opencv/modules/cudaarithm/perf/perf_core.cpp
2018-01-19 00:23:02 +01:00

324 lines
8.7 KiB
C++

/*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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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 the copyright holders 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 "perf_precomp.hpp"
using namespace std;
using namespace testing;
using namespace perf;
#define ARITHM_MAT_DEPTH Values(CV_8U, CV_16U, CV_32F, CV_64F)
//////////////////////////////////////////////////////////////////////
// Merge
DEF_PARAM_TEST(Sz_Depth_Cn, cv::Size, MatDepth, MatCn);
PERF_TEST_P(Sz_Depth_Cn, Merge,
Combine(CUDA_TYPICAL_MAT_SIZES,
ARITHM_MAT_DEPTH,
Values(2, 3, 4)))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int channels = GET_PARAM(2);
std::vector<cv::Mat> src(channels);
for (int i = 0; i < channels; ++i)
{
src[i].create(size, depth);
declare.in(src[i], WARMUP_RNG);
}
if (PERF_RUN_CUDA())
{
std::vector<cv::cuda::GpuMat> d_src(channels);
for (int i = 0; i < channels; ++i)
d_src[i].upload(src[i]);
cv::cuda::GpuMat dst;
TEST_CYCLE() cv::cuda::merge(d_src, dst);
CUDA_SANITY_CHECK(dst, 1e-10);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::merge(src, dst);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// Split
PERF_TEST_P(Sz_Depth_Cn, Split,
Combine(CUDA_TYPICAL_MAT_SIZES,
ARITHM_MAT_DEPTH,
Values(2, 3, 4)))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int channels = GET_PARAM(2);
cv::Mat src(size, CV_MAKE_TYPE(depth, channels));
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
std::vector<cv::cuda::GpuMat> dst;
TEST_CYCLE() cv::cuda::split(d_src, dst);
const cv::cuda::GpuMat& dst0 = dst[0];
const cv::cuda::GpuMat& dst1 = dst[1];
CUDA_SANITY_CHECK(dst0, 1e-10);
CUDA_SANITY_CHECK(dst1, 1e-10);
}
else
{
std::vector<cv::Mat> dst;
TEST_CYCLE() cv::split(src, dst);
const cv::Mat& dst0 = dst[0];
const cv::Mat& dst1 = dst[1];
CPU_SANITY_CHECK(dst0);
CPU_SANITY_CHECK(dst1);
}
}
//////////////////////////////////////////////////////////////////////
// Transpose
PERF_TEST_P(Sz_Type, Transpose,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(CV_8UC1, CV_8UC4, CV_16UC2, CV_16SC2, CV_32SC1, CV_32SC2, CV_64FC1)))
{
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
TEST_CYCLE() cv::cuda::transpose(d_src, dst);
CUDA_SANITY_CHECK(dst, 1e-10);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::transpose(src, dst);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// Flip
enum {FLIP_BOTH = 0, FLIP_X = 1, FLIP_Y = -1};
CV_ENUM(FlipCode, FLIP_BOTH, FLIP_X, FLIP_Y)
DEF_PARAM_TEST(Sz_Depth_Cn_Code, cv::Size, MatDepth, MatCn, FlipCode);
PERF_TEST_P(Sz_Depth_Cn_Code, Flip,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_16U, CV_32F),
CUDA_CHANNELS_1_3_4,
FlipCode::all()))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int channels = GET_PARAM(2);
const int flipCode = GET_PARAM(3);
const int type = CV_MAKE_TYPE(depth, channels);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
TEST_CYCLE() cv::cuda::flip(d_src, dst, flipCode);
CUDA_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::flip(src, dst, flipCode);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// LutOneChannel
PERF_TEST_P(Sz_Type, LutOneChannel,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(CV_8UC1, CV_8UC3)))
{
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
cv::Mat lut(1, 256, CV_8UC1);
declare.in(lut, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
cv::Ptr<cv::cuda::LookUpTable> lutAlg = cv::cuda::createLookUpTable(lut);
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
TEST_CYCLE() lutAlg->transform(d_src, dst);
CUDA_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::LUT(src, lut, dst);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// LutMultiChannel
PERF_TEST_P(Sz_Type, LutMultiChannel,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values<MatType>(CV_8UC3)))
{
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
cv::Mat lut(1, 256, CV_MAKE_TYPE(CV_8U, src.channels()));
declare.in(lut, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
cv::Ptr<cv::cuda::LookUpTable> lutAlg = cv::cuda::createLookUpTable(lut);
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
TEST_CYCLE() lutAlg->transform(d_src, dst);
CUDA_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::LUT(src, lut, dst);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// CopyMakeBorder
DEF_PARAM_TEST(Sz_Depth_Cn_Border, cv::Size, MatDepth, MatCn, BorderMode);
PERF_TEST_P(Sz_Depth_Cn_Border, CopyMakeBorder,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_16U, CV_32F),
CUDA_CHANNELS_1_3_4,
ALL_BORDER_MODES))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int channels = GET_PARAM(2);
const int borderMode = GET_PARAM(3);
const int type = CV_MAKE_TYPE(depth, channels);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
TEST_CYCLE() cv::cuda::copyMakeBorder(d_src, dst, 5, 5, 5, 5, borderMode);
CUDA_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::copyMakeBorder(src, dst, 5, 5, 5, 5, borderMode);
CPU_SANITY_CHECK(dst);
}
}