Merge branch 'opencv:4.x' into 4.x

.github/workflows/PR-4.x.yaml

@@ -6,13 +6,13 @@ on:
       - 4.x
 
 jobs:
-  ARM64:
+  Ubuntu2004-ARM64:
     uses: opencv/ci-gha-workflow/.github/workflows/OCV-Contrib-PR-4.x-ARM64.yaml@main
 
-  U20:
+  Ubuntu2004-x64:
     uses: opencv/ci-gha-workflow/.github/workflows/OCV-Contrib-PR-4.x-U20.yaml@main
 
-  W10:
+  Windows10-x64:
     uses: opencv/ci-gha-workflow/.github/workflows/OCV-Contrib-PR-4.x-W10.yaml@main
 
   macOS-ARM64:

modules/sfm/CMakeLists.txt

@@ -9,17 +9,17 @@ find_package(Ceres QUIET)
 if(NOT Gflags_FOUND)  # Ceres find gflags on the own, so separate search isn't necessary
   find_package(Gflags QUIET)
 endif()
-if(NOT Glog_FOUND)  # Ceres find glog on the own, so separate search isn't necessary
+if(NOT (Glog_FOUND OR glog_FOUND))  # Ceres find glog on the own, so separate search isn't necessary
   find_package(Glog QUIET)
 endif()
 
-if(NOT Gflags_FOUND OR NOT Glog_FOUND)
+if(NOT Gflags_FOUND OR NOT (Glog_FOUND OR glog_FOUND))
   # try local search scripts
   list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/cmake")
   if(NOT Gflags_FOUND)
     find_package(Gflags QUIET)
   endif()
-  if(NOT Glog_FOUND)
+  if(NOT (Glog_FOUND OR glog_FOUND))
     find_package(Glog QUIET)
   endif()
 endif()

modules/xfeatures2d/README.md

@@ -5,4 +5,4 @@ Extra 2D Features Framework
 2. Non-free 2D feature algorithms
 
 Extra 2D Features Framework containing experimental and non-free 2D feature detector/descriptor algorithms:
- SURF, BRIEF, Censure, Freak, LUCID, Daisy, BEBLID, Self-similar.
+ SURF, BRIEF, Censure, Freak, LUCID, Daisy, BEBLID, TEBLID, Self-similar.

modules/xfeatures2d/doc/xfeatures2d.bib

@@ -154,6 +154,18 @@
   author = {Iago Su\'arez and Ghesn Sfeir and Jos\'e M. Buenaposada and Luis Baumela},
 }
 
+@article{Suarez2021TEBLID,
+  title = {Revisiting Binary Local Image Description for Resource Limited Devices},
+  journal = {IEEE Robotics and Automation Letters},
+  volume = {6},
+  pages = {8317--8324},
+  year = {2021},
+  number = {4},
+  doi = {https://doi.org/10.1109/LRA.2021.3107024},
+  url = {https://arxiv.org/pdf/2108.08380.pdf},
+  author = {Iago Su\'arez and Jos\'e M. Buenaposada and Luis Baumela},
+}
+
 @inproceedings{winder2007learning,
   title= {Learning Local Image Descriptors},
   author= {Winder, Simon AJ and Brown, Matthew},

modules/xfeatures2d/include/opencv2/xfeatures2d.hpp

@@ -224,6 +224,49 @@ public:
     CV_WRAP static Ptr<BEBLID> create(float scale_factor, int n_bits = BEBLID::SIZE_512_BITS);
 };
 
+/** @brief 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 .
+*/
+class CV_EXPORTS_W TEBLID : public Feature2D
+{
+public:
+    /**
+     * @brief  Descriptor number of bits, each bit is a box average difference.
+     * The user can choose between 256 or 512 bits.
+     */
+    enum TeblidSize
+    {
+        SIZE_256_BITS = 102, SIZE_512_BITS = 103,
+    };
+    /** @brief Creates the TEBLID descriptor.
+    @param scale_factor Adjust the sampling window around detected keypoints:
+    - <b> 1.00f </b> should be the scale for ORB keypoints
+    - <b> 6.75f </b> should be the scale for SIFT detected keypoints
+    - <b> 6.25f </b> is default and fits for KAZE, SURF detected keypoints
+    - <b> 5.00f </b> should be the scale for AKAZE, MSD, AGAST, FAST, BRISK keypoints
+    @param n_bits Determine the number of bits in the descriptor. Should be either
+     TEBLID::SIZE_256_BITS or TEBLID::SIZE_512_BITS.
+    */
+    CV_WRAP static Ptr<TEBLID> create(float scale_factor, int n_bits = TEBLID::SIZE_256_BITS);
+};
+
 /** @brief Class implementing DAISY descriptor, described in @cite Tola10
 
 @param radius radius of the descriptor at the initial scale

modules/xfeatures2d/perf/perf_teblid.cpp

@@ -0,0 +1,36 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+#include "perf_precomp.hpp"
+
+namespace opencv_test { namespace {
+
+typedef perf::TestBaseWithParam<std::string> teblid;
+
+#define TEBLID_IMAGES \
+    "cv/detectors_descriptors_evaluation/images_datasets/leuven/img1.png",\
+    "stitching/a3.png"
+
+#ifdef OPENCV_ENABLE_NONFREE
+PERF_TEST_P(teblid, extract, testing::Values(TEBLID_IMAGES))
+{
+    string filename = getDataPath(GetParam());
+    Mat frame = imread(filename, IMREAD_GRAYSCALE);
+    ASSERT_FALSE(frame.empty()) << "Unable to load source image " << filename;
+
+    Mat mask;
+    declare.in(frame).time(90);
+
+    Ptr<SURF> detector = SURF::create();
+    vector<KeyPoint> points;
+    detector->detect(frame, points, mask);
+
+    Ptr<TEBLID> descriptor = TEBLID::create(6.25f);
+    cv::Mat descriptors;
+    TEST_CYCLE() descriptor->compute(frame, points, descriptors);
+
+    SANITY_CHECK_NOTHING();
+}
+#endif // NONFREE
+
+}} // namespace

modules/xfeatures2d/src/beblid.cpp

@@ -30,14 +30,21 @@ struct ABWLParams
 {
     int x1, y1, x2, y2, boxRadius, th;
 };
+// Same as previous with floating point threshold
+struct ABWLParamsFloatTh
+{
+    int x1, y1, x2, y2, boxRadius;
+    float th;
+};
 
 // BEBLID implementation
+template <class WeakLearnerT>
 class BEBLID_Impl CV_FINAL: public BEBLID
 {
 public:
 
     // constructor
-    explicit BEBLID_Impl(float scale_factor, int n_bits = SIZE_512_BITS);
+    explicit BEBLID_Impl(float scale_factor, const std::vector<WeakLearnerT>& wl_params);
 
     // destructor
     ~BEBLID_Impl() CV_OVERRIDE = default;
@@ -55,15 +62,65 @@ public:
     void compute(InputArray image, vector<KeyPoint> &keypoints, OutputArray descriptors) CV_OVERRIDE;
 
 private:
-    std::vector<ABWLParams> wl_params_;
+    std::vector<WeakLearnerT> wl_params_;
     float scale_factor_;
     cv::Size patch_size_;
 
-    void computeBEBLID(const cv::Mat &integralImg,
-                       const std::vector<cv::KeyPoint> &keypoints,
-                       cv::Mat &descriptors);
+    void computeBoxDiffsDescriptor(const cv::Mat &integralImg,
+                                   const std::vector<cv::KeyPoint> &keypoints,
+                                   cv::Mat &descriptors);
 }; // END BEBLID_Impl CLASS
 
+
+// TEBLID implementation
+class TEBLID_Impl CV_FINAL: public TEBLID
+{
+public:
+
+    // constructor
+    explicit TEBLID_Impl(float scale_factor, const std::vector<ABWLParamsFloatTh>& wl_params) :
+        impl(scale_factor, wl_params){}
+
+    // destructor
+    ~TEBLID_Impl() CV_OVERRIDE = default;
+
+    // returns the descriptor length in bytes
+    int descriptorSize() const CV_OVERRIDE { return impl.descriptorSize(); }
+
+    // returns the descriptor type
+    int descriptorType() const CV_OVERRIDE { return impl.descriptorType(); }
+
+    // returns the default norm type
+    int defaultNorm() const CV_OVERRIDE { return impl.defaultNorm();  }
+
+    // compute descriptors given keypoints
+    void compute(InputArray image, vector<KeyPoint> &keypoints, OutputArray descriptors) CV_OVERRIDE
+    {
+        impl.compute(image, keypoints, descriptors);
+    }
+
+private:
+    BEBLID_Impl<ABWLParamsFloatTh> impl;
+}; // END TEBLID_Impl CLASS
+
+Ptr<TEBLID> TEBLID::create(float scale_factor, int n_bits)
+{
+    if (n_bits == TEBLID::SIZE_512_BITS)
+    {
+        #include "teblid.p512.hpp"
+        return makePtr<TEBLID_Impl>(scale_factor, teblid_wl_params_512);
+    }
+    else if(n_bits == TEBLID::SIZE_256_BITS)
+    {
+        #include "teblid.p256.hpp"
+        return makePtr<TEBLID_Impl>(scale_factor, teblid_wl_params_256);
+    }
+    else
+    {
+        CV_Error(Error::StsBadArg, "n_bits should be either TEBLID::SIZE_512_BITS or TEBLID::SIZE_256_BITS");
+    }
+}
+
 /**
  * @brief Function that determines if a keypoint is close to the image border.
  * @param kp The detected keypoint
@@ -100,8 +157,9 @@ static inline bool isKeypointInTheBorder(const cv::KeyPoint &kp,
  * @param scaleFactor A scale factor that magnifies the measurement functions w.r.t. the keypoint.
  * @param patchSize The size of the normalized patch where the measurement functions were learnt.
  */
-static inline void rectifyABWL(const std::vector<ABWLParams> &wlPatchParams,
-                               std::vector<ABWLParams> &wlImageParams,
+template< typename WeakLearnerT>
+static inline void rectifyABWL(const std::vector<WeakLearnerT> &wlPatchParams,
+                               std::vector<WeakLearnerT> &wlImageParams,
                                const cv::KeyPoint &kp,
                                float scaleFactor = 1,
                                const cv::Size &patchSize = cv::Size(32, 32))
@@ -151,7 +209,8 @@ static inline void rectifyABWL(const std::vector<ABWLParams> &wlPatchParams,
  * @param integralImage The integral image used to compute the average gray value in the square regions.
  * @return The difference of gray level in the two squares defined by wlImageParams
  */
-static inline float computeABWLResponse(const ABWLParams &wlImageParams,
+template <typename WeakLearnerT>
+static inline float computeABWLResponse(const WeakLearnerT &wlImageParams,
                                         const cv::Mat &integralImage)
 {
     CV_DbgAssert(!integralImage.empty());
@@ -239,7 +298,8 @@ static inline float computeABWLResponse(const ABWLParams &wlImageParams,
 }
 
 // descriptor computation using keypoints
-void BEBLID_Impl::compute(InputArray _image, vector<KeyPoint> &keypoints, OutputArray _descriptors)
+template <class WeakLearnerT>
+void BEBLID_Impl<WeakLearnerT>::compute(InputArray _image, vector<KeyPoint> &keypoints, OutputArray _descriptors)
 {
     Mat image = _image.getMat();
 
@@ -281,27 +341,21 @@ void BEBLID_Impl::compute(InputArray _image, vector<KeyPoint> &keypoints, Output
     CV_DbgAssert(descriptors.type() == CV_8UC1);
 
     // Compute the BEBLID descriptors
-    computeBEBLID(integralImg, keypoints, descriptors);
+    computeBoxDiffsDescriptor(integralImg, keypoints, descriptors);
 }
 
 // constructor
-BEBLID_Impl::BEBLID_Impl(float scale_factor, int n_bits)
-    : scale_factor_(scale_factor), patch_size_(32, 32)
+template <class WeakLearnerT>
+BEBLID_Impl<WeakLearnerT>::BEBLID_Impl(float scale_factor, const std::vector<WeakLearnerT>& wl_params)
+    :  wl_params_(wl_params), scale_factor_(scale_factor),patch_size_(32, 32)
 {
-    #include "beblid.p512.hpp"
-    #include "beblid.p256.hpp"
-    if (n_bits == SIZE_512_BITS)
-        wl_params_.assign(wl_params_512, wl_params_512 + sizeof(wl_params_512) / sizeof(wl_params_512[0]));
-    else if(n_bits == SIZE_256_BITS)
-        wl_params_.assign(wl_params_256, wl_params_256 + sizeof(wl_params_256) / sizeof(wl_params_256[0]));
-    else
-        CV_Error(Error::StsBadArg, "n_wls should be either SIZE_512_BITS or SIZE_256_BITS");
 }
 
 // Internal function that implements the core of BEBLID descriptor
-void BEBLID_Impl::computeBEBLID(const cv::Mat &integralImg,
-                                const std::vector<cv::KeyPoint> &keypoints,
-                                cv::Mat &descriptors)
+template<class WeakLearnerT>
+void BEBLID_Impl<WeakLearnerT>::computeBoxDiffsDescriptor(const cv::Mat &integralImg,
+                                                          const std::vector<cv::KeyPoint> &keypoints,
+                                                          cv::Mat &descriptors)
 {
     CV_DbgAssert(!integralImg.empty());
     CV_DbgAssert(size_t(descriptors.rows) == keypoints.size());
@@ -316,13 +370,13 @@ void BEBLID_Impl::computeBEBLID(const cv::Mat &integralImg,
 #endif
     {
         // Get a pointer to the first element in the range
-        ABWLParams *wl;
+        WeakLearnerT *wl;
         float responseFun;
         int areaResponseFun, kpIdx;
         size_t wlIdx;
         int box1x1, box1y1, box1x2, box1y2, box2x1, box2y1, box2x2, box2y2, bit_idx, side;
         uchar byte = 0;
-        std::vector<ABWLParams> imgWLParams(wl_params_.size());
+        std::vector<WeakLearnerT> imgWLParams(wl_params_.size());
         uchar *d = &descriptors.at<uchar>(range.start, 0);
 
         for (kpIdx = range.start; kpIdx < range.end; kpIdx++)
@@ -397,7 +451,20 @@ void BEBLID_Impl::computeBEBLID(const cv::Mat &integralImg,
 
 Ptr<BEBLID> BEBLID::create(float scale_factor, int n_bits)
 {
-    return makePtr<BEBLID_Impl>(scale_factor, n_bits);
+    if (n_bits == BEBLID::SIZE_512_BITS)
+    {
+        #include "beblid.p512.hpp"
+        return makePtr<BEBLID_Impl<ABWLParams>>(scale_factor, beblid_wl_params_512);
+    }
+    else if(n_bits == BEBLID::SIZE_256_BITS)
+    {
+        #include "beblid.p256.hpp"
+        return makePtr<BEBLID_Impl<ABWLParams>>(scale_factor, beblid_wl_params_256);
+    }
+    else
+    {
+        CV_Error(Error::StsBadArg, "n_bits should be either BEBLID::SIZE_512_BITS or BEBLID::SIZE_256_BITS");
+    }
 }
 } // END NAMESPACE XFEATURES2D
 } // END NAMESPACE CV

modules/xfeatures2d/src/beblid.p256.hpp

@@ -12,7 +12,7 @@
 
 // Pre-trained parameters of BEBLID-256 trained in Liberty data set with
 // a million of patch pairs, 20% positives and 80% negatives
-static const ABWLParams wl_params_256[] = {
+static const ABWLParams beblid_wl_params_256_[] = {
     {26, 20, 14, 16, 5, 16}, {17, 17, 15, 15, 2, 7}, {18, 16, 8, 13, 3, 18},
     {19, 15, 13, 14, 3, 17}, {16, 16, 5, 15, 4, 10}, {25, 10, 16, 16, 6, 11},
     {16, 15, 12, 15, 1, 12}, {18, 17, 14, 17, 1, 13}, {15, 14, 5, 21, 5, 6}, {14, 14, 11, 7, 4, 2},
@@ -79,3 +79,5 @@ static const ABWLParams wl_params_256[] = {
     {2, 14, 1, 9, 1, 1}, {6, 25, 6, 21, 1, 1}, {6, 2, 2, 1, 1, 1}, {30, 19, 29, 20, 1, 0},
     {25, 21, 23, 20, 1, 0}, {16, 10, 16, 9, 1, 0}
 };
+static const std::vector<ABWLParams> beblid_wl_params_256(std::begin(beblid_wl_params_256_),
+                                                          std::end(beblid_wl_params_256_));

modules/xfeatures2d/src/beblid.p512.hpp

@@ -12,7 +12,7 @@
 
 // Pre-trained parameters of BEBLID-512 trained in Liberty data set with
 // a million of patch pairs, 20% positives and 80% negatives
-static const ABWLParams wl_params_512[] = {
+static const ABWLParams beblid_wl_params_512_[] = {
     {24, 18, 15, 17, 6, 13}, {19, 14, 13, 17, 2, 18}, {23, 19, 12, 15, 6, 19},
     {24, 14, 16, 16, 6, 11}, {16, 15, 12, 16, 1, 12}, {16, 15, 7, 10, 4, 10},
     {17, 12, 8, 17, 3, 16}, {24, 12, 11, 17, 7, 19}, {19, 17, 14, 11, 3, 13},
@@ -144,3 +144,5 @@ static const ABWLParams wl_params_512[] = {
     {26, 4, 26, 1, 1, 0}, {5, 21, 2, 20, 1, -1}, {14, 1, 13, 3, 1, 1}, {30, 9, 28, 8, 1, 0},
     {13, 15, 12, 12, 1, 1}, {7, 23, 6, 25, 1, -1}
 };
+static const std::vector<ABWLParams> beblid_wl_params_512(std::begin(beblid_wl_params_512_),
+                                                          std::end(beblid_wl_params_512_));

modules/xfeatures2d/src/teblid.p256.hpp

@@ -0,0 +1,104 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+// Author: Iago Suarez <iagosuarz@gmail.com>
+
+// Implementation of the article:
+//     Iago Suarez, Jose 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.
+
+// ABWLParams: x1, y1, x2, y2, boxRadius, th
+
+// Pre-trained parameters of TEBLID-256 trained in Liberty data set. 10K triplets are sampled per iteration. Each triplet
+// contains an anchor patch, a positive and a negative, selected as the hardest among 256 random negatives.
+static const ABWLParamsFloatTh teblid_wl_params_256_[] = {
+    {25, 14, 13, 15, 6, 21.65f}, {16, 15, 14, 11, 1, 5.65f}, {14, 14, 7, 8, 6, 4.95f},
+    {10, 9, 6, 20, 6, 2.45f}, {13, 26, 13, 19, 5, 2.25f}, {19, 14, 19, 5, 4, 0.85f},
+    {16, 19, 15, 13, 2, 3.35f}, {26, 26, 21, 12, 5, 1.75f}, {18, 23, 15, 20, 2, 4.55f},
+    {12, 15, 10, 20, 1, -1.55f}, {26, 4, 18, 8, 3, 4.55f}, {8, 21, 2, 29, 2, -5.05f},
+    {19, 16, 17, 19, 1, 3.15f}, {10, 3, 5, 13, 3, 4.85f}, {16, 10, 10, 14, 1, 9.95f},
+    {19, 12, 18, 17, 1, 1.35f}, {21, 26, 21, 19, 5, -2.05f}, {6, 7, 5, 5, 5, -0.15f},
+    {22, 12, 20, 14, 2, 1.55f}, {14, 12, 13, 17, 1, 3.35f}, {11, 16, 10, 13, 2, 0.25f},
+    {7, 23, 7, 17, 3, 0.35f}, {27, 13, 25, 8, 4, 2.45f}, {20, 19, 16, 14, 1, 2.75f},
+    {27, 10, 24, 16, 2, -1.65f}, {13, 12, 13, 6, 2, -0.05f}, {14, 18, 13, 23, 1, -0.75f},
+    {14, 8, 11, 1, 1, 0.85f}, {14, 23, 12, 9, 2, 2.95f}, {6, 19, 2, 13, 2, -1.65f},
+    {8, 19, 6, 19, 3, -0.05f}, {18, 28, 17, 25, 3, -0.25f}, {29, 28, 25, 22, 2, -3.85f},
+    {15, 19, 15, 17, 3, -0.05f}, {23, 21, 19, 19, 1, 3.35f}, {20, 20, 20, 16, 3, 0.05f},
+    {29, 4, 25, 8, 2, -3.55f}, {17, 6, 16, 25, 2, 2.65f}, {12, 21, 8, 29, 1, 1.95f},
+    {14, 15, 9, 17, 2, 6.35f}, {18, 5, 17, 3, 3, 0.85f}, {21, 12, 18, 10, 1, 2.65f},
+    {17, 14, 14, 14, 2, 12.45f}, {5, 26, 3, 6, 3, 0.05f}, {16, 13, 15, 14, 1, 3.35f},
+    {28, 21, 24, 22, 3, 1.75f}, {13, 12, 13, 10, 1, -1.05f}, {22, 3, 21, 11, 3, -1.05f},
+    {27, 27, 4, 16, 4, 28.25f}, {12, 13, 7, 10, 1, 0.35f}, {15, 25, 15, 22, 2, -0.15f},
+    {19, 10, 18, 12, 1, 2.05f}, {17, 16, 17, 9, 2, 2.55f}, {21, 17, 21, 14, 2, 0.85f},
+    {13, 19, 12, 16, 1, 1.35f}, {11, 11, 9, 15, 1, 1.15f}, {15, 26, 14, 28, 3, 1.25f},
+    {17, 22, 17, 20, 1, 1.35f}, {10, 26, 2, 27, 2, 1.85f}, {28, 12, 26, 23, 3, 3.95f},
+    {4, 5, 3, 14, 3, 0.75f}, {17, 7, 17, 4, 3, 1.65f}, {19, 15, 17, 15, 1, -3.15f},
+    {7, 8, 2, 5, 2, -6.35f}, {22, 15, 19, 14, 2, 2.05f}, {15, 16, 12, 20, 1, -5.15f},
+    {13, 19, 12, 20, 1, 1.75f}, {17, 10, 17, 8, 2, -0.65f}, {26, 16, 19, 15, 4, -0.65f},
+    {9, 14, 8, 20, 2, 1.05f}, {27, 14, 27, 4, 4, -0.85f}, {17, 14, 15, 9, 1, 0.85f},
+    {5, 4, 5, 3, 3, -0.35f}, {15, 30, 9, 5, 1, 9.05f}, {7, 25, 7, 23, 6, 0.75f},
+    {12, 24, 11, 16, 1, -1.75f}, {20, 29, 20, 20, 2, 0.75f}, {19, 18, 15, 19, 1, 16.05f},
+    {9, 11, 7, 11, 7, 0.35f}, {27, 26, 26, 15, 4, 0.75f}, {10, 28, 10, 27, 3, 0.05f},
+    {8, 12, 8, 6, 3, 0.05f}, {21, 23, 16, 22, 1, 3.75f}, {22, 7, 4, 25, 4, 14.15f},
+    {17, 19, 16, 15, 1, -8.95f}, {28, 21, 11, 15, 3, 67.25f}, {15, 3, 15, 2, 2, -0.45f},
+    {16, 16, 14, 17, 3, 1.65f}, {10, 17, 7, 18, 3, -1.95f}, {12, 18, 12, 15, 1, 1.15f},
+    {18, 16, 16, 13, 1, 1.85f}, {20, 16, 19, 15, 1, 3.95f}, {16, 15, 11, 11, 1, -1.75f},
+    {4, 14, 2, 13, 2, 0.45f}, {29, 18, 27, 17, 2, -1.55f}, {16, 18, 14, 16, 1, 1.05f},
+    {23, 29, 22, 27, 2, -0.25f}, {18, 13, 18, 11, 1, -1.05f}, {26, 23, 21, 27, 4, 3.05f},
+    {18, 22, 17, 18, 1, -1.05f}, {3, 11, 2, 21, 2, 1.95f}, {13, 18, 13, 9, 3, -0.05f},
+    {15, 14, 14, 5, 2, 0.85f}, {1, 14, 1, 1, 1, 3.05f}, {29, 2, 5, 9, 2, 34.85f},
+    {12, 17, 11, 17, 1, -0.15f}, {13, 10, 12, 25, 4, 4.35f}, {5, 13, 1, 25, 1, -10.65f},
+    {13, 16, 13, 12, 1, 2.35f}, {16, 23, 16, 12, 1, -1.35f}, {27, 14, 22, 14, 2, 0.05f},
+    {29, 29, 27, 27, 2, 1.05f}, {23, 6, 22, 4, 4, 1.05f}, {22, 16, 22, 8, 3, -0.15f},
+    {14, 1, 11, 9, 1, 0.45f}, {12, 11, 10, 8, 2, -0.55f}, {24, 19, 7, 16, 7, 10.45f},
+    {5, 29, 2, 20, 2, 1.35f}, {19, 15, 19, 13, 1, -0.95f}, {15, 18, 8, 24, 2, 0.45f},
+    {4, 24, 1, 30, 1, -0.85f}, {17, 30, 17, 26, 1, 1.45f}, {9, 8, 7, 5, 2, -1.85f},
+    {15, 20, 15, 18, 1, 1.65f}, {27, 5, 14, 26, 4, 2.75f}, {18, 19, 18, 15, 1, 1.05f},
+    {24, 14, 9, 12, 1, 81.45f}, {20, 6, 18, 10, 1, 3.35f}, {21, 23, 21, 21, 1, 0.85f},
+    {19, 17, 6, 6, 6, 2.65f}, {10, 13, 6, 12, 3, 9.35f}, {30, 10, 27, 14, 1, 1.15f},
+    {9, 5, 6, 3, 3, 1.35f}, {26, 21, 18, 19, 2, -1.55f}, {23, 5, 23, 4, 4, 0.85f},
+    {14, 11, 11, 12, 1, 20.65f}, {18, 13, 16, 13, 1, 2.05f}, {7, 8, 3, 16, 3, 12.85f},
+    {16, 15, 16, 12, 2, 7.95f}, {25, 20, 24, 25, 3, 2.25f}, {20, 14, 19, 14, 1, 0.05f},
+    {12, 29, 12, 5, 1, 0.85f}, {23, 17, 13, 13, 5, 8.75f}, {27, 27, 23, 22, 4, -8.25f},
+    {11, 4, 11, 3, 3, -0.35f}, {9, 18, 7, 15, 1, 1.65f}, {18, 17, 18, 14, 1, -3.95f},
+    {28, 2, 6, 17, 2, 92.55f}, {5, 20, 3, 22, 3, 0.55f}, {30, 30, 30, 2, 1, 0.35f},
+    {16, 8, 15, 13, 1, -0.75f}, {15, 16, 14, 13, 1, -12.25f}, {28, 5, 27, 5, 3, 0.55f},
+    {13, 13, 12, 12, 1, 1.05f}, {7, 8, 6, 7, 6, 0.95f}, {10, 21, 10, 17, 1, 1.15f},
+    {11, 17, 3, 30, 1, -43.25f}, {16, 17, 9, 14, 7, 3.05f}, {17, 16, 9, 14, 1, 4.35f},
+    {14, 29, 13, 27, 2, 7.15f}, {19, 5, 19, 3, 2, 0.15f}, {18, 16, 14, 14, 1, 57.95f},
+    {10, 23, 8, 25, 2, 4.35f}, {17, 17, 15, 18, 1, 0.75f}, {16, 22, 16, 16, 6, 0.05f},
+    {29, 11, 27, 11, 2, 0.05f}, {13, 9, 7, 11, 1, 5.45f}, {18, 23, 17, 19, 4, 0.55f},
+    {12, 14, 11, 17, 1, 0.95f}, {13, 23, 11, 18, 2, 20.55f}, {27, 8, 23, 20, 4, -4.45f},
+    {18, 18, 18, 11, 4, 0.75f}, {8, 21, 5, 8, 5, 4.55f}, {23, 5, 21, 10, 1, -0.15f},
+    {16, 16, 16, 12, 1, 8.65f}, {18, 17, 14, 19, 1, 42.65f}, {16, 27, 16, 24, 2, -0.45f},
+    {21, 17, 15, 15, 1, -1.25f}, {16, 5, 15, 9, 2, -1.75f}, {24, 16, 1, 30, 1, 11.25f},
+    {15, 14, 14, 19, 1, -8.15f}, {19, 12, 12, 14, 2, 2.85f}, {5, 5, 3, 4, 3, -2.85f},
+    {16, 11, 16, 9, 1, -5.05f}, {16, 9, 6, 18, 6, 44.65f}, {25, 24, 23, 14, 1, 1.45f},
+    {5, 26, 5, 17, 5, -0.75f}, {9, 16, 6, 18, 1, 11.85f}, {29, 25, 9, 24, 2, 2.05f},
+    {25, 22, 24, 30, 1, 1.25f}, {22, 2, 20, 5, 2, 4.45f}, {27, 1, 25, 11, 1, -1.35f},
+    {15, 12, 14, 10, 1, 5.95f}, {17, 6, 16, 8, 1, 1.35f}, {28, 8, 23, 7, 3, -2.55f},
+    {24, 24, 23, 22, 7, 5.05f}, {7, 18, 5, 20, 3, -2.85f}, {22, 15, 20, 20, 1, 7.35f},
+    {30, 21, 28, 20, 1, -1.35f}, {3, 18, 2, 18, 2, -0.45f}, {6, 14, 5, 15, 1, 0.45f},
+    {15, 18, 15, 16, 1, -11.85f}, {7, 11, 5, 2, 1, -39.65f}, {17, 17, 13, 15, 3, 1.65f},
+    {12, 15, 7, 15, 5, -0.05f}, {16, 12, 15, 18, 1, 3.65f}, {14, 26, 14, 25, 5, -0.35f},
+    {11, 17, 8, 18, 1, 0.05f}, {23, 13, 15, 21, 7, 1.85f}, {10, 9, 10, 2, 2, -0.45f},
+    {17, 13, 12, 19, 1, -1.75f}, {20, 25, 19, 22, 1, 3.95f}, {9, 26, 8, 21, 1, 5.25f},
+    {19, 22, 19, 18, 1, -1.05f}, {8, 15, 3, 12, 1, -11.95f}, {26, 13, 16, 19, 5, 37.05f},
+    {24, 12, 21, 13, 1, -1.15f}, {12, 14, 12, 9, 1, 1.25f}, {3, 7, 1, 1, 1, 0.75f},
+    {16, 9, 15, 3, 3, -6.05f}, {23, 20, 23, 8, 7, -1.55f}, {24, 16, 22, 15, 1, -1.65f},
+    {20, 19, 20, 14, 1, 0.85f}, {30, 27, 29, 22, 1, 0.35f}, {27, 17, 4, 16, 4, 101.55f},
+    {8, 13, 5, 13, 5, -5.05f}, {19, 8, 10, 16, 3, 3.65f}, {30, 11, 30, 4, 1, -2.35f},
+    {14, 21, 14, 20, 1, -0.35f}, {14, 11, 13, 13, 1, -1.65f}, {30, 2, 28, 5, 1, 0.65f},
+    {17, 29, 12, 24, 2, 6.35f}, {15, 25, 6, 30, 1, 2.85f}, {4, 1, 1, 1, 1, 5.25f},
+    {12, 16, 5, 20, 5, 24.05f}, {16, 20, 14, 15, 1, 38.15f}, {6, 17, 6, 9, 3, -1.05f},
+    {20, 17, 12, 20, 4, 3.05f}, {15, 15, 12, 4, 4, 0.35f}, {28, 20, 22, 21, 3, -16.05f},
+    {14, 18, 9, 18, 5, -1.25f}, {26, 1, 23, 5, 1, 0.25f}, {21, 24, 11, 10, 7, 1.95f},
+    {15, 19, 14, 12, 1, -0.85f}, {27, 29, 11, 16, 1, 107.65f}, {23, 19, 22, 29, 1, -1.55f},
+    {2, 30, 2, 29, 1, -0.25f}, {14, 16, 6, 5, 3, 26.95f}, {17, 13, 14, 16, 1, 35.95f},
+    {19, 14, 15, 16, 1, -4.85f}, {20, 25, 13, 15, 6, 1.55f}, {19, 18, 11, 12, 5, 10.85f},
+    {30, 30, 30, 13, 1, -7.15f}, {3, 14, 1, 9, 1, -4.25f}, {20, 17, 1, 18, 1, -25.15f},
+    {16, 20, 12, 19, 1, 2.75f}
+};
+static const std::vector<ABWLParamsFloatTh> teblid_wl_params_256(std::begin(teblid_wl_params_256_),
+                                                                 std::end(teblid_wl_params_256_));

modules/xfeatures2d/src/teblid.p512.hpp

@@ -0,0 +1,189 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+// Author: Iago Suarez <iagosuarz@gmail.com>
+
+// Implementation of the article:
+//     Iago Suarez, Jose 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.
+
+// ABWLParams: x1, y1, x2, y2, boxRadius, th
+
+// Pre-trained parameters of TEBLID-512 trained in Liberty data set. 10K triplets are sampled per iteration. Each triplet
+// contains an anchor patch, a positive and a negative, selected as the hardest among 256 random negatives.
+static const ABWLParamsFloatTh teblid_wl_params_512_[] = {
+    {17, 18, 12, 15, 2, 14.45f}, {13, 14, 5, 7, 5, 4.15f}, {21, 16, 16, 14, 1, 7.75f},
+    {27, 11, 18, 20, 3, 9.65f}, {17, 13, 16, 19, 2, 2.25f}, {18, 24, 18, 16, 5, 0.15f},
+    {12, 11, 10, 25, 6, 0.45f}, {14, 17, 14, 13, 1, -0.95f}, {7, 4, 4, 15, 4, 3.65f},
+    {27, 27, 23, 8, 4, -1.75f}, {19, 13, 19, 6, 6, 1.05f}, {14, 15, 10, 16, 1, 5.45f},
+    {13, 15, 12, 22, 1, -0.05f}, {8, 22, 3, 27, 3, -2.65f}, {13, 19, 8, 13, 1, 3.35f},
+    {18, 16, 17, 12, 1, 1.65f}, {27, 7, 25, 11, 4, -1.55f}, {24, 20, 20, 15, 2, 2.85f},
+    {16, 24, 14, 3, 3, 3.05f}, {23, 18, 7, 18, 7, 22.05f}, {8, 7, 2, 1, 1, -3.65f},
+    {17, 28, 17, 26, 3, -0.15f}, {17, 13, 17, 10, 2, -0.55f}, {10, 18, 10, 11, 1, -0.05f},
+    {11, 28, 7, 22, 2, 3.25f}, {18, 13, 15, 15, 1, -2.85f}, {7, 14, 3, 20, 3, -1.25f},
+    {17, 19, 14, 15, 1, 10.45f}, {14, 12, 14, 8, 2, -1.05f}, {14, 12, 13, 11, 1, 1.25f},
+    {21, 9, 19, 19, 2, 3.15f}, {4, 28, 3, 10, 3, 2.05f}, {27, 27, 26, 26, 4, -0.55f},
+    {19, 22, 19, 19, 2, -1.25f}, {12, 25, 12, 20, 1, 3.45f}, {19, 12, 15, 12, 1, 4.35f},
+    {28, 21, 23, 21, 2, 2.45f}, {10, 15, 7, 18, 2, 2.55f}, {12, 7, 10, 3, 3, 1.35f},
+    {21, 16, 19, 15, 1, 1.25f}, {19, 20, 18, 17, 1, 2.75f}, {26, 2, 19, 7, 2, -0.15f},
+    {18, 2, 15, 22, 2, 8.35f}, {24, 26, 24, 22, 5, 0.35f}, {15, 26, 15, 19, 1, -1.25f},
+    {13, 19, 11, 20, 1, 0.75f}, {5, 14, 4, 10, 4, -0.45f}, {15, 7, 15, 4, 2, -0.05f},
+    {13, 16, 11, 7, 1, 0.85f}, {15, 22, 15, 18, 1, 3.65f}, {24, 8, 23, 4, 4, 1.55f},
+    {13, 11, 11, 14, 1, 1.75f}, {4, 19, 3, 19, 3, -0.35f}, {22, 12, 19, 10, 1, 1.35f},
+    {24, 27, 15, 22, 2, 4.85f}, {12, 13, 10, 10, 1, -2.25f}, {11, 25, 9, 29, 2, 0.25f},
+    {15, 21, 15, 10, 1, -2.15f}, {19, 16, 18, 19, 1, 3.35f}, {29, 13, 24, 8, 2, 1.95f},
+    {17, 16, 16, 20, 1, 6.25f}, {12, 17, 12, 15, 1, 0.35f}, {28, 4, 2, 11, 2, 24.45f},
+    {7, 25, 5, 19, 3, -1.15f}, {22, 13, 20, 16, 1, 0.85f}, {14, 16, 13, 17, 1, -1.95f},
+    {10, 3, 8, 11, 3, 5.15f}, {18, 7, 17, 11, 2, 1.35f}, {27, 11, 25, 22, 2, 0.85f},
+    {5, 26, 3, 28, 3, 0.35f}, {28, 13, 27, 13, 3, -0.45f}, {22, 20, 20, 28, 3, 4.95f},
+    {12, 6, 5, 2, 2, -0.25f}, {14, 18, 13, 16, 1, 2.45f}, {17, 29, 3, 25, 2, 11.75f},
+    {20, 20, 19, 19, 1, 0.85f}, {15, 12, 14, 15, 1, -1.65f}, {12, 14, 12, 13, 1, 0.05f},
+    {17, 14, 10, 26, 3, 4.05f}, {11, 15, 6, 12, 6, -0.35f}, {9, 22, 9, 19, 1, -0.95f},
+    {19, 18, 19, 14, 1, -0.25f}, {23, 15, 12, 18, 2, 49.35f}, {12, 15, 11, 14, 1, 0.85f},
+    {28, 2, 27, 9, 2, 1.95f}, {11, 19, 11, 11, 7, 0.25f}, {13, 29, 13, 23, 2, 1.15f},
+    {27, 19, 22, 17, 3, -2.65f}, {17, 3, 17, 2, 2, -0.25f}, {4, 6, 3, 3, 3, 0.85f},
+    {19, 15, 16, 16, 1, -5.65f}, {22, 5, 20, 9, 2, 1.15f}, {14, 6, 13, 9, 2, 3.05f},
+    {17, 16, 13, 16, 2, 4.05f}, {24, 18, 12, 6, 6, 7.35f}, {20, 14, 18, 15, 2, 9.05f},
+    {20, 9, 18, 13, 1, 0.35f}, {18, 20, 17, 8, 2, 1.65f}, {10, 15, 9, 15, 2, 1.65f},
+    {13, 7, 12, 26, 2, 2.55f}, {13, 12, 11, 19, 2, 6.95f}, {15, 2, 2, 29, 2, 1.75f},
+    {15, 12, 14, 13, 1, 0.85f}, {20, 30, 19, 26, 1, -1.15f}, {28, 26, 28, 4, 3, 1.35f},
+    {16, 13, 15, 12, 1, 5.45f}, {18, 11, 17, 25, 2, 1.35f}, {3, 17, 1, 24, 1, -2.35f},
+    {21, 18, 19, 22, 1, -0.15f}, {9, 13, 9, 8, 2, 0.85f}, {19, 18, 16, 16, 1, -3.05f},
+    {21, 22, 17, 20, 1, 0.05f}, {13, 4, 13, 3, 3, -0.35f}, {24, 15, 21, 9, 1, -0.65f},
+    {24, 25, 19, 17, 6, 11.65f}, {4, 14, 3, 14, 2, -0.85f}, {17, 13, 14, 19, 1, 1.15f},
+    {7, 19, 4, 16, 3, 2.35f}, {4, 20, 1, 5, 1, -9.25f}, {15, 13, 12, 14, 3, 16.05f},
+    {19, 26, 19, 21, 2, -1.45f}, {11, 26, 10, 18, 5, 1.95f}, {17, 16, 17, 13, 1, 0.35f},
+    {19, 16, 19, 11, 1, -0.35f}, {4, 26, 4, 23, 4, 0.15f}, {14, 19, 14, 13, 5, 0.25f},
+    {10, 13, 8, 13, 2, -1.35f}, {14, 12, 14, 10, 1, 0.65f}, {29, 24, 26, 19, 2, -4.05f},
+    {26, 9, 19, 19, 5, -2.25f}, {16, 23, 16, 17, 1, 1.05f}, {4, 13, 3, 4, 3, -0.05f},
+    {13, 16, 7, 21, 2, -1.55f}, {17, 16, 16, 17, 1, 0.25f}, {29, 15, 5, 18, 2, 69.45f},
+    {29, 2, 23, 5, 2, 0.15f}, {9, 17, 9, 14, 2, -1.25f}, {25, 26, 25, 22, 5, -1.85f},
+    {13, 21, 13, 20, 1, -0.65f}, {23, 12, 7, 20, 6, 8.75f}, {6, 8, 6, 3, 3, -0.95f},
+    {13, 19, 13, 17, 1, 1.95f}, {25, 21, 22, 20, 1, 1.05f}, {24, 17, 23, 15, 2, -1.45f},
+    {20, 8, 17, 4, 1, 2.15f}, {11, 19, 10, 17, 1, -1.85f}, {9, 11, 6, 9, 1, -1.75f},
+    {25, 9, 24, 14, 1, -2.95f}, {18, 20, 13, 14, 3, 2.65f}, {26, 23, 25, 23, 5, 0.65f},
+    {14, 20, 11, 4, 4, -1.05f}, {28, 7, 25, 13, 3, 4.35f}, {13, 13, 12, 12, 1, 0.25f},
+    {7, 29, 2, 2, 2, 19.65f}, {16, 17, 16, 8, 5, 0.35f}, {20, 6, 19, 12, 3, 1.65f},
+    {19, 7, 19, 6, 6, 0.65f}, {20, 13, 19, 14, 1, 2.75f}, {19, 24, 16, 29, 2, 2.85f},
+    {8, 15, 4, 13, 1, -10.95f}, {7, 9, 2, 10, 2, 3.65f}, {15, 14, 14, 13, 1, -4.15f},
+    {18, 13, 18, 11, 1, 0.25f}, {8, 19, 5, 23, 2, -0.65f}, {3, 13, 1, 14, 1, -2.25f},
+    {23, 20, 16, 14, 1, 3.75f}, {17, 15, 13, 18, 2, 35.75f}, {16, 16, 9, 14, 5, 3.15f},
+    {15, 28, 15, 27, 3, -0.55f}, {18, 20, 16, 19, 1, 1.95f}, {16, 17, 16, 11, 2, -6.55f},
+    {30, 1, 10, 19, 1, 88.35f}, {12, 19, 9, 23, 2, 7.25f}, {25, 13, 21, 13, 1, 1.75f},
+    {9, 23, 5, 24, 5, -4.15f}, {13, 20, 13, 18, 1, 0.25f}, {13, 13, 12, 13, 3, 0.25f},
+    {29, 18, 25, 2, 2, 0.65f}, {30, 30, 25, 26, 1, 3.75f}, {16, 20, 15, 11, 1, 1.65f},
+    {18, 16, 18, 14, 1, 2.85f}, {15, 18, 5, 7, 4, 42.15f}, {16, 13, 15, 19, 1, 11.75f},
+    {26, 24, 16, 9, 5, -1.25f}, {1, 28, 1, 5, 1, -8.25f}, {20, 17, 20, 16, 1, 0.05f},
+    {15, 19, 10, 17, 4, 2.15f}, {12, 9, 10, 5, 1, 0.65f}, {30, 29, 28, 29, 1, -1.55f},
+    {29, 17, 27, 18, 2, -2.75f}, {17, 29, 15, 27, 2, 1.15f}, {9, 29, 9, 28, 2, -0.15f},
+    {23, 24, 21, 22, 1, -0.75f}, {22, 2, 1, 1, 1, 16.85f}, {20, 4, 20, 1, 1, 1.15f},
+    {5, 30, 4, 25, 1, 1.45f}, {20, 8, 17, 12, 7, 15.35f}, {10, 7, 3, 17, 3, 19.45f},
+    {21, 17, 14, 15, 5, 8.65f}, {14, 10, 13, 8, 1, -1.25f}, {4, 21, 4, 13, 3, 0.25f},
+    {30, 1, 24, 10, 1, 2.15f}, {15, 17, 14, 16, 3, 0.15f}, {21, 23, 20, 15, 3, 2.85f},
+    {17, 20, 17, 18, 3, -2.25f}, {12, 11, 12, 6, 5, 1.75f}, {15, 15, 12, 17, 1, -15.15f},
+    {25, 9, 16, 25, 6, 4.15f}, {22, 28, 22, 27, 3, -0.45f}, {5, 8, 3, 3, 3, -8.25f},
+    {9, 5, 9, 1, 1, 1.25f}, {30, 12, 29, 23, 1, -0.85f}, {20, 21, 5, 9, 5, 3.55f},
+    {15, 21, 15, 20, 1, 0.45f}, {11, 17, 10, 23, 2, -1.25f}, {16, 11, 15, 13, 1, 9.65f},
+    {16, 12, 16, 10, 1, 1.25f}, {15, 6, 14, 3, 3, 3.15f}, {2, 4, 1, 1, 1, -2.05f},
+    {15, 16, 11, 15, 1, -6.85f}, {24, 6, 24, 2, 2, -0.05f}, {8, 15, 6, 12, 1, 3.25f},
+    {21, 27, 1, 30, 1, 7.05f}, {17, 10, 14, 16, 3, 2.35f}, {13, 9, 7, 7, 7, -1.45f},
+    {22, 17, 19, 17, 1, -1.05f}, {16, 14, 14, 13, 2, 20.25f}, {14, 21, 13, 23, 1, 1.15f},
+    {18, 2, 15, 7, 2, 1.35f}, {3, 25, 1, 24, 1, 1.05f}, {24, 20, 7, 14, 7, 5.15f},
+    {26, 25, 24, 19, 2, 2.55f}, {6, 25, 6, 23, 6, 0.55f}, {15, 24, 15, 17, 7, 0.55f},
+    {22, 14, 16, 15, 1, -0.15f}, {17, 25, 17, 23, 1, 0.65f}, {12, 18, 2, 26, 2, -28.35f},
+    {30, 30, 26, 11, 1, 3.85f}, {22, 8, 16, 14, 5, 2.05f}, {9, 16, 8, 20, 1, -1.15f},
+    {4, 14, 2, 13, 2, 3.05f}, {28, 7, 27, 8, 1, -0.95f}, {10, 22, 9, 24, 1, -1.55f},
+    {14, 16, 13, 18, 3, -0.35f}, {28, 26, 3, 15, 2, 101.95f}, {12, 15, 10, 15, 1, 0.35f},
+    {18, 17, 17, 15, 1, -2.05f}, {30, 10, 28, 14, 1, -2.15f}, {30, 14, 28, 30, 1, 4.25f},
+    {30, 18, 7, 13, 1, 105.15f}, {3, 19, 2, 20, 1, 1.15f}, {16, 19, 14, 13, 2, 27.45f},
+    {11, 9, 5, 27, 4, 30.15f}, {16, 19, 15, 15, 2, 18.45f}, {24, 22, 18, 19, 7, -2.45f},
+    {12, 17, 12, 12, 1, 1.85f}, {28, 5, 28, 1, 1, 2.95f}, {4, 29, 2, 30, 1, -3.75f},
+    {27, 11, 27, 8, 1, -0.65f}, {8, 3, 8, 1, 1, 0.05f}, {15, 10, 15, 8, 3, 0.25f},
+    {12, 27, 11, 18, 4, 34.45f}, {25, 6, 22, 8, 6, -2.05f}, {15, 3, 15, 2, 2, 0.25f},
+    {19, 22, 17, 19, 1, 3.25f}, {24, 21, 24, 16, 2, 2.05f}, {9, 7, 6, 6, 6, 2.85f},
+    {13, 26, 11, 27, 2, 3.45f}, {24, 10, 19, 12, 4, 20.95f}, {22, 17, 22, 9, 2, 1.45f},
+    {17, 14, 14, 11, 1, -1.95f}, {13, 4, 13, 3, 1, -0.35f}, {15, 18, 15, 17, 1, -6.85f},
+    {29, 30, 29, 24, 1, 1.15f}, {29, 29, 20, 17, 2, -31.95f}, {6, 12, 2, 27, 2, 11.85f},
+    {18, 17, 14, 13, 2, 2.75f}, {11, 27, 11, 26, 4, 0.15f}, {22, 12, 3, 18, 3, 8.35f},
+    {15, 13, 13, 9, 1, 1.15f}, {12, 20, 7, 18, 1, 2.15f}, {16, 6, 15, 9, 1, -1.15f},
+    {3, 6, 1, 7, 1, -1.05f}, {12, 17, 11, 19, 1, 2.45f}, {15, 8, 8, 18, 7, 1.45f},
+    {11, 19, 11, 5, 3, 0.15f}, {17, 20, 16, 23, 3, -1.65f}, {12, 6, 9, 13, 1, 41.85f},
+    {2, 1, 1, 2, 1, 0.35f}, {14, 26, 13, 21, 3, 0.65f}, {25, 16, 16, 14, 3, -0.35f},
+    {30, 14, 29, 14, 1, 0.25f}, {27, 25, 15, 22, 4, 56.75f}, {13, 10, 8, 7, 2, 23.55f},
+    {18, 19, 13, 14, 1, 88.85f}, {28, 28, 28, 22, 3, -7.25f}, {8, 14, 8, 11, 1, 1.05f},
+    {23, 28, 22, 24, 2, 0.75f}, {8, 2, 3, 18, 2, -1.05f}, {22, 24, 22, 23, 7, -1.65f},
+    {20, 17, 15, 16, 1, -9.75f}, {8, 11, 6, 4, 4, -16.95f}, {25, 13, 23, 13, 2, -0.05f},
+    {18, 18, 16, 15, 1, -10.05f}, {20, 16, 16, 15, 1, 34.15f}, {18, 20, 14, 26, 3, 14.75f},
+    {17, 12, 17, 8, 1, -0.95f}, {1, 5, 1, 3, 1, 1.45f}, {22, 13, 13, 20, 2, 4.85f},
+    {17, 16, 17, 14, 3, -0.05f}, {27, 17, 25, 17, 2, -0.35f}, {8, 23, 6, 29, 2, 0.75f},
+    {15, 4, 14, 18, 1, 64.75f}, {10, 24, 10, 17, 4, 24.25f}, {25, 30, 25, 28, 1, -0.35f},
+    {3, 22, 1, 29, 1, -13.65f}, {24, 8, 23, 17, 1, 2.85f}, {26, 3, 26, 1, 1, 1.75f},
+    {18, 22, 18, 17, 2, -0.35f}, {9, 17, 8, 10, 2, 0.25f}, {29, 22, 29, 2, 2, -12.15f},
+    {19, 4, 5, 10, 3, 108.05f}, {3, 28, 3, 27, 1, -0.35f}, {12, 15, 11, 18, 1, -3.35f},
+    {30, 3, 28, 4, 1, 1.95f}, {7, 9, 7, 8, 1, -0.35f}, {24, 15, 8, 14, 7, 21.25f},
+    {30, 6, 20, 16, 1, -20.05f}, {18, 18, 1, 10, 1, 95.85f}, {30, 20, 28, 21, 1, -1.05f},
+    {15, 15, 13, 14, 1, -17.75f}, {6, 3, 5, 1, 1, -0.55f}, {3, 8, 1, 17, 1, 2.75f},
+    {3, 2, 2, 2, 2, 0.65f}, {19, 28, 18, 20, 1, 0.75f}, {20, 20, 20, 17, 2, -1.85f},
+    {21, 30, 19, 29, 1, 2.65f}, {12, 19, 12, 13, 1, -2.15f}, {29, 10, 29, 4, 2, 1.05f},
+    {20, 16, 20, 14, 1, -0.05f}, {15, 9, 11, 16, 2, 4.25f}, {8, 13, 6, 26, 4, 3.75f},
+    {13, 11, 12, 8, 2, -13.55f}, {17, 27, 17, 26, 4, -0.05f}, {29, 29, 14, 12, 1, 105.95f},
+    {29, 2, 28, 3, 2, 0.45f}, {9, 15, 7, 9, 4, 3.05f}, {27, 28, 12, 30, 1, 9.35f},
+    {14, 30, 2, 28, 1, 79.25f}, {19, 12, 18, 14, 1, 1.75f}, {26, 5, 24, 15, 5, 7.65f},
+    {2, 24, 2, 2, 2, -0.45f}, {6, 21, 5, 21, 1, 0.95f}, {22, 16, 9, 17, 2, 79.15f},
+    {16, 19, 15, 17, 1, 28.05f}, {2, 29, 2, 28, 2, 0.05f}, {25, 11, 24, 1, 1, 0.65f},
+    {16, 30, 16, 29, 1, -0.35f}, {14, 20, 14, 17, 3, 0.95f}, {15, 14, 11, 17, 3, 2.05f},
+    {18, 17, 16, 21, 1, 1.15f}, {17, 8, 17, 4, 2, 2.75f}, {11, 4, 11, 3, 3, -1.65f},
+    {25, 16, 9, 17, 6, 8.65f}, {18, 8, 18, 6, 6, 3.55f}, {17, 22, 17, 19, 1, -0.25f},
+    {8, 20, 3, 11, 3, -28.95f}, {20, 17, 4, 17, 1, -7.35f}, {29, 12, 12, 19, 2, 122.25f},
+    {14, 29, 14, 28, 2, 0.55f}, {12, 18, 10, 18, 1, 4.75f}, {13, 15, 13, 11, 2, 1.75f},
+    {18, 15, 14, 15, 2, 11.15f}, {19, 17, 17, 19, 1, 0.15f}, {22, 17, 12, 16, 6, 2.55f},
+    {30, 22, 29, 18, 1, 0.05f}, {30, 2, 29, 20, 1, -7.95f}, {12, 3, 1, 1, 1, 51.05f},
+    {4, 7, 1, 7, 1, 6.25f}, {27, 10, 21, 13, 4, 0.05f}, {18, 21, 18, 13, 3, -1.15f},
+    {12, 4, 3, 6, 2, 0.75f}, {12, 10, 9, 3, 2, 26.65f}, {3, 28, 2, 29, 2, -1.25f},
+    {22, 2, 20, 5, 2, 1.15f}, {27, 18, 20, 3, 3, -0.55f}, {6, 24, 6, 23, 1, 0.05f},
+    {27, 26, 9, 16, 4, 5.85f}, {5, 18, 5, 11, 5, 1.55f}, {20, 14, 15, 12, 3, 2.25f},
+    {19, 16, 19, 15, 1, -1.65f}, {27, 4, 21, 9, 4, -16.85f}, {3, 19, 2, 29, 1, -62.65f},
+    {20, 24, 18, 22, 1, -1.25f}, {18, 7, 18, 2, 1, -1.05f}, {28, 30, 28, 28, 1, -1.55f},
+    {11, 24, 10, 9, 1, 1.35f}, {21, 18, 21, 14, 3, 0.25f}, {27, 19, 26, 18, 2, -0.65f},
+    {16, 18, 10, 6, 6, 0.85f}, {11, 18, 5, 19, 1, 17.45f}, {24, 16, 22, 16, 1, 0.25f},
+    {17, 15, 17, 9, 5, -9.35f}, {27, 29, 20, 11, 2, 34.35f}, {29, 25, 28, 22, 1, 0.95f},
+    {21, 11, 21, 5, 1, -0.95f}, {12, 15, 8, 16, 2, -10.05f}, {2, 29, 1, 30, 1, 1.45f},
+    {18, 12, 4, 21, 3, -6.05f}, {18, 9, 11, 13, 3, 93.25f}, {18, 3, 10, 21, 3, 3.15f},
+    {17, 11, 16, 16, 1, -10.85f}, {15, 17, 13, 14, 1, -1.65f}, {7, 7, 7, 5, 5, -0.15f},
+    {9, 29, 5, 18, 2, -2.45f}, {10, 11, 10, 6, 6, -0.35f}, {28, 26, 25, 26, 1, 0.15f},
+    {19, 30, 8, 20, 1, 118.15f}, {8, 15, 7, 29, 2, -81.85f}, {21, 18, 19, 17, 1, 4.05f},
+    {2, 22, 1, 22, 1, 1.05f}, {12, 20, 4, 17, 1, -0.85f}, {27, 8, 4, 14, 2, 152.35f},
+    {26, 10, 25, 13, 1, 0.65f}, {19, 13, 19, 8, 3, 1.35f}, {12, 16, 7, 18, 7, -12.35f},
+    {20, 26, 12, 3, 3, 95.35f}, {6, 10, 3, 10, 2, -23.25f}, {25, 25, 25, 21, 2, -0.75f},
+    {12, 3, 7, 16, 2, 75.65f}, {8, 4, 4, 17, 4, 50.85f}, {12, 20, 5, 8, 5, 47.85f},
+    {22, 15, 8, 13, 7, 2.55f}, {12, 13, 12, 8, 2, 0.55f}, {20, 15, 19, 13, 1, 0.15f},
+    {30, 5, 29, 8, 1, 0.25f}, {14, 29, 13, 23, 2, 38.15f}, {18, 19, 9, 10, 7, -2.65f},
+    {2, 11, 1, 10, 1, -1.95f}, {12, 13, 12, 11, 1, -1.05f}, {27, 15, 9, 5, 4, 110.25f},
+    {13, 12, 7, 17, 2, 35.25f}, {8, 17, 1, 26, 1, 2.65f}, {20, 24, 11, 12, 4, 3.65f},
+    {12, 24, 10, 22, 6, 18.75f}, {19, 29, 14, 20, 1, 89.45f}, {20, 27, 20, 25, 2, -0.55f},
+    {9, 25, 8, 27, 1, 0.35f}, {7, 11, 5, 11, 1, 0.25f}, {20, 11, 11, 8, 1, 105.05f},
+    {9, 8, 9, 5, 1, 0.25f}, {27, 9, 25, 10, 1, 1.25f}, {30, 20, 22, 20, 1, -34.65f},
+    {26, 21, 26, 20, 1, -0.55f}, {30, 14, 27, 16, 1, -0.15f}, {12, 16, 11, 19, 3, 0.75f},
+    {7, 28, 6, 29, 1, -0.15f}, {17, 23, 17, 22, 2, -0.15f}, {12, 17, 2, 2, 1, -94.25f},
+    {17, 14, 17, 13, 1, -12.05f}, {18, 12, 16, 16, 1, -15.35f}, {7, 23, 7, 17, 1, -1.75f},
+    {25, 12, 9, 15, 4, 31.35f}, {16, 6, 16, 5, 5, 0.15f}, {8, 16, 7, 16, 7, -2.15f},
+    {6, 7, 5, 7, 5, -0.15f}, {15, 13, 15, 12, 2, -12.05f}, {13, 15, 13, 13, 3, -0.35f},
+    {16, 12, 16, 11, 1, -0.65f}, {18, 15, 15, 14, 3, -0.55f}, {17, 8, 14, 5, 4, 28.95f},
+    {9, 26, 6, 22, 5, 39.05f}, {17, 16, 14, 17, 3, 7.05f}, {25, 1, 24, 2, 1, 0.65f},
+    {14, 16, 14, 15, 1, -1.65f}, {24, 22, 4, 23, 4, 2.85f}, {30, 29, 27, 29, 1, 4.85f},
+    {17, 18, 17, 17, 1, -1.85f}, {19, 30, 19, 28, 1, 1.75f}, {21, 27, 21, 23, 3, -31.65f},
+    {16, 18, 15, 20, 1, 0.05f}, {27, 27, 13, 12, 4, 13.75f}, {30, 25, 27, 26, 1, -0.35f},
+    {4, 21, 3, 7, 1, 0.35f}, {10, 5, 10, 4, 4, -5.75f}, {14, 14, 5, 3, 1, 83.85f},
+    {23, 6, 21, 3, 3, 0.95f}, {9, 20, 2, 15, 2, 27.95f}, {23, 9, 20, 13, 1, -0.25f},
+    {15, 14, 12, 3, 3, -19.05f}, {19, 25, 19, 18, 4, 2.45f}, {27, 25, 24, 22, 4, -13.25f},
+    {15, 15, 15, 11, 1, 44.05f}, {17, 16, 14, 13, 1, 51.85f}, {12, 18, 12, 17, 1, -0.05f},
+    {30, 3, 30, 2, 1, 0.05f}, {21, 20, 18, 28, 3, 79.35f}, {25, 25, 7, 14, 5, 111.25f},
+    {3, 11, 2, 3, 2, -61.65f}, {25, 5, 9, 21, 4, 3.05f}, {6, 15, 4, 28, 3, -69.95f},
+    {9, 9, 3, 3, 3, 35.65f}, {16, 19, 14, 16, 2, 62.05f}, {10, 25, 10, 20, 1, -0.25f},
+    {2, 17, 2, 15, 1, -1.05f}, {17, 15, 15, 16, 1, -2.95f}, {20, 15, 19, 15, 1, 0.95f},
+    {22, 2, 22, 1, 1, -0.15f}, {15, 19, 15, 18, 1, -18.15f}, {15, 16, 10, 12, 1, -18.65f},
+    {28, 2, 23, 14, 2, 74.55f}, {11, 3, 9, 2, 1, 2.65f}
+};
+static const std::vector<ABWLParamsFloatTh> teblid_wl_params_512(std::begin(teblid_wl_params_512_),
+                                                                 std::end(teblid_wl_params_512_));

modules/xfeatures2d/test/test_features2d.cpp

@@ -192,6 +192,13 @@ TEST(Features2d_DescriptorExtractor_BEBLID, regression )
     test.safe_run();
 }
 
+TEST(Features2d_DescriptorExtractor_TEBLID, regression )
+{
+    CV_DescriptorExtractorTest<Hamming> test("descriptor-teblid", 1,
+                                             TEBLID::create(6.75));
+    test.safe_run();
+}
+
 #ifdef OPENCV_XFEATURES2D_HAS_VGG_DATA
 TEST( Features2d_DescriptorExtractor_VGG, regression )
 {

modules/xfeatures2d/test/test_rotation_and_scale_invariance.cpp

@@ -34,6 +34,10 @@ INSTANTIATE_TEST_CASE_P(BEBLID, DescriptorRotationInvariance, Values(
     make_tuple(IMAGE_TSUKUBA, SIFT::create(), BEBLID::create(6.75), 0.98f)
 ));
 
+INSTANTIATE_TEST_CASE_P(TEBLID, DescriptorRotationInvariance, Values(
+        make_tuple(IMAGE_TSUKUBA, SIFT::create(), TEBLID::create(6.75), 0.98f)
+));
+
 INSTANTIATE_TEST_CASE_P(DAISY, DescriptorRotationInvariance, Values(
     make_tuple(IMAGE_TSUKUBA,
                BRISK::create(),