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modules/features/test/ocl/test_brute_force_matcher.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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
// Niko Li, newlife20080214@gmail.com
// Jia Haipeng, jiahaipeng95@gmail.com
// Zero Lin, Zero.Lin@amd.com
// Zhang Ying, zhangying913@gmail.com
// Yao Wang, bitwangyaoyao@gmail.com
//
// 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 the copyright holders 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"
#include "cvconfig.h"
#include "opencv2/ts/ocl_test.hpp"
#ifdef HAVE_OPENCL
namespace opencv_test {
namespace ocl {
PARAM_TEST_CASE(BruteForceMatcher, int, int)
{
int distType;
int dim;
int queryDescCount;
int countFactor;
Mat query, train;
UMat uquery, utrain;
virtual void SetUp()
{
distType = GET_PARAM(0);
dim = GET_PARAM(1);
queryDescCount = 300; // must be even number because we split train data in some cases in two
countFactor = 4; // do not change it
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 descriptors 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);
query.copyTo(uquery);
train.copyTo(utrain);
}
};
#ifdef __ANDROID__
OCL_TEST_P(BruteForceMatcher, DISABLED_Match_Single)
#else
OCL_TEST_P(BruteForceMatcher, Match_Single)
#endif
{
BFMatcher matcher(distType);
std::vector<cv::DMatch> matches;
matcher.match(uquery, utrain, 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);
}
#ifdef __ANDROID__
OCL_TEST_P(BruteForceMatcher, DISABLED_KnnMatch_2_Single)
#else
OCL_TEST_P(BruteForceMatcher, KnnMatch_2_Single)
#endif
{
const int knn = 2;
BFMatcher matcher(distType);
std::vector< std::vector<cv::DMatch> > matches;
matcher.knnMatch(uquery, utrain, 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);
}
#ifdef __ANDROID__
OCL_TEST_P(BruteForceMatcher, DISABLED_RadiusMatch_Single)
#else
OCL_TEST_P(BruteForceMatcher, RadiusMatch_Single)
#endif
{
float radius = 1.f / countFactor;
BFMatcher matcher(distType);
std::vector< std::vector<cv::DMatch> > matches;
matcher.radiusMatch(uquery, utrain, 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);
}
OCL_INSTANTIATE_TEST_CASE_P(Matcher, BruteForceMatcher, Combine( Values((int)NORM_L1, (int)NORM_L2),
Values(57, 64, 83, 128, 179, 256, 304) ) );
}//ocl
}//cvtest
#endif //HAVE_OPENCL
modules/features/test/ocl/test_gftt.cpp
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///////////////////////////////////////////////////////////////////////////////////////
//
// 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.
//
//
// License Agreement
// For Open Source Computer Vision Library
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, 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 the copyright holders 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"
#include "opencv2/ts/ocl_test.hpp"
#ifdef HAVE_OPENCL
namespace opencv_test {
namespace ocl {
//////////////////////////// GoodFeaturesToTrack //////////////////////////
PARAM_TEST_CASE(GoodFeaturesToTrack, double, bool)
{
double minDistance;
bool useRoi;
static const int maxCorners;
static const double qualityLevel;
TEST_DECLARE_INPUT_PARAMETER(src);
UMat points, upoints;
std::vector<float> quality, uquality;
virtual void SetUp()
{
minDistance = GET_PARAM(0);
useRoi = GET_PARAM(1);
}
void generateTestData()
{
Mat frame = readImage("../gpu/opticalflow/rubberwhale1.png", IMREAD_GRAYSCALE);
ASSERT_FALSE(frame.empty()) << "could not load gpu/opticalflow/rubberwhale1.png";
Size roiSize = frame.size();
Border srcBorder = randomBorder(0, useRoi ? 2 : 0);
randomSubMat(src, src_roi, roiSize, srcBorder, frame.type(), 5, 256);
src_roi.copyTo(frame);
UMAT_UPLOAD_INPUT_PARAMETER(src);
}
void UMatToVector(const UMat & um, std::vector<Point2f> & v) const
{
v.resize(um.size().area());
um.copyTo(Mat(um.size(), CV_32FC2, &v[0]));
}
};
const int GoodFeaturesToTrack::maxCorners = 1000;
const double GoodFeaturesToTrack::qualityLevel = 0.01;
OCL_TEST_P(GoodFeaturesToTrack, Accuracy)
{
for (int j = 0; j < test_loop_times; ++j)
{
generateTestData();
std::vector<Point2f> upts, pts;
OCL_OFF(cv::goodFeaturesToTrack(src_roi, points, maxCorners, qualityLevel, minDistance, noArray(), quality));
ASSERT_FALSE(points.empty());
UMatToVector(points, pts);
OCL_ON(cv::goodFeaturesToTrack(usrc_roi, upoints, maxCorners, qualityLevel, minDistance, noArray(), uquality));
ASSERT_FALSE(upoints.empty());
UMatToVector(upoints, upts);
ASSERT_EQ(pts.size(), quality.size());
ASSERT_EQ(upts.size(), uquality.size());
ASSERT_EQ(upts.size(), pts.size());
int mistmatch = 0;
for (size_t i = 0; i < pts.size(); ++i)
{
Point2i a = upts[i], b = pts[i];
bool eq = std::abs(a.x - b.x) < 1 && std::abs(a.y - b.y) < 1 &&
std::abs(quality[i] - uquality[i]) <= 3.f * FLT_EPSILON * std::max(quality[i], uquality[i]);
if (!eq)
++mistmatch;
}
double bad_ratio = static_cast<double>(mistmatch) / pts.size();
ASSERT_GE(1e-2, bad_ratio);
}
}
OCL_TEST_P(GoodFeaturesToTrack, EmptyCorners)
{
generateTestData();
usrc_roi.setTo(Scalar::all(0));
OCL_ON(cv::goodFeaturesToTrack(usrc_roi, upoints, maxCorners, qualityLevel, minDistance, noArray(), uquality));
ASSERT_TRUE(upoints.empty());
ASSERT_TRUE(uquality.empty());
}
OCL_INSTANTIATE_TEST_CASE_P(Imgproc, GoodFeaturesToTrack,
::testing::Combine(testing::Values(0.0, 3.0), Bool()));
} } // namespace opencv_test::ocl
#endif