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144 lines
5.8 KiB
C++
144 lines
5.8 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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#if defined(HAVE_OPENCV_XFEATURES2D) && defined(OPENCV_ENABLE_NONFREE)
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TEST(SurfFeaturesFinder, CanFindInROIs)
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{
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Ptr<Feature2D> finder = xfeatures2d::SURF::create();
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Mat img = imread(string(cvtest::TS::ptr()->get_data_path()) + "cv/shared/lena.png");
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vector<Rect> rois;
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rois.push_back(Rect(0, 0, img.cols / 2, img.rows / 2));
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rois.push_back(Rect(img.cols / 2, img.rows / 2, img.cols - img.cols / 2, img.rows - img.rows / 2));
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// construct mask
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Mat mask = Mat::zeros(img.size(), CV_8U);
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for (const Rect &roi : rois)
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{
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Mat(mask, roi) = 1;
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}
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detail::ImageFeatures roi_features;
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detail::computeImageFeatures(finder, img, roi_features, mask);
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int tl_rect_count = 0, br_rect_count = 0, bad_count = 0;
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for (const auto &keypoint : roi_features.keypoints)
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{
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if (rois[0].contains(keypoint.pt))
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tl_rect_count++;
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else if (rois[1].contains(keypoint.pt))
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br_rect_count++;
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else
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bad_count++;
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}
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EXPECT_GT(tl_rect_count, 0);
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EXPECT_GT(br_rect_count, 0);
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EXPECT_EQ(bad_count, 0);
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}
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#endif // HAVE_OPENCV_XFEATURES2D && OPENCV_ENABLE_NONFREE
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TEST(ParallelFeaturesFinder, IsSameWithSerial)
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{
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Ptr<Feature2D> para_finder = ORB::create();
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Ptr<Feature2D> serial_finder = ORB::create();
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Mat img = imread(string(cvtest::TS::ptr()->get_data_path()) + "stitching/a3.png", IMREAD_GRAYSCALE);
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detail::ImageFeatures serial_features;
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detail::computeImageFeatures(serial_finder, img, serial_features);
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vector<Mat> imgs(50, img);
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vector<detail::ImageFeatures> para_features(imgs.size());
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detail::computeImageFeatures(para_finder, imgs, para_features); // FIXIT This call doesn't use parallel_for_()
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// results must be the same
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Mat serial_descriptors;
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serial_features.descriptors.copyTo(serial_descriptors);
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for(size_t i = 0; i < para_features.size(); ++i)
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{
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SCOPED_TRACE(cv::format("i=%zu", i));
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EXPECT_EQ(serial_descriptors.size(), para_features[i].descriptors.size());
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#if 0 // FIXIT ORB descriptors are not bit-exact (perhaps due internal parallel_for usage)
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ASSERT_EQ(0, cv::norm(u_serial_descriptors, para_features[i].descriptors, NORM_L1))
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<< "serial_size=" << u_serial_descriptors.size()
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<< " par_size=" << para_features[i].descriptors.size()
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<< endl << u_serial_descriptors.getMat(ACCESS_READ)
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<< endl << endl << para_features[i].descriptors.getMat(ACCESS_READ);
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#endif
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EXPECT_EQ(serial_features.img_size, para_features[i].img_size);
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EXPECT_EQ(serial_features.keypoints.size(), para_features[i].keypoints.size());
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}
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}
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TEST(RangeMatcher, MatchesRangeOnly)
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{
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Ptr<Feature2D> finder = ORB::create();
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Mat img0 = imread(string(cvtest::TS::ptr()->get_data_path()) + "stitching/a1.png", IMREAD_GRAYSCALE);
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Mat img1 = imread(string(cvtest::TS::ptr()->get_data_path()) + "stitching/a2.png", IMREAD_GRAYSCALE);
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Mat img2 = imread(string(cvtest::TS::ptr()->get_data_path()) + "stitching/a3.png", IMREAD_GRAYSCALE);
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vector<detail::ImageFeatures> features(3);
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computeImageFeatures(finder, img0, features[0]);
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computeImageFeatures(finder, img1, features[1]);
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computeImageFeatures(finder, img2, features[2]);
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vector<detail::MatchesInfo> pairwise_matches;
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Ptr<detail::FeaturesMatcher> matcher = makePtr<detail::BestOf2NearestRangeMatcher>(1);
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(*matcher)(features, pairwise_matches);
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// matches[1] will be image 0 and image 1, should have non-zero confidence
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EXPECT_NE(pairwise_matches[1].confidence, .0);
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// matches[2] will be image 0 and image 2, should have zero confidence due to range_width=1
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EXPECT_DOUBLE_EQ(pairwise_matches[2].confidence, .0);
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}
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}} // namespace
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