Merge pull request #25042 from mshabunin:doc-upgrade

Documentation transition to fresh Doxygen #25042 * current Doxygen version is 1.10, but we will use 1.9.8 for now due to issue with snippets (https://github.com/doxygen/doxygen/pull/10584) * Doxyfile adapted to new version * MathJax updated to 3.x * `@relates` instructions removed temporarily due to issue in Doxygen (to avoid warnings) * refactored matx.hpp - extracted matx.inl.hpp * opencv_contrib - https://github.com/opencv/opencv_contrib/pull/3638
2025-06-07 01:13:28 +08:00 · 2024-03-05 16:19:45 +03:00 · 2024-03-05 16:19:45 +03:00 · bf06e3d09f
commit bf06e3d09f
parent 0f5792a7a1
49 changed files with 1764 additions and 1658 deletions
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@ -572,8 +572,6 @@ if(OPENCV_DISABLE_FILESYSTEM_SUPPORT)
  add_definitions(-DOPENCV_HAVE_FILESYSTEM_SUPPORT=0)
 endif()

-set(OPENCV_MATHJAX_RELPATH "https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0" CACHE STRING "URI to a MathJax installation")
-
 # ----------------------------------------------------------------------------
 #  Get actual OpenCV version number from sources
 # ----------------------------------------------------------------------------
--- a/doc/CMakeLists.txt
+++ b/doc/CMakeLists.txt
@ -16,6 +16,10 @@ endif()

 find_package(Doxygen)
 if(DOXYGEN_FOUND)
+  if (DOXYGEN_VERSION VERSION_LESS 1.9.8)
+    message(WARNING "Found doxygen ${DOXYGEN_VERSION}, version 1.9.8 is used for testing, there is
+                     a chance your documentation will look different or have some limitations.")
+  endif()
  add_custom_target(doxygen)

  # not documented modules list
@ -191,6 +195,8 @@ if(DOXYGEN_FOUND)
  list(APPEND CMAKE_DOXYGEN_HTML_FILES "${CMAKE_CURRENT_SOURCE_DIR}/tutorial-utils.js")
  string(REPLACE ";" " \\\n" CMAKE_DOXYGEN_HTML_FILES "${CMAKE_DOXYGEN_HTML_FILES}")

+  set(OPENCV_MATHJAX_RELPATH "https://cdn.jsdelivr.net/npm/mathjax@3.0.1" CACHE STRING "URI to a MathJax installation")
+
  set(OPENCV_DOCS_DOT_PATH "" CACHE PATH "Doxygen/DOT_PATH value")
  set(CMAKECONFIG_DOT_PATH "${OPENCV_DOCS_DOT_PATH}")

--- a/doc/Doxyfile.in
+++ b/doc/Doxyfile.in
@ -3,8 +3,11 @@ PROJECT_NAME           = OpenCV
 PROJECT_NUMBER         = @OPENCV_VERSION@
 PROJECT_BRIEF          = "Open Source Computer Vision"
 PROJECT_LOGO           = @CMAKE_CURRENT_SOURCE_DIR@/opencv-logo-small.png
+#PROJECT_ICON           =
 OUTPUT_DIRECTORY       = @CMAKE_DOXYGEN_OUTPUT_PATH@
 CREATE_SUBDIRS         = YES
+CREATE_SUBDIRS_LEVEL   = 8
+ALLOW_UNICODE_NAMES    = NO
 OUTPUT_LANGUAGE        = English
 BRIEF_MEMBER_DESC      = YES
 REPEAT_BRIEF           = YES
@ -26,8 +29,10 @@ STRIP_FROM_PATH        = @CMAKE_SOURCE_DIR@/modules @CMAKE_DOXYGEN_INCLUDE_ROOTS
 STRIP_FROM_INC_PATH    = @CMAKE_DOXYGEN_INCLUDE_ROOTS@
 SHORT_NAMES            = NO
 JAVADOC_AUTOBRIEF      = NO
+JAVADOC_BANNER         = NO
 QT_AUTOBRIEF           = NO
 MULTILINE_CPP_IS_BRIEF = NO
+PYTHON_DOCSTRING       = YES
 INHERIT_DOCS           = YES
 SEPARATE_MEMBER_PAGES  = NO
 TAB_SIZE               = 4
@ -43,26 +48,34 @@ OPTIMIZE_OUTPUT_FOR_C  = NO
 OPTIMIZE_OUTPUT_JAVA   = NO
 OPTIMIZE_FOR_FORTRAN   = NO
 OPTIMIZE_OUTPUT_VHDL   = NO
+OPTIMIZE_OUTPUT_SLICE  = NO
 EXTENSION_MAPPING      =
 MARKDOWN_SUPPORT       = YES
+TOC_INCLUDE_HEADINGS   = 5
+MARKDOWN_ID_STYLE      = DOXYGEN
 AUTOLINK_SUPPORT       = YES
 BUILTIN_STL_SUPPORT    = YES
 CPP_CLI_SUPPORT        = NO
 SIP_SUPPORT            = NO
 IDL_PROPERTY_SUPPORT   = YES
 DISTRIBUTE_GROUP_DOC   = NO
+GROUP_NESTED_COMPOUNDS = NO
 SUBGROUPING            = YES
 INLINE_GROUPED_CLASSES = NO
 INLINE_SIMPLE_STRUCTS  = NO
 TYPEDEF_HIDES_STRUCT   = YES
 LOOKUP_CACHE_SIZE      = 0
+NUM_PROC_THREADS       = 1
+TIMESTAMP              = YES
 EXTRACT_ALL            = YES
 EXTRACT_PRIVATE        = NO
+EXTRACT_PRIV_VIRTUAL   = NO
 EXTRACT_PACKAGE        = NO
 EXTRACT_STATIC         = YES
 EXTRACT_LOCAL_CLASSES  = NO
 EXTRACT_LOCAL_METHODS  = NO
 EXTRACT_ANON_NSPACES   = NO
+RESOLVE_UNNAMED_PARAMS = YES
 HIDE_UNDOC_MEMBERS     = NO
 HIDE_UNDOC_CLASSES     = NO
 HIDE_FRIEND_COMPOUNDS  = NO
@ -70,6 +83,8 @@ HIDE_IN_BODY_DOCS      = NO
 INTERNAL_DOCS          = NO
 CASE_SENSE_NAMES       = YES
 HIDE_SCOPE_NAMES       = NO
+HIDE_COMPOUND_REFERENCE= NO
+SHOW_HEADERFILE        = YES
 SHOW_INCLUDE_FILES     = YES
 SHOW_GROUPED_MEMB_INC  = YES
 FORCE_LOCAL_INCLUDES   = NO
@ -96,11 +111,16 @@ QUIET                  = YES
 WARNINGS               = YES
 WARN_IF_UNDOCUMENTED   = YES
 WARN_IF_DOC_ERROR      = YES
+WARN_IF_INCOMPLETE_DOC = YES
 WARN_NO_PARAMDOC       = NO
+WARN_IF_UNDOC_ENUM_VAL = NO
+WARN_AS_ERROR          = NO
 WARN_FORMAT            = "$file:$line: $text"
+WARN_LINE_FORMAT       = "at line $line of file $file"
 WARN_LOGFILE           =
 INPUT                  = @CMAKE_DOXYGEN_INPUT_LIST@
 INPUT_ENCODING         = UTF-8
+INPUT_FILE_ENCODING    =
 FILE_PATTERNS          =
 RECURSIVE              = YES
 EXCLUDE                = @CMAKE_DOXYGEN_EXCLUDE_LIST@
@ -125,8 +145,11 @@ REFERENCES_LINK_SOURCE = YES
 SOURCE_TOOLTIPS        = YES
 USE_HTAGS              = NO
 VERBATIM_HEADERS       = NO
+CLANG_ASSISTED_PARSING = NO
+CLANG_ADD_INC_PATHS    = YES
+CLANG_OPTIONS          =
+CLANG_DATABASE_PATH    =
 ALPHABETICAL_INDEX     = YES
-COLS_IN_ALPHA_INDEX    = 5
 IGNORE_PREFIX          =
 GENERATE_HTML          = YES
 HTML_OUTPUT            = html
@ -136,14 +159,19 @@ HTML_FOOTER            = @CMAKE_CURRENT_SOURCE_DIR@/footer.html
 HTML_STYLESHEET        =
 HTML_EXTRA_STYLESHEET  = @CMAKE_CURRENT_SOURCE_DIR@/stylesheet.css
 HTML_EXTRA_FILES       = @CMAKE_DOXYGEN_HTML_FILES@
+HTML_COLORSTYLE        = LIGHT
 HTML_COLORSTYLE_HUE    = 220
 HTML_COLORSTYLE_SAT    = 100
 HTML_COLORSTYLE_GAMMA  = 80
-HTML_TIMESTAMP         = YES
+HTML_DYNAMIC_MENUS     = YES
 HTML_DYNAMIC_SECTIONS  = NO
+HTML_CODE_FOLDING      = YES
+#HTML_COPY_CLIPBOARD    = YES
+#HTML_PROJECT_COOKIE    =
 HTML_INDEX_NUM_ENTRIES = 100
 GENERATE_DOCSET        = NO
 DOCSET_FEEDNAME        = "Doxygen generated docs"
+DOCSET_FEEDURL         =
 DOCSET_BUNDLE_ID       = org.doxygen.Project
 DOCSET_PUBLISHER_ID    = org.doxygen.Publisher
 DOCSET_PUBLISHER_NAME  = Publisher
@ -154,6 +182,7 @@ GENERATE_CHI           = NO
 CHM_INDEX_ENCODING     =
 BINARY_TOC             = NO
 TOC_EXPAND             = NO
+SITEMAP_URL            =
 GENERATE_QHP           = @CMAKE_DOXYGEN_GENERATE_QHP@
 QCH_FILE               = ../opencv-@OPENCV_VERSION@.qch
 QHP_NAMESPACE          = org.opencv.@OPENCV_VERSION@
@ -166,15 +195,19 @@ GENERATE_ECLIPSEHELP   = NO
 ECLIPSE_DOC_ID         = org.doxygen.Project
 DISABLE_INDEX          = NO
 GENERATE_TREEVIEW      = NO
+FULL_SIDEBAR           = NO
 ENUM_VALUES_PER_LINE   = 1
 TREEVIEW_WIDTH         = 250
 EXT_LINKS_IN_WINDOW    = YES
+OBFUSCATE_EMAILS       = YES
+HTML_FORMULA_FORMAT    = svg
 FORMULA_FONTSIZE       = 14
-FORMULA_TRANSPARENT    = YES
+FORMULA_MACROFILE      =
 USE_MATHJAX            = YES
-MATHJAX_FORMAT         = HTML-CSS
+MATHJAX_VERSION        = MathJax_3
+MATHJAX_FORMAT         = chtml
 MATHJAX_RELPATH        = @OPENCV_MATHJAX_RELPATH@
-MATHJAX_EXTENSIONS     = TeX/AMSmath TeX/AMSsymbols
+MATHJAX_EXTENSIONS     = ams
 MATHJAX_CODEFILE       = @CMAKE_CURRENT_SOURCE_DIR@/mymath.js
 SEARCHENGINE           = YES
 SERVER_BASED_SEARCH    = NO
@ -187,18 +220,20 @@ GENERATE_LATEX         = NO
 LATEX_OUTPUT           = latex
 LATEX_CMD_NAME         = latex
 MAKEINDEX_CMD_NAME     = makeindex
+LATEX_MAKEINDEX_CMD    = makeindex
 COMPACT_LATEX          = NO
 PAPER_TYPE             = a4
 EXTRA_PACKAGES         = mymath
 LATEX_HEADER           =
 LATEX_FOOTER           =
+LATEX_EXTRA_STYLESHEET =
 LATEX_EXTRA_FILES      =
 PDF_HYPERLINKS         = YES
 USE_PDFLATEX           = YES
 LATEX_BATCHMODE        = NO
 LATEX_HIDE_INDICES     = NO
-LATEX_SOURCE_CODE      = NO
 LATEX_BIB_STYLE        = plain
+LATEX_EMOJI_DIRECTORY  =
 GENERATE_RTF           = NO
 RTF_OUTPUT             = rtf
 COMPACT_RTF            = NO
@ -208,13 +243,18 @@ RTF_EXTENSIONS_FILE    =
 GENERATE_MAN           = NO
 MAN_OUTPUT             = man
 MAN_EXTENSION          = .3
+MAN_SUBDIR             =
 MAN_LINKS              = NO
 GENERATE_XML           = NO
 XML_OUTPUT             = xml
 XML_PROGRAMLISTING     = YES
+XML_NS_MEMB_FILE_SCOPE = NO
 GENERATE_DOCBOOK       = NO
 DOCBOOK_OUTPUT         = docbook
 GENERATE_AUTOGEN_DEF   = NO
+GENERATE_SQLITE3       = NO
+SQLITE3_OUTPUT         = sqlite3
+SQLITE3_RECREATE_DB    = YES
 GENERATE_PERLMOD       = NO
 PERLMOD_LATEX          = NO
 PERLMOD_PRETTY         = YES
@ -272,19 +312,20 @@ GENERATE_TAGFILE       = @CMAKE_DOXYGEN_OUTPUT_PATH@/html/opencv.tag
 ALLEXTERNALS           = NO
 EXTERNAL_GROUPS        = YES
 EXTERNAL_PAGES         = YES
-CLASS_DIAGRAMS         = YES
-DIA_PATH               =
 HIDE_UNDOC_RELATIONS   = NO
 HAVE_DOT               = @CMAKECONFIG_HAVE_DOT@
 DOT_NUM_THREADS        = 0
-DOT_FONTNAME           = Helvetica
-DOT_FONTSIZE           = 10
 DOT_FONTPATH           =
+DOT_COMMON_ATTR        = "fontname=Helvetica,fontsize=10"
+DOT_EDGE_ATTR          = "labelfontname=Helvetica,labelfontsize=10"
+DOT_NODE_ATTR          = "shape=box,height=0.2,width=0.4"
 CLASS_GRAPH            = YES
 COLLABORATION_GRAPH    = YES
-GROUP_GRAPHS           = YES
+GROUP_GRAPHS           = NO
 UML_LOOK               = YES
 UML_LIMIT_NUM_FIELDS   = 10
+DOT_UML_DETAILS        = NO
+DOT_WRAP_THRESHOLD     = 17
 TEMPLATE_RELATIONS     = YES
 INCLUDE_GRAPH          = YES
 INCLUDED_BY_GRAPH      = YES
@ -292,15 +333,19 @@ CALL_GRAPH             = YES
 CALLER_GRAPH           = NO
 GRAPHICAL_HIERARCHY    = YES
 DIRECTORY_GRAPH        = YES
+DIR_GRAPH_MAX_DEPTH    = 1
 DOT_IMAGE_FORMAT       = @CMAKECONFIG_DOT_IMAGE_FORMAT@
 INTERACTIVE_SVG        = @CMAKECONFIG_INTERACTIVE_SVG@
 DOT_PATH               = @CMAKECONFIG_DOT_PATH@
 DOTFILE_DIRS           =
-MSCFILE_DIRS           =
 DIAFILE_DIRS           =
-DOT_GRAPH_MAX_NODES    = 50
+PLANTUML_JAR_PATH      =
+PLANTUML_CFG_FILE      =
+PLANTUML_INCLUDE_PATH  =
+DOT_GRAPH_MAX_NODES    = 250
 MAX_DOT_GRAPH_DEPTH    = 0
-DOT_TRANSPARENT        = NO
 DOT_MULTI_TARGETS      = NO
 GENERATE_LEGEND        = YES
 DOT_CLEANUP            = YES
+MSCGEN_TOOL            =
+MSCFILE_DIRS           =
--- a/doc/js_tutorials/js_setup/js_nodejs/js_nodejs.markdown
+++ b/doc/js_tutorials/js_setup/js_nodejs/js_nodejs.markdown
@ -336,7 +336,7 @@ function installDOM(){
 -   Make sure the files `aarcascade_frontalface_default.xml` and `haarcascade_eye.xml` are present in project's directory. They can be obtained from [OpenCV sources](https://github.com/opencv/opencv/tree/4.x/data/haarcascades).
 -   Make sure a sample image file `lena.jpg` exists in project's directory. It should display people's faces for this example to make sense. The following image is known to work:

-![image](lena.jpg)
+![image](js_assets/lena.jpg)

 The following command should generate the file `output3.jpg`:

--- a/doc/mymath.js
+++ b/doc/mymath.js
@ -1,22 +1,20 @@
 //<![CDATA[
-MathJax.Hub.Config(
-{
-  TeX: {
-      Macros: {
-          matTT: [ "\\[ \\left|\\begin{array}{ccc} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \\end{array}\\right| \\]", 9],
-          fork: ["\\left\\{ \\begin{array}{l l} #1 & \\mbox{#2}\\\\ #3 & \\mbox{#4}\\\\ \\end{array} \\right.", 4],
-          forkthree: ["\\left\\{ \\begin{array}{l l} #1 & \\mbox{#2}\\\\ #3 & \\mbox{#4}\\\\ #5 & \\mbox{#6}\\\\ \\end{array} \\right.", 6],
-          forkfour: ["\\left\\{ \\begin{array}{l l} #1 & \\mbox{#2}\\\\ #3 & \\mbox{#4}\\\\ #5 & \\mbox{#6}\\\\ #7 & \\mbox{#8}\\\\ \\end{array} \\right.", 8],
-          vecthree: ["\\begin{bmatrix} #1\\\\ #2\\\\ #3 \\end{bmatrix}", 3],
-          vecthreethree: ["\\begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \\end{bmatrix}", 9],
-          cameramatrix: ["#1 = \\begin{bmatrix} f_x & 0 & c_x\\\\ 0 & f_y & c_y\\\\ 0 & 0 & 1 \\end{bmatrix}", 1],
-          distcoeffs: ["(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \\tau_x, \\tau_y]]]]) \\text{ of 4, 5, 8, 12 or 14 elements}"],
-          distcoeffsfisheye: ["(k_1, k_2, k_3, k_4)"],
-          hdotsfor: ["\\dots", 1],
-          mathbbm: ["\\mathbb{#1}", 1],
-          bordermatrix: ["\\matrix{#1}", 1]
-      }
-  }
-}
-);
+window.MathJax = {
+    tex: {
+        macros: {
+            matTT: [ "\\[ \\left|\\begin{array}{ccc} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \\end{array}\\right| \\]", 9],
+            fork: ["\\left\\{ \\begin{array}{l l} #1 & \\mbox{#2}\\\\ #3 & \\mbox{#4}\\\\ \\end{array} \\right.", 4],
+            forkthree: ["\\left\\{ \\begin{array}{l l} #1 & \\mbox{#2}\\\\ #3 & \\mbox{#4}\\\\ #5 & \\mbox{#6}\\\\ \\end{array} \\right.", 6],
+            forkfour: ["\\left\\{ \\begin{array}{l l} #1 & \\mbox{#2}\\\\ #3 & \\mbox{#4}\\\\ #5 & \\mbox{#6}\\\\ #7 & \\mbox{#8}\\\\ \\end{array} \\right.", 8],
+            vecthree: ["\\begin{bmatrix} #1\\\\ #2\\\\ #3 \\end{bmatrix}", 3],
+            vecthreethree: ["\\begin{bmatrix} #1 & #2 & #3\\\\ #4 & #5 & #6\\\\ #7 & #8 & #9 \\end{bmatrix}", 9],
+            cameramatrix: ["#1 = \\begin{bmatrix} f_x & 0 & c_x\\\\ 0 & f_y & c_y\\\\ 0 & 0 & 1 \\end{bmatrix}", 1],
+            distcoeffs: ["(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \\tau_x, \\tau_y]]]]) \\text{ of 4, 5, 8, 12 or 14 elements}"],
+            distcoeffsfisheye: ["(k_1, k_2, k_3, k_4)"],
+            hdotsfor: ["\\dots", 1],
+            mathbbm: ["\\mathbb{#1}", 1],
+            bordermatrix: ["\\matrix{#1}", 1]
+        }
+    }
+};
 //]]>
--- a/doc/py_tutorials/py_setup/py_setup_in_windows/py_setup_in_windows.markdown
+++ b/doc/py_tutorials/py_setup/py_setup_in_windows/py_setup_in_windows.markdown
@ -79,62 +79,38 @@ Building OpenCV from source
 -#  Extract it to a folder, opencv and create a new folder build in it.
 -#  Open CMake-gui (*Start \> All Programs \> CMake-gui*)
 -#  Fill the fields as follows (see the image below):
-
    -#  Click on **Browse Source...** and locate the opencv folder.
-
    -#  Click on **Browse Build...** and locate the build folder we created.
-
    -#  Click on **Configure**.
-
        ![image](images/Capture1.jpg)
-
    -#  It will open a new window to select the compiler. Choose appropriate compiler (here,
        Visual Studio 11) and click **Finish**.
-
        ![image](images/Capture2.png)
-
    -#  Wait until analysis is finished.
-
 -#  You will see all the fields are marked in red. Click on the **WITH** field to expand it. It
    decides what extra features you need. So mark appropriate fields. See the below image:
-
    ![image](images/Capture3.png)
-
 -#  Now click on **BUILD** field to expand it. First few fields configure the build method. See the
    below image:
-
    ![image](images/Capture5.png)
-
 -#  Remaining fields specify what modules are to be built. Since GPU modules are not yet supported
    by OpenCV-Python, you can completely avoid it to save time (But if you work with them, keep it
    there). See the image below:
-
    ![image](images/Capture6.png)
-
 -#  Now click on **ENABLE** field to expand it. Make sure **ENABLE_SOLUTION_FOLDERS** is unchecked
    (Solution folders are not supported by Visual Studio Express edition). See the image below:
-
    ![image](images/Capture7.png)
-
 -#  Also make sure that in the **PYTHON** field, everything is filled. (Ignore
    PYTHON_DEBUG_LIBRARY). See image below:
-
    ![image](images/Capture80.png)
-
 -#  Finally click the **Generate** button.
-
 -#  Now go to our **opencv/build** folder. There you will find **OpenCV.sln** file. Open it with
    Visual Studio.
-
 -#  Check build mode as **Release** instead of **Debug**.
-
 -#  In the solution explorer, right-click on the **Solution** (or **ALL_BUILD**) and build it. It
    will take some time to finish.
-
 -#  Again, right-click on **INSTALL** and build it. Now OpenCV-Python will be installed.
-
    ![image](images/Capture8.png)
-
 -#  Open Python IDLE and enter 'import cv2 as cv'. If no error, it is installed correctly.

@note We have installed with no other support like TBB, Eigen, Qt, Documentation etc. It would be
--- a/doc/tutorials/core/discrete_fourier_transform/discrete_fourier_transform.markdown
+++ b/doc/tutorials/core/discrete_fourier_transform/discrete_fourier_transform.markdown
@ -87,7 +87,7 @@ Fourier Transform too needs to be of a discrete type resulting in a Discrete Fou
 (*DFT*). You'll want to use this whenever you need to determine the structure of an image from a
 geometrical point of view. Here are the steps to follow (in case of a gray scale input image *I*):

-#### Expand the image to an optimal size
+### Expand the image to an optimal size

 The performance of a DFT is dependent of the image
 size. It tends to be the fastest for image sizes that are multiple of the numbers two, three and
@ -108,7 +108,7 @@ image (the appended pixels are initialized with zero):
@snippet python/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.py expand
@end_toggle

-#### Make place for both the complex and the real values
+### Make place for both the complex and the real values

 The result of a Fourier Transform is
 complex. This implies that for each image value the result is two image values (one per
@ -128,7 +128,7 @@ input image to this type and expand it with another channel to hold the complex
@snippet python/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.py complex_and_real
@end_toggle

-#### Make the Discrete Fourier Transform
+### Make the Discrete Fourier Transform
 It's possible an in-place calculation (same input as
 output):

@ -144,7 +144,7 @@ output):
@snippet python/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.py dft
@end_toggle

-#### Transform the real and complex values to magnitude
+### Transform the real and complex values to magnitude
 A complex number has a real (*Re*) and a
 complex (imaginary - *Im*) part. The results of a DFT are complex numbers. The magnitude of a
 DFT is:
@ -165,7 +165,7 @@ Translated to OpenCV code:
@snippet python/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.py magnitude
@end_toggle

-#### Switch to a logarithmic scale
+### Switch to a logarithmic scale
 It turns out that the dynamic range of the Fourier
 coefficients is too large to be displayed on the screen. We have some small and some high
 changing values that we can't observe like this. Therefore the high values will all turn out as
@ -188,7 +188,7 @@ Translated to OpenCV code:
@snippet python/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.py log
@end_toggle

-#### Crop and rearrange
+### Crop and rearrange
 Remember, that at the first step, we expanded the image? Well, it's time
 to throw away the newly introduced values. For visualization purposes we may also rearrange the
 quadrants of the result, so that the origin (zero, zero) corresponds with the image center.
@ -205,7 +205,7 @@ quadrants of the result, so that the origin (zero, zero) corresponds with the im
@snippet python/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.py crop_rearrange
@end_toggle

-#### Normalize
+### Normalize
 This is done again for visualization purposes. We now have the magnitudes,
 however this are still out of our image display range of zero to one. We normalize our values to
 this range using the @ref cv::normalize() function.
--- a/doc/tutorials/core/mat-mask-operations/mat_mask_operations.markdown
+++ b/doc/tutorials/core/mat-mask-operations/mat_mask_operations.markdown
@ -73,7 +73,7 @@ Here's a function that will do this:
@snippet samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp basic_method

 At first we make sure that the input images data is in unsigned char format. For this we use the
-@ref cv::CV_Assert function that throws an error when the expression inside it is false.
+@ref CV_Assert function (macro) that throws an error when the expression inside it is false.
@snippet samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp 8_bit
@end_toggle

--- a/doc/tutorials/dnn/dnn_OCR/dnn_OCR.markdown
+++ b/doc/tutorials/dnn/dnn_OCR/dnn_OCR.markdown
@ -22,7 +22,7 @@ In this tutorial, we first introduce how to obtain the custom OCR model, then ho

 After completing the model training, please use [transform_to_onnx.py](https://github.com/zihaomu/deep-text-recognition-benchmark/blob/master/transform_to_onnx.py) to convert the model into onnx format.

-#### Execute in webcam
+### Execute in webcam
 The Python version example code can be found at [here](https://github.com/opencv/opencv/blob/4.x/samples/dnn/text_detection.py).

 Example:
@ -44,9 +44,10 @@ Their performance at different text recognition datasets is shown in the table b
 | CRNN_VGG-BiLSTM-CTC | 82.63    | 82.07 | 92.96     | 88.867     | 66.28     | 71.01  | 62.37    | 78.03       | 8.45              |
 | ResNet-CTC           | 84.00        | 84.08  | 92.39     | 88.96     | 67.74     | 74.73  | 67.60    | 79.93    | 44.28             |

-The performance of the text recognition model were tesred on OpenCV DNN, and does not include the text detection model.
+The performance of the text recognition model were tested on OpenCV DNN, and does not include the text detection model.
+
+### Model selection suggestion

-#### Model selection suggestion:
 The input of text recognition model is the output of the text detection model, which causes the performance of text detection to greatly affect the performance of text recognition.

 DenseNet_CTC has the smallest parameters and best FPS, and it is suitable for edge devices, which are very sensitive to the cost of calculation. If you have limited computing resources and want to achieve better accuracy, VGG_CTC is a good choice.
--- a/doc/tutorials/dnn/dnn_googlenet/dnn_googlenet.markdown
+++ b/doc/tutorials/dnn/dnn_googlenet/dnn_googlenet.markdown
@ -17,7 +17,7 @@ In this tutorial you will learn how to use opencv_dnn module for image classific
 GoogLeNet trained network from [Caffe model zoo](http://caffe.berkeleyvision.org/model_zoo.html).

 We will demonstrate results of this example on the following picture.
-![Buran space shuttle](images/space_shuttle.jpg)
+![Buran space shuttle](dnn/images/space_shuttle.jpg)

 Source Code
 -----------
--- a/doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown
+++ b/doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown
@ -14,7 +14,7 @@
 In this tutorial, we will introduce the APIs for TextRecognitionModel and TextDetectionModel in detail.

 ---
-#### TextRecognitionModel:
+### TextRecognitionModel

 In the current version, @ref cv::dnn::TextRecognitionModel only supports CNN+RNN+CTC based algorithms,
 and the greedy decoding method for CTC is provided.
@ -38,7 +38,7 @@ Before recognition, you should `setVocabulary` and `setDecodeType`.

 ---

-#### TextDetectionModel:
+### TextDetectionModel

@ref cv::dnn::TextDetectionModel API provides these methods for text detection:
 - cv::dnn::TextDetectionModel::detect() returns the results in std::vector<std::vector<Point>> (4-points quadrangles)
@ -60,7 +60,7 @@ We encourage you to add new algorithms to these APIs.

 ## Pretrained Models

-#### TextRecognitionModel:
+### TextRecognitionModel

 ```
 crnn.onnx:
@ -92,7 +92,7 @@ More models can be found in [here](https://drive.google.com/drive/folders/1cTbQ3
 which are taken from [clovaai](https://github.com/clovaai/deep-text-recognition-benchmark).
 You can train more models by [CRNN](https://github.com/meijieru/crnn.pytorch), and convert models by `torch.onnx.export`.

-#### TextDetectionModel:
+### TextDetectionModel

 ```
 - DB_IC15_resnet50.onnx:
@ -297,7 +297,7 @@ For more information, please refer to:
 - [samples/dnn/text_detection.cpp](https://github.com/opencv/opencv/blob/4.x/samples/dnn/text_detection.cpp)
 - [samples/dnn/scene_text_spotting.cpp](https://github.com/opencv/opencv/blob/4.x/samples/dnn/scene_text_spotting.cpp)

-#### Test with an image
+### Test with an image
 Examples:
 ```bash
 example_dnn_scene_text_recognition -mp=path/to/crnn_cs.onnx -i=path/to/an/image -rgb=1 -vp=/path/to/alphabet_94.txt
@ -306,7 +306,7 @@ example_dnn_scene_text_spotting -dmp=path/to/DB_IC15_resnet50.onnx -rmp=path/to/
 example_dnn_text_detection -dmp=path/to/EAST.pb -rmp=path/to/crnn_cs.onnx -i=path/to/an/image -rgb=1 -vp=path/to/alphabet_94.txt
 ```

-#### Test on public datasets
+### Test on public datasets
 Text Recognition:

 The download link for testing images can be found in the **Images for Testing**
--- a/doc/tutorials/imgproc/erosion_dilatation/erosion_dilatation.markdown
+++ b/doc/tutorials/imgproc/erosion_dilatation/erosion_dilatation.markdown
@ -111,7 +111,7 @@ called and it will update the output image based on the current trackbar values.

 Let's analyze these two functions:

-#### The erosion function
+### The erosion function (CPP)

@snippet cpp/tutorial_code/ImgProc/Morphology_1.cpp erosion

@ -135,7 +135,7 @@ receives three arguments:

 That is all. We are ready to perform the erosion of our image.

-#### The dilation function
+### The dilation function (CPP)

 The code is below. As you can see, it is completely similar to the snippet of code for **erosion**.
 Here we also have the option of defining our kernel, its anchor point and the size of the operator
@ -175,7 +175,7 @@ In short we
 The action and state changed listeners added call at the end the `update` method which updates
 the image based on the current slider values. So every time we move any slider, the `update` method is triggered.

-#### Updating the image
+### Updating the image (Java)

 To update the image we used the following implementation:

@ -190,7 +190,7 @@ In other words we

 Let's analyze the `erode` and `dilate` methods:

-#### The erosion method
+### The erosion method (Java)

@snippet java/tutorial_code/ImgProc/erosion_dilatation/MorphologyDemo1.java erosion

@ -213,7 +213,7 @@ receives three arguments:

 That is all. We are ready to perform the erosion of our image.

-#### The dilation function
+### The dilation function (Java)

 The code is below. As you can see, it is completely similar to the snippet of code for **erosion**.
 Here we also have the option of defining our kernel, its anchor point and the size of the operator
@ -240,7 +240,7 @@ called and it will update the output image based on the current trackbar values.

 Let's analyze these two functions:

-#### The erosion function
+### The erosion function (Python)

@snippet python/tutorial_code/imgProc/erosion_dilatation/morphology_1.py erosion

@ -262,7 +262,7 @@ specified, it is assumed to be in the center.

 That is all. We are ready to perform the erosion of our image.

-#### The dilation function
+### The dilation function (Python)

 The code is below. As you can see, it is completely similar to the snippet of code for **erosion**.
 Here we also have the option of defining our kernel, its anchor point and the size of the operator
--- a/doc/tutorials/imgproc/gausian_median_blur_bilateral_filter/gausian_median_blur_bilateral_filter.markdown
+++ b/doc/tutorials/imgproc/gausian_median_blur_bilateral_filter/gausian_median_blur_bilateral_filter.markdown
@ -133,7 +133,7 @@ Explanation
 Let's check the OpenCV functions that involve only the smoothing procedure, since the rest is
 already known by now.

-#### Normalized Block Filter:
+### Normalized Block Filter:

 -   OpenCV offers the function **blur()** to perform smoothing with this filter.
    We specify 4 arguments (more details, check the Reference):
@ -157,7 +157,7 @@ already known by now.
@snippet samples/python/tutorial_code/imgProc/Smoothing/smoothing.py blur
@end_toggle

-#### Gaussian Filter:
+### Gaussian Filter:

 -   It is performed by the function **GaussianBlur()** :
    Here we use 4 arguments (more details, check the OpenCV reference):
@ -183,7 +183,7 @@ already known by now.
@snippet samples/python/tutorial_code/imgProc/Smoothing/smoothing.py gaussianblur
@end_toggle

-#### Median Filter:
+### Median Filter:

 -   This filter is provided by the **medianBlur()** function:
    We use three arguments:
@ -203,7 +203,7 @@ already known by now.
@snippet samples/python/tutorial_code/imgProc/Smoothing/smoothing.py medianblur
@end_toggle

-#### Bilateral Filter
+### Bilateral Filter

 -   Provided by OpenCV function **bilateralFilter()**
    We use 5 arguments:
--- a/doc/tutorials/imgproc/imgtrans/copyMakeBorder/copyMakeBorder.markdown
+++ b/doc/tutorials/imgproc/imgtrans/copyMakeBorder/copyMakeBorder.markdown
@ -78,7 +78,7 @@ You can also download it from
 Explanation
 -----------

-#### Declare the variables
+### Declare the variables

 First we declare the variables we are going to use:

@ -97,7 +97,7 @@ First we declare the variables we are going to use:
 Especial attention deserves the variable *rng* which is a random number generator. We use it to
 generate the random border color, as we will see soon.

-#### Load an image
+### Load an image

 As usual we load our source image *src*:

@ -113,7 +113,7 @@ As usual we load our source image *src*:
@snippet python/tutorial_code/ImgTrans/MakeBorder/copy_make_border.py load
@end_toggle

-#### Create a window
+### Create a window

 After giving a short intro of how to use the program, we create a window:

@ -129,7 +129,7 @@ After giving a short intro of how to use the program, we create a window:
@snippet python/tutorial_code/ImgTrans/MakeBorder/copy_make_border.py create_window
@end_toggle

-#### Initialize arguments
+### Initialize arguments

 Now we initialize the argument that defines the size of the borders (*top*, *bottom*, *left* and
 *right*). We give them a value of 5% the size of *src*.
@ -146,7 +146,7 @@ Now we initialize the argument that defines the size of the borders (*top*, *bot
@snippet python/tutorial_code/ImgTrans/MakeBorder/copy_make_border.py init_arguments
@end_toggle

-#### Loop
+### Loop

 The program runs in an infinite loop while the key **ESC** isn't pressed.
 If the user presses '**c**' or '**r**', the *borderType* variable
@ -164,7 +164,7 @@ takes the value of *BORDER_CONSTANT* or *BORDER_REPLICATE* respectively:
@snippet python/tutorial_code/ImgTrans/MakeBorder/copy_make_border.py check_keypress
@end_toggle

-#### Random color
+### Random color

 In each iteration (after 0.5 seconds), the random border color (*value*) is updated...

@ -182,7 +182,7 @@ In each iteration (after 0.5 seconds), the random border color (*value*) is upda

 This value is a set of three numbers picked randomly in the range \f$[0,255]\f$.

-#### Form a border around the image
+### Form a border around the image

 Finally, we call the function **copyMakeBorder()** to apply the respective padding:

@ -209,7 +209,7 @@ Finally, we call the function **copyMakeBorder()** to apply the respective paddi
    -#  *value*: If *borderType* is *BORDER_CONSTANT*, this is the value used to fill the border
        pixels.

-#### Display the results
+### Display the results

 We display our output image in the image created previously

--- a/doc/tutorials/imgproc/imgtrans/filter_2d/filter_2d.markdown
+++ b/doc/tutorials/imgproc/imgtrans/filter_2d/filter_2d.markdown
@ -94,7 +94,7 @@ You can also download it from
 Explanation
 -----------

-####  Load an image
+###  Load an image

@add_toggle_cpp
@snippet cpp/tutorial_code/ImgTrans/filter2D_demo.cpp load
@ -108,7 +108,7 @@ Explanation
@snippet python/tutorial_code/ImgTrans/Filter2D/filter2D.py load
@end_toggle

-####  Initialize the arguments
+###  Initialize the arguments

@add_toggle_cpp
@snippet cpp/tutorial_code/ImgTrans/filter2D_demo.cpp init_arguments
@ -122,7 +122,7 @@ Explanation
@snippet python/tutorial_code/ImgTrans/Filter2D/filter2D.py init_arguments
@end_toggle

-##### Loop
+### Loop

 Perform an infinite loop updating the kernel size and applying our linear filter to the input
 image. Let's analyze that more in detail:
--- a/doc/tutorials/imgproc/imgtrans/hough_circle/hough_circle.markdown
+++ b/doc/tutorials/imgproc/imgtrans/hough_circle/hough_circle.markdown
@ -74,7 +74,7 @@ Explanation

 The image we used can be found [here](https://raw.githubusercontent.com/opencv/opencv/4.x/samples/data/smarties.png)

-####  Load an image:
+###  Load an image:

@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp load
@ -88,7 +88,7 @@ The image we used can be found [here](https://raw.githubusercontent.com/opencv/o
@snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py load
@end_toggle

-####  Convert it to grayscale:
+###  Convert it to grayscale:

@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp convert_to_gray
@ -102,7 +102,7 @@ The image we used can be found [here](https://raw.githubusercontent.com/opencv/o
@snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py convert_to_gray
@end_toggle

-#### Apply a Median blur to reduce noise and avoid false circle detection:
+### Apply a Median blur to reduce noise and avoid false circle detection:

@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp reduce_noise
@ -116,7 +116,7 @@ The image we used can be found [here](https://raw.githubusercontent.com/opencv/o
@snippet samples/python/tutorial_code/ImgTrans/HoughCircle/hough_circle.py reduce_noise
@end_toggle

-#### Proceed to apply Hough Circle Transform:
+### Proceed to apply Hough Circle Transform:

@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp houghcircles
@ -144,7 +144,7 @@ The image we used can be found [here](https://raw.githubusercontent.com/opencv/o
    -   *min_radius = 0*: Minimum radius to be detected. If unknown, put zero as default.
    -   *max_radius = 0*: Maximum radius to be detected. If unknown, put zero as default.

-####  Draw the detected circles:
+###  Draw the detected circles:

@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp draw
@ -160,7 +160,7 @@ The image we used can be found [here](https://raw.githubusercontent.com/opencv/o

 You can see that we will draw the circle(s) on red and the center(s) with a small green dot

-####  Display the detected circle(s) and wait for the user to exit the program:
+###  Display the detected circle(s) and wait for the user to exit the program:

@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp display
--- a/doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.markdown
+++ b/doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.markdown
@ -129,7 +129,7 @@ The sample code that we will explain can be downloaded from
 Explanation
 -----------

-#### Load an image:
+### Load an image:

@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgTrans/houghlines.cpp load
@ -143,7 +143,7 @@ Explanation
@snippet samples/python/tutorial_code/ImgTrans/HoughLine/hough_lines.py load
@end_toggle

-#### Detect the edges of the image by using a Canny detector:
+### Detect the edges of the image by using a Canny detector:

@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgTrans/houghlines.cpp edge_detection
@ -160,7 +160,7 @@ Explanation
 Now we will apply the Hough Line Transform. We will explain how to use both OpenCV functions
 available for this purpose.

-#### Standard Hough Line Transform:
+### Standard Hough Line Transform:
 First, you apply the Transform:

@add_toggle_cpp
@ -199,7 +199,7 @@ And then you display the result by drawing the lines.
@snippet samples/python/tutorial_code/ImgTrans/HoughLine/hough_lines.py draw_lines
@end_toggle

-#### Probabilistic Hough Line Transform
+### Probabilistic Hough Line Transform
 First you apply the transform:

@add_toggle_cpp
@ -242,7 +242,7 @@ And then you display the result by drawing the lines.
@snippet samples/python/tutorial_code/ImgTrans/HoughLine/hough_lines.py draw_lines_p
@end_toggle

-#### Display the original image and the detected lines:
+### Display the original image and the detected lines:

@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgTrans/houghlines.cpp imshow
@ -256,7 +256,7 @@ And then you display the result by drawing the lines.
@snippet samples/python/tutorial_code/ImgTrans/HoughLine/hough_lines.py imshow
@end_toggle

-#### Wait until the user exits the program
+### Wait until the user exits the program

@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgTrans/houghlines.cpp exit
--- a/doc/tutorials/imgproc/imgtrans/laplace_operator/laplace_operator.markdown
+++ b/doc/tutorials/imgproc/imgtrans/laplace_operator/laplace_operator.markdown
@ -81,7 +81,7 @@ Code
 Explanation
 -----------

-#### Declare variables
+### Declare variables

@add_toggle_cpp
@snippet cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp variables
@ -95,7 +95,7 @@ Explanation
@snippet samples/python/tutorial_code/ImgTrans/LaPlace/laplace_demo.py variables
@end_toggle

-#### Load source image
+### Load source image

@add_toggle_cpp
@snippet cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp load
@ -109,7 +109,7 @@ Explanation
@snippet samples/python/tutorial_code/ImgTrans/LaPlace/laplace_demo.py load
@end_toggle

-#### Reduce noise
+### Reduce noise

@add_toggle_cpp
@snippet cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp reduce_noise
@ -123,7 +123,7 @@ Explanation
@snippet samples/python/tutorial_code/ImgTrans/LaPlace/laplace_demo.py reduce_noise
@end_toggle

-#### Grayscale
+### Grayscale

@add_toggle_cpp
@snippet cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp convert_to_gray
@ -137,7 +137,7 @@ Explanation
@snippet samples/python/tutorial_code/ImgTrans/LaPlace/laplace_demo.py convert_to_gray
@end_toggle

-#### Laplacian operator
+### Laplacian operator

@add_toggle_cpp
@snippet cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp laplacian
@ -160,7 +160,7 @@ Explanation
        this example.
    -   *scale*, *delta* and *BORDER_DEFAULT*: We leave them as default values.

-#### Convert output to a *CV_8U* image
+### Convert output to a *CV_8U* image

@add_toggle_cpp
@snippet cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp convert
@ -174,7 +174,7 @@ Explanation
@snippet samples/python/tutorial_code/ImgTrans/LaPlace/laplace_demo.py convert
@end_toggle

-#### Display the result
+### Display the result

@add_toggle_cpp
@snippet cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp display
--- a/doc/tutorials/imgproc/imgtrans/sobel_derivatives/sobel_derivatives.markdown
+++ b/doc/tutorials/imgproc/imgtrans/sobel_derivatives/sobel_derivatives.markdown
@ -58,7 +58,7 @@ Theory
    gradient of an image intensity function.
 -#  The Sobel Operator combines Gaussian smoothing and differentiation.

-#### Formulation
+### Formulation

 Assuming that the image to be operated is \f$I\f$:

@ -140,23 +140,23 @@ You can also download it from
 Explanation
 -----------

-#### Declare variables
+### Declare variables

@snippet cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp variables

-#### Load source image
+### Load source image

@snippet cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp load

-#### Reduce noise
+### Reduce noise

@snippet cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp reduce_noise

-#### Grayscale
+### Grayscale

@snippet cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp convert_to_gray

-#### Sobel Operator
+### Sobel Operator

@snippet cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp sobel

@ -174,18 +174,18 @@ Explanation
    Notice that to calculate the gradient in *x* direction we use: \f$x_{order}= 1\f$ and
    \f$y_{order} = 0\f$. We do analogously for the *y* direction.

-#### Convert output to a CV_8U image
+### Convert output to a CV_8U image

@snippet cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp convert

-#### Gradient
+### Gradient

@snippet cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp blend

 We try to approximate the *gradient* by adding both directional gradients (note that
 this is not an exact calculation at all! but it is good for our purposes).

-#### Show results
+### Show results

@snippet cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp display

--- a/doc/tutorials/imgproc/morph_lines_detection/morph_lines_detection.md
+++ b/doc/tutorials/imgproc/morph_lines_detection/morph_lines_detection.md
@ -80,7 +80,7 @@ Explanation / Result

 Get image from [here](https://raw.githubusercontent.com/opencv/opencv/4.x/doc/tutorials/imgproc/morph_lines_detection/images/src.png) .

-#### Load Image
+### Load Image

@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.cpp load_image
@ -96,7 +96,7 @@ Get image from [here](https://raw.githubusercontent.com/opencv/opencv/4.x/doc/tu

 ![](images/src.png)

-#### Grayscale
+### Grayscale

@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.cpp gray
@ -112,7 +112,7 @@ Get image from [here](https://raw.githubusercontent.com/opencv/opencv/4.x/doc/tu

 ![](images/gray.png)

-#### Grayscale to Binary image
+### Grayscale to Binary image

@add_toggle_cpp
@snippet samples/cpp/tutorial_code/ImgProc/morph_lines_detection/Morphology_3.cpp bin
@ -128,7 +128,7 @@ Get image from [here](https://raw.githubusercontent.com/opencv/opencv/4.x/doc/tu

 ![](images/binary.png)

-#### Output images
+### Output images

 Now we are ready to apply morphological operations in order to extract the horizontal and vertical lines and as a consequence to separate the music notes from the music sheet, but first let's initialize the output images that we will use for that reason:

@ -144,7 +144,7 @@ Now we are ready to apply morphological operations in order to extract the horiz
@snippet samples/python/tutorial_code/imgProc/morph_lines_detection/morph_lines_detection.py init
@end_toggle

-#### Structure elements
+### Structure elements

 As we specified in the theory in order to extract the object that we desire, we need to create the corresponding structure element. Since  we want to extract the horizontal lines, a corresponding structure element for that purpose will have the following shape:
 ![](images/linear_horiz.png)
@ -182,7 +182,7 @@ and again this is represented as follows:

 ![](images/vert.png)

-#### Refine edges / Result
+### Refine edges / Result

 As you can see we are almost there. However, at that point you will notice that the edges of the notes are a bit rough. For that reason we need to refine the edges in order to obtain a smoother result:

--- a/doc/tutorials/imgproc/pyramids/pyramids.markdown
+++ b/doc/tutorials/imgproc/pyramids/pyramids.markdown
@ -43,7 +43,7 @@ Theory
        pyramid (with less resolution)
 -   In this tutorial we'll use the *Gaussian pyramid*.

-#### Gaussian Pyramid
+### Gaussian Pyramid

 -   Imagine the pyramid as a set of layers in which the higher the layer, the smaller the size.

@ -100,7 +100,7 @@ Explanation

 Let's check the general structure of the program:

-#### Load an image
+### Load an image

@add_toggle_cpp
@snippet cpp/tutorial_code/ImgProc/Pyramids/Pyramids.cpp load
@ -114,7 +114,7 @@ Let's check the general structure of the program:
@snippet python/tutorial_code/imgProc/Pyramids/pyramids.py load
@end_toggle

-#### Create window
+### Create window

@add_toggle_cpp
@snippet cpp/tutorial_code/ImgProc/Pyramids/Pyramids.cpp show_image
@ -128,7 +128,7 @@ Let's check the general structure of the program:
@snippet python/tutorial_code/imgProc/Pyramids/pyramids.py show_image
@end_toggle

-#### Loop
+### Loop

@add_toggle_cpp
@snippet cpp/tutorial_code/ImgProc/Pyramids/Pyramids.cpp loop
--- a/doc/tutorials/introduction/android_binary_package/android_dev_intro.markdown
+++ b/doc/tutorials/introduction/android_binary_package/android_dev_intro.markdown
@ -85,7 +85,7 @@ Setup Device for Testing and Debugging
 Usually the recipe above works as expected, but in some cases there are additional actions that must
 be performed. In this section we'll cover some cases.

-#### Windows host computer
+### Windows host computer

 If you have Windows 10 or higher then you don't have to do additional actions to connect
 a phone and run samples on it. However, earlier Windows versions require a longer procedure:
@ -163,7 +163,7 @@ a phone and run samples on it. However, earlier Windows versions require a longe
 -#  Now, in Eclipse go Run -\> Run/Debug to run your application in regular or debugging mode.
    Device Chooser will let you choose among the devices.

-#### Linux host computer
+### Linux host computer

 While the latest Ubuntu versions work well with connected Android devices, there can be issues on older versions.
 However, most of them can be fixed easily. You have to create a new **/etc/udev/rules.d/51-android.rules** configuration file that contains
@ -184,7 +184,7 @@ R58MB40Q3VP     device
 savuor@rostislav-laptop:~/Android/Sdk/platform-tools$
 ```

-#### Mac OS host computer
+### Mac OS host computer

 No actions are required, just connect your device via USB and run adb devices to check connection.

--- a/doc/tutorials/introduction/building_tegra_cuda/building_tegra_cuda.markdown
+++ b/doc/tutorials/introduction/building_tegra_cuda/building_tegra_cuda.markdown
@ -15,7 +15,7 @@ This tutorial is deprecated.
@tableofcontents

 OpenCV with CUDA for Tegra
-==========================
+--------------------------

 This document is a basic guide to building the OpenCV libraries with CUDA support for use in the Tegra environment. It covers the basic elements of building the version 3.1.0 libraries from source code for three (3) different types of platforms:

@ -36,7 +36,7 @@ The OpenCV build system supports native compilation for all the supported platfo
 At the present time, this document focuses only on native compilation.

 Getting the Source Code {#tutorial_building_tegra_cuda_getting_the_code}
-=======================
+-----------------------

 There are two (2) ways to get the OpenCV source code:

@ -45,8 +45,7 @@ There are two (2) ways to get the OpenCV source code:

 For this guide, the focus is on using the git repositories. This is because the 3.1.0 version of OpenCV will not build with CUDA 8.0 without applying a few small upstream changes from the git repository.

-OpenCV
------
+### OpenCV

 Start with the `opencv` repository:

@ -93,8 +92,7 @@ You should see output similar to:

 At this point, the `opencv` repository is ready for building.

-OpenCV Extra
------------
+### OpenCV Extra

 The `opencv_extra` repository contains extra data for the OpenCV library, including the data files used by the tests and demos. It must be cloned separately:

@ -111,12 +109,11 @@ You may opt to not fetch this repository if you do not plan on running the tests
 __Note:__ If you plan to run the tests, some tests expect the data to be present and will fail without it.

 Preparation and Prerequisites {#tutorial_building_tegra_cuda_preparation}
-=============================
+-----------------------------

 To build OpenCV, you need a directory to create the configuration and build the libraries. You also need a number of 3rd-party libraries upon which OpenCV depends.

-Prerequisites for Ubuntu Linux
------------------------------
+### Prerequisites for Ubuntu Linux

 These are the basic requirements for building OpenCV for Tegra on Linux:

@ -186,8 +183,7 @@ The commands that will do this:

 Once all the necessary packages are installed, you can configure the build.

-Preparing the Build Area
------------------------
+### Preparing the Build Area

 Software projects that use the CMake system for configuring their builds expect the actual builds to be done outside of the source tree itself. For configuring and building OpenCV, create a directory called "build" in the same base directory into which you cloned the git repositories:

@ -197,7 +193,7 @@ Software projects that use the CMake system for configuring their builds expect
 You are now ready to configure and build OpenCV.

 Configuring OpenCV for Building {#tutorial_building_tegra_cuda_configuring}
-===============================
+-------------------------------

 The CMake configuration options given below for the different platforms are targeted towards the functionality needed for Tegra. They are based on the original configuration options used for building OpenCV 2.4.13.

@ -209,8 +205,7 @@ For the Linux-based platforms, the shown value for the `CMAKE_INSTALL_PREFIX` pa

 In each of the `cmake` invocations below, the last parameter, `OPENCV_TEST_DATA_PATH`, tells the build system where to find the test-data that is provided by the `opencv_extra` repository. When this is included, a `make install` installs this test-data alongside the libraries and example code, and a `make test` automatically provides this path to the tests that have to load data from it. If you did not clone the `opencv_extra` repository, do not include this parameter.

-Vibrante V4L Configuration
--------------------------
+### Vibrante V4L Configuration

 Supported platform: Drive PX 2

@ -251,8 +246,7 @@ The configuration provided above builds the Python bindings for Python 2 (but no

    -DBUILD_opencv_python2=OFF

-Jetson L4T Configuration
------------------------
+### Jetson L4T Configuration

 Supported platforms:

@ -261,7 +255,7 @@ Supported platforms:

 Configuration is slightly different for the Jetson TK1 and the Jetson TX1 systems.

-### Jetson TK1
+#### Jetson TK1

    $ cmake \
        -DCMAKE_BUILD_TYPE=Release \
@ -299,7 +293,7 @@ Configuration is slightly different for the Jetson TK1 and the Jetson TX1 system

 __Note:__ This uses CUDA 6.5, not 8.0.

-### Jetson TX1
+#### Jetson TX1

    $ cmake \
        -DCMAKE_BUILD_TYPE=Release \
@ -336,8 +330,7 @@ __Note:__ This uses CUDA 6.5, not 8.0.

 __Note:__ This configuration does not set the `ENABLE_NEON` parameter.

-Ubuntu Desktop Linux Configuration
----------------------------------
+### Ubuntu Desktop Linux Configuration

 Supported platforms:

@ -383,12 +376,11 @@ This configuration is nearly identical to that for V4L and L4T, except that the
 As with previous examples, the configuration given above builds the Python bindings for Python 2 (but not Python 3) as part of the build process.

 Building OpenCV {#tutorial_building_tegra_cuda_building}
-===============
+---------------

 Once `cmake` finishes configuring OpenCV, building is done using the standard `make` utility.

-Building with `make`
--------------------
+### Building with `make`

 The only parameter that is needed for the invocation of `make` is the `-j` parameter for specifying how many parallel threads to use. This varies depending on the system and how much memory is available, other running processes, etc. The following table offers suggested values for this parameter:

@ -408,12 +400,11 @@ By default, CMake hides the details of the build steps. If you need to see more
    $ make -j6 VERBOSE=1

 Testing OpenCV {#tutorial_building_tegra_cuda_testing}
-==============
+--------------

 Once the build completes successfully, you have the option of running the extensive set of tests that OpenCV provides. If you did not clone the `opencv_extra` repository and specify the path to `testdata` in the `cmake` invocation, then testing is not recommended.

-Testing under Linux
-------------------
+### Testing under Linux

 To run the basic tests under Linux, execute:

@ -425,7 +416,7 @@ This executes `ctest` to carry out the tests, as specified in CTest syntax withi

 In this example, there are two (2) arguments passed to `ctest`: `--verbose` and `--parallel 3`. The first argument causes the output from `ctest` to be more detailed, and the second causes `ctest` to run as many as three (3) tests in parallel. As with choosing a thread count for building, base any choice for testing on the available number of processor cores, physical memory, etc. Some of the tests do attempt to allocate significant amounts of memory.

-### Known Issues with Tests
+#### Known Issues with Tests

 At present, not all of the tests in the OpenCV test suite pass. There are tests that fail whether or not CUDA is compiled, and there are tests that are only specific to CUDA that also do not currently pass.

@ -434,7 +425,7 @@ __Note:__ There are no tests that pass without CUDA but fail only when CUDA is i
 As the full lists of failing tests vary based on platform, it is impractical to list them here.

 Installing OpenCV {#tutorial_building_tegra_cuda_installing}
-=================
+-----------------

 Installing OpenCV is very straightforward. For the Linux-based platforms, the command is:

@ -443,14 +434,13 @@ Installing OpenCV is very straightforward. For the Linux-based platforms, the co
 Depending on the chosen installation location, you may need root privilege to install.

 Building OpenCV 2.4.X {#tutorial_building_tegra_cuda_opencv_24X}
-=====================
+---------------------

 If you wish to build your own version of the 2.4 version of OpenCV, there are only a few adjustments that must be made. At the time of this writing, the latest version on the 2.4 tree is 2.4.13. These instructions may work for later versions of 2.4, though they have not been tested for any earlier versions.

 __Note:__ The 2.4.X OpenCV source does not have the extra modules and code for Tegra that was upstreamed into the 3.X versions of OpenCV. This part of the guide is only for cases where you want to build a vanilla version of OpenCV 2.4.

-Selecting the 2.4 Source
------------------------
+### Selecting the 2.4 Source

 First you must select the correct source branch or tag. If you want a specific version such as 2.4.13, you want to make a local branch based on the tag, as was done with the 3.1.0 tag above:

@ -466,14 +456,13 @@ If you simply want the newest code from the 2.4 line of OpenCV, there is a `2.4`

 There is no need for the `git cherry-pick` commands used with 3.1.0 when building the 2.4.13 source.

-Configuring
-----------
+### Configuring

 Configuring is done with CMake as before. The primary difference is that OpenCV 2.4 only provides Python bindings for Python 2, and thus does not distinguish between Python 2 and Python 3 in the CMake parameters. There is only one parameter, `BUILD_opencv_python`. In addition, there is a build-related parameter that controls features in 2.4 that are not in 3.1.0. This parameter is `BUILD_opencv_nonfree`.

 Configuration still takes place in a separate directory that must be a sibling to the `opencv` and `opencv_extra` directories.

-### Configuring Vibrante V4L
+#### Configuring Vibrante V4L

 For DRIVE PX 2:

@ -510,7 +499,7 @@ For DRIVE PX 2:
        -DOPENCV_TEST_DATA_PATH=../opencv_extra/testdata \
        ../opencv

-### Configuring Jetson L4T
+#### Configuring Jetson L4T

 For Jetson TK1:

@ -582,7 +571,7 @@ For Jetson TX1:
        -DOPENCV_TEST_DATA_PATH=../opencv_extra/testdata \
        ../opencv

-### Configuring Desktop Ubuntu Linux
+#### Configuring Desktop Ubuntu Linux

 For both 14.04 LTS and 16.04 LTS:

@ -618,13 +607,12 @@ For both 14.04 LTS and 16.04 LTS:
        -DOPENCV_TEST_DATA_PATH=../opencv_extra/testdata \
        ../opencv

-Building, Testing and Installing
--------------------------------
+### Building, Testing and Installing

 Once configured, the steps of building, testing, and installing are the same as above for the 3.1.0 source.

 CMake Parameter Reference {#tutorial_building_tegra_cuda_parameter_reference}
-=========================
+-------------------------

 The following is a table of all the parameters passed to CMake in the recommended invocations above. Some of these are parameters from CMake itself, while most are specific to OpenCV.

--- a/doc/tutorials/introduction/clojure_dev_intro/clojure_dev_intro.markdown
+++ b/doc/tutorials/introduction/clojure_dev_intro/clojure_dev_intro.markdown
@ -35,11 +35,11 @@ issue the following command to run the sample from the command line.
 cd path/to/samples/java/clojure/simple-sample
 lein run
@endcode
+
 Preamble
 --------

-For detailed instruction on installing OpenCV with desktop Java support refer to the @ref tutorial_java_dev_intro "corresponding
-tutorial".
+For detailed instruction on installing OpenCV with desktop Java support refer to the @ref tutorial_java_dev_intro "corresponding tutorial".

 If you are in hurry, here is a minimum quick start guide to install OpenCV on Mac OS X:

@ -63,6 +63,7 @@ make -j8
 # optional
 # make install
@endcode
+
 Install Leiningen
 -----------------

@ -170,6 +171,7 @@ i386     -> x86
 arm      -> arm
 sparc    -> sparc
@endcode
+
 ### Package the native lib as a jar

 Next you need to package the native lib in a jar file by using the jar command to create a new jar
@ -193,6 +195,7 @@ tree

 3 directories, 3 files
@endcode
+
 ### Locally install the jars

 We are now ready to add the two jars as artifacts to the local maven repository with the help of the
@ -402,6 +405,7 @@ Let's now try to port to Clojure the @ref tutorial_java_dev_intro "OpenCV Java t
 Instead of writing it in a source file we're going to evaluate it at the REPL.

 Following is the original Java source code of the cited sample.
+
@code{.java}
 import org.opencv.core.Mat;
 import org.opencv.core.CvType;
@ -430,20 +434,25 @@ Before start coding, we'd like to eliminate the boring need of interactively loa
 opencv lib any time we start a new REPL to interact with it.

 First, stop the REPL by evaluating the (exit) expression at the REPL prompt.
+
@code{.clojure}
 user=> (exit)
 Bye for now!
@endcode
+
 Then open your project.clj file and edit it as follows:
+
@code{.clojure}
 (defproject simple-sample "0.1.0-SNAPSHOT"
  ...
 injections [(clojure.lang.RT/loadLibrary org.opencv.core.Core/NATIVE_LIBRARY_NAME)])
@endcode
+
 Here we're saying to load the opencv native lib anytime we run the REPL in such a way that we have
 not anymore to remember to manually do it.

 Rerun the lein repl task
+
@code{.bash}
 lein repl
 nREPL server started on port 51645 on host 127.0.0.1
@ -458,11 +467,14 @@ Clojure 1.5.1

 user=>
@endcode
+
 Import the interested OpenCV java interfaces.
+
@code{.clojure}
 user=> (import '[org.opencv.core Mat CvType Scalar])
 org.opencv.core.Scalar
@endcode
+
 We're going to mimic almost verbatim the original OpenCV java tutorial to:

 -   create a 5x10 matrix with all its elements initialized to 0
--- a/doc/tutorials/introduction/env_reference/env_reference.markdown
+++ b/doc/tutorials/introduction/env_reference/env_reference.markdown
@ -6,7 +6,7 @@ OpenCV environment variables reference {#tutorial_env_reference}

@tableofcontents

-### Introduction
+## Introduction

 OpenCV can change its behavior depending on the runtime environment:
 - enable extra debugging output or performance tracing
@ -18,7 +18,7 @@ OpenCV can change its behavior depending on the runtime environment:
 - ⭐ marks most popular variables
 - variables with names like this `VAR_${NAME}` describes family of variables, where `${NAME}` should be changed to one of predefined values, e.g. `VAR_TBB`, `VAR_OPENMP`, ...

-##### Setting environment variable in Windows
+### Setting environment variable in Windows
 In terminal or cmd-file (bat-file):
 ```.bat
 set MY_ENV_VARIABLE=true
@ -30,7 +30,7 @@ In GUI:
 - In new window click on the "Environment variables" button
 - Add an entry to the "User variables" list

-##### Setting environment variable in Linux
+### Setting environment variable in Linux

 In terminal or shell script:
 ```.sh
@ -42,7 +42,7 @@ or as a single command:
 MY_ENV_VARIABLE=true ./my_app
 ```

-##### Setting environment variable in Python
+### Setting environment variable in Python

 ```.py
 import os
@ -51,7 +51,7 @@ import cv2 # variables set after this may not have effect
 ```


-### Types
+## Types

 - _non-null_ - set to anything to enable feature, in some cases can be interpreted as other types (e.g. path)
 - _bool_ - `1`, `True`, `true`, `TRUE` / `0`, `False`, `false`, `FALSE`
@ -61,7 +61,7 @@ import cv2 # variables set after this may not have effect
 - _paths_ - `;`-separated on Windows, `:`-separated on others


-### General, core
+## General, core
 | name | type | default | description |
 |------|------|---------|-------------|
 | OPENCV_SKIP_CPU_BASELINE_CHECK | non-null | | do not check that current CPU supports all features used by the build (baseline) |
@ -84,14 +84,14 @@ Links:
 - https://github.com/opencv/opencv/wiki/CPU-optimizations-build-options


-### Logging
+## Logging
 | name | type | default | description |
 |------|------|---------|-------------|
 | ⭐ OPENCV_LOG_LEVEL | string | | logging level (see accepted values below) |
 | OPENCV_LOG_TIMESTAMP | bool | true | logging with timestamps |
 | OPENCV_LOG_TIMESTAMP_NS | bool | false | add nsec to logging timestamps |

-##### Levels:
+### Levels
 - `0`, `O`, `OFF`, `S`, `SILENT`, `DISABLE`, `DISABLED`
 - `F`, `FATAL`
 - `E`, `ERROR`
@ -101,7 +101,7 @@ Links:
 - `V`, `VERBOSE`


-### core/parallel_for
+## core/parallel_for
 | name | type | default | description |
 |------|------|---------|-------------|
 | ⭐ OPENCV_FOR_THREADS_NUM | num | 0 | set number of threads |
@ -112,7 +112,7 @@ Links:
 | OPENCV_FOR_OPENMP_DYNAMIC_DISABLE | bool | false | use single OpenMP thread |


-### backends
+## backends
 OPENCV_LEGACY_WAITKEY
 Some modules have multiple available backends, following variables allow choosing specific backend or changing default priorities in which backends will be probed (e.g. when opening a video file).

@ -128,7 +128,7 @@ Some modules have multiple available backends, following variables allow choosin
 | OPENCV_VIDEOIO_PRIORITY_LIST | string, `,`-separated | | list of videoio backends in priority order |


-### plugins
+## plugins
 Some external dependencies can be detached into a dynamic library, which will be loaded at runtime (plugin). Following variables allow changing default search locations and naming pattern for these plugins.
 | name | type | default | description |
 |------|------|---------|-------------|
@ -141,7 +141,7 @@ Some external dependencies can be detached into a dynamic library, which will be
 | OPENCV_VIDEOIO_PLUGIN_PATH | paths | | directories to search for _videoio_ plugins |
 | OPENCV_VIDEOIO_PLUGIN_${NAME} | string, glob | | _videoio_ plugin library name (glob) |

-### OpenCL
+## OpenCL

 **Note:** OpenCL device specification format is `<Platform>:<CPU|GPU|ACCELERATOR|nothing=GPU/CPU>:<deviceName>`, e.g. `AMD:GPU:`

@ -170,7 +170,7 @@ Some external dependencies can be detached into a dynamic library, which will be
 | OPENCV_OPENCL_FORCE | bool | false | force running OpenCL kernel even if usual conditions are not met (e.g. dst.isUMat) |
 | OPENCV_OPENCL_PERF_CHECK_BYPASS | bool | false | force running OpenCL kernel even if usual performance-related conditions are not met (e.g. image is very small) |

-##### SVM (Shared Virtual Memory) - disabled by default
+### SVM (Shared Virtual Memory) - disabled by default
 | name | type | default | description |
 |------|------|---------|-------------|
 | OPENCV_OPENCL_SVM_DISABLE | bool | false | disable SVM |
@ -179,11 +179,11 @@ Some external dependencies can be detached into a dynamic library, which will be
 | OPENCV_OPENCL_SVM_CAPABILITIES_MASK | num | | |
 | OPENCV_OPENCL_SVM_BUFFERPOOL_LIMIT | num | | same as OPENCV_OPENCL_BUFFERPOOL_LIMIT, but for SVM buffers |

-##### Links:
+### Links:
 - https://github.com/opencv/opencv/wiki/OpenCL-optimizations


-### Tracing/Profiling
+## Tracing/Profiling
 | name | type | default | description |
 |------|------|---------|-------------|
 | ⭐ OPENCV_TRACE | bool | false | enable trace |
@ -196,11 +196,11 @@ Some external dependencies can be detached into a dynamic library, which will be
 | OPENCV_TRACE_ITT_PARENT | bool | false | set parentID for ITT task |
 | OPENCV_TRACE_ITT_SET_THREAD_NAME | bool | false | set name for OpenCV's threads "OpenCVThread-%03d" |

-##### Links:
+### Links:
 - https://github.com/opencv/opencv/wiki/Profiling-OpenCV-Applications


-##### Cache
+## Cache
 **Note:** Default tmp location is `%TMPDIR%` (Windows); `$XDG_CACHE_HOME`, `$HOME/.cache`, `/var/tmp`, `/tmp` (others)
 | name | type | default | description |
 |------|------|---------|-------------|
@ -210,7 +210,7 @@ Some external dependencies can be detached into a dynamic library, which will be
 | OPENCV_OPENCL_CACHE_DIR | path | default tmp location | cache directory for OpenCL kernels cache (subdirectory `opencl_cache`) |


-### dnn
+## dnn
 **Note:** In the table below `dump_base_name` equals to `ocv_dnn_net_%05d_%02d` where first argument is internal network ID and the second - dump level.
 | name | type | default | description |
 |------|------|---------|-------------|
@ -240,7 +240,7 @@ Some external dependencies can be detached into a dynamic library, which will be
 | OPENCV_OCL4DNN_TUNING_RAISE_CHECK_ERROR | bool | false | raise exception on errors (auto-tuning) |


-### Tests
+## Tests
 | name | type | default | description |
 |------|------|---------|-------------|
 | ⭐ OPENCV_TEST_DATA_PATH | dir path | | set test data search location (e.g. `/home/user/opencv_extra/testdata`) |
@ -254,11 +254,11 @@ Some external dependencies can be detached into a dynamic library, which will be
 | OPENCV_PERF_VALIDATION_DIR | dir path | | location of files read/written by `--perf_read_validation_results`/`--perf_write_validation_results` |
 | ⭐ OPENCV_PYTEST_FILTER | string (glob) | | test filter for Python tests |

-##### Links:
+### Links:
 * https://github.com/opencv/opencv/wiki/QA_in_OpenCV


-### videoio
+## videoio
 **Note:** extra FFmpeg options should be pased in form `key;value|key;value|key;value`, for example `hwaccel;cuvid|video_codec;h264_cuvid|vsync;0` or `vcodec;x264|vprofile;high|vlevel;4.0`

 | name | type | default | description |
@ -288,7 +288,7 @@ Some external dependencies can be detached into a dynamic library, which will be
 | OPENCV_VIDEOWRITER_DEBUG | bool | false | enable debug messages for VideoWriter |
 | ⭐ OPENCV_VIDEOIO_DEBUG | bool | false | debug messages for both VideoCapture and VideoWriter |

-##### videoio tests
+### videoio tests
 | name | type | default | description |
 |------|------|---------|-------------|
 | OPENCV_TEST_VIDEOIO_BACKEND_REQUIRE_FFMPEG | | | test app will exit if no FFmpeg backend is available |
@ -297,14 +297,14 @@ Some external dependencies can be detached into a dynamic library, which will be
 | OPENCV_TEST_CAMERA_%d_FPS | num | | fps to set for N-th camera (0-based index) (waitAny_V4L test) |


-### gapi
+## gapi
 | name | type | default | description |
 |------|------|---------|-------------|
 | ⭐ GRAPH_DUMP_PATH | file path | | dump graph (dot format) |
 | PIPELINE_MODELS_PATH | dir path | | pipeline_modeling_tool sample application uses this var |
 | OPENCV_GAPI_INFERENCE_ENGINE_CORE_LIFETIME_WORKAROUND | bool | true (Windows, Apple), false (others) | similar to OPENCV_DNN_INFERENCE_ENGINE_CORE_LIFETIME_WORKAROUND |

-##### gapi tests/samples
+### gapi tests/samples
 | name | type | default | description |
 |------|------|---------|-------------|
 | PLAIDML_DEVICE | string | | specific to PlaidML backend test |
@ -312,12 +312,12 @@ Some external dependencies can be detached into a dynamic library, which will be
 | OPENCV_GAPI_ONNX_MODEL_PATH | dir path | | search location for ONNX models test |
 | OPENCV_TEST_FREETYPE_FONT_PATH | file path | | location of TrueType font for one of tests |

-##### Links:
+### Links:
 * https://github.com/opencv/opencv/wiki/Using-G-API-with-OpenVINO-Toolkit
 * https://github.com/opencv/opencv/wiki/Using-G-API-with-MS-ONNX-Runtime


-### highgui
+## highgui

 | name | type | default | description |
 |------|------|---------|-------------|
@ -325,14 +325,14 @@ Some external dependencies can be detached into a dynamic library, which will be
 | $XDG_RUNTIME_DIR | | | Wayland backend specific - create shared memory-mapped file for interprocess communication (named `opencv-shared-??????`) |


-### imgproc
+## imgproc
 | name | type | default | description |
 |------|------|---------|-------------|
 | OPENCV_OPENCL_IMGPROC_MORPH_SPECIAL_KERNEL | bool | true (Apple), false (others) | use special OpenCL kernel for small morph kernel (Intel devices) |
 | OPENCV_GAUSSIANBLUR_CHECK_BITEXACT_KERNELS | bool | false | validate Gaussian kernels before running (src is CV_16U, bit-exact version) |


-### imgcodecs
+## imgcodecs
 | name | type | default | description |
 |------|------|---------|-------------|
 | OPENCV_IMGCODECS_AVIF_MAX_FILE_SIZE | num | 64MB | limit input AVIF size |
--- a/doc/tutorials/introduction/windows_install/windows_install.markdown
+++ b/doc/tutorials/introduction/windows_install/windows_install.markdown
@ -22,8 +22,7 @@ best to help you out.
 with the latest Microsoft Visual Studio IDE and do not take advantage of the most advanced
 technologies we integrate into our library. .. _Windows_Install_Prebuild:

-Installation by Using the Pre-built Libraries {#tutorial_windows_install_prebuilt}
-=============================================
+## Installation by Using the Pre-built Libraries {#tutorial_windows_install_prebuilt}

 -#  Launch a web browser of choice and go to our [page on
    Sourceforge](http://sourceforge.net/projects/opencvlibrary/files/).
@ -35,8 +34,7 @@ Installation by Using the Pre-built Libraries {#tutorial_windows_install_prebuil

 -#  To finalize the installation go to the @ref tutorial_windows_install_path section.

-Installation by Using git-bash (version>=2.14.1) and cmake (version >=3.9.1){#tutorial_windows_gitbash_build}
-===============================================================
+## Installation by Using git-bash (version>=2.14.1) and cmake (version >=3.9.1){#tutorial_windows_gitbash_build}

 -#  You must download [cmake (version >=3.9.1)](https://cmake.org) and install it. You must add cmake to PATH variable during installation

@ -108,8 +106,7 @@ CMAKE_OPTIONS=(-DBUILD_PERF_TESTS:BOOL=OFF -DBUILD_TESTS:BOOL=OFF -DBUILD_DOCS:B
 -# Next time you run this script, opencv and opencv_contrib will be updated and rebuild


-Installation by Making Your Own Libraries from the Source Files {#tutorial_windows_install_build}
-===============================================================
+## Installation by Making Your Own Libraries from the Source Files {#tutorial_windows_install_build}

 You may find the content of this tutorial also inside the following videos:
 [Part 1](https://www.youtube.com/watch?v=NnovZ1cTlMs) and [Part 2](https://www.youtube.com/watch?v=qGNWMcfWwPU), hosted on YouTube.
@ -364,8 +361,7 @@ libraries). If you do not need the support for some of these, you can just freel
    caused mostly by old video card drivers. For testing the GPU (if built) run the
    *performance_gpu.exe* sample application.

-Set the OpenCV environment variable and add it to the systems path {#tutorial_windows_install_path}
-=================================================================
+## Set the OpenCV environment variable and add it to the systems path {#tutorial_windows_install_path}

 First, we set an environment variable to make our work easier. This will hold the build directory of
 our OpenCV library that we use in our projects. Start up a command window and enter:
--- a/modules/core/include/opencv2/core.hpp
+++ b/modules/core/include/opencv2/core.hpp
@ -62,10 +62,6 @@
@defgroup core Core functionality
@{
    @defgroup core_basic Basic structures
-    @defgroup core_c C structures and operations
-    @{
-        @defgroup core_c_glue Connections with C++
-    @}
    @defgroup core_array Operations on arrays
    @defgroup core_async Asynchronous API
    @defgroup core_xml XML/YAML Persistence
--- a/modules/core/include/opencv2/core/cvstd.hpp
+++ b/modules/core/include/opencv2/core/cvstd.hpp
@ -140,7 +140,6 @@ public:

 //! @} core_utils

-//! @endcond

 //! @addtogroup core_basic
 //! @{
--- a/modules/core/include/opencv2/core/matx.hpp
+++ b/modules/core/include/opencv2/core/matx.hpp
--- a/modules/core/include/opencv2/core/matx.inl.hpp
+++ b/modules/core/include/opencv2/core/matx.inl.hpp
--- a/modules/core/include/opencv2/core/persistence.hpp
+++ b/modules/core/include/opencv2/core/persistence.hpp
@ -696,9 +696,6 @@ protected:

 /////////////////// XML & YAML I/O implementation //////////////////

-//! @relates cv::FileStorage
-//! @{
-
 CV_EXPORTS void write( FileStorage& fs, const String& name, int value );
 CV_EXPORTS void write( FileStorage& fs, const String& name, float value );
 CV_EXPORTS void write( FileStorage& fs, const String& name, double value );
@ -715,11 +712,6 @@ CV_EXPORTS void writeScalar( FileStorage& fs, float value );
 CV_EXPORTS void writeScalar( FileStorage& fs, double value );
 CV_EXPORTS void writeScalar( FileStorage& fs, const String& value );

-//! @}
-
-//! @relates cv::FileNode
-//! @{
-
 CV_EXPORTS void read(const FileNode& node, int& value, int default_value);
 CV_EXPORTS void read(const FileNode& node, float& value, float default_value);
 CV_EXPORTS void read(const FileNode& node, double& value, double default_value);
@ -796,10 +788,7 @@ static inline void read(const FileNode& node, Range& value, const Range& default
    value.start = temp.x; value.end = temp.y;
 }

-//! @}
-
 /** @brief Writes string to a file storage.
-@relates cv::FileStorage
 */
 CV_EXPORTS FileStorage& operator << (FileStorage& fs, const String& str);

@ -884,9 +873,6 @@ namespace internal

 //! @endcond

-//! @relates cv::FileStorage
-//! @{
-
 template<typename _Tp> static inline
 void write(FileStorage& fs, const _Tp& value)
 {
@ -1118,10 +1104,6 @@ static inline void write(FileStorage& fs, const std::vector<DMatch>& vec)
 }
 #endif

-//! @} FileStorage
-
-//! @relates cv::FileNode
-//! @{

 static inline
 void read(const FileNode& node, bool& value, bool default_value)
@ -1208,11 +1190,6 @@ void read( const FileNode& node, std::vector<DMatch>& vec, const std::vector<DMa
        read(node, vec);
 }

-//! @} FileNode
-
-//! @relates cv::FileStorage
-//! @{
-
 /** @brief Writes data to a file storage.
 */
 template<typename _Tp> static inline
@ -1244,11 +1221,6 @@ FileStorage& operator << (FileStorage& fs, char* value)
    return (fs << String(value));
 }

-//! @} FileStorage
-
-//! @relates cv::FileNodeIterator
-//! @{
-
 /** @brief Reads data from a file storage.
 */
 template<typename _Tp> static inline
@ -1268,11 +1240,6 @@ FileNodeIterator& operator >> (FileNodeIterator& it, std::vector<_Tp>& vec)
    return it;
 }

-//! @} FileNodeIterator
-
-//! @relates cv::FileNode
-//! @{
-
 /** @brief Reads data from a file storage.
 */
 template<typename _Tp> static inline
@ -1323,11 +1290,6 @@ void operator >> (const FileNode& n, DMatch& m)
    it >> m.queryIdx >> m.trainIdx >> m.imgIdx >> m.distance;
 }

-//! @} FileNode
-
-//! @relates cv::FileNodeIterator
-//! @{
-
 CV_EXPORTS bool operator == (const FileNodeIterator& it1, const FileNodeIterator& it2);
 CV_EXPORTS bool operator != (const FileNodeIterator& it1, const FileNodeIterator& it2);

@ -1343,8 +1305,6 @@ bool operator < (const FileNodeIterator& it1, const FileNodeIterator& it2)
    return it1.remaining() > it2.remaining();
 }

-//! @} FileNodeIterator
-
 } // cv

 #endif // OPENCV_CORE_PERSISTENCE_HPP
--- a/modules/features2d/include/opencv2/features2d.hpp
+++ b/modules/features2d/include/opencv2/features2d.hpp
@ -56,15 +56,15 @@
    @defgroup features2d_main Feature Detection and Description
    @defgroup features2d_match Descriptor Matchers

-Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to
-easily switch between different algorithms solving the same problem. This section is devoted to
-matching descriptors that are represented as vectors in a multidimensional space. All objects that
-implement vector descriptor matchers inherit the DescriptorMatcher interface.
+    Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables
+    you to easily switch between different algorithms solving the same problem. This section is
+    devoted to matching descriptors that are represented as vectors in a multidimensional space.
+    All objects that implement vector descriptor matchers inherit the DescriptorMatcher interface.

    @defgroup features2d_draw Drawing Function of Keypoints and Matches
    @defgroup features2d_category Object Categorization

-This section describes approaches based on local 2D features and used to categorize objects.
+    This section describes approaches based on local 2D features and used to categorize objects.

    @defgroup feature2d_hal Hardware Acceleration Layer
    @{
@ -567,10 +567,6 @@ public:
    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
 };

-//! @} features2d_main
-
-//! @addtogroup features2d_main
-//! @{

 /** @brief Wrapping class for feature detection using the FAST method. :
 */
@ -627,10 +623,6 @@ detection, use cv.FAST.detect() method.
 CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
                      int threshold, bool nonmaxSuppression, FastFeatureDetector::DetectorType type );

-//! @} features2d_main
-
-//! @addtogroup features2d_main
-//! @{

 /** @brief Wrapping class for feature detection using the AGAST method. :
 */
@ -793,10 +785,6 @@ public:
  CV_WRAP virtual const std::vector<std::vector<cv::Point> >& getBlobContours() const;
 };

-//! @} features2d_main
-
-//! @addtogroup features2d_main
-//! @{

 /** @brief Class implementing the KAZE keypoint detector and descriptor extractor, described in @cite ABD12 .

@ -925,7 +913,6 @@ public:
    CV_WRAP virtual int getMaxPoints() const = 0;
 };

-//! @} features2d_main

 /****************************************************************************************\
 *                                      Distance                                          *
@ -990,6 +977,8 @@ struct L1
    }
 };

+//! @} features2d_main
+
 /****************************************************************************************\
 *                                  DescriptorMatcher                                     *
 \****************************************************************************************/
@ -1431,6 +1420,9 @@ CV_EXPORTS_AS(drawMatchesKnn) void drawMatches( InputArray img1, const std::vect
 *   Functions to evaluate the feature detectors and [generic] descriptor extractors      *
 \****************************************************************************************/

+//! @addtogroup features2d_main
+//! @{
+
 CV_EXPORTS void evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H1to2,
                                         std::vector<KeyPoint>* keypoints1, std::vector<KeyPoint>* keypoints2,
                                         float& repeatability, int& correspCount,
@ -1443,6 +1435,8 @@ CV_EXPORTS void computeRecallPrecisionCurve( const std::vector<std::vector<DMatc
 CV_EXPORTS float getRecall( const std::vector<Point2f>& recallPrecisionCurve, float l_precision );
 CV_EXPORTS int getNearestPoint( const std::vector<Point2f>& recallPrecisionCurve, float l_precision );

+//! @}
+
 /****************************************************************************************\
 *                                     Bag of visual words                                *
 \****************************************************************************************/
@ -1603,8 +1597,6 @@ protected:

 //! @} features2d_category

-//! @} features2d
-
 } /* namespace cv */

 #endif
--- a/modules/gapi/doc/00-root.markdown
+++ b/modules/gapi/doc/00-root.markdown
@ -17,7 +17,7 @@ is volatile at the moment and there may be minor but
 compatibility-breaking changes in the future.

 # Contents
-
+gapi
 G-API documentation is organized into the following chapters:

 - @subpage gapi_purposes
@ -41,6 +41,10 @@ G-API documentation is organized into the following chapters:

 - API Reference: functions and classes

+    - @subpage gapi_ref
+
+      Core G-API classes, data types, backends, etc.
+
    - @subpage gapi_core

      Core G-API operations - arithmetic, boolean, and other matrix
@ -51,6 +55,14 @@ G-API documentation is organized into the following chapters:
      Image processing functions: color space conversions, various
      filters, etc.

+    - @subpage gapi_video
+
+      Video processing functionality.
+
+    - @subpage gapi_draw
+
+      Drawing and composition functionality
+
 # API Example {#gapi_example}

 A very basic example of G-API pipeline is shown below:
--- a/modules/gapi/doc/30-implementation.markdown
+++ b/modules/gapi/doc/30-implementation.markdown
@ -4,7 +4,7 @@

 # G-API Implementation details

-Note -- this section is still in progress.
+@note this section is still in progress.

 # API layer {#gapi_detail_api}

--- a/modules/gapi/include/opencv2/gapi.hpp
+++ b/modules/gapi/include/opencv2/gapi.hpp
@ -10,7 +10,7 @@

 #include <memory>

-/** \defgroup gapi G-API framework
+/** \defgroup gapi_ref G-API framework
@{
    @defgroup gapi_main_classes G-API Main Classes
    @defgroup gapi_data_objects G-API Data Types
--- a/modules/gapi/include/opencv2/gapi/gcommon.hpp
+++ b/modules/gapi/include/opencv2/gapi/gcommon.hpp
@ -249,6 +249,8 @@ template<typename T> struct wrap_serialize
 } // namespace s11n
 } // namespace gapi

+/** @} gapi_compile_args */
+
 /**
 * @brief Ask G-API to dump compiled graph in Graphviz format under
 * the given file name.
@ -261,7 +263,6 @@ struct graph_dump_path
 {
    std::string m_dump_path;
 };
-/** @} */

 /**
 * @brief Ask G-API to use threaded executor when cv::GComputation
@ -276,7 +277,6 @@ struct GAPI_EXPORTS use_threaded_executor

    uint32_t num_threads;
 };
-/** @} */

 namespace detail
 {
--- a/modules/gapi/include/opencv2/gapi/gstreaming.hpp
+++ b/modules/gapi/include/opencv2/gapi/gstreaming.hpp
@ -388,7 +388,6 @@ protected:
    /// @private
    std::shared_ptr<Priv> m_priv;
 };
-/** @} */

 namespace gapi {

@ -413,7 +412,6 @@ struct GAPI_EXPORTS_W_SIMPLE queue_capacity
    GAPI_PROP_RW
    size_t capacity;
 };
-/** @} */
 } // namespace streaming
 } // namespace gapi

@ -425,6 +423,8 @@ template<> struct CompileArgTag<cv::gapi::streaming::queue_capacity>
 };
 }

+/** @} gapi_main_classes */
+
 }

 #endif // OPENCV_GAPI_GSTREAMING_COMPILED_HPP
--- a/modules/gapi/include/opencv2/gapi/infer/ov.hpp
+++ b/modules/gapi/include/opencv2/gapi/infer/ov.hpp
@ -690,7 +690,6 @@ namespace wip { namespace ov {
 * taking into account the i/o data transfer.
 */
 struct benchmark_mode { };
-/** @} */

 } // namespace ov
 } // namespace wip
--- a/modules/highgui/doc/highgui_qt.cpp
+++ b/modules/highgui/doc/highgui_qt.cpp
@ -0,0 +1,39 @@
+#include "opencv2/highgui.hpp"
+
+int main(int argc, char *argv[])
+{
+    int value = 50;
+    int value2 = 0;
+
+    namedWindow("main1",WINDOW_NORMAL);
+    namedWindow("main2",WINDOW_AUTOSIZE | WINDOW_GUI_NORMAL);
+    createTrackbar( "track1", "main1", &value, 255,  NULL);
+
+    String nameb1 = "button1";
+    String nameb2 = "button2";
+
+    createButton(nameb1,callbackButton,&nameb1,QT_CHECKBOX,1);
+    createButton(nameb2,callbackButton,NULL,QT_CHECKBOX,0);
+    createTrackbar( "track2", NULL, &value2, 255, NULL);
+    createButton("button5",callbackButton1,NULL,QT_RADIOBOX,0);
+    createButton("button6",callbackButton2,NULL,QT_RADIOBOX,1);
+
+    setMouseCallback( "main2",on_mouse,NULL );
+
+    Mat img1 = imread("files/flower.jpg");
+    VideoCapture video;
+    video.open("files/hockey.avi");
+
+    Mat img2,img3;
+    while( waitKey(33) != 27 )
+    {
+        img1.convertTo(img2,-1,1,value);
+        video >> img3;
+
+        imshow("main1",img2);
+        imshow("main2",img3);
+    }
+
+    destroyAllWindows();
+    return 0;
+}
--- a/modules/highgui/include/opencv2/highgui.hpp
+++ b/modules/highgui/include/opencv2/highgui.hpp
@ -85,50 +85,8 @@ It provides easy interface to:
        created. Then, a new button is attached to it.

    See below the example used to generate the figure:
-    @code
-        int main(int argc, char *argv[])
-        {
-
-            int value = 50;
-            int value2 = 0;
-
-
-            namedWindow("main1",WINDOW_NORMAL);
-            namedWindow("main2",WINDOW_AUTOSIZE | WINDOW_GUI_NORMAL);
-            createTrackbar( "track1", "main1", &value, 255,  NULL);
-
-            String nameb1 = "button1";
-            String nameb2 = "button2";
-
-            createButton(nameb1,callbackButton,&nameb1,QT_CHECKBOX,1);
-            createButton(nameb2,callbackButton,NULL,QT_CHECKBOX,0);
-            createTrackbar( "track2", NULL, &value2, 255, NULL);
-            createButton("button5",callbackButton1,NULL,QT_RADIOBOX,0);
-            createButton("button6",callbackButton2,NULL,QT_RADIOBOX,1);
-
-            setMouseCallback( "main2",on_mouse,NULL );
-
-            Mat img1 = imread("files/flower.jpg");
-            VideoCapture video;
-            video.open("files/hockey.avi");
-
-            Mat img2,img3;
-
-            while( waitKey(33) != 27 )
-            {
-                img1.convertTo(img2,-1,1,value);
-                video >> img3;
-
-                imshow("main1",img2);
-                imshow("main2",img3);
-            }
-
-            destroyAllWindows();
-
-            return 0;
-        }
-    @endcode

+    @include highgui_qt.cpp

    @defgroup highgui_winrt WinRT support

@ -139,36 +97,34 @@ It provides easy interface to:

    See below the example used to generate the figure:
    @code
-        void sample_app::MainPage::ShowWindow()
+    void sample_app::MainPage::ShowWindow()
+    {
+        static cv::String windowName("sample");
+        cv::winrt_initContainer(this->cvContainer);
+        cv::namedWindow(windowName); // not required
+
+        cv::Mat image = cv::imread("Assets/sample.jpg");
+        cv::Mat converted = cv::Mat(image.rows, image.cols, CV_8UC4);
+        cv::cvtColor(image, converted, COLOR_BGR2BGRA);
+        cv::imshow(windowName, converted); // this will create window if it hasn't been created before
+
+        int state = 42;
+        cv::TrackbarCallback callback = [](int pos, void* userdata)
        {
-            static cv::String windowName("sample");
-            cv::winrt_initContainer(this->cvContainer);
-            cv::namedWindow(windowName); // not required
-
-            cv::Mat image = cv::imread("Assets/sample.jpg");
-            cv::Mat converted = cv::Mat(image.rows, image.cols, CV_8UC4);
-            cv::cvtColor(image, converted, COLOR_BGR2BGRA);
-            cv::imshow(windowName, converted); // this will create window if it hasn't been created before
-
-            int state = 42;
-            cv::TrackbarCallback callback = [](int pos, void* userdata)
-            {
-                if (pos == 0) {
-                    cv::destroyWindow(windowName);
-                }
-            };
-            cv::TrackbarCallback callbackTwin = [](int pos, void* userdata)
-            {
-                if (pos >= 70) {
-                    cv::destroyAllWindows();
-                }
-            };
-            cv::createTrackbar("Sample trackbar", windowName, &state, 100, callback);
-            cv::createTrackbar("Twin brother", windowName, &state, 100, callbackTwin);
-        }
+            if (pos == 0) {
+                cv::destroyWindow(windowName);
+            }
+        };
+        cv::TrackbarCallback callbackTwin = [](int pos, void* userdata)
+        {
+            if (pos >= 70) {
+                cv::destroyAllWindows();
+            }
+        };
+        cv::createTrackbar("Sample trackbar", windowName, &state, 100, callback);
+        cv::createTrackbar("Twin brother", windowName, &state, 100, callbackTwin);
+    }
    @endcode
-
-    @defgroup highgui_c C API
@}
 */

--- a/modules/imgcodecs/include/opencv2/imgcodecs.hpp
+++ b/modules/imgcodecs/include/opencv2/imgcodecs.hpp
@ -48,7 +48,6 @@
 /**
  @defgroup imgcodecs Image file reading and writing
  @{
-    @defgroup imgcodecs_c C API
    @defgroup imgcodecs_flags Flags used for image file reading and writing
    @defgroup imgcodecs_ios iOS glue
    @defgroup imgcodecs_macosx MacOS(OSX) glue
@ -297,7 +296,7 @@ It also demonstrates how to save multiple images in a TIFF file:
 CV_EXPORTS_W bool imwrite( const String& filename, InputArray img,
              const std::vector<int>& params = std::vector<int>());

-/// @overload multi-image overload for bindings
+//! @brief multi-image overload for bindings
 CV_WRAP static inline
 bool imwritemulti(const String& filename, InputArrayOfArrays img,
                  const std::vector<int>& params = std::vector<int>())
--- a/modules/imgproc/doc/colors.markdown
+++ b/modules/imgproc/doc/colors.markdown
@ -1,10 +1,12 @@
 Color conversions {#imgproc_color_conversions}
 =================
+
 See cv::cvtColor and cv::ColorConversionCodes
+
@todo document other conversion modes

@anchor color_convert_rgb_gray
-RGB \f$\leftrightarrow\f$ GRAY
+RGB \emoji arrow_right GRAY
 ------------------------------
 Transformations within RGB space like adding/removing the alpha channel, reversing the channel
 order, conversion to/from 16-bit RGB color (R5:G6:B5 or R5:G5:B5), as well as conversion
@ -20,7 +22,7 @@ More advanced channel reordering can also be done with cv::mixChannels.
@see cv::COLOR_BGR2GRAY, cv::COLOR_RGB2GRAY, cv::COLOR_GRAY2BGR, cv::COLOR_GRAY2RGB

@anchor color_convert_rgb_xyz
-RGB \f$\leftrightarrow\f$ CIE XYZ.Rec 709 with D65 white point
+RGB \emoji arrow_right CIE XYZ.Rec 709 with D65 white point
 --------------------------------------------------------------
 \f[\begin{bmatrix} X  \\ Y  \\ Z
  \end{bmatrix} \leftarrow \begin{bmatrix} 0.412453 & 0.357580 & 0.180423 \\ 0.212671 & 0.715160 & 0.072169 \\ 0.019334 & 0.119193 & 0.950227
@ -35,7 +37,7 @@ RGB \f$\leftrightarrow\f$ CIE XYZ.Rec 709 with D65 white point
@see cv::COLOR_BGR2XYZ, cv::COLOR_RGB2XYZ, cv::COLOR_XYZ2BGR, cv::COLOR_XYZ2RGB

@anchor color_convert_rgb_ycrcb
-RGB \f$\leftrightarrow\f$ YCrCb JPEG (or YCC)
+RGB \emoji arrow_right YCrCb JPEG (or YCC)
 ---------------------------------------------
 \f[Y  \leftarrow 0.299  \cdot R + 0.587  \cdot G + 0.114  \cdot B\f]
 \f[Cr  \leftarrow (R-Y)  \cdot 0.713 + delta\f]
@ -49,7 +51,7 @@ Y, Cr, and Cb cover the whole value range.
@see cv::COLOR_BGR2YCrCb, cv::COLOR_RGB2YCrCb, cv::COLOR_YCrCb2BGR, cv::COLOR_YCrCb2RGB

@anchor color_convert_rgb_hsv
-RGB \f$\leftrightarrow\f$ HSV
+RGB \emoji arrow_right HSV
 -----------------------------
 In case of 8-bit and 16-bit images, R, G, and B are converted to the floating-point format and
 scaled to fit the 0 to 1 range.
@ -71,7 +73,7 @@ The values are then converted to the destination data type:
@see cv::COLOR_BGR2HSV, cv::COLOR_RGB2HSV, cv::COLOR_HSV2BGR, cv::COLOR_HSV2RGB

@anchor color_convert_rgb_hls
-RGB \f$\leftrightarrow\f$ HLS
+RGB \emoji arrow_right HLS
 -----------------------------
 In case of 8-bit and 16-bit images, R, G, and B are converted to the floating-point format and
 scaled to fit the 0 to 1 range.
@ -96,7 +98,7 @@ The values are then converted to the destination data type:
@see cv::COLOR_BGR2HLS, cv::COLOR_RGB2HLS, cv::COLOR_HLS2BGR, cv::COLOR_HLS2RGB

@anchor color_convert_rgb_lab
-RGB \f$\leftrightarrow\f$ CIE L\*a\*b\*
+RGB \emoji arrow_right CIE L\*a\*b\*
 ---------------------------------------
 In case of 8-bit and 16-bit images, R, G, and B are converted to the floating-point format and
 scaled to fit the 0 to 1 range.
@ -121,7 +123,7 @@ are then converted to the destination data type:
@see cv::COLOR_BGR2Lab, cv::COLOR_RGB2Lab, cv::COLOR_Lab2BGR, cv::COLOR_Lab2RGB

@anchor color_convert_rgb_luv
-RGB \f$\leftrightarrow\f$ CIE L\*u\*v\*
+RGB \emoji arrow_right CIE L\*u\*v\*
 ---------------------------------------
 In case of 8-bit and 16-bit images, R, G, and B are converted to the floating-point format and
 scaled to fit 0 to 1 range.
@ -148,7 +150,7 @@ sources on the web, primarily from the Charles Poynton site <http://www.poynton.
@see cv::COLOR_BGR2Luv, cv::COLOR_RGB2Luv, cv::COLOR_Luv2BGR, cv::COLOR_Luv2RGB

@anchor color_convert_bayer
-Bayer \f$\rightarrow\f$ RGB
+Bayer \emoji arrow_right RGB
 ---------------------------
 The Bayer pattern is widely used in CCD and CMOS cameras. It enables you to get color pictures
 from a single plane where R, G, and B pixels (sensors of a particular component) are interleaved
--- a/modules/imgproc/include/opencv2/imgproc.hpp
+++ b/modules/imgproc/include/opencv2/imgproc.hpp
@ -46,143 +46,143 @@
 #include "opencv2/core.hpp"

 /**
-  @defgroup imgproc Image Processing
+@defgroup imgproc Image Processing

 This module includes image-processing functions.

-  @{
+@{
    @defgroup imgproc_filter Image Filtering

-Functions and classes described in this section are used to perform various linear or non-linear
-filtering operations on 2D images (represented as Mat's). It means that for each pixel location
-\f$(x,y)\f$ in the source image (normally, rectangular), its neighborhood is considered and used to
-compute the response. In case of a linear filter, it is a weighted sum of pixel values. In case of
-morphological operations, it is the minimum or maximum values, and so on. The computed response is
-stored in the destination image at the same location \f$(x,y)\f$. It means that the output image
-will be of the same size as the input image. Normally, the functions support multi-channel arrays,
-in which case every channel is processed independently. Therefore, the output image will also have
-the same number of channels as the input one.
+    Functions and classes described in this section are used to perform various linear or non-linear
+    filtering operations on 2D images (represented as Mat's). It means that for each pixel location
+    \f$(x,y)\f$ in the source image (normally, rectangular), its neighborhood is considered and used to
+    compute the response. In case of a linear filter, it is a weighted sum of pixel values. In case of
+    morphological operations, it is the minimum or maximum values, and so on. The computed response is
+    stored in the destination image at the same location \f$(x,y)\f$. It means that the output image
+    will be of the same size as the input image. Normally, the functions support multi-channel arrays,
+    in which case every channel is processed independently. Therefore, the output image will also have
+    the same number of channels as the input one.

-Another common feature of the functions and classes described in this section is that, unlike
-simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For
-example, if you want to smooth an image using a Gaussian \f$3 \times 3\f$ filter, then, when
-processing the left-most pixels in each row, you need pixels to the left of them, that is, outside
-of the image. You can let these pixels be the same as the left-most image pixels ("replicated
-border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant
-border" extrapolation method), and so on. OpenCV enables you to specify the extrapolation method.
-For details, see #BorderTypes
+    Another common feature of the functions and classes described in this section is that, unlike
+    simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For
+    example, if you want to smooth an image using a Gaussian \f$3 \times 3\f$ filter, then, when
+    processing the left-most pixels in each row, you need pixels to the left of them, that is, outside
+    of the image. You can let these pixels be the same as the left-most image pixels ("replicated
+    border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant
+    border" extrapolation method), and so on. OpenCV enables you to specify the extrapolation method.
+    For details, see #BorderTypes

-@anchor filter_depths
-### Depth combinations
-Input depth (src.depth()) | Output depth (ddepth)
--------------------------|----------------------
-CV_8U                     | -1/CV_16S/CV_32F/CV_64F
-CV_16U/CV_16S             | -1/CV_32F/CV_64F
-CV_32F                    | -1/CV_32F
-CV_64F                    | -1/CV_64F
+    @anchor filter_depths
+    ### Depth combinations
+    Input depth (src.depth()) | Output depth (ddepth)
+    --------------------------|----------------------
+    CV_8U                     | -1/CV_16S/CV_32F/CV_64F
+    CV_16U/CV_16S             | -1/CV_32F/CV_64F
+    CV_32F                    | -1/CV_32F
+    CV_64F                    | -1/CV_64F

-@note when ddepth=-1, the output image will have the same depth as the source.
+    @note when ddepth=-1, the output image will have the same depth as the source.

-@note if you need double floating-point accuracy and using single floating-point input data
-(CV_32F input and CV_64F output depth combination), you can use @ref Mat.convertTo to convert
-the input data to the desired precision.
+    @note if you need double floating-point accuracy and using single floating-point input data
+    (CV_32F input and CV_64F output depth combination), you can use @ref Mat.convertTo to convert
+    the input data to the desired precision.

    @defgroup imgproc_transform Geometric Image Transformations

-The functions in this section perform various geometrical transformations of 2D images. They do not
-change the image content but deform the pixel grid and map this deformed grid to the destination
-image. In fact, to avoid sampling artifacts, the mapping is done in the reverse order, from
-destination to the source. That is, for each pixel \f$(x, y)\f$ of the destination image, the
-functions compute coordinates of the corresponding "donor" pixel in the source image and copy the
-pixel value:
+    The functions in this section perform various geometrical transformations of 2D images. They do not
+    change the image content but deform the pixel grid and map this deformed grid to the destination
+    image. In fact, to avoid sampling artifacts, the mapping is done in the reverse order, from
+    destination to the source. That is, for each pixel \f$(x, y)\f$ of the destination image, the
+    functions compute coordinates of the corresponding "donor" pixel in the source image and copy the
+    pixel value:

-\f[\texttt{dst} (x,y)= \texttt{src} (f_x(x,y), f_y(x,y))\f]
+    \f[\texttt{dst} (x,y)= \texttt{src} (f_x(x,y), f_y(x,y))\f]

-In case when you specify the forward mapping \f$\left<g_x, g_y\right>: \texttt{src} \rightarrow
-\texttt{dst}\f$, the OpenCV functions first compute the corresponding inverse mapping
-\f$\left<f_x, f_y\right>: \texttt{dst} \rightarrow \texttt{src}\f$ and then use the above formula.
+    In case when you specify the forward mapping \f$\left<g_x, g_y\right>: \texttt{src} \rightarrow
+    \texttt{dst}\f$, the OpenCV functions first compute the corresponding inverse mapping
+    \f$\left<f_x, f_y\right>: \texttt{dst} \rightarrow \texttt{src}\f$ and then use the above formula.

-The actual implementations of the geometrical transformations, from the most generic remap and to
-the simplest and the fastest resize, need to solve two main problems with the above formula:
+    The actual implementations of the geometrical transformations, from the most generic remap and to
+    the simplest and the fastest resize, need to solve two main problems with the above formula:

- Extrapolation of non-existing pixels. Similarly to the filtering functions described in the
-previous section, for some \f$(x,y)\f$, either one of \f$f_x(x,y)\f$, or \f$f_y(x,y)\f$, or both
-of them may fall outside of the image. In this case, an extrapolation method needs to be used.
-OpenCV provides the same selection of extrapolation methods as in the filtering functions. In
-addition, it provides the method #BORDER_TRANSPARENT. This means that the corresponding pixels in
-the destination image will not be modified at all.
+    - Extrapolation of non-existing pixels. Similarly to the filtering functions described in the
+    previous section, for some \f$(x,y)\f$, either one of \f$f_x(x,y)\f$, or \f$f_y(x,y)\f$, or both
+    of them may fall outside of the image. In this case, an extrapolation method needs to be used.
+    OpenCV provides the same selection of extrapolation methods as in the filtering functions. In
+    addition, it provides the method #BORDER_TRANSPARENT. This means that the corresponding pixels in
+    the destination image will not be modified at all.

- Interpolation of pixel values. Usually \f$f_x(x,y)\f$ and \f$f_y(x,y)\f$ are floating-point
-numbers. This means that \f$\left<f_x, f_y\right>\f$ can be either an affine or perspective
-transformation, or radial lens distortion correction, and so on. So, a pixel value at fractional
-coordinates needs to be retrieved. In the simplest case, the coordinates can be just rounded to the
-nearest integer coordinates and the corresponding pixel can be used. This is called a
-nearest-neighbor interpolation. However, a better result can be achieved by using more
-sophisticated [interpolation methods](http://en.wikipedia.org/wiki/Multivariate_interpolation) ,
-where a polynomial function is fit into some neighborhood of the computed pixel \f$(f_x(x,y),
-f_y(x,y))\f$, and then the value of the polynomial at \f$(f_x(x,y), f_y(x,y))\f$ is taken as the
-interpolated pixel value. In OpenCV, you can choose between several interpolation methods. See
-#resize for details.
+    - Interpolation of pixel values. Usually \f$f_x(x,y)\f$ and \f$f_y(x,y)\f$ are floating-point
+    numbers. This means that \f$\left<f_x, f_y\right>\f$ can be either an affine or perspective
+    transformation, or radial lens distortion correction, and so on. So, a pixel value at fractional
+    coordinates needs to be retrieved. In the simplest case, the coordinates can be just rounded to the
+    nearest integer coordinates and the corresponding pixel can be used. This is called a
+    nearest-neighbor interpolation. However, a better result can be achieved by using more
+    sophisticated [interpolation methods](http://en.wikipedia.org/wiki/Multivariate_interpolation) ,
+    where a polynomial function is fit into some neighborhood of the computed pixel \f$(f_x(x,y),
+    f_y(x,y))\f$, and then the value of the polynomial at \f$(f_x(x,y), f_y(x,y))\f$ is taken as the
+    interpolated pixel value. In OpenCV, you can choose between several interpolation methods. See
+    #resize for details.

-@note The geometrical transformations do not work with `CV_8S` or `CV_32S` images.
+    @note The geometrical transformations do not work with `CV_8S` or `CV_32S` images.

    @defgroup imgproc_misc Miscellaneous Image Transformations
    @defgroup imgproc_draw Drawing Functions

-Drawing functions work with matrices/images of arbitrary depth. The boundaries of the shapes can be
-rendered with antialiasing (implemented only for 8-bit images for now). All the functions include
-the parameter color that uses an RGB value (that may be constructed with the Scalar constructor )
-for color images and brightness for grayscale images. For color images, the channel ordering is
-normally *Blue, Green, Red*. This is what imshow, imread, and imwrite expect. So, if you form a
-color using the Scalar constructor, it should look like:
+    Drawing functions work with matrices/images of arbitrary depth. The boundaries of the shapes can be
+    rendered with antialiasing (implemented only for 8-bit images for now). All the functions include
+    the parameter color that uses an RGB value (that may be constructed with the Scalar constructor )
+    for color images and brightness for grayscale images. For color images, the channel ordering is
+    normally *Blue, Green, Red*. This is what imshow, imread, and imwrite expect. So, if you form a
+    color using the Scalar constructor, it should look like:

-\f[\texttt{Scalar} (blue \_ component, green \_ component, red \_ component[, alpha \_ component])\f]
+    \f[\texttt{Scalar} (blue \_ component, green \_ component, red \_ component[, alpha \_ component])\f]

-If you are using your own image rendering and I/O functions, you can use any channel ordering. The
-drawing functions process each channel independently and do not depend on the channel order or even
-on the used color space. The whole image can be converted from BGR to RGB or to a different color
-space using cvtColor .
+    If you are using your own image rendering and I/O functions, you can use any channel ordering. The
+    drawing functions process each channel independently and do not depend on the channel order or even
+    on the used color space. The whole image can be converted from BGR to RGB or to a different color
+    space using cvtColor .

-If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also,
-many drawing functions can handle pixel coordinates specified with sub-pixel accuracy. This means
-that the coordinates can be passed as fixed-point numbers encoded as integers. The number of
-fractional bits is specified by the shift parameter and the real point coordinates are calculated as
-\f$\texttt{Point}(x,y)\rightarrow\texttt{Point2f}(x*2^{-shift},y*2^{-shift})\f$ . This feature is
-especially effective when rendering antialiased shapes.
+    If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also,
+    many drawing functions can handle pixel coordinates specified with sub-pixel accuracy. This means
+    that the coordinates can be passed as fixed-point numbers encoded as integers. The number of
+    fractional bits is specified by the shift parameter and the real point coordinates are calculated as
+    \f$\texttt{Point}(x,y)\rightarrow\texttt{Point2f}(x*2^{-shift},y*2^{-shift})\f$ . This feature is
+    especially effective when rendering antialiased shapes.

-@note The functions do not support alpha-transparency when the target image is 4-channel. In this
-case, the color[3] is simply copied to the repainted pixels. Thus, if you want to paint
-semi-transparent shapes, you can paint them in a separate buffer and then blend it with the main
-image.
+    @note The functions do not support alpha-transparency when the target image is 4-channel. In this
+    case, the color[3] is simply copied to the repainted pixels. Thus, if you want to paint
+    semi-transparent shapes, you can paint them in a separate buffer and then blend it with the main
+    image.

    @defgroup imgproc_color_conversions Color Space Conversions
    @defgroup imgproc_colormap ColorMaps in OpenCV

-The human perception isn't built for observing fine changes in grayscale images. Human eyes are more
-sensitive to observing changes between colors, so you often need to recolor your grayscale images to
-get a clue about them. OpenCV now comes with various colormaps to enhance the visualization in your
-computer vision application.
+    The human perception isn't built for observing fine changes in grayscale images. Human eyes are more
+    sensitive to observing changes between colors, so you often need to recolor your grayscale images to
+    get a clue about them. OpenCV now comes with various colormaps to enhance the visualization in your
+    computer vision application.

-In OpenCV you only need applyColorMap to apply a colormap on a given image. The following sample
-code reads the path to an image from command line, applies a Jet colormap on it and shows the
-result:
+    In OpenCV you only need applyColorMap to apply a colormap on a given image. The following sample
+    code reads the path to an image from command line, applies a Jet colormap on it and shows the
+    result:

-@include snippets/imgproc_applyColorMap.cpp
+    @include snippets/imgproc_applyColorMap.cpp

-@see #ColormapTypes
+    @see #ColormapTypes

    @defgroup imgproc_subdiv2d Planar Subdivision

-The Subdiv2D class described in this section is used to perform various planar subdivision on
-a set of 2D points (represented as vector of Point2f). OpenCV subdivides a plane into triangles
-using the Delaunay's algorithm, which corresponds to the dual graph of the Voronoi diagram.
-In the figure below, the Delaunay's triangulation is marked with black lines and the Voronoi
-diagram with red lines.
+    The Subdiv2D class described in this section is used to perform various planar subdivision on
+    a set of 2D points (represented as vector of Point2f). OpenCV subdivides a plane into triangles
+    using the Delaunay's algorithm, which corresponds to the dual graph of the Voronoi diagram.
+    In the figure below, the Delaunay's triangulation is marked with black lines and the Voronoi
+    diagram with red lines.

-![Delaunay triangulation (black) and Voronoi (red)](pics/delaunay_voronoi.png)
+    ![Delaunay triangulation (black) and Voronoi (red)](pics/delaunay_voronoi.png)

-The subdivisions can be used for the 3D piece-wise transformation of a plane, morphing, fast
-location of points on the plane, building special graphs (such as NNG,RNG), and so forth.
+    The subdivisions can be used for the 3D piece-wise transformation of a plane, morphing, fast
+    location of points on the plane, building special graphs (such as NNG,RNG), and so forth.

    @defgroup imgproc_hist Histograms
    @defgroup imgproc_shape Structural Analysis and Shape Descriptors
@ -190,7 +190,6 @@ location of points on the plane, building special graphs (such as NNG,RNG), and
    @defgroup imgproc_feature Feature Detection
    @defgroup imgproc_object Object Detection
    @defgroup imgproc_segmentation Image Segmentation
-    @defgroup imgproc_c C API
    @defgroup imgproc_hal Hardware Acceleration Layer
    @{
        @defgroup imgproc_hal_functions Functions
--- a/modules/java/jar/build.xml.in
+++ b/modules/java/jar/build.xml.in
@ -52,7 +52,7 @@
              var pos = url.lastIndexOf('/javadoc/');
              url = pos >= 0 ? (url.substring(0, pos) + '/javadoc/mymath.js') : (window.location.origin + '/mymath.js');
              var script = document.createElement('script');
-              script.src = '@OPENCV_MATHJAX_RELPATH@/MathJax.js?config=TeX-AMS-MML_HTMLorMML,' + url;
+              script.src = '@OPENCV_MATHJAX_RELPATH@/es5/tex-chtml.js,' + url;
              document.getElementsByTagName('head')[0].appendChild(script);
            </script>
         ]]>
--- a/modules/objdetect/include/opencv2/objdetect.hpp
+++ b/modules/objdetect/include/opencv2/objdetect.hpp
@ -54,59 +54,61 @@
@{
    @defgroup objdetect_cascade_classifier Cascade Classifier for Object Detection

-The object detector described below has been initially proposed by Paul Viola @cite Viola01 and
-improved by Rainer Lienhart @cite Lienhart02 .
+    The object detector described below has been initially proposed by Paul Viola @cite Viola01 and
+    improved by Rainer Lienhart @cite Lienhart02 .

-First, a classifier (namely a *cascade of boosted classifiers working with haar-like features*) is
-trained with a few hundred sample views of a particular object (i.e., a face or a car), called
-positive examples, that are scaled to the same size (say, 20x20), and negative examples - arbitrary
-images of the same size.
+    First, a classifier (namely a *cascade of boosted classifiers working with haar-like features*) is
+    trained with a few hundred sample views of a particular object (i.e., a face or a car), called
+    positive examples, that are scaled to the same size (say, 20x20), and negative examples - arbitrary
+    images of the same size.

-After a classifier is trained, it can be applied to a region of interest (of the same size as used
-during the training) in an input image. The classifier outputs a "1" if the region is likely to show
-the object (i.e., face/car), and "0" otherwise. To search for the object in the whole image one can
-move the search window across the image and check every location using the classifier. The
-classifier is designed so that it can be easily "resized" in order to be able to find the objects of
-interest at different sizes, which is more efficient than resizing the image itself. So, to find an
-object of an unknown size in the image the scan procedure should be done several times at different
-scales.
+    After a classifier is trained, it can be applied to a region of interest (of the same size as used
+    during the training) in an input image. The classifier outputs a "1" if the region is likely to show
+    the object (i.e., face/car), and "0" otherwise. To search for the object in the whole image one can
+    move the search window across the image and check every location using the classifier. The
+    classifier is designed so that it can be easily "resized" in order to be able to find the objects of
+    interest at different sizes, which is more efficient than resizing the image itself. So, to find an
+    object of an unknown size in the image the scan procedure should be done several times at different
+    scales.

-The word "cascade" in the classifier name means that the resultant classifier consists of several
-simpler classifiers (*stages*) that are applied subsequently to a region of interest until at some
-stage the candidate is rejected or all the stages are passed. The word "boosted" means that the
-classifiers at every stage of the cascade are complex themselves and they are built out of basic
-classifiers using one of four different boosting techniques (weighted voting). Currently Discrete
-Adaboost, Real Adaboost, Gentle Adaboost and Logitboost are supported. The basic classifiers are
-decision-tree classifiers with at least 2 leaves. Haar-like features are the input to the basic
-classifiers, and are calculated as described below. The current algorithm uses the following
-Haar-like features:
+    The word "cascade" in the classifier name means that the resultant classifier consists of several
+    simpler classifiers (*stages*) that are applied subsequently to a region of interest until at some
+    stage the candidate is rejected or all the stages are passed. The word "boosted" means that the
+    classifiers at every stage of the cascade are complex themselves and they are built out of basic
+    classifiers using one of four different boosting techniques (weighted voting). Currently Discrete
+    Adaboost, Real Adaboost, Gentle Adaboost and Logitboost are supported. The basic classifiers are
+    decision-tree classifiers with at least 2 leaves. Haar-like features are the input to the basic
+    classifiers, and are calculated as described below. The current algorithm uses the following
+    Haar-like features:

-![image](pics/haarfeatures.png)
+    ![image](pics/haarfeatures.png)

-The feature used in a particular classifier is specified by its shape (1a, 2b etc.), position within
-the region of interest and the scale (this scale is not the same as the scale used at the detection
-stage, though these two scales are multiplied). For example, in the case of the third line feature
-(2c) the response is calculated as the difference between the sum of image pixels under the
-rectangle covering the whole feature (including the two white stripes and the black stripe in the
-middle) and the sum of the image pixels under the black stripe multiplied by 3 in order to
-compensate for the differences in the size of areas. The sums of pixel values over a rectangular
-regions are calculated rapidly using integral images (see below and the integral description).
+    The feature used in a particular classifier is specified by its shape (1a, 2b etc.), position within
+    the region of interest and the scale (this scale is not the same as the scale used at the detection
+    stage, though these two scales are multiplied). For example, in the case of the third line feature
+    (2c) the response is calculated as the difference between the sum of image pixels under the
+    rectangle covering the whole feature (including the two white stripes and the black stripe in the
+    middle) and the sum of the image pixels under the black stripe multiplied by 3 in order to
+    compensate for the differences in the size of areas. The sums of pixel values over a rectangular
+    regions are calculated rapidly using integral images (see below and the integral description).

-Check @ref tutorial_cascade_classifier "the corresponding tutorial" for more details.
+    Check @ref tutorial_cascade_classifier "the corresponding tutorial" for more details.

-The following reference is for the detection part only. There is a separate application called
-opencv_traincascade that can train a cascade of boosted classifiers from a set of samples.
+    The following reference is for the detection part only. There is a separate application called
+    opencv_traincascade that can train a cascade of boosted classifiers from a set of samples.

-@note In the new C++ interface it is also possible to use LBP (local binary pattern) features in
-addition to Haar-like features. .. [Viola01] Paul Viola and Michael J. Jones. Rapid Object Detection
-using a Boosted Cascade of Simple Features. IEEE CVPR, 2001. The paper is available online at
-<https://github.com/SvHey/thesis/blob/master/Literature/ObjectDetection/violaJones_CVPR2001.pdf>
+    @note In the new C++ interface it is also possible to use LBP (local binary pattern) features in
+    addition to Haar-like features. .. [Viola01] Paul Viola and Michael J. Jones. Rapid Object Detection
+    using a Boosted Cascade of Simple Features. IEEE CVPR, 2001. The paper is available online at
+    <https://github.com/SvHey/thesis/blob/master/Literature/ObjectDetection/violaJones_CVPR2001.pdf>

    @defgroup objdetect_hog HOG (Histogram of Oriented Gradients) descriptor and object detector
    @defgroup objdetect_barcode Barcode detection and decoding
    @defgroup objdetect_qrcode QRCode detection and encoding
    @defgroup objdetect_dnn_face DNN-based face detection and recognition
-Check @ref tutorial_dnn_face "the corresponding tutorial" for more details.
+
+    Check @ref tutorial_dnn_face "the corresponding tutorial" for more details.
+
    @defgroup objdetect_common Common functions and classes
    @defgroup objdetect_aruco ArUco markers and boards detection for robust camera pose estimation
    @{
--- a/modules/photo/include/opencv2/photo.hpp
+++ b/modules/photo/include/opencv2/photo.hpp
@ -55,30 +55,29 @@ This module includes photo processing algorithms
    @defgroup photo_denoise Denoising
    @defgroup photo_hdr HDR imaging

-This section describes high dynamic range imaging algorithms namely tonemapping, exposure alignment,
-camera calibration with multiple exposures and exposure fusion.
+    This section describes high dynamic range imaging algorithms namely tonemapping, exposure alignment,
+    camera calibration with multiple exposures and exposure fusion.

    @defgroup photo_decolor Contrast Preserving Decolorization

-Useful links:
+    Useful links:

-http://www.cse.cuhk.edu.hk/leojia/projects/color2gray/index.html
+    http://www.cse.cuhk.edu.hk/leojia/projects/color2gray/index.html

    @defgroup photo_clone Seamless Cloning

-Useful links:
+    Useful links:

-https://www.learnopencv.com/seamless-cloning-using-opencv-python-cpp
+    https://www.learnopencv.com/seamless-cloning-using-opencv-python-cpp

    @defgroup photo_render Non-Photorealistic Rendering

-Useful links:
+    Useful links:

-http://www.inf.ufrgs.br/~eslgastal/DomainTransform
+    http://www.inf.ufrgs.br/~eslgastal/DomainTransform

-https://www.learnopencv.com/non-photorealistic-rendering-using-opencv-python-c/
+    https://www.learnopencv.com/non-photorealistic-rendering-using-opencv-python-c/

-    @defgroup photo_c C API
@}
  */

--- a/modules/video/include/opencv2/video.hpp
+++ b/modules/video/include/opencv2/video.hpp
@ -49,7 +49,6 @@
  @{
    @defgroup video_motion Motion Analysis
    @defgroup video_track Object Tracking
-    @defgroup video_c C API
  @}
 */

--- a/modules/videoio/doc/videoio_overview.markdown
+++ b/modules/videoio/doc/videoio_overview.markdown
@ -1,12 +1,14 @@
 Video I/O with OpenCV Overview {#videoio_overview}
-===================================
+==============================

-### See also:
+@tableofcontents
+
+@sa
  - @ref videoio "Video I/O Code Reference"
  - Tutorials: @ref tutorial_table_of_content_app

 General Information
-===================
+-------------------

 The OpenCV @ref videoio module is a set of classes and functions to read and write video or images sequence.

@ -53,10 +55,11 @@ cv::VideoCapture cap(filename, cv::CAP_MSMF);
 //or specify the apiPreference with open
 cap.open(filename, cv::CAP_MSMF);
 ```
-
@sa cv::VideoCapture::open() , cv::VideoCapture::VideoCapture()

-#### How to enable backends
+
+How to enable backends
+----------------------

 There are two kinds of videoio backends: built-in backends and plugins which will be loaded at runtime (since OpenCV 4.1.0). Use functions cv::videoio_registry::getBackends, cv::videoio_registry::hasBackend and cv::videoio_registry::getBackendName to check actual presence of backend during runtime.

@ -71,7 +74,9 @@ To enable dynamically-loaded videoio backend (currently supported: GStreamer and

@note Don't forget to clean CMake cache when switching between these two modes

-#### Use 3rd party drivers or cameras
+
+Use 3rd party drivers or cameras
+--------------------------------

 Many industrial cameras or some video I/O devices don't provide standard driver interfaces
 for the operating system. Thus you can't use  VideoCapture or VideoWriter with these devices.
@ -83,6 +88,7 @@ It is a common case that these libraries read/write images from/to a memory buff
 possible to make a `Mat` header for memory buffer (user-allocated data) and process it
 in-place using OpenCV functions. See cv::Mat::Mat() for more details.

+
 The FFmpeg library
 ------------------

--- a/samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp
+++ b/samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp
@ -45,9 +45,9 @@ int main( int argc, char** argv )
    printf( " ** Press 'r' to set the border to be replicated \n");
    printf( " ** Press 'ESC' to exit the program \n");

-    //![create_window]
+//![create_window]
    namedWindow( window_name, WINDOW_AUTOSIZE );
-    //![create_window]
+//![create_window]

    //![init_arguments]
    // Initialize arguments for the filter