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features2d(test): extract common extract/invariance test code
to share with opencv_contrib/xfeatures2d
This commit is contained in:
parent
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@ -5,163 +5,13 @@
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#include "test_precomp.hpp"
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#include "test_invariance_utils.hpp"
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#include "test_descriptors_invariance.impl.hpp"
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namespace opencv_test { namespace {
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#define SHOW_DEBUG_LOG 1
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typedef tuple<std::string, Ptr<FeatureDetector>, Ptr<DescriptorExtractor>, float>
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String_FeatureDetector_DescriptorExtractor_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_DescriptorExtractor_Float_t(__VA_ARGS__))
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static
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void rotateKeyPoints(const vector<KeyPoint>& src, const Mat& H, float angle, vector<KeyPoint>& dst)
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{
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// suppose that H is rotation given from rotateImage() and angle has value passed to rotateImage()
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vector<Point2f> srcCenters, dstCenters;
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KeyPoint::convert(src, srcCenters);
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perspectiveTransform(srcCenters, dstCenters, H);
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dst = src;
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for(size_t i = 0; i < dst.size(); i++)
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{
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dst[i].pt = dstCenters[i];
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float dstAngle = src[i].angle + angle;
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if(dstAngle >= 360.f)
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dstAngle -= 360.f;
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dst[i].angle = dstAngle;
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}
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}
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class DescriptorInvariance : public TestWithParam<String_FeatureDetector_DescriptorExtractor_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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descriptorExtractor = 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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Ptr<DescriptorExtractor> descriptorExtractor;
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float minInliersRatio;
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Mat image0;
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};
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typedef DescriptorInvariance DescriptorScaleInvariance;
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typedef DescriptorInvariance DescriptorRotationInvariance;
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TEST_P(DescriptorRotationInvariance, 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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Mat descriptors0;
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featureDetector->detect(image0, keypoints0, mask0);
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std::cout << "Keypoints: " << keypoints0.size() << std::endl;
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EXPECT_GE(keypoints0.size(), 15u);
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descriptorExtractor->compute(image0, keypoints0, descriptors0);
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BFMatcher bfmatcher(descriptorExtractor->defaultNorm());
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const float minIntersectRatio = 0.5f;
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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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rotateKeyPoints(keypoints0, H, static_cast<float>(angle), keypoints1);
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Mat descriptors1;
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descriptorExtractor->compute(image1, keypoints1, descriptors1);
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vector<DMatch> descMatches;
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bfmatcher.match(descriptors0, descriptors1, descMatches);
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int descInliersCount = 0;
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for(size_t m = 0; m < descMatches.size(); m++)
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{
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const KeyPoint& transformed_p0 = keypoints1[descMatches[m].queryIdx];
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const KeyPoint& p1 = keypoints1[descMatches[m].trainIdx];
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if(calcIntersectRatio(transformed_p0.pt, 0.5f * transformed_p0.size,
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p1.pt, 0.5f * p1.size) >= minIntersectRatio)
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{
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descInliersCount++;
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}
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}
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float descInliersRatio = static_cast<float>(descInliersCount) / keypoints0.size();
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EXPECT_GE(descInliersRatio, minInliersRatio);
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#if SHOW_DEBUG_LOG
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std::cout
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<< "angle = " << angle
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<< ", inliers = " << descInliersCount
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<< ", descInliersRatio = " << static_cast<float>(descInliersCount) / keypoints0.size()
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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(DescriptorScaleInvariance, scale)
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{
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vector<KeyPoint> keypoints0;
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featureDetector->detect(image0, keypoints0);
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std::cout << "Keypoints: " << keypoints0.size() << std::endl;
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EXPECT_GE(keypoints0.size(), 15u);
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Mat descriptors0;
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descriptorExtractor->compute(image0, keypoints0, descriptors0);
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BFMatcher bfmatcher(descriptorExtractor->defaultNorm());
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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;
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scaleKeyPoints(keypoints0, keypoints1, 1.0f/scale);
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Mat descriptors1;
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descriptorExtractor->compute(image1, keypoints1, descriptors1);
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vector<DMatch> descMatches;
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bfmatcher.match(descriptors0, descriptors1, descMatches);
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const float minIntersectRatio = 0.5f;
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int descInliersCount = 0;
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for(size_t m = 0; m < descMatches.size(); m++)
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{
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const KeyPoint& transformed_p0 = keypoints0[descMatches[m].queryIdx];
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const KeyPoint& p1 = keypoints0[descMatches[m].trainIdx];
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if(calcIntersectRatio(transformed_p0.pt, 0.5f * transformed_p0.size,
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p1.pt, 0.5f * p1.size) >= minIntersectRatio)
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{
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descInliersCount++;
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}
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}
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float descInliersRatio = static_cast<float>(descInliersCount) / keypoints0.size();
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EXPECT_GE(descInliersRatio, minInliersRatio);
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#if SHOW_DEBUG_LOG
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std::cout
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<< "scale = " << scale
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<< ", inliers = " << descInliersCount
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<< ", descInliersRatio = " << static_cast<float>(descInliersCount) / keypoints0.size()
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<< std::endl;
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#endif
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}
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}
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#define Value(...) Values(make_tuple(__VA_ARGS__))
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/*
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* Descriptors's rotation invariance check
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modules/features2d/test/test_descriptors_invariance.impl.hpp
Normal file
174
modules/features2d/test/test_descriptors_invariance.impl.hpp
Normal file
@ -0,0 +1,174 @@
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// 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_invariance_utils.hpp"
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namespace opencv_test { namespace {
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#define SHOW_DEBUG_LOG 1
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typedef tuple<std::string, Ptr<FeatureDetector>, Ptr<DescriptorExtractor>, float>
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String_FeatureDetector_DescriptorExtractor_Float_t;
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static
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void rotateKeyPoints(const vector<KeyPoint>& src, const Mat& H, float angle, vector<KeyPoint>& dst)
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{
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// suppose that H is rotation given from rotateImage() and angle has value passed to rotateImage()
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vector<Point2f> srcCenters, dstCenters;
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KeyPoint::convert(src, srcCenters);
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perspectiveTransform(srcCenters, dstCenters, H);
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dst = src;
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for(size_t i = 0; i < dst.size(); i++)
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{
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dst[i].pt = dstCenters[i];
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float dstAngle = src[i].angle + angle;
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if(dstAngle >= 360.f)
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dstAngle -= 360.f;
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dst[i].angle = dstAngle;
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}
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}
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class DescriptorInvariance : public TestWithParam<String_FeatureDetector_DescriptorExtractor_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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descriptorExtractor = 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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Ptr<DescriptorExtractor> descriptorExtractor;
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float minInliersRatio;
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Mat image0;
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};
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typedef DescriptorInvariance DescriptorScaleInvariance;
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typedef DescriptorInvariance DescriptorRotationInvariance;
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TEST_P(DescriptorRotationInvariance, 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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Mat descriptors0;
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featureDetector->detect(image0, keypoints0, mask0);
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std::cout << "Keypoints: " << keypoints0.size() << std::endl;
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EXPECT_GE(keypoints0.size(), 15u);
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descriptorExtractor->compute(image0, keypoints0, descriptors0);
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BFMatcher bfmatcher(descriptorExtractor->defaultNorm());
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const float minIntersectRatio = 0.5f;
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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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rotateKeyPoints(keypoints0, H, static_cast<float>(angle), keypoints1);
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Mat descriptors1;
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descriptorExtractor->compute(image1, keypoints1, descriptors1);
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vector<DMatch> descMatches;
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bfmatcher.match(descriptors0, descriptors1, descMatches);
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int descInliersCount = 0;
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for(size_t m = 0; m < descMatches.size(); m++)
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{
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const KeyPoint& transformed_p0 = keypoints1[descMatches[m].queryIdx];
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const KeyPoint& p1 = keypoints1[descMatches[m].trainIdx];
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if(calcIntersectRatio(transformed_p0.pt, 0.5f * transformed_p0.size,
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p1.pt, 0.5f * p1.size) >= minIntersectRatio)
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{
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descInliersCount++;
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}
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}
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float descInliersRatio = static_cast<float>(descInliersCount) / keypoints0.size();
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EXPECT_GE(descInliersRatio, minInliersRatio);
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#if SHOW_DEBUG_LOG
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std::cout
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<< "angle = " << angle
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<< ", inliers = " << descInliersCount
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<< ", descInliersRatio = " << static_cast<float>(descInliersCount) / keypoints0.size()
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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(DescriptorScaleInvariance, scale)
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{
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vector<KeyPoint> keypoints0;
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featureDetector->detect(image0, keypoints0);
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std::cout << "Keypoints: " << keypoints0.size() << std::endl;
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EXPECT_GE(keypoints0.size(), 15u);
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Mat descriptors0;
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descriptorExtractor->compute(image0, keypoints0, descriptors0);
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BFMatcher bfmatcher(descriptorExtractor->defaultNorm());
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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;
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scaleKeyPoints(keypoints0, keypoints1, 1.0f/scale);
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Mat descriptors1;
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descriptorExtractor->compute(image1, keypoints1, descriptors1);
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vector<DMatch> descMatches;
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bfmatcher.match(descriptors0, descriptors1, descMatches);
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const float minIntersectRatio = 0.5f;
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int descInliersCount = 0;
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for(size_t m = 0; m < descMatches.size(); m++)
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{
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const KeyPoint& transformed_p0 = keypoints0[descMatches[m].queryIdx];
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const KeyPoint& p1 = keypoints0[descMatches[m].trainIdx];
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if(calcIntersectRatio(transformed_p0.pt, 0.5f * transformed_p0.size,
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p1.pt, 0.5f * p1.size) >= minIntersectRatio)
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{
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descInliersCount++;
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}
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}
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float descInliersRatio = static_cast<float>(descInliersCount) / keypoints0.size();
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EXPECT_GE(descInliersRatio, minInliersRatio);
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#if SHOW_DEBUG_LOG
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std::cout
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<< "scale = " << scale
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<< ", inliers = " << descInliersCount
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<< ", descInliersRatio = " << static_cast<float>(descInliersCount) / keypoints0.size()
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<< std::endl;
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#endif
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}
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}
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#undef SHOW_DEBUG_LOG
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}} // namespace
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namespace std {
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using namespace opencv_test;
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static inline void PrintTo(const String_FeatureDetector_DescriptorExtractor_Float_t& v, std::ostream* os)
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{
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*os << "(\"" << get<0>(v)
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<< "\", " << get<3>(v)
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<< ")";
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}
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} // namespace
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@ -1,342 +1,18 @@
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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||||
// loss of use, data, or profits; or business interruption) however caused
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||||
// and on any theory of liability, whether in contract, strict liability,
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||||
// or tort (including negligence or otherwise) arising in any way out of
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||||
// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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// 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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namespace opencv_test { namespace {
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const string FEATURES2D_DIR = "features2d";
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const string IMAGE_FILENAME = "tsukuba.png";
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const string DESCRIPTOR_DIR = FEATURES2D_DIR + "/descriptor_extractors";
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}} // namespace
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/****************************************************************************************\
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* Regression tests for descriptor extractors. *
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\****************************************************************************************/
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static void writeMatInBin( const Mat& mat, const string& filename )
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{
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FILE* f = fopen( filename.c_str(), "wb");
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if( f )
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{
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CV_Assert(4 == sizeof(int));
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int type = mat.type();
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fwrite( (void*)&mat.rows, sizeof(int), 1, f );
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fwrite( (void*)&mat.cols, sizeof(int), 1, f );
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fwrite( (void*)&type, sizeof(int), 1, f );
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int dataSize = (int)(mat.step * mat.rows);
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fwrite( (void*)&dataSize, sizeof(int), 1, f );
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fwrite( (void*)mat.ptr(), 1, dataSize, f );
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fclose(f);
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}
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}
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#include "test_descriptors_regression.impl.hpp"
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static Mat readMatFromBin( const string& filename )
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{
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FILE* f = fopen( filename.c_str(), "rb" );
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if( f )
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{
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CV_Assert(4 == sizeof(int));
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int rows, cols, type, dataSize;
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size_t elements_read1 = fread( (void*)&rows, sizeof(int), 1, f );
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size_t elements_read2 = fread( (void*)&cols, sizeof(int), 1, f );
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size_t elements_read3 = fread( (void*)&type, sizeof(int), 1, f );
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size_t elements_read4 = fread( (void*)&dataSize, sizeof(int), 1, f );
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CV_Assert(elements_read1 == 1 && elements_read2 == 1 && elements_read3 == 1 && elements_read4 == 1);
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int step = dataSize / rows / CV_ELEM_SIZE(type);
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CV_Assert(step >= cols);
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Mat returnMat = Mat(rows, step, type).colRange(0, cols);
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size_t elements_read = fread( returnMat.ptr(), 1, dataSize, f );
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CV_Assert(elements_read == (size_t)(dataSize));
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fclose(f);
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return returnMat;
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}
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return Mat();
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||||
}
|
||||
|
||||
template<class Distance>
|
||||
class CV_DescriptorExtractorTest : public cvtest::BaseTest
|
||||
{
|
||||
public:
|
||||
typedef typename Distance::ValueType ValueType;
|
||||
typedef typename Distance::ResultType DistanceType;
|
||||
|
||||
CV_DescriptorExtractorTest( const string _name, DistanceType _maxDist, const Ptr<DescriptorExtractor>& _dextractor,
|
||||
Distance d = Distance(), Ptr<FeatureDetector> _detector = Ptr<FeatureDetector>()):
|
||||
name(_name), maxDist(_maxDist), dextractor(_dextractor), distance(d) , detector(_detector) {}
|
||||
|
||||
~CV_DescriptorExtractorTest()
|
||||
{
|
||||
}
|
||||
protected:
|
||||
virtual void createDescriptorExtractor() {}
|
||||
|
||||
void compareDescriptors( const Mat& validDescriptors, const Mat& calcDescriptors )
|
||||
{
|
||||
if( validDescriptors.size != calcDescriptors.size || validDescriptors.type() != calcDescriptors.type() )
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Valid and computed descriptors matrices must have the same size and type.\n");
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
|
||||
CV_Assert( DataType<ValueType>::type == validDescriptors.type() );
|
||||
|
||||
int dimension = validDescriptors.cols;
|
||||
DistanceType curMaxDist = 0;
|
||||
size_t exact_count = 0, failed_count = 0;
|
||||
for( int y = 0; y < validDescriptors.rows; y++ )
|
||||
{
|
||||
DistanceType dist = distance( validDescriptors.ptr<ValueType>(y), calcDescriptors.ptr<ValueType>(y), dimension );
|
||||
if (dist == 0)
|
||||
exact_count++;
|
||||
if( dist > curMaxDist )
|
||||
{
|
||||
if (dist > maxDist)
|
||||
failed_count++;
|
||||
curMaxDist = dist;
|
||||
}
|
||||
#if 0
|
||||
if (dist > 0)
|
||||
{
|
||||
std::cout << "i=" << y << " fail_count=" << failed_count << " dist=" << dist << std::endl;
|
||||
std::cout << "valid: " << validDescriptors.row(y) << std::endl;
|
||||
std::cout << " calc: " << calcDescriptors.row(y) << std::endl;
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
float exact_percents = (100 * (float)exact_count / validDescriptors.rows);
|
||||
float failed_percents = (100 * (float)failed_count / validDescriptors.rows);
|
||||
std::stringstream ss;
|
||||
ss << "Exact count (dist == 0): " << exact_count << " (" << (int)exact_percents << "%)" << std::endl
|
||||
<< "Failed count (dist > " << maxDist << "): " << failed_count << " (" << (int)failed_percents << "%)" << std::endl
|
||||
<< "Max distance between valid and computed descriptors (" << validDescriptors.size() << "): " << curMaxDist;
|
||||
EXPECT_LE(failed_percents, 20.0f);
|
||||
std::cout << ss.str() << std::endl;
|
||||
}
|
||||
|
||||
void emptyDataTest()
|
||||
{
|
||||
assert( dextractor );
|
||||
|
||||
// One image.
|
||||
Mat image;
|
||||
vector<KeyPoint> keypoints;
|
||||
Mat descriptors;
|
||||
|
||||
try
|
||||
{
|
||||
dextractor->compute( image, keypoints, descriptors );
|
||||
}
|
||||
catch(...)
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "compute() on empty image and empty keypoints must not generate exception (1).\n");
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
}
|
||||
|
||||
RNG rng;
|
||||
image = cvtest::randomMat(rng, Size(50, 50), CV_8UC3, 0, 255, false);
|
||||
try
|
||||
{
|
||||
dextractor->compute( image, keypoints, descriptors );
|
||||
}
|
||||
catch(...)
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "compute() on nonempty image and empty keypoints must not generate exception (1).\n");
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
}
|
||||
|
||||
// Several images.
|
||||
vector<Mat> images;
|
||||
vector<vector<KeyPoint> > keypointsCollection;
|
||||
vector<Mat> descriptorsCollection;
|
||||
try
|
||||
{
|
||||
dextractor->compute( images, keypointsCollection, descriptorsCollection );
|
||||
}
|
||||
catch(...)
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "compute() on empty images and empty keypoints collection must not generate exception (2).\n");
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
}
|
||||
}
|
||||
|
||||
void regressionTest()
|
||||
{
|
||||
assert( dextractor );
|
||||
|
||||
// Read the test image.
|
||||
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
|
||||
Mat img = imread( imgFilename );
|
||||
if( img.empty() )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Image %s can not be read.\n", imgFilename.c_str() );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
const std::string keypoints_filename = string(ts->get_data_path()) +
|
||||
(detector.empty()
|
||||
? (FEATURES2D_DIR + "/" + std::string("keypoints.xml.gz"))
|
||||
: (DESCRIPTOR_DIR + "/" + name + "_keypoints.xml.gz"));
|
||||
FileStorage fs(keypoints_filename, FileStorage::READ);
|
||||
|
||||
vector<KeyPoint> keypoints;
|
||||
EXPECT_TRUE(fs.isOpened()) << "Keypoint testdata is missing. Re-computing and re-writing keypoints testdata...";
|
||||
if (!fs.isOpened())
|
||||
{
|
||||
fs.open(keypoints_filename, FileStorage::WRITE);
|
||||
ASSERT_TRUE(fs.isOpened()) << "File for writing keypoints can not be opened.";
|
||||
if (detector.empty())
|
||||
{
|
||||
Ptr<ORB> fd = ORB::create();
|
||||
fd->detect(img, keypoints);
|
||||
}
|
||||
else
|
||||
{
|
||||
detector->detect(img, keypoints);
|
||||
}
|
||||
write(fs, "keypoints", keypoints);
|
||||
fs.release();
|
||||
}
|
||||
else
|
||||
{
|
||||
read(fs.getFirstTopLevelNode(), keypoints);
|
||||
fs.release();
|
||||
}
|
||||
|
||||
if(!detector.empty())
|
||||
{
|
||||
vector<KeyPoint> calcKeypoints;
|
||||
detector->detect(img, calcKeypoints);
|
||||
// TODO validate received keypoints
|
||||
int diff = abs((int)calcKeypoints.size() - (int)keypoints.size());
|
||||
if (diff > 0)
|
||||
{
|
||||
std::cout << "Keypoints difference: " << diff << std::endl;
|
||||
EXPECT_LE(diff, (int)(keypoints.size() * 0.03f));
|
||||
}
|
||||
}
|
||||
ASSERT_FALSE(keypoints.empty());
|
||||
{
|
||||
Mat calcDescriptors;
|
||||
double t = (double)getTickCount();
|
||||
dextractor->compute(img, keypoints, calcDescriptors);
|
||||
t = getTickCount() - t;
|
||||
ts->printf(cvtest::TS::LOG, "\nAverage time of computing one descriptor = %g ms.\n", t/((double)getTickFrequency()*1000.)/calcDescriptors.rows);
|
||||
|
||||
if (calcDescriptors.rows != (int)keypoints.size())
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Count of computed descriptors and keypoints count must be equal.\n" );
|
||||
ts->printf( cvtest::TS::LOG, "Count of keypoints is %d.\n", (int)keypoints.size() );
|
||||
ts->printf( cvtest::TS::LOG, "Count of computed descriptors is %d.\n", calcDescriptors.rows );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||||
return;
|
||||
}
|
||||
|
||||
if (calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType())
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Incorrect descriptor size or descriptor type.\n" );
|
||||
ts->printf( cvtest::TS::LOG, "Expected size is %d.\n", dextractor->descriptorSize() );
|
||||
ts->printf( cvtest::TS::LOG, "Calculated size is %d.\n", calcDescriptors.cols );
|
||||
ts->printf( cvtest::TS::LOG, "Expected type is %d.\n", dextractor->descriptorType() );
|
||||
ts->printf( cvtest::TS::LOG, "Calculated type is %d.\n", calcDescriptors.type() );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||||
return;
|
||||
}
|
||||
|
||||
// TODO read and write descriptor extractor parameters and check them
|
||||
Mat validDescriptors = readDescriptors();
|
||||
EXPECT_FALSE(validDescriptors.empty()) << "Descriptors testdata is missing. Re-writing descriptors testdata...";
|
||||
if (!validDescriptors.empty())
|
||||
{
|
||||
compareDescriptors(validDescriptors, calcDescriptors);
|
||||
}
|
||||
else
|
||||
{
|
||||
ASSERT_TRUE(writeDescriptors(calcDescriptors)) << "Descriptors can not be written.";
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void run(int)
|
||||
{
|
||||
createDescriptorExtractor();
|
||||
if( !dextractor )
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Descriptor extractor is empty.\n");
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
|
||||
emptyDataTest();
|
||||
regressionTest();
|
||||
|
||||
ts->set_failed_test_info( cvtest::TS::OK );
|
||||
}
|
||||
|
||||
virtual Mat readDescriptors()
|
||||
{
|
||||
Mat res = readMatFromBin( string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) );
|
||||
return res;
|
||||
}
|
||||
|
||||
virtual bool writeDescriptors( Mat& descs )
|
||||
{
|
||||
writeMatInBin( descs, string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) );
|
||||
return true;
|
||||
}
|
||||
|
||||
string name;
|
||||
const DistanceType maxDist;
|
||||
Ptr<DescriptorExtractor> dextractor;
|
||||
Distance distance;
|
||||
Ptr<FeatureDetector> detector;
|
||||
|
||||
private:
|
||||
CV_DescriptorExtractorTest& operator=(const CV_DescriptorExtractorTest&) { return *this; }
|
||||
};
|
||||
namespace opencv_test { namespace {
|
||||
|
||||
/****************************************************************************************\
|
||||
* Tests registrations *
|
||||
|
298
modules/features2d/test/test_descriptors_regression.impl.hpp
Normal file
298
modules/features2d/test/test_descriptors_regression.impl.hpp
Normal file
@ -0,0 +1,298 @@
|
||||
// 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
|
||||
|
||||
namespace opencv_test { namespace {
|
||||
|
||||
/****************************************************************************************\
|
||||
* Regression tests for descriptor extractors. *
|
||||
\****************************************************************************************/
|
||||
static void writeMatInBin( const Mat& mat, const string& filename )
|
||||
{
|
||||
FILE* f = fopen( filename.c_str(), "wb");
|
||||
if( f )
|
||||
{
|
||||
CV_Assert(4 == sizeof(int));
|
||||
int type = mat.type();
|
||||
fwrite( (void*)&mat.rows, sizeof(int), 1, f );
|
||||
fwrite( (void*)&mat.cols, sizeof(int), 1, f );
|
||||
fwrite( (void*)&type, sizeof(int), 1, f );
|
||||
int dataSize = (int)(mat.step * mat.rows);
|
||||
fwrite( (void*)&dataSize, sizeof(int), 1, f );
|
||||
fwrite( (void*)mat.ptr(), 1, dataSize, f );
|
||||
fclose(f);
|
||||
}
|
||||
}
|
||||
|
||||
static Mat readMatFromBin( const string& filename )
|
||||
{
|
||||
FILE* f = fopen( filename.c_str(), "rb" );
|
||||
if( f )
|
||||
{
|
||||
CV_Assert(4 == sizeof(int));
|
||||
int rows, cols, type, dataSize;
|
||||
size_t elements_read1 = fread( (void*)&rows, sizeof(int), 1, f );
|
||||
size_t elements_read2 = fread( (void*)&cols, sizeof(int), 1, f );
|
||||
size_t elements_read3 = fread( (void*)&type, sizeof(int), 1, f );
|
||||
size_t elements_read4 = fread( (void*)&dataSize, sizeof(int), 1, f );
|
||||
CV_Assert(elements_read1 == 1 && elements_read2 == 1 && elements_read3 == 1 && elements_read4 == 1);
|
||||
|
||||
int step = dataSize / rows / CV_ELEM_SIZE(type);
|
||||
CV_Assert(step >= cols);
|
||||
|
||||
Mat returnMat = Mat(rows, step, type).colRange(0, cols);
|
||||
|
||||
size_t elements_read = fread( returnMat.ptr(), 1, dataSize, f );
|
||||
CV_Assert(elements_read == (size_t)(dataSize));
|
||||
|
||||
fclose(f);
|
||||
|
||||
return returnMat;
|
||||
}
|
||||
return Mat();
|
||||
}
|
||||
|
||||
template<class Distance>
|
||||
class CV_DescriptorExtractorTest : public cvtest::BaseTest
|
||||
{
|
||||
public:
|
||||
typedef typename Distance::ValueType ValueType;
|
||||
typedef typename Distance::ResultType DistanceType;
|
||||
|
||||
CV_DescriptorExtractorTest( const string _name, DistanceType _maxDist, const Ptr<DescriptorExtractor>& _dextractor,
|
||||
Distance d = Distance(), Ptr<FeatureDetector> _detector = Ptr<FeatureDetector>()):
|
||||
name(_name), maxDist(_maxDist), dextractor(_dextractor), distance(d) , detector(_detector) {}
|
||||
|
||||
~CV_DescriptorExtractorTest()
|
||||
{
|
||||
}
|
||||
protected:
|
||||
virtual void createDescriptorExtractor() {}
|
||||
|
||||
void compareDescriptors( const Mat& validDescriptors, const Mat& calcDescriptors )
|
||||
{
|
||||
if( validDescriptors.size != calcDescriptors.size || validDescriptors.type() != calcDescriptors.type() )
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Valid and computed descriptors matrices must have the same size and type.\n");
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
|
||||
CV_Assert( DataType<ValueType>::type == validDescriptors.type() );
|
||||
|
||||
int dimension = validDescriptors.cols;
|
||||
DistanceType curMaxDist = 0;
|
||||
size_t exact_count = 0, failed_count = 0;
|
||||
for( int y = 0; y < validDescriptors.rows; y++ )
|
||||
{
|
||||
DistanceType dist = distance( validDescriptors.ptr<ValueType>(y), calcDescriptors.ptr<ValueType>(y), dimension );
|
||||
if (dist == 0)
|
||||
exact_count++;
|
||||
if( dist > curMaxDist )
|
||||
{
|
||||
if (dist > maxDist)
|
||||
failed_count++;
|
||||
curMaxDist = dist;
|
||||
}
|
||||
#if 0
|
||||
if (dist > 0)
|
||||
{
|
||||
std::cout << "i=" << y << " fail_count=" << failed_count << " dist=" << dist << std::endl;
|
||||
std::cout << "valid: " << validDescriptors.row(y) << std::endl;
|
||||
std::cout << " calc: " << calcDescriptors.row(y) << std::endl;
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
float exact_percents = (100 * (float)exact_count / validDescriptors.rows);
|
||||
float failed_percents = (100 * (float)failed_count / validDescriptors.rows);
|
||||
std::stringstream ss;
|
||||
ss << "Exact count (dist == 0): " << exact_count << " (" << (int)exact_percents << "%)" << std::endl
|
||||
<< "Failed count (dist > " << maxDist << "): " << failed_count << " (" << (int)failed_percents << "%)" << std::endl
|
||||
<< "Max distance between valid and computed descriptors (" << validDescriptors.size() << "): " << curMaxDist;
|
||||
EXPECT_LE(failed_percents, 20.0f);
|
||||
std::cout << ss.str() << std::endl;
|
||||
}
|
||||
|
||||
void emptyDataTest()
|
||||
{
|
||||
assert( dextractor );
|
||||
|
||||
// One image.
|
||||
Mat image;
|
||||
vector<KeyPoint> keypoints;
|
||||
Mat descriptors;
|
||||
|
||||
try
|
||||
{
|
||||
dextractor->compute( image, keypoints, descriptors );
|
||||
}
|
||||
catch(...)
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "compute() on empty image and empty keypoints must not generate exception (1).\n");
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
}
|
||||
|
||||
RNG rng;
|
||||
image = cvtest::randomMat(rng, Size(50, 50), CV_8UC3, 0, 255, false);
|
||||
try
|
||||
{
|
||||
dextractor->compute( image, keypoints, descriptors );
|
||||
}
|
||||
catch(...)
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "compute() on nonempty image and empty keypoints must not generate exception (1).\n");
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
}
|
||||
|
||||
// Several images.
|
||||
vector<Mat> images;
|
||||
vector<vector<KeyPoint> > keypointsCollection;
|
||||
vector<Mat> descriptorsCollection;
|
||||
try
|
||||
{
|
||||
dextractor->compute( images, keypointsCollection, descriptorsCollection );
|
||||
}
|
||||
catch(...)
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "compute() on empty images and empty keypoints collection must not generate exception (2).\n");
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
}
|
||||
}
|
||||
|
||||
void regressionTest()
|
||||
{
|
||||
assert( dextractor );
|
||||
|
||||
// Read the test image.
|
||||
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
|
||||
Mat img = imread( imgFilename );
|
||||
if( img.empty() )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Image %s can not be read.\n", imgFilename.c_str() );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
const std::string keypoints_filename = string(ts->get_data_path()) +
|
||||
(detector.empty()
|
||||
? (FEATURES2D_DIR + "/" + std::string("keypoints.xml.gz"))
|
||||
: (DESCRIPTOR_DIR + "/" + name + "_keypoints.xml.gz"));
|
||||
FileStorage fs(keypoints_filename, FileStorage::READ);
|
||||
|
||||
vector<KeyPoint> keypoints;
|
||||
EXPECT_TRUE(fs.isOpened()) << "Keypoint testdata is missing. Re-computing and re-writing keypoints testdata...";
|
||||
if (!fs.isOpened())
|
||||
{
|
||||
fs.open(keypoints_filename, FileStorage::WRITE);
|
||||
ASSERT_TRUE(fs.isOpened()) << "File for writing keypoints can not be opened.";
|
||||
if (detector.empty())
|
||||
{
|
||||
Ptr<ORB> fd = ORB::create();
|
||||
fd->detect(img, keypoints);
|
||||
}
|
||||
else
|
||||
{
|
||||
detector->detect(img, keypoints);
|
||||
}
|
||||
write(fs, "keypoints", keypoints);
|
||||
fs.release();
|
||||
}
|
||||
else
|
||||
{
|
||||
read(fs.getFirstTopLevelNode(), keypoints);
|
||||
fs.release();
|
||||
}
|
||||
|
||||
if(!detector.empty())
|
||||
{
|
||||
vector<KeyPoint> calcKeypoints;
|
||||
detector->detect(img, calcKeypoints);
|
||||
// TODO validate received keypoints
|
||||
int diff = abs((int)calcKeypoints.size() - (int)keypoints.size());
|
||||
if (diff > 0)
|
||||
{
|
||||
std::cout << "Keypoints difference: " << diff << std::endl;
|
||||
EXPECT_LE(diff, (int)(keypoints.size() * 0.03f));
|
||||
}
|
||||
}
|
||||
ASSERT_FALSE(keypoints.empty());
|
||||
{
|
||||
Mat calcDescriptors;
|
||||
double t = (double)getTickCount();
|
||||
dextractor->compute(img, keypoints, calcDescriptors);
|
||||
t = getTickCount() - t;
|
||||
ts->printf(cvtest::TS::LOG, "\nAverage time of computing one descriptor = %g ms.\n", t/((double)getTickFrequency()*1000.)/calcDescriptors.rows);
|
||||
|
||||
if (calcDescriptors.rows != (int)keypoints.size())
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Count of computed descriptors and keypoints count must be equal.\n" );
|
||||
ts->printf( cvtest::TS::LOG, "Count of keypoints is %d.\n", (int)keypoints.size() );
|
||||
ts->printf( cvtest::TS::LOG, "Count of computed descriptors is %d.\n", calcDescriptors.rows );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||||
return;
|
||||
}
|
||||
|
||||
if (calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType())
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Incorrect descriptor size or descriptor type.\n" );
|
||||
ts->printf( cvtest::TS::LOG, "Expected size is %d.\n", dextractor->descriptorSize() );
|
||||
ts->printf( cvtest::TS::LOG, "Calculated size is %d.\n", calcDescriptors.cols );
|
||||
ts->printf( cvtest::TS::LOG, "Expected type is %d.\n", dextractor->descriptorType() );
|
||||
ts->printf( cvtest::TS::LOG, "Calculated type is %d.\n", calcDescriptors.type() );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||||
return;
|
||||
}
|
||||
|
||||
// TODO read and write descriptor extractor parameters and check them
|
||||
Mat validDescriptors = readDescriptors();
|
||||
EXPECT_FALSE(validDescriptors.empty()) << "Descriptors testdata is missing. Re-writing descriptors testdata...";
|
||||
if (!validDescriptors.empty())
|
||||
{
|
||||
compareDescriptors(validDescriptors, calcDescriptors);
|
||||
}
|
||||
else
|
||||
{
|
||||
ASSERT_TRUE(writeDescriptors(calcDescriptors)) << "Descriptors can not be written.";
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void run(int)
|
||||
{
|
||||
createDescriptorExtractor();
|
||||
if( !dextractor )
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Descriptor extractor is empty.\n");
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
|
||||
emptyDataTest();
|
||||
regressionTest();
|
||||
|
||||
ts->set_failed_test_info( cvtest::TS::OK );
|
||||
}
|
||||
|
||||
virtual Mat readDescriptors()
|
||||
{
|
||||
Mat res = readMatFromBin( string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) );
|
||||
return res;
|
||||
}
|
||||
|
||||
virtual bool writeDescriptors( Mat& descs )
|
||||
{
|
||||
writeMatInBin( descs, string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) );
|
||||
return true;
|
||||
}
|
||||
|
||||
string name;
|
||||
const DistanceType maxDist;
|
||||
Ptr<DescriptorExtractor> dextractor;
|
||||
Distance distance;
|
||||
Ptr<FeatureDetector> detector;
|
||||
|
||||
private:
|
||||
CV_DescriptorExtractorTest& operator=(const CV_DescriptorExtractorTest&) { return *this; }
|
||||
};
|
||||
|
||||
}} // namespace
|
@ -5,216 +5,13 @@
|
||||
#include "test_precomp.hpp"
|
||||
#include "test_invariance_utils.hpp"
|
||||
|
||||
#include "test_detectors_invariance.impl.hpp"
|
||||
|
||||
namespace opencv_test { namespace {
|
||||
using namespace perf;
|
||||
|
||||
#define SHOW_DEBUG_LOG 1
|
||||
|
||||
typedef 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, INTER_LINEAR_EXACT);
|
||||
|
||||
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
|
||||
}
|
||||
}
|
||||
#define Value(...) Values(make_tuple(__VA_ARGS__))
|
||||
|
||||
/*
|
||||
* Detector's rotation invariance check
|
||||
|
227
modules/features2d/test/test_detectors_invariance.impl.hpp
Normal file
227
modules/features2d/test/test_detectors_invariance.impl.hpp
Normal file
@ -0,0 +1,227 @@
|
||||
// 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_invariance_utils.hpp"
|
||||
|
||||
namespace opencv_test { namespace {
|
||||
|
||||
#define SHOW_DEBUG_LOG 1
|
||||
|
||||
typedef tuple<std::string, Ptr<FeatureDetector>, float, float> String_FeatureDetector_Float_Float_t;
|
||||
|
||||
|
||||
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, INTER_LINEAR_EXACT);
|
||||
|
||||
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
|
||||
}
|
||||
}
|
||||
|
||||
#undef SHOW_DEBUG_LOG
|
||||
}} // namespace
|
||||
|
||||
namespace std {
|
||||
using namespace opencv_test;
|
||||
static inline void PrintTo(const String_FeatureDetector_Float_Float_t& v, std::ostream* os)
|
||||
{
|
||||
*os << "(\"" << get<0>(v)
|
||||
<< "\", " << get<2>(v)
|
||||
<< ", " << get<3>(v)
|
||||
<< ")";
|
||||
}
|
||||
} // namespace
|
@ -1,245 +1,18 @@
|
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// Intel License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000, Intel Corporation, all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of Intel Corporation may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
// 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"
|
||||
|
||||
namespace opencv_test { namespace {
|
||||
|
||||
const string FEATURES2D_DIR = "features2d";
|
||||
const string IMAGE_FILENAME = "tsukuba.png";
|
||||
const string DETECTOR_DIR = FEATURES2D_DIR + "/feature_detectors";
|
||||
}} // namespace
|
||||
|
||||
/****************************************************************************************\
|
||||
* Regression tests for feature detectors comparing keypoints. *
|
||||
\****************************************************************************************/
|
||||
#include "test_detectors_regression.impl.hpp"
|
||||
|
||||
class CV_FeatureDetectorTest : public cvtest::BaseTest
|
||||
{
|
||||
public:
|
||||
CV_FeatureDetectorTest( const string& _name, const Ptr<FeatureDetector>& _fdetector ) :
|
||||
name(_name), fdetector(_fdetector) {}
|
||||
|
||||
protected:
|
||||
bool isSimilarKeypoints( const KeyPoint& p1, const KeyPoint& p2 );
|
||||
void compareKeypointSets( const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints );
|
||||
|
||||
void emptyDataTest();
|
||||
void regressionTest(); // TODO test of detect() with mask
|
||||
|
||||
virtual void run( int );
|
||||
|
||||
string name;
|
||||
Ptr<FeatureDetector> fdetector;
|
||||
};
|
||||
|
||||
void CV_FeatureDetectorTest::emptyDataTest()
|
||||
{
|
||||
// One image.
|
||||
Mat image;
|
||||
vector<KeyPoint> keypoints;
|
||||
try
|
||||
{
|
||||
fdetector->detect( image, keypoints );
|
||||
}
|
||||
catch(...)
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "detect() on empty image must not generate exception (1).\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||||
}
|
||||
|
||||
if( !keypoints.empty() )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "detect() on empty image must return empty keypoints vector (1).\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||||
return;
|
||||
}
|
||||
|
||||
// Several images.
|
||||
vector<Mat> images;
|
||||
vector<vector<KeyPoint> > keypointCollection;
|
||||
try
|
||||
{
|
||||
fdetector->detect( images, keypointCollection );
|
||||
}
|
||||
catch(...)
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "detect() on empty image vector must not generate exception (2).\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||||
}
|
||||
}
|
||||
|
||||
bool CV_FeatureDetectorTest::isSimilarKeypoints( const KeyPoint& p1, const KeyPoint& p2 )
|
||||
{
|
||||
const float maxPtDif = 1.f;
|
||||
const float maxSizeDif = 1.f;
|
||||
const float maxAngleDif = 2.f;
|
||||
const float maxResponseDif = 0.1f;
|
||||
|
||||
float dist = (float)cv::norm( p1.pt - p2.pt );
|
||||
return (dist < maxPtDif &&
|
||||
fabs(p1.size - p2.size) < maxSizeDif &&
|
||||
abs(p1.angle - p2.angle) < maxAngleDif &&
|
||||
abs(p1.response - p2.response) < maxResponseDif &&
|
||||
p1.octave == p2.octave &&
|
||||
p1.class_id == p2.class_id );
|
||||
}
|
||||
|
||||
void CV_FeatureDetectorTest::compareKeypointSets( const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints )
|
||||
{
|
||||
const float maxCountRatioDif = 0.01f;
|
||||
|
||||
// Compare counts of validation and calculated keypoints.
|
||||
float countRatio = (float)validKeypoints.size() / (float)calcKeypoints.size();
|
||||
if( countRatio < 1 - maxCountRatioDif || countRatio > 1.f + maxCountRatioDif )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Bad keypoints count ratio (validCount = %d, calcCount = %d).\n",
|
||||
validKeypoints.size(), calcKeypoints.size() );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||||
return;
|
||||
}
|
||||
|
||||
int progress = 0, progressCount = (int)(validKeypoints.size() * calcKeypoints.size());
|
||||
int badPointCount = 0, commonPointCount = max((int)validKeypoints.size(), (int)calcKeypoints.size());
|
||||
for( size_t v = 0; v < validKeypoints.size(); v++ )
|
||||
{
|
||||
int nearestIdx = -1;
|
||||
float minDist = std::numeric_limits<float>::max();
|
||||
|
||||
for( size_t c = 0; c < calcKeypoints.size(); c++ )
|
||||
{
|
||||
progress = update_progress( progress, (int)(v*calcKeypoints.size() + c), progressCount, 0 );
|
||||
float curDist = (float)cv::norm( calcKeypoints[c].pt - validKeypoints[v].pt );
|
||||
if( curDist < minDist )
|
||||
{
|
||||
minDist = curDist;
|
||||
nearestIdx = (int)c;
|
||||
}
|
||||
}
|
||||
|
||||
assert( minDist >= 0 );
|
||||
if( !isSimilarKeypoints( validKeypoints[v], calcKeypoints[nearestIdx] ) )
|
||||
badPointCount++;
|
||||
}
|
||||
ts->printf( cvtest::TS::LOG, "badPointCount = %d; validPointCount = %d; calcPointCount = %d\n",
|
||||
badPointCount, validKeypoints.size(), calcKeypoints.size() );
|
||||
if( badPointCount > 0.9 * commonPointCount )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, " - Bad accuracy!\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
return;
|
||||
}
|
||||
ts->printf( cvtest::TS::LOG, " - OK\n" );
|
||||
}
|
||||
|
||||
void CV_FeatureDetectorTest::regressionTest()
|
||||
{
|
||||
assert( !fdetector.empty() );
|
||||
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
|
||||
string resFilename = string(ts->get_data_path()) + DETECTOR_DIR + "/" + string(name) + ".xml.gz";
|
||||
|
||||
// Read the test image.
|
||||
Mat image = imread( imgFilename );
|
||||
if( image.empty() )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Image %s can not be read.\n", imgFilename.c_str() );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
|
||||
FileStorage fs( resFilename, FileStorage::READ );
|
||||
|
||||
// Compute keypoints.
|
||||
vector<KeyPoint> calcKeypoints;
|
||||
fdetector->detect( image, calcKeypoints );
|
||||
|
||||
if( fs.isOpened() ) // Compare computed and valid keypoints.
|
||||
{
|
||||
// TODO compare saved feature detector params with current ones
|
||||
|
||||
// Read validation keypoints set.
|
||||
vector<KeyPoint> validKeypoints;
|
||||
read( fs["keypoints"], validKeypoints );
|
||||
if( validKeypoints.empty() )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Keypoints can not be read.\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
|
||||
compareKeypointSets( validKeypoints, calcKeypoints );
|
||||
}
|
||||
else // Write detector parameters and computed keypoints as validation data.
|
||||
{
|
||||
fs.open( resFilename, FileStorage::WRITE );
|
||||
if( !fs.isOpened() )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "File %s can not be opened to write.\n", resFilename.c_str() );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
else
|
||||
{
|
||||
fs << "detector_params" << "{";
|
||||
fdetector->write( fs );
|
||||
fs << "}";
|
||||
|
||||
write( fs, "keypoints", calcKeypoints );
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void CV_FeatureDetectorTest::run( int /*start_from*/ )
|
||||
{
|
||||
if( !fdetector )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Feature detector is empty.\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
|
||||
emptyDataTest();
|
||||
regressionTest();
|
||||
|
||||
ts->set_failed_test_info( cvtest::TS::OK );
|
||||
}
|
||||
namespace opencv_test { namespace {
|
||||
|
||||
/****************************************************************************************\
|
||||
* Tests registrations *
|
||||
|
201
modules/features2d/test/test_detectors_regression.impl.hpp
Normal file
201
modules/features2d/test/test_detectors_regression.impl.hpp
Normal file
@ -0,0 +1,201 @@
|
||||
// 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
|
||||
|
||||
namespace opencv_test { namespace {
|
||||
|
||||
/****************************************************************************************\
|
||||
* Regression tests for feature detectors comparing keypoints. *
|
||||
\****************************************************************************************/
|
||||
|
||||
class CV_FeatureDetectorTest : public cvtest::BaseTest
|
||||
{
|
||||
public:
|
||||
CV_FeatureDetectorTest( const string& _name, const Ptr<FeatureDetector>& _fdetector ) :
|
||||
name(_name), fdetector(_fdetector) {}
|
||||
|
||||
protected:
|
||||
bool isSimilarKeypoints( const KeyPoint& p1, const KeyPoint& p2 );
|
||||
void compareKeypointSets( const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints );
|
||||
|
||||
void emptyDataTest();
|
||||
void regressionTest(); // TODO test of detect() with mask
|
||||
|
||||
virtual void run( int );
|
||||
|
||||
string name;
|
||||
Ptr<FeatureDetector> fdetector;
|
||||
};
|
||||
|
||||
void CV_FeatureDetectorTest::emptyDataTest()
|
||||
{
|
||||
// One image.
|
||||
Mat image;
|
||||
vector<KeyPoint> keypoints;
|
||||
try
|
||||
{
|
||||
fdetector->detect( image, keypoints );
|
||||
}
|
||||
catch(...)
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "detect() on empty image must not generate exception (1).\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||||
}
|
||||
|
||||
if( !keypoints.empty() )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "detect() on empty image must return empty keypoints vector (1).\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||||
return;
|
||||
}
|
||||
|
||||
// Several images.
|
||||
vector<Mat> images;
|
||||
vector<vector<KeyPoint> > keypointCollection;
|
||||
try
|
||||
{
|
||||
fdetector->detect( images, keypointCollection );
|
||||
}
|
||||
catch(...)
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "detect() on empty image vector must not generate exception (2).\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||||
}
|
||||
}
|
||||
|
||||
bool CV_FeatureDetectorTest::isSimilarKeypoints( const KeyPoint& p1, const KeyPoint& p2 )
|
||||
{
|
||||
const float maxPtDif = 1.f;
|
||||
const float maxSizeDif = 1.f;
|
||||
const float maxAngleDif = 2.f;
|
||||
const float maxResponseDif = 0.1f;
|
||||
|
||||
float dist = (float)cv::norm( p1.pt - p2.pt );
|
||||
return (dist < maxPtDif &&
|
||||
fabs(p1.size - p2.size) < maxSizeDif &&
|
||||
abs(p1.angle - p2.angle) < maxAngleDif &&
|
||||
abs(p1.response - p2.response) < maxResponseDif &&
|
||||
p1.octave == p2.octave &&
|
||||
p1.class_id == p2.class_id );
|
||||
}
|
||||
|
||||
void CV_FeatureDetectorTest::compareKeypointSets( const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints )
|
||||
{
|
||||
const float maxCountRatioDif = 0.01f;
|
||||
|
||||
// Compare counts of validation and calculated keypoints.
|
||||
float countRatio = (float)validKeypoints.size() / (float)calcKeypoints.size();
|
||||
if( countRatio < 1 - maxCountRatioDif || countRatio > 1.f + maxCountRatioDif )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Bad keypoints count ratio (validCount = %d, calcCount = %d).\n",
|
||||
validKeypoints.size(), calcKeypoints.size() );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||||
return;
|
||||
}
|
||||
|
||||
int progress = 0, progressCount = (int)(validKeypoints.size() * calcKeypoints.size());
|
||||
int badPointCount = 0, commonPointCount = max((int)validKeypoints.size(), (int)calcKeypoints.size());
|
||||
for( size_t v = 0; v < validKeypoints.size(); v++ )
|
||||
{
|
||||
int nearestIdx = -1;
|
||||
float minDist = std::numeric_limits<float>::max();
|
||||
|
||||
for( size_t c = 0; c < calcKeypoints.size(); c++ )
|
||||
{
|
||||
progress = update_progress( progress, (int)(v*calcKeypoints.size() + c), progressCount, 0 );
|
||||
float curDist = (float)cv::norm( calcKeypoints[c].pt - validKeypoints[v].pt );
|
||||
if( curDist < minDist )
|
||||
{
|
||||
minDist = curDist;
|
||||
nearestIdx = (int)c;
|
||||
}
|
||||
}
|
||||
|
||||
assert( minDist >= 0 );
|
||||
if( !isSimilarKeypoints( validKeypoints[v], calcKeypoints[nearestIdx] ) )
|
||||
badPointCount++;
|
||||
}
|
||||
ts->printf( cvtest::TS::LOG, "badPointCount = %d; validPointCount = %d; calcPointCount = %d\n",
|
||||
badPointCount, validKeypoints.size(), calcKeypoints.size() );
|
||||
if( badPointCount > 0.9 * commonPointCount )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, " - Bad accuracy!\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
return;
|
||||
}
|
||||
ts->printf( cvtest::TS::LOG, " - OK\n" );
|
||||
}
|
||||
|
||||
void CV_FeatureDetectorTest::regressionTest()
|
||||
{
|
||||
assert( !fdetector.empty() );
|
||||
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
|
||||
string resFilename = string(ts->get_data_path()) + DETECTOR_DIR + "/" + string(name) + ".xml.gz";
|
||||
|
||||
// Read the test image.
|
||||
Mat image = imread( imgFilename );
|
||||
if( image.empty() )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Image %s can not be read.\n", imgFilename.c_str() );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
|
||||
FileStorage fs( resFilename, FileStorage::READ );
|
||||
|
||||
// Compute keypoints.
|
||||
vector<KeyPoint> calcKeypoints;
|
||||
fdetector->detect( image, calcKeypoints );
|
||||
|
||||
if( fs.isOpened() ) // Compare computed and valid keypoints.
|
||||
{
|
||||
// TODO compare saved feature detector params with current ones
|
||||
|
||||
// Read validation keypoints set.
|
||||
vector<KeyPoint> validKeypoints;
|
||||
read( fs["keypoints"], validKeypoints );
|
||||
if( validKeypoints.empty() )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Keypoints can not be read.\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
|
||||
compareKeypointSets( validKeypoints, calcKeypoints );
|
||||
}
|
||||
else // Write detector parameters and computed keypoints as validation data.
|
||||
{
|
||||
fs.open( resFilename, FileStorage::WRITE );
|
||||
if( !fs.isOpened() )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "File %s can not be opened to write.\n", resFilename.c_str() );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
else
|
||||
{
|
||||
fs << "detector_params" << "{";
|
||||
fdetector->write( fs );
|
||||
fs << "}";
|
||||
|
||||
write( fs, "keypoints", calcKeypoints );
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void CV_FeatureDetectorTest::run( int /*start_from*/ )
|
||||
{
|
||||
if( !fdetector )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Feature detector is empty.\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
|
||||
emptyDataTest();
|
||||
regressionTest();
|
||||
|
||||
ts->set_failed_test_info( cvtest::TS::OK );
|
||||
}
|
||||
|
||||
}} // namespace
|
Loading…
Reference in New Issue
Block a user