Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
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// @Authors
// Peng Xiao, pengxiao@multicorewareinc.com
//
// Redistribution and use in source and binary forms, with or without modification,
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// * Redistribution's of source code must retain the above copyright notice,
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// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
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#include "test_precomp.hpp"
#ifdef HAVE_OPENCV_OCL
using namespace std;
using std::tr1::get;
static bool keyPointsEquals(const cv::KeyPoint& p1, const cv::KeyPoint& p2)
{
const double maxPtDif = 0.1;
const double maxSizeDif = 0.1;
const double maxAngleDif = 0.1;
const double maxResponseDif = 0.01;
double dist = 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)
{
return true;
}
return false;
}
static int getMatchedPointsCount(std::vector<cv::KeyPoint>& gold, std::vector<cv::KeyPoint>& actual)
{
std::sort(actual.begin(), actual.end(), perf::comparators::KeypointGreater());
std::sort(gold.begin(), gold.end(), perf::comparators::KeypointGreater());
int validCount = 0;
for (size_t i = 0; i < gold.size(); ++i)
{
const cv::KeyPoint& p1 = gold[i];
const cv::KeyPoint& p2 = actual[i];
if (keyPointsEquals(p1, p2))
++validCount;
}
return validCount;
}
static int getMatchedPointsCount(const std::vector<cv::KeyPoint>& keypoints1, const std::vector<cv::KeyPoint>& keypoints2, const std::vector<cv::DMatch>& matches)
{
int validCount = 0;
for (size_t i = 0; i < matches.size(); ++i)
{
const cv::DMatch& m = matches[i];
const cv::KeyPoint& p1 = keypoints1[m.queryIdx];
const cv::KeyPoint& p2 = keypoints2[m.trainIdx];
if (keyPointsEquals(p1, p2))
++validCount;
}
return validCount;
}
IMPLEMENT_PARAM_CLASS(HessianThreshold, double)
IMPLEMENT_PARAM_CLASS(Octaves, int)
IMPLEMENT_PARAM_CLASS(OctaveLayers, int)
IMPLEMENT_PARAM_CLASS(Extended, bool)
IMPLEMENT_PARAM_CLASS(Upright, bool)
PARAM_TEST_CASE(SURF, HessianThreshold, Octaves, OctaveLayers, Extended, Upright)
{
double hessianThreshold;
int nOctaves;
int nOctaveLayers;
bool extended;
bool upright;
virtual void SetUp()
{
hessianThreshold = get<0>(GetParam());
nOctaves = get<1>(GetParam());
nOctaveLayers = get<2>(GetParam());
extended = get<3>(GetParam());
upright = get<4>(GetParam());
}
};
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TEST_P(SURF, DISABLED_Detector)
{
cv::Mat image = cv::imread(string(cvtest::TS::ptr()->get_data_path()) + "shared/fruits.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(image.empty());
cv::ocl::SURF_OCL surf;
surf.hessianThreshold = static_cast<float>(hessianThreshold);
surf.nOctaves = nOctaves;
surf.nOctaveLayers = nOctaveLayers;
surf.extended = extended;
surf.upright = upright;
surf.keypointsRatio = 0.05f;
std::vector<cv::KeyPoint> keypoints;
surf(cv::ocl::oclMat(image), cv::ocl::oclMat(), keypoints);
cv::SURF surf_gold;
surf_gold.hessianThreshold = hessianThreshold;
surf_gold.nOctaves = nOctaves;
surf_gold.nOctaveLayers = nOctaveLayers;
surf_gold.extended = extended;
surf_gold.upright = upright;
std::vector<cv::KeyPoint> keypoints_gold;
surf_gold(image, cv::noArray(), keypoints_gold);
ASSERT_EQ(keypoints_gold.size(), keypoints.size());
int matchedCount = getMatchedPointsCount(keypoints_gold, keypoints);
double matchedRatio = static_cast<double>(matchedCount) / keypoints_gold.size();
EXPECT_GT(matchedRatio, 0.99);
}
TEST_P(SURF, DISABLED_Descriptor)
{
cv::Mat image = cv::imread(string(cvtest::TS::ptr()->get_data_path()) + "shared/fruits.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(image.empty());
cv::ocl::SURF_OCL surf;
surf.hessianThreshold = static_cast<float>(hessianThreshold);
surf.nOctaves = nOctaves;
surf.nOctaveLayers = nOctaveLayers;
surf.extended = extended;
surf.upright = upright;
surf.keypointsRatio = 0.05f;
cv::SURF surf_gold;
surf_gold.hessianThreshold = hessianThreshold;
surf_gold.nOctaves = nOctaves;
surf_gold.nOctaveLayers = nOctaveLayers;
surf_gold.extended = extended;
surf_gold.upright = upright;
std::vector<cv::KeyPoint> keypoints;
surf_gold(image, cv::noArray(), keypoints);
cv::ocl::oclMat descriptors;
surf(cv::ocl::oclMat(image), cv::ocl::oclMat(), keypoints, descriptors, true);
cv::Mat descriptors_gold;
surf_gold(image, cv::noArray(), keypoints, descriptors_gold, true);
cv::BFMatcher matcher(surf.defaultNorm());
std::vector<cv::DMatch> matches;
matcher.match(descriptors_gold, cv::Mat(descriptors), matches);
int matchedCount = getMatchedPointsCount(keypoints, keypoints, matches);
double matchedRatio = static_cast<double>(matchedCount) / keypoints.size();
EXPECT_GT(matchedRatio, 0.35);
}
INSTANTIATE_TEST_CASE_P(OCL_Features2D, SURF, testing::Combine(
testing::Values(HessianThreshold(500.0), HessianThreshold(1000.0)),
testing::Values(Octaves(3), Octaves(4)),
testing::Values(OctaveLayers(2), OctaveLayers(3)),
testing::Values(Extended(false), Extended(true)),
testing::Values(Upright(false), Upright(true))));
#endif // HAVE_OPENCV_OCL