732 lines
23 KiB
C++
732 lines
23 KiB
C++
/*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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#include "test_precomp.hpp"
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#ifdef HAVE_CUDA
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using namespace cvtest;
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using namespace testing;
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int getValidMatchesCount(const std::vector<cv::KeyPoint>& keypoints1, const std::vector<cv::KeyPoint>& keypoints2, const std::vector<cv::DMatch>& matches)
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{
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int validCount = 0;
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for (size_t i = 0; i < matches.size(); ++i)
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{
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const cv::DMatch& m = matches[i];
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const cv::KeyPoint& p1 = keypoints1[m.queryIdx];
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const cv::KeyPoint& p2 = keypoints2[m.trainIdx];
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const float maxPtDif = 1.f;
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const float maxSizeDif = 1.f;
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const float maxAngleDif = 2.f;
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const float maxResponseDif = 0.1f;
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float dist = (float) cv::norm(p1.pt - p2.pt);
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if (dist < maxPtDif &&
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fabs(p1.size - p2.size) < maxSizeDif &&
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abs(p1.angle - p2.angle) < maxAngleDif &&
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abs(p1.response - p2.response) < maxResponseDif &&
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p1.octave == p2.octave &&
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p1.class_id == p2.class_id)
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{
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++validCount;
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}
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}
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return validCount;
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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// SURF
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struct SURF : TestWithParam<cv::gpu::DeviceInfo>
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{
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cv::gpu::DeviceInfo devInfo;
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cv::Mat image;
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cv::Mat mask;
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std::vector<cv::KeyPoint> keypoints_gold;
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std::vector<float> descriptors_gold;
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virtual void SetUp()
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{
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devInfo = GetParam();
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cv::gpu::setDevice(devInfo.deviceID());
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image = readImage("features2d/aloe.png", CV_LOAD_IMAGE_GRAYSCALE);
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ASSERT_FALSE(image.empty());
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mask = cv::Mat(image.size(), CV_8UC1, cv::Scalar::all(1));
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mask(cv::Range(0, image.rows / 2), cv::Range(0, image.cols / 2)).setTo(cv::Scalar::all(0));
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cv::SURF fdetector_gold;
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fdetector_gold.extended = false;
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fdetector_gold(image, mask, keypoints_gold, descriptors_gold);
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}
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};
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TEST_P(SURF, EmptyDataTest)
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{
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cv::gpu::SURF_GPU fdetector;
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cv::gpu::GpuMat image;
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std::vector<cv::KeyPoint> keypoints;
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std::vector<float> descriptors;
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ASSERT_NO_THROW(
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fdetector(image, cv::gpu::GpuMat(), keypoints, descriptors);
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);
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EXPECT_TRUE(keypoints.empty());
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EXPECT_TRUE(descriptors.empty());
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}
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TEST_P(SURF, Accuracy)
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{
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std::vector<cv::KeyPoint> keypoints;
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cv::Mat descriptors;
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ASSERT_NO_THROW(
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cv::gpu::GpuMat dev_descriptors;
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cv::gpu::SURF_GPU fdetector; fdetector.extended = false;
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fdetector(loadMat(image), loadMat(mask), keypoints, dev_descriptors);
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dev_descriptors.download(descriptors);
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);
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cv::BruteForceMatcher< cv::L2<float> > matcher;
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std::vector<cv::DMatch> matches;
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matcher.match(cv::Mat(static_cast<int>(keypoints_gold.size()), 64, CV_32FC1, &descriptors_gold[0]), descriptors, matches);
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int validCount = getValidMatchesCount(keypoints_gold, keypoints, matches);
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double validRatio = (double) validCount / matches.size();
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EXPECT_GT(validRatio, 0.5);
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}
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INSTANTIATE_TEST_CASE_P(Features2D, SURF, DEVICES(cv::gpu::GLOBAL_ATOMICS));
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/////////////////////////////////////////////////////////////////////////////////////////////////
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// BruteForceMatcher
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PARAM_TEST_CASE(BruteForceMatcher, cv::gpu::DeviceInfo, DistType, int)
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{
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static const int queryDescCount = 300; // must be even number because we split train data in some cases in two
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static const int countFactor = 4; // do not change it
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cv::gpu::DeviceInfo devInfo;
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cv::gpu::BruteForceMatcher_GPU_base::DistType distType;
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int dim;
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cv::Mat query, train;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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distType = (cv::gpu::BruteForceMatcher_GPU_base::DistType)(int)GET_PARAM(1);
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dim = GET_PARAM(2);
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cv::gpu::setDevice(devInfo.deviceID());
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cv::RNG& rng = cvtest::TS::ptr()->get_rng();
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cv::Mat queryBuf, trainBuf;
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// Generate query descriptors randomly.
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// Descriptor vector elements are integer values.
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queryBuf.create(queryDescCount, dim, CV_32SC1);
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rng.fill(queryBuf, cv::RNG::UNIFORM, cv::Scalar::all(0), cv::Scalar::all(3));
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queryBuf.convertTo(queryBuf, CV_32FC1);
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// Generate train decriptors as follows:
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// copy each query descriptor to train set countFactor times
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// and perturb some one element of the copied descriptors in
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// in ascending order. General boundaries of the perturbation
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// are (0.f, 1.f).
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trainBuf.create(queryDescCount * countFactor, dim, CV_32FC1);
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float step = 1.f / countFactor;
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for (int qIdx = 0; qIdx < queryDescCount; qIdx++)
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{
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cv::Mat queryDescriptor = queryBuf.row(qIdx);
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for (int c = 0; c < countFactor; c++)
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{
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int tIdx = qIdx * countFactor + c;
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cv::Mat trainDescriptor = trainBuf.row(tIdx);
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queryDescriptor.copyTo(trainDescriptor);
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int elem = rng(dim);
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float diff = rng.uniform(step * c, step * (c + 1));
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trainDescriptor.at<float>(0, elem) += diff;
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}
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}
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queryBuf.convertTo(query, CV_32F);
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trainBuf.convertTo(train, CV_32F);
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}
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};
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TEST_P(BruteForceMatcher, Match)
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{
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std::vector<cv::DMatch> matches;
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ASSERT_NO_THROW(
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cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
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matcher.match(loadMat(query), loadMat(train), matches);
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);
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ASSERT_EQ(queryDescCount, matches.size());
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int badCount = 0;
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for (size_t i = 0; i < matches.size(); i++)
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{
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cv::DMatch match = matches[i];
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if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0))
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badCount++;
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}
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ASSERT_EQ(0, badCount);
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}
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TEST_P(BruteForceMatcher, MatchAdd)
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{
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std::vector<cv::DMatch> matches;
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bool isMaskSupported;
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ASSERT_NO_THROW(
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cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
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cv::gpu::GpuMat d_train(train);
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// make add() twice to test such case
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matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(0, train.rows/2)));
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matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(train.rows/2, train.rows)));
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// prepare masks (make first nearest match illegal)
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std::vector<cv::gpu::GpuMat> masks(2);
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for (int mi = 0; mi < 2; mi++)
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{
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masks[mi] = cv::gpu::GpuMat(query.rows, train.rows/2, CV_8UC1, cv::Scalar::all(1));
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for (int di = 0; di < queryDescCount/2; di++)
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masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0));
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}
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matcher.match(cv::gpu::GpuMat(query), matches, masks);
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isMaskSupported = matcher.isMaskSupported();
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);
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ASSERT_EQ(queryDescCount, matches.size());
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int badCount = 0;
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for (size_t i = 0; i < matches.size(); i++)
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{
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cv::DMatch match = matches[i];
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int shift = isMaskSupported ? 1 : 0;
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{
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if (i < queryDescCount / 2)
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{
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if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + shift) || (match.imgIdx != 0))
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badCount++;
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}
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else
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{
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if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + shift) || (match.imgIdx != 1))
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badCount++;
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}
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}
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}
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ASSERT_EQ(0, badCount);
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}
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TEST_P(BruteForceMatcher, KnnMatch2)
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{
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const int knn = 2;
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std::vector< std::vector<cv::DMatch> > matches;
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ASSERT_NO_THROW(
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cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
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matcher.knnMatch(loadMat(query), loadMat(train), matches, knn);
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);
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ASSERT_EQ(queryDescCount, matches.size());
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int badCount = 0;
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for (size_t i = 0; i < matches.size(); i++)
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{
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if ((int)matches[i].size() != knn)
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badCount++;
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else
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{
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int localBadCount = 0;
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for (int k = 0; k < knn; k++)
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{
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cv::DMatch match = matches[i][k];
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if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k) || (match.imgIdx != 0))
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localBadCount++;
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}
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badCount += localBadCount > 0 ? 1 : 0;
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}
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}
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ASSERT_EQ(0, badCount);
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}
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TEST_P(BruteForceMatcher, KnnMatch3)
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{
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const int knn = 3;
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std::vector< std::vector<cv::DMatch> > matches;
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ASSERT_NO_THROW(
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cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
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matcher.knnMatch(loadMat(query), loadMat(train), matches, knn);
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);
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ASSERT_EQ(queryDescCount, matches.size());
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int badCount = 0;
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for (size_t i = 0; i < matches.size(); i++)
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{
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if ((int)matches[i].size() != knn)
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badCount++;
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else
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{
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int localBadCount = 0;
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for (int k = 0; k < knn; k++)
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{
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cv::DMatch match = matches[i][k];
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if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k) || (match.imgIdx != 0))
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localBadCount++;
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}
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badCount += localBadCount > 0 ? 1 : 0;
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}
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}
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ASSERT_EQ(0, badCount);
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}
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TEST_P(BruteForceMatcher, KnnMatchAdd2)
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{
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const int knn = 2;
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std::vector< std::vector<cv::DMatch> > matches;
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bool isMaskSupported;
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ASSERT_NO_THROW(
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cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
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cv::gpu::GpuMat d_train(train);
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// make add() twice to test such case
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matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
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matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
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// prepare masks (make first nearest match illegal)
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std::vector<cv::gpu::GpuMat> masks(2);
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for (int mi = 0; mi < 2; mi++ )
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{
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masks[mi] = cv::gpu::GpuMat(query.rows, train.rows / 2, CV_8UC1, cv::Scalar::all(1));
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for (int di = 0; di < queryDescCount / 2; di++)
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masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0));
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}
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matcher.knnMatch(cv::gpu::GpuMat(query), matches, knn, masks);
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isMaskSupported = matcher.isMaskSupported();
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);
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ASSERT_EQ(queryDescCount, matches.size());
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int badCount = 0;
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int shift = isMaskSupported ? 1 : 0;
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for (size_t i = 0; i < matches.size(); i++)
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{
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if ((int)matches[i].size() != knn)
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badCount++;
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else
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{
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int localBadCount = 0;
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for (int k = 0; k < knn; k++)
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{
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cv::DMatch match = matches[i][k];
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{
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if (i < queryDescCount / 2)
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{
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if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) )
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localBadCount++;
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}
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else
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{
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if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) )
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localBadCount++;
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}
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}
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}
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badCount += localBadCount > 0 ? 1 : 0;
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}
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}
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ASSERT_EQ(0, badCount);
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}
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TEST_P(BruteForceMatcher, KnnMatchAdd3)
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{
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const int knn = 3;
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std::vector< std::vector<cv::DMatch> > matches;
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bool isMaskSupported;
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ASSERT_NO_THROW(
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cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
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cv::gpu::GpuMat d_train(train);
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// make add() twice to test such case
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matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
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matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
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// prepare masks (make first nearest match illegal)
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std::vector<cv::gpu::GpuMat> masks(2);
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for (int mi = 0; mi < 2; mi++ )
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{
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masks[mi] = cv::gpu::GpuMat(query.rows, train.rows / 2, CV_8UC1, cv::Scalar::all(1));
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for (int di = 0; di < queryDescCount / 2; di++)
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masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0));
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}
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matcher.knnMatch(cv::gpu::GpuMat(query), matches, knn, masks);
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isMaskSupported = matcher.isMaskSupported();
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);
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ASSERT_EQ(queryDescCount, matches.size());
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int badCount = 0;
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int shift = isMaskSupported ? 1 : 0;
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for (size_t i = 0; i < matches.size(); i++)
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{
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if ((int)matches[i].size() != knn)
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badCount++;
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else
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{
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int localBadCount = 0;
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for (int k = 0; k < knn; k++)
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{
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cv::DMatch match = matches[i][k];
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{
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if (i < queryDescCount / 2)
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{
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if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) )
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localBadCount++;
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}
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else
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{
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if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) )
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localBadCount++;
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}
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}
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}
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badCount += localBadCount > 0 ? 1 : 0;
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}
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}
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ASSERT_EQ(0, badCount);
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}
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TEST_P(BruteForceMatcher, RadiusMatch)
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{
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if (!supportFeature(devInfo, cv::gpu::SHARED_ATOMICS))
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return;
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const float radius = 1.f / countFactor;
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std::vector< std::vector<cv::DMatch> > matches;
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ASSERT_NO_THROW(
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cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
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matcher.radiusMatch(loadMat(query), loadMat(train), matches, radius);
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);
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ASSERT_EQ(queryDescCount, matches.size());
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int badCount = 0;
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for (size_t i = 0; i < matches.size(); i++)
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{
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if ((int)matches[i].size() != 1)
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badCount++;
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else
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{
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cv::DMatch match = matches[i][0];
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if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor) || (match.imgIdx != 0))
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badCount++;
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}
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}
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|
|
|
ASSERT_EQ(0, badCount);
|
|
}
|
|
|
|
TEST_P(BruteForceMatcher, RadiusMatchAdd)
|
|
{
|
|
if (!supportFeature(devInfo, cv::gpu::SHARED_ATOMICS))
|
|
return;
|
|
|
|
int n = 3;
|
|
const float radius = 1.f / countFactor * n;
|
|
|
|
std::vector< std::vector<cv::DMatch> > 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<cv::gpu::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
|
|
matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
|
|
|
|
// prepare masks (make first nearest match illegal)
|
|
std::vector<cv::gpu::GpuMat> 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, Combine(
|
|
ALL_DEVICES,
|
|
Values(cv::gpu::BruteForceMatcher_GPU_base::L1Dist, cv::gpu::BruteForceMatcher_GPU_base::L2Dist),
|
|
Values(57, 64, 83, 128, 179, 256, 304)));
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// FAST
|
|
|
|
struct FAST : TestWithParam<cv::gpu::DeviceInfo>
|
|
{
|
|
cv::gpu::DeviceInfo devInfo;
|
|
|
|
cv::Mat image;
|
|
|
|
int threshold;
|
|
|
|
std::vector<cv::KeyPoint> keypoints_gold;
|
|
|
|
virtual void SetUp()
|
|
{
|
|
devInfo = GetParam();
|
|
|
|
cv::gpu::setDevice(devInfo.deviceID());
|
|
|
|
image = readImage("features2d/aloe.png", CV_LOAD_IMAGE_GRAYSCALE);
|
|
ASSERT_FALSE(image.empty());
|
|
|
|
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
|
|
threshold = 30;
|
|
|
|
cv::FAST(image, keypoints_gold, threshold);
|
|
}
|
|
};
|
|
|
|
struct HashEq
|
|
{
|
|
size_t hash;
|
|
inline HashEq(size_t hash_) : hash(hash_) {}
|
|
inline bool operator ()(const cv::KeyPoint& kp) const
|
|
{
|
|
return kp.hash() == hash;
|
|
}
|
|
};
|
|
|
|
struct KeyPointCompare
|
|
{
|
|
inline bool operator ()(const cv::KeyPoint& kp1, const cv::KeyPoint& kp2) const
|
|
{
|
|
return kp1.pt.y < kp2.pt.y || (kp1.pt.y == kp2.pt.y && kp1.pt.x < kp2.pt.x);
|
|
}
|
|
};
|
|
|
|
TEST_P(FAST, Accuracy)
|
|
{
|
|
std::vector<cv::KeyPoint> keypoints;
|
|
|
|
ASSERT_NO_THROW(
|
|
cv::gpu::FAST_GPU fastGPU(threshold);
|
|
|
|
fastGPU(cv::gpu::GpuMat(image), cv::gpu::GpuMat(), keypoints);
|
|
);
|
|
|
|
ASSERT_EQ(keypoints.size(), keypoints_gold.size());
|
|
|
|
std::sort(keypoints.begin(), keypoints.end(), KeyPointCompare());
|
|
|
|
for (size_t i = 0; i < keypoints_gold.size(); ++i)
|
|
{
|
|
const cv::KeyPoint& kp1 = keypoints[i];
|
|
const cv::KeyPoint& kp2 = keypoints_gold[i];
|
|
|
|
size_t h1 = kp1.hash();
|
|
size_t h2 = kp2.hash();
|
|
|
|
ASSERT_EQ(h1, h2);
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(Features2D, FAST, DEVICES(cv::gpu::GLOBAL_ATOMICS));
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// ORB
|
|
|
|
struct ORB : TestWithParam<cv::gpu::DeviceInfo>
|
|
{
|
|
cv::gpu::DeviceInfo devInfo;
|
|
|
|
cv::Mat image;
|
|
cv::Mat mask;
|
|
|
|
int npoints;
|
|
|
|
std::vector<cv::KeyPoint> keypoints_gold;
|
|
cv::Mat descriptors_gold;
|
|
|
|
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));
|
|
|
|
npoints = 1000;
|
|
|
|
cv::ORB orbCPU(npoints);
|
|
|
|
orbCPU(image, mask, keypoints_gold, descriptors_gold);
|
|
}
|
|
};
|
|
|
|
TEST_P(ORB, Accuracy)
|
|
{
|
|
std::vector<cv::KeyPoint> keypoints;
|
|
cv::Mat descriptors;
|
|
|
|
ASSERT_NO_THROW(
|
|
cv::gpu::ORB_GPU orbGPU(npoints);
|
|
cv::gpu::GpuMat d_descriptors;
|
|
|
|
orbGPU(cv::gpu::GpuMat(image), cv::gpu::GpuMat(mask), keypoints, d_descriptors);
|
|
|
|
d_descriptors.download(descriptors);
|
|
);
|
|
|
|
cv::BruteForceMatcher<cv::Hamming> matcher;
|
|
std::vector<cv::DMatch> matches;
|
|
|
|
matcher.match(descriptors_gold, descriptors, matches);
|
|
|
|
int count = getValidMatchesCount(keypoints_gold, keypoints, matches);
|
|
double ratio = (double) count / matches.size();
|
|
|
|
ASSERT_GE(ratio, 0.65);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(Features2D, ORB, DEVICES(cv::gpu::GLOBAL_ATOMICS));
|
|
|
|
#endif // HAVE_CUDA
|