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#include "test_precomp.hpp"
#include <numeric>

namespace opencv_test { namespace {

class CV_DisTransTest : public cvtest::ArrayTest
{
public:
    CV_DisTransTest();

protected:
    void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
    double get_success_error_level( int test_case_idx, int i, int j );
    void run_func();
    void prepare_to_validation( int );

    void get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high );
    int prepare_test_case( int test_case_idx );

    int mask_size;
    int dist_type;
    int fill_labels;
    float mask[3];
};


CV_DisTransTest::CV_DisTransTest()
{
    test_array[INPUT].push_back(NULL);
    test_array[OUTPUT].push_back(NULL);
    test_array[OUTPUT].push_back(NULL);
    test_array[REF_OUTPUT].push_back(NULL);
    test_array[REF_OUTPUT].push_back(NULL);
    optional_mask = false;
    element_wise_relative_error = true;
}


void CV_DisTransTest::get_test_array_types_and_sizes( int test_case_idx,
                                                vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
    RNG& rng = ts->get_rng();
    cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );

    types[INPUT][0] = CV_8UC1;
    types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_32FC1;
    types[OUTPUT][1] = types[REF_OUTPUT][1] = CV_32SC1;

    if( cvtest::randInt(rng) & 1 )
    {
        mask_size = 3;
    }
    else
    {
        mask_size = 5;
    }

    dist_type = cvtest::randInt(rng) % 3;
    dist_type = dist_type == 0 ? CV_DIST_C : dist_type == 1 ? CV_DIST_L1 : CV_DIST_L2;

    // for now, check only the "labeled" distance transform mode
    fill_labels = 0;

    if( !fill_labels )
        sizes[OUTPUT][1] = sizes[REF_OUTPUT][1] = cvSize(0,0);
}


double CV_DisTransTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
    Size sz = test_mat[INPUT][0].size();
    return dist_type == CV_DIST_C || dist_type == CV_DIST_L1 ? 0 : 0.01*MAX(sz.width, sz.height);
}


void CV_DisTransTest::get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high )
{
    cvtest::ArrayTest::get_minmax_bounds( i, j, type, low, high );
    if( i == INPUT && CV_MAT_DEPTH(type) == CV_8U )
    {
        low = Scalar::all(0);
        high = Scalar::all(10);
    }
}

int CV_DisTransTest::prepare_test_case( int test_case_idx )
{
    int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
    if( code > 0 )
    {
        // the function's response to an "all-nonzeros" image is not determined,
        // so put at least one zero point
        Mat& mat = test_mat[INPUT][0];
        RNG& rng = ts->get_rng();
        int i = cvtest::randInt(rng) % mat.rows;
        int j = cvtest::randInt(rng) % mat.cols;
        mat.at<uchar>(i,j) = 0;
    }

    return code;
}


void CV_DisTransTest::run_func()
{
    cvDistTransform( test_array[INPUT][0], test_array[OUTPUT][0], dist_type, mask_size,
                     dist_type == CV_DIST_USER ? mask : 0, test_array[OUTPUT][1] );
}


static void
cvTsDistTransform( const CvMat* _src, CvMat* _dst, int dist_type,
                   int mask_size, float* _mask, CvMat* /*_labels*/ )
{
    int i, j, k;
    int width = _src->cols, height = _src->rows;
    const float init_val = 1e6;
    float mask[3];
    CvMat* temp;
    int ofs[16] = {0};
    float delta[16];
    int tstep, count;

    CV_Assert( mask_size == 3 || mask_size == 5 );

    if( dist_type == CV_DIST_USER )
        memcpy( mask, _mask, sizeof(mask) );
    else if( dist_type == CV_DIST_C )
    {
        mask_size = 3;
        mask[0] = mask[1] = 1.f;
    }
    else if( dist_type == CV_DIST_L1 )
    {
        mask_size = 3;
        mask[0] = 1.f;
        mask[1] = 2.f;
    }
    else if( mask_size == 3 )
    {
        mask[0] = 0.955f;
        mask[1] = 1.3693f;
    }
    else
    {
        mask[0] = 1.0f;
        mask[1] = 1.4f;
        mask[2] = 2.1969f;
    }

    temp = cvCreateMat( height + mask_size-1, width + mask_size-1, CV_32F );
    tstep = temp->step / sizeof(float);

    if( mask_size == 3 )
    {
        count = 4;
        ofs[0] = -1; delta[0] = mask[0];
        ofs[1] = -tstep-1; delta[1] = mask[1];
        ofs[2] = -tstep; delta[2] = mask[0];
        ofs[3] = -tstep+1; delta[3] = mask[1];
    }
    else
    {
        count = 8;
        ofs[0] = -1; delta[0] = mask[0];
        ofs[1] = -tstep-2; delta[1] = mask[2];
        ofs[2] = -tstep-1; delta[2] = mask[1];
        ofs[3] = -tstep; delta[3] = mask[0];
        ofs[4] = -tstep+1; delta[4] = mask[1];
        ofs[5] = -tstep+2; delta[5] = mask[2];
        ofs[6] = -tstep*2-1; delta[6] = mask[2];
        ofs[7] = -tstep*2+1; delta[7] = mask[2];
    }

    for( i = 0; i < mask_size/2; i++ )
    {
        float* t0 = (float*)(temp->data.ptr + i*temp->step);
        float* t1 = (float*)(temp->data.ptr + (temp->rows - i - 1)*temp->step);

        for( j = 0; j < width + mask_size - 1; j++ )
            t0[j] = t1[j] = init_val;
    }

    for( i = 0; i < height; i++ )
    {
        uchar* s = _src->data.ptr + i*_src->step;
        float* tmp = (float*)(temp->data.ptr + temp->step*(i + (mask_size/2))) + (mask_size/2);

        for( j = 0; j < mask_size/2; j++ )
            tmp[-j-1] = tmp[j + width] = init_val;

        for( j = 0; j < width; j++ )
        {
            if( s[j] == 0 )
                tmp[j] = 0;
            else
            {
                float min_dist = init_val;
                for( k = 0; k < count; k++ )
                {
                    float t = tmp[j+ofs[k]] + delta[k];
                    if( min_dist > t )
                        min_dist = t;
                }
                tmp[j] = min_dist;
            }
        }
    }

    for( i = height - 1; i >= 0; i-- )
    {
        float* d = (float*)(_dst->data.ptr + i*_dst->step);
        float* tmp = (float*)(temp->data.ptr + temp->step*(i + (mask_size/2))) + (mask_size/2);

        for( j = width - 1; j >= 0; j-- )
        {
            float min_dist = tmp[j];
            if( min_dist > mask[0] )
            {
                for( k = 0; k < count; k++ )
                {
                    float t = tmp[j-ofs[k]] + delta[k];
                    if( min_dist > t )
                        min_dist = t;
                }
                tmp[j] = min_dist;
            }
            d[j] = min_dist;
        }
    }

    cvReleaseMat( &temp );
}


void CV_DisTransTest::prepare_to_validation( int /*test_case_idx*/ )
{
    CvMat _input = cvMat(test_mat[INPUT][0]), _output = cvMat(test_mat[REF_OUTPUT][0]);

    cvTsDistTransform( &_input, &_output, dist_type, mask_size, mask, 0 );
}


TEST(Imgproc_DistanceTransform, accuracy) { CV_DisTransTest test; test.safe_run(); }

BIGDATA_TEST(Imgproc_DistanceTransform, large_image_12218)
{
    const int lls_maxcnt = 79992000;   // labels's maximum count
    const int lls_mincnt = 1;          // labels's minimum count
    int i, j, nz;
    Mat src(8000, 20000, CV_8UC1), dst, labels;
    for( i = 0; i < src.rows; i++ )
        for( j = 0; j < src.cols; j++ )
            src.at<uchar>(i, j) = (j > (src.cols / 2)) ? 0 : 255;

    distanceTransform(src, dst, labels, cv::DIST_L2, cv::DIST_MASK_3, DIST_LABEL_PIXEL);

    double scale = (double)lls_mincnt / (double)lls_maxcnt;
    labels.convertTo(labels, CV_32SC1, scale);
    Size size = labels.size();
    nz = cv::countNonZero(labels);
    EXPECT_EQ(nz, (size.height*size.width / 2));
}

TEST(Imgproc_DistanceTransform, wide_image_22732)
{
    Mat src = Mat::zeros(1, 4099, CV_8U); // 4099 or larger used to be bad
    Mat dist(src.rows, src.cols, CV_32F);
    distanceTransform(src, dist, DIST_L2, DIST_MASK_PRECISE, CV_32F);
    int nz = countNonZero(dist);
    EXPECT_EQ(nz, 0);
}

TEST(Imgproc_DistanceTransform, large_square_22732)
{
    Mat src = Mat::zeros(8000, 8005, CV_8U), dist;
    distanceTransform(src, dist, DIST_L2, DIST_MASK_PRECISE, CV_32F);
    int nz = countNonZero(dist);
    EXPECT_EQ(dist.size(), src.size());
    EXPECT_EQ(dist.type(), CV_32F);
    EXPECT_EQ(nz, 0);

    Point p0(src.cols-1, src.rows-1);
    src.setTo(1);
    src.at<uchar>(p0) = 0;
    distanceTransform(src, dist, DIST_L2, DIST_MASK_PRECISE, CV_32F);
    EXPECT_EQ(dist.size(), src.size());
    EXPECT_EQ(dist.type(), CV_32F);
    bool first = true;
    int nerrs = 0;
    for (int y = 0; y < dist.rows; y++)
        for (int x = 0; x < dist.cols; x++) {
            float d = dist.at<float>(y, x);
            double dx = (double)(x - p0.x), dy = (double)(y - p0.y);
            float d0 = (float)sqrt(dx*dx + dy*dy);
            if (std::abs(d0 - d) > 1) {
                if (first) {
                    printf("y=%d, x=%d. dist_ref=%.2f, dist=%.2f\n", y, x, d0, d);
                    first = false;
                }
                nerrs++;
            }
        }
    EXPECT_EQ(0, nerrs) << "reference distance map is different from computed one at " << nerrs << " pixels\n";
}

BIGDATA_TEST(Imgproc_DistanceTransform, issue_23895_3x3)
{
    Mat src = Mat::zeros(50000, 50000, CV_8U), dist;
    distanceTransform(src.col(0), dist, DIST_L2, DIST_MASK_3);
    int nz = countNonZero(dist);
    EXPECT_EQ(nz, 0);
}

BIGDATA_TEST(Imgproc_DistanceTransform, issue_23895_5x5)
{
    Mat src = Mat::zeros(50000, 50000, CV_8U), dist;
    distanceTransform(src.col(0), dist, DIST_L2, DIST_MASK_5);
    int nz = countNonZero(dist);
    EXPECT_EQ(nz, 0);
}

BIGDATA_TEST(Imgproc_DistanceTransform, issue_23895_5x5_labels)
{
    Mat src = Mat::zeros(50000, 50000, CV_8U), dist, labels;
    distanceTransform(src.col(0), dist, labels, DIST_L2, DIST_MASK_5);
    int nz = countNonZero(dist);
    EXPECT_EQ(nz, 0);
}

TEST(Imgproc_DistanceTransform, max_distance_3x3)
{
    Mat src = Mat::ones(1, 70000, CV_8U), dist;
    src.at<uint8_t>(0, 0) = 0;
    distanceTransform(src, dist, DIST_L2, DIST_MASK_3);

    double minVal, maxVal;
    minMaxLoc(dist, &minVal, &maxVal);
    EXPECT_GE(maxVal, 65533);
}

TEST(Imgproc_DistanceTransform, max_distance_5x5)
{
    Mat src = Mat::ones(1, 70000, CV_8U), dist;
    src.at<uint8_t>(0, 0) = 0;
    distanceTransform(src, dist, DIST_L2, DIST_MASK_5);

    double minVal, maxVal;
    minMaxLoc(dist, &minVal, &maxVal);
    EXPECT_GE(maxVal, 65533);
}

TEST(Imgproc_DistanceTransform, max_distance_5x5_labels)
{
    Mat src = Mat::ones(1, 70000, CV_8U), dist, labels;
    src.at<uint8_t>(0, 0) = 0;
    distanceTransform(src, dist, labels, DIST_L2, DIST_MASK_5);

    double minVal, maxVal;
    minMaxLoc(dist, &minVal, &maxVal);
    EXPECT_GE(maxVal, 65533);
}

TEST(Imgproc_DistanceTransform, precise_long_dist)
{
    static const int maxDist = 1 << 16;
    Mat src = Mat::ones(1, 70000, CV_8U), dist;
    src.at<uint8_t>(0, 0) = 0;
    distanceTransform(src, dist, DIST_L2, DIST_MASK_PRECISE, CV_32F);

    Mat expected(src.size(), CV_32F);
    std::iota(expected.begin<float>(), expected.end<float>(), 0.f);
    expected.colRange(maxDist, expected.cols).setTo(maxDist);

    EXPECT_EQ(cv::norm(expected, dist, NORM_INF), 0);
}

TEST(Imgproc_DistanceTransform, ipp_deterministic_corner)
{
    setNumThreads(1);

    Mat src(1, 4096, CV_8U, Scalar(255)), dist;
    src.at<uint8_t>(0, 0) = 0;
    distanceTransform(src, dist, DIST_L2, DIST_MASK_PRECISE);
    for (int i = 0; i < src.cols; ++i)
    {
        float expected = static_cast<float>(i);
        ASSERT_EQ(expected, dist.at<float>(0, i)) << cv::format("diff: %e", expected - dist.at<float>(0, i));
    }
}

TEST(Imgproc_DistanceTransform, ipp_deterministic)
{
    setNumThreads(1);
    RNG& rng = TS::ptr()->get_rng();
    Mat src(1, 800, CV_8U, Scalar(255)), dist;
    int p1 = cvtest::randInt(rng) % src.cols;
    int p2 = cvtest::randInt(rng) % src.cols;
    int p3 = cvtest::randInt(rng) % src.cols;
    src.at<uint8_t>(0, p1) = 0;
    src.at<uint8_t>(0, p2) = 0;
    src.at<uint8_t>(0, p3) = 0;
    distanceTransform(src, dist, DIST_L2, DIST_MASK_PRECISE);
    for (int i = 0; i < src.cols; ++i)
    {
        float expected = static_cast<float>(min(min(abs(i - p1), abs(i - p2)), abs(i - p3)));
        ASSERT_EQ(expected, dist.at<float>(0, i)) << cv::format("diff: %e", expected - dist.at<float>(0, i));
    }
}

}} // namespace
