Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
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#include "precomp.hpp"
#ifdef HAVE_OPENCL
namespace
{
/////////////////////////////////////////////////////////////////////////////////////////////////
// BruteForceMatcher
CV_ENUM(DistType, cv::ocl::BruteForceMatcher_OCL_base::L1Dist, cv::ocl::BruteForceMatcher_OCL_base::L2Dist, cv::ocl::BruteForceMatcher_OCL_base::HammingDist)
IMPLEMENT_PARAM_CLASS(DescriptorSize, int)
PARAM_TEST_CASE(BruteForceMatcher/*, NormCode*/, DistType, DescriptorSize)
{
//std::vector<cv::ocl::Info> oclinfo;
cv::ocl::BruteForceMatcher_OCL_base::DistType distType;
int normCode;
int dim;
int queryDescCount;
int countFactor;
cv::Mat query, train;
virtual void SetUp()
{
//normCode = GET_PARAM(0);
distType = (cv::ocl::BruteForceMatcher_OCL_base::DistType)(int)GET_PARAM(0);
dim = GET_PARAM(1);
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
//CV_Assert(devnums > 0);
queryDescCount = 300; // must be even number because we split train data in some cases in two
countFactor = 4; // do not change it
cv::RNG &rng = cvtest::TS::ptr()->get_rng();
cv::Mat queryBuf, trainBuf;
// Generate query descriptors randomly.
// Descriptor vector elements are integer values.
queryBuf.create(queryDescCount, dim, CV_32SC1);
rng.fill(queryBuf, cv::RNG::UNIFORM, cv::Scalar::all(0), cv::Scalar::all(3));
queryBuf.convertTo(queryBuf, CV_32FC1);
// Generate train decriptors as follows:
// copy each query descriptor to train set countFactor times
// and perturb some one element of the copied descriptors in
// in ascending order. General boundaries of the perturbation
// are (0.f, 1.f).
trainBuf.create(queryDescCount * countFactor, dim, CV_32FC1);
float step = 1.f / countFactor;
for (int qIdx = 0; qIdx < queryDescCount; qIdx++)
{
cv::Mat queryDescriptor = queryBuf.row(qIdx);
for (int c = 0; c < countFactor; c++)
{
int tIdx = qIdx * countFactor + c;
cv::Mat trainDescriptor = trainBuf.row(tIdx);
queryDescriptor.copyTo(trainDescriptor);
int elem = rng(dim);
float diff = rng.uniform(step * c, step * (c + 1));
trainDescriptor.at<float>(0, elem) += diff;
}
}
queryBuf.convertTo(query, CV_32F);
trainBuf.convertTo(train, CV_32F);
}
};
TEST_P(BruteForceMatcher, Match_Single)
{
cv::ocl::BruteForceMatcher_OCL_base matcher(distType);
std::vector<cv::DMatch> matches;
matcher.match(cv::ocl::oclMat(query), cv::ocl::oclMat(train), matches);
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
int badCount = 0;
for (size_t i = 0; i < matches.size(); i++)
{
cv::DMatch match = matches[i];
if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0))
badCount++;
}
ASSERT_EQ(0, badCount);
}
TEST_P(BruteForceMatcher, KnnMatch_2_Single)
{
const int knn = 2;
cv::ocl::BruteForceMatcher_OCL_base matcher(distType);
std::vector< std::vector<cv::DMatch> > matches;
matcher.knnMatch(cv::ocl::oclMat(query), cv::ocl::oclMat(train), matches, knn);
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
int badCount = 0;
for (size_t i = 0; i < matches.size(); i++)
{
if ((int)matches[i].size() != knn)
badCount++;
else
{
int localBadCount = 0;
for (int k = 0; k < knn; k++)
{
cv::DMatch match = matches[i][k];
if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k) || (match.imgIdx != 0))
localBadCount++;
}
badCount += localBadCount > 0 ? 1 : 0;
}
}
ASSERT_EQ(0, badCount);
}
TEST_P(BruteForceMatcher, RadiusMatch_Single)
{
float radius;
if(distType == cv::ocl::BruteForceMatcher_OCL_base::L2Dist)
radius = 1.f / countFactor / countFactor;
else
radius = 1.f / countFactor;
cv::ocl::BruteForceMatcher_OCL_base matcher(distType);
// assume support atomic.
//if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS))
//{
// try
// {
// std::vector< std::vector<cv::DMatch> > matches;
// matcher.radiusMatch(loadMat(query), loadMat(train), matches, radius);
// }
// catch (const cv::Exception& e)
// {
// ASSERT_EQ(CV_StsNotImplemented, e.code);
// }
//}
//else
{
std::vector< std::vector<cv::DMatch> > matches;
matcher.radiusMatch(cv::ocl::oclMat(query), cv::ocl::oclMat(train), matches, radius);
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
int badCount = 0;
for (size_t i = 0; i < matches.size(); i++)
{
if ((int)matches[i].size() != 1)
{
badCount++;
}
else
{
cv::DMatch match = matches[i][0];
if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0))
badCount++;
}
}
ASSERT_EQ(0, badCount);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Features2D, BruteForceMatcher, testing::Combine(
//ALL_DEVICES,
testing::Values(DistType(cv::ocl::BruteForceMatcher_OCL_base::L1Dist), DistType(cv::ocl::BruteForceMatcher_OCL_base::L2Dist)),
testing::Values(DescriptorSize(57), DescriptorSize(64), DescriptorSize(83), DescriptorSize(128), DescriptorSize(179), DescriptorSize(256), DescriptorSize(304))));
} // namespace
#endif