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Revert "Merge pull request #836 from jet47:gpu-modules"

Browse Source 
This reverts commit fba72cb60d, reversing
changes made to 02131ffb62.
This commit is contained in:
Andrey Kamaev
2013-04-18 15:03:50 +04:00
parent fba72cb60d
commit 416fb50594
472 changed files with 22945 additions and 29803 deletions
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236
modules/gpu/perf/perf_calib3d.cpp
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@@ -46,8 +46,181 @@ using namespace std;
using namespace testing;
using namespace perf;
//////////////////////////////////////////////////////////////////////
// StereoBM
typedef std::tr1::tuple<string, string> pair_string;
DEF_PARAM_TEST_1(ImagePair, pair_string);
PERF_TEST_P(ImagePair, Calib3D_StereoBM,
Values(pair_string("gpu/perf/aloe.png", "gpu/perf/aloeR.png")))
{
declare.time(300.0);
const cv::Mat imgLeft = readImage(GET_PARAM(0), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(imgLeft.empty());
const cv::Mat imgRight = readImage(GET_PARAM(1), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(imgRight.empty());
const int preset = 0;
const int ndisp = 256;
if (PERF_RUN_GPU())
{
cv::gpu::StereoBM_GPU d_bm(preset, ndisp);
const cv::gpu::GpuMat d_imgLeft(imgLeft);
const cv::gpu::GpuMat d_imgRight(imgRight);
cv::gpu::GpuMat dst;
TEST_CYCLE() d_bm(d_imgLeft, d_imgRight, dst);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Ptr<cv::StereoBM> bm = cv::createStereoBM(ndisp);
cv::Mat dst;
TEST_CYCLE() bm->compute(imgLeft, imgRight, dst);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// StereoBeliefPropagation
PERF_TEST_P(ImagePair, Calib3D_StereoBeliefPropagation,
Values(pair_string("gpu/stereobp/aloe-L.png", "gpu/stereobp/aloe-R.png")))
{
declare.time(300.0);
const cv::Mat imgLeft = readImage(GET_PARAM(0));
ASSERT_FALSE(imgLeft.empty());
const cv::Mat imgRight = readImage(GET_PARAM(1));
ASSERT_FALSE(imgRight.empty());
const int ndisp = 64;
if (PERF_RUN_GPU())
{
cv::gpu::StereoBeliefPropagation d_bp(ndisp);
const cv::gpu::GpuMat d_imgLeft(imgLeft);
const cv::gpu::GpuMat d_imgRight(imgRight);
cv::gpu::GpuMat dst;
TEST_CYCLE() d_bp(d_imgLeft, d_imgRight, dst);
GPU_SANITY_CHECK(dst);
}
else
{
FAIL_NO_CPU();
}
}
//////////////////////////////////////////////////////////////////////
// StereoConstantSpaceBP
PERF_TEST_P(ImagePair, Calib3D_StereoConstantSpaceBP,
Values(pair_string("gpu/stereobm/aloe-L.png", "gpu/stereobm/aloe-R.png")))
{
declare.time(300.0);
const cv::Mat imgLeft = readImage(GET_PARAM(0), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(imgLeft.empty());
const cv::Mat imgRight = readImage(GET_PARAM(1), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(imgRight.empty());
const int ndisp = 128;
if (PERF_RUN_GPU())
{
cv::gpu::StereoConstantSpaceBP d_csbp(ndisp);
const cv::gpu::GpuMat d_imgLeft(imgLeft);
const cv::gpu::GpuMat d_imgRight(imgRight);
cv::gpu::GpuMat dst;
TEST_CYCLE() d_csbp(d_imgLeft, d_imgRight, dst);
GPU_SANITY_CHECK(dst);
}
else
{
FAIL_NO_CPU();
}
}
//////////////////////////////////////////////////////////////////////
// DisparityBilateralFilter
PERF_TEST_P(ImagePair, Calib3D_DisparityBilateralFilter,
Values(pair_string("gpu/stereobm/aloe-L.png", "gpu/stereobm/aloe-disp.png")))
{
const cv::Mat img = readImage(GET_PARAM(0), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
const cv::Mat disp = readImage(GET_PARAM(1), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(disp.empty());
const int ndisp = 128;
if (PERF_RUN_GPU())
{
cv::gpu::DisparityBilateralFilter d_filter(ndisp);
const cv::gpu::GpuMat d_img(img);
const cv::gpu::GpuMat d_disp(disp);
cv::gpu::GpuMat dst;
TEST_CYCLE() d_filter(d_disp, d_img, dst);
GPU_SANITY_CHECK(dst);
}
else
{
FAIL_NO_CPU();
}
}
//////////////////////////////////////////////////////////////////////
// TransformPoints
DEF_PARAM_TEST_1(Count, int);
PERF_TEST_P(Count, Calib3D_TransformPoints,
Values(5000, 10000, 20000))
{
const int count = GetParam();
cv::Mat src(1, count, CV_32FC3);
declare.in(src, WARMUP_RNG);
const cv::Mat rvec = cv::Mat::ones(1, 3, CV_32FC1);
const cv::Mat tvec = cv::Mat::ones(1, 3, CV_32FC1);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::transformPoints(d_src, rvec, tvec, dst);
GPU_SANITY_CHECK(dst);
}
else
{
FAIL_NO_CPU();
}
}
//////////////////////////////////////////////////////////////////////
// ProjectPoints
@@ -133,3 +306,66 @@ PERF_TEST_P(Count, Calib3D_SolvePnPRansac,
CPU_SANITY_CHECK(tvec, 1e-6);
}
}
//////////////////////////////////////////////////////////////////////
// ReprojectImageTo3D
PERF_TEST_P(Sz_Depth, Calib3D_ReprojectImageTo3D,
Combine(GPU_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_16S)))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
cv::Mat src(size, depth);
declare.in(src, WARMUP_RNG);
cv::Mat Q(4, 4, CV_32FC1);
cv::randu(Q, 0.1, 1.0);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::reprojectImageTo3D(d_src, dst, Q);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::reprojectImageTo3D(src, dst, Q);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// DrawColorDisp
PERF_TEST_P(Sz_Depth, Calib3D_DrawColorDisp,
Combine(GPU_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_16S)))
{
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_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::drawColorDisp(d_src, dst, 255);
GPU_SANITY_CHECK(dst);
}
else
{
FAIL_NO_CPU();
}
}

2157
modules/gpu/perf/perf_core.cpp Normal file
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230
modules/gpu/perf/perf_denoising.cpp Normal file
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@@ -0,0 +1,230 @@
/*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 GPU_DENOISING_IMAGE_SIZES testing::Values(perf::szVGA, perf::sz720p)
//////////////////////////////////////////////////////////////////////
// BilateralFilter
DEF_PARAM_TEST(Sz_Depth_Cn_KernelSz, cv::Size, MatDepth, MatCn, int);
PERF_TEST_P(Sz_Depth_Cn_KernelSz, Denoising_BilateralFilter,
Combine(GPU_DENOISING_IMAGE_SIZES,
Values(CV_8U, CV_32F),
GPU_CHANNELS_1_3,
Values(3, 5, 9)))
{
declare.time(60.0);
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int channels = GET_PARAM(2);
const int kernel_size = GET_PARAM(3);
const float sigma_color = 7;
const float sigma_spatial = 5;
const int borderMode = cv::BORDER_REFLECT101;
const int type = CV_MAKE_TYPE(depth, channels);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::bilateralFilter(d_src, dst, kernel_size, sigma_color, sigma_spatial, borderMode);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::bilateralFilter(src, dst, kernel_size, sigma_color, sigma_spatial, borderMode);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// nonLocalMeans
DEF_PARAM_TEST(Sz_Depth_Cn_WinSz_BlockSz, cv::Size, MatDepth, MatCn, int, int);
PERF_TEST_P(Sz_Depth_Cn_WinSz_BlockSz, Denoising_NonLocalMeans,
Combine(GPU_DENOISING_IMAGE_SIZES,
Values<MatDepth>(CV_8U),
GPU_CHANNELS_1_3,
Values(21),
Values(5)))
{
declare.time(600.0);
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int channels = GET_PARAM(2);
const int search_widow_size = GET_PARAM(3);
const int block_size = GET_PARAM(4);
const float h = 10;
const int borderMode = cv::BORDER_REFLECT101;
const int type = CV_MAKE_TYPE(depth, channels);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::nonLocalMeans(d_src, dst, h, search_widow_size, block_size, borderMode);
GPU_SANITY_CHECK(dst);
}
else
{
FAIL_NO_CPU();
}
}
//////////////////////////////////////////////////////////////////////
// fastNonLocalMeans
DEF_PARAM_TEST(Sz_Depth_Cn_WinSz_BlockSz, cv::Size, MatDepth, MatCn, int, int);
PERF_TEST_P(Sz_Depth_Cn_WinSz_BlockSz, Denoising_FastNonLocalMeans,
Combine(GPU_DENOISING_IMAGE_SIZES,
Values<MatDepth>(CV_8U),
GPU_CHANNELS_1_3,
Values(21),
Values(7)))
{
declare.time(60.0);
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int search_widow_size = GET_PARAM(2);
const int block_size = GET_PARAM(3);
const float h = 10;
const int type = CV_MAKE_TYPE(depth, 1);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
cv::gpu::FastNonLocalMeansDenoising fnlmd;
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() fnlmd.simpleMethod(d_src, dst, h, search_widow_size, block_size);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::fastNlMeansDenoising(src, dst, h, block_size, search_widow_size);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// fastNonLocalMeans (colored)
DEF_PARAM_TEST(Sz_Depth_WinSz_BlockSz, cv::Size, MatDepth, int, int);
PERF_TEST_P(Sz_Depth_WinSz_BlockSz, Denoising_FastNonLocalMeansColored,
Combine(GPU_DENOISING_IMAGE_SIZES,
Values<MatDepth>(CV_8U),
Values(21),
Values(7)))
{
declare.time(60.0);
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int search_widow_size = GET_PARAM(2);
const int block_size = GET_PARAM(3);
const float h = 10;
const int type = CV_MAKE_TYPE(depth, 3);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
cv::gpu::FastNonLocalMeansDenoising fnlmd;
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() fnlmd.labMethod(d_src, dst, h, h, search_widow_size, block_size);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::fastNlMeansDenoisingColored(src, dst, h, h, block_size, search_widow_size);
CPU_SANITY_CHECK(dst);
}
}

309
modules/gpu/perf/perf_features2d.cpp Normal file
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@@ -0,0 +1,309 @@
/*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;
//////////////////////////////////////////////////////////////////////
// FAST
DEF_PARAM_TEST(Image_Threshold_NonMaxSupression, string, int, bool);
PERF_TEST_P(Image_Threshold_NonMaxSupression, Features2D_FAST,
Combine(Values<string>("gpu/perf/aloe.png"),
Values(20),
Bool()))
{
const cv::Mat img = readImage(GET_PARAM(0), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
const int threshold = GET_PARAM(1);
const bool nonMaxSuppersion = GET_PARAM(2);
if (PERF_RUN_GPU())
{
cv::gpu::FAST_GPU d_fast(threshold, nonMaxSuppersion, 0.5);
const cv::gpu::GpuMat d_img(img);
cv::gpu::GpuMat d_keypoints;
TEST_CYCLE() d_fast(d_img, cv::gpu::GpuMat(), d_keypoints);
std::vector<cv::KeyPoint> gpu_keypoints;
d_fast.downloadKeypoints(d_keypoints, gpu_keypoints);
sortKeyPoints(gpu_keypoints);
SANITY_CHECK_KEYPOINTS(gpu_keypoints);
}
else
{
std::vector<cv::KeyPoint> cpu_keypoints;
TEST_CYCLE() cv::FAST(img, cpu_keypoints, threshold, nonMaxSuppersion);
SANITY_CHECK_KEYPOINTS(cpu_keypoints);
}
}
//////////////////////////////////////////////////////////////////////
// ORB
DEF_PARAM_TEST(Image_NFeatures, string, int);
PERF_TEST_P(Image_NFeatures, Features2D_ORB,
Combine(Values<string>("gpu/perf/aloe.png"),
Values(4000)))
{
declare.time(300.0);
const cv::Mat img = readImage(GET_PARAM(0), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
const int nFeatures = GET_PARAM(1);
if (PERF_RUN_GPU())
{
cv::gpu::ORB_GPU d_orb(nFeatures);
const cv::gpu::GpuMat d_img(img);
cv::gpu::GpuMat d_keypoints, d_descriptors;
TEST_CYCLE() d_orb(d_img, cv::gpu::GpuMat(), d_keypoints, d_descriptors);
std::vector<cv::KeyPoint> gpu_keypoints;
d_orb.downloadKeyPoints(d_keypoints, gpu_keypoints);
cv::Mat gpu_descriptors(d_descriptors);
gpu_keypoints.resize(10);
gpu_descriptors = gpu_descriptors.rowRange(0, 10);
sortKeyPoints(gpu_keypoints, gpu_descriptors);
SANITY_CHECK_KEYPOINTS(gpu_keypoints);
SANITY_CHECK(gpu_descriptors);
}
else
{
cv::ORB orb(nFeatures);
std::vector<cv::KeyPoint> cpu_keypoints;
cv::Mat cpu_descriptors;
TEST_CYCLE() orb(img, cv::noArray(), cpu_keypoints, cpu_descriptors);
SANITY_CHECK_KEYPOINTS(cpu_keypoints);
SANITY_CHECK(cpu_descriptors);
}
}
//////////////////////////////////////////////////////////////////////
// BFMatch
DEF_PARAM_TEST(DescSize_Norm, int, NormType);
PERF_TEST_P(DescSize_Norm, Features2D_BFMatch,
Combine(Values(64, 128, 256),
Values(NormType(cv::NORM_L1), NormType(cv::NORM_L2), NormType(cv::NORM_HAMMING))))
{
declare.time(20.0);
const int desc_size = GET_PARAM(0);
const int normType = GET_PARAM(1);
const int type = normType == cv::NORM_HAMMING ? CV_8U : CV_32F;
cv::Mat query(3000, desc_size, type);
declare.in(query, WARMUP_RNG);
cv::Mat train(3000, desc_size, type);
declare.in(train, WARMUP_RNG);
if (PERF_RUN_GPU())
{
cv::gpu::BFMatcher_GPU d_matcher(normType);
const cv::gpu::GpuMat d_query(query);
const cv::gpu::GpuMat d_train(train);
cv::gpu::GpuMat d_trainIdx, d_distance;
TEST_CYCLE() d_matcher.matchSingle(d_query, d_train, d_trainIdx, d_distance);
std::vector<cv::DMatch> gpu_matches;
d_matcher.matchDownload(d_trainIdx, d_distance, gpu_matches);
SANITY_CHECK_MATCHES(gpu_matches);
}
else
{
cv::BFMatcher matcher(normType);
std::vector<cv::DMatch> cpu_matches;
TEST_CYCLE() matcher.match(query, train, cpu_matches);
SANITY_CHECK_MATCHES(cpu_matches);
}
}
//////////////////////////////////////////////////////////////////////
// BFKnnMatch
static void toOneRowMatches(const std::vector< std::vector<cv::DMatch> >& src, std::vector<cv::DMatch>& dst)
{
dst.clear();
for (size_t i = 0; i < src.size(); ++i)
for (size_t j = 0; j < src[i].size(); ++j)
dst.push_back(src[i][j]);
}
DEF_PARAM_TEST(DescSize_K_Norm, int, int, NormType);
PERF_TEST_P(DescSize_K_Norm, Features2D_BFKnnMatch,
Combine(Values(64, 128, 256),
Values(2, 3),
Values(NormType(cv::NORM_L1), NormType(cv::NORM_L2))))
{
declare.time(30.0);
const int desc_size = GET_PARAM(0);
const int k = GET_PARAM(1);
const int normType = GET_PARAM(2);
const int type = normType == cv::NORM_HAMMING ? CV_8U : CV_32F;
cv::Mat query(3000, desc_size, type);
declare.in(query, WARMUP_RNG);
cv::Mat train(3000, desc_size, type);
declare.in(train, WARMUP_RNG);
if (PERF_RUN_GPU())
{
cv::gpu::BFMatcher_GPU d_matcher(normType);
const cv::gpu::GpuMat d_query(query);
const cv::gpu::GpuMat d_train(train);
cv::gpu::GpuMat d_trainIdx, d_distance, d_allDist;
TEST_CYCLE() d_matcher.knnMatchSingle(d_query, d_train, d_trainIdx, d_distance, d_allDist, k);
std::vector< std::vector<cv::DMatch> > matchesTbl;
d_matcher.knnMatchDownload(d_trainIdx, d_distance, matchesTbl);
std::vector<cv::DMatch> gpu_matches;
toOneRowMatches(matchesTbl, gpu_matches);
SANITY_CHECK_MATCHES(gpu_matches);
}
else
{
cv::BFMatcher matcher(normType);
std::vector< std::vector<cv::DMatch> > matchesTbl;
TEST_CYCLE() matcher.knnMatch(query, train, matchesTbl, k);
std::vector<cv::DMatch> cpu_matches;
toOneRowMatches(matchesTbl, cpu_matches);
SANITY_CHECK_MATCHES(cpu_matches);
}
}
//////////////////////////////////////////////////////////////////////
// BFRadiusMatch
PERF_TEST_P(DescSize_Norm, Features2D_BFRadiusMatch,
Combine(Values(64, 128, 256),
Values(NormType(cv::NORM_L1), NormType(cv::NORM_L2))))
{
declare.time(30.0);
const int desc_size = GET_PARAM(0);
const int normType = GET_PARAM(1);
const int type = normType == cv::NORM_HAMMING ? CV_8U : CV_32F;
const float maxDistance = 10000;
cv::Mat query(3000, desc_size, type);
declare.in(query, WARMUP_RNG);
cv::Mat train(3000, desc_size, type);
declare.in(train, WARMUP_RNG);
if (PERF_RUN_GPU())
{
cv::gpu::BFMatcher_GPU d_matcher(normType);
const cv::gpu::GpuMat d_query(query);
const cv::gpu::GpuMat d_train(train);
cv::gpu::GpuMat d_trainIdx, d_nMatches, d_distance;
TEST_CYCLE() d_matcher.radiusMatchSingle(d_query, d_train, d_trainIdx, d_distance, d_nMatches, maxDistance);
std::vector< std::vector<cv::DMatch> > matchesTbl;
d_matcher.radiusMatchDownload(d_trainIdx, d_distance, d_nMatches, matchesTbl);
std::vector<cv::DMatch> gpu_matches;
toOneRowMatches(matchesTbl, gpu_matches);
SANITY_CHECK_MATCHES(gpu_matches);
}
else
{
cv::BFMatcher matcher(normType);
std::vector< std::vector<cv::DMatch> > matchesTbl;
TEST_CYCLE() matcher.radiusMatch(query, train, matchesTbl, maxDistance);
std::vector<cv::DMatch> cpu_matches;
toOneRowMatches(matchesTbl, cpu_matches);
SANITY_CHECK_MATCHES(cpu_matches);
}
}

366
modules/gpu/perf/perf_filters.cpp Normal file
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@@ -0,0 +1,366 @@
/*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;
//////////////////////////////////////////////////////////////////////
// Blur
DEF_PARAM_TEST(Sz_Type_KernelSz, cv::Size, MatType, int);
PERF_TEST_P(Sz_Type_KernelSz, Filters_Blur,
Combine(GPU_TYPICAL_MAT_SIZES,
Values(CV_8UC1, CV_8UC4),
Values(3, 5, 7)))
{
declare.time(20.0);
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
const int ksize = GET_PARAM(2);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::blur(d_src, dst, cv::Size(ksize, ksize));
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::blur(src, dst, cv::Size(ksize, ksize));
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// Sobel
PERF_TEST_P(Sz_Type_KernelSz, Filters_Sobel, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4, CV_32FC1), Values(3, 5, 7, 9, 11, 13, 15)))
{
declare.time(20.0);
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
const int ksize = GET_PARAM(2);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat d_buf;
TEST_CYCLE() cv::gpu::Sobel(d_src, dst, -1, 1, 1, d_buf, ksize);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::Sobel(src, dst, -1, 1, 1, ksize);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// Scharr
PERF_TEST_P(Sz_Type, Filters_Scharr, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4, CV_32FC1)))
{
declare.time(20.0);
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_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat d_buf;
TEST_CYCLE() cv::gpu::Scharr(d_src, dst, -1, 1, 0, d_buf);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::Scharr(src, dst, -1, 1, 0);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// GaussianBlur
PERF_TEST_P(Sz_Type_KernelSz, Filters_GaussianBlur, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4, CV_32FC1), Values(3, 5, 7, 9, 11, 13, 15)))
{
declare.time(20.0);
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
const int ksize = GET_PARAM(2);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat d_buf;
TEST_CYCLE() cv::gpu::GaussianBlur(d_src, dst, cv::Size(ksize, ksize), d_buf, 0.5);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::GaussianBlur(src, dst, cv::Size(ksize, ksize), 0.5);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// Laplacian
PERF_TEST_P(Sz_Type_KernelSz, Filters_Laplacian, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4, CV_32FC1, CV_32FC4), Values(1, 3)))
{
declare.time(20.0);
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
const int ksize = GET_PARAM(2);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::Laplacian(d_src, dst, -1, ksize);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::Laplacian(src, dst, -1, ksize);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// Erode
PERF_TEST_P(Sz_Type, Filters_Erode, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4)))
{
declare.time(20.0);
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
const cv::Mat ker = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3));
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat d_buf;
TEST_CYCLE() cv::gpu::erode(d_src, dst, ker, d_buf);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::erode(src, dst, ker);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// Dilate
PERF_TEST_P(Sz_Type, Filters_Dilate, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4)))
{
declare.time(20.0);
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
const cv::Mat ker = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3));
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat d_buf;
TEST_CYCLE() cv::gpu::dilate(d_src, dst, ker, d_buf);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::dilate(src, dst, ker);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// MorphologyEx
CV_ENUM(MorphOp, MORPH_OPEN, MORPH_CLOSE, MORPH_GRADIENT, MORPH_TOPHAT, MORPH_BLACKHAT)
DEF_PARAM_TEST(Sz_Type_Op, cv::Size, MatType, MorphOp);
PERF_TEST_P(Sz_Type_Op, Filters_MorphologyEx, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4), MorphOp::all()))
{
declare.time(20.0);
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
const int morphOp = GET_PARAM(2);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
const cv::Mat ker = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3));
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat d_buf1;
cv::gpu::GpuMat d_buf2;
TEST_CYCLE() cv::gpu::morphologyEx(d_src, dst, morphOp, ker, d_buf1, d_buf2);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::morphologyEx(src, dst, morphOp, ker);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// Filter2D
PERF_TEST_P(Sz_Type_KernelSz, Filters_Filter2D, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4, CV_32FC1, CV_32FC4), Values(3, 5, 7, 9, 11, 13, 15)))
{
declare.time(20.0);
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
const int ksize = GET_PARAM(2);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
cv::Mat kernel(ksize, ksize, CV_32FC1);
declare.in(kernel, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::filter2D(d_src, dst, -1, kernel);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::filter2D(src, dst, -1, kernel);
CPU_SANITY_CHECK(dst);
}
}

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11
modules/gpu/perf/perf_precomp.hpp
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@@ -51,12 +51,21 @@
#ifndef __OPENCV_PERF_PRECOMP_HPP__
#define __OPENCV_PERF_PRECOMP_HPP__
#include <cstdio>
#include <iostream>
#include "opencv2/ts.hpp"
#include "opencv2/ts/gpu_perf.hpp"
#include "opencv2/core.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/gpu.hpp"
#include "opencv2/calib3d.hpp"
#include "opencv2/objdetect.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/video.hpp"
#include "opencv2/photo.hpp"
#include "opencv2/core/gpu_private.hpp"
#ifdef GTEST_CREATE_SHARED_LIBRARY
#error no modules except ts should have GTEST_CREATE_SHARED_LIBRARY defined

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