Open Source Computer Vision Library https://opencv.org/
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#ifndef __OPENCV_TEST_UTILITY_HPP__
#define __OPENCV_TEST_UTILITY_HPP__
extern int LOOP_TIMES;
#define MWIDTH 256
#define MHEIGHT 256
#define MIN_VALUE 171
#define MAX_VALUE 357
namespace cvtest {
void showDiff(const Mat& src, const Mat& gold, const Mat& actual, double eps, bool alwaysShow = false);
cv::ocl::oclMat createMat_ocl(cv::RNG& rng, Size size, int type, bool useRoi);
cv::ocl::oclMat loadMat_ocl(cv::RNG& rng, const Mat& m, bool useRoi);
// This function test if gpu_rst matches cpu_rst.
// If the two vectors are not equal, it will return the difference in vector size
// Else it will return (total diff of each cpu and gpu rects covered pixels)/(total cpu rects covered pixels)
// The smaller, the better matched
double checkRectSimilarity(cv::Size sz, std::vector<cv::Rect>& ob1, std::vector<cv::Rect>& ob2);
//! read image from testdata folder.
cv::Mat readImage(const std::string &fileName, int flags = cv::IMREAD_COLOR);
cv::Mat readImageType(const std::string &fname, int type);
double checkNorm(const cv::Mat &m);
double checkNorm(const cv::Mat &m1, const cv::Mat &m2);
double checkSimilarity(const cv::Mat &m1, const cv::Mat &m2);
inline double checkNormRelative(const Mat &m1, const Mat &m2)
{
return cv::norm(m1, m2, cv::NORM_INF) /
std::max((double)std::numeric_limits<float>::epsilon(),
(double)std::max(cv::norm(m1, cv::NORM_INF), norm(m2, cv::NORM_INF)));
}
#define EXPECT_MAT_NORM(mat, eps) \
{ \
EXPECT_LE(checkNorm(cv::Mat(mat)), eps) \
}
#define EXPECT_MAT_NEAR(mat1, mat2, eps) \
{ \
ASSERT_EQ(mat1.type(), mat2.type()); \
ASSERT_EQ(mat1.size(), mat2.size()); \
EXPECT_LE(checkNorm(cv::Mat(mat1), cv::Mat(mat2)), eps) \
<< cv::format("Size: %d x %d", mat1.cols, mat1.rows) << std::endl; \
}
#define EXPECT_MAT_NEAR_RELATIVE(mat1, mat2, eps) \
{ \
ASSERT_EQ(mat1.type(), mat2.type()); \
ASSERT_EQ(mat1.size(), mat2.size()); \
EXPECT_LE(checkNormRelative(cv::Mat(mat1), cv::Mat(mat2)), eps) \
<< cv::format("Size: %d x %d", mat1.cols, mat1.rows) << std::endl; \
}
#define EXPECT_MAT_SIMILAR(mat1, mat2, eps) \
{ \
ASSERT_EQ(mat1.type(), mat2.type()); \
ASSERT_EQ(mat1.size(), mat2.size()); \
EXPECT_LE(checkSimilarity(cv::Mat(mat1), cv::Mat(mat2)), eps); \
}
using perf::MatDepth;
using perf::MatType;
//! return vector with types from specified range.
std::vector<MatType> types(int depth_start, int depth_end, int cn_start, int cn_end);
//! return vector with all types (depth: CV_8U-CV_64F, channels: 1-4).
const std::vector<MatType> &all_types();
class Inverse
{
public:
inline Inverse(bool val = false) : val_(val) {}
inline operator bool() const
{
return val_;
}
private:
bool val_;
};
void PrintTo(const Inverse &useRoi, std::ostream *os);
#define OCL_RNG_SEED 123456
template <typename T>
struct TSTestWithParam : public ::testing::TestWithParam<T>
{
cv::RNG rng;
TSTestWithParam()
{
rng = cv::RNG(OCL_RNG_SEED);
}
int randomInt(int minVal, int maxVal)
{
return rng.uniform(minVal, maxVal);
}
double randomDouble(double minVal, double maxVal)
{
return rng.uniform(minVal, maxVal);
}
double randomDoubleLog(double minVal, double maxVal)
{
double logMin = log((double)minVal + 1);
double logMax = log((double)maxVal + 1);
double pow = rng.uniform(logMin, logMax);
double v = exp(pow) - 1;
CV_Assert(v >= minVal && (v < maxVal || (v == minVal && v == maxVal)));
return v;
}
Size randomSize(int minVal, int maxVal)
{
#if 1
return cv::Size((int)randomDoubleLog(minVal, maxVal), (int)randomDoubleLog(minVal, maxVal));
#else
return cv::Size(randomInt(minVal, maxVal), randomInt(minVal, maxVal));
#endif
}
Size randomSize(int minValX, int maxValX, int minValY, int maxValY)
{
#if 1
return cv::Size(randomDoubleLog(minValX, maxValX), randomDoubleLog(minValY, maxValY));
#else
return cv::Size(randomInt(minVal, maxVal), randomInt(minVal, maxVal));
#endif
}
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, bool useRoi = false)
{
RNG dataRng(rng.next());
return cvtest::randomMat(dataRng, size, type, minVal, maxVal, useRoi);
}
struct Border
{
int top, bot, lef, rig;
};
Border randomBorder(int minValue = 0, int maxValue = MAX_VALUE)
{
Border border = {
(int)randomDoubleLog(minValue, maxValue),
(int)randomDoubleLog(minValue, maxValue),
(int)randomDoubleLog(minValue, maxValue),
(int)randomDoubleLog(minValue, maxValue)
};
return border;
}
void randomSubMat(Mat& whole, Mat& subMat, const Size& roiSize, const Border& border, int type, double minVal, double maxVal)
{
Size wholeSize = Size(roiSize.width + border.lef + border.rig, roiSize.height + border.top + border.bot);
whole = randomMat(wholeSize, type, minVal, maxVal, false);
subMat = whole(Rect(border.lef, border.top, roiSize.width, roiSize.height));
}
void generateOclMat(cv::ocl::oclMat& whole, cv::ocl::oclMat& subMat, const Mat& wholeMat, const Size& roiSize, const Border& border)
{
whole = wholeMat;
subMat = whole(Rect(border.lef, border.top, roiSize.width, roiSize.height));
}
};
#define PARAM_TEST_CASE(name, ...) struct name : public TSTestWithParam< std::tr1::tuple< __VA_ARGS__ > >
#define GET_PARAM(k) std::tr1::get< k >(GetParam())
#define ALL_TYPES testing::ValuesIn(all_types())
#define TYPES(depth_start, depth_end, cn_start, cn_end) testing::ValuesIn(types(depth_start, depth_end, cn_start, cn_end))
#define DIFFERENT_SIZES testing::Values(cv::Size(128, 128), cv::Size(113, 113), cv::Size(1300, 1300))
#define IMAGE_CHANNELS testing::Values(Channels(1), Channels(3), Channels(4))
#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)) << ")"; \
}
IMPLEMENT_PARAM_CLASS(Channels, int)
#endif // IMPLEMENT_PARAM_CLASS
} // namespace cvtest
enum {FLIP_BOTH = 0, FLIP_X = 1, FLIP_Y = -1};
CV_ENUM(FlipCode, FLIP_BOTH, FLIP_X, FLIP_Y)
CV_ENUM(CmpCode, CMP_EQ, CMP_GT, CMP_GE, CMP_LT, CMP_LE, CMP_NE)
CV_ENUM(NormCode, NORM_INF, NORM_L1, NORM_L2, NORM_TYPE_MASK, NORM_RELATIVE, NORM_MINMAX)
CV_ENUM(ReduceOp, CV_REDUCE_SUM, CV_REDUCE_AVG, CV_REDUCE_MAX, CV_REDUCE_MIN)
CV_ENUM(MorphOp, MORPH_OPEN, MORPH_CLOSE, MORPH_GRADIENT, MORPH_TOPHAT, MORPH_BLACKHAT)
CV_ENUM(ThreshOp, THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV)
CV_ENUM(Interpolation, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_AREA)
CV_ENUM(Border, BORDER_REFLECT101, BORDER_REPLICATE, BORDER_CONSTANT, BORDER_REFLECT, BORDER_WRAP)
CV_ENUM(TemplateMethod, TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED)
CV_FLAGS(GemmFlags, GEMM_1_T, GEMM_2_T, GEMM_3_T);
CV_FLAGS(WarpFlags, INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, WARP_INVERSE_MAP)
CV_FLAGS(DftFlags, DFT_INVERSE, DFT_SCALE, DFT_ROWS, DFT_COMPLEX_OUTPUT, DFT_REAL_OUTPUT)
# define OCL_TEST_P(test_case_name, test_name) \
class GTEST_TEST_CLASS_NAME_(test_case_name, test_name) : \
public test_case_name { \
public: \
GTEST_TEST_CLASS_NAME_(test_case_name, test_name)() { } \
virtual void TestBody(); \
void OCLTestBody(); \
private: \
static int AddToRegistry() \
{ \
::testing::UnitTest::GetInstance()->parameterized_test_registry(). \
GetTestCasePatternHolder<test_case_name>(\
#test_case_name, __FILE__, __LINE__)->AddTestPattern(\
#test_case_name, \
#test_name, \
new ::testing::internal::TestMetaFactory< \
GTEST_TEST_CLASS_NAME_(test_case_name, test_name)>()); \
return 0; \
} \
\
static int gtest_registering_dummy_; \
GTEST_DISALLOW_COPY_AND_ASSIGN_(\
GTEST_TEST_CLASS_NAME_(test_case_name, test_name)); \
}; \
\
int GTEST_TEST_CLASS_NAME_(test_case_name, \
test_name)::gtest_registering_dummy_ = \
GTEST_TEST_CLASS_NAME_(test_case_name, test_name)::AddToRegistry(); \
\
void GTEST_TEST_CLASS_NAME_(test_case_name, test_name)::TestBody() \
{ \
try \
{ \
OCLTestBody(); \
} \
catch (const cv::Exception & ex) \
{ \
if (ex.code == CV_OpenCLDoubleNotSupported)\
std::cout << "Test skipped (selected device does not support double)" << std::endl; \
else if (ex.code == CV_OpenCLNoAMDBlasFft) \
std::cout << "Test skipped (AMD Blas / Fft libraries are not available)" << std::endl; \
else \
throw; \
} \
} \
\
void GTEST_TEST_CLASS_NAME_(test_case_name, test_name)::OCLTestBody()
#endif // __OPENCV_TEST_UTILITY_HPP__