313 lines
9.8 KiB
C++
313 lines
9.8 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"
|
|
|
|
namespace opencv_test { namespace {
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// FAST
|
|
|
|
DEF_PARAM_TEST(Image_Threshold_NonMaxSuppression, string, int, bool);
|
|
|
|
PERF_TEST_P(Image_Threshold_NonMaxSuppression, 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_CUDA())
|
|
{
|
|
cv::Ptr<cv::cuda::FastFeatureDetector> d_fast =
|
|
cv::cuda::FastFeatureDetector::create(threshold, nonMaxSuppersion,
|
|
cv::FastFeatureDetector::TYPE_9_16,
|
|
0.5 * img.size().area());
|
|
|
|
const cv::cuda::GpuMat d_img(img);
|
|
cv::cuda::GpuMat d_keypoints;
|
|
|
|
TEST_CYCLE() d_fast->detectAsync(d_img, d_keypoints);
|
|
|
|
std::vector<cv::KeyPoint> gpu_keypoints;
|
|
d_fast->convert(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, 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_CUDA())
|
|
{
|
|
cv::Ptr<cv::cuda::ORB> d_orb = cv::cuda::ORB::create(nFeatures);
|
|
|
|
const cv::cuda::GpuMat d_img(img);
|
|
cv::cuda::GpuMat d_keypoints, d_descriptors;
|
|
|
|
TEST_CYCLE() d_orb->detectAndComputeAsync(d_img, cv::noArray(), d_keypoints, d_descriptors);
|
|
|
|
std::vector<cv::KeyPoint> gpu_keypoints;
|
|
d_orb->convert(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, 1e-4);
|
|
SANITY_CHECK(gpu_descriptors);
|
|
}
|
|
else
|
|
{
|
|
cv::Ptr<cv::ORB> orb = cv::ORB::create(nFeatures);
|
|
|
|
std::vector<cv::KeyPoint> cpu_keypoints;
|
|
cv::Mat cpu_descriptors;
|
|
|
|
TEST_CYCLE() orb->detectAndCompute(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, 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_CUDA())
|
|
{
|
|
cv::Ptr<cv::cuda::DescriptorMatcher> d_matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normType);
|
|
|
|
const cv::cuda::GpuMat d_query(query);
|
|
const cv::cuda::GpuMat d_train(train);
|
|
cv::cuda::GpuMat d_matches;
|
|
|
|
TEST_CYCLE() d_matcher->matchAsync(d_query, d_train, d_matches);
|
|
|
|
std::vector<cv::DMatch> gpu_matches;
|
|
d_matcher->matchConvert(d_matches, 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, 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_CUDA())
|
|
{
|
|
cv::Ptr<cv::cuda::DescriptorMatcher> d_matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normType);
|
|
|
|
const cv::cuda::GpuMat d_query(query);
|
|
const cv::cuda::GpuMat d_train(train);
|
|
cv::cuda::GpuMat d_matches;
|
|
|
|
TEST_CYCLE() d_matcher->knnMatchAsync(d_query, d_train, d_matches, k);
|
|
|
|
std::vector< std::vector<cv::DMatch> > matchesTbl;
|
|
d_matcher->knnMatchConvert(d_matches, 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, 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_CUDA())
|
|
{
|
|
cv::Ptr<cv::cuda::DescriptorMatcher> d_matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normType);
|
|
|
|
const cv::cuda::GpuMat d_query(query);
|
|
const cv::cuda::GpuMat d_train(train);
|
|
cv::cuda::GpuMat d_matches;
|
|
|
|
TEST_CYCLE() d_matcher->radiusMatchAsync(d_query, d_train, d_matches, maxDistance);
|
|
|
|
std::vector< std::vector<cv::DMatch> > matchesTbl;
|
|
d_matcher->radiusMatchConvert(d_matches, 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);
|
|
}
|
|
}
|
|
|
|
}} // namespace
|