mirror of https://github.com/opencv/opencv.git
Merge pull request #12464 from alalek:fix_contrib_1754
Alexander Alekhin
6 years ago
commit
40b1dc12de
8 changed files with 918 additions and 922 deletions
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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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@ -0,0 +1,298 @@ |
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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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namespace opencv_test { 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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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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} |
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template<class Distance> |
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class CV_DescriptorExtractorTest : public cvtest::BaseTest |
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{ |
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public: |
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typedef typename Distance::ValueType ValueType; |
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typedef typename Distance::ResultType DistanceType; |
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CV_DescriptorExtractorTest( const string _name, DistanceType _maxDist, const Ptr<DescriptorExtractor>& _dextractor, |
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Distance d = Distance(), Ptr<FeatureDetector> _detector = Ptr<FeatureDetector>()): |
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name(_name), maxDist(_maxDist), dextractor(_dextractor), distance(d) , detector(_detector) {} |
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~CV_DescriptorExtractorTest() |
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{ |
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} |
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protected: |
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virtual void createDescriptorExtractor() {} |
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void compareDescriptors( const Mat& validDescriptors, const Mat& calcDescriptors ) |
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{ |
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if( validDescriptors.size != calcDescriptors.size || validDescriptors.type() != calcDescriptors.type() ) |
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{ |
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ts->printf(cvtest::TS::LOG, "Valid and computed descriptors matrices must have the same size and type.\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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return; |
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} |
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CV_Assert( DataType<ValueType>::type == validDescriptors.type() ); |
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int dimension = validDescriptors.cols; |
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DistanceType curMaxDist = 0; |
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size_t exact_count = 0, failed_count = 0; |
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for( int y = 0; y < validDescriptors.rows; y++ ) |
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{ |
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DistanceType dist = distance( validDescriptors.ptr<ValueType>(y), calcDescriptors.ptr<ValueType>(y), dimension ); |
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if (dist == 0) |
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exact_count++; |
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if( dist > curMaxDist ) |
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{ |
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if (dist > maxDist) |
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failed_count++; |
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curMaxDist = dist; |
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} |
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#if 0 |
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if (dist > 0) |
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{ |
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std::cout << "i=" << y << " fail_count=" << failed_count << " dist=" << dist << std::endl; |
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std::cout << "valid: " << validDescriptors.row(y) << std::endl; |
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std::cout << " calc: " << calcDescriptors.row(y) << std::endl; |
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} |
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#endif |
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} |
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float exact_percents = (100 * (float)exact_count / validDescriptors.rows); |
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float failed_percents = (100 * (float)failed_count / validDescriptors.rows); |
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std::stringstream ss; |
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ss << "Exact count (dist == 0): " << exact_count << " (" << (int)exact_percents << "%)" << std::endl |
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<< "Failed count (dist > " << maxDist << "): " << failed_count << " (" << (int)failed_percents << "%)" << std::endl |
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<< "Max distance between valid and computed descriptors (" << validDescriptors.size() << "): " << curMaxDist; |
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EXPECT_LE(failed_percents, 20.0f); |
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std::cout << ss.str() << std::endl; |
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} |
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void emptyDataTest() |
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{ |
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assert( dextractor ); |
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// One image.
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Mat image; |
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vector<KeyPoint> keypoints; |
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Mat descriptors; |
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try |
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{ |
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dextractor->compute( image, keypoints, descriptors ); |
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} |
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catch(...) |
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{ |
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ts->printf( cvtest::TS::LOG, "compute() on empty image and empty keypoints must not generate exception (1).\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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} |
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RNG rng; |
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image = cvtest::randomMat(rng, Size(50, 50), CV_8UC3, 0, 255, false); |
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try |
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{ |
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dextractor->compute( image, keypoints, descriptors ); |
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} |
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catch(...) |
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{ |
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ts->printf( cvtest::TS::LOG, "compute() on nonempty image and empty keypoints must not generate exception (1).\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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} |
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// Several images.
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vector<Mat> images; |
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vector<vector<KeyPoint> > keypointsCollection; |
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vector<Mat> descriptorsCollection; |
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try |
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{ |
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dextractor->compute( images, keypointsCollection, descriptorsCollection ); |
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} |
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catch(...) |
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{ |
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ts->printf( cvtest::TS::LOG, "compute() on empty images and empty keypoints collection must not generate exception (2).\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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} |
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} |
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void regressionTest() |
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{ |
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assert( dextractor ); |
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// Read the test image.
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string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME; |
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Mat img = imread( imgFilename ); |
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if( img.empty() ) |
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{ |
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ts->printf( cvtest::TS::LOG, "Image %s can not be read.\n", imgFilename.c_str() ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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return; |
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} |
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const std::string keypoints_filename = string(ts->get_data_path()) + |
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(detector.empty() |
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? (FEATURES2D_DIR + "/" + std::string("keypoints.xml.gz")) |
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: (DESCRIPTOR_DIR + "/" + name + "_keypoints.xml.gz")); |
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FileStorage fs(keypoints_filename, FileStorage::READ); |
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vector<KeyPoint> keypoints; |
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EXPECT_TRUE(fs.isOpened()) << "Keypoint testdata is missing. Re-computing and re-writing keypoints testdata..."; |
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if (!fs.isOpened()) |
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{ |
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fs.open(keypoints_filename, FileStorage::WRITE); |
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ASSERT_TRUE(fs.isOpened()) << "File for writing keypoints can not be opened."; |
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if (detector.empty()) |
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{ |
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Ptr<ORB> fd = ORB::create(); |
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fd->detect(img, keypoints); |
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} |
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else |
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{ |
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detector->detect(img, keypoints); |
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} |
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write(fs, "keypoints", keypoints); |
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fs.release(); |
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} |
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else |
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{ |
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read(fs.getFirstTopLevelNode(), keypoints); |
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fs.release(); |
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} |
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if(!detector.empty()) |
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{ |
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vector<KeyPoint> calcKeypoints; |
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detector->detect(img, calcKeypoints); |
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// TODO validate received keypoints
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int diff = abs((int)calcKeypoints.size() - (int)keypoints.size()); |
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if (diff > 0) |
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{ |
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std::cout << "Keypoints difference: " << diff << std::endl; |
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EXPECT_LE(diff, (int)(keypoints.size() * 0.03f)); |
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} |
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} |
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ASSERT_FALSE(keypoints.empty()); |
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{ |
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Mat calcDescriptors; |
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double t = (double)getTickCount(); |
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dextractor->compute(img, keypoints, calcDescriptors); |
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t = getTickCount() - t; |
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ts->printf(cvtest::TS::LOG, "\nAverage time of computing one descriptor = %g ms.\n", t/((double)getTickFrequency()*1000.)/calcDescriptors.rows); |
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if (calcDescriptors.rows != (int)keypoints.size()) |
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{ |
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ts->printf( cvtest::TS::LOG, "Count of computed descriptors and keypoints count must be equal.\n" ); |
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ts->printf( cvtest::TS::LOG, "Count of keypoints is %d.\n", (int)keypoints.size() ); |
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ts->printf( cvtest::TS::LOG, "Count of computed descriptors is %d.\n", calcDescriptors.rows ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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return; |
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} |
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if (calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType()) |
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{ |
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ts->printf( cvtest::TS::LOG, "Incorrect descriptor size or descriptor type.\n" ); |
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ts->printf( cvtest::TS::LOG, "Expected size is %d.\n", dextractor->descriptorSize() ); |
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ts->printf( cvtest::TS::LOG, "Calculated size is %d.\n", calcDescriptors.cols ); |
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ts->printf( cvtest::TS::LOG, "Expected type is %d.\n", dextractor->descriptorType() ); |
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ts->printf( cvtest::TS::LOG, "Calculated type is %d.\n", calcDescriptors.type() ); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); |
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return; |
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} |
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// TODO read and write descriptor extractor parameters and check them
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Mat validDescriptors = readDescriptors(); |
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EXPECT_FALSE(validDescriptors.empty()) << "Descriptors testdata is missing. Re-writing descriptors testdata..."; |
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if (!validDescriptors.empty()) |
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{ |
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compareDescriptors(validDescriptors, calcDescriptors); |
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} |
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else |
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{ |
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ASSERT_TRUE(writeDescriptors(calcDescriptors)) << "Descriptors can not be written."; |
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} |
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} |
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} |
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void run(int) |
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{ |
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createDescriptorExtractor(); |
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if( !dextractor ) |
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{ |
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ts->printf(cvtest::TS::LOG, "Descriptor extractor is empty.\n"); |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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return; |
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} |
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emptyDataTest(); |
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regressionTest(); |
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ts->set_failed_test_info( cvtest::TS::OK ); |
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} |
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virtual Mat readDescriptors() |
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{ |
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Mat res = readMatFromBin( string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) ); |
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return res; |
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} |
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|
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virtual bool writeDescriptors( Mat& descs ) |
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{ |
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writeMatInBin( descs, string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) ); |
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return true; |
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} |
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|
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string name; |
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const DistanceType maxDist; |
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Ptr<DescriptorExtractor> dextractor; |
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Distance distance; |
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Ptr<FeatureDetector> detector; |
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private: |
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CV_DescriptorExtractorTest& operator=(const CV_DescriptorExtractorTest&) { return *this; } |
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}; |
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}} // namespace
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@ -0,0 +1,227 @@ |
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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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|
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#include "test_invariance_utils.hpp" |
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|
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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>, float, float> String_FeatureDetector_Float_Float_t; |
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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(); |
||||
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
|
||||
@ -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