mirror of
https://github.com/opencv/opencv.git
synced 2024-11-25 11:40:44 +08:00
97156897b2
change the download channels to oclchannles() fix bugs of arithm functions perf fix of bilateral bug fix of split test case add build_warps functions
221 lines
8.4 KiB
C++
221 lines
8.4 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.
|
|
//
|
|
//
|
|
// Intel License Agreement
|
|
// For Open Source Computer Vision Library
|
|
//
|
|
// Copyright (C) 2010-2012, Multicoreware 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 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
|
|
// 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 "precomp.hpp"
|
|
#ifdef HAVE_OPENCL
|
|
namespace
|
|
{
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// BruteForceMatcher
|
|
|
|
CV_ENUM(DistType, cv::ocl::BruteForceMatcher_OCL_base::L1Dist, cv::ocl::BruteForceMatcher_OCL_base::L2Dist, cv::ocl::BruteForceMatcher_OCL_base::HammingDist)
|
|
IMPLEMENT_PARAM_CLASS(DescriptorSize, int)
|
|
|
|
PARAM_TEST_CASE(BruteForceMatcher/*, NormCode*/, DistType, DescriptorSize)
|
|
{
|
|
//std::vector<cv::ocl::Info> oclinfo;
|
|
cv::ocl::BruteForceMatcher_OCL_base::DistType distType;
|
|
int normCode;
|
|
int dim;
|
|
|
|
int queryDescCount;
|
|
int countFactor;
|
|
|
|
cv::Mat query, train;
|
|
|
|
virtual void SetUp()
|
|
{
|
|
//normCode = GET_PARAM(0);
|
|
distType = (cv::ocl::BruteForceMatcher_OCL_base::DistType)(int)GET_PARAM(0);
|
|
dim = GET_PARAM(1);
|
|
|
|
//int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
|
|
//CV_Assert(devnums > 0);
|
|
|
|
queryDescCount = 300; // must be even number because we split train data in some cases in two
|
|
countFactor = 4; // do not change it
|
|
|
|
cv::RNG &rng = cvtest::TS::ptr()->get_rng();
|
|
|
|
cv::Mat queryBuf, trainBuf;
|
|
|
|
// Generate query descriptors randomly.
|
|
// Descriptor vector elements are integer values.
|
|
queryBuf.create(queryDescCount, dim, CV_32SC1);
|
|
rng.fill(queryBuf, cv::RNG::UNIFORM, cv::Scalar::all(0), cv::Scalar::all(3));
|
|
queryBuf.convertTo(queryBuf, CV_32FC1);
|
|
|
|
// Generate train decriptors as follows:
|
|
// copy each query descriptor to train set countFactor times
|
|
// and perturb some one element of the copied descriptors in
|
|
// in ascending order. General boundaries of the perturbation
|
|
// are (0.f, 1.f).
|
|
trainBuf.create(queryDescCount * countFactor, dim, CV_32FC1);
|
|
float step = 1.f / countFactor;
|
|
for (int qIdx = 0; qIdx < queryDescCount; qIdx++)
|
|
{
|
|
cv::Mat queryDescriptor = queryBuf.row(qIdx);
|
|
for (int c = 0; c < countFactor; c++)
|
|
{
|
|
int tIdx = qIdx * countFactor + c;
|
|
cv::Mat trainDescriptor = trainBuf.row(tIdx);
|
|
queryDescriptor.copyTo(trainDescriptor);
|
|
int elem = rng(dim);
|
|
float diff = rng.uniform(step * c, step * (c + 1));
|
|
trainDescriptor.at<float>(0, elem) += diff;
|
|
}
|
|
}
|
|
|
|
queryBuf.convertTo(query, CV_32F);
|
|
trainBuf.convertTo(train, CV_32F);
|
|
}
|
|
};
|
|
|
|
TEST_P(BruteForceMatcher, Match_Single)
|
|
{
|
|
cv::ocl::BruteForceMatcher_OCL_base matcher(distType);
|
|
|
|
std::vector<cv::DMatch> matches;
|
|
matcher.match(cv::ocl::oclMat(query), cv::ocl::oclMat(train), matches);
|
|
|
|
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
|
|
|
|
int badCount = 0;
|
|
for (size_t i = 0; i < matches.size(); i++)
|
|
{
|
|
cv::DMatch match = matches[i];
|
|
if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0))
|
|
badCount++;
|
|
}
|
|
|
|
ASSERT_EQ(0, badCount);
|
|
}
|
|
|
|
TEST_P(BruteForceMatcher, KnnMatch_2_Single)
|
|
{
|
|
const int knn = 2;
|
|
|
|
cv::ocl::BruteForceMatcher_OCL_base matcher(distType);
|
|
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
matcher.knnMatch(cv::ocl::oclMat(query), cv::ocl::oclMat(train), matches, knn);
|
|
|
|
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() != knn)
|
|
badCount++;
|
|
else
|
|
{
|
|
int localBadCount = 0;
|
|
for (int k = 0; k < knn; k++)
|
|
{
|
|
cv::DMatch match = matches[i][k];
|
|
if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k) || (match.imgIdx != 0))
|
|
localBadCount++;
|
|
}
|
|
badCount += localBadCount > 0 ? 1 : 0;
|
|
}
|
|
}
|
|
|
|
ASSERT_EQ(0, badCount);
|
|
}
|
|
|
|
TEST_P(BruteForceMatcher, RadiusMatch_Single)
|
|
{
|
|
float radius;
|
|
if(distType == cv::ocl::BruteForceMatcher_OCL_base::L2Dist)
|
|
radius = 1.f / countFactor / countFactor;
|
|
else
|
|
radius = 1.f / countFactor;
|
|
|
|
cv::ocl::BruteForceMatcher_OCL_base matcher(distType);
|
|
|
|
// assume support atomic.
|
|
//if (!supportFeature(devInfo, cv::gpu::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_StsNotImplemented, e.code);
|
|
// }
|
|
//}
|
|
//else
|
|
{
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
matcher.radiusMatch(cv::ocl::oclMat(query), cv::ocl::oclMat(train), matches, radius);
|
|
|
|
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);
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(GPU_Features2D, BruteForceMatcher, testing::Combine(
|
|
//ALL_DEVICES,
|
|
testing::Values(DistType(cv::ocl::BruteForceMatcher_OCL_base::L1Dist), DistType(cv::ocl::BruteForceMatcher_OCL_base::L2Dist)),
|
|
testing::Values(DescriptorSize(57), DescriptorSize(64), DescriptorSize(83), DescriptorSize(128), DescriptorSize(179), DescriptorSize(256), DescriptorSize(304))));
|
|
|
|
} // namespace
|
|
#endif
|