761 lines
25 KiB
C++
761 lines
25 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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#ifdef HAVE_CUDA
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#include <cuda_runtime_api.h>
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namespace opencv_test { namespace {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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// FAST
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namespace
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{
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IMPLEMENT_PARAM_CLASS(FAST_Threshold, int)
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IMPLEMENT_PARAM_CLASS(FAST_NonmaxSuppression, bool)
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}
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PARAM_TEST_CASE(FAST, cv::cuda::DeviceInfo, FAST_Threshold, FAST_NonmaxSuppression)
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{
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cv::cuda::DeviceInfo devInfo;
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int threshold;
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bool nonmaxSuppression;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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threshold = GET_PARAM(1);
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nonmaxSuppression = GET_PARAM(2);
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cv::cuda::setDevice(devInfo.deviceID());
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}
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};
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CUDA_TEST_P(FAST, Accuracy)
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{
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cv::Mat image = readImage("features2d/aloe.png", cv::IMREAD_GRAYSCALE);
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ASSERT_FALSE(image.empty());
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cv::Ptr<cv::cuda::FastFeatureDetector> fast = cv::cuda::FastFeatureDetector::create(threshold, nonmaxSuppression);
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if (!supportFeature(devInfo, cv::cuda::GLOBAL_ATOMICS))
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{
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throw SkipTestException("CUDA device doesn't support global atomics");
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}
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else
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{
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std::vector<cv::KeyPoint> keypoints;
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fast->detect(loadMat(image), keypoints);
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std::vector<cv::KeyPoint> keypoints_gold;
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cv::FAST(image, keypoints_gold, threshold, nonmaxSuppression);
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ASSERT_KEYPOINTS_EQ(keypoints_gold, keypoints);
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}
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}
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class FastAsyncParallelLoopBody : public cv::ParallelLoopBody
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{
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public:
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FastAsyncParallelLoopBody(cv::cuda::HostMem& src, cv::cuda::GpuMat* d_kpts, cv::Ptr<cv::cuda::FastFeatureDetector>* d_fast)
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: src_(src), kpts_(d_kpts), fast_(d_fast) {}
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~FastAsyncParallelLoopBody() {};
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void operator()(const cv::Range& r) const
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{
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for (int i = r.start; i < r.end; i++) {
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cv::cuda::Stream stream;
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cv::cuda::GpuMat d_src_(src_.rows, src_.cols, CV_8UC1);
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d_src_.upload(src_);
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fast_[i]->detectAsync(d_src_, kpts_[i], noArray(), stream);
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}
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}
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protected:
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cv::cuda::HostMem src_;
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cv::cuda::GpuMat* kpts_;
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cv::Ptr<cv::cuda::FastFeatureDetector>* fast_;
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};
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CUDA_TEST_P(FAST, Async)
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{
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if (!supportFeature(devInfo, cv::cuda::GLOBAL_ATOMICS))
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{
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throw SkipTestException("CUDA device doesn't support global atomics");
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}
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else
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{
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cv::Mat image_ = readImage("features2d/aloe.png", cv::IMREAD_GRAYSCALE);
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ASSERT_FALSE(image_.empty());
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cv::cuda::HostMem image(image_);
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cv::cuda::GpuMat d_keypoints[2];
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cv::Ptr<cv::cuda::FastFeatureDetector> d_fast[2];
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d_fast[0] = cv::cuda::FastFeatureDetector::create(threshold, nonmaxSuppression);
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d_fast[1] = cv::cuda::FastFeatureDetector::create(threshold, nonmaxSuppression);
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cv::parallel_for_(cv::Range(0, 2), FastAsyncParallelLoopBody(image, d_keypoints, d_fast));
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cudaDeviceSynchronize();
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std::vector<cv::KeyPoint> keypoints[2];
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d_fast[0]->convert(d_keypoints[0], keypoints[0]);
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d_fast[1]->convert(d_keypoints[1], keypoints[1]);
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std::vector<cv::KeyPoint> keypoints_gold;
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cv::FAST(image, keypoints_gold, threshold, nonmaxSuppression);
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ASSERT_KEYPOINTS_EQ(keypoints_gold, keypoints[0]);
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ASSERT_KEYPOINTS_EQ(keypoints_gold, keypoints[1]);
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}
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}
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INSTANTIATE_TEST_CASE_P(CUDA_Features2D, FAST, testing::Combine(
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ALL_DEVICES,
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testing::Values(FAST_Threshold(25), FAST_Threshold(50)),
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testing::Values(FAST_NonmaxSuppression(false), FAST_NonmaxSuppression(true))));
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/////////////////////////////////////////////////////////////////////////////////////////////////
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// ORB
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namespace
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{
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IMPLEMENT_PARAM_CLASS(ORB_FeaturesCount, int)
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IMPLEMENT_PARAM_CLASS(ORB_ScaleFactor, float)
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IMPLEMENT_PARAM_CLASS(ORB_LevelsCount, int)
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IMPLEMENT_PARAM_CLASS(ORB_EdgeThreshold, int)
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IMPLEMENT_PARAM_CLASS(ORB_firstLevel, int)
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IMPLEMENT_PARAM_CLASS(ORB_WTA_K, int)
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IMPLEMENT_PARAM_CLASS(ORB_PatchSize, int)
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IMPLEMENT_PARAM_CLASS(ORB_BlurForDescriptor, bool)
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}
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PARAM_TEST_CASE(ORB, cv::cuda::DeviceInfo, ORB_FeaturesCount, ORB_ScaleFactor, ORB_LevelsCount, ORB_EdgeThreshold, ORB_firstLevel, ORB_WTA_K, cv::ORB::ScoreType, ORB_PatchSize, ORB_BlurForDescriptor)
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{
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cv::cuda::DeviceInfo devInfo;
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int nFeatures;
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float scaleFactor;
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int nLevels;
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int edgeThreshold;
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int firstLevel;
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int WTA_K;
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cv::ORB::ScoreType scoreType;
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int patchSize;
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bool blurForDescriptor;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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nFeatures = GET_PARAM(1);
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scaleFactor = GET_PARAM(2);
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nLevels = GET_PARAM(3);
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edgeThreshold = GET_PARAM(4);
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firstLevel = GET_PARAM(5);
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WTA_K = GET_PARAM(6);
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scoreType = GET_PARAM(7);
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patchSize = GET_PARAM(8);
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blurForDescriptor = GET_PARAM(9);
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cv::cuda::setDevice(devInfo.deviceID());
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}
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};
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CUDA_TEST_P(ORB, Accuracy)
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{
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cv::Mat image = readImage("features2d/aloe.png", cv::IMREAD_GRAYSCALE);
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ASSERT_FALSE(image.empty());
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cv::Mat mask(image.size(), CV_8UC1, cv::Scalar::all(1));
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mask(cv::Range(0, image.rows / 2), cv::Range(0, image.cols / 2)).setTo(cv::Scalar::all(0));
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cv::Ptr<cv::cuda::ORB> orb =
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cv::cuda::ORB::create(nFeatures, scaleFactor, nLevels, edgeThreshold, firstLevel,
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WTA_K, scoreType, patchSize, 20, blurForDescriptor);
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if (!supportFeature(devInfo, cv::cuda::GLOBAL_ATOMICS))
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{
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try
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{
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std::vector<cv::KeyPoint> keypoints;
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cv::cuda::GpuMat descriptors;
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orb->detectAndComputeAsync(loadMat(image), loadMat(mask), rawOut(keypoints), descriptors);
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}
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catch (const cv::Exception& e)
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{
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ASSERT_EQ(cv::Error::StsNotImplemented, e.code);
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}
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}
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else
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{
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std::vector<cv::KeyPoint> keypoints;
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cv::cuda::GpuMat descriptors;
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orb->detectAndCompute(loadMat(image), loadMat(mask), keypoints, descriptors);
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cv::Ptr<cv::ORB> orb_gold = cv::ORB::create(nFeatures, scaleFactor, nLevels, edgeThreshold, firstLevel, WTA_K, scoreType, patchSize);
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std::vector<cv::KeyPoint> keypoints_gold;
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cv::Mat descriptors_gold;
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orb_gold->detectAndCompute(image, mask, keypoints_gold, descriptors_gold);
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cv::BFMatcher matcher(cv::NORM_HAMMING);
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std::vector<cv::DMatch> matches;
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matcher.match(descriptors_gold, cv::Mat(descriptors), matches);
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int matchedCount = getMatchedPointsCount(keypoints_gold, keypoints, matches);
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double matchedRatio = static_cast<double>(matchedCount) / keypoints.size();
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EXPECT_GT(matchedRatio, 0.35);
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}
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}
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INSTANTIATE_TEST_CASE_P(CUDA_Features2D, ORB, testing::Combine(
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ALL_DEVICES,
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testing::Values(ORB_FeaturesCount(1000)),
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testing::Values(ORB_ScaleFactor(1.2f)),
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testing::Values(ORB_LevelsCount(4), ORB_LevelsCount(8)),
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testing::Values(ORB_EdgeThreshold(31)),
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testing::Values(ORB_firstLevel(0)),
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testing::Values(ORB_WTA_K(2), ORB_WTA_K(3), ORB_WTA_K(4)),
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testing::Values(cv::ORB::HARRIS_SCORE),
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testing::Values(ORB_PatchSize(31), ORB_PatchSize(29)),
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testing::Values(ORB_BlurForDescriptor(false), ORB_BlurForDescriptor(true))));
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/////////////////////////////////////////////////////////////////////////////////////////////////
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// BruteForceMatcher
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namespace
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{
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IMPLEMENT_PARAM_CLASS(DescriptorSize, int)
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IMPLEMENT_PARAM_CLASS(UseMask, bool)
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}
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PARAM_TEST_CASE(BruteForceMatcher, cv::cuda::DeviceInfo, NormCode, DescriptorSize, UseMask)
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{
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cv::cuda::DeviceInfo devInfo;
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int normCode;
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int dim;
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bool useMask;
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int queryDescCount;
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int countFactor;
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cv::Mat query, train;
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virtual void SetUp()
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{
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devInfo = GET_PARAM(0);
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normCode = GET_PARAM(1);
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dim = GET_PARAM(2);
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useMask = GET_PARAM(3);
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cv::cuda::setDevice(devInfo.deviceID());
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queryDescCount = 300; // must be even number because we split train data in some cases in two
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countFactor = 4; // do not change it
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cv::RNG& rng = cvtest::TS::ptr()->get_rng();
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cv::Mat queryBuf, trainBuf;
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// Generate query descriptors randomly.
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// Descriptor vector elements are integer values.
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queryBuf.create(queryDescCount, dim, CV_32SC1);
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rng.fill(queryBuf, cv::RNG::UNIFORM, cv::Scalar::all(0), cv::Scalar::all(3));
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queryBuf.convertTo(queryBuf, CV_32FC1);
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// Generate train descriptors as follows:
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// copy each query descriptor to train set countFactor times
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// and perturb some one element of the copied descriptors in
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// in ascending order. General boundaries of the perturbation
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// are (0.f, 1.f).
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trainBuf.create(queryDescCount * countFactor, dim, CV_32FC1);
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float step = 1.f / countFactor;
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for (int qIdx = 0; qIdx < queryDescCount; qIdx++)
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{
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cv::Mat queryDescriptor = queryBuf.row(qIdx);
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for (int c = 0; c < countFactor; c++)
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{
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int tIdx = qIdx * countFactor + c;
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cv::Mat trainDescriptor = trainBuf.row(tIdx);
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queryDescriptor.copyTo(trainDescriptor);
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int elem = rng(dim);
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float diff = rng.uniform(step * c, step * (c + 1));
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trainDescriptor.at<float>(0, elem) += diff;
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}
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}
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queryBuf.convertTo(query, CV_32F);
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trainBuf.convertTo(train, CV_32F);
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}
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};
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CUDA_TEST_P(BruteForceMatcher, Match_Single)
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{
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cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
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cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
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cv::cuda::GpuMat mask;
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if (useMask)
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{
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mask.create(query.rows, train.rows, CV_8UC1);
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mask.setTo(cv::Scalar::all(1));
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}
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std::vector<cv::DMatch> matches;
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matcher->match(loadMat(query), loadMat(train), matches, mask);
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ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
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int badCount = 0;
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for (size_t i = 0; i < matches.size(); i++)
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{
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cv::DMatch match = matches[i];
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if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0))
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badCount++;
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}
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ASSERT_EQ(0, badCount);
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}
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CUDA_TEST_P(BruteForceMatcher, Match_Collection)
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{
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cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
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cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
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cv::cuda::GpuMat d_train(train);
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// make add() twice to test such case
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matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
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matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
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// prepare masks (make first nearest match illegal)
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std::vector<cv::cuda::GpuMat> masks(2);
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for (int mi = 0; mi < 2; mi++)
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{
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masks[mi] = cv::cuda::GpuMat(query.rows, train.rows/2, CV_8UC1, cv::Scalar::all(1));
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for (int di = 0; di < queryDescCount/2; di++)
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masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0));
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}
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std::vector<cv::DMatch> matches;
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if (useMask)
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matcher->match(cv::cuda::GpuMat(query), matches, masks);
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else
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matcher->match(cv::cuda::GpuMat(query), matches);
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ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
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int badCount = 0;
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int shift = useMask ? 1 : 0;
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for (size_t i = 0; i < matches.size(); i++)
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{
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cv::DMatch match = matches[i];
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if ((int)i < queryDescCount / 2)
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{
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bool validQueryIdx = (match.queryIdx == (int)i);
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bool validTrainIdx = (match.trainIdx == (int)i * countFactor + shift);
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bool validImgIdx = (match.imgIdx == 0);
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if (!validQueryIdx || !validTrainIdx || !validImgIdx)
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badCount++;
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}
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else
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{
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bool validQueryIdx = (match.queryIdx == (int)i);
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bool validTrainIdx = (match.trainIdx == ((int)i - queryDescCount / 2) * countFactor + shift);
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bool validImgIdx = (match.imgIdx == 1);
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if (!validQueryIdx || !validTrainIdx || !validImgIdx)
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badCount++;
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}
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}
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ASSERT_EQ(0, badCount);
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}
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CUDA_TEST_P(BruteForceMatcher, KnnMatch_2_Single)
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{
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cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
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cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
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const int knn = 2;
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cv::cuda::GpuMat mask;
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if (useMask)
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{
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mask.create(query.rows, train.rows, CV_8UC1);
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mask.setTo(cv::Scalar::all(1));
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}
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std::vector< std::vector<cv::DMatch> > matches;
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matcher->knnMatch(loadMat(query), loadMat(train), matches, knn, mask);
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ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
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int badCount = 0;
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for (size_t i = 0; i < matches.size(); i++)
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{
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if ((int)matches[i].size() != knn)
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badCount++;
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else
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{
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int localBadCount = 0;
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for (int k = 0; k < knn; k++)
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{
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cv::DMatch match = matches[i][k];
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if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k) || (match.imgIdx != 0))
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localBadCount++;
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}
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badCount += localBadCount > 0 ? 1 : 0;
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}
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}
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ASSERT_EQ(0, badCount);
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}
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CUDA_TEST_P(BruteForceMatcher, KnnMatch_3_Single)
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{
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cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
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cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
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const int knn = 3;
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cv::cuda::GpuMat mask;
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if (useMask)
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{
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mask.create(query.rows, train.rows, CV_8UC1);
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mask.setTo(cv::Scalar::all(1));
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}
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std::vector< std::vector<cv::DMatch> > matches;
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matcher->knnMatch(loadMat(query), loadMat(train), matches, knn, mask);
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ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
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int badCount = 0;
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for (size_t i = 0; i < matches.size(); i++)
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{
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if ((int)matches[i].size() != knn)
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badCount++;
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else
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{
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int localBadCount = 0;
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for (int k = 0; k < knn; k++)
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{
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cv::DMatch match = matches[i][k];
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if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k) || (match.imgIdx != 0))
|
|
localBadCount++;
|
|
}
|
|
badCount += localBadCount > 0 ? 1 : 0;
|
|
}
|
|
}
|
|
|
|
ASSERT_EQ(0, badCount);
|
|
}
|
|
|
|
CUDA_TEST_P(BruteForceMatcher, KnnMatch_2_Collection)
|
|
{
|
|
cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
|
|
cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
|
|
|
|
const int knn = 2;
|
|
|
|
cv::cuda::GpuMat d_train(train);
|
|
|
|
// make add() twice to test such case
|
|
matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
|
|
matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
|
|
|
|
// prepare masks (make first nearest match illegal)
|
|
std::vector<cv::cuda::GpuMat> masks(2);
|
|
for (int mi = 0; mi < 2; mi++ )
|
|
{
|
|
masks[mi] = cv::cuda::GpuMat(query.rows, train.rows / 2, CV_8UC1, cv::Scalar::all(1));
|
|
for (int di = 0; di < queryDescCount / 2; di++)
|
|
masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0));
|
|
}
|
|
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
|
|
if (useMask)
|
|
matcher->knnMatch(cv::cuda::GpuMat(query), matches, knn, masks);
|
|
else
|
|
matcher->knnMatch(cv::cuda::GpuMat(query), matches, knn);
|
|
|
|
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
|
|
|
|
int badCount = 0;
|
|
int shift = useMask ? 1 : 0;
|
|
for (size_t i = 0; i < matches.size(); i++)
|
|
{
|
|
if ((int)matches[i].size() != knn)
|
|
badCount++;
|
|
else
|
|
{
|
|
int localBadCount = 0;
|
|
for (int k = 0; k < knn; k++)
|
|
{
|
|
cv::DMatch match = matches[i][k];
|
|
{
|
|
if ((int)i < queryDescCount / 2)
|
|
{
|
|
if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) )
|
|
localBadCount++;
|
|
}
|
|
else
|
|
{
|
|
if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) )
|
|
localBadCount++;
|
|
}
|
|
}
|
|
}
|
|
badCount += localBadCount > 0 ? 1 : 0;
|
|
}
|
|
}
|
|
|
|
ASSERT_EQ(0, badCount);
|
|
}
|
|
|
|
CUDA_TEST_P(BruteForceMatcher, KnnMatch_3_Collection)
|
|
{
|
|
cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
|
|
cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
|
|
|
|
const int knn = 3;
|
|
|
|
cv::cuda::GpuMat d_train(train);
|
|
|
|
// make add() twice to test such case
|
|
matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
|
|
matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
|
|
|
|
// prepare masks (make first nearest match illegal)
|
|
std::vector<cv::cuda::GpuMat> masks(2);
|
|
for (int mi = 0; mi < 2; mi++ )
|
|
{
|
|
masks[mi] = cv::cuda::GpuMat(query.rows, train.rows / 2, CV_8UC1, cv::Scalar::all(1));
|
|
for (int di = 0; di < queryDescCount / 2; di++)
|
|
masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0));
|
|
}
|
|
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
|
|
if (useMask)
|
|
matcher->knnMatch(cv::cuda::GpuMat(query), matches, knn, masks);
|
|
else
|
|
matcher->knnMatch(cv::cuda::GpuMat(query), matches, knn);
|
|
|
|
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
|
|
|
|
int badCount = 0;
|
|
int shift = useMask ? 1 : 0;
|
|
for (size_t i = 0; i < matches.size(); i++)
|
|
{
|
|
if ((int)matches[i].size() != knn)
|
|
badCount++;
|
|
else
|
|
{
|
|
int localBadCount = 0;
|
|
for (int k = 0; k < knn; k++)
|
|
{
|
|
cv::DMatch match = matches[i][k];
|
|
{
|
|
if ((int)i < queryDescCount / 2)
|
|
{
|
|
if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) )
|
|
localBadCount++;
|
|
}
|
|
else
|
|
{
|
|
if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) )
|
|
localBadCount++;
|
|
}
|
|
}
|
|
}
|
|
badCount += localBadCount > 0 ? 1 : 0;
|
|
}
|
|
}
|
|
|
|
ASSERT_EQ(0, badCount);
|
|
}
|
|
|
|
CUDA_TEST_P(BruteForceMatcher, RadiusMatch_Single)
|
|
{
|
|
cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
|
|
cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
|
|
|
|
const float radius = 1.f / countFactor;
|
|
|
|
if (!supportFeature(devInfo, cv::cuda::GLOBAL_ATOMICS))
|
|
{
|
|
try
|
|
{
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
matcher->radiusMatch(loadMat(query), loadMat(train), matches, radius);
|
|
}
|
|
catch (const cv::Exception& e)
|
|
{
|
|
ASSERT_EQ(cv::Error::StsNotImplemented, e.code);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
cv::cuda::GpuMat mask;
|
|
if (useMask)
|
|
{
|
|
mask.create(query.rows, train.rows, CV_8UC1);
|
|
mask.setTo(cv::Scalar::all(1));
|
|
}
|
|
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
matcher->radiusMatch(loadMat(query), loadMat(train), matches, radius, mask);
|
|
|
|
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
|
|
|
|
int badCount = 0;
|
|
for (size_t i = 0; i < matches.size(); i++)
|
|
{
|
|
if ((int)matches[i].size() != 1)
|
|
badCount++;
|
|
else
|
|
{
|
|
cv::DMatch match = matches[i][0];
|
|
if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor) || (match.imgIdx != 0))
|
|
badCount++;
|
|
}
|
|
}
|
|
|
|
ASSERT_EQ(0, badCount);
|
|
}
|
|
}
|
|
|
|
CUDA_TEST_P(BruteForceMatcher, RadiusMatch_Collection)
|
|
{
|
|
cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
|
|
cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
|
|
|
|
const int n = 3;
|
|
const float radius = 1.f / countFactor * n;
|
|
|
|
cv::cuda::GpuMat d_train(train);
|
|
|
|
// make add() twice to test such case
|
|
matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
|
|
matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
|
|
|
|
// prepare masks (make first nearest match illegal)
|
|
std::vector<cv::cuda::GpuMat> masks(2);
|
|
for (int mi = 0; mi < 2; mi++)
|
|
{
|
|
masks[mi] = cv::cuda::GpuMat(query.rows, train.rows / 2, CV_8UC1, cv::Scalar::all(1));
|
|
for (int di = 0; di < queryDescCount / 2; di++)
|
|
masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0));
|
|
}
|
|
|
|
if (!supportFeature(devInfo, cv::cuda::GLOBAL_ATOMICS))
|
|
{
|
|
try
|
|
{
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
matcher->radiusMatch(cv::cuda::GpuMat(query), matches, radius, masks);
|
|
}
|
|
catch (const cv::Exception& e)
|
|
{
|
|
ASSERT_EQ(cv::Error::StsNotImplemented, e.code);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
|
|
if (useMask)
|
|
matcher->radiusMatch(cv::cuda::GpuMat(query), matches, radius, masks);
|
|
else
|
|
matcher->radiusMatch(cv::cuda::GpuMat(query), matches, radius);
|
|
|
|
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
|
|
|
|
int badCount = 0;
|
|
int shift = useMask ? 1 : 0;
|
|
int needMatchCount = useMask ? n-1 : n;
|
|
for (size_t i = 0; i < matches.size(); i++)
|
|
{
|
|
if ((int)matches[i].size() != needMatchCount)
|
|
badCount++;
|
|
else
|
|
{
|
|
int localBadCount = 0;
|
|
for (int k = 0; k < needMatchCount; k++)
|
|
{
|
|
cv::DMatch match = matches[i][k];
|
|
{
|
|
if ((int)i < queryDescCount / 2)
|
|
{
|
|
if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) )
|
|
localBadCount++;
|
|
}
|
|
else
|
|
{
|
|
if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) )
|
|
localBadCount++;
|
|
}
|
|
}
|
|
}
|
|
badCount += localBadCount > 0 ? 1 : 0;
|
|
}
|
|
}
|
|
|
|
ASSERT_EQ(0, badCount);
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(CUDA_Features2D, BruteForceMatcher, testing::Combine(
|
|
ALL_DEVICES,
|
|
testing::Values(NormCode(cv::NORM_L1), NormCode(cv::NORM_L2)),
|
|
testing::Values(DescriptorSize(57), DescriptorSize(64), DescriptorSize(83), DescriptorSize(128), DescriptorSize(179), DescriptorSize(256), DescriptorSize(304)),
|
|
testing::Values(UseMask(false), UseMask(true))));
|
|
|
|
}} // namespace
|
|
#endif // HAVE_CUDA
|