opencv/modules/core/test/test_misc.cpp

// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"

namespace opencv_test { namespace {

TEST(Core_OutputArrayCreate, _1997)
{
    struct local {
        static void create(OutputArray arr, Size submatSize, int type)
        {
            int sizes[] = {submatSize.width, submatSize.height};
            arr.create(sizeof(sizes)/sizeof(sizes[0]), sizes, type);
        }
    };

    Mat mat(Size(512, 512), CV_8U);
    Size submatSize = Size(256, 256);

    ASSERT_NO_THROW(local::create( mat(Rect(Point(), submatSize)), submatSize, mat.type() ));
}

TEST(Core_SaturateCast, NegativeNotClipped)
{
    double d = -1.0;
    unsigned int val = cv::saturate_cast<unsigned int>(d);

    ASSERT_EQ(0xffffffff, val);
}

template<typename T, typename U>
static double maxAbsDiff(const T &t, const U &u)
{
  Mat_<double> d;
  absdiff(t, u, d);
  double ret;
  minMaxLoc(d, NULL, &ret);
  return ret;
}

TEST(Core_OutputArrayAssign, _Matxd_Matd)
{
    Mat expected = (Mat_<double>(2,3) << 1, 2, 3, .1, .2, .3);
    Matx23d actualx;

    {
        OutputArray oa(actualx);
        oa.assign(expected);
    }

    Mat actual = (Mat) actualx;

    EXPECT_LE(maxAbsDiff(expected, actual), 0.0);
}

TEST(Core_OutputArrayAssign, _Matxd_Matf)
{
    Mat expected = (Mat_<float>(2,3) << 1, 2, 3, .1, .2, .3);
    Matx23d actualx;

    {
        OutputArray oa(actualx);
        oa.assign(expected);
    }

    Mat actual = (Mat) actualx;

    EXPECT_LE(maxAbsDiff(expected, actual), FLT_EPSILON);
}

TEST(Core_OutputArrayAssign, _Matxf_Matd)
{
    Mat expected = (Mat_<double>(2,3) << 1, 2, 3, .1, .2, .3);
    Matx23f actualx;

    {
        OutputArray oa(actualx);
        oa.assign(expected);
    }

    Mat actual = (Mat) actualx;

    EXPECT_LE(maxAbsDiff(expected, actual), FLT_EPSILON);
}

TEST(Core_OutputArrayAssign, _Matxd_UMatd)
{
    Mat expected = (Mat_<double>(2,3) << 1, 2, 3, .1, .2, .3);
    UMat uexpected = expected.getUMat(ACCESS_READ);
    Matx23d actualx;

    {
        OutputArray oa(actualx);
        oa.assign(uexpected);
    }

    Mat actual = (Mat) actualx;

    EXPECT_LE(maxAbsDiff(expected, actual), 0.0);
}

TEST(Core_OutputArrayAssign, _Matxd_UMatf)
{
    Mat expected = (Mat_<float>(2,3) << 1, 2, 3, .1, .2, .3);
    UMat uexpected = expected.getUMat(ACCESS_READ);
    Matx23d actualx;

    {
        OutputArray oa(actualx);
        oa.assign(uexpected);
    }

    Mat actual = (Mat) actualx;

    EXPECT_LE(maxAbsDiff(expected, actual), FLT_EPSILON);
}

TEST(Core_OutputArrayAssign, _Matxf_UMatd)
{
    Mat expected = (Mat_<double>(2,3) << 1, 2, 3, .1, .2, .3);
    UMat uexpected = expected.getUMat(ACCESS_READ);
    Matx23f actualx;

    {
        OutputArray oa(actualx);
        oa.assign(uexpected);
    }

    Mat actual = (Mat) actualx;

    EXPECT_LE(maxAbsDiff(expected, actual), FLT_EPSILON);
}



int fixedType_handler(OutputArray dst)
{
    int type = CV_32FC2; // return points only {x, y}
    if (dst.fixedType())
    {
        type = dst.type();
        CV_Assert(type == CV_32FC2 || type == CV_32FC3); // allow points + confidence level: {x, y, confidence}
    }
    const int N = 100;
    dst.create(Size(1, N), type);
    Mat m = dst.getMat();
    if (m.type() == CV_32FC2)
    {
        for (int i = 0; i < N; i++)
            m.at<Vec2f>(i) = Vec2f((float)i, (float)(i*2));
    }
    else if (m.type() == CV_32FC3)
    {
        for (int i = 0; i < N; i++)
            m.at<Vec3f>(i) = Vec3f((float)i, (float)(i*2), 1.0f / (i + 1));
    }
    else
    {
        CV_Assert(0 && "Internal error");
    }
    return CV_MAT_CN(type);
}

TEST(Core_OutputArray, FixedType)
{
    Mat_<Vec2f> pointsOnly;
    int num_pointsOnly = fixedType_handler(pointsOnly);
    EXPECT_EQ(2, num_pointsOnly);

    Mat_<Vec3f> pointsWithConfidence;
    int num_pointsWithConfidence = fixedType_handler(pointsWithConfidence);
    EXPECT_EQ(3, num_pointsWithConfidence);

    Mat defaultResult;
    int num_defaultResult = fixedType_handler(defaultResult);
    EXPECT_EQ(2, num_defaultResult);
}



TEST(Core_String, find_last_of__with__empty_string)
{
    cv::String s;
    size_t p = s.find_last_of("q", 0);
    // npos is not exported: EXPECT_EQ(cv::String::npos, p);
    EXPECT_EQ(std::string::npos, p);
}

TEST(Core_String, end_method_regression)
{
    cv::String old_string = "012345";
    cv::String new_string(old_string.begin(), old_string.end());
    EXPECT_EQ(6u, new_string.size());
}

TEST(Core_Copy, repeat_regression_8972)
{
    Mat src = (Mat_<int>(1, 4) << 1, 2, 3, 4);

    ASSERT_ANY_THROW({
                         repeat(src, 5, 1, src);
                     });
}


class ThrowErrorParallelLoopBody : public cv::ParallelLoopBody
{
public:
    ThrowErrorParallelLoopBody(cv::Mat& dst, int i) : dst_(dst), i_(i) {}
    ~ThrowErrorParallelLoopBody() {}
    void operator()(const cv::Range& r) const
    {
        for (int i = r.start; i < r.end; i++)
        {
            CV_Assert(i != i_);
            dst_.row(i).setTo(1);
        }
    }
protected:
    Mat dst_;
    int i_;
};

TEST(Core_Parallel, propagate_exceptions)
{
    Mat dst1(1000, 100, CV_8SC1, Scalar::all(0));
    ASSERT_NO_THROW({
        parallel_for_(cv::Range(0, dst1.rows), ThrowErrorParallelLoopBody(dst1, -1));
    });

    Mat dst2(1000, 100, CV_8SC1, Scalar::all(0));
    ASSERT_THROW({
        parallel_for_(cv::Range(0, dst2.rows), ThrowErrorParallelLoopBody(dst2, dst2.rows / 2));
    }, cv::Exception);
}

TEST(Core_Version, consistency)
{
    // this test verifies that OpenCV version loaded in runtime
    //   is the same this test has been built with
    EXPECT_EQ(CV_VERSION_MAJOR, cv::getVersionMajor());
    EXPECT_EQ(CV_VERSION_MINOR, cv::getVersionMinor());
    EXPECT_EQ(CV_VERSION_REVISION, cv::getVersionRevision());
    EXPECT_EQ(String(CV_VERSION), cv::getVersionString());
}

}} // namespace