using OpenCVForUnity.CoreModule; using OpenCVForUnity.Features2dModule; using OpenCVForUnity.UtilsModule; using System; using System.Collections.Generic; using System.Runtime.InteropServices; namespace OpenCVForUnity.Xfeatures2dModule { // C++: class TEBLID /** * Class implementing TEBLID (Triplet-based Efficient Binary Local Image Descriptor), * described in CITE: Suarez2021TEBLID. * * TEBLID stands for Triplet-based Efficient Binary Local Image Descriptor, although originally it was called BAD * \cite Suarez2021TEBLID. It is an improvement over BEBLID \cite Suarez2020BEBLID, that uses triplet loss, * hard negative mining, and anchor swap to improve the image matching results. * It is able to describe keypoints from any detector just by changing the scale_factor parameter. * TEBLID is as efficient as ORB, BEBLID or BRISK, but the triplet-based training objective selected more * discriminative features that explain the accuracy gain. It is also more compact than BEBLID, * when running the [AKAZE example](https://github.com/opencv/opencv/blob/4.x/samples/cpp/tutorial_code/features2D/AKAZE_match.cpp) * with 10000 keypoints detected by ORB, BEBLID obtains 561 inliers (75%) with 512 bits, whereas * TEBLID obtains 621 (75.2%) with 256 bits. ORB obtains only 493 inliers (63%). * * If you find this code useful, please add a reference to the following paper: * <BLOCKQUOTE> Iago Suárez, José M. Buenaposada, and Luis Baumela. * Revisiting Binary Local Image Description for Resource Limited Devices. * IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 8317-8324, Oct. 2021. </BLOCKQUOTE> * * The descriptor was trained in Liberty split of the UBC datasets \cite winder2007learning . */ public class TEBLID : Feature2D { protected override void Dispose(bool disposing) { try { if (disposing) { } if (IsEnabledDispose) { if (nativeObj != IntPtr.Zero) xfeatures2d_TEBLID_delete(nativeObj); nativeObj = IntPtr.Zero; } } finally { base.Dispose(disposing); } } protected internal TEBLID(IntPtr addr) : base(addr) { } // internal usage only public static new TEBLID __fromPtr__(IntPtr addr) { return new TEBLID(addr); } // C++: enum cv.xfeatures2d.TEBLID.TeblidSize public const int SIZE_256_BITS = 102; public const int SIZE_512_BITS = 103; // // C++: static Ptr_TEBLID cv::xfeatures2d::TEBLID::create(float scale_factor, int n_bits = TEBLID::SIZE_256_BITS) // /** * Creates the TEBLID descriptor. * param scale_factor Adjust the sampling window around detected keypoints: *