opencv/modules/ocl/test/test_brute_force_matcher.cpp

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#include "precomp.hpp"

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