opencv/modules/gpu/test/test_features2d.cpp

1338 lines
44 KiB
C++

/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
#ifdef HAVE_CUDA
/////////////////////////////////////////////////////////////////////////////////////////////////
// SURF
struct SURF : testing::TestWithParam<cv::gpu::DeviceInfo>
{
static cv::Mat image;
static cv::Mat mask;
static std::vector<cv::KeyPoint> keypoints_gold;
static std::vector<float> descriptors_gold;
static void SetUpTestCase()
{
image = readImage("features2d/aloe.png", CV_LOAD_IMAGE_GRAYSCALE);
mask = cv::Mat(image.size(), CV_8UC1, cv::Scalar::all(1));
mask(cv::Range(0, image.rows / 2), cv::Range(0, image.cols / 2)).setTo(cv::Scalar::all(0));
cv::SURF fdetector_gold; fdetector_gold.extended = false;
fdetector_gold(image, mask, keypoints_gold, descriptors_gold);
}
static void TearDownTestCase()
{
image.release();
mask.release();
keypoints_gold.clear();
descriptors_gold.clear();
}
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
bool isSimilarKeypoints(const cv::KeyPoint& p1, const cv::KeyPoint& p2)
{
const float maxPtDif = 1.f;
const float maxSizeDif = 1.f;
const float maxAngleDif = 2.f;
const float maxResponseDif = 0.1f;
float dist = (float)cv::norm(p1.pt - p2.pt);
return (dist < maxPtDif &&
fabs(p1.size - p2.size) < maxSizeDif &&
abs(p1.angle - p2.angle) < maxAngleDif &&
abs(p1.response - p2.response) < maxResponseDif &&
p1.octave == p2.octave &&
p1.class_id == p2.class_id );
}
};
cv::Mat SURF::image;
cv::Mat SURF::mask;
std::vector<cv::KeyPoint> SURF::keypoints_gold;
std::vector<float> SURF::descriptors_gold;
TEST_P(SURF, EmptyDataTest)
{
PRINT_PARAM(devInfo);
cv::gpu::SURF_GPU fdetector;
cv::gpu::GpuMat image;
std::vector<cv::KeyPoint> keypoints;
std::vector<float> descriptors;
ASSERT_NO_THROW(
fdetector(image, cv::gpu::GpuMat(), keypoints, descriptors);
);
EXPECT_TRUE(keypoints.empty());
EXPECT_TRUE(descriptors.empty());
}
TEST_P(SURF, Accuracy)
{
ASSERT_TRUE(!image.empty());
PRINT_PARAM(devInfo);
// Compute keypoints.
std::vector<cv::KeyPoint> keypoints;
cv::Mat descriptors;
ASSERT_NO_THROW(
cv::gpu::GpuMat dev_descriptors;
cv::gpu::SURF_GPU fdetector; fdetector.extended = false;
fdetector(cv::gpu::GpuMat(image), cv::gpu::GpuMat(mask), keypoints, dev_descriptors);
dev_descriptors.download(descriptors);
);
cv::BruteForceMatcher< cv::L2<float> > matcher;
std::vector<cv::DMatch> matches;
matcher.match(cv::Mat(keypoints_gold.size(), 64, CV_32FC1, &descriptors_gold[0]), descriptors, matches);
int validCount = 0;
for (size_t i = 0; i < matches.size(); ++i)
{
const cv::DMatch& m = matches[i];
const cv::KeyPoint& p1 = keypoints_gold[m.queryIdx];
const cv::KeyPoint& p2 = keypoints[m.trainIdx];
const float maxPtDif = 1.f;
const float maxSizeDif = 1.f;
const float maxAngleDif = 2.f;
const float maxResponseDif = 0.1f;
float dist = (float)cv::norm(p1.pt - p2.pt);
if (dist < maxPtDif &&
fabs(p1.size - p2.size) < maxSizeDif &&
abs(p1.angle - p2.angle) < maxAngleDif &&
abs(p1.response - p2.response) < maxResponseDif &&
p1.octave == p2.octave &&
p1.class_id == p2.class_id )
{
++validCount;
}
}
double validRatio = (double)validCount / matches.size();
EXPECT_GT(validRatio, 0.5);
}
INSTANTIATE_TEST_CASE_P(Features2D, SURF, testing::ValuesIn(devices(cv::gpu::GLOBAL_ATOMICS)));
/////////////////////////////////////////////////////////////////////////////////////////////////
// BruteForceMatcher
static const char* dists[] = {"L1Dist", "L2Dist", "HammingDist"};
struct BruteForceMatcher : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, cv::gpu::BruteForceMatcher_GPU_base::DistType, int> >
{
static const int queryDescCount = 300; // must be even number because we split train data in some cases in two
static const int countFactor = 4; // do not change it
cv::gpu::DeviceInfo devInfo;
cv::gpu::BruteForceMatcher_GPU_base::DistType distType;
int dim;
cv::Mat query, train;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
distType = std::tr1::get<1>(GetParam());
dim = std::tr1::get<2>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
cv::Mat queryBuf, trainBuf;
// Generate query descriptors randomly.
// Descriptor vector elements are integer values.
queryBuf.create(queryDescCount, dim, CV_32SC1);
rng.fill(queryBuf, cv::RNG::UNIFORM, cv::Scalar::all(0), cv::Scalar::all(3));
queryBuf.convertTo(queryBuf, CV_32FC1);
// Generate train decriptors as follows:
// copy each query descriptor to train set countFactor times
// and perturb some one element of the copied descriptors in
// in ascending order. General boundaries of the perturbation
// are (0.f, 1.f).
trainBuf.create(queryDescCount * countFactor, dim, CV_32FC1);
float step = 1.f / countFactor;
for (int qIdx = 0; qIdx < queryDescCount; qIdx++)
{
cv::Mat queryDescriptor = queryBuf.row(qIdx);
for (int c = 0; c < countFactor; c++)
{
int tIdx = qIdx * countFactor + c;
cv::Mat trainDescriptor = trainBuf.row(tIdx);
queryDescriptor.copyTo(trainDescriptor);
int elem = rng(dim);
float diff = rng.uniform(step * c, step * (c + 1));
trainDescriptor.at<float>(0, elem) += diff;
}
}
queryBuf.convertTo(query, CV_32F);
trainBuf.convertTo(train, CV_32F);
}
};
const int BruteForceMatcher::queryDescCount;
const int BruteForceMatcher::countFactor;
TEST_P(BruteForceMatcher, Match)
{
const char* distStr = dists[distType];
PRINT_PARAM(devInfo);
PRINT_PARAM(distStr);
PRINT_PARAM(dim);
std::vector<cv::DMatch> matches;
ASSERT_NO_THROW(
cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
matcher.match(cv::gpu::GpuMat(query), cv::gpu::GpuMat(train), matches);
);
ASSERT_EQ(queryDescCount, matches.size());
int badCount = 0;
for (size_t i = 0; i < matches.size(); i++)
{
cv::DMatch match = matches[i];
if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0))
badCount++;
}
ASSERT_EQ(0, badCount);
}
TEST_P(BruteForceMatcher, MatchAdd)
{
const char* distStr = dists[distType];
PRINT_PARAM(devInfo);
PRINT_PARAM(distStr);
PRINT_PARAM(dim);
std::vector<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.match(cv::gpu::GpuMat(query), matches, masks);
isMaskSupported = matcher.isMaskSupported();
);
ASSERT_EQ(queryDescCount, matches.size());
int badCount = 0;
for (size_t i = 0; i < matches.size(); i++)
{
cv::DMatch match = matches[i];
int shift = isMaskSupported ? 1 : 0;
{
if (i < queryDescCount / 2)
{
if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + shift) || (match.imgIdx != 0))
badCount++;
}
else
{
if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + shift) || (match.imgIdx != 1))
badCount++;
}
}
}
ASSERT_EQ(0, badCount);
}
TEST_P(BruteForceMatcher, KnnMatch)
{
const char* distStr = dists[distType];
PRINT_PARAM(devInfo);
PRINT_PARAM(distStr);
PRINT_PARAM(dim);
const int knn = 3;
std::vector< std::vector<cv::DMatch> > matches;
ASSERT_NO_THROW(
cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
matcher.knnMatch(cv::gpu::GpuMat(query), cv::gpu::GpuMat(train), matches, knn);
);
ASSERT_EQ(queryDescCount, matches.size());
int badCount = 0;
for (size_t i = 0; i < matches.size(); i++)
{
if ((int)matches[i].size() != knn)
badCount++;
else
{
int localBadCount = 0;
for (int k = 0; k < knn; k++)
{
cv::DMatch match = matches[i][k];
if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k) || (match.imgIdx != 0))
localBadCount++;
}
badCount += localBadCount > 0 ? 1 : 0;
}
}
ASSERT_EQ(0, badCount);
}
TEST_P(BruteForceMatcher, KnnMatchAdd)
{
const char* distStr = dists[distType];
PRINT_PARAM(devInfo);
PRINT_PARAM(distStr);
PRINT_PARAM(dim);
const int knn = 2;
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.knnMatch(cv::gpu::GpuMat(query), matches, knn, masks);
isMaskSupported = matcher.isMaskSupported();
);
ASSERT_EQ(queryDescCount, matches.size());
int badCount = 0;
int shift = isMaskSupported ? 1 : 0;
for (size_t i = 0; i < matches.size(); i++)
{
if ((int)matches[i].size() != knn)
badCount++;
else
{
int localBadCount = 0;
for (int k = 0; k < knn; k++)
{
cv::DMatch match = matches[i][k];
{
if (i < queryDescCount / 2)
{
if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) )
localBadCount++;
}
else
{
if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) )
localBadCount++;
}
}
}
badCount += localBadCount > 0 ? 1 : 0;
}
}
ASSERT_EQ(0, badCount);
}
TEST_P(BruteForceMatcher, RadiusMatch)
{
if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS))
return;
const char* distStr = dists[distType];
PRINT_PARAM(devInfo);
PRINT_PARAM(distStr);
PRINT_PARAM(dim);
const float radius = 1.f / countFactor;
std::vector< std::vector<cv::DMatch> > matches;
ASSERT_NO_THROW(
cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
matcher.radiusMatch(cv::gpu::GpuMat(query), cv::gpu::GpuMat(train), matches, radius);
);
ASSERT_EQ(queryDescCount, matches.size());
int badCount = 0;
for (size_t i = 0; i < matches.size(); i++)
{
if ((int)matches[i].size() != 1)
badCount++;
else
{
cv::DMatch match = matches[i][0];
if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor) || (match.imgIdx != 0))
badCount++;
}
}
ASSERT_EQ(0, badCount);
}
TEST_P(BruteForceMatcher, RadiusMatchAdd)
{
if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS))
return;
const char* distStr = dists[distType];
PRINT_PARAM(devInfo);
PRINT_PARAM(distStr);
PRINT_PARAM(dim);
int n = 3;
const float radius = 1.f / countFactor * n;
std::vector< std::vector<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, 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(); }