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#include "test_precomp.hpp"

namespace opencv_test { namespace {

static void Canny_reference_follow( int x, int y, float lowThreshold, const Mat& mag, Mat& dst )
{
    static const int ofs[][2] = {{1,0},{1,-1},{0,-1},{-1,-1},{-1,0},{-1,1},{0,1},{1,1}};
    int i;

    dst.at<uchar>(y, x) = (uchar)255;

    for( i = 0; i < 8; i++ )
    {
        int x1 = x + ofs[i][0];
        int y1 = y + ofs[i][1];
        if( (unsigned)x1 < (unsigned)mag.cols &&
            (unsigned)y1 < (unsigned)mag.rows &&
            mag.at<float>(y1, x1) > lowThreshold &&
            !dst.at<uchar>(y1, x1) )
            Canny_reference_follow( x1, y1, lowThreshold, mag, dst );
    }
}

static void Canny_reference( const Mat& src, Mat& dst,
            double threshold1, double threshold2,
            int aperture_size, bool use_true_gradient )
{
    dst.create(src.size(), src.type());
    int m = aperture_size;
    Point anchor(m/2, m/2);
    const double tan_pi_8 = tan(CV_PI/8.);
    const double tan_3pi_8 = tan(CV_PI*3/8);
    float lowThreshold = (float)MIN(threshold1, threshold2);
    float highThreshold = (float)MAX(threshold1, threshold2);

    int x, y, width = src.cols, height = src.rows;

    Mat dxkernel = cvtest::calcSobelKernel2D( 1, 0, m, 0 );
    Mat dykernel = cvtest::calcSobelKernel2D( 0, 1, m, 0 );
    Mat dx, dy, mag(height, width, CV_32F);
    cvtest::filter2D(src, dx, CV_32S, dxkernel, anchor, 0, BORDER_REPLICATE);
    cvtest::filter2D(src, dy, CV_32S, dykernel, anchor, 0, BORDER_REPLICATE);

    // calc gradient magnitude
    for( y = 0; y < height; y++ )
    {
        for( x = 0; x < width; x++ )
        {
            int dxval = dx.at<int>(y, x), dyval = dy.at<int>(y, x);
            mag.at<float>(y, x) = use_true_gradient ?
                (float)sqrt((double)(dxval*dxval + dyval*dyval)) :
                (float)(fabs((double)dxval) + fabs((double)dyval));
        }
    }

    // calc gradient direction, do nonmaxima suppression
    for( y = 0; y < height; y++ )
    {
        for( x = 0; x < width; x++ )
        {

            float a = mag.at<float>(y, x), b = 0, c = 0;
            int y1 = 0, y2 = 0, x1 = 0, x2 = 0;

            if( a <= lowThreshold )
                continue;

            int dxval = dx.at<int>(y, x);
            int dyval = dy.at<int>(y, x);

            double tg = dxval ? (double)dyval/dxval : DBL_MAX*CV_SIGN(dyval);

            if( fabs(tg) < tan_pi_8 )
            {
                y1 = y2 = y; x1 = x + 1; x2 = x - 1;
            }
            else if( tan_pi_8 <= tg && tg <= tan_3pi_8 )
            {
                y1 = y + 1; y2 = y - 1; x1 = x + 1; x2 = x - 1;
            }
            else if( -tan_3pi_8 <= tg && tg <= -tan_pi_8 )
            {
                y1 = y - 1; y2 = y + 1; x1 = x + 1; x2 = x - 1;
            }
            else
            {
                CV_Assert( fabs(tg) > tan_3pi_8 );
                x1 = x2 = x; y1 = y + 1; y2 = y - 1;
            }

            if( (unsigned)y1 < (unsigned)height && (unsigned)x1 < (unsigned)width )
                b = (float)fabs(mag.at<float>(y1, x1));

            if( (unsigned)y2 < (unsigned)height && (unsigned)x2 < (unsigned)width )
                c = (float)fabs(mag.at<float>(y2, x2));

            if( (a > b || (a == b && ((x1 == x+1 && y1 == y) || (x1 == x && y1 == y+1)))) && a > c )
                ;
            else
                mag.at<float>(y, x) = -a;
        }
    }

    dst = Scalar::all(0);

    // hysteresis threshold
    for( y = 0; y < height; y++ )
    {
        for( x = 0; x < width; x++ )
            if( mag.at<float>(y, x) > highThreshold && !dst.at<uchar>(y, x) )
                Canny_reference_follow( x, y, lowThreshold, mag, dst );
    }
}

//==============================================================================

// aperture, true gradient
typedef testing::TestWithParam<testing::tuple<int, bool>> Canny_Modes;

TEST_P(Canny_Modes, accuracy)
{
    const int aperture = get<0>(GetParam());
    const bool trueGradient = get<1>(GetParam());
    const double range = aperture == 3 ? 300. : 1000.;
    RNG & rng = TS::ptr()->get_rng();

    for (int ITER = 0; ITER < 20; ++ITER)
    {
        SCOPED_TRACE(cv::format("iteration %d", ITER));

        const std::string fname = cvtest::findDataFile("shared/fruits.png");
        const Mat original = cv::imread(fname, IMREAD_GRAYSCALE);

        const double thresh1 = rng.uniform(0., range);
        const double thresh2 = rng.uniform(0., range * 0.3);
        const Size sz(rng.uniform(127, 800), rng.uniform(127, 600));
        const Size osz = original.size();

        // preparation
        Mat img;
        if (sz.width >= osz.width || sz.height >= osz.height)
        {
            // larger image -> scale
            resize(original, img, sz, 0, 0, INTER_LINEAR_EXACT);
        }
        else
        {
            // smaller image -> crop
            Point origin(rng.uniform(0, osz.width - sz.width), rng.uniform(0, osz.height - sz.height));
            Rect roi(origin, sz);
            original(roi).copyTo(img);
        }
        GaussianBlur(img, img, Size(5, 5), 0);

        // regular function
        Mat result;
        {
            cv::Canny(img, result, thresh1, thresh2, aperture, trueGradient);
        }

        // custom derivatives
        Mat customResult;
        {
            Mat dxkernel = cvtest::calcSobelKernel2D(1, 0, aperture, 0);
            Mat dykernel = cvtest::calcSobelKernel2D(0, 1, aperture, 0);
            Point anchor(aperture / 2, aperture / 2);
            cv::Mat dx, dy;
            cvtest::filter2D(img, dx, CV_16S, dxkernel, anchor, 0, BORDER_REPLICATE);
            cvtest::filter2D(img, dy, CV_16S, dykernel, anchor, 0, BORDER_REPLICATE);
            cv::Canny(dx, dy, customResult, thresh1, thresh2, trueGradient);
        }

        Mat reference;
        Canny_reference(img, reference, thresh1, thresh2, aperture, trueGradient);

        EXPECT_MAT_NEAR(result, reference, 0);
        EXPECT_MAT_NEAR(customResult, reference, 0);
    }
}

INSTANTIATE_TEST_CASE_P(/**/, Canny_Modes,
    testing::Combine(
        testing::Values(3, 5),
        testing::Values(true, false)));


/*
 * Comparing OpenVX based implementation with the main one
*/

#ifndef IMPLEMENT_PARAM_CLASS
#define IMPLEMENT_PARAM_CLASS(name, type) \
    class name \
    { \
    public: \
        name ( type arg = type ()) : val_(arg) {} \
        operator type () const {return val_;} \
    private: \
        type val_; \
    }; \
    inline void PrintTo( name param, std::ostream* os) \
    { \
        *os << #name <<  "(" << testing::PrintToString(static_cast< type >(param)) << ")"; \
    }
#endif // IMPLEMENT_PARAM_CLASS

IMPLEMENT_PARAM_CLASS(ImagePath, string)
IMPLEMENT_PARAM_CLASS(ApertureSize, int)
IMPLEMENT_PARAM_CLASS(L2gradient, bool)

PARAM_TEST_CASE(CannyVX, ImagePath, ApertureSize, L2gradient)
{
    string imgPath;
    int kSize;
    bool useL2;
    Mat src, dst;

    virtual void SetUp()
    {
        imgPath = GET_PARAM(0);
        kSize = GET_PARAM(1);
        useL2 = GET_PARAM(2);
    }

    void loadImage()
    {
        src = cv::imread(cvtest::TS::ptr()->get_data_path() + imgPath, IMREAD_GRAYSCALE);
        ASSERT_FALSE(src.empty()) << "can't load image: " << imgPath;
    }
};

TEST_P(CannyVX, Accuracy)
{
    if(haveOpenVX())
    {
        loadImage();

        setUseOpenVX(false);
        Mat canny;
        cv::Canny(src, canny, 100, 150, 3);

        setUseOpenVX(true);
        Mat cannyVX;
        cv::Canny(src, cannyVX, 100, 150, 3);

        // 'smart' diff check (excluding isolated pixels)
        Mat diff, diff1;
        absdiff(canny, cannyVX, diff);
        boxFilter(diff, diff1, -1, Size(3,3));
        const int minPixelsAroud = 3; // empirical number
        diff1 = diff1 > 255/9 * minPixelsAroud;
        erode(diff1, diff1, Mat());
        double error = cv::norm(diff1, NORM_L1) / 255;
        const int maxError = std::min(10, diff.size().area()/100); // empirical number
        if(error > maxError)
        {
            string outPath =
                    string("CannyVX-diff-") +
                    imgPath + '-' +
                    'k' + char(kSize+'0') + '-' +
                    (useL2 ? "l2" : "l1");
            std::replace(outPath.begin(), outPath.end(), '/', '_');
            std::replace(outPath.begin(), outPath.end(), '\\', '_');
            std::replace(outPath.begin(), outPath.end(), '.', '_');
            imwrite(outPath+".png", diff);
        }
        ASSERT_LE(error, maxError);

    }
}

    INSTANTIATE_TEST_CASE_P(
                ImgProc, CannyVX,
                testing::Combine(
                    testing::Values(
                        string("shared/baboon.png"),
                        string("shared/fruits.png"),
                        string("shared/lena.png"),
                        string("shared/pic1.png"),
                        string("shared/pic3.png"),
                        string("shared/pic5.png"),
                        string("shared/pic6.png")
                    ),
                    testing::Values(ApertureSize(3), ApertureSize(5)),
                    testing::Values(L2gradient(false), L2gradient(true))
                )
    );

}} // namespace
/* End of file. */