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//                For Open Source Computer Vision Library
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#include "precomp.hpp"
#define VARNAME(A) #A
using namespace std;
using namespace cv;
using namespace cv::gpu;
using namespace cvtest;


//std::string generateVarList(int first,...)
//{
//	vector<std::string> varname;
//
//	va_list argp;
//	string s;
//	stringstream ss;
//	va_start(argp,first);
//	int i=first;
//	while(i!=-1)
//	{
//		ss<<i<<",";
//		i=va_arg(argp,int);
//	};
//	s=ss.str();
//	va_end(argp);
//	return s;
//};

//std::string generateVarList(int& p1,int& p2)
//{
//	stringstream ss;
//	ss<<VARNAME(p1)<<":"<<src1x<<","<<VARNAME(p2)<<":"<<src1y;
//	return ss.str();
//};

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);
}







/*
void showDiff(InputArray gold_, InputArray actual_, double eps)
{
    Mat gold;
    if (gold_.kind() == _InputArray::MAT)
        gold = gold_.getMat();
    else
        gold_.getGpuMat().download(gold);

    Mat actual;
    if (actual_.kind() == _InputArray::MAT)
        actual = actual_.getMat();
    else
        actual_.getGpuMat().download(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();
}
*/

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

const vector<DeviceInfo>& devices()
{
    static vector<DeviceInfo> devs;
    static bool first = true;

    if (first)
    {
        int deviceCount = getCudaEnabledDeviceCount();

        devs.reserve(deviceCount);

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

        first = false;
    }

    return devs;
}

vector<DeviceInfo> devices(FeatureSet feature)
{
    const vector<DeviceInfo>& d = devices();

    vector<DeviceInfo> devs_filtered;

    if (TargetArchs::builtWith(feature))
    {
        devs_filtered.reserve(d.size());

        for (size_t i = 0, size = d.size(); i < size; ++i)
        {
            const DeviceInfo& info = d[i];

            if (info.supports(feature))
                devs_filtered.push_back(info);
        }
    }

    return devs_filtered;
}
*/

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;
}

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

Mat readImageType(const 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));
    return src;
}

double checkNorm(const Mat &m)
{
    return norm(m, NORM_INF);
}

double checkNorm(const Mat &m1, const Mat &m2)
{
    return norm(m1, m2, NORM_INF);
}

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

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

void PrintTo(const Inverse &inverse, std::ostream *os)
{
    if (inverse)
        (*os) << "inverse";
    else
        (*os) << "direct";
}
	cv::ocl::oclMat createMat(cv::Size size,int type,bool useRoi)
	{
		cv::Size size0 = size;
		if (useRoi)
		{
			size0.width += randomInt(5, 15);
			size0.height += randomInt(5, 15);
		}
		cv::ocl::oclMat d_m(size0, type);
		if (size0 != size)
			d_m = cv::ocl::oclMat(size.width,size.height,type); // suspicious point
		return d_m;
	}
	cv::ocl::oclMat loadMat(const cv::Mat& m, bool useRoi)
	{
		cv::ocl::oclMat d_m = ::createMat(m.size(), m.type(), useRoi);
		d_m.upload(m);
		return d_m;
	}
