Doxygen documentation: more fixes and cleanups
This commit is contained in:
Maksim Shabunin
@@ -59,11 +59,11 @@ implement vector descriptor matchers inherit the DescriptorMatcher interface.
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@note
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- An example explaining keypoint matching can be found at
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opencv\_source\_code/samples/cpp/descriptor\_extractor\_matcher.cpp
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opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
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- An example on descriptor matching evaluation can be found at
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opencv\_source\_code/samples/cpp/detector\_descriptor\_matcher\_evaluation.cpp
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opencv_source_code/samples/cpp/detector_descriptor_matcher_evaluation.cpp
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- An example on one to many image matching can be found at
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opencv\_source\_code/samples/cpp/matching\_to\_many\_images.cpp
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opencv_source_code/samples/cpp/matching_to_many_images.cpp
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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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@@ -72,9 +72,9 @@ This section describes approaches based on local 2D features and used to categor
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@note
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- A complete Bag-Of-Words sample can be found at
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opencv\_source\_code/samples/cpp/bagofwords\_classification.cpp
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opencv_source_code/samples/cpp/bagofwords_classification.cpp
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- (Python) An example using the features2D framework to perform object categorization can be
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found at opencv\_source\_code/samples/python2/find\_obj.py
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found at opencv_source_code/samples/python2/find_obj.py
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@}
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*/
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@@ -265,22 +265,22 @@ public:
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will mean that to cover certain scale range you will need more pyramid levels and so the speed
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will suffer.
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@param nlevels The number of pyramid levels. The smallest level will have linear size equal to
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input\_image\_linear\_size/pow(scaleFactor, nlevels).
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input_image_linear_size/pow(scaleFactor, nlevels).
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@param edgeThreshold This is size of the border where the features are not detected. It should
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roughly match the patchSize parameter.
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@param firstLevel It should be 0 in the current implementation.
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@param WTA\_K The number of points that produce each element of the oriented BRIEF descriptor. The
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@param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The
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default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
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so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
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random points (of course, those point coordinates are random, but they are generated from the
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pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
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rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
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output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
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denoted as NORM\_HAMMING2 (2 bits per bin). When WTA\_K=4, we take 4 random points to compute each
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denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
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bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
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@param scoreType The default HARRIS\_SCORE means that Harris algorithm is used to rank features
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@param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features
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(the score is written to KeyPoint::score and is used to retain best nfeatures features);
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FAST\_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
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FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
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but it is a little faster to compute.
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@param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller
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pyramid layers the perceived image area covered by a feature will be larger.
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@@ -325,7 +325,7 @@ The class encapsulates all the parameters of the MSER extraction algorithm (see
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@note
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- (Python) A complete example showing the use of the MSER detector can be found at
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opencv\_source\_code/samples/python2/mser.py
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opencv_source_code/samples/python2/mser.py
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*/
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class CV_EXPORTS_W MSER : public Feature2D
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{
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@@ -366,13 +366,13 @@ circle around this pixel.
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@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners
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(keypoints).
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@param type one of the three neighborhoods as defined in the paper:
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FastFeatureDetector::TYPE\_9\_16, FastFeatureDetector::TYPE\_7\_12,
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FastFeatureDetector::TYPE\_5\_8
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FastFeatureDetector::TYPE_9_16, FastFeatureDetector::TYPE_7_12,
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FastFeatureDetector::TYPE_5_8
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Detects corners using the FAST algorithm by @cite Rosten06.
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@note In Python API, types are given as cv2.FAST\_FEATURE\_DETECTOR\_TYPE\_5\_8,
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cv2.FAST\_FEATURE\_DETECTOR\_TYPE\_7\_12 and cv2.FAST\_FEATURE\_DETECTOR\_TYPE\_9\_16. For corner
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@note In Python API, types are given as cv2.FAST_FEATURE_DETECTOR_TYPE_5_8,
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cv2.FAST_FEATURE_DETECTOR_TYPE_7_12 and cv2.FAST_FEATURE_DETECTOR_TYPE_9_16. For corner
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detection, use cv2.FAST.detect() method.
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*/
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CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
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@@ -529,8 +529,8 @@ public:
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@param threshold Detector response threshold to accept point
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@param nOctaves Maximum octave evolution of the image
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@param nOctaveLayers Default number of sublevels per scale level
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@param diffusivity Diffusivity type. DIFF\_PM\_G1, DIFF\_PM\_G2, DIFF\_WEICKERT or
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DIFF\_CHARBONNIER
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@param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or
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DIFF_CHARBONNIER
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*/
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CV_WRAP static Ptr<KAZE> create(bool extended=false, bool upright=false,
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float threshold = 0.001f,
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@@ -577,15 +577,15 @@ public:
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/** @brief The AKAZE constructor
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@param descriptor\_type Type of the extracted descriptor: DESCRIPTOR\_KAZE,
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DESCRIPTOR\_KAZE\_UPRIGHT, DESCRIPTOR\_MLDB or DESCRIPTOR\_MLDB\_UPRIGHT.
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@param descriptor\_size Size of the descriptor in bits. 0 -\> Full size
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@param descriptor\_channels Number of channels in the descriptor (1, 2, 3)
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@param descriptor_type Type of the extracted descriptor: DESCRIPTOR_KAZE,
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DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT.
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@param descriptor_size Size of the descriptor in bits. 0 -\> Full size
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@param descriptor_channels Number of channels in the descriptor (1, 2, 3)
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@param threshold Detector response threshold to accept point
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@param nOctaves Maximum octave evolution of the image
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@param nOctaveLayers Default number of sublevels per scale level
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@param diffusivity Diffusivity type. DIFF\_PM\_G1, DIFF\_PM\_G2, DIFF\_WEICKERT or
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DIFF\_CHARBONNIER
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@param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or
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DIFF_CHARBONNIER
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*/
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CV_WRAP static Ptr<AKAZE> create(int descriptor_type=AKAZE::DESCRIPTOR_MLDB,
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int descriptor_size = 0, int descriptor_channels = 3,
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@@ -947,9 +947,9 @@ class CV_EXPORTS_W BFMatcher : public DescriptorMatcher
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public:
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/** @brief Brute-force matcher constructor.
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@param normType One of NORM\_L1, NORM\_L2, NORM\_HAMMING, NORM\_HAMMING2. L1 and L2 norms are
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preferable choices for SIFT and SURF descriptors, NORM\_HAMMING should be used with ORB, BRISK and
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BRIEF, NORM\_HAMMING2 should be used with ORB when WTA\_K==3 or 4 (see ORB::ORB constructor
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@param normType One of NORM_L1, NORM_L2, NORM_HAMMING, NORM_HAMMING2. L1 and L2 norms are
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preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and
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BRIEF, NORM_HAMMING2 should be used with ORB when WTA_K==3 or 4 (see ORB::ORB constructor
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description).
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@param crossCheck If it is false, this is will be default BFMatcher behaviour when it finds the k
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nearest neighbors for each query descriptor. If crossCheck==true, then the knnMatch() method with
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@@ -977,7 +977,7 @@ protected:
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/** @brief Flann-based descriptor matcher.
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This matcher trains flann::Index\_ on a train descriptor collection and calls its nearest search
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This matcher trains flann::Index_ on a train descriptor collection and calls its nearest search
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methods to find the best matches. So, this matcher may be faster when matching a large train
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collection than the brute force matcher. FlannBasedMatcher does not support masking permissible
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matches of descriptor sets because flann::Index does not support this. :
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@@ -1053,9 +1053,9 @@ output image. See possible flags bit values below.
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DrawMatchesFlags. See details above in drawMatches .
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@note
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For Python API, flags are modified as cv2.DRAW\_MATCHES\_FLAGS\_DEFAULT,
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cv2.DRAW\_MATCHES\_FLAGS\_DRAW\_RICH\_KEYPOINTS, cv2.DRAW\_MATCHES\_FLAGS\_DRAW\_OVER\_OUTIMG,
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cv2.DRAW\_MATCHES\_FLAGS\_NOT\_DRAW\_SINGLE\_POINTS
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For Python API, flags are modified as cv2.DRAW_MATCHES_FLAGS_DEFAULT,
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cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS, cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG,
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cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS
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*/
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CV_EXPORTS_W void drawKeypoints( InputArray image, const std::vector<KeyPoint>& keypoints, InputOutputArray outImage,
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const Scalar& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT );
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