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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,
// 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"
namespace opencv_test { namespace {
#if defined(HAVE_OPENCV_XFEATURES2D) && defined(OPENCV_ENABLE_NONFREE)
TEST(SurfFeaturesFinder, CanFindInROIs)
{
Ptr<Feature2D> finder = xfeatures2d::SURF::create();
Mat img = imread(string(cvtest::TS::ptr()->get_data_path()) + "cv/shared/lena.png");
vector<Rect> rois;
rois.push_back(Rect(0, 0, img.cols / 2, img.rows / 2));
rois.push_back(Rect(img.cols / 2, img.rows / 2, img.cols - img.cols / 2, img.rows - img.rows / 2));
// construct mask
Mat mask = Mat::zeros(img.size(), CV_8U);
for (const Rect &roi : rois)
{
Mat(mask, roi) = 1;
}
detail::ImageFeatures roi_features;
detail::computeImageFeatures(finder, img, roi_features, mask);
int tl_rect_count = 0, br_rect_count = 0, bad_count = 0;
for (const auto &keypoint : roi_features.keypoints)
{
if (rois[0].contains(keypoint.pt))
tl_rect_count++;
else if (rois[1].contains(keypoint.pt))
br_rect_count++;
else
bad_count++;
}
EXPECT_GT(tl_rect_count, 0);
EXPECT_GT(br_rect_count, 0);
EXPECT_EQ(bad_count, 0);
}
#endif // HAVE_OPENCV_XFEATURES2D && OPENCV_ENABLE_NONFREE
TEST(ParallelFeaturesFinder, IsSameWithSerial)
{
Ptr<Feature2D> para_finder = ORB::create();
Ptr<Feature2D> serial_finder = ORB::create();
Mat img = imread(string(cvtest::TS::ptr()->get_data_path()) + "stitching/a3.png", IMREAD_GRAYSCALE);
detail::ImageFeatures serial_features;
detail::computeImageFeatures(serial_finder, img, serial_features);
vector<Mat> imgs(50, img);
vector<detail::ImageFeatures> para_features(imgs.size());
detail::computeImageFeatures(para_finder, imgs, para_features); // FIXIT This call doesn't use parallel_for_()
// results must be the same
Mat serial_descriptors;
serial_features.descriptors.copyTo(serial_descriptors);
for(size_t i = 0; i < para_features.size(); ++i)
{
SCOPED_TRACE(cv::format("i=%zu", i));
EXPECT_EQ(serial_descriptors.size(), para_features[i].descriptors.size());
#if 0 // FIXIT ORB descriptors are not bit-exact (perhaps due internal parallel_for usage)
ASSERT_EQ(0, cv::norm(u_serial_descriptors, para_features[i].descriptors, NORM_L1))
<< "serial_size=" << u_serial_descriptors.size()
<< " par_size=" << para_features[i].descriptors.size()
<< endl << u_serial_descriptors.getMat(ACCESS_READ)
<< endl << endl << para_features[i].descriptors.getMat(ACCESS_READ);
#endif
EXPECT_EQ(serial_features.img_size, para_features[i].img_size);
EXPECT_EQ(serial_features.keypoints.size(), para_features[i].keypoints.size());
}
}
TEST(RangeMatcher, MatchesRangeOnly)
{
Ptr<Feature2D> finder = ORB::create();
Mat img0 = imread(string(cvtest::TS::ptr()->get_data_path()) + "stitching/a1.png", IMREAD_GRAYSCALE);
Mat img1 = imread(string(cvtest::TS::ptr()->get_data_path()) + "stitching/a2.png", IMREAD_GRAYSCALE);
Mat img2 = imread(string(cvtest::TS::ptr()->get_data_path()) + "stitching/a3.png", IMREAD_GRAYSCALE);
vector<detail::ImageFeatures> features(3);
computeImageFeatures(finder, img0, features[0]);
computeImageFeatures(finder, img1, features[1]);
computeImageFeatures(finder, img2, features[2]);
vector<detail::MatchesInfo> pairwise_matches;
Ptr<detail::FeaturesMatcher> matcher = makePtr<detail::BestOf2NearestRangeMatcher>(1);
(*matcher)(features, pairwise_matches);
// matches[1] will be image 0 and image 1, should have non-zero confidence
EXPECT_NE(pairwise_matches[1].confidence, .0);
// matches[2] will be image 0 and image 2, should have zero confidence due to range_width=1
EXPECT_DOUBLE_EQ(pairwise_matches[2].confidence, .0);
}
}} // namespace
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