/*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*/ #include "test_precomp.hpp" #ifdef HAVE_CUDA ///////////////////////////////////////////////////////////////////////////////////////////////// // SURF struct SURF : testing::TestWithParam { cv::Mat image; cv::Mat mask; std::vector keypoints_gold; std::vector descriptors_gold; cv::gpu::DeviceInfo devInfo; virtual void SetUp() { devInfo = GetParam(); cv::gpu::setDevice(devInfo.deviceID()); image = readImage("features2d/aloe.png", CV_LOAD_IMAGE_GRAYSCALE); ASSERT_FALSE(image.empty()); mask = cv::Mat(image.size(), CV_8UC1, cv::Scalar::all(1)); mask(cv::Range(0, image.rows / 2), cv::Range(0, image.cols / 2)).setTo(cv::Scalar::all(0)); cv::SURF fdetector_gold; fdetector_gold.extended = false; fdetector_gold(image, mask, keypoints_gold, descriptors_gold); } bool isSimilarKeypoints(const cv::KeyPoint& p1, const cv::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 ); } }; TEST_P(SURF, EmptyDataTest) { PRINT_PARAM(devInfo); cv::gpu::SURF_GPU fdetector; cv::gpu::GpuMat image; std::vector keypoints; std::vector descriptors; ASSERT_NO_THROW( fdetector(image, cv::gpu::GpuMat(), keypoints, descriptors); ); EXPECT_TRUE(keypoints.empty()); EXPECT_TRUE(descriptors.empty()); } TEST_P(SURF, Accuracy) { PRINT_PARAM(devInfo); // Compute keypoints. std::vector keypoints; cv::Mat descriptors; ASSERT_NO_THROW( cv::gpu::GpuMat dev_descriptors; cv::gpu::SURF_GPU fdetector; fdetector.extended = false; fdetector(cv::gpu::GpuMat(image), cv::gpu::GpuMat(mask), keypoints, dev_descriptors); dev_descriptors.download(descriptors); ); cv::BruteForceMatcher< cv::L2 > matcher; std::vector matches; matcher.match(cv::Mat(static_cast(keypoints_gold.size()), 64, CV_32FC1, &descriptors_gold[0]), descriptors, matches); int validCount = 0; for (size_t i = 0; i < matches.size(); ++i) { const cv::DMatch& m = matches[i]; const cv::KeyPoint& p1 = keypoints_gold[m.queryIdx]; const cv::KeyPoint& p2 = keypoints[m.trainIdx]; 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); if (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) { ++validCount; } } double validRatio = (double)validCount / matches.size(); EXPECT_GT(validRatio, 0.5); } INSTANTIATE_TEST_CASE_P(Features2D, SURF, testing::ValuesIn(devices(cv::gpu::GLOBAL_ATOMICS))); ///////////////////////////////////////////////////////////////////////////////////////////////// // BruteForceMatcher static const char* dists[] = {"L1Dist", "L2Dist", "HammingDist"}; struct BruteForceMatcher : testing::TestWithParam< std::tr1::tuple > { static const int queryDescCount = 300; // must be even number because we split train data in some cases in two static const int countFactor = 4; // do not change it cv::gpu::DeviceInfo devInfo; cv::gpu::BruteForceMatcher_GPU_base::DistType distType; int dim; cv::Mat query, train; virtual void SetUp() { devInfo = std::tr1::get<0>(GetParam()); distType = std::tr1::get<1>(GetParam()); dim = std::tr1::get<2>(GetParam()); cv::gpu::setDevice(devInfo.deviceID()); cv::RNG& rng = cvtest::TS::ptr()->get_rng(); cv::Mat queryBuf, trainBuf; // Generate query descriptors randomly. // Descriptor vector elements are integer values. queryBuf.create(queryDescCount, dim, CV_32SC1); rng.fill(queryBuf, cv::RNG::UNIFORM, cv::Scalar::all(0), cv::Scalar::all(3)); queryBuf.convertTo(queryBuf, CV_32FC1); // Generate train decriptors as follows: // copy each query descriptor to train set countFactor times // and perturb some one element of the copied descriptors in // in ascending order. General boundaries of the perturbation // are (0.f, 1.f). trainBuf.create(queryDescCount * countFactor, dim, CV_32FC1); float step = 1.f / countFactor; for (int qIdx = 0; qIdx < queryDescCount; qIdx++) { cv::Mat queryDescriptor = queryBuf.row(qIdx); for (int c = 0; c < countFactor; c++) { int tIdx = qIdx * countFactor + c; cv::Mat trainDescriptor = trainBuf.row(tIdx); queryDescriptor.copyTo(trainDescriptor); int elem = rng(dim); float diff = rng.uniform(step * c, step * (c + 1)); trainDescriptor.at(0, elem) += diff; } } queryBuf.convertTo(query, CV_32F); trainBuf.convertTo(train, CV_32F); } }; const int BruteForceMatcher::queryDescCount; const int BruteForceMatcher::countFactor; TEST_P(BruteForceMatcher, Match) { const char* distStr = dists[distType]; PRINT_PARAM(devInfo); PRINT_PARAM(distStr); PRINT_PARAM(dim); std::vector matches; ASSERT_NO_THROW( cv::gpu::BruteForceMatcher_GPU_base matcher(distType); matcher.match(cv::gpu::GpuMat(query), cv::gpu::GpuMat(train), matches); ); ASSERT_EQ(queryDescCount, matches.size()); int badCount = 0; for (size_t i = 0; i < matches.size(); i++) { cv::DMatch match = matches[i]; if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0)) badCount++; } ASSERT_EQ(0, badCount); } TEST_P(BruteForceMatcher, MatchAdd) { const char* distStr = dists[distType]; PRINT_PARAM(devInfo); PRINT_PARAM(distStr); PRINT_PARAM(dim); std::vector matches; bool isMaskSupported; ASSERT_NO_THROW( cv::gpu::BruteForceMatcher_GPU_base matcher(distType); cv::gpu::GpuMat d_train(train); // make add() twice to test such case matcher.add(std::vector(1, d_train.rowRange(0, train.rows/2))); matcher.add(std::vector(1, d_train.rowRange(train.rows/2, train.rows))); // prepare masks (make first nearest match illegal) std::vector masks(2); for (int mi = 0; mi < 2; mi++) { masks[mi] = cv::gpu::GpuMat(query.rows, train.rows/2, CV_8UC1, cv::Scalar::all(1)); for (int di = 0; di < queryDescCount/2; di++) masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0)); } matcher.match(cv::gpu::GpuMat(query), matches, masks); isMaskSupported = matcher.isMaskSupported(); ); ASSERT_EQ(queryDescCount, matches.size()); int badCount = 0; for (size_t i = 0; i < matches.size(); i++) { cv::DMatch match = matches[i]; int shift = isMaskSupported ? 1 : 0; { if (i < queryDescCount / 2) { if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + shift) || (match.imgIdx != 0)) badCount++; } else { if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + shift) || (match.imgIdx != 1)) badCount++; } } } ASSERT_EQ(0, badCount); } TEST_P(BruteForceMatcher, KnnMatch2) { const char* distStr = dists[distType]; PRINT_PARAM(devInfo); PRINT_PARAM(distStr); PRINT_PARAM(dim); const int knn = 2; std::vector< std::vector > matches; ASSERT_NO_THROW( cv::gpu::BruteForceMatcher_GPU_base matcher(distType); matcher.knnMatch(cv::gpu::GpuMat(query), cv::gpu::GpuMat(train), matches, knn); ); ASSERT_EQ(queryDescCount, matches.size()); int badCount = 0; for (size_t i = 0; i < matches.size(); i++) { if ((int)matches[i].size() != knn) badCount++; else { int localBadCount = 0; for (int k = 0; k < knn; k++) { cv::DMatch match = matches[i][k]; if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k) || (match.imgIdx != 0)) localBadCount++; } badCount += localBadCount > 0 ? 1 : 0; } } ASSERT_EQ(0, badCount); } TEST_P(BruteForceMatcher, KnnMatch3) { const char* distStr = dists[distType]; PRINT_PARAM(devInfo); PRINT_PARAM(distStr); PRINT_PARAM(dim); const int knn = 3; std::vector< std::vector > matches; ASSERT_NO_THROW( cv::gpu::BruteForceMatcher_GPU_base matcher(distType); matcher.knnMatch(cv::gpu::GpuMat(query), cv::gpu::GpuMat(train), matches, knn); ); ASSERT_EQ(queryDescCount, matches.size()); int badCount = 0; for (size_t i = 0; i < matches.size(); i++) { if ((int)matches[i].size() != knn) badCount++; else { int localBadCount = 0; for (int k = 0; k < knn; k++) { cv::DMatch match = matches[i][k]; if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k) || (match.imgIdx != 0)) localBadCount++; } badCount += localBadCount > 0 ? 1 : 0; } } ASSERT_EQ(0, badCount); } TEST_P(BruteForceMatcher, KnnMatchAdd2) { const char* distStr = dists[distType]; PRINT_PARAM(devInfo); PRINT_PARAM(distStr); PRINT_PARAM(dim); const int knn = 2; std::vector< std::vector > matches; bool isMaskSupported; ASSERT_NO_THROW( cv::gpu::BruteForceMatcher_GPU_base matcher(distType); cv::gpu::GpuMat d_train(train); // make add() twice to test such case matcher.add(std::vector(1, d_train.rowRange(0, train.rows / 2))); matcher.add(std::vector(1, d_train.rowRange(train.rows / 2, train.rows))); // prepare masks (make first nearest match illegal) std::vector masks(2); for (int mi = 0; mi < 2; mi++ ) { masks[mi] = cv::gpu::GpuMat(query.rows, train.rows / 2, CV_8UC1, cv::Scalar::all(1)); for (int di = 0; di < queryDescCount / 2; di++) masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0)); } matcher.knnMatch(cv::gpu::GpuMat(query), matches, knn, masks); isMaskSupported = matcher.isMaskSupported(); ); ASSERT_EQ(queryDescCount, matches.size()); int badCount = 0; int shift = isMaskSupported ? 1 : 0; for (size_t i = 0; i < matches.size(); i++) { if ((int)matches[i].size() != knn) badCount++; else { int localBadCount = 0; for (int k = 0; k < knn; k++) { cv::DMatch match = matches[i][k]; { if (i < queryDescCount / 2) { if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) ) localBadCount++; } else { if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) ) localBadCount++; } } } badCount += localBadCount > 0 ? 1 : 0; } } ASSERT_EQ(0, badCount); } TEST_P(BruteForceMatcher, KnnMatchAdd3) { const char* distStr = dists[distType]; PRINT_PARAM(devInfo); PRINT_PARAM(distStr); PRINT_PARAM(dim); const int knn = 3; std::vector< std::vector > matches; bool isMaskSupported; ASSERT_NO_THROW( cv::gpu::BruteForceMatcher_GPU_base matcher(distType); cv::gpu::GpuMat d_train(train); // make add() twice to test such case matcher.add(std::vector(1, d_train.rowRange(0, train.rows / 2))); matcher.add(std::vector(1, d_train.rowRange(train.rows / 2, train.rows))); // prepare masks (make first nearest match illegal) std::vector masks(2); for (int mi = 0; mi < 2; mi++ ) { masks[mi] = cv::gpu::GpuMat(query.rows, train.rows / 2, CV_8UC1, cv::Scalar::all(1)); for (int di = 0; di < queryDescCount / 2; di++) masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0)); } matcher.knnMatch(cv::gpu::GpuMat(query), matches, knn, masks); isMaskSupported = matcher.isMaskSupported(); ); ASSERT_EQ(queryDescCount, matches.size()); int badCount = 0; int shift = isMaskSupported ? 1 : 0; for (size_t i = 0; i < matches.size(); i++) { if ((int)matches[i].size() != knn) badCount++; else { int localBadCount = 0; for (int k = 0; k < knn; k++) { cv::DMatch match = matches[i][k]; { if (i < queryDescCount / 2) { if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) ) localBadCount++; } else { if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) ) localBadCount++; } } } badCount += localBadCount > 0 ? 1 : 0; } } ASSERT_EQ(0, badCount); } TEST_P(BruteForceMatcher, RadiusMatch) { if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS)) return; const char* distStr = dists[distType]; PRINT_PARAM(devInfo); PRINT_PARAM(distStr); PRINT_PARAM(dim); const float radius = 1.f / countFactor; std::vector< std::vector > matches; ASSERT_NO_THROW( cv::gpu::BruteForceMatcher_GPU_base matcher(distType); matcher.radiusMatch(cv::gpu::GpuMat(query), cv::gpu::GpuMat(train), matches, radius); ); ASSERT_EQ(queryDescCount, matches.size()); int badCount = 0; for (size_t i = 0; i < matches.size(); i++) { if ((int)matches[i].size() != 1) badCount++; else { cv::DMatch match = matches[i][0]; if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor) || (match.imgIdx != 0)) badCount++; } } ASSERT_EQ(0, badCount); } TEST_P(BruteForceMatcher, RadiusMatchAdd) { if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS)) return; const char* distStr = dists[distType]; PRINT_PARAM(devInfo); PRINT_PARAM(distStr); PRINT_PARAM(dim); int n = 3; const float radius = 1.f / countFactor * n; std::vector< std::vector > matches; bool isMaskSupported; ASSERT_NO_THROW( cv::gpu::BruteForceMatcher_GPU_base matcher(distType); cv::gpu::GpuMat d_train(train); // make add() twice to test such case matcher.add(std::vector(1, d_train.rowRange(0, train.rows / 2))); matcher.add(std::vector(1, d_train.rowRange(train.rows / 2, train.rows))); // prepare masks (make first nearest match illegal) std::vector masks(2); for (int mi = 0; mi < 2; mi++) { masks[mi] = cv::gpu::GpuMat(query.rows, train.rows / 2, CV_8UC1, cv::Scalar::all(1)); for (int di = 0; di < queryDescCount / 2; di++) masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0)); } matcher.radiusMatch(cv::gpu::GpuMat(query), matches, radius, masks); isMaskSupported = matcher.isMaskSupported(); ); ASSERT_EQ(queryDescCount, matches.size()); int badCount = 0; int shift = isMaskSupported ? 1 : 0; int needMatchCount = isMaskSupported ? n-1 : n; for (size_t i = 0; i < matches.size(); i++) { if ((int)matches[i].size() != needMatchCount) badCount++; else { int localBadCount = 0; for (int k = 0; k < needMatchCount; k++) { cv::DMatch match = matches[i][k]; { if (i < queryDescCount / 2) { if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) ) localBadCount++; } else { if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) ) localBadCount++; } } } badCount += localBadCount > 0 ? 1 : 0; } } ASSERT_EQ(0, badCount); } INSTANTIATE_TEST_CASE_P(Features2D, BruteForceMatcher, testing::Combine( testing::ValuesIn(devices()), testing::Values(cv::gpu::BruteForceMatcher_GPU_base::L1Dist, cv::gpu::BruteForceMatcher_GPU_base::L2Dist), testing::Values(57, 64, 83, 128, 179, 256, 304))); #endif // HAVE_CUDA