opencv · asmorkalov · Mar 5, 2024 · Feb 17, 2024 · Feb 17, 2024
diff --git a/modules/bioinspired/include/opencv2/bioinspired/retina.hpp b/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

diff --git a/modules/bioinspired/samples/default_retina_config.xml b/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>
diff --git a/modules/bioinspired/samples/realistic_retina_config.xml b/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>
diff --git a/modules/bioinspired/tutorials/retina_model/retina_model.markdown b/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

diff --git a/modules/cannops/include/opencv2/cann.hpp b/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
     @}
   @}
  */

diff --git a/modules/cannops/include/opencv2/cann_interface.hpp b/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

diff --git 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);
 ```
 
 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());

diff --git a/modules/dnn_superres/tutorials/benchmark/sr_benchmark.markdown b/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 | ||
 |:-------------:|:-------------------:|:-------------:|:----:|
@@ -131,13 +122,11 @@ 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 | |
 |:-------------:|:-------------------:|:-------------:|
 |![Original](images/orig_div2k.jpg)|![Bicubic interpolation](images/bicubic_div2k.jpg)|![Nearest neighbor interpolation](images/nearest_div2k.jpg)|
 |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>
diff --git a/modules/face/include/opencv2/face/facemark.hpp b/modules/face/include/opencv2/face/facemark.hpp
@@ -12,19 +12,15 @@ 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>
 
 
 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

diff --git 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
 #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>

diff --git 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
 ./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)

diff --git a/modules/fuzzy/include/opencv2/fuzzy.hpp b/modules/fuzzy/include/opencv2/fuzzy.hpp
@@ -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.
+@}
 
 */