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

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Aaron Webster 3 years ago committed by GitHub
parent 67d1f83bb7 3a3e40a447
commit bc42a05bd2
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13 changed files with 501 additions and 35 deletions
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  1. 6
      .github/workflows/PR-4.x.yaml
  2. 6
      modules/sfm/CMakeLists.txt
  3. 2
      modules/xfeatures2d/README.md
  4. 12
      modules/xfeatures2d/doc/xfeatures2d.bib
  5. 43
      modules/xfeatures2d/include/opencv2/xfeatures2d.hpp
  6. 36
      modules/xfeatures2d/perf/perf_teblid.cpp
  7. 119
      modules/xfeatures2d/src/beblid.cpp
  8. 4
      modules/xfeatures2d/src/beblid.p256.hpp
  9. 4
      modules/xfeatures2d/src/beblid.p512.hpp
  10. 104
      modules/xfeatures2d/src/teblid.p256.hpp
  11. 189
      modules/xfeatures2d/src/teblid.p512.hpp
  12. 7
      modules/xfeatures2d/test/test_features2d.cpp
  13. 4
      modules/xfeatures2d/test/test_rotation_and_scale_invariance.cpp

6
.github/workflows/PR-4.x.yaml
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@ -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:

6
modules/sfm/CMakeLists.txt
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@ -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()

2
modules/xfeatures2d/README.md
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@ -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.

12
modules/xfeatures2d/doc/xfeatures2d.bib
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@ -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},

43
modules/xfeatures2d/include/opencv2/xfeatures2d.hpp
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@ -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

36
modules/xfeatures2d/perf/perf_teblid.cpp
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@ -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

119
modules/xfeatures2d/src/beblid.cpp
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@ -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

4
modules/xfeatures2d/src/beblid.p256.hpp
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@ -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_));

4
modules/xfeatures2d/src/beblid.p512.hpp
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@ -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_));

104
modules/xfeatures2d/src/teblid.p256.hpp
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@ -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_));

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modules/xfeatures2d/src/teblid.p512.hpp
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// 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_));

7
modules/xfeatures2d/test/test_features2d.cpp
Unescape View File

@ -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 )
{

4
modules/xfeatures2d/test/test_rotation_and_scale_invariance.cpp
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@ -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(),

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