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#include "test_precomp.hpp"

#ifdef HAVE_CUDA

using namespace std;
using namespace cv;
using namespace cv::gpu;
using namespace cvtest;
using namespace testing;
using namespace testing::internal;

//////////////////////////////////////////////////////////////////////
// random generators

int randomInt(int minVal, int maxVal)
{
    RNG& rng = TS::ptr()->get_rng();
    return rng.uniform(minVal, maxVal);
}

double randomDouble(double minVal, double maxVal)
{
    RNG& rng = TS::ptr()->get_rng();
    return rng.uniform(minVal, maxVal);
}

Size randomSize(int minVal, int maxVal)
{
    return cv::Size(randomInt(minVal, maxVal), randomInt(minVal, maxVal));
}

Scalar randomScalar(double minVal, double maxVal)
{
    return Scalar(randomDouble(minVal, maxVal), randomDouble(minVal, maxVal), randomDouble(minVal, maxVal), randomDouble(minVal, maxVal));
}

Mat randomMat(Size size, int type, double minVal, double maxVal)
{
    return randomMat(TS::ptr()->get_rng(), size, type, minVal, maxVal, false);
}

//////////////////////////////////////////////////////////////////////
// GpuMat create

cv::gpu::GpuMat createMat(cv::Size size, int type, bool useRoi)
{
    Size size0 = size;

    if (useRoi)
    {
        size0.width += randomInt(5, 15);
        size0.height += randomInt(5, 15);
    }

    GpuMat d_m(size0, type);

    if (size0 != size)
        d_m = d_m(Rect((size0.width - size.width) / 2, (size0.height - size.height) / 2, size.width, size.height));

    return d_m;
}

GpuMat loadMat(const Mat& m, bool useRoi)
{
    GpuMat d_m = createMat(m.size(), m.type(), useRoi);
    d_m.upload(m);
    return d_m;
}

//////////////////////////////////////////////////////////////////////
// Image load

Mat readImage(const std::string& fileName, int flags)
{
    return imread(TS::ptr()->get_data_path() + fileName, flags);
}

Mat readImageType(const std::string& fname, int type)
{
    Mat src = readImage(fname, CV_MAT_CN(type) == 1 ? IMREAD_GRAYSCALE : IMREAD_COLOR);
    if (CV_MAT_CN(type) == 4)
    {
        Mat temp;
        cvtColor(src, temp, cv::COLOR_BGR2BGRA);
        swap(src, temp);
    }
    src.convertTo(src, CV_MAT_DEPTH(type), CV_MAT_DEPTH(type) == CV_32F ? 1.0 / 255.0 : 1.0);
    return src;
}

//////////////////////////////////////////////////////////////////////
// Image dumping

void dumpImage(const std::string& fileName, const cv::Mat& image)
{
    cv::imwrite(TS::ptr()->get_data_path() + fileName, image);
}

//////////////////////////////////////////////////////////////////////
// Gpu devices

bool supportFeature(const DeviceInfo& info, FeatureSet feature)
{
    return TargetArchs::builtWith(feature) && info.supports(feature);
}

DeviceManager& DeviceManager::instance()
{
    static DeviceManager obj;
    return obj;
}

void DeviceManager::load(int i)
{
    devices_.clear();
    devices_.reserve(1);

    ostringstream msg;

    if (i < 0 || i >= getCudaEnabledDeviceCount())
    {
        msg << "Incorrect device number - " << i;
        throw runtime_error(msg.str());
    }

    DeviceInfo info(i);

    if (!info.isCompatible())
    {
        msg << "Device " << i << " [" << info.name() << "] is NOT compatible with current GPU module build";
        throw runtime_error(msg.str());
    }

    devices_.push_back(info);
}

void DeviceManager::loadAll()
{
    int deviceCount = getCudaEnabledDeviceCount();

    devices_.clear();
    devices_.reserve(deviceCount);

    for (int i = 0; i < deviceCount; ++i)
    {
        DeviceInfo info(i);
        if (info.isCompatible())
        {
            devices_.push_back(info);
        }
    }
}

//////////////////////////////////////////////////////////////////////
// Additional assertion

Mat getMat(InputArray arr)
{
    if (arr.kind() == _InputArray::GPU_MAT)
    {
        Mat m;
        arr.getGpuMat().download(m);
        return m;
    }

    return arr.getMat();
}

double checkNorm(InputArray m1, InputArray m2)
{
    return norm(getMat(m1), getMat(m2), NORM_INF);
}

void minMaxLocGold(const Mat& src, double* minVal_, double* maxVal_, Point* minLoc_, Point* maxLoc_, const Mat& mask)
{
    if (src.depth() != CV_8S)
    {
        minMaxLoc(src, minVal_, maxVal_, minLoc_, maxLoc_, mask);
        return;
    }

    // OpenCV's minMaxLoc doesn't support CV_8S type
    double minVal = numeric_limits<double>::max();
    Point minLoc(-1, -1);

    double maxVal = -numeric_limits<double>::max();
    Point maxLoc(-1, -1);

    for (int y = 0; y < src.rows; ++y)
    {
        const schar* src_row = src.ptr<signed char>(y);
        const uchar* mask_row = mask.empty() ? 0 : mask.ptr<unsigned char>(y);

        for (int x = 0; x < src.cols; ++x)
        {
            if (!mask_row || mask_row[x])
            {
                schar val = src_row[x];

                if (val < minVal)
                {
                    minVal = val;
                    minLoc = cv::Point(x, y);
                }

                if (val > maxVal)
                {
                    maxVal = val;
                    maxLoc = cv::Point(x, y);
                }
            }
        }
    }

    if (minVal_) *minVal_ = minVal;
    if (maxVal_) *maxVal_ = maxVal;

    if (minLoc_) *minLoc_ = minLoc;
    if (maxLoc_) *maxLoc_ = maxLoc;
}

namespace
{
    template <typename T, typename OutT> std::string printMatValImpl(const Mat& m, Point p)
    {
        const int cn = m.channels();

        ostringstream ostr;
        ostr << "(";

        p.x /= cn;

        ostr << static_cast<OutT>(m.at<T>(p.y, p.x * cn));
        for (int c = 1; c < m.channels(); ++c)
        {
            ostr << ", " << static_cast<OutT>(m.at<T>(p.y, p.x * cn + c));
        }
        ostr << ")";

        return ostr.str();
    }

    std::string printMatVal(const Mat& m, Point p)
    {
        typedef std::string (*func_t)(const Mat& m, Point p);

        static const func_t funcs[] =
        {
            printMatValImpl<uchar, int>, printMatValImpl<schar, int>, printMatValImpl<ushort, int>, printMatValImpl<short, int>,
            printMatValImpl<int, int>, printMatValImpl<float, float>, printMatValImpl<double, double>
        };

        return funcs[m.depth()](m, p);
    }
}

testing::AssertionResult assertMatNear(const char* expr1, const char* expr2, const char* eps_expr, cv::InputArray m1_, cv::InputArray m2_, double eps)
{
    Mat m1 = getMat(m1_);
    Mat m2 = getMat(m2_);

    if (m1.size() != m2.size())
    {
        return AssertionFailure() << "Matrices \"" << expr1 << "\" and \"" << expr2 << "\" have different sizes : \""
                                  << expr1 << "\" [" << PrintToString(m1.size()) << "] vs \""
                                  << expr2 << "\" [" << PrintToString(m2.size()) << "]";
    }

    if (m1.type() != m2.type())
    {
        return AssertionFailure() << "Matrices \"" << expr1 << "\" and \"" << expr2 << "\" have different types : \""
                                  << expr1 << "\" [" << PrintToString(MatType(m1.type())) << "] vs \""
                                  << expr2 << "\" [" << PrintToString(MatType(m2.type())) << "]";
    }

    Mat diff;
    absdiff(m1.reshape(1), m2.reshape(1), diff);

    double maxVal = 0.0;
    Point maxLoc;
    minMaxLocGold(diff, 0, &maxVal, 0, &maxLoc);

    if (maxVal > eps)
    {
        return AssertionFailure() << "The max difference between matrices \"" << expr1 << "\" and \"" << expr2
                                  << "\" is " << maxVal << " at (" << maxLoc.y << ", " << maxLoc.x / m1.channels() << ")"
                                  << ", which exceeds \"" << eps_expr << "\", where \""
                                  << expr1 << "\" at (" << maxLoc.y << ", " << maxLoc.x / m1.channels() << ") evaluates to " << printMatVal(m1, maxLoc) << ", \""
                                  << expr2 << "\" at (" << maxLoc.y << ", " << maxLoc.x / m1.channels() << ") evaluates to " << printMatVal(m2, maxLoc) << ", \""
                                  << eps_expr << "\" evaluates to " << eps;
    }

    return AssertionSuccess();
}

double checkSimilarity(InputArray m1, InputArray m2)
{
    Mat diff;
    matchTemplate(getMat(m1), getMat(m2), diff, CV_TM_CCORR_NORMED);
    return std::abs(diff.at<float>(0, 0) - 1.f);
}

//////////////////////////////////////////////////////////////////////
// Helper structs for value-parameterized tests

vector<MatDepth> depths(int depth_start, int depth_end)
{
    vector<MatDepth> v;

    v.reserve((depth_end - depth_start + 1));

    for (int depth = depth_start; depth <= depth_end; ++depth)
        v.push_back(depth);

    return v;
}

vector<MatType> types(int depth_start, int depth_end, int cn_start, int cn_end)
{
    vector<MatType> v;

    v.reserve((depth_end - depth_start + 1) * (cn_end - cn_start + 1));

    for (int depth = depth_start; depth <= depth_end; ++depth)
    {
        for (int cn = cn_start; cn <= cn_end; ++cn)
        {
            v.push_back(CV_MAKETYPE(depth, cn));
        }
    }

    return v;
}

const vector<MatType>& all_types()
{
    static vector<MatType> v = types(CV_8U, CV_64F, 1, 4);

    return v;
}

void cv::gpu::PrintTo(const DeviceInfo& info, ostream* os)
{
    (*os) << info.name();
}

void PrintTo(const UseRoi& useRoi, std::ostream* os)
{
    if (useRoi)
        (*os) << "sub matrix";
    else
        (*os) << "whole matrix";
}

void PrintTo(const Inverse& inverse, std::ostream* os)
{
    if (inverse)
        (*os) << "inverse";
    else
        (*os) << "direct";
}

void showDiff(InputArray gold_, InputArray actual_, double eps)
{
    Mat gold = getMat(gold_);
    Mat actual = getMat(actual_);

    Mat diff;
    absdiff(gold, actual, diff);
    threshold(diff, diff, eps, 255.0, cv::THRESH_BINARY);

    namedWindow("gold", WINDOW_NORMAL);
    namedWindow("actual", WINDOW_NORMAL);
    namedWindow("diff", WINDOW_NORMAL);

    imshow("gold", gold);
    imshow("actual", actual);
    imshow("diff", diff);

    waitKey();
}

#endif // HAVE_CUDA