/*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. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., 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 the copyright holders 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 using namespace cvtest; ///////////////////////////////////////////////////////////////////////////////////////////////// // FAST namespace { IMPLEMENT_PARAM_CLASS(FAST_Threshold, int) IMPLEMENT_PARAM_CLASS(FAST_NonmaxSuppression, bool) } PARAM_TEST_CASE(FAST, cv::cuda::DeviceInfo, FAST_Threshold, FAST_NonmaxSuppression) { cv::cuda::DeviceInfo devInfo; int threshold; bool nonmaxSuppression; virtual void SetUp() { devInfo = GET_PARAM(0); threshold = GET_PARAM(1); nonmaxSuppression = GET_PARAM(2); cv::cuda::setDevice(devInfo.deviceID()); } }; CUDA_TEST_P(FAST, Accuracy) { cv::Mat image = readImage("features2d/aloe.png", cv::IMREAD_GRAYSCALE); ASSERT_FALSE(image.empty()); cv::Ptr<cv::cuda::FastFeatureDetector> fast = cv::cuda::FastFeatureDetector::create(threshold, nonmaxSuppression); if (!supportFeature(devInfo, cv::cuda::GLOBAL_ATOMICS)) { try { std::vector<cv::KeyPoint> keypoints; fast->detect(loadMat(image), keypoints); } catch (const cv::Exception& e) { ASSERT_EQ(cv::Error::StsNotImplemented, e.code); } } else { std::vector<cv::KeyPoint> keypoints; fast->detect(loadMat(image), keypoints); std::vector<cv::KeyPoint> keypoints_gold; cv::FAST(image, keypoints_gold, threshold, nonmaxSuppression); ASSERT_KEYPOINTS_EQ(keypoints_gold, keypoints); } } INSTANTIATE_TEST_CASE_P(CUDA_Features2D, FAST, testing::Combine( ALL_DEVICES, testing::Values(FAST_Threshold(25), FAST_Threshold(50)), testing::Values(FAST_NonmaxSuppression(false), FAST_NonmaxSuppression(true)))); ///////////////////////////////////////////////////////////////////////////////////////////////// // ORB namespace { IMPLEMENT_PARAM_CLASS(ORB_FeaturesCount, int) IMPLEMENT_PARAM_CLASS(ORB_ScaleFactor, float) IMPLEMENT_PARAM_CLASS(ORB_LevelsCount, int) IMPLEMENT_PARAM_CLASS(ORB_EdgeThreshold, int) IMPLEMENT_PARAM_CLASS(ORB_firstLevel, int) IMPLEMENT_PARAM_CLASS(ORB_WTA_K, int) IMPLEMENT_PARAM_CLASS(ORB_PatchSize, int) IMPLEMENT_PARAM_CLASS(ORB_BlurForDescriptor, bool) } CV_ENUM(ORB_ScoreType, cv::ORB::HARRIS_SCORE, cv::ORB::FAST_SCORE) PARAM_TEST_CASE(ORB, cv::cuda::DeviceInfo, ORB_FeaturesCount, ORB_ScaleFactor, ORB_LevelsCount, ORB_EdgeThreshold, ORB_firstLevel, ORB_WTA_K, ORB_ScoreType, ORB_PatchSize, ORB_BlurForDescriptor) { cv::cuda::DeviceInfo devInfo; int nFeatures; float scaleFactor; int nLevels; int edgeThreshold; int firstLevel; int WTA_K; int scoreType; int patchSize; bool blurForDescriptor; virtual void SetUp() { devInfo = GET_PARAM(0); nFeatures = GET_PARAM(1); scaleFactor = GET_PARAM(2); nLevels = GET_PARAM(3); edgeThreshold = GET_PARAM(4); firstLevel = GET_PARAM(5); WTA_K = GET_PARAM(6); scoreType = GET_PARAM(7); patchSize = GET_PARAM(8); blurForDescriptor = GET_PARAM(9); cv::cuda::setDevice(devInfo.deviceID()); } }; CUDA_TEST_P(ORB, Accuracy) { cv::Mat image = readImage("features2d/aloe.png", cv::IMREAD_GRAYSCALE); ASSERT_FALSE(image.empty()); cv::Mat mask(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::Ptr<cv::cuda::ORB> orb = cv::cuda::ORB::create(nFeatures, scaleFactor, nLevels, edgeThreshold, firstLevel, WTA_K, scoreType, patchSize, 20, blurForDescriptor); if (!supportFeature(devInfo, cv::cuda::GLOBAL_ATOMICS)) { try { std::vector<cv::KeyPoint> keypoints; cv::cuda::GpuMat descriptors; orb->detectAndComputeAsync(loadMat(image), loadMat(mask), keypoints, descriptors); } catch (const cv::Exception& e) { ASSERT_EQ(cv::Error::StsNotImplemented, e.code); } } else { std::vector<cv::KeyPoint> keypoints; cv::cuda::GpuMat descriptors; orb->detectAndCompute(loadMat(image), loadMat(mask), keypoints, descriptors); cv::Ptr<cv::ORB> orb_gold = cv::ORB::create(nFeatures, scaleFactor, nLevels, edgeThreshold, firstLevel, WTA_K, scoreType, patchSize); std::vector<cv::KeyPoint> keypoints_gold; cv::Mat descriptors_gold; orb_gold->detectAndCompute(image, mask, keypoints_gold, descriptors_gold); cv::BFMatcher matcher(cv::NORM_HAMMING); std::vector<cv::DMatch> matches; matcher.match(descriptors_gold, cv::Mat(descriptors), matches); int matchedCount = getMatchedPointsCount(keypoints_gold, keypoints, matches); double matchedRatio = static_cast<double>(matchedCount) / keypoints.size(); EXPECT_GT(matchedRatio, 0.35); } } INSTANTIATE_TEST_CASE_P(CUDA_Features2D, ORB, testing::Combine( ALL_DEVICES, testing::Values(ORB_FeaturesCount(1000)), testing::Values(ORB_ScaleFactor(1.2f)), testing::Values(ORB_LevelsCount(4), ORB_LevelsCount(8)), testing::Values(ORB_EdgeThreshold(31)), testing::Values(ORB_firstLevel(0)), testing::Values(ORB_WTA_K(2), ORB_WTA_K(3), ORB_WTA_K(4)), testing::Values(ORB_ScoreType(cv::ORB::HARRIS_SCORE)), testing::Values(ORB_PatchSize(31), ORB_PatchSize(29)), testing::Values(ORB_BlurForDescriptor(false), ORB_BlurForDescriptor(true)))); ///////////////////////////////////////////////////////////////////////////////////////////////// // BruteForceMatcher namespace { IMPLEMENT_PARAM_CLASS(DescriptorSize, int) IMPLEMENT_PARAM_CLASS(UseMask, bool) } PARAM_TEST_CASE(BruteForceMatcher, cv::cuda::DeviceInfo, NormCode, DescriptorSize, UseMask) { cv::cuda::DeviceInfo devInfo; int normCode; int dim; bool useMask; int queryDescCount; int countFactor; cv::Mat query, train; virtual void SetUp() { devInfo = GET_PARAM(0); normCode = GET_PARAM(1); dim = GET_PARAM(2); useMask = GET_PARAM(3); cv::cuda::setDevice(devInfo.deviceID()); queryDescCount = 300; // must be even number because we split train data in some cases in two countFactor = 4; // do not change it 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<float>(0, elem) += diff; } } queryBuf.convertTo(query, CV_32F); trainBuf.convertTo(train, CV_32F); } }; CUDA_TEST_P(BruteForceMatcher, Match_Single) { cv::Ptr<cv::cuda::DescriptorMatcher> matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normCode); cv::cuda::GpuMat mask; if (useMask) { mask.create(query.rows, train.rows, CV_8UC1); mask.setTo(cv::Scalar::all(1)); } std::vector<cv::DMatch> matches; matcher->match(loadMat(query), loadMat(train), matches, mask); ASSERT_EQ(static_cast<size_t>(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); } CUDA_TEST_P(BruteForceMatcher, Match_Collection) { cv::Ptr<cv::cuda::DescriptorMatcher> matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normCode); cv::cuda::GpuMat d_train(train); // make add() twice to test such case matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2))); matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows))); // prepare masks (make first nearest match illegal) std::vector<cv::cuda::GpuMat> masks(2); for (int mi = 0; mi < 2; mi++) { masks[mi] = cv::cuda::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)); } std::vector<cv::DMatch> matches; if (useMask) matcher->match(cv::cuda::GpuMat(query), matches, masks); else matcher->match(cv::cuda::GpuMat(query), matches); ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size()); int badCount = 0; int shift = useMask ? 1 : 0; for (size_t i = 0; i < matches.size(); i++) { cv::DMatch match = matches[i]; if ((int)i < queryDescCount / 2) { bool validQueryIdx = (match.queryIdx == (int)i); bool validTrainIdx = (match.trainIdx == (int)i * countFactor + shift); bool validImgIdx = (match.imgIdx == 0); if (!validQueryIdx || !validTrainIdx || !validImgIdx) badCount++; } else { bool validQueryIdx = (match.queryIdx == (int)i); bool validTrainIdx = (match.trainIdx == ((int)i - queryDescCount / 2) * countFactor + shift); bool validImgIdx = (match.imgIdx == 1); if (!validQueryIdx || !validTrainIdx || !validImgIdx) badCount++; } } ASSERT_EQ(0, badCount); } CUDA_TEST_P(BruteForceMatcher, KnnMatch_2_Single) { cv::Ptr<cv::cuda::DescriptorMatcher> matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normCode); const int knn = 2; cv::cuda::GpuMat mask; if (useMask) { mask.create(query.rows, train.rows, CV_8UC1); mask.setTo(cv::Scalar::all(1)); } std::vector< std::vector<cv::DMatch> > matches; matcher->knnMatch(loadMat(query), loadMat(train), matches, knn, mask); ASSERT_EQ(static_cast<size_t>(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); } CUDA_TEST_P(BruteForceMatcher, KnnMatch_3_Single) { cv::Ptr<cv::cuda::DescriptorMatcher> matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normCode); const int knn = 3; cv::cuda::GpuMat mask; if (useMask) { mask.create(query.rows, train.rows, CV_8UC1); mask.setTo(cv::Scalar::all(1)); } std::vector< std::vector<cv::DMatch> > matches; matcher->knnMatch(loadMat(query), loadMat(train), matches, knn, mask); ASSERT_EQ(static_cast<size_t>(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); } CUDA_TEST_P(BruteForceMatcher, KnnMatch_2_Collection) { cv::Ptr<cv::cuda::DescriptorMatcher> matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normCode); const int knn = 2; cv::cuda::GpuMat d_train(train); // make add() twice to test such case matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2))); matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows))); // prepare masks (make first nearest match illegal) std::vector<cv::cuda::GpuMat> masks(2); for (int mi = 0; mi < 2; mi++ ) { masks[mi] = cv::cuda::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)); } std::vector< std::vector<cv::DMatch> > matches; if (useMask) matcher->knnMatch(cv::cuda::GpuMat(query), matches, knn, masks); else matcher->knnMatch(cv::cuda::GpuMat(query), matches, knn); ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size()); int badCount = 0; int shift = useMask ? 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 ((int)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); } CUDA_TEST_P(BruteForceMatcher, KnnMatch_3_Collection) { cv::Ptr<cv::cuda::DescriptorMatcher> matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normCode); const int knn = 3; cv::cuda::GpuMat d_train(train); // make add() twice to test such case matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2))); matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows))); // prepare masks (make first nearest match illegal) std::vector<cv::cuda::GpuMat> masks(2); for (int mi = 0; mi < 2; mi++ ) { masks[mi] = cv::cuda::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)); } std::vector< std::vector<cv::DMatch> > matches; if (useMask) matcher->knnMatch(cv::cuda::GpuMat(query), matches, knn, masks); else matcher->knnMatch(cv::cuda::GpuMat(query), matches, knn); ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size()); int badCount = 0; int shift = useMask ? 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 ((int)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); } CUDA_TEST_P(BruteForceMatcher, RadiusMatch_Single) { cv::Ptr<cv::cuda::DescriptorMatcher> matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normCode); const float radius = 1.f / countFactor; if (!supportFeature(devInfo, cv::cuda::GLOBAL_ATOMICS)) { try { std::vector< std::vector<cv::DMatch> > matches; matcher->radiusMatch(loadMat(query), loadMat(train), matches, radius); } catch (const cv::Exception& e) { ASSERT_EQ(cv::Error::StsNotImplemented, e.code); } } else { cv::cuda::GpuMat mask; if (useMask) { mask.create(query.rows, train.rows, CV_8UC1); mask.setTo(cv::Scalar::all(1)); } std::vector< std::vector<cv::DMatch> > matches; matcher->radiusMatch(loadMat(query), loadMat(train), matches, radius, mask); ASSERT_EQ(static_cast<size_t>(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); } } CUDA_TEST_P(BruteForceMatcher, RadiusMatch_Collection) { cv::Ptr<cv::cuda::DescriptorMatcher> matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normCode); const int n = 3; const float radius = 1.f / countFactor * n; cv::cuda::GpuMat d_train(train); // make add() twice to test such case matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2))); matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows))); // prepare masks (make first nearest match illegal) std::vector<cv::cuda::GpuMat> masks(2); for (int mi = 0; mi < 2; mi++) { masks[mi] = cv::cuda::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)); } if (!supportFeature(devInfo, cv::cuda::GLOBAL_ATOMICS)) { try { std::vector< std::vector<cv::DMatch> > matches; matcher->radiusMatch(cv::cuda::GpuMat(query), matches, radius, masks); } catch (const cv::Exception& e) { ASSERT_EQ(cv::Error::StsNotImplemented, e.code); } } else { std::vector< std::vector<cv::DMatch> > matches; if (useMask) matcher->radiusMatch(cv::cuda::GpuMat(query), matches, radius, masks); else matcher->radiusMatch(cv::cuda::GpuMat(query), matches, radius); ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size()); int badCount = 0; int shift = useMask ? 1 : 0; int needMatchCount = useMask ? 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 ((int)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(CUDA_Features2D, BruteForceMatcher, testing::Combine( ALL_DEVICES, testing::Values(NormCode(cv::NORM_L1), NormCode(cv::NORM_L2)), testing::Values(DescriptorSize(57), DescriptorSize(64), DescriptorSize(83), DescriptorSize(128), DescriptorSize(179), DescriptorSize(256), DescriptorSize(304)), testing::Values(UseMask(false), UseMask(true)))); #endif // HAVE_CUDA