Merge pull request #25161 from mshabunin:doc-upgrade-5.x

Documentation transition to fresh Doxygen (5.x) #25161 

Port of #25042

Merge with opencv/opencv_contrib#3687
CI part: opencv/ci-gha-workflow#162
pull/25216/head
Maksim Shabunin 9 months ago committed by GitHub
parent c6776ec136
commit de29223217
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  1. 2
      CMakeLists.txt
  2. 6
      doc/CMakeLists.txt
  3. 73
      doc/Doxyfile.in
  4. 2
      doc/js_tutorials/js_setup/js_nodejs/js_nodejs.markdown
  5. 10
      doc/mymath.js
  6. 24
      doc/py_tutorials/py_setup/py_setup_in_windows/py_setup_in_windows.markdown
  7. 16
      doc/tutorials/calib3d/camera_multiview_calibration/multiview_calibration.markdown
  8. 14
      doc/tutorials/core/discrete_fourier_transform/discrete_fourier_transform.markdown
  9. 2
      doc/tutorials/core/mat-mask-operations/mat_mask_operations.markdown
  10. 7
      doc/tutorials/dnn/dnn_OCR/dnn_OCR.markdown
  11. 2
      doc/tutorials/dnn/dnn_googlenet/dnn_googlenet.markdown
  12. 12
      doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown
  13. 14
      doc/tutorials/imgproc/erosion_dilatation/erosion_dilatation.markdown
  14. 8
      doc/tutorials/imgproc/gausian_median_blur_bilateral_filter/gausian_median_blur_bilateral_filter.markdown
  15. 16
      doc/tutorials/imgproc/imgtrans/copyMakeBorder/copyMakeBorder.markdown
  16. 6
      doc/tutorials/imgproc/imgtrans/filter_2d/filter_2d.markdown
  17. 12
      doc/tutorials/imgproc/imgtrans/hough_circle/hough_circle.markdown
  18. 12
      doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.markdown
  19. 14
      doc/tutorials/imgproc/imgtrans/laplace_operator/laplace_operator.markdown
  20. 18
      doc/tutorials/imgproc/imgtrans/sobel_derivatives/sobel_derivatives.markdown
  21. 12
      doc/tutorials/imgproc/morph_lines_detection/morph_lines_detection.md
  22. 8
      doc/tutorials/imgproc/pyramids/pyramids.markdown
  23. 6
      doc/tutorials/introduction/android_binary_package/android_dev_intro.markdown
  24. 66
      doc/tutorials/introduction/building_tegra_cuda/building_tegra_cuda.markdown
  25. 16
      doc/tutorials/introduction/clojure_dev_intro/clojure_dev_intro.markdown
  26. 56
      doc/tutorials/introduction/env_reference/env_reference.markdown
  27. 12
      doc/tutorials/introduction/windows_install/windows_install.markdown
  28. 4
      doc/tutorials/others/_old/table_of_content_objdetect.markdown
  29. 34
      modules/calib/include/opencv2/calib.hpp
  30. 4
      modules/core/include/opencv2/core.hpp
  31. 1
      modules/core/include/opencv2/core/cvstd.hpp
  32. 1188
      modules/core/include/opencv2/core/matx.hpp
  33. 1115
      modules/core/include/opencv2/core/matx.inl.hpp
  34. 40
      modules/core/include/opencv2/core/persistence.hpp
  35. 30
      modules/features2d/include/opencv2/features2d.hpp
  36. 12
      modules/gapi/doc/00-root.markdown
  37. 2
      modules/gapi/doc/30-implementation.markdown
  38. 2
      modules/gapi/include/opencv2/gapi.hpp
  39. 4
      modules/gapi/include/opencv2/gapi/gcommon.hpp
  40. 4
      modules/gapi/include/opencv2/gapi/gstreaming.hpp
  41. 1
      modules/gapi/include/opencv2/gapi/infer/ov.hpp
  42. 39
      modules/highgui/doc/highgui_qt.cpp
  43. 44
      modules/highgui/include/opencv2/highgui.hpp
  44. 3
      modules/imgcodecs/include/opencv2/imgcodecs.hpp
  45. 18
      modules/imgproc/doc/colors.markdown
  46. 2
      modules/java/jar/build.xml.in
  47. 2
      modules/objdetect/include/opencv2/objdetect.hpp
  48. 1
      modules/photo/include/opencv2/photo.hpp
  49. 2
      modules/stereo/include/opencv2/stereo.hpp
  50. 1
      modules/video/include/opencv2/video.hpp
  51. 18
      modules/videoio/doc/videoio_overview.markdown

@ -573,8 +573,6 @@ 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
# ----------------------------------------------------------------------------

@ -16,6 +16,10 @@ endif()
find_package(Doxygen)
if(DOXYGEN_FOUND)
if (DOXYGEN_VERSION VERSION_LESS 1.9.8)
message(WARNING "Found doxygen ${DOXYGEN_VERSION}, version 1.9.8 is used for testing, there is
a chance your documentation will look different or have some limitations.")
endif()
add_custom_target(doxygen)
# not documented modules list
@ -196,6 +200,8 @@ if(DOXYGEN_FOUND)
list(APPEND CMAKE_DOXYGEN_HTML_FILES "${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}")

@ -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@/doxygen-awesome.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 =

@ -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/5.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`:

@ -1,8 +1,7 @@
//<![CDATA[
MathJax.Hub.Config(
{
TeX: {
Macros: {
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],
@ -17,6 +16,5 @@ MathJax.Hub.Config(
bordermatrix: ["\\matrix{#1}", 1]
}
}
}
);
};
//]]>

@ -82,62 +82,38 @@ 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

@ -102,10 +102,10 @@ python3 multiview_calibration.py --help
```
The expected output in Linux terminal for `multiview_calibration_images` data (from `opencv_extra/testdata/python/` generated in Blender) should be the following:
![](images/terminal-demo.jpg)
![](camera_multiview_calibration/images/terminal-demo.jpg)
The expected output for real-life calibration images in `opencv_extra/testdata/python/real_multiview_calibration_images` is the following:
![](images/terminal-real-demo.jpg)
![](camera_multiview_calibration/images/terminal-real-demo.jpg)
Python visualization
@ -116,21 +116,21 @@ Firstly, the sample shows positions of cameras, checkerboard (of a random frame)
If images are not known, then a simple plot with arrows (from given point to the back-projected one) visualizing errors is shown. The color of arrows highlights the error values. Additionally, the title reports mean error on this frame, and its accuracy among other frames used in calibration.
The following test instances were synthetically generated (see `opencv/apps/python-calibration-generator/calibration_generator.py`):
![](images/1.jpg)
![](camera_multiview_calibration/images/1.jpg)
![](images/2.jpg)
![](camera_multiview_calibration/images/2.jpg)
This instance has large Gaussian points noise.
![](images/3.jpg)
![](camera_multiview_calibration/images/3.jpg)
Another example, with more complex tree structure is here, it shows a weak connection between two groups of cameras.
![](images/4.jpg)
![](camera_multiview_calibration/images/4.jpg)
If files to images are provided, then the output is an image with plotted arrows:
![](images/checkerboard.png)
![](camera_multiview_calibration/images/checkerboard.png)
Details Of The Algorithm
@ -153,7 +153,7 @@ Details Of The Algorithm
* a. To reduce the total number of parameters, all rotation and translation vectors estimated in the first step from intrinsics calibration with the lowest error are transformed to be relative with respect to the root camera.
* b. The total number of parameters is (NUM_CAMERAS - 1) x (3 + 3) + NUM_FRAMES x (3 + 3), where 3 stands for a rotation vector and 3 for a translation vector. The first part of parameters are extrinsics, and the second part is for rotation and translation vectors per frame.
* c. Robust function is additionally applied to mitigate impact of outlier points during the optimization. The function has the shape of derivative of Gaussian, or it is $x * exp(-x/s)$ (efficiently implemented by approximation of the `exp`), where `x` is a square pixel error, and `s` is manually pre-defined scale. The choice of this function is that it is increasing on the interval of `0` to `y` pixel error, and it’s decreasing thereafter. The idea is that the function slightly decreases errors until it reaches `y`, and if error is too high (more than `y`) then its robust value limits to `0`. The value of scale factor was found by exhaustive evaluation that forces robust function to almost linearly increase until the robust value of an error is 10 px and decrease afterwards (see graph of the function below). The value itself is equal to 30, but could be modified in OpenCV source code.
![](images/exp.jpg)
![](camera_multiview_calibration/images/exp.jpg)
Method Input
----

@ -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.

@ -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

@ -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/5.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.

@ -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
-----------

@ -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/5.x/samples/dnn/text_detection.cpp)
- [samples/dnn/scene_text_spotting.cpp](https://github.com/opencv/opencv/blob/5.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**

@ -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

@ -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:

@ -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

@ -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:

@ -74,7 +74,7 @@ Explanation
The image we used can be found [here](https://raw.githubusercontent.com/opencv/opencv/5.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

@ -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

@ -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

@ -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

@ -80,7 +80,7 @@ Explanation / Result
Get image from [here](https://raw.githubusercontent.com/opencv/opencv/5.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/5.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/5.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/5.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:

@ -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

@ -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.

@ -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.

@ -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

@ -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 |

@ -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:

@ -1,4 +0,0 @@
Object Detection (objdetect module) {#tutorial_table_of_content_objdetect}
===================================
Content has been moved to this page: @ref tutorial_table_of_content_other

@ -1601,7 +1601,7 @@ objectPoints[i].size() for each i.
@param image_size Size of the image used only to initialize the camera intrinsic matrix.
@param K Output 3x3 floating-point camera intrinsic matrix
\f$\cameramatrix{A}\f$ . If
@ref CALIB_USE_INTRINSIC_GUESS is specified, some or all of fx, fy, cx, cy must be
@ref cv::CALIB_USE_INTRINSIC_GUESS is specified, some or all of fx, fy, cx, cy must be
initialized before calling the function.
@param D Output vector of distortion coefficients \f$\distcoeffsfisheye\f$.
@param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view.
@ -1611,19 +1611,19 @@ space (in which object points are specified) to the world coordinate space, that
position of the calibration pattern in the k-th pattern view (k=0.. *M* -1).
@param tvecs Output vector of translation vectors estimated for each pattern view.
@param flags Different flags that may be zero or a combination of the following values:
- @ref CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of
- @ref cv::CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of
fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
center ( imageSize is used), and focal distances are computed in a least-squares fashion.
- @ref CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration
- @ref cv::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration
of intrinsic optimization.
- @ref CALIB_CHECK_COND The functions will check validity of condition number.
- @ref CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero.
- @ref CALIB_FIX_K1,..., @ref CALIB_FIX_K4 Selected distortion coefficients
- @ref cv::CALIB_CHECK_COND The functions will check validity of condition number.
- @ref cv::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero.
- @ref cv::CALIB_FIX_K1,..., @ref cv::CALIB_FIX_K4 Selected distortion coefficients
are set to zeros and stay zero.
- @ref CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global
optimization. It stays at the center or at a different location specified when @ref CALIB_USE_INTRINSIC_GUESS is set too.
- @ref CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global
optimization. It is the \f$max(width,height)/\pi\f$ or the provided \f$f_x\f$, \f$f_y\f$ when @ref CALIB_USE_INTRINSIC_GUESS is set too.
- @ref cv::CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global
optimization. It stays at the center or at a different location specified when @ref cv::CALIB_USE_INTRINSIC_GUESS is set too.
- @ref cv::CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global
optimization. It is the \f$max(width,height)/\pi\f$ or the provided \f$f_x\f$, \f$f_y\f$ when @ref cv::CALIB_USE_INTRINSIC_GUESS is set too.
@param criteria Termination criteria for the iterative optimization algorithm.
*/
CV_EXPORTS_W double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, const Size& image_size,
@ -1639,7 +1639,7 @@ observed by the first camera.
observed by the second camera.
@param K1 Input/output first camera intrinsic matrix:
\f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If
any of @ref CALIB_USE_INTRINSIC_GUESS , @ref CALIB_FIX_INTRINSIC are specified,
any of @ref cv::CALIB_USE_INTRINSIC_GUESS , @ref cv::CALIB_FIX_INTRINSIC are specified,
some or all of the matrix components must be initialized.
@param D1 Input/output vector of distortion coefficients \f$\distcoeffsfisheye\f$ of 4 elements.
@param K2 Input/output second camera intrinsic matrix. The parameter is similar to K1 .
@ -1658,16 +1658,16 @@ to camera coordinate space of the first camera of the stereo pair.
@param tvecs Output vector of translation vectors estimated for each pattern view, see parameter description
of previous output parameter ( rvecs ).
@param flags Different flags that may be zero or a combination of the following values:
- @ref CALIB_FIX_INTRINSIC Fix K1, K2? and D1, D2? so that only R, T matrices
- @ref cv::CALIB_FIX_INTRINSIC Fix K1, K2? and D1, D2? so that only R, T matrices
are estimated.
- @ref CALIB_USE_INTRINSIC_GUESS K1, K2 contains valid initial values of
- @ref cv::CALIB_USE_INTRINSIC_GUESS K1, K2 contains valid initial values of
fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
center (imageSize is used), and focal distances are computed in a least-squares fashion.
- @ref CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration
- @ref cv::CALIB_RECOMPUTE_EXTRINSIC Extrinsic will be recomputed after each iteration
of intrinsic optimization.
- @ref CALIB_CHECK_COND The functions will check validity of condition number.
- @ref CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero.
- @ref CALIB_FIX_K1,..., @ref CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay
- @ref cv::CALIB_CHECK_COND The functions will check validity of condition number.
- @ref cv::CALIB_FIX_SKEW Skew coefficient (alpha) is set to zero and stay zero.
- @ref cv::CALIB_FIX_K1,..., @ref cv::CALIB_FIX_K4 Selected distortion coefficients are set to zeros and stay
zero.
@param criteria Termination criteria for the iterative optimization algorithm.
*/

@ -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

@ -140,7 +140,6 @@ public:
//! @} core_utils
//! @endcond
//! @addtogroup core_basic
//! @{

File diff suppressed because it is too large Load Diff

File diff suppressed because it is too large Load Diff

@ -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

@ -56,10 +56,10 @@
@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
@ -560,10 +560,6 @@ public:
CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
};
//! @} features2d_main
//! @addtogroup features2d_main
//! @{
/** @brief Wrapping class for feature detection using the FAST method.
@ -615,10 +611,6 @@ Check @ref tutorial_py_fast "the corresponding tutorial" for more details.
CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
int threshold, bool nonmaxSuppression=true, FastFeatureDetector::DetectorType type=FastFeatureDetector::TYPE_9_16 );
//! @} features2d_main
//! @addtogroup features2d_main
//! @{
/** @brief Wrapping class for feature detection using the AGAST method. :
*/
@ -776,10 +768,6 @@ public:
CV_WRAP virtual const std::vector<std::vector<cv::Point> >& getBlobContours() const = 0;
};
//! @} features2d_main
//! @addtogroup features2d_main
//! @{
/** @brief Class implementing the KAZE keypoint detector and descriptor extractor, described in @cite ABD12 .
@ -908,7 +896,6 @@ public:
CV_WRAP virtual int getMaxPoints() const = 0;
};
//! @} features2d_main
/****************************************************************************************\
* Distance *
@ -973,6 +960,8 @@ struct L1
}
};
//! @} features2d_main
/****************************************************************************************\
* DescriptorMatcher *
\****************************************************************************************/
@ -1414,6 +1403,9 @@ 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,
@ -1426,6 +1418,8 @@ 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 *
\****************************************************************************************/
@ -1586,8 +1580,6 @@ protected:
//! @} features2d_category
//! @} features2d
} /* namespace cv */
#endif

@ -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:

@ -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}

@ -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

@ -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
{

@ -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

@ -690,7 +690,6 @@ namespace wip { namespace ov {
* taking into account the i/o data transfer.
*/
struct benchmark_mode { };
/** @} */
} // namespace ov
} // namespace wip

@ -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;
}

@ -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

@ -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>())

@ -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

@ -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>
]]>

@ -106,7 +106,9 @@ using a Boosted Cascade of Simple Features. IEEE CVPR, 2001. The paper is availa
@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.
@defgroup objdetect_common Common functions and classes
@defgroup objdetect_aruco ArUco markers and boards detection for robust camera pose estimation
@{

@ -78,7 +78,6 @@ http://www.inf.ufrgs.br/~eslgastal/DomainTransform
https://www.learnopencv.com/non-photorealistic-rendering-using-opencv-python-c/
@defgroup photo_c C API
@}
*/

@ -217,7 +217,7 @@ camera.
@param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
camera.
@param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ).
@param flags Operation flags that may be zero or @ref CALIB_ZERO_DISPARITY . If the flag is set,
@param flags Operation flags that may be zero or @ref cv::CALIB_ZERO_DISPARITY . If the flag is set,
the function makes the principal points of each camera have the same pixel coordinates in the
rectified views. And if the flag is not set, the function may still shift the images in the
horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the

@ -49,7 +49,6 @@
@{
@defgroup video_motion Motion Analysis
@defgroup video_track Object Tracking
@defgroup video_c C API
@}
*/

@ -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
------------------

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