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4064d4c7eb
Added/Fixed testcases for SIFT * merge perf_sift into conventional perf tests * Fix disabled SIFT scale invariance tests allows trainIdx duplication in matching scaled keypoints
256 lines
9.3 KiB
C++
256 lines
9.3 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html
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#include "test_precomp.hpp"
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#include "test_invariance_utils.hpp"
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namespace opencv_test { namespace {
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using namespace perf;
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#define SHOW_DEBUG_LOG 1
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typedef tuple<std::string, Ptr<FeatureDetector>, float, float> String_FeatureDetector_Float_Float_t;
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const static std::string IMAGE_TSUKUBA = "features2d/tsukuba.png";
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const static std::string IMAGE_BIKES = "detectors_descriptors_evaluation/images_datasets/bikes/img1.png";
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#define Value(...) Values(String_FeatureDetector_Float_Float_t(__VA_ARGS__))
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static
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void matchKeyPoints(const vector<KeyPoint>& keypoints0, const Mat& H,
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const vector<KeyPoint>& keypoints1,
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vector<DMatch>& matches)
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{
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vector<Point2f> points0;
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KeyPoint::convert(keypoints0, points0);
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Mat points0t;
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if(H.empty())
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points0t = Mat(points0);
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else
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perspectiveTransform(Mat(points0), points0t, H);
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matches.clear();
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for(int i0 = 0; i0 < static_cast<int>(keypoints0.size()); i0++)
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{
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int nearestPointIndex = -1;
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float maxIntersectRatio = 0.f;
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const float r0 = 0.5f * keypoints0[i0].size;
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for(size_t i1 = 0; i1 < keypoints1.size(); i1++)
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{
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float r1 = 0.5f * keypoints1[i1].size;
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float intersectRatio = calcIntersectRatio(points0t.at<Point2f>(i0), r0,
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keypoints1[i1].pt, r1);
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if(intersectRatio > maxIntersectRatio)
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{
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maxIntersectRatio = intersectRatio;
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nearestPointIndex = static_cast<int>(i1);
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}
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}
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matches.push_back(DMatch(i0, nearestPointIndex, maxIntersectRatio));
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}
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}
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class DetectorInvariance : public TestWithParam<String_FeatureDetector_Float_Float_t>
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{
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protected:
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virtual void SetUp() {
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// Read test data
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const std::string filename = cvtest::TS::ptr()->get_data_path() + get<0>(GetParam());
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image0 = imread(filename);
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ASSERT_FALSE(image0.empty()) << "couldn't read input image";
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featureDetector = get<1>(GetParam());
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minKeyPointMatchesRatio = get<2>(GetParam());
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minInliersRatio = get<3>(GetParam());
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}
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Ptr<FeatureDetector> featureDetector;
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float minKeyPointMatchesRatio;
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float minInliersRatio;
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Mat image0;
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};
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typedef DetectorInvariance DetectorScaleInvariance;
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typedef DetectorInvariance DetectorRotationInvariance;
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TEST_P(DetectorRotationInvariance, rotation)
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{
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Mat image1, mask1;
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const int borderSize = 16;
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Mat mask0(image0.size(), CV_8UC1, Scalar(0));
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mask0(Rect(borderSize, borderSize, mask0.cols - 2*borderSize, mask0.rows - 2*borderSize)).setTo(Scalar(255));
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vector<KeyPoint> keypoints0;
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featureDetector->detect(image0, keypoints0, mask0);
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EXPECT_GE(keypoints0.size(), 15u);
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const int maxAngle = 360, angleStep = 15;
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for(int angle = 0; angle < maxAngle; angle += angleStep)
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{
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Mat H = rotateImage(image0, mask0, static_cast<float>(angle), image1, mask1);
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vector<KeyPoint> keypoints1;
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featureDetector->detect(image1, keypoints1, mask1);
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vector<DMatch> matches;
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matchKeyPoints(keypoints0, H, keypoints1, matches);
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int angleInliersCount = 0;
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const float minIntersectRatio = 0.5f;
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int keyPointMatchesCount = 0;
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for(size_t m = 0; m < matches.size(); m++)
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{
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if(matches[m].distance < minIntersectRatio)
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continue;
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keyPointMatchesCount++;
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// Check does this inlier have consistent angles
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const float maxAngleDiff = 15.f; // grad
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float angle0 = keypoints0[matches[m].queryIdx].angle;
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float angle1 = keypoints1[matches[m].trainIdx].angle;
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ASSERT_FALSE(angle0 == -1 || angle1 == -1) << "Given FeatureDetector is not rotation invariant, it can not be tested here.";
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ASSERT_GE(angle0, 0.f);
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ASSERT_LT(angle0, 360.f);
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ASSERT_GE(angle1, 0.f);
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ASSERT_LT(angle1, 360.f);
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float rotAngle0 = angle0 + angle;
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if(rotAngle0 >= 360.f)
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rotAngle0 -= 360.f;
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float angleDiff = std::max(rotAngle0, angle1) - std::min(rotAngle0, angle1);
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angleDiff = std::min(angleDiff, static_cast<float>(360.f - angleDiff));
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ASSERT_GE(angleDiff, 0.f);
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bool isAngleCorrect = angleDiff < maxAngleDiff;
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if(isAngleCorrect)
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angleInliersCount++;
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}
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float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints0.size();
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EXPECT_GE(keyPointMatchesRatio, minKeyPointMatchesRatio) << "angle: " << angle;
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if(keyPointMatchesCount)
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{
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float angleInliersRatio = static_cast<float>(angleInliersCount) / keyPointMatchesCount;
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EXPECT_GE(angleInliersRatio, minInliersRatio) << "angle: " << angle;
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}
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#if SHOW_DEBUG_LOG
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std::cout
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<< "angle = " << angle
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<< ", keypoints = " << keypoints1.size()
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<< ", keyPointMatchesRatio = " << keyPointMatchesRatio
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<< ", angleInliersRatio = " << (keyPointMatchesCount ? (static_cast<float>(angleInliersCount) / keyPointMatchesCount) : 0)
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<< std::endl;
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#endif
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}
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}
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TEST_P(DetectorScaleInvariance, scale)
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{
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vector<KeyPoint> keypoints0;
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featureDetector->detect(image0, keypoints0);
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EXPECT_GE(keypoints0.size(), 15u);
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for(int scaleIdx = 1; scaleIdx <= 3; scaleIdx++)
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{
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float scale = 1.f + scaleIdx * 0.5f;
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Mat image1;
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resize(image0, image1, Size(), 1./scale, 1./scale, INTER_LINEAR_EXACT);
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vector<KeyPoint> keypoints1, osiKeypoints1; // osi - original size image
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featureDetector->detect(image1, keypoints1);
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EXPECT_GE(keypoints1.size(), 15u);
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EXPECT_LE(keypoints1.size(), keypoints0.size()) << "Strange behavior of the detector. "
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"It gives more points count in an image of the smaller size.";
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scaleKeyPoints(keypoints1, osiKeypoints1, scale);
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vector<DMatch> matches;
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// image1 is query image (it's reduced image0)
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// image0 is train image
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matchKeyPoints(osiKeypoints1, Mat(), keypoints0, matches);
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const float minIntersectRatio = 0.5f;
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int keyPointMatchesCount = 0;
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int scaleInliersCount = 0;
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for(size_t m = 0; m < matches.size(); m++)
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{
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if(matches[m].distance < minIntersectRatio)
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continue;
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keyPointMatchesCount++;
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// Check does this inlier have consistent sizes
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const float maxSizeDiff = 0.8f;//0.9f; // grad
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float size0 = keypoints0[matches[m].trainIdx].size;
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float size1 = osiKeypoints1[matches[m].queryIdx].size;
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ASSERT_GT(size0, 0);
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ASSERT_GT(size1, 0);
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if(std::min(size0, size1) > maxSizeDiff * std::max(size0, size1))
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scaleInliersCount++;
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}
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float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints1.size();
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EXPECT_GE(keyPointMatchesRatio, minKeyPointMatchesRatio);
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if(keyPointMatchesCount)
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{
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float scaleInliersRatio = static_cast<float>(scaleInliersCount) / keyPointMatchesCount;
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EXPECT_GE(scaleInliersRatio, minInliersRatio);
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}
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#if SHOW_DEBUG_LOG
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std::cout
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<< "scale = " << scale
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<< ", keyPointMatchesRatio = " << keyPointMatchesRatio
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<< ", scaleInliersRatio = " << (keyPointMatchesCount ? static_cast<float>(scaleInliersCount) / keyPointMatchesCount : 0)
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<< std::endl;
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#endif
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}
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}
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/*
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* Detector's rotation invariance check
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*/
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INSTANTIATE_TEST_CASE_P(SIFT, DetectorRotationInvariance,
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Value(IMAGE_TSUKUBA, SIFT::create(), 0.45f, 0.70f));
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INSTANTIATE_TEST_CASE_P(BRISK, DetectorRotationInvariance,
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Value(IMAGE_TSUKUBA, BRISK::create(), 0.45f, 0.76f));
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INSTANTIATE_TEST_CASE_P(ORB, DetectorRotationInvariance,
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Value(IMAGE_TSUKUBA, ORB::create(), 0.5f, 0.76f));
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INSTANTIATE_TEST_CASE_P(AKAZE, DetectorRotationInvariance,
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Value(IMAGE_TSUKUBA, AKAZE::create(), 0.5f, 0.71f));
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INSTANTIATE_TEST_CASE_P(AKAZE_DESCRIPTOR_KAZE, DetectorRotationInvariance,
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Value(IMAGE_TSUKUBA, AKAZE::create(AKAZE::DESCRIPTOR_KAZE), 0.5f, 0.71f));
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/*
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* Detector's scale invariance check
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*/
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INSTANTIATE_TEST_CASE_P(SIFT, DetectorScaleInvariance,
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Value(IMAGE_BIKES, SIFT::create(0, 3, 0.09), 0.69f, 0.98f));
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INSTANTIATE_TEST_CASE_P(BRISK, DetectorScaleInvariance,
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Value(IMAGE_BIKES, BRISK::create(), 0.08f, 0.49f));
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INSTANTIATE_TEST_CASE_P(ORB, DetectorScaleInvariance,
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Value(IMAGE_BIKES, ORB::create(), 0.08f, 0.49f));
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INSTANTIATE_TEST_CASE_P(KAZE, DetectorScaleInvariance,
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Value(IMAGE_BIKES, KAZE::create(), 0.08f, 0.49f));
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INSTANTIATE_TEST_CASE_P(AKAZE, DetectorScaleInvariance,
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Value(IMAGE_BIKES, AKAZE::create(), 0.08f, 0.49f));
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INSTANTIATE_TEST_CASE_P(AKAZE_DESCRIPTOR_KAZE, DetectorScaleInvariance,
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Value(IMAGE_BIKES, AKAZE::create(AKAZE::DESCRIPTOR_KAZE), 0.08f, 0.49f));
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}} // namespace
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