mirror of
https://github.com/opencv/opencv.git
synced 2024-11-30 06:10:02 +08:00
656 lines
21 KiB
C++
656 lines
21 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) 2000, Intel Corporation, 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 "test_precomp.hpp"
|
|
|
|
#ifdef HAVE_CUDA
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// SURF
|
|
|
|
struct SURF : testing::TestWithParam<cv::gpu::DeviceInfo>
|
|
{
|
|
cv::Mat image;
|
|
cv::Mat mask;
|
|
std::vector<cv::KeyPoint> keypoints_gold;
|
|
std::vector<float> descriptors_gold;
|
|
|
|
cv::gpu::DeviceInfo devInfo;
|
|
|
|
virtual void SetUp()
|
|
{
|
|
devInfo = GetParam();
|
|
|
|
cv::gpu::setDevice(devInfo.deviceID());
|
|
|
|
image = readImage("features2d/aloe.png", CV_LOAD_IMAGE_GRAYSCALE);
|
|
ASSERT_FALSE(image.empty());
|
|
|
|
mask = cv::Mat(image.size(), CV_8UC1, cv::Scalar::all(1));
|
|
mask(cv::Range(0, image.rows / 2), cv::Range(0, image.cols / 2)).setTo(cv::Scalar::all(0));
|
|
|
|
cv::SURF fdetector_gold; fdetector_gold.extended = false;
|
|
fdetector_gold(image, mask, keypoints_gold, descriptors_gold);
|
|
}
|
|
|
|
bool isSimilarKeypoints(const cv::KeyPoint& p1, const cv::KeyPoint& p2)
|
|
{
|
|
const float maxPtDif = 1.f;
|
|
const float maxSizeDif = 1.f;
|
|
const float maxAngleDif = 2.f;
|
|
const float maxResponseDif = 0.1f;
|
|
|
|
float dist = (float)cv::norm(p1.pt - p2.pt);
|
|
return (dist < maxPtDif &&
|
|
fabs(p1.size - p2.size) < maxSizeDif &&
|
|
abs(p1.angle - p2.angle) < maxAngleDif &&
|
|
abs(p1.response - p2.response) < maxResponseDif &&
|
|
p1.octave == p2.octave &&
|
|
p1.class_id == p2.class_id );
|
|
}
|
|
};
|
|
|
|
TEST_P(SURF, EmptyDataTest)
|
|
{
|
|
PRINT_PARAM(devInfo);
|
|
|
|
cv::gpu::SURF_GPU fdetector;
|
|
|
|
cv::gpu::GpuMat image;
|
|
std::vector<cv::KeyPoint> keypoints;
|
|
std::vector<float> descriptors;
|
|
|
|
ASSERT_NO_THROW(
|
|
fdetector(image, cv::gpu::GpuMat(), keypoints, descriptors);
|
|
);
|
|
|
|
EXPECT_TRUE(keypoints.empty());
|
|
EXPECT_TRUE(descriptors.empty());
|
|
}
|
|
|
|
TEST_P(SURF, Accuracy)
|
|
{
|
|
PRINT_PARAM(devInfo);
|
|
|
|
// Compute keypoints.
|
|
std::vector<cv::KeyPoint> keypoints;
|
|
cv::Mat descriptors;
|
|
|
|
ASSERT_NO_THROW(
|
|
cv::gpu::GpuMat dev_descriptors;
|
|
cv::gpu::SURF_GPU fdetector; fdetector.extended = false;
|
|
|
|
fdetector(cv::gpu::GpuMat(image), cv::gpu::GpuMat(mask), keypoints, dev_descriptors);
|
|
|
|
dev_descriptors.download(descriptors);
|
|
);
|
|
|
|
cv::BruteForceMatcher< cv::L2<float> > matcher;
|
|
std::vector<cv::DMatch> matches;
|
|
|
|
matcher.match(cv::Mat(static_cast<int>(keypoints_gold.size()), 64, CV_32FC1, &descriptors_gold[0]), descriptors, matches);
|
|
|
|
int validCount = 0;
|
|
|
|
for (size_t i = 0; i < matches.size(); ++i)
|
|
{
|
|
const cv::DMatch& m = matches[i];
|
|
|
|
const cv::KeyPoint& p1 = keypoints_gold[m.queryIdx];
|
|
const cv::KeyPoint& p2 = keypoints[m.trainIdx];
|
|
|
|
const float maxPtDif = 1.f;
|
|
const float maxSizeDif = 1.f;
|
|
const float maxAngleDif = 2.f;
|
|
const float maxResponseDif = 0.1f;
|
|
|
|
float dist = (float)cv::norm(p1.pt - p2.pt);
|
|
if (dist < maxPtDif &&
|
|
fabs(p1.size - p2.size) < maxSizeDif &&
|
|
abs(p1.angle - p2.angle) < maxAngleDif &&
|
|
abs(p1.response - p2.response) < maxResponseDif &&
|
|
p1.octave == p2.octave &&
|
|
p1.class_id == p2.class_id)
|
|
{
|
|
++validCount;
|
|
}
|
|
}
|
|
|
|
double validRatio = (double)validCount / matches.size();
|
|
|
|
EXPECT_GT(validRatio, 0.5);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(Features2D, SURF, testing::ValuesIn(devices(cv::gpu::GLOBAL_ATOMICS)));
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// BruteForceMatcher
|
|
|
|
static const char* dists[] = {"L1Dist", "L2Dist", "HammingDist"};
|
|
|
|
struct BruteForceMatcher : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, cv::gpu::BruteForceMatcher_GPU_base::DistType, int> >
|
|
{
|
|
static const int queryDescCount = 300; // must be even number because we split train data in some cases in two
|
|
static const int countFactor = 4; // do not change it
|
|
|
|
cv::gpu::DeviceInfo devInfo;
|
|
cv::gpu::BruteForceMatcher_GPU_base::DistType distType;
|
|
int dim;
|
|
|
|
cv::Mat query, train;
|
|
|
|
virtual void SetUp()
|
|
{
|
|
devInfo = std::tr1::get<0>(GetParam());
|
|
distType = std::tr1::get<1>(GetParam());
|
|
dim = std::tr1::get<2>(GetParam());
|
|
|
|
cv::gpu::setDevice(devInfo.deviceID());
|
|
|
|
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);
|
|
}
|
|
};
|
|
|
|
const int BruteForceMatcher::queryDescCount;
|
|
const int BruteForceMatcher::countFactor;
|
|
|
|
TEST_P(BruteForceMatcher, Match)
|
|
{
|
|
const char* distStr = dists[distType];
|
|
|
|
PRINT_PARAM(devInfo);
|
|
PRINT_PARAM(distStr);
|
|
PRINT_PARAM(dim);
|
|
|
|
std::vector<cv::DMatch> matches;
|
|
|
|
ASSERT_NO_THROW(
|
|
cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
|
|
|
|
matcher.match(cv::gpu::GpuMat(query), cv::gpu::GpuMat(train), matches);
|
|
);
|
|
|
|
ASSERT_EQ(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, MatchAdd)
|
|
{
|
|
const char* distStr = dists[distType];
|
|
|
|
PRINT_PARAM(devInfo);
|
|
PRINT_PARAM(distStr);
|
|
PRINT_PARAM(dim);
|
|
|
|
std::vector<cv::DMatch> matches;
|
|
|
|
bool isMaskSupported;
|
|
|
|
ASSERT_NO_THROW(
|
|
cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
|
|
|
|
cv::gpu::GpuMat d_train(train);
|
|
|
|
// make add() twice to test such case
|
|
matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(0, train.rows/2)));
|
|
matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(train.rows/2, train.rows)));
|
|
|
|
// prepare masks (make first nearest match illegal)
|
|
std::vector<cv::gpu::GpuMat> masks(2);
|
|
for (int mi = 0; mi < 2; mi++)
|
|
{
|
|
masks[mi] = cv::gpu::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));
|
|
}
|
|
|
|
matcher.match(cv::gpu::GpuMat(query), matches, masks);
|
|
|
|
isMaskSupported = matcher.isMaskSupported();
|
|
);
|
|
|
|
ASSERT_EQ(queryDescCount, matches.size());
|
|
|
|
int badCount = 0;
|
|
for (size_t i = 0; i < matches.size(); i++)
|
|
{
|
|
cv::DMatch match = matches[i];
|
|
int shift = isMaskSupported ? 1 : 0;
|
|
{
|
|
if (i < queryDescCount / 2)
|
|
{
|
|
if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + shift) || (match.imgIdx != 0))
|
|
badCount++;
|
|
}
|
|
else
|
|
{
|
|
if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + shift) || (match.imgIdx != 1))
|
|
badCount++;
|
|
}
|
|
}
|
|
}
|
|
|
|
ASSERT_EQ(0, badCount);
|
|
}
|
|
|
|
TEST_P(BruteForceMatcher, KnnMatch2)
|
|
{
|
|
const char* distStr = dists[distType];
|
|
|
|
PRINT_PARAM(devInfo);
|
|
PRINT_PARAM(distStr);
|
|
PRINT_PARAM(dim);
|
|
|
|
const int knn = 2;
|
|
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
|
|
ASSERT_NO_THROW(
|
|
cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
|
|
matcher.knnMatch(cv::gpu::GpuMat(query), cv::gpu::GpuMat(train), matches, knn);
|
|
);
|
|
|
|
ASSERT_EQ(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, KnnMatch3)
|
|
{
|
|
const char* distStr = dists[distType];
|
|
|
|
PRINT_PARAM(devInfo);
|
|
PRINT_PARAM(distStr);
|
|
PRINT_PARAM(dim);
|
|
|
|
const int knn = 3;
|
|
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
|
|
ASSERT_NO_THROW(
|
|
cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
|
|
matcher.knnMatch(cv::gpu::GpuMat(query), cv::gpu::GpuMat(train), matches, knn);
|
|
);
|
|
|
|
ASSERT_EQ(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, KnnMatchAdd2)
|
|
{
|
|
const char* distStr = dists[distType];
|
|
|
|
PRINT_PARAM(devInfo);
|
|
PRINT_PARAM(distStr);
|
|
PRINT_PARAM(dim);
|
|
|
|
const int knn = 2;
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
|
|
bool isMaskSupported;
|
|
|
|
ASSERT_NO_THROW(
|
|
cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
|
|
|
|
cv::gpu::GpuMat d_train(train);
|
|
|
|
// make add() twice to test such case
|
|
matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
|
|
matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
|
|
|
|
// prepare masks (make first nearest match illegal)
|
|
std::vector<cv::gpu::GpuMat> masks(2);
|
|
for (int mi = 0; mi < 2; mi++ )
|
|
{
|
|
masks[mi] = cv::gpu::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));
|
|
}
|
|
|
|
matcher.knnMatch(cv::gpu::GpuMat(query), matches, knn, masks);
|
|
|
|
isMaskSupported = matcher.isMaskSupported();
|
|
);
|
|
|
|
ASSERT_EQ(queryDescCount, matches.size());
|
|
|
|
int badCount = 0;
|
|
int shift = isMaskSupported ? 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 (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);
|
|
}
|
|
|
|
TEST_P(BruteForceMatcher, KnnMatchAdd3)
|
|
{
|
|
const char* distStr = dists[distType];
|
|
|
|
PRINT_PARAM(devInfo);
|
|
PRINT_PARAM(distStr);
|
|
PRINT_PARAM(dim);
|
|
|
|
const int knn = 3;
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
|
|
bool isMaskSupported;
|
|
|
|
ASSERT_NO_THROW(
|
|
cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
|
|
|
|
cv::gpu::GpuMat d_train(train);
|
|
|
|
// make add() twice to test such case
|
|
matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
|
|
matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
|
|
|
|
// prepare masks (make first nearest match illegal)
|
|
std::vector<cv::gpu::GpuMat> masks(2);
|
|
for (int mi = 0; mi < 2; mi++ )
|
|
{
|
|
masks[mi] = cv::gpu::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));
|
|
}
|
|
|
|
matcher.knnMatch(cv::gpu::GpuMat(query), matches, knn, masks);
|
|
|
|
isMaskSupported = matcher.isMaskSupported();
|
|
);
|
|
|
|
ASSERT_EQ(queryDescCount, matches.size());
|
|
|
|
int badCount = 0;
|
|
int shift = isMaskSupported ? 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 (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);
|
|
}
|
|
|
|
TEST_P(BruteForceMatcher, RadiusMatch)
|
|
{
|
|
if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS))
|
|
return;
|
|
|
|
const char* distStr = dists[distType];
|
|
|
|
PRINT_PARAM(devInfo);
|
|
PRINT_PARAM(distStr);
|
|
PRINT_PARAM(dim);
|
|
|
|
const float radius = 1.f / countFactor;
|
|
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
|
|
ASSERT_NO_THROW(
|
|
cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
|
|
|
|
matcher.radiusMatch(cv::gpu::GpuMat(query), cv::gpu::GpuMat(train), matches, radius);
|
|
);
|
|
|
|
ASSERT_EQ(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);
|
|
}
|
|
|
|
TEST_P(BruteForceMatcher, RadiusMatchAdd)
|
|
{
|
|
if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS))
|
|
return;
|
|
|
|
const char* distStr = dists[distType];
|
|
|
|
PRINT_PARAM(devInfo);
|
|
PRINT_PARAM(distStr);
|
|
PRINT_PARAM(dim);
|
|
|
|
int n = 3;
|
|
const float radius = 1.f / countFactor * n;
|
|
|
|
std::vector< std::vector<cv::DMatch> > matches;
|
|
|
|
bool isMaskSupported;
|
|
|
|
ASSERT_NO_THROW(
|
|
cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
|
|
|
|
cv::gpu::GpuMat d_train(train);
|
|
|
|
// make add() twice to test such case
|
|
matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
|
|
matcher.add(std::vector<cv::gpu::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
|
|
|
|
// prepare masks (make first nearest match illegal)
|
|
std::vector<cv::gpu::GpuMat> masks(2);
|
|
for (int mi = 0; mi < 2; mi++)
|
|
{
|
|
masks[mi] = cv::gpu::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));
|
|
}
|
|
|
|
matcher.radiusMatch(cv::gpu::GpuMat(query), matches, radius, masks);
|
|
|
|
isMaskSupported = matcher.isMaskSupported();
|
|
);
|
|
|
|
ASSERT_EQ(queryDescCount, matches.size());
|
|
|
|
int badCount = 0;
|
|
int shift = isMaskSupported ? 1 : 0;
|
|
int needMatchCount = isMaskSupported ? 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 (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(Features2D, BruteForceMatcher, testing::Combine(
|
|
testing::ValuesIn(devices()),
|
|
testing::Values(cv::gpu::BruteForceMatcher_GPU_base::L1Dist, cv::gpu::BruteForceMatcher_GPU_base::L2Dist),
|
|
testing::Values(57, 64, 83, 128, 179, 256, 304)));
|
|
|
|
#endif // HAVE_CUDA
|