doc: tutorial refactor

doc/tools/scan_tutorials.py

@@ -0,0 +1,96 @@
+#!/usr/bin/env python
+
+from pathlib import Path
+import re
+
+# Tasks
+# 1. Find all tutorials
+# 2. Generate tree (@subpage)
+# 3. Check prev/next nodes
+
+class Tutorial(object):
+    def __init__(self, path):
+        self.path = path
+        self.title = None # doxygen title
+        self.children = [] # ordered titles
+        self.prev = None
+        self.next = None
+        with open(path, "rt") as f:
+            self.parse(f)
+
+    def parse(self, f):
+        rx_title = re.compile(r"\{#(\w+)\}")
+        rx_subpage = re.compile(r"@subpage\s+(\w+)")
+        rx_prev = re.compile(r"@prev_tutorial\{(\w+)\}")
+        rx_next = re.compile(r"@next_tutorial\{(\w+)\}")
+        for line in f:
+            if self.title is None:
+                m = rx_title.search(line)
+                if m:
+                    self.title = m.group(1)
+                    continue
+            if self.prev is None:
+                m = rx_prev.search(line)
+                if m:
+                    self.prev = m.group(1)
+                    continue
+            if self.next is None:
+                m = rx_next.search(line)
+                if m:
+                    self.next = m.group(1)
+                    continue
+            m = rx_subpage.search(line)
+            if m:
+                self.children.append(m.group(1))
+                continue
+
+    def verify_prev_next(self, storage):
+        res = True
+
+        if self.title is None:
+            print("[W] No title")
+            res = False
+
+        prev = None
+        for one in self.children:
+            c = storage[one]
+            if c.prev is not None and c.prev != prev:
+                print("[W] Wrong prev_tutorial: expected {} / actual {}".format(c.prev, prev))
+                res = False
+            prev = c.title
+
+        next = None
+        for one in reversed(self.children):
+            c = storage[one]
+            if c.next is not None and c.next != next:
+                print("[W] Wrong next_tutorial: expected {} / actual {}".format(c.next, next))
+                res = False
+            next = c.title
+
+        if len(self.children) == 0 and self.prev is None and self.next is None:
+            print("[W] No prev and next tutorials")
+            res = False
+
+        return res
+
+if __name__ == "__main__":
+
+    p = Path('tutorials')
+    print("Looking for tutorials in: '{}'".format(p))
+
+    all_tutorials = dict()
+    for f in p.glob('**/*'):
+        if f.suffix.lower() in ('.markdown', '.md'):
+            t = Tutorial(f)
+            all_tutorials[t.title] = t
+
+    res = 0
+    print("Found: {}".format(len(all_tutorials)))
+    print("------")
+    for title, t in all_tutorials.items():
+        if not t.verify_prev_next(all_tutorials):
+            print("[E] Verification failed: {}".format(t.path))
+            print("------")
+            res = 1
+
+    exit(res)

doc/tutorials/app/_old/table_of_content_highgui.markdown

@@ -0,0 +1,4 @@
+High Level GUI and Media (highgui module) {#tutorial_table_of_content_highgui}
+=========================================
+
+Content has been moved to this page: @ref tutorial_table_of_content_app

doc/tutorials/app/_old/table_of_content_imgcodecs.markdown

@@ -0,0 +1,4 @@
+Image Input and Output (imgcodecs module) {#tutorial_table_of_content_imgcodecs}
+=========================================
+
+Content has been moved to this page: @ref tutorial_table_of_content_app

doc/tutorials/app/_old/table_of_content_videoio.markdown

@@ -0,0 +1,4 @@
+Video Input and Output (videoio module) {#tutorial_table_of_content_videoio}
+=========================================
+
+Content has been moved to this page: @ref tutorial_table_of_content_app

doc/tutorials/app/raster_io_gdal.markdown

@@ -1,6 +1,14 @@
 Reading Geospatial Raster files with GDAL {#tutorial_raster_io_gdal}
 =========================================
 
+@prev_tutorial{tutorial_trackbar}
+@next_tutorial{tutorial_video_input_psnr_ssim}
+
+|    |    |
+| -: | :- |
+| Original author | Marvin Smith |
+| Compatibility | OpenCV >= 3.0 |
+
 Geospatial raster data is a heavily used product in Geographic Information Systems and
 Photogrammetry. Raster data typically can represent imagery and Digital Elevation Models (DEM). The
 standard library for loading GIS imagery is the Geographic Data Abstraction Library [(GDAL)](http://www.gdal.org). In this

doc/tutorials/app/table_of_content_app.markdown

@@ -0,0 +1,10 @@
+Application utils (highgui, imgcodecs, videoio modules) {#tutorial_table_of_content_app}
+=======================================================
+
+-   @subpage tutorial_trackbar
+-   @subpage tutorial_raster_io_gdal
+-   @subpage tutorial_video_input_psnr_ssim
+-   @subpage tutorial_video_write
+-   @subpage tutorial_kinect_openni
+-   @subpage tutorial_orbbec_astra
+-   @subpage tutorial_intelperc

doc/tutorials/app/trackbar.markdown

@@ -1,6 +1,14 @@
 Adding a Trackbar to our applications! {#tutorial_trackbar}
 ======================================
 
+@next_tutorial{tutorial_raster_io_gdal}
+
+|    |    |
+| -: | :- |
+| Original author | Ana Huamán |
+| Compatibility | OpenCV >= 3.0 |
+
+
 -   In the previous tutorials (about @ref tutorial_adding_images and the @ref tutorial_basic_linear_transform)
     you might have noted that we needed to give some **input** to our programs, such
     as \f$\alpha\f$ and \f$beta\f$. We accomplished that by entering this data using the Terminal.

doc/tutorials/app/video_input_psnr_ssim.markdown

@@ -1,8 +1,14 @@
 Video Input with OpenCV and similarity measurement {#tutorial_video_input_psnr_ssim}
 ==================================================
 
+@prev_tutorial{tutorial_raster_io_gdal}
 @next_tutorial{tutorial_video_write}
 
+|    |    |
+| -: | :- |
+| Original author | Bernát Gábor |
+| Compatibility | OpenCV >= 3.0 |
+
 Goal
 ----
 

doc/tutorials/app/video_write.markdown

@@ -4,6 +4,11 @@ Creating a video with OpenCV {#tutorial_video_write}
 @prev_tutorial{tutorial_video_input_psnr_ssim}
 @next_tutorial{tutorial_kinect_openni}
 
+|    |    |
+| -: | :- |
+| Original author | Bernát Gábor |
+| Compatibility | OpenCV >= 3.0 |
+
 Goal
 ----
 

doc/tutorials/calib3d/camera_calibration/camera_calibration.markdown

@@ -4,6 +4,11 @@ Camera calibration With OpenCV {#tutorial_camera_calibration}
 @prev_tutorial{tutorial_camera_calibration_square_chess}
 @next_tutorial{tutorial_real_time_pose}
 
+|    |    |
+| -: | :- |
+| Original author | Bernát Gábor |
+| Compatibility | OpenCV >= 4.0 |
+
 
 Cameras have been around for a long-long time. However, with the introduction of the cheap *pinhole*
 cameras in the late 20th century, they became a common occurrence in our everyday life.

doc/tutorials/calib3d/camera_calibration_pattern/camera_calibration_pattern.markdown

@@ -3,6 +3,11 @@ Create calibration pattern {#tutorial_camera_calibration_pattern}
 
 @next_tutorial{tutorial_camera_calibration_square_chess}
 
+|    |    |
+| -: | :- |
+| Original author | Laurent Berger |
+| Compatibility | OpenCV >= 3.0 |
+
 
 The goal of this tutorial is to learn how to create calibration pattern.
 

doc/tutorials/calib3d/camera_calibration_square_chess/camera_calibration_square_chess.markdown

@@ -4,6 +4,11 @@ Camera calibration with square chessboard {#tutorial_camera_calibration_square_c
 @prev_tutorial{tutorial_camera_calibration_pattern}
 @next_tutorial{tutorial_camera_calibration}
 
+|    |    |
+| -: | :- |
+| Original author | Victor Eruhimov |
+| Compatibility | OpenCV >= 4.0 |
+
 
 The goal of this tutorial is to learn how to calibrate a camera given a set of chessboard images.
 

doc/tutorials/calib3d/interactive_calibration/interactive_calibration.markdown

@@ -3,6 +3,11 @@ Interactive camera calibration application {#tutorial_interactive_calibration}
 
 @prev_tutorial{tutorial_real_time_pose}
 
+|    |    |
+| -: | :- |
+| Original author | Vladislav Sovrasov |
+| Compatibility | OpenCV >= 3.1 |
+
 
 According to classical calibration technique user must collect all data first and when run @ref cv::calibrateCamera function
 to obtain camera parameters. If average re-projection error is huge or if estimated parameters seems to be wrong, process of

doc/tutorials/calib3d/real_time_pose/real_time_pose.markdown

@@ -4,6 +4,11 @@ Real Time pose estimation of a textured object {#tutorial_real_time_pose}
 @prev_tutorial{tutorial_camera_calibration}
 @next_tutorial{tutorial_interactive_calibration}
 
+|    |    |
+| -: | :- |
+| Original author | Edgar Riba |
+| Compatibility | OpenCV >= 3.0 |
+
 
 Nowadays, augmented reality is one of the top research topic in computer vision and robotics fields.
 The most elemental problem in augmented reality is the estimation of the camera pose respect of an

doc/tutorials/calib3d/table_of_content_calib3d.markdown

@@ -1,58 +1,8 @@
 Camera calibration and 3D reconstruction (calib3d module) {#tutorial_table_of_content_calib3d}
 ==========================================================
 
-Although we get most of our images in a 2D format they do come from a 3D world. Here you will learn how to find out 3D world information from 2D images.
-
 -   @subpage tutorial_camera_calibration_pattern
-
-    *Languages:* Python
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Laurent Berger
-
-    You will learn how to create some calibration pattern.
-
 -   @subpage tutorial_camera_calibration_square_chess
-
-    *Languages:* C++
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Victor Eruhimov
-
-    You will use some chessboard images to calibrate your camera.
-
 -   @subpage tutorial_camera_calibration
-
-    *Languages:* C++
-
-    *Compatibility:* \> OpenCV 4.0
-
-    *Author:* Bernát Gábor
-
-    Camera calibration by using either the chessboard, circle or the asymmetrical circle
-    pattern. Get the images either from a camera attached, a video file or from an image
-    collection.
-
 -   @subpage tutorial_real_time_pose
-
-    *Languages:* C++
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Edgar Riba
-
-    Real time pose estimation of a textured object using ORB features, FlannBased matcher, PnP
-    approach plus Ransac and Linear Kalman Filter to reject possible bad poses.
-
 -   @subpage tutorial_interactive_calibration
-
-    *Compatibility:* \> OpenCV 3.1
-
-    *Author:* Vladislav Sovrasov
-
-    Camera calibration by using either the chessboard, chAruco, asymmetrical circle or dual asymmetrical circle
-    pattern. Calibration process is continuous, so you can see results after each new pattern shot.
-    As an output you get average reprojection error, intrinsic camera parameters, distortion coefficients and
-     confidence intervals for all of evaluated variables.

doc/tutorials/core/adding_images/adding_images.markdown

@@ -4,6 +4,12 @@ Adding (blending) two images using OpenCV {#tutorial_adding_images}
 @prev_tutorial{tutorial_mat_operations}
 @next_tutorial{tutorial_basic_linear_transform}
 
+|    |    |
+| -: | :- |
+| Original author | Ana Huamán |
+| Compatibility | OpenCV >= 3.0 |
+
+We will learn how to blend two images!
 Goal
 ----
 

doc/tutorials/core/basic_linear_transform/basic_linear_transform.markdown

@@ -4,6 +4,11 @@ Changing the contrast and brightness of an image! {#tutorial_basic_linear_transf
 @prev_tutorial{tutorial_adding_images}
 @next_tutorial{tutorial_discrete_fourier_transform}
 
+|    |    |
+| -: | :- |
+| Original author | Ana Huamán |
+| Compatibility | OpenCV >= 3.0 |
+
 Goal
 ----
 

doc/tutorials/core/discrete_fourier_transform/discrete_fourier_transform.markdown

@@ -4,6 +4,11 @@ Discrete Fourier Transform {#tutorial_discrete_fourier_transform}
 @prev_tutorial{tutorial_basic_linear_transform}
 @next_tutorial{tutorial_file_input_output_with_xml_yml}
 
+|    |    |
+| -: | :- |
+| Original author | Bernát Gábor |
+| Compatibility | OpenCV >= 3.0 |
+
 Goal
 ----
 

doc/tutorials/core/file_input_output_with_xml_yml/file_input_output_with_xml_yml.markdown

@@ -4,6 +4,11 @@ File Input and Output using XML and YAML files {#tutorial_file_input_output_with
 @prev_tutorial{tutorial_discrete_fourier_transform}
 @next_tutorial{tutorial_how_to_use_OpenCV_parallel_for_}
 
+|    |    |
+| -: | :- |
+| Original author | Bernát Gábor |
+| Compatibility | OpenCV >= 3.0 |
+
 Goal
 ----
 

doc/tutorials/core/how_to_scan_images/how_to_scan_images.markdown

@@ -4,6 +4,11 @@ How to scan images, lookup tables and time measurement with OpenCV {#tutorial_ho
 @prev_tutorial{tutorial_mat_the_basic_image_container}
 @next_tutorial{tutorial_mat_mask_operations}
 
+|    |    |
+| -: | :- |
+| Original author | Bernát Gábor |
+| Compatibility | OpenCV >= 3.0 |
+
 Goal
 ----
 

doc/tutorials/core/how_to_use_OpenCV_parallel_for_/how_to_use_OpenCV_parallel_for_.markdown

@@ -3,6 +3,10 @@ How to use the OpenCV parallel_for_ to parallelize your code {#tutorial_how_to_u
 
 @prev_tutorial{tutorial_file_input_output_with_xml_yml}
 
+|    |    |
+| -: | :- |
+| Compatibility | OpenCV >= 3.0 |
+
 Goal
 ----
 

doc/tutorials/core/mat-mask-operations/mat_mask_operations.markdown

@@ -4,6 +4,11 @@ Mask operations on matrices {#tutorial_mat_mask_operations}
 @prev_tutorial{tutorial_how_to_scan_images}
 @next_tutorial{tutorial_mat_operations}
 
+|    |    |
+| -: | :- |
+| Original author | Bernát Gábor |
+| Compatibility | OpenCV >= 3.0 |
+
 Mask operations on matrices are quite simple. The idea is that we recalculate each pixel's value in
 an image according to a mask matrix (also known as kernel). This mask holds values that will adjust
 how much influence neighboring pixels (and the current pixel) have on the new pixel value. From a

doc/tutorials/core/mat_operations.markdown

@@ -4,6 +4,10 @@ Operations with images {#tutorial_mat_operations}
 @prev_tutorial{tutorial_mat_mask_operations}
 @next_tutorial{tutorial_adding_images}
 
+|    |    |
+| -: | :- |
+| Compatibility | OpenCV >= 3.0 |
+
 Input/Output
 ------------
 

doc/tutorials/core/mat_the_basic_image_container/mat_the_basic_image_container.markdown

@@ -3,6 +3,11 @@ Mat - The Basic Image Container {#tutorial_mat_the_basic_image_container}
 
 @next_tutorial{tutorial_how_to_scan_images}
 
+|    |    |
+| -: | :- |
+| Original author | Bernát Gábor |
+| Compatibility | OpenCV >= 3.0 |
+
 Goal
 ----
 

doc/tutorials/core/table_of_content_core.markdown

@@ -1,97 +1,12 @@
 The Core Functionality (core module) {#tutorial_table_of_content_core}
 =====================================
 
-Here you will learn the about the basic building blocks of the library. A must read and know for
-understanding how to manipulate the images on a pixel level.
-
 -   @subpage tutorial_mat_the_basic_image_container
-
-    *Languages:* C++
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Bernát Gábor
-
-    You will learn how to store images in the memory and how to print out their content to the
-    console.
-
 -   @subpage tutorial_how_to_scan_images
-
-    *Languages:* C++
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Bernát Gábor
-
-    You'll find out how to scan images (go through each of the image pixels) with OpenCV.
-    Bonus: time measurement with OpenCV.
-
-
 -   @subpage tutorial_mat_mask_operations
-
-    *Languages:* C++, Java, Python
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Bernát Gábor
-
-    You'll find out how to scan images with neighbor access and use the @ref cv::filter2D
-    function to apply kernel filters on images.
-
 -   @subpage tutorial_mat_operations
-
-    *Languages:* C++, Java, Python
-
-    *Compatibility:* \> OpenCV 2.0
-
-    Reading/writing images from file, accessing pixels, primitive operations, visualizing images.
-
 -   @subpage tutorial_adding_images
-
-    *Languages:* C++, Java, Python
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Ana Huamán
-
-    We will learn how to blend two images!
-
 -   @subpage tutorial_basic_linear_transform
-
-    *Languages:* C++, Java, Python
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Ana Huamán
-
-    We will learn how to change our image appearance!
-
 -   @subpage tutorial_discrete_fourier_transform
-
-    *Languages:* C++, Java, Python
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Bernát Gábor
-
-    You will see how and why use the Discrete Fourier transformation with OpenCV.
-
-
 -   @subpage tutorial_file_input_output_with_xml_yml
-
-    *Languages:* C++, Python
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Bernát Gábor
-
-    You will see how to use the @ref cv::FileStorage data structure of OpenCV to write and read
-    data to XML or YAML file format.
-
 -   @subpage tutorial_how_to_use_OpenCV_parallel_for_
-
-    *Languages:* C++
-
-    *Compatibility:* \>= OpenCV 2.4.3
-
-    You will see how to use the OpenCV parallel_for_ to easily parallelize your code.

doc/tutorials/dnn/dnn_OCR/dnn_OCR.markdown

@@ -3,6 +3,11 @@
 @prev_tutorial{tutorial_dnn_custom_layers}
 @next_tutorial{tutorial_dnn_text_spotting}
 
+|    |    |
+| -: | :- |
+| Original author | Zihao Mu |
+| Compatibility | OpenCV >= 4.3 |
+
 ## Introduction
 
 In this tutorial, we first introduce how to obtain the custom OCR model, then how to transform your own OCR models so that they can be run correctly by the opencv_dnn module. and finally we will provide some pre-trained models.

doc/tutorials/dnn/dnn_android/dnn_android.markdown

@@ -3,6 +3,11 @@
 @prev_tutorial{tutorial_dnn_halide_scheduling}
 @next_tutorial{tutorial_dnn_yolo}
 
+|    |    |
+| -: | :- |
+| Original author | Dmitry Kurtaev |
+| Compatibility | OpenCV >= 3.3 |
+
 ## Introduction
 In this tutorial you'll know how to run deep learning networks on Android device
 using OpenCV deep learning module.

doc/tutorials/dnn/dnn_custom_layers/dnn_custom_layers.md

@@ -3,6 +3,11 @@
 @prev_tutorial{tutorial_dnn_javascript}
 @next_tutorial{tutorial_dnn_OCR}
 
+|    |    |
+| -: | :- |
+| Original author | Dmitry Kurtaev |
+| Compatibility | OpenCV >= 3.4.1 |
+
 ## Introduction
 Deep learning is a fast growing area. The new approaches to build neural networks
 usually introduce new types of layers. They could be modifications of existing

doc/tutorials/dnn/dnn_googlenet/dnn_googlenet.markdown

@@ -3,6 +3,11 @@ Load Caffe framework models  {#tutorial_dnn_googlenet}
 
 @next_tutorial{tutorial_dnn_halide}
 
+|    |    |
+| -: | :- |
+| Original author | Vitaliy Lyudvichenko |
+| Compatibility | OpenCV >= 3.3 |
+
 Introduction
 ------------
 

doc/tutorials/dnn/dnn_halide/dnn_halide.markdown

@@ -3,6 +3,11 @@
 @prev_tutorial{tutorial_dnn_googlenet}
 @next_tutorial{tutorial_dnn_halide_scheduling}
 
+|    |    |
+| -: | :- |
+| Original author | Dmitry Kurtaev |
+| Compatibility | OpenCV >= 3.3 |
+
 ## Introduction
 This tutorial guidelines how to run your models in OpenCV deep learning module
 using Halide language backend. Halide is an open-source project that let us

doc/tutorials/dnn/dnn_halide_scheduling/dnn_halide_scheduling.markdown

@@ -3,6 +3,11 @@
 @prev_tutorial{tutorial_dnn_halide}
 @next_tutorial{tutorial_dnn_android}
 
+|    |    |
+| -: | :- |
+| Original author | Dmitry Kurtaev |
+| Compatibility | OpenCV >= 3.3 |
+
 ## Introduction
 Halide code is the same for every device we use. But for achieving the satisfied
 efficiency we should schedule computations properly. In this tutorial we describe

doc/tutorials/dnn/dnn_javascript/dnn_javascript.markdown

@@ -3,6 +3,11 @@
 @prev_tutorial{tutorial_dnn_yolo}
 @next_tutorial{tutorial_dnn_custom_layers}
 
+|    |    |
+| -: | :- |
+| Original author | Dmitry Kurtaev |
+| Compatibility | OpenCV >= 3.3.1 |
+
 ## Introduction
 This tutorial will show us how to run deep learning models using OpenCV.js right
 in a browser. Tutorial refers a sample of face detection and face recognition

doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown

@@ -2,6 +2,11 @@
 
 @prev_tutorial{tutorial_dnn_OCR}
 
+|    |    |
+| -: | :- |
+| Original author | Wenqing Zhang |
+| Compatibility | OpenCV >= 4.5 |
+
 ## Introduction
 In this tutorial, we will introduce the APIs for TextRecognitionModel and TextDetectionModel in detail.
 

doc/tutorials/dnn/dnn_yolo/dnn_yolo.markdown

@@ -4,6 +4,11 @@ YOLO DNNs  {#tutorial_dnn_yolo}
 @prev_tutorial{tutorial_dnn_android}
 @next_tutorial{tutorial_dnn_javascript}
 
+|    |    |
+| -: | :- |
+| Original author | Alessandro de Oliveira Faria |
+| Compatibility | OpenCV >= 3.3.1 |
+
 Introduction
 ------------
 

doc/tutorials/dnn/table_of_content_dnn.markdown

@@ -2,91 +2,11 @@ Deep Neural Networks (dnn module) {#tutorial_table_of_content_dnn}
 =====================================
 
 -   @subpage tutorial_dnn_googlenet
-
-    *Languages:* C++
-
-    *Compatibility:* \> OpenCV 3.3
-
-    *Author:* Vitaliy Lyudvichenko
-
-    In this tutorial you will learn how to use opencv_dnn module for image classification by using GoogLeNet trained network from Caffe model zoo.
-
 -   @subpage tutorial_dnn_halide
-
-    *Languages:* Halide
-
-    *Compatibility:* \> OpenCV 3.3
-
-    *Author:* Dmitry Kurtaev
-
-    This tutorial guidelines how to run your models in OpenCV deep learning module using Halide language backend.
-
 -   @subpage tutorial_dnn_halide_scheduling
-
-    *Languages:* Halide
-
-    *Compatibility:* \> OpenCV 3.3
-
-    *Author:* Dmitry Kurtaev
-
-    In this tutorial we describe the ways to schedule your networks using Halide backend in OpenCV deep learning module.
-
 -   @subpage tutorial_dnn_android
-
-    *Languages:* Java
-
-    *Compatibility:* \> OpenCV 3.3
-
-    *Author:* Dmitry Kurtaev
-
-    This tutorial will show you how to run deep learning model using OpenCV on Android device.
-
 -   @subpage tutorial_dnn_yolo
-
-    *Languages:* C++, Python
-
-    *Compatibility:* \> OpenCV 3.3.1
-
-    *Author:* Alessandro de Oliveira Faria
-
-    In this tutorial you will learn how to use opencv_dnn module using yolo_object_detection with device capture, video file or image.
-
 -   @subpage tutorial_dnn_javascript
-
-    *Languages:* JavaScript
-
-    *Compatibility:* \> OpenCV 3.3.1
-
-    *Author:* Dmitry Kurtaev
-
-    In this tutorial we'll run deep learning models in browser using OpenCV.js.
-
 -   @subpage tutorial_dnn_custom_layers
-
-    *Languages:* C++, Python
-
-    *Compatibility:* \> OpenCV 3.4.1
-
-    *Author:* Dmitry Kurtaev
-
-    How to define custom layers to import networks.
-
 -   @subpage tutorial_dnn_OCR
-
-    *Languages:* C++
-
-    *Compatibility:* \> OpenCV 4.3
-
-    *Author:* Zihao Mu
-
-    In this tutorial you will learn how to use opencv_dnn module using custom OCR models.
-
 -   @subpage tutorial_dnn_text_spotting
-
-    *Languages:* C++
-
-    *Compatibility:* \> OpenCV 4.5
-
-    *Author:* Wenqing Zhang
-
-    In these tutorial, we'll introduce how to use the high-level APIs for text recognition and text detection

doc/tutorials/features2d/akaze_matching/akaze_matching.markdown

@@ -4,6 +4,11 @@ AKAZE local features matching {#tutorial_akaze_matching}
 @prev_tutorial{tutorial_detection_of_planar_objects}
 @next_tutorial{tutorial_akaze_tracking}
 
+|    |    |
+| -: | :- |
+| Original author | Fedor Morozov |
+| Compatibility | OpenCV >= 3.0 |
+
 Introduction
 ------------
 

doc/tutorials/features2d/akaze_tracking/akaze_tracking.markdown

@@ -4,6 +4,11 @@ AKAZE and ORB planar tracking {#tutorial_akaze_tracking}
 @prev_tutorial{tutorial_akaze_matching}
 @next_tutorial{tutorial_homography}
 
+|    |    |
+| -: | :- |
+| Original author | Fedor Morozov |
+| Compatibility | OpenCV >= 3.0 |
+
 Introduction
 ------------
 

doc/tutorials/features2d/detection_of_planar_objects/detection_of_planar_objects.markdown

@@ -4,6 +4,10 @@ Detection of planar objects {#tutorial_detection_of_planar_objects}
 @prev_tutorial{tutorial_feature_homography}
 @next_tutorial{tutorial_akaze_matching}
 
+|    |    |
+| -: | :- |
+| Original author | Victor Eruhimov |
+| Compatibility | OpenCV >= 3.0 |
 
 The goal of this tutorial is to learn how to use *features2d* and *calib3d* modules for detecting
 known planar objects in scenes.

doc/tutorials/features2d/feature_description/feature_description.markdown

@@ -4,6 +4,11 @@ Feature Description {#tutorial_feature_description}
 @prev_tutorial{tutorial_feature_detection}
 @next_tutorial{tutorial_feature_flann_matcher}
 
+|    |    |
+| -: | :- |
+| Original author | Ana Huamán |
+| Compatibility | OpenCV >= 3.0 |
+
 Goal
 ----
 

doc/tutorials/features2d/feature_detection/feature_detection.markdown

@@ -4,6 +4,11 @@ Feature Detection {#tutorial_feature_detection}
 @prev_tutorial{tutorial_corner_subpixels}
 @next_tutorial{tutorial_feature_description}
 
+|    |    |
+| -: | :- |
+| Original author | Ana Huamán |
+| Compatibility | OpenCV >= 3.0 |
+
 Goal
 ----
 

doc/tutorials/features2d/feature_flann_matcher/feature_flann_matcher.markdown

@@ -4,6 +4,11 @@ Feature Matching with FLANN {#tutorial_feature_flann_matcher}
 @prev_tutorial{tutorial_feature_description}
 @next_tutorial{tutorial_feature_homography}
 
+|    |    |
+| -: | :- |
+| Original author | Ana Huamán |
+| Compatibility | OpenCV >= 3.0 |
+
 Goal
 ----
 

doc/tutorials/features2d/feature_homography/feature_homography.markdown

@@ -4,6 +4,11 @@ Features2D + Homography to find a known object {#tutorial_feature_homography}
 @prev_tutorial{tutorial_feature_flann_matcher}
 @next_tutorial{tutorial_detection_of_planar_objects}
 
+|    |    |
+| -: | :- |
+| Original author | Ana Huamán |
+| Compatibility | OpenCV >= 3.0 |
+
 Goal
 ----
 

doc/tutorials/features2d/homography/homography.markdown

@@ -3,6 +3,10 @@ Basic concepts of the homography explained with code {#tutorial_homography}
 
 @prev_tutorial{tutorial_akaze_tracking}
 
+|    |    |
+| -: | :- |
+| Compatibility | OpenCV >= 3.0 |
+
 @tableofcontents
 
 Introduction {#tutorial_homography_Introduction}

doc/tutorials/features2d/table_of_content_features2d.markdown

@@ -1,128 +1,15 @@
 2D Features framework (feature2d module) {#tutorial_table_of_content_features2d}
 =========================================
 
-Learn about how to use the feature points detectors, descriptors and matching framework found inside
-OpenCV.
-
 -   @subpage tutorial_harris_detector
-
-    *Languages:* C++, Java, Python
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Ana Huamán
-
-    Why is it a good idea to track corners? We learn how to use the Harris method to detect
-    corners.
-
 -   @subpage tutorial_good_features_to_track
-
-    *Languages:* C++, Java, Python
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Ana Huamán
-
-    Where we use an improved method to detect corners more accurately.
-
 -   @subpage tutorial_generic_corner_detector
-
-    *Languages:* C++, Java, Python
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Ana Huamán
-
-    Here you will learn how to use OpenCV functions to make your personalized corner detector!
-
-    *Languages:* C++, Java, Python
-
 -   @subpage tutorial_corner_subpixels
-
-    *Languages:* C++, Java, Python
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Ana Huamán
-
-    Is pixel resolution enough? Here we learn a simple method to improve our corner location accuracy.
-
 -   @subpage tutorial_feature_detection
-
-    *Languages:* C++, Java, Python
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Ana Huamán
-
-    In this tutorial, you will use *features2d* to detect interest points.
-
 -   @subpage tutorial_feature_description
-
-    *Languages:* C++, Java, Python
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Ana Huamán
-
-    In this tutorial, you will use *features2d* to calculate feature vectors.
-
 -   @subpage tutorial_feature_flann_matcher
-
-    *Languages:* C++, Java, Python
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Ana Huamán
-
-    In this tutorial, you will use the FLANN library to make a fast matching.
-
 -   @subpage tutorial_feature_homography
-
-    *Languages:* C++, Java, Python
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Ana Huamán
-
-    In this tutorial, you will use *features2d* and *calib3d* to detect an object in a scene.
-
 -   @subpage tutorial_detection_of_planar_objects
-
-    *Languages:* C++
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Victor Eruhimov
-
-    You will use *features2d* and *calib3d* modules for detecting known planar objects in
-    scenes.
-
 -   @subpage tutorial_akaze_matching
-
-    *Languages:* C++, Java, Python
-
-    *Compatibility:* \> OpenCV 3.0
-
-    *Author:* Fedor Morozov
-
-    Using *AKAZE* local features to find correspondence between two images.
-
 -   @subpage tutorial_akaze_tracking
-
-    *Languages:* C++
-
-    *Compatibility:* \> OpenCV 3.0
-
-    *Author:* Fedor Morozov
-
-    Using *AKAZE* and *ORB* for planar object tracking.
-
 -   @subpage tutorial_homography
-
-    *Languages:* C++, Java, Python
-
-    *Compatibility:* \> OpenCV 3.0
-
-    This tutorial will explain the basic concepts of the homography with some
-    demonstration codes.

doc/tutorials/features2d/trackingmotion/corner_subpixels/corner_subpixels.markdown

@@ -4,6 +4,11 @@ Detecting corners location in subpixels {#tutorial_corner_subpixels}
 @prev_tutorial{tutorial_generic_corner_detector}
 @next_tutorial{tutorial_feature_detection}
 
+|    |    |
+| -: | :- |
+| Original author | Ana Huamán |
+| Compatibility | OpenCV >= 3.0 |
+
 Goal
 ----
 

doc/tutorials/features2d/trackingmotion/generic_corner_detector/generic_corner_detector.markdown

@@ -4,6 +4,10 @@ Creating your own corner detector {#tutorial_generic_corner_detector}
 @prev_tutorial{tutorial_good_features_to_track}
 @next_tutorial{tutorial_corner_subpixels}
 
+|    |    |
+| -: | :- |
+| Original author | Ana Huamán |
+| Compatibility | OpenCV >= 3.0 |
 
 Goal
 ----

doc/tutorials/features2d/trackingmotion/good_features_to_track/good_features_to_track.markdown

@@ -4,6 +4,11 @@ Shi-Tomasi corner detector {#tutorial_good_features_to_track}
 @prev_tutorial{tutorial_harris_detector}
 @next_tutorial{tutorial_generic_corner_detector}
 
+|    |    |
+| -: | :- |
+| Original author | Ana Huamán |
+| Compatibility | OpenCV >= 3.0 |
+
 Goal
 ----
 

doc/tutorials/features2d/trackingmotion/harris_detector/harris_detector.markdown

@@ -3,6 +3,11 @@ Harris corner detector {#tutorial_harris_detector}
 
 @next_tutorial{tutorial_good_features_to_track}
 
+|    |    |
+| -: | :- |
+| Original author | Ana Huamán |
+| Compatibility | OpenCV >= 3.0 |
+
 Goal
 ----
 

doc/tutorials/gapi/anisotropic_segmentation/porting_anisotropic_segmentation.markdown

@@ -1,5 +1,8 @@
 # Porting anisotropic image segmentation on G-API {#tutorial_gapi_anisotropic_segmentation}
 
+@prev_tutorial{tutorial_gapi_interactive_face_detection}
+@next_tutorial{tutorial_gapi_face_beautification}
+
 [TOC]
 
 # Introduction {#gapi_anisotropic_intro}

doc/tutorials/gapi/face_beautification/face_beautification.markdown

@@ -1,5 +1,7 @@
 # Implementing a face beautification algorithm with G-API {#tutorial_gapi_face_beautification}
 
+@prev_tutorial{tutorial_gapi_anisotropic_segmentation}
+
 [TOC]
 
 # Introduction {#gapi_fb_intro}

doc/tutorials/gapi/interactive_face_detection/interactive_face_detection.markdown

@@ -1,5 +1,7 @@
 # Face analytics pipeline with G-API {#tutorial_gapi_interactive_face_detection}
 
+@next_tutorial{tutorial_gapi_anisotropic_segmentation}
+
 [TOC]
 
 # Overview {#gapi_ifd_intro}

doc/tutorials/highgui/table_of_content_highgui.markdown

@@ -1,14 +0,0 @@
-High Level GUI and Media (highgui module) {#tutorial_table_of_content_highgui}
-=========================================
-
-This section contains tutorials about how to use the built-in graphical user interface of the library.
-
--   @subpage tutorial_trackbar
-
-    *Languages:* C++, Java, Python
-
-    *Compatibility:* \> OpenCV 2.0
-
-    *Author:* Ana Huamán
-
-    We will learn how to add a Trackbar to our applications