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opencv/modules/gpu/test/test_imgproc.cpp

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refactored gpu module tests
2011-06-29 12:14:16 +02:00
/*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
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// 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
///////////////////////////////////////////////////////////////////////////////////////////////////////
// threshold
struct Threshold : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
int threshOp;
cv::Size size;
cv::Mat src;
double maxVal;
double thresh;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
threshOp = std::tr1::get<2>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(20, 150), rng.uniform(20, 150));
src = cvtest::randomMat(rng, size, type, 0.0, 127.0, false);
maxVal = rng.uniform(20.0, 127.0);
thresh = rng.uniform(0.0, maxVal);
cv::threshold(src, dst_gold, thresh, maxVal, threshOp);
}
};
TEST_P(Threshold, Accuracy)
{
static const char* ops[] = {"THRESH_BINARY", "THRESH_BINARY_INV", "THRESH_TRUNC", "THRESH_TOZERO", "THRESH_TOZERO_INV"};
const char* threshOpStr = ops[threshOp];
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
PRINT_PARAM(threshOpStr);
PRINT_PARAM(maxVal);
PRINT_PARAM(thresh);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::threshold(cv::gpu::GpuMat(src), gpuRes, thresh, maxVal, threshOp);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(ImgProc, Threshold, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8U, CV_32F),
testing::Values((int)cv::THRESH_BINARY, (int)cv::THRESH_BINARY_INV, (int)cv::THRESH_TRUNC, (int)cv::THRESH_TOZERO, (int)cv::THRESH_TOZERO_INV)));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// resize
struct Resize : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
int interpolation;
cv::Size size;
cv::Mat src;
cv::Mat dst_gold1;
cv::Mat dst_gold2;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
interpolation = std::tr1::get<2>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(20, 150), rng.uniform(20, 150));
src = cvtest::randomMat(rng, size, type, 0.0, 127.0, false);
cv::resize(src, dst_gold1, cv::Size(), 2.0, 2.0, interpolation);
cv::resize(src, dst_gold2, cv::Size(), 0.5, 0.5, interpolation);
}
};
TEST_P(Resize, Accuracy)
{
static const char* interpolations[] = {"INTER_NEAREST", "INTER_LINEAR"};
const char* interpolationStr = interpolations[interpolation];
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
PRINT_PARAM(interpolationStr);
cv::Mat dst1;
cv::Mat dst2;
ASSERT_NO_THROW(
cv::gpu::GpuMat dev_src(src);
cv::gpu::GpuMat gpuRes1;
cv::gpu::GpuMat gpuRes2;
cv::gpu::resize(dev_src, gpuRes1, cv::Size(), 2.0, 2.0, interpolation);
cv::gpu::resize(dev_src, gpuRes2, cv::Size(), 0.5, 0.5, interpolation);
gpuRes1.download(dst1);
gpuRes2.download(dst2);
);
EXPECT_MAT_SIMILAR(dst_gold1, dst1, 0.5);
EXPECT_MAT_SIMILAR(dst_gold2, dst2, 0.5);
}
INSTANTIATE_TEST_CASE_P(ImgProc, Resize, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8UC1, CV_8UC4),
testing::Values((int)cv::INTER_NEAREST, (int)cv::INTER_LINEAR)));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// copyMakeBorder
struct CopyMakeBorder : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
cv::Size size;
cv::Mat src;
int top;
int botton;
int left;
int right;
cv::Scalar val;
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(20, 150), rng.uniform(20, 150));
src = cvtest::randomMat(rng, size, type, 0.0, 127.0, false);
top = rng.uniform(1, 10);
botton = rng.uniform(1, 10);
left = rng.uniform(1, 10);
right = rng.uniform(1, 10);
val = cv::Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
cv::copyMakeBorder(src, dst_gold, top, botton, left, right, cv::BORDER_CONSTANT, val);
}
};
TEST_P(CopyMakeBorder, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
PRINT_PARAM(top);
PRINT_PARAM(botton);
PRINT_PARAM(left);
PRINT_PARAM(right);
PRINT_PARAM(val);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::copyMakeBorder(cv::gpu::GpuMat(src), gpuRes, top, botton, left, right, val);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(ImgProc, CopyMakeBorder, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8UC1, CV_8UC4, CV_32SC1)));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// warpAffine & warpPerspective
static const int warpFlags[] = {cv::INTER_NEAREST, cv::INTER_LINEAR, cv::INTER_CUBIC, cv::INTER_NEAREST | cv::WARP_INVERSE_MAP, cv::INTER_LINEAR | cv::WARP_INVERSE_MAP, cv::INTER_CUBIC | cv::WARP_INVERSE_MAP};
static const char* warpFlags_str[] = {"INTER_NEAREST", "INTER_LINEAR", "INTER_CUBIC", "INTER_NEAREST | WARP_INVERSE_MAP", "INTER_LINEAR | WARP_INVERSE_MAP", "INTER_CUBIC | WARP_INVERSE_MAP"};
struct WarpAffine : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
int flagIdx;
cv::Size size;
cv::Mat src;
cv::Mat M;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
flagIdx = std::tr1::get<2>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(20, 150), rng.uniform(20, 150));
src = cvtest::randomMat(rng, size, type, 0.0, 127.0, false);
static double reflect[2][3] = { {-1, 0, 0},
{ 0, -1, 0}};
reflect[0][2] = size.width;
reflect[1][2] = size.height;
M = cv::Mat(2, 3, CV_64F, (void*)reflect);
cv::warpAffine(src, dst_gold, M, src.size(), warpFlags[flagIdx]);
}
};
TEST_P(WarpAffine, Accuracy)
{
const char* warpFlagStr = warpFlags_str[flagIdx];
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
PRINT_PARAM(warpFlagStr);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::warpAffine(cv::gpu::GpuMat(src), gpuRes, M, src.size(), warpFlags[flagIdx]);
gpuRes.download(dst);
);
// Check inner parts (ignoring 1 pixel width border)
cv::Mat dst_gold_roi = dst_gold.rowRange(1, dst_gold.rows - 1).colRange(1, dst_gold.cols - 1);
cv::Mat dst_roi = dst.rowRange(1, dst.rows - 1).colRange(1, dst.cols - 1);
EXPECT_MAT_NEAR(dst_gold_roi, dst_roi, 1e-3);
}
struct WarpPerspective : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
int flagIdx;
cv::Size size;
cv::Mat src;
cv::Mat M;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
flagIdx = std::tr1::get<2>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(rng.uniform(20, 150), rng.uniform(20, 150));
src = cvtest::randomMat(rng, size, type, 0.0, 127.0, false);
static double reflect[3][3] = { { -1, 0, 0},
{ 0, -1, 0},
{ 0, 0, 1}};
reflect[0][2] = size.width;
reflect[1][2] = size.height;
M = cv::Mat(3, 3, CV_64F, (void*)reflect);
cv::warpPerspective(src, dst_gold, M, src.size(), warpFlags[flagIdx]);
}
};
TEST_P(WarpPerspective, Accuracy)
{
const char* warpFlagStr = warpFlags_str[flagIdx];
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
PRINT_PARAM(warpFlagStr);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::warpPerspective(cv::gpu::GpuMat(src), gpuRes, M, src.size(), warpFlags[flagIdx]);
gpuRes.download(dst);
);
// Check inner parts (ignoring 1 pixel width border)
cv::Mat dst_gold_roi = dst_gold.rowRange(1, dst_gold.rows - 1).colRange(1, dst_gold.cols - 1);
cv::Mat dst_roi = dst.rowRange(1, dst.rows - 1).colRange(1, dst.cols - 1);
EXPECT_MAT_NEAR(dst_gold_roi, dst_roi, 1e-3);
}
INSTANTIATE_TEST_CASE_P(ImgProc, WarpAffine, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4),
testing::Range(0, 6)));
INSTANTIATE_TEST_CASE_P(ImgProc, WarpPerspective, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4),
testing::Range(0, 6)));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// integral
struct Integral : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
cv::Mat src;
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(20, 150), rng.uniform(20, 150));
src = cvtest::randomMat(rng, size, CV_8UC1, 0.0, 255.0, false);
cv::integral(src, dst_gold, CV_32S);
}
};
TEST_P(Integral, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_PARAM(size);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::integral(cv::gpu::GpuMat(src), gpuRes);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(ImgProc, Integral, testing::ValuesIn(devices()));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// cvtColor
struct CvtColor : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
static cv::Mat imgBase;
static void SetUpTestCase()
{
imgBase = readImage("stereobm/aloe-L.png");
}
static void TearDownTestCase()
{
imgBase.release();
}
cv::gpu::DeviceInfo devInfo;
int type;
cv::Mat img;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
imgBase.convertTo(img, type, type == CV_32F ? 1.0 / 255.0 : 1.0);
}
};
cv::Mat CvtColor::imgBase;
TEST_P(CvtColor, BGR2RGB)
{
ASSERT_TRUE(!img.empty());
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
cv::Mat src = img;
cv::Mat dst_gold;
cv::cvtColor(src, dst_gold, CV_BGR2RGB);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::cvtColor(cv::gpu::GpuMat(src), gpuRes, CV_BGR2RGB);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
TEST_P(CvtColor, BGR2RGBA)
{
ASSERT_TRUE(!img.empty());
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
cv::Mat src = img;
cv::Mat dst_gold;
cv::cvtColor(src, dst_gold, CV_BGR2RGBA);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::cvtColor(cv::gpu::GpuMat(src), gpuRes, CV_BGR2RGBA);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
TEST_P(CvtColor, BGRA2RGB)
{
ASSERT_TRUE(!img.empty());
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
cv::Mat src;
cv::cvtColor(img, src, CV_BGR2BGRA);
cv::Mat dst_gold;
cv::cvtColor(src, dst_gold, CV_BGRA2RGB);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::cvtColor(cv::gpu::GpuMat(src), gpuRes, CV_BGRA2RGB);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
TEST_P(CvtColor, BGR2YCrCb)
{
ASSERT_TRUE(!img.empty());
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
cv::Mat src = img;
cv::Mat dst_gold;
cv::cvtColor(src, dst_gold, CV_BGR2YCrCb);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::cvtColor(cv::gpu::GpuMat(src), gpuRes, CV_BGR2YCrCb);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-5);
}
TEST_P(CvtColor, YCrCb2RGB)
{
ASSERT_TRUE(!img.empty());
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
cv::Mat src;
cv::cvtColor(img, src, CV_BGR2YCrCb);
cv::Mat dst_gold;
cv::cvtColor(src, dst_gold, CV_YCrCb2RGB);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::cvtColor(cv::gpu::GpuMat(src), gpuRes, CV_YCrCb2RGB);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-5);
}
TEST_P(CvtColor, BGR2YUV)
{
ASSERT_TRUE(!img.empty());
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
cv::Mat src = img;
cv::Mat dst_gold;
cv::cvtColor(src, dst_gold, CV_BGR2YUV);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::cvtColor(cv::gpu::GpuMat(src), gpuRes, CV_BGR2YUV);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-5);
}
TEST_P(CvtColor, YUV2BGR)
{
ASSERT_TRUE(!img.empty());
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
cv::Mat src;
cv::cvtColor(img, src, CV_BGR2YUV);
cv::Mat dst_gold;
cv::cvtColor(src, dst_gold, CV_YUV2BGR);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::cvtColor(cv::gpu::GpuMat(src), gpuRes, CV_YUV2BGR);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-5);
}
TEST_P(CvtColor, BGR2XYZ)
{
ASSERT_TRUE(!img.empty());
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
cv::Mat src = img;
cv::Mat dst_gold;
cv::cvtColor(src, dst_gold, CV_BGR2XYZ);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::cvtColor(cv::gpu::GpuMat(src), gpuRes, CV_BGR2XYZ);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-5);
}
TEST_P(CvtColor, XYZ2BGR)
{
ASSERT_TRUE(!img.empty());
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
cv::Mat src;
cv::cvtColor(img, src, CV_BGR2XYZ);
cv::Mat dst_gold;
cv::cvtColor(src, dst_gold, CV_XYZ2BGR);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::cvtColor(cv::gpu::GpuMat(src), gpuRes, CV_XYZ2BGR);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-5);
}
TEST_P(CvtColor, BGR2HSV)
{
if (type == CV_16U)
return;
ASSERT_TRUE(!img.empty());
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
cv::Mat src = img;
cv::Mat dst_gold;
cv::cvtColor(src, dst_gold, CV_BGR2HSV);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::cvtColor(cv::gpu::GpuMat(src), gpuRes, CV_BGR2HSV);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, type == CV_32F ? 1e-2 : 1);
}
TEST_P(CvtColor, HSV2BGR)
{
if (type == CV_16U)
return;
ASSERT_TRUE(!img.empty());
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
cv::Mat src;
cv::cvtColor(img, src, CV_BGR2HSV);
cv::Mat dst_gold;
cv::cvtColor(src, dst_gold, CV_HSV2BGR);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::cvtColor(cv::gpu::GpuMat(src), gpuRes, CV_HSV2BGR);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, type == CV_32F ? 1e-2 : 1);
}
TEST_P(CvtColor, BGR2HSV_FULL)
{
if (type == CV_16U)
return;
ASSERT_TRUE(!img.empty());
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
cv::Mat src = img;
cv::Mat dst_gold;
cv::cvtColor(src, dst_gold, CV_BGR2HSV_FULL);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::cvtColor(cv::gpu::GpuMat(src), gpuRes, CV_BGR2HSV_FULL);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, type == CV_32F ? 1e-2 : 1);
}
TEST_P(CvtColor, HSV2BGR_FULL)
{
if (type == CV_16U)
return;
ASSERT_TRUE(!img.empty());
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
cv::Mat src;
cv::cvtColor(img, src, CV_BGR2HSV_FULL);
cv::Mat dst_gold;
cv::cvtColor(src, dst_gold, CV_HSV2BGR_FULL);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::cvtColor(cv::gpu::GpuMat(src), gpuRes, CV_HSV2BGR_FULL);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, type == CV_32F ? 1e-2 : 1);
}
TEST_P(CvtColor, BGR2HLS)
{
if (type == CV_16U)
return;
ASSERT_TRUE(!img.empty());
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
cv::Mat src = img;
cv::Mat dst_gold;
cv::cvtColor(src, dst_gold, CV_BGR2HLS);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::cvtColor(cv::gpu::GpuMat(src), gpuRes, CV_BGR2HLS);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, type == CV_32F ? 1e-2 : 1);
}
TEST_P(CvtColor, HLS2BGR)
{
if (type == CV_16U)
return;
ASSERT_TRUE(!img.empty());
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
cv::Mat src;
cv::cvtColor(img, src, CV_BGR2HLS);
cv::Mat dst_gold;
cv::cvtColor(src, dst_gold, CV_HLS2BGR);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::cvtColor(cv::gpu::GpuMat(src), gpuRes, CV_HLS2BGR);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, type == CV_32F ? 1e-2 : 1);
}
TEST_P(CvtColor, BGR2HLS_FULL)
{
if (type == CV_16U)
return;
ASSERT_TRUE(!img.empty());
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
cv::Mat src = img;
cv::Mat dst_gold;
cv::cvtColor(src, dst_gold, CV_BGR2HLS_FULL);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::cvtColor(cv::gpu::GpuMat(src), gpuRes, CV_BGR2HLS_FULL);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, type == CV_32F ? 1e-2 : 1);
}
TEST_P(CvtColor, HLS2BGR_FULL)
{
if (type == CV_16U)
return;
ASSERT_TRUE(!img.empty());
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
cv::Mat src;
cv::cvtColor(img, src, CV_BGR2HLS_FULL);
cv::Mat dst_gold;
cv::cvtColor(src, dst_gold, CV_HLS2BGR_FULL);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::cvtColor(cv::gpu::GpuMat(src), gpuRes, CV_HLS2BGR_FULL);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, type == CV_32F ? 1e-2 : 1);
}
TEST_P(CvtColor, BGR2GRAY)
{
ASSERT_TRUE(!img.empty());
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
cv::Mat src = img;
cv::Mat dst_gold;
cv::cvtColor(src, dst_gold, CV_BGR2GRAY);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::cvtColor(cv::gpu::GpuMat(src), gpuRes, CV_BGR2GRAY);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-5);
}
TEST_P(CvtColor, GRAY2RGB)
{
ASSERT_TRUE(!img.empty());
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
cv::Mat src;
cv::cvtColor(img, src, CV_BGR2GRAY);
cv::Mat dst_gold;
cv::cvtColor(src, dst_gold, CV_GRAY2RGB);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpuRes;
cv::gpu::cvtColor(cv::gpu::GpuMat(src), gpuRes, CV_GRAY2RGB);
gpuRes.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(ImgProc, CvtColor, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8U, CV_16U, CV_32F)));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// histograms
struct Histograms : testing::TestWithParam<cv::gpu::DeviceInfo>
{
static cv::Mat hsv;
static void SetUpTestCase()
{
cv::Mat img = readImage("stereobm/aloe-L.png");
cv::cvtColor(img, hsv, CV_BGR2HSV);
}
static void TearDownTestCase()
{
hsv.release();
}
cv::gpu::DeviceInfo devInfo;
int hbins;
float hranges[2];
cv::Mat hist_gold;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
hbins = 30;
hranges[0] = 0;
hranges[1] = 180;
int histSize[] = {hbins};
const float* ranges[] = {hranges};
cv::MatND histnd;
int channels[] = {0};
cv::calcHist(&hsv, 1, channels, cv::Mat(), histnd, 1, histSize, ranges);
hist_gold = histnd;
hist_gold = hist_gold.t();
hist_gold.convertTo(hist_gold, CV_32S);
}
};
cv::Mat Histograms::hsv;
TEST_P(Histograms, Accuracy)
{
ASSERT_TRUE(!hsv.empty());
PRINT_PARAM(devInfo);
cv::Mat hist;
ASSERT_NO_THROW(
std::vector<cv::gpu::GpuMat> srcs;
cv::gpu::split(cv::gpu::GpuMat(hsv), srcs);
cv::gpu::GpuMat gpuHist;
cv::gpu::histEven(srcs[0], gpuHist, hbins, (int)hranges[0], (int)hranges[1]);
gpuHist.download(hist);
);
EXPECT_MAT_NEAR(hist_gold, hist, 0.0);
}
INSTANTIATE_TEST_CASE_P(ImgProc, Histograms, testing::ValuesIn(devices()));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// cornerHarris
static const int borderTypes[] = {cv::BORDER_REPLICATE, cv::BORDER_CONSTANT, cv::BORDER_REFLECT, cv::BORDER_WRAP, cv::BORDER_REFLECT101, cv::BORDER_TRANSPARENT};
static const char* borderTypes_str[] = {"BORDER_REPLICATE", "BORDER_CONSTANT", "BORDER_REFLECT", "BORDER_WRAP", "BORDER_REFLECT101", "BORDER_TRANSPARENT"};
struct CornerHarris : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int, int> >
{
static cv::Mat img;
static void SetUpTestCase()
{
img = readImage("stereobm/aloe-L.png", CV_LOAD_IMAGE_GRAYSCALE);
}
static void TearDownTestCase()
{
img.release();
}
cv::gpu::DeviceInfo devInfo;
int type;
int borderTypeIdx;
cv::Mat src;
int blockSize;
int apertureSize;
double k;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
borderTypeIdx = std::tr1::get<2>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
img.convertTo(src, type, type == CV_32F ? 1.0 / 255.0 : 1.0);
blockSize = 1 + rng.next() % 5;
apertureSize = 1 + 2 * (rng.next() % 4);
k = rng.uniform(0.1, 0.9);
cv::cornerHarris(src, dst_gold, blockSize, apertureSize, k, borderTypes[borderTypeIdx]);
}
};
cv::Mat CornerHarris::img;
TEST_P(CornerHarris, Accuracy)
{
const char* borderTypeStr = borderTypes_str[borderTypeIdx];
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(borderTypeStr);
PRINT_PARAM(blockSize);
PRINT_PARAM(apertureSize);
PRINT_PARAM(k);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat dev_dst;
cv::gpu::cornerHarris(cv::gpu::GpuMat(src), dev_dst, blockSize, apertureSize, k, borderTypes[borderTypeIdx]);
dev_dst.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-3);
}
INSTANTIATE_TEST_CASE_P(ImgProc, CornerHarris, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8UC1, CV_32FC1),
testing::Values(0, 4)));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// cornerMinEigen
struct CornerMinEigen : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int, int> >
{
static cv::Mat img;
static void SetUpTestCase()
{
img = readImage("stereobm/aloe-L.png", CV_LOAD_IMAGE_GRAYSCALE);
}
static void TearDownTestCase()
{
img.release();
}
cv::gpu::DeviceInfo devInfo;
int type;
int borderTypeIdx;
cv::Mat src;
int blockSize;
int apertureSize;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
borderTypeIdx = std::tr1::get<2>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
img.convertTo(src, type, type == CV_32F ? 1.0 / 255.0 : 1.0);
blockSize = 1 + rng.next() % 5;
apertureSize = 1 + 2 * (rng.next() % 4);
cv::cornerMinEigenVal(src, dst_gold, blockSize, apertureSize, borderTypes[borderTypeIdx]);
}
};
cv::Mat CornerMinEigen::img;
TEST_P(CornerMinEigen, Accuracy)
{
const char* borderTypeStr = borderTypes_str[borderTypeIdx];
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(borderTypeStr);
PRINT_PARAM(blockSize);
PRINT_PARAM(apertureSize);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat dev_dst;
cv::gpu::cornerMinEigenVal(cv::gpu::GpuMat(src), dev_dst, blockSize, apertureSize, borderTypes[borderTypeIdx]);
dev_dst.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-2);
}
INSTANTIATE_TEST_CASE_P(ImgProc, CornerMinEigen, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8UC1, CV_32FC1),
testing::Values(0, 4)));
////////////////////////////////////////////////////////////////////////
// ColumnSum
struct ColumnSum : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
cv::Mat src;
virtual void SetUp()
{
devInfo = 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, CV_32F, 0.0, 1.0, false);
}
};
TEST_P(ColumnSum, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_PARAM(size);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat dev_dst;
cv::gpu::columnSum(cv::gpu::GpuMat(src), dev_dst);
dev_dst.download(dst);
);
for (int j = 0; j < src.cols; ++j)
{
float gold = src.at<float>(0, j);
float res = dst.at<float>(0, j);
ASSERT_NEAR(res, gold, 0.5);
}
for (int i = 1; i < src.rows; ++i)
{
for (int j = 0; j < src.cols; ++j)
{
float gold = src.at<float>(i, j) += src.at<float>(i - 1, j);
float res = dst.at<float>(i, j);
ASSERT_NEAR(res, gold, 0.5);
}
}
}
INSTANTIATE_TEST_CASE_P(ImgProc, ColumnSum, testing::ValuesIn(devices()));
////////////////////////////////////////////////////////////////////////
// Norm
static const int normTypes[] = {cv::NORM_INF, cv::NORM_L1, cv::NORM_L2};
static const char* normTypes_str[] = {"NORM_INF", "NORM_L1", "NORM_L2"};
struct Norm : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int, int> >
{
cv::gpu::DeviceInfo devInfo;
int type;
int normTypeIdx;
cv::Size size;
cv::Mat src;
double gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
type = std::tr1::get<1>(GetParam());
normTypeIdx = std::tr1::get<2>(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, 10.0, false);
gold = cv::norm(src, normTypes[normTypeIdx]);
}
};
TEST_P(Norm, Accuracy)
{
const char* normTypeStr = normTypes_str[normTypeIdx];
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
PRINT_PARAM(normTypeStr);
double res;
ASSERT_NO_THROW(
res = cv::gpu::norm(cv::gpu::GpuMat(src), normTypes[normTypeIdx]);
);
ASSERT_NEAR(res, gold, 0.5);
}
INSTANTIATE_TEST_CASE_P(ImgProc, Norm, testing::Combine(
testing::ValuesIn(devices()),
testing::ValuesIn(types(CV_8U, CV_32F, 1, 1)),
testing::Range(0, 3)));
////////////////////////////////////////////////////////////////////////////////
// reprojectImageTo3D
struct ReprojectImageTo3D : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
cv::Mat disp;
cv::Mat Q;
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, 500), rng.uniform(100, 500));
disp = cvtest::randomMat(rng, size, CV_8UC1, 5.0, 30.0, false);
Q = cvtest::randomMat(rng, cv::Size(4, 4), CV_32FC1, 0.1, 1.0, false);
cv::reprojectImageTo3D(disp, dst_gold, Q, false);
}
};
TEST_P(ReprojectImageTo3D, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_PARAM(size);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpures;
cv::gpu::reprojectImageTo3D(cv::gpu::GpuMat(disp), gpures, Q);
gpures.download(dst);
);
ASSERT_EQ(dst_gold.size(), dst.size());
for (int y = 0; y < dst_gold.rows; ++y)
{
const cv::Vec3f* cpu_row = dst_gold.ptr<cv::Vec3f>(y);
const cv::Vec4f* gpu_row = dst.ptr<cv::Vec4f>(y);
for (int x = 0; x < dst_gold.cols; ++x)
{
cv::Vec3f gold = cpu_row[x];
cv::Vec4f res = gpu_row[x];
ASSERT_NEAR(res[0], gold[0], 1e-5);
ASSERT_NEAR(res[1], gold[1], 1e-5);
ASSERT_NEAR(res[2], gold[2], 1e-5);
}
}
}
INSTANTIATE_TEST_CASE_P(ImgProc, ReprojectImageTo3D, testing::ValuesIn(devices()));
////////////////////////////////////////////////////////////////////////////////
// Downsample
struct Downsample : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
cv::gpu::DeviceInfo devInfo;
int k;
cv::Size size;
cv::Size dst_gold_size;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
k = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(200 + cvtest::randInt(rng) % 1000, 200 + cvtest::randInt(rng) % 1000);
dst_gold_size = cv::Size((size.width + k - 1) / k, (size.height + k - 1) / k);
}
};
TEST_P(Downsample, Accuracy8U)
{
PRINT_PARAM(devInfo);
PRINT_PARAM(size);
PRINT_PARAM(k);
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
cv::Mat src = cvtest::randomMat(rng, size, CV_8U, 0, 255, false);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpures;
cv::gpu::downsample(cv::gpu::GpuMat(src), gpures, k);
gpures.download(dst);
);
ASSERT_EQ(dst_gold_size, dst.size());
for (int y = 0; y < dst.rows; ++y)
{
for (int x = 0; x < dst.cols; ++x)
{
int gold = src.at<uchar>(y * k, x * k);
int res = dst.at<uchar>(y, x);
ASSERT_EQ(gold, res);
}
}
}
TEST_P(Downsample, Accuracy32F)
{
PRINT_PARAM(devInfo);
PRINT_PARAM(size);
PRINT_PARAM(k);
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
cv::Mat src = cvtest::randomMat(rng, size, CV_32F, 0, 1.0, false);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat gpures;
cv::gpu::downsample(cv::gpu::GpuMat(src), gpures, k);
gpures.download(dst);
);
ASSERT_EQ(dst_gold_size, dst.size());
for (int y = 0; y < dst.rows; ++y)
{
for (int x = 0; x < dst.cols; ++x)
{
float gold = src.at<float>(y * k, x * k);
float res = dst.at<float>(y, x);
ASSERT_FLOAT_EQ(gold, res);
}
}
}
INSTANTIATE_TEST_CASE_P(ImgProc, Downsample, testing::Combine(
testing::ValuesIn(devices()),
testing::Range(2, 6)));
////////////////////////////////////////////////////////////////////////////////
// meanShift
struct MeanShift : testing::TestWithParam<cv::gpu::DeviceInfo>
{
static cv::Mat rgba;
static void SetUpTestCase()
{
cv::Mat img = readImage("meanshift/cones.png");
cv::cvtColor(img, rgba, CV_BGR2BGRA);
}
static void TearDownTestCase()
{
rgba.release();
}
cv::gpu::DeviceInfo devInfo;
int spatialRad;
int colorRad;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
spatialRad = 30;
colorRad = 30;
}
};
cv::Mat MeanShift::rgba;
TEST_P(MeanShift, Filtering)
{
cv::Mat img_template;
if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20))
img_template = readImage("meanshift/con_result.png");
else
img_template = readImage("meanshift/con_result_CC1X.png");
ASSERT_TRUE(!rgba.empty() && !img_template.empty());
PRINT_PARAM(devInfo);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat dev_dst;
cv::gpu::meanShiftFiltering(cv::gpu::GpuMat(rgba), dev_dst, spatialRad, colorRad);
dev_dst.download(dst);
);
ASSERT_EQ(CV_8UC4, dst.type());
cv::Mat result;
cv::cvtColor(dst, result, CV_BGRA2BGR);
EXPECT_MAT_NEAR(img_template, result, 0.0);
}
TEST_P(MeanShift, Proc)
{
cv::Mat spmap_template;
cv::FileStorage fs;
if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20))
fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap.yaml", cv::FileStorage::READ);
else
fs.open(std::string(cvtest::TS::ptr()->get_data_path()) + "meanshift/spmap_CC1X.yaml", cv::FileStorage::READ);
ASSERT_TRUE(fs.isOpened());
fs["spmap"] >> spmap_template;
ASSERT_TRUE(!rgba.empty() && !spmap_template.empty());
PRINT_PARAM(devInfo);
cv::Mat rmap_filtered;
cv::Mat rmap;
cv::Mat spmap;
ASSERT_NO_THROW(
cv::gpu::GpuMat d_rmap_filtered;
cv::gpu::meanShiftFiltering(cv::gpu::GpuMat(rgba), d_rmap_filtered, spatialRad, colorRad);
cv::gpu::GpuMat d_rmap;
cv::gpu::GpuMat d_spmap;
cv::gpu::meanShiftProc(cv::gpu::GpuMat(rgba), d_rmap, d_spmap, spatialRad, colorRad);
d_rmap_filtered.download(rmap_filtered);
d_rmap.download(rmap);
d_spmap.download(spmap);
);
ASSERT_EQ(CV_8UC4, rmap.type());
EXPECT_MAT_NEAR(rmap_filtered, rmap, 0.0);
EXPECT_MAT_NEAR(spmap_template, spmap, 0.0);
}
INSTANTIATE_TEST_CASE_P(ImgProc, MeanShift, testing::ValuesIn(devices(cv::gpu::FEATURE_SET_COMPUTE_12)));
struct MeanShiftSegmentation : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
static cv::Mat rgba;
static void SetUpTestCase()
{
cv::Mat img = readImage("meanshift/cones.png");
cv::cvtColor(img, rgba, CV_BGR2BGRA);
}
static void TearDownTestCase()
{
rgba.release();
}
cv::gpu::DeviceInfo devInfo;
int minsize;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
minsize = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
std::ostringstream path;
path << "meanshift/cones_segmented_sp10_sr10_minsize" << minsize;
if (supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20))
path << ".png";
else
path << "_CC1X.png";
dst_gold = readImage(path.str());
}
};
cv::Mat MeanShiftSegmentation::rgba;
TEST_P(MeanShiftSegmentation, Regression)
{
ASSERT_TRUE(!rgba.empty() && !dst_gold.empty());
PRINT_PARAM(devInfo);
PRINT_PARAM(minsize);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::meanShiftSegmentation(cv::gpu::GpuMat(rgba), dst, 10, 10, minsize);
);
cv::Mat dst_rgb;
cv::cvtColor(dst, dst_rgb, CV_BGRA2BGR);
EXPECT_MAT_SIMILAR(dst_gold, dst_rgb, 1e-5);
}
INSTANTIATE_TEST_CASE_P(ImgProc, MeanShiftSegmentation, testing::Combine(
testing::ValuesIn(devices(cv::gpu::FEATURE_SET_COMPUTE_12)),
testing::Values(0, 4, 20, 84, 340, 1364)));
////////////////////////////////////////////////////////////////////////////////
// matchTemplate
static const char* matchTemplateMethods[] = {"SQDIFF", "SQDIFF_NORMED", "CCORR", "CCORR_NORMED", "CCOEFF", "CCOEFF_NORMED"};
struct MatchTemplate8U : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int, int> >
{
cv::gpu::DeviceInfo devInfo;
int cn;
int method;
int n, m, h, w;
cv::Mat image, templ;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
cn = std::tr1::get<1>(GetParam());
method = std::tr1::get<2>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
n = rng.uniform(30, 100);
m = rng.uniform(30, 100);
h = rng.uniform(5, n - 1);
w = rng.uniform(5, m - 1);
image = cvtest::randomMat(rng, cv::Size(m, n), CV_MAKETYPE(CV_8U, cn), 1.0, 10.0, false);
templ = cvtest::randomMat(rng, cv::Size(w, h), CV_MAKETYPE(CV_8U, cn), 1.0, 10.0, false);
cv::matchTemplate(image, templ, dst_gold, method);
}
};
TEST_P(MatchTemplate8U, Regression)
{
const char* matchTemplateMethodStr = matchTemplateMethods[method];
PRINT_PARAM(devInfo);
PRINT_PARAM(cn);
PRINT_PARAM(matchTemplateMethodStr);
PRINT_PARAM(n);
PRINT_PARAM(m);
PRINT_PARAM(h);
PRINT_PARAM(w);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat dev_dst;
cv::gpu::matchTemplate(cv::gpu::GpuMat(image), cv::gpu::GpuMat(templ), dev_dst, method);
dev_dst.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 5 * h * w * 1e-4);
}
INSTANTIATE_TEST_CASE_P(ImgProc, MatchTemplate8U, testing::Combine(
testing::ValuesIn(devices()),
testing::Range(1, 5),
testing::Values((int)CV_TM_SQDIFF, (int)CV_TM_SQDIFF_NORMED, (int)CV_TM_CCORR, (int)CV_TM_CCORR_NORMED, (int)CV_TM_CCOEFF, (int)CV_TM_CCOEFF_NORMED)));
struct MatchTemplate32F : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int, int> >
{
cv::gpu::DeviceInfo devInfo;
int cn;
int method;
int n, m, h, w;
cv::Mat image, templ;
cv::Mat dst_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
cn = std::tr1::get<1>(GetParam());
method = std::tr1::get<2>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
n = rng.uniform(30, 100);
m = rng.uniform(30, 100);
h = rng.uniform(5, n - 1);
w = rng.uniform(5, m - 1);
image = cvtest::randomMat(rng, cv::Size(m, n), CV_MAKETYPE(CV_32F, cn), 0.001, 1.0, false);
templ = cvtest::randomMat(rng, cv::Size(w, h), CV_MAKETYPE(CV_32F, cn), 0.001, 1.0, false);
cv::matchTemplate(image, templ, dst_gold, method);
}
};
TEST_P(MatchTemplate32F, Regression)
{
const char* matchTemplateMethodStr = matchTemplateMethods[method];
PRINT_PARAM(devInfo);
PRINT_PARAM(cn);
PRINT_PARAM(matchTemplateMethodStr);
PRINT_PARAM(n);
PRINT_PARAM(m);
PRINT_PARAM(h);
PRINT_PARAM(w);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat dev_dst;
cv::gpu::matchTemplate(cv::gpu::GpuMat(image), cv::gpu::GpuMat(templ), dev_dst, method);
dev_dst.download(dst);
);
EXPECT_MAT_NEAR(dst_gold, dst, 0.25 * h * w * 1e-4);
}
INSTANTIATE_TEST_CASE_P(ImgProc, MatchTemplate32F, testing::Combine(
testing::ValuesIn(devices()),
testing::Range(1, 5),
testing::Values((int)CV_TM_SQDIFF, (int)CV_TM_CCORR)));
struct MatchTemplate : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
static cv::Mat image;
static cv::Mat pattern;
static cv::Point maxLocGold;
static void SetUpTestCase()
{
image = readImage("matchtemplate/black.png");
pattern = readImage("matchtemplate/cat.png");
maxLocGold = cv::Point(284, 12);
}
static void TearDownTestCase()
{
image.release();
pattern.release();
}
cv::gpu::DeviceInfo devInfo;
int method;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
method = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
}
};
cv::Mat MatchTemplate::image;
cv::Mat MatchTemplate::pattern;
cv::Point MatchTemplate::maxLocGold;
TEST_P(MatchTemplate, FindPatternInBlack)
{
ASSERT_TRUE(!image.empty() && !pattern.empty());
const char* matchTemplateMethodStr = matchTemplateMethods[method];
PRINT_PARAM(devInfo);
PRINT_PARAM(matchTemplateMethodStr);
cv::Mat dst;
ASSERT_NO_THROW(
cv::gpu::GpuMat dev_dst;
cv::gpu::matchTemplate(cv::gpu::GpuMat(image), cv::gpu::GpuMat(pattern), dev_dst, method);
dev_dst.download(dst);
);
double maxValue;
cv::Point maxLoc;
cv::minMaxLoc(dst, NULL, &maxValue, NULL, &maxLoc);
ASSERT_EQ(maxLocGold, maxLoc);
}
INSTANTIATE_TEST_CASE_P(ImgProc, MatchTemplate, testing::Combine(
testing::ValuesIn(devices()),
testing::Values((int)CV_TM_CCOEFF_NORMED, (int)CV_TM_CCORR_NORMED)));
////////////////////////////////////////////////////////////////////////////
// MulSpectrums
struct MulSpectrums : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int> >
{
cv::gpu::DeviceInfo devInfo;
int flag;
cv::Mat a, b;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
flag = std::tr1::get<1>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
a = cvtest::randomMat(rng, cv::Size(rng.uniform(100, 200), rng.uniform(100, 200)), CV_32FC2, 0.0, 10.0, false);
b = cvtest::randomMat(rng, a.size(), CV_32FC2, 0.0, 10.0, false);
}
};
TEST_P(MulSpectrums, Simple)
{
PRINT_PARAM(devInfo);
PRINT_PARAM(flag);
cv::Mat c_gold;
cv::mulSpectrums(a, b, c_gold, flag, false);
cv::Mat c;
ASSERT_NO_THROW(
cv::gpu::GpuMat d_c;
cv::gpu::mulSpectrums(cv::gpu::GpuMat(a), cv::gpu::GpuMat(b), d_c, flag, false);
d_c.download(c);
);
EXPECT_MAT_NEAR(c_gold, c, 1e-4);
}
TEST_P(MulSpectrums, Scaled)
{
PRINT_PARAM(devInfo);
PRINT_PARAM(flag);
float scale = 1.f / a.size().area();
cv::Mat c_gold;
cv::mulSpectrums(a, b, c_gold, flag, false);
c_gold.convertTo(c_gold, c_gold.type(), scale);
cv::Mat c;
ASSERT_NO_THROW(
cv::gpu::GpuMat d_c;
cv::gpu::mulAndScaleSpectrums(cv::gpu::GpuMat(a), cv::gpu::GpuMat(b), d_c, flag, scale, false);
d_c.download(c);
);
EXPECT_MAT_NEAR(c_gold, c, 1e-4);
}
INSTANTIATE_TEST_CASE_P(ImgProc, MulSpectrums, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(0, (int)cv::DFT_ROWS)));
////////////////////////////////////////////////////////////////////////////
// Dft
struct Dft : testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::gpu::setDevice(devInfo.deviceID());
}
};
static void testC2C(const std::string& hint, int cols, int rows, int flags, bool inplace)
{
PRINT_PARAM(hint);
PRINT_PARAM(cols);
PRINT_PARAM(rows);
PRINT_PARAM(flags);
PRINT_PARAM(inplace);
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
cv::Mat a = cvtest::randomMat(rng, cv::Size(cols, rows), CV_32FC2, 0.0, 10.0, false);
cv::Mat b_gold;
cv::dft(a, b_gold, flags);
cv::gpu::GpuMat d_b;
cv::gpu::GpuMat d_b_data;
if (inplace)
{
d_b_data.create(1, a.size().area(), CV_32FC2);
d_b = cv::gpu::GpuMat(a.rows, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize());
}
cv::gpu::dft(cv::gpu::GpuMat(a), d_b, cv::Size(cols, rows), flags);
EXPECT_TRUE(!inplace || d_b.ptr() == d_b_data.ptr());
ASSERT_EQ(CV_32F, d_b.depth());
ASSERT_EQ(2, d_b.channels());
EXPECT_MAT_NEAR(b_gold, d_b, rows * cols * 1e-4);
}
TEST_P(Dft, C2C)
{
PRINT_PARAM(devInfo);
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
int cols = 2 + rng.next() % 100, rows = 2 + rng.next() % 100;
ASSERT_NO_THROW(
for (int i = 0; i < 2; ++i)
{
bool inplace = i != 0;
testC2C("no flags", cols, rows, 0, inplace);
testC2C("no flags 0 1", cols, rows + 1, 0, inplace);
testC2C("no flags 1 0", cols, rows + 1, 0, inplace);
testC2C("no flags 1 1", cols + 1, rows, 0, inplace);
testC2C("DFT_INVERSE", cols, rows, cv::DFT_INVERSE, inplace);
testC2C("DFT_ROWS", cols, rows, cv::DFT_ROWS, inplace);
testC2C("single col", 1, rows, 0, inplace);
testC2C("single row", cols, 1, 0, inplace);
testC2C("single col inversed", 1, rows, cv::DFT_INVERSE, inplace);
testC2C("single row inversed", cols, 1, cv::DFT_INVERSE, inplace);
testC2C("single row DFT_ROWS", cols, 1, cv::DFT_ROWS, inplace);
testC2C("size 1 2", 1, 2, 0, inplace);
testC2C("size 2 1", 2, 1, 0, inplace);
}
);
}
static void testR2CThenC2R(const std::string& hint, int cols, int rows, bool inplace)
{
PRINT_PARAM(hint);
PRINT_PARAM(cols);
PRINT_PARAM(rows);
PRINT_PARAM(inplace);
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
cv::Mat a = cvtest::randomMat(rng, cv::Size(cols, rows), CV_32FC1, 0.0, 10.0, false);
cv::gpu::GpuMat d_b, d_c;
cv::gpu::GpuMat d_b_data, d_c_data;
if (inplace)
{
if (a.cols == 1)
{
d_b_data.create(1, (a.rows / 2 + 1) * a.cols, CV_32FC2);
d_b = cv::gpu::GpuMat(a.rows / 2 + 1, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize());
}
else
{
d_b_data.create(1, a.rows * (a.cols / 2 + 1), CV_32FC2);
d_b = cv::gpu::GpuMat(a.rows, a.cols / 2 + 1, CV_32FC2, d_b_data.ptr(), (a.cols / 2 + 1) * d_b_data.elemSize());
}
d_c_data.create(1, a.size().area(), CV_32F);
d_c = cv::gpu::GpuMat(a.rows, a.cols, CV_32F, d_c_data.ptr(), a.cols * d_c_data.elemSize());
}
cv::gpu::dft(cv::gpu::GpuMat(a), d_b, cv::Size(cols, rows), 0);
cv::gpu::dft(d_b, d_c, cv::Size(cols, rows), cv::DFT_REAL_OUTPUT | cv::DFT_SCALE);
EXPECT_TRUE(!inplace || d_b.ptr() == d_b_data.ptr());
EXPECT_TRUE(!inplace || d_c.ptr() == d_c_data.ptr());
ASSERT_EQ(CV_32F, d_c.depth());
ASSERT_EQ(1, d_c.channels());
cv::Mat c(d_c);
EXPECT_MAT_NEAR(a, c, rows * cols * 1e-5);
}
TEST_P(Dft, R2CThenC2R)
{
PRINT_PARAM(devInfo);
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
int cols = 2 + rng.next() % 100, rows = 2 + rng.next() % 100;
ASSERT_NO_THROW(
testR2CThenC2R("sanity", cols, rows, false);
testR2CThenC2R("sanity 0 1", cols, rows + 1, false);
testR2CThenC2R("sanity 1 0", cols + 1, rows, false);
testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, false);
testR2CThenC2R("single col", 1, rows, false);
testR2CThenC2R("single col 1", 1, rows + 1, false);
testR2CThenC2R("single row", cols, 1, false);
testR2CThenC2R("single row 1", cols + 1, 1, false);
testR2CThenC2R("sanity", cols, rows, true);
testR2CThenC2R("sanity 0 1", cols, rows + 1, true);
testR2CThenC2R("sanity 1 0", cols + 1, rows, true);
testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, true);
testR2CThenC2R("single row", cols, 1, true);
testR2CThenC2R("single row 1", cols + 1, 1, true);
);
}
INSTANTIATE_TEST_CASE_P(ImgProc, Dft, testing::ValuesIn(devices()));
////////////////////////////////////////////////////////////////////////////
// blend
template <typename T> static void blendLinearGold(const cv::Mat& img1, const cv::Mat& img2, const cv::Mat& weights1, const cv::Mat& weights2, cv::Mat& result_gold)
{
result_gold.create(img1.size(), img1.type());
int cn = img1.channels();
for (int y = 0; y < img1.rows; ++y)
{
const float* weights1_row = weights1.ptr<float>(y);
const float* weights2_row = weights2.ptr<float>(y);
const T* img1_row = img1.ptr<T>(y);
const T* img2_row = img2.ptr<T>(y);
T* result_gold_row = result_gold.ptr<T>(y);
for (int x = 0; x < img1.cols * cn; ++x)
{
float w1 = weights1_row[x / cn];
float w2 = weights2_row[x / cn];
result_gold_row[x] = static_cast<T>((img1_row[x] * w1 + img2_row[x] * w2) / (w1 + w2 + 1e-5f));
}
}
}
struct Blend : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int, int> >
{
cv::gpu::DeviceInfo devInfo;
int depth;
int cn;
int type;
cv::Size size;
cv::Mat img1;
cv::Mat img2;
cv::Mat weights1;
cv::Mat weights2;
cv::Mat result_gold;
virtual void SetUp()
{
devInfo = std::tr1::get<0>(GetParam());
depth = std::tr1::get<1>(GetParam());
cn = std::tr1::get<2>(GetParam());
cv::gpu::setDevice(devInfo.deviceID());
type = CV_MAKETYPE(depth, cn);
cv::RNG& rng = cvtest::TS::ptr()->get_rng();
size = cv::Size(200 + cvtest::randInt(rng) % 1000, 200 + cvtest::randInt(rng) % 1000);
img1 = cvtest::randomMat(rng, size, type, 0.0, depth == CV_8U ? 255.0 : 1.0, false);
img2 = cvtest::randomMat(rng, size, type, 0.0, depth == CV_8U ? 255.0 : 1.0, false);
weights1 = cvtest::randomMat(rng, size, CV_32F, 0, 1, false);
weights2 = cvtest::randomMat(rng, size, CV_32F, 0, 1, false);
if (depth == CV_8U)
blendLinearGold<uchar>(img1, img2, weights1, weights2, result_gold);
else
blendLinearGold<float>(img1, img2, weights1, weights2, result_gold);
}
};
TEST_P(Blend, Accuracy)
{
PRINT_PARAM(devInfo);
PRINT_TYPE(type);
PRINT_PARAM(size);
cv::Mat result;
ASSERT_NO_THROW(
cv::gpu::GpuMat d_result;
cv::gpu::blendLinear(cv::gpu::GpuMat(img1), cv::gpu::GpuMat(img2), cv::gpu::GpuMat(weights1), cv::gpu::GpuMat(weights2), d_result);
d_result.download(result);
);
EXPECT_MAT_NEAR(result_gold, result, depth == CV_8U ? 1.0 : 1e-5);
}
INSTANTIATE_TEST_CASE_P(ImgProc, Blend, testing::Combine(
testing::ValuesIn(devices()),
testing::Values(CV_8U, CV_32F),
testing::Range(1, 5)));
#endif // HAVE_CUDA
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