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Merge pull request #8869 from hrnr:akaze_part1

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[GSOC] Speeding-up AKAZE, part #1 (#8869)

* ts: expand arguments before stringifications in CV_ENUM and CV_FLAGS

added protective macros to always force macro expansion of arguments. This allows using CV_ENUM and CV_FLAGS with macro arguments.

* feature2d: unify perf test

use the same test for all detectors/descriptors we have.

* added AKAZE tests

* features2d: extend perf tests

* add BRISK, KAZE, MSER
* run all extract tests on AKAZE keypoints, so that the test si more comparable for the speed of extraction

* feature2d: rework opencl perf tests

use the same configuration as cpu tests

* feature2d: fix descriptors allocation for AKAZE and KAZE

fix crash when descriptors are UMat

* feature2d: name enum to fix build with older gcc

* Revert "ts: expand arguments before stringifications in CV_ENUM and CV_FLAGS"

This reverts commit 19538cac1e.

This wasn't a great idea after all. There is a lot of flags implemented as #define, that we don't want to expand.

* feature2d: fix expansion problems with CV_ENUM in perf

* expand arguments before passing them to CV_ENUM. This does not need modifications of CV_ENUM.
* added include guards to `perf_feature2d.hpp`

* feature2d: fix crash in AKAZE when using KAZE descriptors

* out-of-bound access in Get_MSURF_Descriptor_64
* this happened reliably when running on provided keypoints (not computed by the same instance)

* feature2d: added regression tests for AKAZE

* test with both MLDB and KAZE keypoints

* feature2d: do not compute keypoints orientation twice

* always compute keypoints orientation, when computing keypoints
* do not recompute keypoint orientation when computing descriptors

this allows to test detection and extraction separately

* features2d: fix crash in AKAZE

* out-of-bound reads near the image edge
* same as the bug in KAZE descriptors

* feature2d: refactor invariance testing

* split detectors and descriptors tests
* rewrite to google test to simplify debugging
* add tests for AKAZE and one test for ORB

* stitching: add tests with AKAZE feature finder

* added basic stitching cpu and ocl tests
* fix bug in AKAZE wrapper for stitching pipeline causing lots of
! OPENCV warning: getUMat()/getMat() call chain possible problem.
!                 Base object is dead, while nested/derived object is still alive or processed.
!                 Please check lifetime of UMat/Mat objects!
This commit is contained in:
Jiri Horner
2017-06-21 13:33:09 +02:00
committed by Alexander Alekhin
parent 437ca0b62a
commit 5f20e802d2
21 changed files with 849 additions and 1085 deletions
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modules/features2d/test/test_detectors_invariance.cpp
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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
#include "test_precomp.hpp"
#include "test_invariance_utils.hpp"
using namespace std;
using namespace cv;
using std::tr1::make_tuple;
using std::tr1::get;
using namespace testing;
#define SHOW_DEBUG_LOG 0
typedef std::tr1::tuple<std::string, Ptr<FeatureDetector>, float, float> String_FeatureDetector_Float_Float_t;
const static std::string IMAGE_TSUKUBA = "features2d/tsukuba.png";
const static std::string IMAGE_BIKES = "detectors_descriptors_evaluation/images_datasets/bikes/img1.png";
#define Value(...) Values(String_FeatureDetector_Float_Float_t(__VA_ARGS__))
static
void matchKeyPoints(const vector<KeyPoint>& keypoints0, const Mat& H,
const vector<KeyPoint>& keypoints1,
vector<DMatch>& matches)
{
vector<Point2f> points0;
KeyPoint::convert(keypoints0, points0);
Mat points0t;
if(H.empty())
points0t = Mat(points0);
else
perspectiveTransform(Mat(points0), points0t, H);
matches.clear();
vector<uchar> usedMask(keypoints1.size(), 0);
for(int i0 = 0; i0 < static_cast<int>(keypoints0.size()); i0++)
{
int nearestPointIndex = -1;
float maxIntersectRatio = 0.f;
const float r0 = 0.5f * keypoints0[i0].size;
for(size_t i1 = 0; i1 < keypoints1.size(); i1++)
{
if(nearestPointIndex >= 0 && usedMask[i1])
continue;
float r1 = 0.5f * keypoints1[i1].size;
float intersectRatio = calcIntersectRatio(points0t.at<Point2f>(i0), r0,
keypoints1[i1].pt, r1);
if(intersectRatio > maxIntersectRatio)
{
maxIntersectRatio = intersectRatio;
nearestPointIndex = static_cast<int>(i1);
}
}
matches.push_back(DMatch(i0, nearestPointIndex, maxIntersectRatio));
if(nearestPointIndex >= 0)
usedMask[nearestPointIndex] = 1;
}
}
class DetectorInvariance : public TestWithParam<String_FeatureDetector_Float_Float_t>
{
protected:
virtual void SetUp() {
// Read test data
const std::string filename = cvtest::TS::ptr()->get_data_path() + get<0>(GetParam());
image0 = imread(filename);
ASSERT_FALSE(image0.empty()) << "couldn't read input image";
featureDetector = get<1>(GetParam());
minKeyPointMatchesRatio = get<2>(GetParam());
minInliersRatio = get<3>(GetParam());
}
Ptr<FeatureDetector> featureDetector;
float minKeyPointMatchesRatio;
float minInliersRatio;
Mat image0;
};
typedef DetectorInvariance DetectorScaleInvariance;
typedef DetectorInvariance DetectorRotationInvariance;
TEST_P(DetectorRotationInvariance, rotation)
{
Mat image1, mask1;
const int borderSize = 16;
Mat mask0(image0.size(), CV_8UC1, Scalar(0));
mask0(Rect(borderSize, borderSize, mask0.cols - 2*borderSize, mask0.rows - 2*borderSize)).setTo(Scalar(255));
vector<KeyPoint> keypoints0;
featureDetector->detect(image0, keypoints0, mask0);
EXPECT_GE(keypoints0.size(), 15u);
const int maxAngle = 360, angleStep = 15;
for(int angle = 0; angle < maxAngle; angle += angleStep)
{
Mat H = rotateImage(image0, mask0, static_cast<float>(angle), image1, mask1);
vector<KeyPoint> keypoints1;
featureDetector->detect(image1, keypoints1, mask1);
vector<DMatch> matches;
matchKeyPoints(keypoints0, H, keypoints1, matches);
int angleInliersCount = 0;
const float minIntersectRatio = 0.5f;
int keyPointMatchesCount = 0;
for(size_t m = 0; m < matches.size(); m++)
{
if(matches[m].distance < minIntersectRatio)
continue;
keyPointMatchesCount++;
// Check does this inlier have consistent angles
const float maxAngleDiff = 15.f; // grad
float angle0 = keypoints0[matches[m].queryIdx].angle;
float angle1 = keypoints1[matches[m].trainIdx].angle;
ASSERT_FALSE(angle0 == -1 || angle1 == -1) << "Given FeatureDetector is not rotation invariant, it can not be tested here.";
ASSERT_GE(angle0, 0.f);
ASSERT_LT(angle0, 360.f);
ASSERT_GE(angle1, 0.f);
ASSERT_LT(angle1, 360.f);
float rotAngle0 = angle0 + angle;
if(rotAngle0 >= 360.f)
rotAngle0 -= 360.f;
float angleDiff = std::max(rotAngle0, angle1) - std::min(rotAngle0, angle1);
angleDiff = std::min(angleDiff, static_cast<float>(360.f - angleDiff));
ASSERT_GE(angleDiff, 0.f);
bool isAngleCorrect = angleDiff < maxAngleDiff;
if(isAngleCorrect)
angleInliersCount++;
}
float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints0.size();
EXPECT_GE(keyPointMatchesRatio, minKeyPointMatchesRatio) << "angle: " << angle;
if(keyPointMatchesCount)
{
float angleInliersRatio = static_cast<float>(angleInliersCount) / keyPointMatchesCount;
EXPECT_GE(angleInliersRatio, minInliersRatio) << "angle: " << angle;
}
#if SHOW_DEBUG_LOG
std::cout
<< "angle = " << angle
<< ", keypoints = " << keypoints1.size()
<< ", keyPointMatchesRatio = " << keyPointMatchesRatio
<< ", angleInliersRatio = " << (keyPointMatchesCount ? (static_cast<float>(angleInliersCount) / keyPointMatchesCount) : 0)
<< std::endl;
#endif
}
}
TEST_P(DetectorScaleInvariance, scale)
{
vector<KeyPoint> keypoints0;
featureDetector->detect(image0, keypoints0);
EXPECT_GE(keypoints0.size(), 15u);
for(int scaleIdx = 1; scaleIdx <= 3; scaleIdx++)
{
float scale = 1.f + scaleIdx * 0.5f;
Mat image1;
resize(image0, image1, Size(), 1./scale, 1./scale);
vector<KeyPoint> keypoints1, osiKeypoints1; // osi - original size image
featureDetector->detect(image1, keypoints1);
EXPECT_GE(keypoints1.size(), 15u);
EXPECT_LE(keypoints1.size(), keypoints0.size()) << "Strange behavior of the detector. "
"It gives more points count in an image of the smaller size.";
scaleKeyPoints(keypoints1, osiKeypoints1, scale);
vector<DMatch> matches;
// image1 is query image (it's reduced image0)
// image0 is train image
matchKeyPoints(osiKeypoints1, Mat(), keypoints0, matches);
const float minIntersectRatio = 0.5f;
int keyPointMatchesCount = 0;
int scaleInliersCount = 0;
for(size_t m = 0; m < matches.size(); m++)
{
if(matches[m].distance < minIntersectRatio)
continue;
keyPointMatchesCount++;
// Check does this inlier have consistent sizes
const float maxSizeDiff = 0.8f;//0.9f; // grad
float size0 = keypoints0[matches[m].trainIdx].size;
float size1 = osiKeypoints1[matches[m].queryIdx].size;
ASSERT_GT(size0, 0);
ASSERT_GT(size1, 0);
if(std::min(size0, size1) > maxSizeDiff * std::max(size0, size1))
scaleInliersCount++;
}
float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints1.size();
EXPECT_GE(keyPointMatchesRatio, minKeyPointMatchesRatio);
if(keyPointMatchesCount)
{
float scaleInliersRatio = static_cast<float>(scaleInliersCount) / keyPointMatchesCount;
EXPECT_GE(scaleInliersRatio, minInliersRatio);
}
#if SHOW_DEBUG_LOG
std::cout
<< "scale = " << scale
<< ", keyPointMatchesRatio = " << keyPointMatchesRatio
<< ", scaleInliersRatio = " << (keyPointMatchesCount ? static_cast<float>(scaleInliersCount) / keyPointMatchesCount : 0)
<< std::endl;
#endif
}
}
/*
* Detector's rotation invariance check
*/
INSTANTIATE_TEST_CASE_P(BRISK, DetectorRotationInvariance,
Value(IMAGE_TSUKUBA, BRISK::create(), 0.45f, 0.76f));
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));
INSTANTIATE_TEST_CASE_P(AKAZE_DESCRIPTOR_KAZE, DetectorRotationInvariance,
Value(IMAGE_TSUKUBA, AKAZE::create(AKAZE::DESCRIPTOR_KAZE), 0.5f, 0.76f));
/*
* Detector's scale invariance check
*/
INSTANTIATE_TEST_CASE_P(BRISK, DetectorScaleInvariance,
Value(IMAGE_BIKES, BRISK::create(), 0.08f, 0.49f));
INSTANTIATE_TEST_CASE_P(ORB, DetectorScaleInvariance,
Value(IMAGE_BIKES, ORB::create(), 0.08f, 0.49f));
INSTANTIATE_TEST_CASE_P(KAZE, DetectorScaleInvariance,
Value(IMAGE_BIKES, KAZE::create(), 0.08f, 0.49f));
INSTANTIATE_TEST_CASE_P(AKAZE, DetectorScaleInvariance,
Value(IMAGE_BIKES, AKAZE::create(), 0.08f, 0.49f));
INSTANTIATE_TEST_CASE_P(AKAZE_DESCRIPTOR_KAZE, DetectorScaleInvariance,
Value(IMAGE_BIKES, AKAZE::create(AKAZE::DESCRIPTOR_KAZE), 0.08f, 0.49f));