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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
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@@ -56,15 +56,15 @@
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@defgroup features2d_main Feature Detection and Description
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@defgroup features2d_match Descriptor Matchers
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Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to
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easily switch between different algorithms solving the same problem. This section is devoted to
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matching descriptors that are represented as vectors in a multidimensional space. All objects that
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implement vector descriptor matchers inherit the DescriptorMatcher interface.
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Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables
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you to easily switch between different algorithms solving the same problem. This section is
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devoted to matching descriptors that are represented as vectors in a multidimensional space.
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All objects that implement vector descriptor matchers inherit the DescriptorMatcher interface.
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@defgroup features2d_draw Drawing Function of Keypoints and Matches
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@defgroup features2d_category Object Categorization
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This section describes approaches based on local 2D features and used to categorize objects.
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This section describes approaches based on local 2D features and used to categorize objects.
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@defgroup feature2d_hal Hardware Acceleration Layer
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@{
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@@ -560,10 +560,6 @@ public:
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CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
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};
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//! @} features2d_main
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//! @addtogroup features2d_main
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//! @{
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/** @brief Wrapping class for feature detection using the FAST method.
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@@ -615,10 +611,6 @@ Check @ref tutorial_py_fast "the corresponding tutorial" for more details.
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CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
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int threshold, bool nonmaxSuppression=true, FastFeatureDetector::DetectorType type=FastFeatureDetector::TYPE_9_16 );
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//! @} features2d_main
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//! @addtogroup features2d_main
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//! @{
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/** @brief Wrapping class for feature detection using the AGAST method. :
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*/
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@@ -776,10 +768,6 @@ public:
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CV_WRAP virtual const std::vector<std::vector<cv::Point> >& getBlobContours() const = 0;
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};
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//! @} features2d_main
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//! @addtogroup features2d_main
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//! @{
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/** @brief Class implementing the KAZE keypoint detector and descriptor extractor, described in @cite ABD12 .
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@@ -908,7 +896,6 @@ public:
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CV_WRAP virtual int getMaxPoints() const = 0;
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};
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//! @} features2d_main
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/****************************************************************************************\
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* Distance *
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@@ -973,6 +960,8 @@ struct L1
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}
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};
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//! @} features2d_main
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/****************************************************************************************\
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* DescriptorMatcher *
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\****************************************************************************************/
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@@ -1414,6 +1403,9 @@ CV_EXPORTS_AS(drawMatchesKnn) void drawMatches( InputArray img1, const std::vect
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* Functions to evaluate the feature detectors and [generic] descriptor extractors *
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\****************************************************************************************/
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//! @addtogroup features2d_main
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//! @{
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CV_EXPORTS void evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H1to2,
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std::vector<KeyPoint>* keypoints1, std::vector<KeyPoint>* keypoints2,
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float& repeatability, int& correspCount,
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@@ -1426,6 +1418,8 @@ CV_EXPORTS void computeRecallPrecisionCurve( const std::vector<std::vector<DMatc
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CV_EXPORTS float getRecall( const std::vector<Point2f>& recallPrecisionCurve, float l_precision );
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CV_EXPORTS int getNearestPoint( const std::vector<Point2f>& recallPrecisionCurve, float l_precision );
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//! @}
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/****************************************************************************************\
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* Bag of visual words *
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\****************************************************************************************/
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@@ -1586,8 +1580,6 @@ protected:
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//! @} features2d_category
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//! @} features2d
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} /* namespace cv */
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#endif
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