Commit 3166d0c6 authored by Jiri Horner's avatar Jiri Horner Committed by Alexander Alekhin

Merge pull request #9249 from hrnr:akaze_part3

[GSOC] Speeding-up AKAZE, part #3 (#9249)

* use finding of scale extremas from fast_akaze

* incorporade finding of extremas and subpixel refinement from Hideaki Suzuki's fast_akaze (https://github.com/h2suzuki/fast_akaze)
* use opencv parallel framework
* do not search for keypoints near the border, where we can't compute sensible descriptors (bugs fixed in ffd9ad99f4946e31508677dab09bddbecb82ae9f, 2c5389594bb560b62097de3602755ef97e60135f), but the descriptors were not 100% correct. this is a better solution

this version produces less keypoints with the same treshold. It is more effective in pruning similar keypoints (which do not bring any new information), so we have less keypoints, but with high quality. Accuracy is about the same.

* incorporate bugfix from upstream

* fix bug in subpixel refinement
* see commit db3dc22981e856ca8111f2f7fe57d9c2e0286efc in Pablo's repo

* rework finding of scale space extremas

* store just keypoints positions
* store positions in uchar mask for effective spatial search for neighbours
* construct keypoints structs at the very end

* lower inlier threshold in test

* win32 has lower accuracy
parent 0bd357e7
......@@ -44,6 +44,7 @@ struct Evolution
int sublevel; ///< Image sublevel in each octave
int sigma_size; ///< Integer esigma. For computing the feature detector responses
float octave_ratio; ///< Scaling ratio of this octave. ratio = 2^octave
int border; ///< Width of border where descriptors cannot be computed
};
/* ************************************************************************* */
......@@ -76,8 +77,9 @@ public:
void Create_Nonlinear_Scale_Space(InputArray img);
void Feature_Detection(std::vector<cv::KeyPoint>& kpts);
void Compute_Determinant_Hessian_Response(void);
void Find_Scale_Space_Extrema(std::vector<cv::KeyPoint>& kpts);
void Do_Subpixel_Refinement(std::vector<cv::KeyPoint>& kpts);
void Find_Scale_Space_Extrema(std::vector<Mat>& keypoints_by_layers);
void Do_Subpixel_Refinement(std::vector<Mat>& keypoints_by_layers,
std::vector<KeyPoint>& kpts);
/// Feature description methods
void Compute_Descriptors(std::vector<cv::KeyPoint>& kpts, OutputArray desc);
......
......@@ -230,10 +230,10 @@ INSTANTIATE_TEST_CASE_P(ORB, DetectorRotationInvariance,
Value(IMAGE_TSUKUBA, ORB::create(), 0.5f, 0.76f));
INSTANTIATE_TEST_CASE_P(AKAZE, DetectorRotationInvariance,
Value(IMAGE_TSUKUBA, AKAZE::create(), 0.5f, 0.76f));
Value(IMAGE_TSUKUBA, AKAZE::create(), 0.5f, 0.71f));
INSTANTIATE_TEST_CASE_P(AKAZE_DESCRIPTOR_KAZE, DetectorRotationInvariance,
Value(IMAGE_TSUKUBA, AKAZE::create(AKAZE::DESCRIPTOR_KAZE), 0.5f, 0.76f));
Value(IMAGE_TSUKUBA, AKAZE::create(AKAZE::DESCRIPTOR_KAZE), 0.5f, 0.71f));
/*
* Detector's scale invariance check
......
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