122 lines
4.1 KiB
C
122 lines
4.1 KiB
C
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//
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// This file is auto-generated. Please don't modify it!
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//
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#pragma once
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#ifdef __cplusplus
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//#import "opencv.hpp"
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#import "opencv2/xfeatures2d.hpp"
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#else
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#define CV_EXPORTS
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#endif
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#import <Foundation/Foundation.h>
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#import "Feature2D.h"
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// C++: enum BeblidSize (cv.xfeatures2d.BEBLID.BeblidSize)
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typedef NS_ENUM(int, BeblidSize) {
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BEBLID_SIZE_512_BITS NS_SWIFT_NAME(SIZE_512_BITS) = 100,
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BEBLID_SIZE_256_BITS NS_SWIFT_NAME(SIZE_256_BITS) = 101
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};
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NS_ASSUME_NONNULL_BEGIN
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// C++: class BEBLID
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/**
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* Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor),
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* described in CITE: Suarez2020BEBLID .
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*
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* BEBLID \cite Suarez2020BEBLID is a efficient binary descriptor learned with boosting.
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* It is able to describe keypoints from any detector just by changing the scale_factor parameter.
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* In several benchmarks it has proved to largely improve other binary descriptors like ORB or
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* BRISK with the same efficiency. BEBLID describes using the difference of mean gray values in
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* different regions of the image around the KeyPoint, the descriptor is specifically optimized for
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* image matching and patch retrieval addressing the asymmetries of these problems.
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*
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* If you find this code useful, please add a reference to the following paper:
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* <BLOCKQUOTE> Iago Suárez, Ghesn Sfeir, José M. Buenaposada, and Luis Baumela.
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* BEBLID: Boosted efficient binary local image descriptor.
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* Pattern Recognition Letters, 133:366–372, 2020. </BLOCKQUOTE>
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*
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* The descriptor was trained using 1 million of randomly sampled pairs of patches
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* (20% positives and 80% negatives) from the Liberty split of the UBC datasets
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* \cite winder2007learning as described in the paper CITE: Suarez2020BEBLID.
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* You can check in the [AKAZE example](https://raw.githubusercontent.com/opencv/opencv/master/samples/cpp/tutorial_code/features2D/AKAZE_match.cpp)
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* how well BEBLID works. Detecting 10000 keypoints with ORB and describing with BEBLID obtains
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* 561 inliers (75%) whereas describing with ORB obtains only 493 inliers (63%).
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*
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* Member of `Xfeatures2d`
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*/
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CV_EXPORTS @interface BEBLID : Feature2D
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#ifdef __cplusplus
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@property(readonly)cv::Ptr<cv::xfeatures2d::BEBLID> nativePtrBEBLID;
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#endif
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#ifdef __cplusplus
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- (instancetype)initWithNativePtr:(cv::Ptr<cv::xfeatures2d::BEBLID>)nativePtr;
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+ (instancetype)fromNative:(cv::Ptr<cv::xfeatures2d::BEBLID>)nativePtr;
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#endif
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#pragma mark - Methods
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//
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// static Ptr_BEBLID cv::xfeatures2d::BEBLID::create(float scale_factor, int n_bits = BEBLID::SIZE_512_BITS)
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//
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/**
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* Creates the BEBLID descriptor.
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* @param scale_factor Adjust the sampling window around detected keypoints:
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* - <b> 1.00f </b> should be the scale for ORB keypoints
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* - <b> 6.75f </b> should be the scale for SIFT detected keypoints
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* - <b> 6.25f </b> is default and fits for KAZE, SURF detected keypoints
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* - <b> 5.00f </b> should be the scale for AKAZE, MSD, AGAST, FAST, BRISK keypoints
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* @param n_bits Determine the number of bits in the descriptor. Should be either
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* BEBLID::SIZE_512_BITS or BEBLID::SIZE_256_BITS.
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*/
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+ (BEBLID*)create:(float)scale_factor n_bits:(int)n_bits NS_SWIFT_NAME(create(scale_factor:n_bits:));
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/**
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* Creates the BEBLID descriptor.
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* @param scale_factor Adjust the sampling window around detected keypoints:
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* - <b> 1.00f </b> should be the scale for ORB keypoints
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* - <b> 6.75f </b> should be the scale for SIFT detected keypoints
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* - <b> 6.25f </b> is default and fits for KAZE, SURF detected keypoints
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* - <b> 5.00f </b> should be the scale for AKAZE, MSD, AGAST, FAST, BRISK keypoints
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* BEBLID::SIZE_512_BITS or BEBLID::SIZE_256_BITS.
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*/
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+ (BEBLID*)create:(float)scale_factor NS_SWIFT_NAME(create(scale_factor:));
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//
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// void cv::xfeatures2d::BEBLID::setScaleFactor(float scale_factor)
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//
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- (void)setScaleFactor:(float)scale_factor NS_SWIFT_NAME(setScaleFactor(scale_factor:));
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//
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// float cv::xfeatures2d::BEBLID::getScaleFactor()
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//
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- (float)getScaleFactor NS_SWIFT_NAME(getScaleFactor());
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//
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// String cv::xfeatures2d::BEBLID::getDefaultName()
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//
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- (NSString*)getDefaultName NS_SWIFT_NAME(getDefaultName());
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@end
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NS_ASSUME_NONNULL_END
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