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opencv/modules/gpu/test/test_arithm.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
#ifdef HAVE_CUDA
struct ArithmTest : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
cv::Size size;
cv::Mat mat1, mat2;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat1 = cvtest::randomMat(rng, size, type, 1, 16, false);
mat2 = cvtest::randomMat(rng, size, type, 1, 16, false);
}
};
////////////////////////////////////////////////////////////////////////////////
// add
struct AddArray : ArithmTest {};
TEST_P(AddArray, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::Mat dst_gold;
cv::add(mat1, mat2, dst_gold);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::add(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), gpuRes);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(Arithm, AddArray, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1)));
struct AddScalar : ArithmTest {};
TEST_P(AddScalar, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
cv::Scalar val(rng.uniform(0.1, 3.0), rng.uniform(0.1, 3.0));
PRINT_PARAM(val);
cv::Mat dst_gold;
cv::add(mat1, val, dst_gold);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::add(cv::gpu::GpuMat(mat1), val, gpuRes);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-5);
}
INSTANTIATE_TEST_CASE_P(Arithm, AddScalar, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_32FC1, CV_32FC2)));
////////////////////////////////////////////////////////////////////////////////
// subtract
struct SubtractArray : ArithmTest {};
TEST_P(SubtractArray, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::Mat dst_gold;
cv::subtract(mat1, mat2, dst_gold);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::subtract(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), gpuRes);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(Arithm, SubtractArray, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1)));
struct SubtractScalar : ArithmTest {};
TEST_P(SubtractScalar, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
cv::Scalar val(rng.uniform(0.1, 3.0), rng.uniform(0.1, 3.0));
PRINT_PARAM(val);
cv::Mat dst_gold;
cv::subtract(mat1, val, dst_gold);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::subtract(cv::gpu::GpuMat(mat1), val, gpuRes);
gpuRes.download(dst);
);
ASSERT_LE(checkNorm(dst_gold, dst), 1e-5);
}
INSTANTIATE_TEST_CASE_P(Arithm, SubtractScalar, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_32FC1, CV_32FC2)));
////////////////////////////////////////////////////////////////////////////////
// multiply
struct MultiplyArray : ArithmTest {};
TEST_P(MultiplyArray, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::Mat dst_gold;
cv::multiply(mat1, mat2, dst_gold);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::multiply(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), gpuRes);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(Arithm, MultiplyArray, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1)));
struct MultiplyScalar : ArithmTest {};
TEST_P(MultiplyScalar, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
cv::Scalar val(rng.uniform(0.1, 3.0), rng.uniform(0.1, 3.0));
PRINT_PARAM(val);
cv::Mat dst_gold;
cv::multiply(mat1, val, dst_gold);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::multiply(cv::gpu::GpuMat(mat1), val, gpuRes);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-5);
}
INSTANTIATE_TEST_CASE_P(Arithm, MultiplyScalar, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_32FC1)));
////////////////////////////////////////////////////////////////////////////////
// divide
struct DivideArray : ArithmTest {};
TEST_P(DivideArray, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::Mat dst_gold;
cv::divide(mat1, mat2, dst_gold);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::divide(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), gpuRes);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 1.0);
}
INSTANTIATE_TEST_CASE_P(Arithm, DivideArray, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1)));
struct DivideScalar : ArithmTest {};
TEST_P(DivideScalar, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
cv::Scalar val(rng.uniform(0.1, 3.0), rng.uniform(0.1, 3.0));
PRINT_PARAM(val);
cv::Mat dst_gold;
cv::divide(mat1, val, dst_gold);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::divide(cv::gpu::GpuMat(mat1), val, gpuRes);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-5);
}
INSTANTIATE_TEST_CASE_P(Arithm, DivideScalar, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_32FC1)));
////////////////////////////////////////////////////////////////////////////////
// transpose
struct Transpose : ArithmTest {};
TEST_P(Transpose, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::Mat dst_gold;
cv::transpose(mat1, dst_gold);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::transpose(cv::gpu::GpuMat(mat1), gpuRes);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(Arithm, Transpose, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8UC1, CV_8UC4, CV_8SC1, CV_8SC4, CV_16UC2, CV_16SC2, CV_32SC1, CV_32SC2, CV_32FC1, CV_32FC2, CV_64FC1)));
////////////////////////////////////////////////////////////////////////////////
// absdiff
struct AbsdiffArray : ArithmTest {};
TEST_P(AbsdiffArray, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::Mat dst_gold;
cv::absdiff(mat1, mat2, dst_gold);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::absdiff(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), gpuRes);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(Arithm, AbsdiffArray, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1)));
struct AbsdiffScalar : ArithmTest {};
TEST_P(AbsdiffScalar, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
cv::Scalar val(rng.uniform(0.1, 3.0), rng.uniform(0.1, 3.0));
PRINT_PARAM(val);
cv::Mat dst_gold;
cv::absdiff(mat1, val, dst_gold);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::absdiff(cv::gpu::GpuMat(mat1), val, gpuRes);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-5);
}
INSTANTIATE_TEST_CASE_P(Arithm, AbsdiffScalar, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_32FC1)));
////////////////////////////////////////////////////////////////////////////////
// compare
struct Compare : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
cv::gpu::DeviceInfo devInfo;
int cmp_code;
cv::Size size;
cv::Mat mat1, mat2;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
cmp_code = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat1 = cvtest::randomMat(rng, size, CV_32FC1, 1, 16, false);
mat2 = cvtest::randomMat(rng, size, CV_32FC1, 1, 16, false);
cv::compare(mat1, mat2, dst_gold, cmp_code);
}
};
TEST_P(Compare, Accuracy)
{
static const char* cmp_codes[] = {"CMP_EQ", "CMP_GT", "CMP_GE", "CMP_LT", "CMP_LE", "CMP_NE"};
const char* cmpCodeStr = cmp_codes[cmp_code];
PRINT_PARAM(devInfo);
PRINT_PARAM(size);
PRINT_PARAM(cmpCodeStr);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::compare(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), gpuRes, cmp_code);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(Arithm, Compare, testing::Combine(
testing::ValuesIn(devices()),
testing::Values((int)cv::CMP_EQ, (int)cv::CMP_GT, (int)cv::CMP_GE, (int)cv::CMP_LT, (int)cv::CMP_LE, (int)cv::CMP_NE)));
////////////////////////////////////////////////////////////////////////////////
// meanStdDev
struct MeanStdDev : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
cv::Mat mat;
cv::Scalar mean_gold;
cv::Scalar stddev_gold;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat = cvtest::randomMat(rng, size, CV_8UC1, 1, 255, false);
cv::meanStdDev(mat, mean_gold, stddev_gold);
}
};
TEST_P(MeanStdDev, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_PARAM(size);
cv::Scalar mean;
cv::Scalar stddev;
ASSERT_NO_THROW(
cv::gpu::meanStdDev(cv::gpu::GpuMat(mat), mean, stddev);
);
EXPECT_NEAR(mean_gold[0], mean[0], 1e-5);
EXPECT_NEAR(mean_gold[1], mean[1], 1e-5);
EXPECT_NEAR(mean_gold[2], mean[2], 1e-5);
EXPECT_NEAR(mean_gold[3], mean[3], 1e-5);
EXPECT_NEAR(stddev_gold[0], stddev[0], 1e-5);
EXPECT_NEAR(stddev_gold[1], stddev[1], 1e-5);
EXPECT_NEAR(stddev_gold[2], stddev[2], 1e-5);
EXPECT_NEAR(stddev_gold[3], stddev[3], 1e-5);
}
INSTANTIATE_TEST_CASE_P(Arithm, MeanStdDev, testing::ValuesIn(devices()));
////////////////////////////////////////////////////////////////////////////////
// normDiff
static const int norms[] = {cv::NORM_INF, cv::NORM_L1, cv::NORM_L2};
static const char* norms_str[] = {"NORM_INF", "NORM_L1", "NORM_L2"};
struct NormDiff : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
cv::gpu::DeviceInfo devInfo;
int normIdx;
cv::Size size;
cv::Mat mat1, mat2;
double norm_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
normIdx = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat1 = cvtest::randomMat(rng, size, CV_8UC1, 1, 255, false);
mat2 = cvtest::randomMat(rng, size, CV_8UC1, 1, 255, false);
norm_gold = cv::norm(mat1, mat2, norms[normIdx]);
}
};
TEST_P(NormDiff, Accuracy)
{
const char* normStr = norms_str[normIdx];
PRINT_PARAM(devInfo);
PRINT_PARAM(size);
PRINT_PARAM(normStr);
double norm;
ASSERT_NO_THROW(
norm = cv::gpu::norm(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), norms[normIdx]);
);
EXPECT_NEAR(norm_gold, norm, 1e-6);
}
INSTANTIATE_TEST_CASE_P(Arithm, NormDiff, testing::Combine(
testing::ValuesIn(devices()),
testing::Range(0, 3)));
////////////////////////////////////////////////////////////////////////////////
// flip
struct Flip : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
int flip_code;
cv::Size size;
cv::Mat mat;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
flip_code = std::tr1::get<2>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat = cvtest::randomMat(rng, size, type, 1, 255, false);
cv::flip(mat, dst_gold, flip_code);
}
};
TEST_P(Flip, Accuracy)
{
static const char* flip_axis[] = {"Both", "X", "Y"};
const char* flipAxisStr = flip_axis[flip_code + 1];
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
PRINT_PARAM(flipAxisStr);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpu_res;
cv::gpu::flip(cv::gpu::GpuMat(mat), gpu_res, flip_code);
gpu_res.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(Arithm, Flip, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8UC1, CV_8UC4),
testing::Values(0, 1, -1)));
////////////////////////////////////////////////////////////////////////////////
// LUT
struct LUT : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
cv::Size size;
cv::Mat mat;
cv::Mat lut;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat = cvtest::randomMat(rng, size, type, 1, 255, false);
lut = cvtest::randomMat(rng, cv::Size(256, 1), CV_8UC1, 100, 200, false);
cv::LUT(mat, lut, dst_gold);
}
};
TEST_P(LUT, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpu_res;
cv::gpu::LUT(cv::gpu::GpuMat(mat), lut, gpu_res);
gpu_res.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(Arithm, LUT, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8UC1, CV_8UC3)));
////////////////////////////////////////////////////////////////////////////////
// exp
struct Exp : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
cv::Mat mat;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat = cvtest::randomMat(rng, size, CV_32FC1, -10.0, 2.0, false);
cv::exp(mat, dst_gold);
}
};
TEST_P(Exp, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_PARAM(size);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpu_res;
cv::gpu::exp(cv::gpu::GpuMat(mat), gpu_res);
gpu_res.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-5);
}
INSTANTIATE_TEST_CASE_P(Arithm, Exp, testing::ValuesIn(devices()));
////////////////////////////////////////////////////////////////////////////////
// pow
struct Pow : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
double power;
cv::Size size;
cv::Mat mat;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
//size = cv::Size(2, 2);
mat = cvtest::randomMat(rng, size, type, 0.0, 100.0, false);
if (mat.depth() == CV_32F)
power = rng.uniform(1.2f, 3.f);
else
{
int ipower = rng.uniform(2, 8);
power = (float)ipower;
}
cv::pow(mat, power, dst_gold);
}
};
TEST_P(Pow, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
PRINT_PARAM(power);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpu_res;
cv::gpu::pow(cv::gpu::GpuMat(mat), power, gpu_res);
gpu_res.download(dst);
);
/*std::cout << mat << std::endl << std::endl;
std::cout << dst << std::endl << std::endl;
std::cout << dst_gold << std::endl;*/
EXPECT_MAT_NEAR(dst_gold, dst, 1);
}
INSTANTIATE_TEST_CASE_P(Arithm, Pow, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_32F, CV_32FC3)));
////////////////////////////////////////////////////////////////////////////////
// log
struct Log : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
cv::Mat mat;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat = cvtest::randomMat(rng, size, CV_32FC1, 0.0, 100.0, false);
cv::log(mat, dst_gold);
}
};
TEST_P(Log, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_PARAM(size);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpu_res;
cv::gpu::log(cv::gpu::GpuMat(mat), gpu_res);
gpu_res.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-5);
}
INSTANTIATE_TEST_CASE_P(Arithm, Log, testing::ValuesIn(devices()));
////////////////////////////////////////////////////////////////////////////////
// magnitude
struct Magnitude : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
cv::Mat mat1, mat2;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat1 = cvtest::randomMat(rng, size, CV_32FC1, 0.0, 100.0, false);
mat2 = cvtest::randomMat(rng, size, CV_32FC1, 0.0, 100.0, false);
cv::magnitude(mat1, mat2, dst_gold);
}
};
TEST_P(Magnitude, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_PARAM(size);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpu_res;
cv::gpu::magnitude(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), gpu_res);
gpu_res.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-4);
}
INSTANTIATE_TEST_CASE_P(Arithm, Magnitude, testing::ValuesIn(devices()));
////////////////////////////////////////////////////////////////////////////////
// phase
struct Phase : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
cv::Mat mat1, mat2;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat1 = cvtest::randomMat(rng, size, CV_32FC1, 0.0, 100.0, false);
mat2 = cvtest::randomMat(rng, size, CV_32FC1, 0.0, 100.0, false);
cv::phase(mat1, mat2, dst_gold);
}
};
TEST_P(Phase, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_PARAM(size);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpu_res;
cv::gpu::phase(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), gpu_res);
gpu_res.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-3);
}
INSTANTIATE_TEST_CASE_P(Arithm, Phase, testing::ValuesIn(devices()));
////////////////////////////////////////////////////////////////////////////////
// cartToPolar
struct CartToPolar : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
cv::Mat mat1, mat2;
cv::Mat mag_gold;
cv::Mat angle_gold;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat1 = cvtest::randomMat(rng, size, CV_32FC1, -100.0, 100.0, false);
mat2 = cvtest::randomMat(rng, size, CV_32FC1, -100.0, 100.0, false);
cv::cartToPolar(mat1, mat2, mag_gold, angle_gold);
}
};
TEST_P(CartToPolar, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_PARAM(size);
cv::Mat mag, angle;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuMag;
cv::gpu::GpuMat gpuAngle;
cv::gpu::cartToPolar(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), gpuMag, gpuAngle);
gpuMag.download(mag);
gpuAngle.download(angle);
);
EXPECT_MAT_NEAR(mag_gold, mag, 1e-4);
EXPECT_MAT_NEAR(angle_gold, angle, 1e-3);
}
INSTANTIATE_TEST_CASE_P(Arithm, CartToPolar, testing::ValuesIn(devices()));
////////////////////////////////////////////////////////////////////////////////
// polarToCart
struct PolarToCart : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
cv::Mat mag;
cv::Mat angle;
cv::Mat x_gold;
cv::Mat y_gold;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mag = cvtest::randomMat(rng, size, CV_32FC1, -100.0, 100.0, false);
angle = cvtest::randomMat(rng, size, CV_32FC1, 0.0, 2.0 * CV_PI, false);
cv::polarToCart(mag, angle, x_gold, y_gold);
}
};
TEST_P(PolarToCart, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_PARAM(size);
cv::Mat x, y;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuX;
cv::gpu::GpuMat gpuY;
cv::gpu::polarToCart(cv::gpu::GpuMat(mag), cv::gpu::GpuMat(angle), gpuX, gpuY);
gpuX.download(x);
gpuY.download(y);
);
EXPECT_MAT_NEAR(x_gold, x, 1e-4);
EXPECT_MAT_NEAR(y_gold, y, 1e-4);
}
INSTANTIATE_TEST_CASE_P(Arithm, PolarToCart, testing::ValuesIn(devices()));
////////////////////////////////////////////////////////////////////////////////
// minMax
struct MinMax : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
cv::Size size;
cv::Mat mat;
cv::Mat mask;
double minVal_gold;
double maxVal_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat = cvtest::randomMat(rng, size, type, 0.0, 127.0, false);
mask = cvtest::randomMat(rng, size, CV_8UC1, 0, 2, false);
if (type != CV_8S)
{
cv::minMaxLoc(mat, &minVal_gold, &maxVal_gold, 0, 0, mask);
}
else
{
// OpenCV's minMaxLoc doesn't support CV_8S type
minVal_gold = std::numeric_limits<double>::max();
maxVal_gold = -std::numeric_limits<double>::max();
for (int i = 0; i < mat.rows; ++i)
{
const signed char* mat_row = mat.ptr<signed char>(i);
const unsigned char* mask_row = mask.ptr<unsigned char>(i);
for (int j = 0; j < mat.cols; ++j)
{
if (mask_row[j])
{
signed char val = mat_row[j];
if (val < minVal_gold) minVal_gold = val;
if (val > maxVal_gold) maxVal_gold = val;
}
}
}
}
}
};
TEST_P(MinMax, Accuracy)
{
if (type == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
return;
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
double minVal, maxVal;
ASSERT_NO_THROW(
cv::gpu::minMax(cv::gpu::GpuMat(mat), &minVal, &maxVal, cv::gpu::GpuMat(mask));
);
EXPECT_DOUBLE_EQ(minVal_gold, minVal);
EXPECT_DOUBLE_EQ(maxVal_gold, maxVal);
}
INSTANTIATE_TEST_CASE_P(Arithm, MinMax, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F)));
////////////////////////////////////////////////////////////////////////////////
// minMaxLoc
struct MinMaxLoc : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
cv::Size size;
cv::Mat mat;
cv::Mat mask;
double minVal_gold;
double maxVal_gold;
cv::Point minLoc_gold;
cv::Point maxLoc_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat = cvtest::randomMat(rng, size, type, 0.0, 127.0, false);
mask = cvtest::randomMat(rng, size, CV_8UC1, 0, 2, false);
if (type != CV_8S)
{
cv::minMaxLoc(mat, &minVal_gold, &maxVal_gold, &minLoc_gold, &maxLoc_gold, mask);
}
else
{
// OpenCV's minMaxLoc doesn't support CV_8S type
minVal_gold = std::numeric_limits<double>::max();
maxVal_gold = -std::numeric_limits<double>::max();
for (int i = 0; i < mat.rows; ++i)
{
const signed char* mat_row = mat.ptr<signed char>(i);
const unsigned char* mask_row = mask.ptr<unsigned char>(i);
for (int j = 0; j < mat.cols; ++j)
{
if (mask_row[j])
{
signed char val = mat_row[j];
if (val < minVal_gold) { minVal_gold = val; minLoc_gold = cv::Point(j, i); }
if (val > maxVal_gold) { maxVal_gold = val; maxLoc_gold = cv::Point(j, i); }
}
}
}
}
}
};
TEST_P(MinMaxLoc, Accuracy)
{
if (type == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
return;
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
double minVal, maxVal;
cv::Point minLoc, maxLoc;
ASSERT_NO_THROW(
cv::gpu::minMaxLoc(cv::gpu::GpuMat(mat), &minVal, &maxVal, &minLoc, &maxLoc, cv::gpu::GpuMat(mask));
);
EXPECT_DOUBLE_EQ(minVal_gold, minVal);
EXPECT_DOUBLE_EQ(maxVal_gold, maxVal);
int cmpMinVals = memcmp(mat.data + minLoc_gold.y * mat.step + minLoc_gold.x * mat.elemSize(),
mat.data + minLoc.y * mat.step + minLoc.x * mat.elemSize(),
mat.elemSize());
int cmpMaxVals = memcmp(mat.data + maxLoc_gold.y * mat.step + maxLoc_gold.x * mat.elemSize(),
mat.data + maxLoc.y * mat.step + maxLoc.x * mat.elemSize(),
mat.elemSize());
EXPECT_EQ(0, cmpMinVals);
EXPECT_EQ(0, cmpMaxVals);
}
INSTANTIATE_TEST_CASE_P(Arithm, MinMaxLoc, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F)));
////////////////////////////////////////////////////////////////////////////
// countNonZero
struct CountNonZero : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
cv::Size size;
cv::Mat mat;
int n_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
cv::Mat matBase = cvtest::randomMat(rng, size, CV_8U, 0.0, 1.0, false);
matBase.convertTo(mat, type);
n_gold = cv::countNonZero(mat);
}
};
TEST_P(CountNonZero, Accuracy)
{
if (type == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
return;
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
int n;
ASSERT_NO_THROW(
n = cv::gpu::countNonZero(cv::gpu::GpuMat(mat));
);
ASSERT_EQ(n_gold, n);
}
INSTANTIATE_TEST_CASE_P(Arithm, CountNonZero, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F)));
//////////////////////////////////////////////////////////////////////////////
// sum
struct Sum : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
cv::Size size;
cv::Mat mat;
cv::Scalar sum_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat = cvtest::randomMat(rng, size, CV_8U, 0.0, 10.0, false);
sum_gold = cv::sum(mat);
}
};
TEST_P(Sum, Accuracy)
{
if (type == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
return;
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::Scalar sum;
ASSERT_NO_THROW(
sum = cv::gpu::sum(cv::gpu::GpuMat(mat));
);
EXPECT_NEAR(sum[0], sum_gold[0], mat.size().area() * 1e-5);
EXPECT_NEAR(sum[1], sum_gold[1], mat.size().area() * 1e-5);
EXPECT_NEAR(sum[2], sum_gold[2], mat.size().area() * 1e-5);
EXPECT_NEAR(sum[3], sum_gold[3], mat.size().area() * 1e-5);
}
INSTANTIATE_TEST_CASE_P(Arithm, Sum, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F)));
struct AbsSum : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
cv::Size size;
cv::Mat mat;
cv::Scalar sum_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat = cvtest::randomMat(rng, size, CV_8U, 0.0, 10.0, false);
sum_gold = cv::norm(mat, cv::NORM_L1);
}
};
TEST_P(AbsSum, Accuracy)
{
if (type == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
return;
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::Scalar sum;
ASSERT_NO_THROW(
sum = cv::gpu::absSum(cv::gpu::GpuMat(mat));
);
EXPECT_NEAR(sum[0], sum_gold[0], mat.size().area() * 1e-5);
EXPECT_NEAR(sum[1], sum_gold[1], mat.size().area() * 1e-5);
EXPECT_NEAR(sum[2], sum_gold[2], mat.size().area() * 1e-5);
EXPECT_NEAR(sum[3], sum_gold[3], mat.size().area() * 1e-5);
}
INSTANTIATE_TEST_CASE_P(Arithm, AbsSum, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F)));
struct SqrSum : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
cv::Size size;
cv::Mat mat;
cv::Scalar sum_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat = cvtest::randomMat(rng, size, CV_8U, 0.0, 10.0, false);
cv::Mat sqrmat;
cv::multiply(mat, mat, sqrmat);
sum_gold = cv::sum(sqrmat);
}
};
TEST_P(SqrSum, Accuracy)
{
if (type == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
return;
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::Scalar sum;
ASSERT_NO_THROW(
sum = cv::gpu::sqrSum(cv::gpu::GpuMat(mat));
);
EXPECT_NEAR(sum[0], sum_gold[0], mat.size().area() * 1e-5);
EXPECT_NEAR(sum[1], sum_gold[1], mat.size().area() * 1e-5);
EXPECT_NEAR(sum[2], sum_gold[2], mat.size().area() * 1e-5);
EXPECT_NEAR(sum[3], sum_gold[3], mat.size().area() * 1e-5);
}
INSTANTIATE_TEST_CASE_P(Arithm, SqrSum, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F)));
//////////////////////////////////////////////////////////////////////////////
// bitwise
struct BitwiseNot : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
cv::Size size;
cv::Mat mat;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat.create(size, type);
for (int i = 0; i < mat.rows; ++i)
{
cv::Mat row(1, static_cast<int>(mat.cols * mat.elemSize()), CV_8U, (void*)mat.ptr(i));
rng.fill(row, cv::RNG::UNIFORM, cv::Scalar(0), cv::Scalar(255));
}
dst_gold = ~mat;
}
};
TEST_P(BitwiseNot, Accuracy)
{
if (mat.depth() == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
return;
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat dev_dst;
cv::gpu::bitwise_not(cv::gpu::GpuMat(mat), dev_dst);
dev_dst.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(Arithm, BitwiseNot, testing::Combine(
testing::ValuesIn(devices()),
testing::ValuesIn(all_types())));
struct BitwiseOr : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
cv::Size size;
cv::Mat mat1;
cv::Mat mat2;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat1.create(size, type);
mat2.create(size, type);
for (int i = 0; i < mat1.rows; ++i)
{
cv::Mat row1(1, static_cast<int>(mat1.cols * mat1.elemSize()), CV_8U, (void*)mat1.ptr(i));
rng.fill(row1, cv::RNG::UNIFORM, cv::Scalar(0), cv::Scalar(255));
cv::Mat row2(1, static_cast<int>(mat2.cols * mat2.elemSize()), CV_8U, (void*)mat2.ptr(i));
rng.fill(row2, cv::RNG::UNIFORM, cv::Scalar(0), cv::Scalar(255));
}
dst_gold = mat1 | mat2;
}
};
TEST_P(BitwiseOr, Accuracy)
{
if (mat1.depth() == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
return;
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat dev_dst;
cv::gpu::bitwise_or(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), dev_dst);
dev_dst.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(Arithm, BitwiseOr, testing::Combine(
testing::ValuesIn(devices()),
testing::ValuesIn(all_types())));
struct BitwiseAnd : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
cv::Size size;
cv::Mat mat1;
cv::Mat mat2;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat1.create(size, type);
mat2.create(size, type);
for (int i = 0; i < mat1.rows; ++i)
{
cv::Mat row1(1, static_cast<int>(mat1.cols * mat1.elemSize()), CV_8U, (void*)mat1.ptr(i));
rng.fill(row1, cv::RNG::UNIFORM, cv::Scalar(0), cv::Scalar(255));
cv::Mat row2(1, static_cast<int>(mat2.cols * mat2.elemSize()), CV_8U, (void*)mat2.ptr(i));
rng.fill(row2, cv::RNG::UNIFORM, cv::Scalar(0), cv::Scalar(255));
}
dst_gold = mat1 & mat2;
}
};
TEST_P(BitwiseAnd, Accuracy)
{
if (mat1.depth() == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
return;
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat dev_dst;
cv::gpu::bitwise_and(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), dev_dst);
dev_dst.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(Arithm, BitwiseAnd, testing::Combine(
testing::ValuesIn(devices()),
testing::ValuesIn(all_types())));
struct BitwiseXor : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
cv::Size size;
cv::Mat mat1;
cv::Mat mat2;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
mat1.create(size, type);
mat2.create(size, type);
for (int i = 0; i < mat1.rows; ++i)
{
cv::Mat row1(1, static_cast<int>(mat1.cols * mat1.elemSize()), CV_8U, (void*)mat1.ptr(i));
rng.fill(row1, cv::RNG::UNIFORM, cv::Scalar(0), cv::Scalar(255));
cv::Mat row2(1, static_cast<int>(mat2.cols * mat2.elemSize()), CV_8U, (void*)mat2.ptr(i));
rng.fill(row2, cv::RNG::UNIFORM, cv::Scalar(0), cv::Scalar(255));
}
dst_gold = mat1 ^ mat2;
}
};
TEST_P(BitwiseXor, Accuracy)
{
if (mat1.depth() == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
return;
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat dev_dst;
cv::gpu::bitwise_xor(cv::gpu::GpuMat(mat1), cv::gpu::GpuMat(mat2), dev_dst);
dev_dst.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(Arithm, BitwiseXor, testing::Combine(
testing::ValuesIn(devices()),
testing::ValuesIn(all_types())));
//////////////////////////////////////////////////////////////////////////////
// addWeighted
struct AddWeighted : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int, int, int> >
{
cv::gpu::DeviceInfo devInfo;
int type1;
int type2;
int dtype;
cv::Size size;
cv::Mat src1;
cv::Mat src2;
double alpha;
double beta;
double gamma;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type1 = std::tr1::get<1>(GetParam());
type2 = std::tr1::get<2>(GetParam());
dtype = std::tr1::get<3>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));
src1 = cvtest::randomMat(rng, size, type1, 0.0, 255.0, false);
src2 = cvtest::randomMat(rng, size, type2, 0.0, 255.0, false);
alpha = rng.uniform(-10.0, 10.0);
beta = rng.uniform(-10.0, 10.0);
gamma = rng.uniform(-10.0, 10.0);
cv::addWeighted(src1, alpha, src2, beta, gamma, dst_gold, dtype);
}
};
TEST_P(AddWeighted, Accuracy)
{
if ((src1.depth() == CV_64F || src2.depth() == CV_64F || dst_gold.depth() == CV_64F) && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
return;
PRINT_PARAM(devInfo);
PRINT_TYPE(type1);
PRINT_TYPE(type2);
PRINT_TYPE(dtype);
PRINT_PARAM(size);
PRINT_PARAM(alpha);
PRINT_PARAM(beta);
PRINT_PARAM(gamma);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat dev_dst;
cv::gpu::addWeighted(cv::gpu::GpuMat(src1), alpha, cv::gpu::GpuMat(src2), beta, gamma, dev_dst, dtype);
dev_dst.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, dtype < CV_32F ? 1.0 : 1e-12);
}
INSTANTIATE_TEST_CASE_P(Arithm, AddWeighted, testing::Combine(
testing::ValuesIn(devices()),
testing::ValuesIn(types(CV_8U, CV_64F, 1, 1)),
testing::ValuesIn(types(CV_8U, CV_64F, 1, 1)),
testing::ValuesIn(types(CV_8U, CV_64F, 1, 1))));
//////////////////////////////////////////////////////////////////////////////
// reduce
struct Reduce : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int, int, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
int dim;
int reduceOp;
cv::Size size;
cv::Mat src;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
dim = std::tr1::get<2>(GetParam());
reduceOp = std::tr1::get<3>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(100, 400), rng.uniform(100, 400));
src = cvtest::randomMat(rng, size, type, 0.0, 255.0, false);
cv::reduce(src, dst_gold, dim, reduceOp, reduceOp == CV_REDUCE_SUM || reduceOp == CV_REDUCE_AVG ? CV_32F : CV_MAT_DEPTH(type));
if (dim == 1)
{
dst_gold.cols = dst_gold.rows;
dst_gold.rows = 1;
dst_gold.step = dst_gold.cols * dst_gold.elemSize();
}
}
};
TEST_P(Reduce, Accuracy)
{
static const char* reduceOpStrs[] = {"CV_REDUCE_SUM", "CV_REDUCE_AVG", "CV_REDUCE_MAX", "CV_REDUCE_MIN"};
const char* reduceOpStr = reduceOpStrs[reduceOp];
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(dim);
PRINT_PARAM(reduceOpStr);
PRINT_PARAM(size);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat dev_dst;
cv::gpu::reduce(cv::gpu::GpuMat(src), dev_dst, dim, reduceOp, reduceOp == CV_REDUCE_SUM || reduceOp == CV_REDUCE_AVG ? CV_32F : CV_MAT_DEPTH(type));
dev_dst.download(dst);
);
double norm = reduceOp == CV_REDUCE_SUM || reduceOp == CV_REDUCE_AVG ? 1e-1 : 0.0;
EXPECT_MAT_NEAR(dst_gold, dst, norm);
}
INSTANTIATE_TEST_CASE_P(Arithm, Reduce, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_16UC1, CV_16UC3, CV_16UC4, CV_32FC1, CV_32FC3, CV_32FC4),
testing::Values(0, 1),
testing::Values((int)CV_REDUCE_SUM, (int)CV_REDUCE_AVG, (int)CV_REDUCE_MAX, (int)CV_REDUCE_MIN)));
#endif // HAVE_CUDA