Add DAISY descriptor for wide-baseline / keypoints.

pull/229/head
cbalint13 10 years ago
parent ea682d6f04
commit 5785a6a58a
  1. 11
      modules/xfeatures2d/doc/xfeatures2d.bib
  2. 31
      modules/xfeatures2d/include/opencv2/xfeatures2d.hpp
  3. 33
      modules/xfeatures2d/perf/perf_daisy.cpp
  4. 2054
      modules/xfeatures2d/src/daisy.cpp
  5. 7
      modules/xfeatures2d/test/test_features2d.cpp
  6. 18
      modules/xfeatures2d/test/test_rotation_and_scale_invariance.cpp

@ -53,3 +53,14 @@
year={2012}
publisher={NIPS}
}
@article{Tola10,
author = "E. Tola and V. Lepetit and P. Fua",
title = {{DAISY: An Efficient Dense Descriptor Applied to Wide Baseline Stereo}},
journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
year = 2010,
month = "May",
pages = "815--830",
volume = "32",
number = "5"
}

@ -144,6 +144,37 @@ public:
static Ptr<LUCID> create(const int lucid_kernel, const int blur_kernel);
};
/** @brief Class implementing DAISY descriptor, described in @cite Tola10
@param radius radius of the descriptor at the initial scale
@param q_radius amount of radial range division quantity
@param q_theta amount of angular range division quantity
@param q_hist amount of gradient orientations range division quantity
@param mode choose computation mode of descriptors where
DAISY::ONLY_KEYS means to compute descriptors only for keypoints in the list (default) and
DAISY::COMP_FULL will compute descriptors for all pixels in the given image
@param norm choose descriptors normalization type, where
DAISY::NRM_NONE will not do any normalization (default),
DAISY::NRM_PARTIAL mean that histograms are normalized independently for L2 norm equal to 1.0,
DAISY::NRM_FULL mean that descriptors are normalized for L2 norm equal to 1.0,
DAISY::NRM_SIFT mean that descriptors are normalized for L2 norm equal to 1.0 but no individual one is bigger than 0.154 as in SIFT
@param H optional 3x3 homography matrix used to warp the grid of daisy but sampling keypoints remains unwarped on image
@param interpolation switch to disable interpolation for speed improvement at minor quality loss
@param use_orientation sample patterns using keypoints orientation, disabled by default.
*/
class CV_EXPORTS DAISY : public DescriptorExtractor
{
public:
enum
{
ONLY_KEYS = 0, COMP_FULL = 1,
NRM_NONE = 100, NRM_PARTIAL = 101, NRM_FULL = 102, NRM_SIFT = 103,
};
static Ptr<DAISY> create( float radius = 15, int q_radius = 3, int q_theta = 8,
int q_hist = 8, int mode = DAISY::ONLY_KEYS, int norm = DAISY::NRM_NONE,
InputArray H = noArray() , bool interpolation = true, bool use_orientation = false );
};
//! @}

@ -0,0 +1,33 @@
#include "perf_precomp.hpp"
using namespace std;
using namespace cv;
using namespace cv::xfeatures2d;
using namespace perf;
using std::tr1::make_tuple;
using std::tr1::get;
typedef perf::TestBaseWithParam<std::string> daisy;
#define DAISY_IMAGES \
"cv/detectors_descriptors_evaluation/images_datasets/leuven/img1.png",\
"stitching/a3.png"
PERF_TEST_P(daisy, extract, testing::Values(DAISY_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);
// use DAISY in COMP_FULL mode (every pixel, dense baseline mode)
Ptr<DAISY> descriptor = DAISY::create(15, 3, 8, 8, DAISY::COMP_FULL, DAISY::NRM_NONE, noArray(), true, false);
vector<KeyPoint> points;
vector<float> descriptors;
TEST_CYCLE() descriptor->compute(frame, points, descriptors);
SANITY_CHECK(descriptors, 1e-4);
}

File diff suppressed because it is too large Load Diff

@ -1010,6 +1010,13 @@ TEST( Features2d_DescriptorExtractor_SURF, regression )
test.safe_run();
}
TEST( Features2d_DescriptorExtractor_DAISY, regression )
{
CV_DescriptorExtractorTest<L2<float> > test( "descriptor-daisy", 0.05f,
DAISY::create() );
test.safe_run();
}
TEST( Features2d_DescriptorExtractor_FREAK, regression )
{
// TODO adjust the parameters below

@ -651,6 +651,15 @@ TEST(Features2d_RotationInvariance_Descriptor_SIFT, regression)
test.safe_run();
}
TEST(Features2d_RotationInvariance_Descriptor_DAISY, regression)
{
DescriptorRotationInvarianceTest test(BRISK::create(),
DAISY::create(15, 3, 8, 8, DAISY::ONLY_KEYS, DAISY::NRM_NONE, noArray(), true, true),
NORM_L1,
0.79f);
test.safe_run();
}
/*
* Detector's scale invariance check
*/
@ -708,3 +717,12 @@ TEST(Features2d_RotationInvariance2_Detector_SURF, regression)
ASSERT_LT( fabs(keypoints[1].response - keypoints[3].response), 1e-6);
ASSERT_LT( fabs(keypoints[1].response - keypoints[4].response), 1e-6);
}
TEST(Features2d_ScaleInvariance_Descriptor_DAISY, regression)
{
DescriptorScaleInvarianceTest test(BRISK::create(),
DAISY::create(15, 3, 8, 8, DAISY::ONLY_KEYS, DAISY::NRM_NONE, noArray(), true, true),
NORM_L1,
0.075f);
test.safe_run();
}

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