1338 lines
44 KiB
C++
1338 lines
44 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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/////////////////////////////////////////////////////////////////////////////////////////////////
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// SURF
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struct SURF : testing::TestWithParam<cv::gpu::DeviceInfo>
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{
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static cv::Mat image;
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static cv::Mat mask;
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static std::vector<cv::KeyPoint> keypoints_gold;
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static std::vector<float> descriptors_gold;
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static void SetUpTestCase()
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{
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image = readImage("features2d/aloe.png", CV_LOAD_IMAGE_GRAYSCALE);
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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; fdetector_gold.extended = false;
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fdetector_gold(image, mask, keypoints_gold, descriptors_gold);
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}
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static void TearDownTestCase()
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{
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image.release();
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mask.release();
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keypoints_gold.clear();
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descriptors_gold.clear();
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}
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cv::gpu::DeviceInfo devInfo;
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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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}
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bool isSimilarKeypoints(const cv::KeyPoint& p1, const cv::KeyPoint& p2)
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{
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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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return (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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};
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cv::Mat SURF::image;
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cv::Mat SURF::mask;
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std::vector<cv::KeyPoint> SURF::keypoints_gold;
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std::vector<float> SURF::descriptors_gold;
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TEST_P(SURF, EmptyDataTest)
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{
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PRINT_PARAM(devInfo);
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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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ASSERT_TRUE(!image.empty());
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PRINT_PARAM(devInfo);
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// Compute keypoints.
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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(cv::gpu::GpuMat(image), cv::gpu::GpuMat(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(keypoints_gold.size(), 64, CV_32FC1, &descriptors_gold[0]), descriptors, matches);
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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 = keypoints_gold[m.queryIdx];
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const cv::KeyPoint& p2 = keypoints[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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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, testing::ValuesIn(devices(cv::gpu::GLOBAL_ATOMICS)));
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/////////////////////////////////////////////////////////////////////////////////////////////////
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// BruteForceMatcher
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static const char* dists[] = {"L1Dist", "L2Dist", "HammingDist"};
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struct BruteForceMatcher : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, cv::gpu::BruteForceMatcher_GPU_base::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 = std::tr1::get<0>(GetParam());
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distType = std::tr1::get<1>(GetParam());
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dim = std::tr1::get<2>(GetParam());
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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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const int BruteForceMatcher::queryDescCount;
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const int BruteForceMatcher::countFactor;
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TEST_P(BruteForceMatcher, Match)
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{
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const char* distStr = dists[distType];
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PRINT_PARAM(devInfo);
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PRINT_PARAM(distStr);
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PRINT_PARAM(dim);
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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(cv::gpu::GpuMat(query), cv::gpu::GpuMat(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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const char* distStr = dists[distType];
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PRINT_PARAM(devInfo);
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PRINT_PARAM(distStr);
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PRINT_PARAM(dim);
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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, KnnMatch)
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{
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const char* distStr = dists[distType];
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PRINT_PARAM(devInfo);
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PRINT_PARAM(distStr);
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PRINT_PARAM(dim);
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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(cv::gpu::GpuMat(query), cv::gpu::GpuMat(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, KnnMatchAdd)
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{
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const char* distStr = dists[distType];
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PRINT_PARAM(devInfo);
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PRINT_PARAM(distStr);
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PRINT_PARAM(dim);
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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, RadiusMatch)
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{
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if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS))
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return;
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const char* distStr = dists[distType];
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PRINT_PARAM(devInfo);
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PRINT_PARAM(distStr);
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PRINT_PARAM(dim);
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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(cv::gpu::GpuMat(query), cv::gpu::GpuMat(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);
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}
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TEST_P(BruteForceMatcher, RadiusMatchAdd)
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{
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if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS))
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return;
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const char* distStr = dists[distType];
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PRINT_PARAM(devInfo);
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PRINT_PARAM(distStr);
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PRINT_PARAM(dim);
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int n = 3;
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const float radius = 1.f / countFactor * n;
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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++)
|
|
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
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
//struct CV_GpuBFMTest : CV_GpuTestBase
|
|
//{
|
|
// void run_gpu_test();
|
|
//
|
|
// void generateData(GpuMat& query, GpuMat& train, int dim, int depth);
|
|
//
|
|
// virtual void test(const GpuMat& query, const GpuMat& train, BruteForceMatcher_GPU_base& matcher) = 0;
|
|
//
|
|
// 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
|
|
//};
|
|
//
|
|
//void CV_GpuBFMTest::run_gpu_test()
|
|
//{
|
|
// BruteForceMatcher_GPU_base::DistType dists[] = {BruteForceMatcher_GPU_base::L1Dist, BruteForceMatcher_GPU_base::L2Dist, BruteForceMatcher_GPU_base::HammingDist};
|
|
// const char* dists_str[] = {"L1Dist", "L2Dist", "HammingDist"};
|
|
// int dists_count = sizeof(dists) / sizeof(dists[0]);
|
|
//
|
|
// RNG rng = ts->get_rng();
|
|
//
|
|
// int dims[] = {rng.uniform(30, 60), 64, rng.uniform(70, 110), 128, rng.uniform(130, 250), 256, rng.uniform(260, 350)};
|
|
// int dims_count = sizeof(dims) / sizeof(dims[0]);
|
|
//
|
|
// for (int dist = 0; dist < dists_count; ++dist)
|
|
// {
|
|
// int depth_end = dists[dist] == BruteForceMatcher_GPU_base::HammingDist ? CV_32S : CV_32F;
|
|
//
|
|
// for (int depth = CV_8U; depth <= depth_end; ++depth)
|
|
// {
|
|
// for (int dim = 0; dim < dims_count; ++dim)
|
|
// {
|
|
// PRINT_ARGS("dist=%s depth=%s dim=%d", dists_str[dist], getTypeName(depth), dims[dim]);
|
|
//
|
|
// BruteForceMatcher_GPU_base matcher(dists[dist]);
|
|
//
|
|
// GpuMat query, train;
|
|
// generateData(query, train, dim, depth);
|
|
//
|
|
// test(query, train, matcher);
|
|
// }
|
|
// }
|
|
// }
|
|
//}
|
|
//
|
|
//void CV_GpuBFMTest::generateData(GpuMat& queryGPU, GpuMat& trainGPU, int dim, int depth)
|
|
//{
|
|
// RNG& rng = ts->get_rng();
|
|
//
|
|
// Mat queryBuf, trainBuf;
|
|
//
|
|
// // Generate query descriptors randomly.
|
|
// // Descriptor vector elements are integer values.
|
|
// queryBuf.create(queryDescCount, dim, CV_32SC1);
|
|
// rng.fill(queryBuf, RNG::UNIFORM, Scalar::all(0), Scalar(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++)
|
|
// {
|
|
// Mat queryDescriptor = queryBuf.row(qIdx);
|
|
// for (int c = 0; c < countFactor; c++)
|
|
// {
|
|
// int tIdx = qIdx * countFactor + c;
|
|
// 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;
|
|
// }
|
|
// }
|
|
//
|
|
// Mat query, train;
|
|
// queryBuf.convertTo(query, depth);
|
|
// trainBuf.convertTo(train, depth);
|
|
//
|
|
// queryGPU.upload(query);
|
|
// trainGPU.upload(train);
|
|
//}
|
|
//
|
|
//#define GPU_BFM_TEST(test_name)
|
|
// struct CV_GpuBFM_ ##test_name ## _Test : CV_GpuBFMTest
|
|
// {
|
|
// void test(const GpuMat& query, const GpuMat& train, BruteForceMatcher_GPU_base& matcher);
|
|
// };
|
|
// TEST(BruteForceMatcher, test_name) { CV_GpuBFM_ ##test_name ## _Test test; test.safe_run(); }
|
|
// void CV_GpuBFM_ ##test_name ## _Test::test(const GpuMat& query, const GpuMat& train, BruteForceMatcher_GPU_base& matcher)
|
|
//
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
//// match
|
|
//
|
|
//GPU_BFM_TEST(match)
|
|
//{
|
|
// vector<DMatch> matches;
|
|
//
|
|
// matcher.match(query, train, matches);
|
|
//
|
|
// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT);
|
|
//
|
|
// int badCount = 0;
|
|
// for (size_t i = 0; i < matches.size(); i++)
|
|
// {
|
|
// DMatch match = matches[i];
|
|
// if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0))
|
|
// badCount++;
|
|
// }
|
|
//
|
|
// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT);
|
|
//}
|
|
//
|
|
//GPU_BFM_TEST(match_add)
|
|
//{
|
|
// vector<DMatch> matches;
|
|
//
|
|
// // make add() twice to test such case
|
|
// matcher.add(vector<GpuMat>(1, train.rowRange(0, train.rows/2)));
|
|
// matcher.add(vector<GpuMat>(1, train.rowRange(train.rows/2, train.rows)));
|
|
//
|
|
// // prepare masks (make first nearest match illegal)
|
|
// vector<GpuMat> masks(2);
|
|
// for (int mi = 0; mi < 2; mi++)
|
|
// {
|
|
// masks[mi] = GpuMat(query.rows, train.rows/2, CV_8UC1, Scalar::all(1));
|
|
// for (int di = 0; di < queryDescCount/2; di++)
|
|
// masks[mi].col(di * countFactor).setTo(Scalar::all(0));
|
|
// }
|
|
//
|
|
// matcher.match(query, matches, masks);
|
|
//
|
|
// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT);
|
|
//
|
|
// int badCount = 0;
|
|
// for (size_t i = 0; i < matches.size(); i++)
|
|
// {
|
|
// DMatch match = matches[i];
|
|
// int shift = matcher.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++;
|
|
// }
|
|
// }
|
|
// }
|
|
//
|
|
// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT);
|
|
//}
|
|
//
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
//// knnMatch
|
|
//
|
|
//GPU_BFM_TEST(knnMatch)
|
|
//{
|
|
// const int knn = 3;
|
|
//
|
|
// vector< vector<DMatch> > matches;
|
|
//
|
|
// matcher.knnMatch(query, train, matches, knn);
|
|
//
|
|
// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT);
|
|
//
|
|
// 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++)
|
|
// {
|
|
// 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;
|
|
// }
|
|
// }
|
|
//
|
|
// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT);
|
|
//}
|
|
//
|
|
//GPU_BFM_TEST(knnMatch_add)
|
|
//{
|
|
// const int knn = 2;
|
|
// vector<vector<DMatch> > matches;
|
|
//
|
|
// // make add() twice to test such case
|
|
// matcher.add(vector<GpuMat>(1,train.rowRange(0, train.rows / 2)));
|
|
// matcher.add(vector<GpuMat>(1,train.rowRange(train.rows / 2, train.rows)));
|
|
//
|
|
// // prepare masks (make first nearest match illegal)
|
|
// vector<GpuMat> masks(2);
|
|
// for (int mi = 0; mi < 2; mi++ )
|
|
// {
|
|
// masks[mi] = GpuMat(query.rows, train.rows / 2, CV_8UC1, Scalar::all(1));
|
|
// for (int di = 0; di < queryDescCount / 2; di++)
|
|
// masks[mi].col(di * countFactor).setTo(Scalar::all(0));
|
|
// }
|
|
//
|
|
// matcher.knnMatch(query, matches, knn, masks);
|
|
//
|
|
// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT);
|
|
//
|
|
// int badCount = 0;
|
|
// int shift = matcher.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++)
|
|
// {
|
|
// 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;
|
|
// }
|
|
// }
|
|
//
|
|
// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT);
|
|
//}
|
|
//
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
//// radiusMatch
|
|
//
|
|
//GPU_BFM_TEST(radiusMatch)
|
|
//{
|
|
// CHECK_RETURN(support(GLOBAL_ATOMICS), TS::SKIPPED);
|
|
//
|
|
// const float radius = 1.f / countFactor;
|
|
//
|
|
// vector< vector<DMatch> > matches;
|
|
//
|
|
// matcher.radiusMatch(query, train, matches, radius);
|
|
//
|
|
// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT);
|
|
//
|
|
// int badCount = 0;
|
|
// for (size_t i = 0; i < matches.size(); i++)
|
|
// {
|
|
// if ((int)matches[i].size() != 1)
|
|
// badCount++;
|
|
// else
|
|
// {
|
|
// DMatch match = matches[i][0];
|
|
// if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor) || (match.imgIdx != 0))
|
|
// badCount++;
|
|
// }
|
|
// }
|
|
//
|
|
// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT);
|
|
//}
|
|
//
|
|
//GPU_BFM_TEST(radiusMatch_add)
|
|
//{
|
|
// CHECK_RETURN(support(GLOBAL_ATOMICS), TS::SKIPPED);
|
|
//
|
|
// int n = 3;
|
|
// const float radius = 1.f / countFactor * n;
|
|
// vector< vector<DMatch> > matches;
|
|
//
|
|
// // make add() twice to test such case
|
|
// matcher.add(vector<GpuMat>(1,train.rowRange(0, train.rows / 2)));
|
|
// matcher.add(vector<GpuMat>(1,train.rowRange(train.rows / 2, train.rows)));
|
|
//
|
|
// // prepare masks (make first nearest match illegal)
|
|
// vector<GpuMat> masks(2);
|
|
// for (int mi = 0; mi < 2; mi++)
|
|
// {
|
|
// masks[mi] = GpuMat(query.rows, train.rows / 2, CV_8UC1, Scalar::all(1));
|
|
// for (int di = 0; di < queryDescCount / 2; di++)
|
|
// masks[mi].col(di * countFactor).setTo(Scalar::all(0));
|
|
// }
|
|
//
|
|
// matcher.radiusMatch(query, matches, radius, masks);
|
|
//
|
|
// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT);
|
|
//
|
|
// int badCount = 0;
|
|
// int shift = matcher.isMaskSupported() ? 1 : 0;
|
|
// int needMatchCount = matcher.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++)
|
|
// {
|
|
// 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;
|
|
// }
|
|
// }
|
|
//
|
|
// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT);
|
|
//}
|
|
//
|
|
//
|
|
//
|
|
//
|
|
//
|
|
//
|
|
//
|
|
//
|
|
//
|
|
//
|
|
//
|
|
//
|
|
//
|
|
//
|
|
//
|
|
//
|
|
//
|
|
//
|
|
//
|
|
//
|
|
//
|
|
////struct CV_GpuBruteForceMatcherTest : CV_GpuTestBase
|
|
////{
|
|
//// void run_gpu_test();
|
|
////
|
|
//// void emptyDataTest();
|
|
//// void dataTest(int dim);
|
|
////
|
|
//// void generateData(GpuMat& query, GpuMat& train, int dim);
|
|
////
|
|
//// void matchTest(const GpuMat& query, const GpuMat& train);
|
|
//// void knnMatchTest(const GpuMat& query, const GpuMat& train);
|
|
//// void radiusMatchTest(const GpuMat& query, const GpuMat& train);
|
|
////
|
|
//// BruteForceMatcher_GPU< L2<float> > dmatcher;
|
|
////
|
|
//// 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
|
|
////};
|
|
////
|
|
////void CV_GpuBruteForceMatcherTest::emptyDataTest()
|
|
////{
|
|
//// GpuMat queryDescriptors, trainDescriptors, mask;
|
|
//// vector<GpuMat> trainDescriptorCollection, masks;
|
|
//// vector<DMatch> matches;
|
|
//// vector< vector<DMatch> > vmatches;
|
|
////
|
|
//// try
|
|
//// {
|
|
//// dmatcher.match(queryDescriptors, trainDescriptors, matches, mask);
|
|
//// }
|
|
//// catch(...)
|
|
//// {
|
|
//// PRINTLN("match() on empty descriptors must not generate exception (1)");
|
|
//// ts->set_failed_test_info(TS::FAIL_EXCEPTION);
|
|
//// }
|
|
////
|
|
//// try
|
|
//// {
|
|
//// dmatcher.knnMatch(queryDescriptors, trainDescriptors, vmatches, 2, mask);
|
|
//// }
|
|
//// catch(...)
|
|
//// {
|
|
//// PRINTLN("knnMatch() on empty descriptors must not generate exception (1)");
|
|
//// ts->set_failed_test_info(TS::FAIL_EXCEPTION);
|
|
//// }
|
|
////
|
|
//// try
|
|
//// {
|
|
//// dmatcher.radiusMatch(queryDescriptors, trainDescriptors, vmatches, 10.f, mask);
|
|
//// }
|
|
//// catch(...)
|
|
//// {
|
|
//// PRINTLN("radiusMatch() on empty descriptors must not generate exception (1)");
|
|
//// ts->set_failed_test_info(TS::FAIL_EXCEPTION);
|
|
//// }
|
|
////
|
|
//// try
|
|
//// {
|
|
//// dmatcher.add(trainDescriptorCollection);
|
|
//// }
|
|
//// catch(...)
|
|
//// {
|
|
//// PRINTLN("add() on empty descriptors must not generate exception");
|
|
//// ts->set_failed_test_info(TS::FAIL_EXCEPTION);
|
|
//// }
|
|
////
|
|
//// try
|
|
//// {
|
|
//// dmatcher.match(queryDescriptors, matches, masks);
|
|
//// }
|
|
//// catch(...)
|
|
//// {
|
|
//// PRINTLN("match() on empty descriptors must not generate exception (2)");
|
|
//// ts->set_failed_test_info(TS::FAIL_EXCEPTION);
|
|
//// }
|
|
////
|
|
//// try
|
|
//// {
|
|
//// dmatcher.knnMatch(queryDescriptors, vmatches, 2, masks);
|
|
//// }
|
|
//// catch(...)
|
|
//// {
|
|
//// PRINTLN("knnMatch() on empty descriptors must not generate exception (2)");
|
|
//// ts->set_failed_test_info(TS::FAIL_EXCEPTION);
|
|
//// }
|
|
////
|
|
//// try
|
|
//// {
|
|
//// dmatcher.radiusMatch( queryDescriptors, vmatches, 10.f, masks );
|
|
//// }
|
|
//// catch(...)
|
|
//// {
|
|
//// PRINTLN("radiusMatch() on empty descriptors must not generate exception (2)");
|
|
//// ts->set_failed_test_info(TS::FAIL_EXCEPTION);
|
|
//// }
|
|
////
|
|
////}
|
|
////
|
|
////void CV_GpuBruteForceMatcherTest::generateData(GpuMat& queryGPU, GpuMat& trainGPU, int dim)
|
|
////{
|
|
//// Mat query, train;
|
|
//// RNG& rng = ts->get_rng();
|
|
////
|
|
//// // Generate query descriptors randomly.
|
|
//// // Descriptor vector elements are integer values.
|
|
//// Mat buf(queryDescCount, dim, CV_32SC1);
|
|
//// rng.fill(buf, RNG::UNIFORM, Scalar::all(0), Scalar(3));
|
|
//// buf.convertTo(query, 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).
|
|
//// train.create( query.rows*countFactor, query.cols, CV_32FC1 );
|
|
//// float step = 1.f / countFactor;
|
|
//// for (int qIdx = 0; qIdx < query.rows; qIdx++)
|
|
//// {
|
|
//// Mat queryDescriptor = query.row(qIdx);
|
|
//// for (int c = 0; c < countFactor; c++)
|
|
//// {
|
|
//// int tIdx = qIdx * countFactor + c;
|
|
//// Mat trainDescriptor = train.row(tIdx);
|
|
//// queryDescriptor.copyTo(trainDescriptor);
|
|
//// int elem = rng(dim);
|
|
//// float diff = rng.uniform(step * c, step * (c + 1));
|
|
//// trainDescriptor.at<float>(0, elem) += diff;
|
|
//// }
|
|
//// }
|
|
////
|
|
//// queryGPU.upload(query);
|
|
//// trainGPU.upload(train);
|
|
////}
|
|
////
|
|
////void CV_GpuBruteForceMatcherTest::matchTest(const GpuMat& query, const GpuMat& train)
|
|
////{
|
|
//// dmatcher.clear();
|
|
////
|
|
//// // test const version of match()
|
|
//// {
|
|
//// vector<DMatch> matches;
|
|
//// dmatcher.match(query, train, matches);
|
|
////
|
|
//// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT);
|
|
////
|
|
//// int badCount = 0;
|
|
//// for (size_t i = 0; i < matches.size(); i++)
|
|
//// {
|
|
//// DMatch match = matches[i];
|
|
//// if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0))
|
|
//// badCount++;
|
|
//// }
|
|
////
|
|
//// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT);
|
|
//// }
|
|
////
|
|
//// // test version of match() with add()
|
|
//// {
|
|
//// vector<DMatch> matches;
|
|
////
|
|
//// // make add() twice to test such case
|
|
//// dmatcher.add(vector<GpuMat>(1, train.rowRange(0, train.rows/2)));
|
|
//// dmatcher.add(vector<GpuMat>(1, train.rowRange(train.rows/2, train.rows)));
|
|
////
|
|
//// // prepare masks (make first nearest match illegal)
|
|
//// vector<GpuMat> masks(2);
|
|
//// for (int mi = 0; mi < 2; mi++)
|
|
//// {
|
|
//// masks[mi] = GpuMat(query.rows, train.rows/2, CV_8UC1, Scalar::all(1));
|
|
//// for (int di = 0; di < queryDescCount/2; di++)
|
|
//// masks[mi].col(di * countFactor).setTo(Scalar::all(0));
|
|
//// }
|
|
////
|
|
//// dmatcher.match(query, matches, masks);
|
|
////
|
|
//// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT);
|
|
////
|
|
//// int badCount = 0;
|
|
//// for (size_t i = 0; i < matches.size(); i++)
|
|
//// {
|
|
//// DMatch match = matches[i];
|
|
//// int shift = dmatcher.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++;
|
|
//// }
|
|
//// }
|
|
//// }
|
|
////
|
|
//// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT);
|
|
//// }
|
|
////}
|
|
////
|
|
////void CV_GpuBruteForceMatcherTest::knnMatchTest(const GpuMat& query, const GpuMat& train)
|
|
////{
|
|
//// dmatcher.clear();
|
|
////
|
|
//// // test const version of knnMatch()
|
|
//// {
|
|
//// const int knn = 3;
|
|
////
|
|
//// vector< vector<DMatch> > matches;
|
|
//// dmatcher.knnMatch(query, train, matches, knn);
|
|
////
|
|
//// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT);
|
|
////
|
|
//// 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++)
|
|
//// {
|
|
//// 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;
|
|
//// }
|
|
//// }
|
|
////
|
|
//// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT);
|
|
//// }
|
|
////
|
|
//// // test version of knnMatch() with add()
|
|
//// {
|
|
//// const int knn = 2;
|
|
//// vector<vector<DMatch> > matches;
|
|
////
|
|
//// // make add() twice to test such case
|
|
//// dmatcher.add(vector<GpuMat>(1,train.rowRange(0, train.rows / 2)));
|
|
//// dmatcher.add(vector<GpuMat>(1,train.rowRange(train.rows / 2, train.rows)));
|
|
////
|
|
//// // prepare masks (make first nearest match illegal)
|
|
//// vector<GpuMat> masks(2);
|
|
//// for (int mi = 0; mi < 2; mi++ )
|
|
//// {
|
|
//// masks[mi] = GpuMat(query.rows, train.rows / 2, CV_8UC1, Scalar::all(1));
|
|
//// for (int di = 0; di < queryDescCount / 2; di++)
|
|
//// masks[mi].col(di * countFactor).setTo(Scalar::all(0));
|
|
//// }
|
|
////
|
|
//// dmatcher.knnMatch(query, matches, knn, masks);
|
|
////
|
|
//// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT);
|
|
////
|
|
//// int badCount = 0;
|
|
//// int shift = dmatcher.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++)
|
|
//// {
|
|
//// 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;
|
|
//// }
|
|
//// }
|
|
////
|
|
//// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT);
|
|
//// }
|
|
////}
|
|
////
|
|
////void CV_GpuBruteForceMatcherTest::radiusMatchTest(const GpuMat& query, const GpuMat& train)
|
|
////{
|
|
//// CHECK_RETURN(support(GLOBAL_ATOMICS), TS::SKIPPED);
|
|
////
|
|
//// dmatcher.clear();
|
|
////
|
|
//// // test const version of match()
|
|
//// {
|
|
//// const float radius = 1.f / countFactor;
|
|
////
|
|
//// vector< vector<DMatch> > matches;
|
|
//// dmatcher.radiusMatch(query, train, matches, radius);
|
|
////
|
|
//// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT);
|
|
////
|
|
//// int badCount = 0;
|
|
//// for (size_t i = 0; i < matches.size(); i++)
|
|
//// {
|
|
//// if ((int)matches[i].size() != 1)
|
|
//// badCount++;
|
|
//// else
|
|
//// {
|
|
//// DMatch match = matches[i][0];
|
|
//// if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor) || (match.imgIdx != 0))
|
|
//// badCount++;
|
|
//// }
|
|
//// }
|
|
////
|
|
//// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT);
|
|
//// }
|
|
////
|
|
//// // test version of match() with add()
|
|
//// {
|
|
//// int n = 3;
|
|
//// const float radius = 1.f / countFactor * n;
|
|
//// vector< vector<DMatch> > matches;
|
|
////
|
|
//// // make add() twice to test such case
|
|
//// dmatcher.add(vector<GpuMat>(1,train.rowRange(0, train.rows / 2)));
|
|
//// dmatcher.add(vector<GpuMat>(1,train.rowRange(train.rows / 2, train.rows)));
|
|
////
|
|
//// // prepare masks (make first nearest match illegal)
|
|
//// vector<GpuMat> masks(2);
|
|
//// for (int mi = 0; mi < 2; mi++)
|
|
//// {
|
|
//// masks[mi] = GpuMat(query.rows, train.rows / 2, CV_8UC1, Scalar::all(1));
|
|
//// for (int di = 0; di < queryDescCount / 2; di++)
|
|
//// masks[mi].col(di * countFactor).setTo(Scalar::all(0));
|
|
//// }
|
|
////
|
|
//// dmatcher.radiusMatch(query, matches, radius, masks);
|
|
////
|
|
//// CHECK((int)matches.size() == queryDescCount, TS::FAIL_INVALID_OUTPUT);
|
|
////
|
|
//// int badCount = 0;
|
|
//// int shift = dmatcher.isMaskSupported() ? 1 : 0;
|
|
//// int needMatchCount = dmatcher.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++)
|
|
//// {
|
|
//// 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;
|
|
//// }
|
|
//// }
|
|
////
|
|
//// CHECK(badCount == 0, TS::FAIL_INVALID_OUTPUT);
|
|
//// }
|
|
////}
|
|
////
|
|
////void CV_GpuBruteForceMatcherTest::dataTest(int dim)
|
|
////{
|
|
//// GpuMat query, train;
|
|
//// generateData(query, train, dim);
|
|
////
|
|
//// matchTest(query, train);
|
|
//// knnMatchTest(query, train);
|
|
//// radiusMatchTest(query, train);
|
|
////
|
|
//// dmatcher.clear();
|
|
////}
|
|
////
|
|
////void CV_GpuBruteForceMatcherTest::run_gpu_test()
|
|
////{
|
|
//// emptyDataTest();
|
|
////
|
|
//// dataTest(50);
|
|
//// dataTest(64);
|
|
//// dataTest(100);
|
|
//// dataTest(128);
|
|
//// dataTest(200);
|
|
//// dataTest(256);
|
|
//// dataTest(300);
|
|
////}
|
|
////
|
|
////TEST(BruteForceMatcher, accuracy) { CV_GpuBruteForceMatcherTest test; test.safe_run(); }
|