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dfc94c58f0
Fix ORB integer overflow * set size_t step to fix integer overflow in ptr0 offset * added issue_537 test * minor fix tags, points * added size_t_step and offset to remove mixed unsigned and signed operations * features2d: update ORB checks Co-authored-by: Alexander Alekhin <alexander.a.alekhin@gmail.com>
172 lines
5.5 KiB
C++
172 lines
5.5 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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// (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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TEST(Features2D_ORB, _1996)
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{
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Ptr<FeatureDetector> fd = ORB::create(10000, 1.2f, 8, 31, 0, 2, ORB::HARRIS_SCORE, 31, 20);
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Ptr<DescriptorExtractor> de = fd;
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Mat image = imread(string(cvtest::TS::ptr()->get_data_path()) + "shared/lena.png");
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ASSERT_FALSE(image.empty());
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Mat roi(image.size(), CV_8UC1, Scalar(0));
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Point poly[] = {Point(100, 20), Point(300, 50), Point(400, 200), Point(10, 500)};
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fillConvexPoly(roi, poly, int(sizeof(poly) / sizeof(poly[0])), Scalar(255));
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std::vector<KeyPoint> keypoints;
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fd->detect(image, keypoints, roi);
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Mat descriptors;
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de->compute(image, keypoints, descriptors);
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//image.setTo(Scalar(255,255,255), roi);
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int roiViolations = 0;
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for(std::vector<KeyPoint>::const_iterator kp = keypoints.begin(); kp != keypoints.end(); ++kp)
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{
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int x = cvRound(kp->pt.x);
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int y = cvRound(kp->pt.y);
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ASSERT_LE(0, x);
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ASSERT_LE(0, y);
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ASSERT_GT(image.cols, x);
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ASSERT_GT(image.rows, y);
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// if (!roi.at<uchar>(y,x))
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// {
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// roiViolations++;
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// circle(image, kp->pt, 3, Scalar(0,0,255));
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// }
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}
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// if(roiViolations)
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// {
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// imshow("img", image);
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// waitKey();
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// }
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ASSERT_EQ(0, roiViolations);
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}
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TEST(Features2D_ORB, crash_5031)
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{
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cv::Mat image = cv::Mat::zeros(cv::Size(1920, 1080), CV_8UC3);
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int nfeatures = 8000;
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float orbScaleFactor = 1.2f;
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int nlevels = 18;
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int edgeThreshold = 4;
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int firstLevel = 0;
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int WTA_K = 2;
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int scoreType = cv::ORB::HARRIS_SCORE;
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int patchSize = 47;
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int fastThreshold = 20;
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Ptr<ORB> orb = cv::ORB::create(nfeatures, orbScaleFactor, nlevels, edgeThreshold, firstLevel, WTA_K, scoreType, patchSize, fastThreshold);
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std::vector<cv::KeyPoint> keypoints;
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cv::Mat descriptors;
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cv::KeyPoint kp;
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kp.pt.x = 443;
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kp.pt.y = 5;
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kp.size = 47;
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kp.angle = 53.4580612f;
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kp.response = 0.0000470733867f;
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kp.octave = 0;
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kp.class_id = -1;
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keypoints.push_back(kp);
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ASSERT_NO_THROW(orb->compute(image, keypoints, descriptors));
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}
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TEST(Features2D_ORB, regression_16197)
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{
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Mat img(Size(72, 72), CV_8UC1, Scalar::all(0));
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Ptr<ORB> orbPtr = ORB::create();
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orbPtr->setNLevels(5);
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orbPtr->setFirstLevel(3);
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orbPtr->setScaleFactor(1.8);
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orbPtr->setPatchSize(8);
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orbPtr->setEdgeThreshold(8);
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std::vector<KeyPoint> kps;
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Mat fv;
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// exception in debug mode, crash in release
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ASSERT_NO_THROW(orbPtr->detectAndCompute(img, noArray(), kps, fv));
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}
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// https://github.com/opencv/opencv-python/issues/537
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BIGDATA_TEST(Features2D_ORB, regression_opencv_python_537) // memory usage: ~3 Gb
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{
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applyTestTag(
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CV_TEST_TAG_LONG,
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CV_TEST_TAG_DEBUG_VERYLONG,
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CV_TEST_TAG_MEMORY_6GB
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);
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const int width = 25000;
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const int height = 25000;
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Mat img(Size(width, height), CV_8UC1, Scalar::all(0));
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const int border = 23, num_lines = 23;
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for (int i = 0; i < num_lines; i++)
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{
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cv::Point2i point1(border + i * 100, border + i * 100);
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cv::Point2i point2(width - border - i * 100, height - border * i * 100);
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cv::line(img, point1, point2, 255, 1, LINE_AA);
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}
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Ptr<ORB> orbPtr = ORB::create(31);
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std::vector<KeyPoint> kps;
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Mat fv;
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ASSERT_NO_THROW(orbPtr->detectAndCompute(img, noArray(), kps, fv));
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}
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}} // namespace
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