opencv/modules/gpu/test/test_features2d.cpp

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/*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,
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// This software is provided by the copyright holders and contributors "as is" and
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#include "test_precomp.hpp"
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#ifdef HAVE_CUDA
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/////////////////////////////////////////////////////////////////////////////////////////////////
// SURF
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struct SURF : testing::TestWithParam<cv::gpu::DeviceInfo>
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{
cv::Mat image;
cv::Mat mask;
std::vector<cv::KeyPoint> keypoints_gold;
std::vector<float> descriptors_gold;
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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);
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}
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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;
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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 );
}
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};
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TEST_P(SURF, EmptyDataTest)
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{
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PRINT_PARAM(devInfo);
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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;
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matcher.match(cv::Mat(static_cast<int>(keypoints_gold.size()), 64, CV_32FC1, &descriptors_gold[0]), descriptors, matches);
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int validCount = 0;
for (size_t i = 0; i < matches.size(); ++i)
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{
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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)
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{
++validCount;
}
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}
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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()
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{
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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);
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}
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};
const int BruteForceMatcher::queryDescCount;
const int BruteForceMatcher::countFactor;
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TEST_P(BruteForceMatcher, Match)
{
const char* distStr = dists[distType];
PRINT_PARAM(devInfo);
PRINT_PARAM(distStr);
PRINT_PARAM(dim);
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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++)
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{
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cv::DMatch match = matches[i];
if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0))
badCount++;
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}
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ASSERT_EQ(0, badCount);
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}
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TEST_P(BruteForceMatcher, MatchAdd)
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{
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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);
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}
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TEST_P(BruteForceMatcher, KnnMatch2)
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{
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const char* distStr = dists[distType];
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PRINT_PARAM(devInfo);
PRINT_PARAM(distStr);
PRINT_PARAM(dim);
const int knn = 2;
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std::vector< std::vector<cv::DMatch> > matches;
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ASSERT_NO_THROW(
cv::gpu::BruteForceMatcher_GPU_base matcher(distType);
matcher.knnMatch(cv::gpu::GpuMat(query), cv::gpu::GpuMat(train), matches, knn);
);
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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;
}
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}
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ASSERT_EQ(0, badCount);
}
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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)
{
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const char* distStr = dists[distType];
PRINT_PARAM(devInfo);
PRINT_PARAM(distStr);
PRINT_PARAM(dim);
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const int knn = 2;
std::vector< std::vector<cv::DMatch> > matches;
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bool isMaskSupported;
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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);
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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);
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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++)
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{
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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;
}
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}
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ASSERT_EQ(0, badCount);
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}
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TEST_P(BruteForceMatcher, RadiusMatch)
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{
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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;
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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++)
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{
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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++;
}
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}
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ASSERT_EQ(0, badCount);
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}
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TEST_P(BruteForceMatcher, RadiusMatchAdd)
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{
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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);
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}
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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