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Merge pull request #3638 from mshabunin:doc-upgrade
Documentation transition to fresh Doxygen #3638 Merge with opencv/opencv#25042
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‎modules/bioinspired/include/opencv2/bioinspired/retina.hpp

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Here is the default configuration file of the retina module. It gives results such as the first
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retina output shown on the top of this page.
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@code{xml}
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<?xml version="1.0"?>
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<opencv_storage>
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<OPLandIPLparvo>
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<colorMode>1</colorMode>
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<normaliseOutput>1</normaliseOutput>
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<photoreceptorsLocalAdaptationSensitivity>7.5e-01</photoreceptorsLocalAdaptationSensitivity>
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<photoreceptorsTemporalConstant>9.0e-01</photoreceptorsTemporalConstant>
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<photoreceptorsSpatialConstant>5.3e-01</photoreceptorsSpatialConstant>
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<horizontalCellsGain>0.01</horizontalCellsGain>
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<hcellsTemporalConstant>0.5</hcellsTemporalConstant>
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<hcellsSpatialConstant>7.</hcellsSpatialConstant>
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<ganglionCellsSensitivity>7.5e-01</ganglionCellsSensitivity></OPLandIPLparvo>
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<IPLmagno>
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<normaliseOutput>1</normaliseOutput>
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<parasolCells_beta>0.</parasolCells_beta>
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<parasolCells_tau>0.</parasolCells_tau>
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<parasolCells_k>7.</parasolCells_k>
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<amacrinCellsTemporalCutFrequency>2.0e+00</amacrinCellsTemporalCutFrequency>
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<V0CompressionParameter>9.5e-01</V0CompressionParameter>
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<localAdaptintegration_tau>0.</localAdaptintegration_tau>
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<localAdaptintegration_k>7.</localAdaptintegration_k></IPLmagno>
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</opencv_storage>
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@endcode
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@include default_retina_config.xml
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Here is the 'realistic" setup used to obtain the second retina output shown on the top of this page.
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@code{xml}
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<?xml version="1.0"?>
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<opencv_storage>
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<OPLandIPLparvo>
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<colorMode>1</colorMode>
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<normaliseOutput>1</normaliseOutput>
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<photoreceptorsLocalAdaptationSensitivity>8.9e-01</photoreceptorsLocalAdaptationSensitivity>
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<photoreceptorsTemporalConstant>9.0e-01</photoreceptorsTemporalConstant>
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<photoreceptorsSpatialConstant>5.3e-01</photoreceptorsSpatialConstant>
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<horizontalCellsGain>0.3</horizontalCellsGain>
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<hcellsTemporalConstant>0.5</hcellsTemporalConstant>
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<hcellsSpatialConstant>7.</hcellsSpatialConstant>
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<ganglionCellsSensitivity>8.9e-01</ganglionCellsSensitivity></OPLandIPLparvo>
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<IPLmagno>
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<normaliseOutput>1</normaliseOutput>
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<parasolCells_beta>0.</parasolCells_beta>
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<parasolCells_tau>0.</parasolCells_tau>
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<parasolCells_k>7.</parasolCells_k>
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<amacrinCellsTemporalCutFrequency>2.0e+00</amacrinCellsTemporalCutFrequency>
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<V0CompressionParameter>9.5e-01</V0CompressionParameter>
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<localAdaptintegration_tau>0.</localAdaptintegration_tau>
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<localAdaptintegration_k>7.</localAdaptintegration_k></IPLmagno>
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</opencv_storage>
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@endcode
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@include realistic_retina_config.xml
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*/
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struct RetinaParameters{
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//! Outer Plexiform Layer (OPL) and Inner Plexiform Layer Parvocellular (IplParvo) parameters

‎modules/bioinspired/samples/default_retina_config.xml

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<?xml version="1.0"?>
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<opencv_storage>
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<OPLandIPLparvo>
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<colorMode>1</colorMode>
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<normaliseOutput>1</normaliseOutput>
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<photoreceptorsLocalAdaptationSensitivity>7.5e-01</photoreceptorsLocalAdaptationSensitivity>
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<photoreceptorsTemporalConstant>9.0e-01</photoreceptorsTemporalConstant>
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<photoreceptorsSpatialConstant>5.3e-01</photoreceptorsSpatialConstant>
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<horizontalCellsGain>0.01</horizontalCellsGain>
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<hcellsTemporalConstant>0.5</hcellsTemporalConstant>
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<hcellsSpatialConstant>7.</hcellsSpatialConstant>
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<ganglionCellsSensitivity>7.5e-01</ganglionCellsSensitivity>
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</OPLandIPLparvo>
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<IPLmagno>
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<normaliseOutput>1</normaliseOutput>
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<parasolCells_beta>0.</parasolCells_beta>
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<parasolCells_tau>0.</parasolCells_tau>
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<parasolCells_k>7.</parasolCells_k>
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<amacrinCellsTemporalCutFrequency>2.0e+00</amacrinCellsTemporalCutFrequency>
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<V0CompressionParameter>9.5e-01</V0CompressionParameter>
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<localAdaptintegration_tau>0.</localAdaptintegration_tau>
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<localAdaptintegration_k>7.</localAdaptintegration_k>
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</IPLmagno>
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</opencv_storage>

‎modules/bioinspired/samples/realistic_retina_config.xml

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<?xml version="1.0"?>
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<opencv_storage>
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<OPLandIPLparvo>
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<colorMode>1</colorMode>
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<normaliseOutput>1</normaliseOutput>
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<photoreceptorsLocalAdaptationSensitivity>8.9e-01</photoreceptorsLocalAdaptationSensitivity>
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<photoreceptorsTemporalConstant>9.0e-01</photoreceptorsTemporalConstant>
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<photoreceptorsSpatialConstant>5.3e-01</photoreceptorsSpatialConstant>
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<horizontalCellsGain>0.3</horizontalCellsGain>
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<hcellsTemporalConstant>0.5</hcellsTemporalConstant>
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<hcellsSpatialConstant>7.</hcellsSpatialConstant>
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<ganglionCellsSensitivity>8.9e-01</ganglionCellsSensitivity>
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</OPLandIPLparvo>
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<IPLmagno>
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<normaliseOutput>1</normaliseOutput>
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<parasolCells_beta>0.</parasolCells_beta>
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<parasolCells_tau>0.</parasolCells_tau>
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<parasolCells_k>7.</parasolCells_k>
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<amacrinCellsTemporalCutFrequency>2.0e+00</amacrinCellsTemporalCutFrequency>
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<V0CompressionParameter>9.5e-01</V0CompressionParameter>
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<localAdaptintegration_tau>0.</localAdaptintegration_tau>
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<localAdaptintegration_k>7.</localAdaptintegration_k>
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</IPLmagno>
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</opencv_storage>

‎modules/bioinspired/tutorials/retina_model/retina_model.markdown

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Retina and real-world vision {#tutorial_bioinspired_retina_model}
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=============================================================
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@tableofcontents
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Goal
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----
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significantly reduce its energy thus giving more visibility to higher spatial frequency details.
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#### Basic parameters
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## Basic parameters
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The simplest parameters are as follows :
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product per pixel for all of the retina processes and it has recently been parallelized for multicore
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architectures.
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#### Photo-receptors parameters
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## Photo-receptors parameters
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The following parameters act on the entry point of the retina - photo-receptors - and has impact on all
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of the following processes. These sensors are low pass spatio-temporal filters that smooth temporal and
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A good compromise for color images is a 0.53 value since such choice won't affect too much the color spectrum.
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Higher values would lead to gray and blurred output images.
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#### Horizontal cells parameters
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## Horizontal cells parameters
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This parameter set tunes the neural network connected to the photo-receptors, the horizontal cells.
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It modulates photo-receptors sensitivity and completes the processing for final spectral whitening
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and luminance is already partly enhanced. The following parameters act on the last processing stages
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of the two outing retina signals.
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#### Parvo (details channel) dedicated parameter
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## Parvo (details channel) dedicated parameter
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- **ganglionCellsSensitivity** specifies the strength of the final local adaptation occurring at
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the output of this details' dedicated channel. Parameter values remain between 0 and 1. Low value
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**Note :** this parameter can correct eventual burned images by favoring low energetic details of
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the visual scene, even in bright areas.
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#### IPL Magno (motion/transient channel) parameters
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## IPL Magno (motion/transient channel) parameters
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Once image's information are cleaned, this channel acts as a high pass temporal filter that
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selects only the signals related to transient signals (events, motion, etc.). A low pass spatial filter

‎modules/cannops/include/opencv2/cann.hpp

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#include "opencv2/core.hpp"
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/**
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@defgroup cann Ascend-accelerated Computer Vision
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@defgroup cannops Ascend-accelerated Computer Vision
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@{
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@defgroup canncore Core part
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@{
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@defgroup cann_struct Data Structures
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@defgroup cann_init Initializeation and Information
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@defgroup cann_init Initialization and Information
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@}
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@}
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*/

‎modules/cannops/include/opencv2/cann_interface.hpp

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{
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/**
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@addtogroup cann
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@addtogroup cannops
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@{
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@defgroup cannops Operations for Ascend Backend.
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@defgroup cannops_ops Operations for Ascend Backend.
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@{
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@defgroup cannops_elem Per-element Operations
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@defgroup cannops_core Core Operations on Matrices

‎modules/cudaimgproc/include/opencv2/cudaimgproc.hpp

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```
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see the \a CUDA_TEST_P(Moments, Async) test inside opencv_contrib_source_code/modules/cudaimgproc/test/test_moments.cpp for an example.
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@returns cv::Moments.
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@sa cuda::moments, cuda::convertSpatialMoments, cuda::numMoments, cuda::MomentsOrder
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*/
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CV_EXPORTS_W void spatialMoments(InputArray src, OutputArray moments, const bool binaryImage = false, const MomentsOrder order = MomentsOrder::THIRD_ORDER_MOMENTS, const int momentsType = CV_64F, Stream& stream = Stream::Null());

‎modules/dnn_superres/tutorials/benchmark/sr_benchmark.markdown

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Benchmarking results
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-----------
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Dataset benchmarking
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----
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###General100 dataset
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<center>
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## General100 dataset
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#####2x scaling factor
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### 2x scaling factor
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| | Avg inference time in sec (CPU)| Avg PSNR | Avg SSIM |
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| Nearest neighbor | 0.000114 | 29.1665 | 0.9049 |
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| Lanczos | 0.001094 | 32.4687 | 0.9327 |
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#####3x scaling factor
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### 3x scaling factor
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| | Avg inference time in sec (CPU)| Avg PSNR | Avg SSIM |
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| ------------- |:-------------------:| ---------:|--------:|
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| Lanczos | 0.001012 |25.9115 |0.8706 |
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#####4x scaling factor
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### 4x scaling factor
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| | Avg inference time in sec (CPU)| Avg PSNR | Avg SSIM |
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| ------------- |:-------------------:| ---------:|--------:|
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| Lanczos | 0.001012 |25.9115 |0.8706 |
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</center>
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Images
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----
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<center>
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## Images
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####2x scaling factor
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### 2x scaling factor
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|Set5: butterfly.png | size: 256x256 | ||
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|:-------------:|:-------------------:|:-------------:|:----:|
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![ESPCN](images/espcn_butterfly.jpg)| ![FSRCNN](images/fsrcnn_butterfly.jpg) | ![LapSRN](images/lapsrn_butterfly.jpg) | ![EDSR](images/edsr_butterfly.jpg)
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|29.0341 / 0.9354 / **0.004157**| 29.0077 / 0.9345 / 0.006325 | 27.8212 / 0.9230 / 0.037937 | **30.0347** / **0.9453** / 2.077280 |
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####3x scaling factor
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### 3x scaling factor
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|Urban100: img_001.png | size: 1024x644 | ||
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|:-------------:|:-------------------:|:-------------:|:----:|
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|28.0118 / 0.8588 / **0.030748**| 28.0184 / 0.8597 / 0.094173 | | **30.5671** / **0.9019** / 9.517580 |
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####4x scaling factor
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### 4x scaling factor
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|Set14: comic.png | size: 250x361 | ||
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|:-------------:|:-------------------:|:-------------:|:----:|
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|![ESPCN](images/espcn_comic.jpg)| ![FSRCNN](images/fsrcnn_comic.jpg) | ![LapSRN](images/lapsrn_comic.jpg) | ![EDSR](images/edsr_comic.jpg)
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|20.0417 / 0.6302 / **0.001894**| 20.0885 / 0.6384 / 0.002103 | 20.0676 / 0.6339 / 0.061640 | **20.5233** / **0.6901** / 0.665876 |
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####8x scaling factor
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### 8x scaling factor
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|Div2K: 0006.png | size: 1356x2040 | |
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|:-------------:|:-------------------:|:-------------:|
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|![Original](images/orig_div2k.jpg)|![Bicubic interpolation](images/bicubic_div2k.jpg)|![Nearest neighbor interpolation](images/nearest_div2k.jpg)|
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|PSRN / SSIM / Speed (CPU)| 26.3139 / **0.8033** / 0.001107| 23.8291 / 0.7340 / **0.000611** |
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|![Lanczos interpolation](images/lanczos_div2k.jpg)| ![LapSRN](images/lapsrn_div2k.jpg) | |
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|26.1565 / 0.7962 / 0.004782| **26.7046** / 0.7987 / 2.274290 | |
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</center>

‎modules/face/include/opencv2/face/facemark.hpp

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#ifndef __OPENCV_FACELANDMARK_HPP__
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#define __OPENCV_FACELANDMARK_HPP__
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/**
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@defgroup face Face Analysis
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- @ref tutorial_table_of_content_facemark
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- The Facemark API
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*/
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#include "opencv2/core.hpp"
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#include <vector>
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namespace cv {
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namespace face {
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//! @addtogroup face
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//! @{
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/** @brief Abstract base class for all facemark models
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//! construct a Kazemi facemark detector
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CV_EXPORTS_W Ptr<Facemark> createFacemarkKazemi();
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//! @}
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} // face
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} // cv

‎modules/face/include/opencv2/face/facemark_train.hpp

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#ifndef __OPENCV_FACELANDMARKTRAIN_HPP__
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#define __OPENCV_FACELANDMARKTRAIN_HPP__
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/**
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@defgroup face Face Analysis
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- @ref tutorial_table_of_content_facemark
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- The Facemark API
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*/
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#include "opencv2/face/facemark.hpp"
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#include "opencv2/objdetect.hpp"
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‎modules/face/tutorials/face_landmark/face_landmark_trainer.markdown

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./sample_train_landmark_detector -annotations=/home/sukhad/Downloads/code/trainset/ -config=config.xml -face_cascade=lbpcascadefrontalface.xml -model=trained_model.dat -width=460 -height=460
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```
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### Description of command parameters
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## Description of command parameters
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> * **annotations** a : (REQUIRED) Path to annotations txt file [example - /data/annotations.txt]
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> * **config** c : (REQUIRED) Path to configuration xml file containing parameters for training.[ example - /data/config.xml]
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> * **height** h : (OPTIONAL) The height which you want all images to get to scale the annotations. Large images are slow to process [default = 460]
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> * **face_cascade** f (REQUIRED) Path to the face cascade xml file which you want to use as a detector.
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### Description of training parameters
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## Description of training parameters
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The configuration file described above which is used while training contains the training parameters which are required for training.
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To get more detailed description about the training parameters you can refer to the [Research paper](https://pdfs.semanticscholar.org/d78b/6a5b0dcaa81b1faea5fb0000045a62513567.pdf).
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### Understanding code
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## Understanding code
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![](images/3.jpg)

‎modules/fuzzy/include/opencv2/fuzzy.hpp

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Namespace for all functions is `ft`. The module brings implementation of the last image processing algorithms based on fuzzy mathematics. Method are named based on the pattern `FT`_degree_dimension`_`method.
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@{
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@{
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@defgroup f0_math Math with F0-transform support
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Fuzzy transform (\f$F^0\f$-transform) of the 0th degree transforms whole image to a matrix of its components. These components are used in latter computation where each of them represents average color of certain subarea.
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Fuzzy transform (\f$F^0\f$-transform) of the 0th degree transforms whole image to a matrix of its components. These components are used in latter computation where each of them represents average color of certain subarea.
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@defgroup f1_math Math with F1-transform support
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Fuzzy transform (\f$F^1\f$-transform) of the 1th degree transforms whole image to a matrix of its components. Each component is polynomial of the 1th degree carrying information about average color and average gradient of certain subarea.
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Fuzzy transform (\f$F^1\f$-transform) of the 1th degree transforms whole image to a matrix of its components. Each component is polynomial of the 1th degree carrying information about average color and average gradient of certain subarea.
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@defgroup f_image Fuzzy image processing
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Image proceesing based on fuzzy mathematics namely F-transform.
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@}
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Image proceesing based on fuzzy mathematics namely F-transform.
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@}
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*/
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