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711 lines
26 KiB
C++
711 lines
26 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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#include "opencv2/highgui/highgui.hpp"
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using namespace std;
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using namespace cv;
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const string IMAGE_TSUKUBA = "/features2d/tsukuba.png";
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const string IMAGE_BIKES = "/detectors_descriptors_evaluation/images_datasets/bikes/img1.png";
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#define SHOW_DEBUG_LOG 0
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static
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Mat generateHomography(float angle)
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{
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// angle - rotation around Oz in degrees
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float angleRadian = static_cast<float>(angle * CV_PI / 180);
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Mat H = Mat::eye(3, 3, CV_32FC1);
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H.at<float>(0,0) = H.at<float>(1,1) = std::cos(angleRadian);
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H.at<float>(0,1) = -std::sin(angleRadian);
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H.at<float>(1,0) = std::sin(angleRadian);
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return H;
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}
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static
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Mat rotateImage(const Mat& srcImage, float angle, Mat& dstImage, Mat& dstMask)
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{
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// angle - rotation around Oz in degrees
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float diag = std::sqrt(static_cast<float>(srcImage.cols * srcImage.cols + srcImage.rows * srcImage.rows));
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Mat LUShift = Mat::eye(3, 3, CV_32FC1); // left up
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LUShift.at<float>(0,2) = static_cast<float>(-srcImage.cols/2);
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LUShift.at<float>(1,2) = static_cast<float>(-srcImage.rows/2);
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Mat RDShift = Mat::eye(3, 3, CV_32FC1); // right down
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RDShift.at<float>(0,2) = diag/2;
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RDShift.at<float>(1,2) = diag/2;
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Size sz(cvRound(diag), cvRound(diag));
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Mat srcMask(srcImage.size(), CV_8UC1, Scalar(255));
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Mat H = RDShift * generateHomography(angle) * LUShift;
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warpPerspective(srcImage, dstImage, H, sz);
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warpPerspective(srcMask, dstMask, H, sz);
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return H;
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}
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void rotateKeyPoints(const vector<KeyPoint>& src, const Mat& H, float angle, vector<KeyPoint>& dst)
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{
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// suppose that H is rotation given from rotateImage() and angle has value passed to rotateImage()
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vector<Point2f> srcCenters, dstCenters;
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KeyPoint::convert(src, srcCenters);
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perspectiveTransform(srcCenters, dstCenters, H);
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dst = src;
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for(size_t i = 0; i < dst.size(); i++)
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{
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dst[i].pt = dstCenters[i];
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float dstAngle = src[i].angle + angle;
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if(dstAngle >= 360.f)
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dstAngle -= 360.f;
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dst[i].angle = dstAngle;
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}
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}
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void scaleKeyPoints(const vector<KeyPoint>& src, vector<KeyPoint>& dst, float scale)
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{
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dst.resize(src.size());
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for(size_t i = 0; i < src.size(); i++)
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dst[i] = KeyPoint(src[i].pt.x * scale, src[i].pt.y * scale, src[i].size * scale, src[i].angle);
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}
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static
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float calcCirclesIntersectArea(const Point2f& p0, float r0, const Point2f& p1, float r1)
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{
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float c = static_cast<float>(norm(p0 - p1)), sqr_c = c * c;
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float sqr_r0 = r0 * r0;
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float sqr_r1 = r1 * r1;
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if(r0 + r1 <= c)
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return 0;
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float minR = std::min(r0, r1);
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float maxR = std::max(r0, r1);
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if(c + minR <= maxR)
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return static_cast<float>(CV_PI * minR * minR);
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float cos_halfA0 = (sqr_r0 + sqr_c - sqr_r1) / (2 * r0 * c);
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float cos_halfA1 = (sqr_r1 + sqr_c - sqr_r0) / (2 * r1 * c);
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float A0 = 2 * acos(cos_halfA0);
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float A1 = 2 * acos(cos_halfA1);
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return 0.5f * sqr_r0 * (A0 - sin(A0)) +
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0.5f * sqr_r1 * (A1 - sin(A1));
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}
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static
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float calcIntersectRatio(const Point2f& p0, float r0, const Point2f& p1, float r1)
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{
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float intersectArea = calcCirclesIntersectArea(p0, r0, p1, r1);
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float unionArea = static_cast<float>(CV_PI) * (r0 * r0 + r1 * r1) - intersectArea;
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return intersectArea / unionArea;
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}
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static
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void matchKeyPoints(const vector<KeyPoint>& keypoints0, const Mat& H,
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const vector<KeyPoint>& keypoints1,
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vector<DMatch>& matches)
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{
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vector<Point2f> points0;
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KeyPoint::convert(keypoints0, points0);
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Mat points0t;
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if(H.empty())
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points0t = Mat(points0);
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else
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perspectiveTransform(Mat(points0), points0t, H);
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matches.clear();
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vector<uchar> usedMask(keypoints1.size(), 0);
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for(int i0 = 0; i0 < static_cast<int>(keypoints0.size()); i0++)
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{
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int nearestPointIndex = -1;
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float maxIntersectRatio = 0.f;
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const float r0 = 0.5f * keypoints0[i0].size;
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for(size_t i1 = 0; i1 < keypoints1.size(); i1++)
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{
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if(nearestPointIndex >= 0 && usedMask[i1])
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continue;
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float r1 = 0.5f * keypoints1[i1].size;
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float intersectRatio = calcIntersectRatio(points0t.at<Point2f>(i0), r0,
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keypoints1[i1].pt, r1);
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if(intersectRatio > maxIntersectRatio)
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{
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maxIntersectRatio = intersectRatio;
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nearestPointIndex = static_cast<int>(i1);
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}
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}
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matches.push_back(DMatch(i0, nearestPointIndex, maxIntersectRatio));
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if(nearestPointIndex >= 0)
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usedMask[nearestPointIndex] = 1;
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}
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}
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static void removeVerySmallKeypoints(vector<KeyPoint>& keypoints)
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{
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size_t i, j = 0, n = keypoints.size();
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for( i = 0; i < n; i++ )
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{
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if( (keypoints[i].octave & 128) != 0 )
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;
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else
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keypoints[j++] = keypoints[i];
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}
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keypoints.resize(j);
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}
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class DetectorRotationInvarianceTest : public cvtest::BaseTest
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{
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public:
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DetectorRotationInvarianceTest(const Ptr<FeatureDetector>& _featureDetector,
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float _minKeyPointMatchesRatio,
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float _minAngleInliersRatio) :
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featureDetector(_featureDetector),
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minKeyPointMatchesRatio(_minKeyPointMatchesRatio),
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minAngleInliersRatio(_minAngleInliersRatio)
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{
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CV_Assert(!featureDetector.empty());
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}
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protected:
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void run(int)
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{
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const string imageFilename = string(ts->get_data_path()) + IMAGE_TSUKUBA;
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// Read test data
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Mat image0 = imread(imageFilename), image1, mask1;
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if(image0.empty())
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{
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ts->printf(cvtest::TS::LOG, "Image %s can not be read.\n", imageFilename.c_str());
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
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return;
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}
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vector<KeyPoint> keypoints0;
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featureDetector->detect(image0, keypoints0);
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removeVerySmallKeypoints(keypoints0);
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if(keypoints0.size() < 15)
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CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
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const int maxAngle = 360, angleStep = 15;
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for(int angle = 0; angle < maxAngle; angle += angleStep)
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{
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Mat H = rotateImage(image0, static_cast<float>(angle), image1, mask1);
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vector<KeyPoint> keypoints1;
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featureDetector->detect(image1, keypoints1, mask1);
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removeVerySmallKeypoints(keypoints1);
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vector<DMatch> matches;
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matchKeyPoints(keypoints0, H, keypoints1, matches);
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int angleInliersCount = 0;
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const float minIntersectRatio = 0.5f;
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int keyPointMatchesCount = 0;
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for(size_t m = 0; m < matches.size(); m++)
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{
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if(matches[m].distance < minIntersectRatio)
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continue;
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keyPointMatchesCount++;
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// Check does this inlier have consistent angles
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const float maxAngleDiff = 15.f; // grad
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float angle0 = keypoints0[matches[m].queryIdx].angle;
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float angle1 = keypoints1[matches[m].trainIdx].angle;
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if(angle0 == -1 || angle1 == -1)
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CV_Error(CV_StsBadArg, "Given FeatureDetector is not rotation invariant, it can not be tested here.\n");
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CV_Assert(angle0 >= 0.f && angle0 < 360.f);
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CV_Assert(angle1 >= 0.f && angle1 < 360.f);
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float rotAngle0 = angle0 + angle;
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if(rotAngle0 >= 360.f)
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rotAngle0 -= 360.f;
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float angleDiff = std::max(rotAngle0, angle1) - std::min(rotAngle0, angle1);
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angleDiff = std::min(angleDiff, static_cast<float>(360.f - angleDiff));
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CV_Assert(angleDiff >= 0.f);
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bool isAngleCorrect = angleDiff < maxAngleDiff;
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if(isAngleCorrect)
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angleInliersCount++;
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}
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float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints0.size();
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if(keyPointMatchesRatio < minKeyPointMatchesRatio)
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{
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ts->printf(cvtest::TS::LOG, "Incorrect keyPointMatchesRatio: curr = %f, min = %f.\n",
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keyPointMatchesRatio, minKeyPointMatchesRatio);
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
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return;
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}
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if(keyPointMatchesCount)
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{
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float angleInliersRatio = static_cast<float>(angleInliersCount) / keyPointMatchesCount;
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if(angleInliersRatio < minAngleInliersRatio)
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{
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ts->printf(cvtest::TS::LOG, "Incorrect angleInliersRatio: curr = %f, min = %f.\n",
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angleInliersRatio, minAngleInliersRatio);
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
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return;
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}
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}
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#if SHOW_DEBUG_LOG
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std::cout << "keyPointMatchesRatio - " << keyPointMatchesRatio
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<< " - angleInliersRatio " << static_cast<float>(angleInliersCount) / keyPointMatchesCount << std::endl;
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#endif
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}
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ts->set_failed_test_info( cvtest::TS::OK );
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}
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Ptr<FeatureDetector> featureDetector;
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float minKeyPointMatchesRatio;
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float minAngleInliersRatio;
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};
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class DescriptorRotationInvarianceTest : public cvtest::BaseTest
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{
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public:
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DescriptorRotationInvarianceTest(const Ptr<FeatureDetector>& _featureDetector,
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const Ptr<DescriptorExtractor>& _descriptorExtractor,
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int _normType,
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float _minDescInliersRatio) :
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featureDetector(_featureDetector),
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descriptorExtractor(_descriptorExtractor),
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normType(_normType),
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minDescInliersRatio(_minDescInliersRatio)
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{
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CV_Assert(!featureDetector.empty());
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CV_Assert(!descriptorExtractor.empty());
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}
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protected:
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void run(int)
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{
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const string imageFilename = string(ts->get_data_path()) + IMAGE_TSUKUBA;
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// Read test data
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Mat image0 = imread(imageFilename), image1, mask1;
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if(image0.empty())
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{
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ts->printf(cvtest::TS::LOG, "Image %s can not be read.\n", imageFilename.c_str());
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
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return;
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}
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vector<KeyPoint> keypoints0;
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Mat descriptors0;
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featureDetector->detect(image0, keypoints0);
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removeVerySmallKeypoints(keypoints0);
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if(keypoints0.size() < 15)
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CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
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descriptorExtractor->compute(image0, keypoints0, descriptors0);
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BFMatcher bfmatcher(normType);
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const float minIntersectRatio = 0.5f;
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const int maxAngle = 360, angleStep = 15;
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for(int angle = 0; angle < maxAngle; angle += angleStep)
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{
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Mat H = rotateImage(image0, static_cast<float>(angle), image1, mask1);
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vector<KeyPoint> keypoints1;
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rotateKeyPoints(keypoints0, H, static_cast<float>(angle), keypoints1);
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Mat descriptors1;
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descriptorExtractor->compute(image1, keypoints1, descriptors1);
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vector<DMatch> descMatches;
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bfmatcher.match(descriptors0, descriptors1, descMatches);
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int descInliersCount = 0;
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for(size_t m = 0; m < descMatches.size(); m++)
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{
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const KeyPoint& transformed_p0 = keypoints1[descMatches[m].queryIdx];
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const KeyPoint& p1 = keypoints1[descMatches[m].trainIdx];
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if(calcIntersectRatio(transformed_p0.pt, 0.5f * transformed_p0.size,
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p1.pt, 0.5f * p1.size) >= minIntersectRatio)
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{
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descInliersCount++;
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}
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}
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float descInliersRatio = static_cast<float>(descInliersCount) / keypoints0.size();
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if(descInliersRatio < minDescInliersRatio)
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{
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ts->printf(cvtest::TS::LOG, "Incorrect descInliersRatio: curr = %f, min = %f.\n",
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descInliersRatio, minDescInliersRatio);
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
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return;
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}
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#if SHOW_DEBUG_LOG
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std::cout << "descInliersRatio " << static_cast<float>(descInliersCount) / keypoints0.size() << std::endl;
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#endif
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}
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ts->set_failed_test_info( cvtest::TS::OK );
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}
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Ptr<FeatureDetector> featureDetector;
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Ptr<DescriptorExtractor> descriptorExtractor;
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int normType;
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float minDescInliersRatio;
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};
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class DetectorScaleInvarianceTest : public cvtest::BaseTest
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{
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public:
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DetectorScaleInvarianceTest(const Ptr<FeatureDetector>& _featureDetector,
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float _minKeyPointMatchesRatio,
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float _minScaleInliersRatio) :
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featureDetector(_featureDetector),
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minKeyPointMatchesRatio(_minKeyPointMatchesRatio),
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minScaleInliersRatio(_minScaleInliersRatio)
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{
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CV_Assert(!featureDetector.empty());
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}
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protected:
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void run(int)
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{
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const string imageFilename = string(ts->get_data_path()) + IMAGE_BIKES;
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// Read test data
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Mat image0 = imread(imageFilename);
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if(image0.empty())
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{
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ts->printf(cvtest::TS::LOG, "Image %s can not be read.\n", imageFilename.c_str());
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
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return;
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}
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vector<KeyPoint> keypoints0;
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featureDetector->detect(image0, keypoints0);
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removeVerySmallKeypoints(keypoints0);
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if(keypoints0.size() < 15)
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CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
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for(int scaleIdx = 1; scaleIdx <= 3; scaleIdx++)
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{
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float scale = 1.f + scaleIdx * 0.5f;
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Mat image1;
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resize(image0, image1, Size(), 1./scale, 1./scale);
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vector<KeyPoint> keypoints1, osiKeypoints1; // osi - original size image
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featureDetector->detect(image1, keypoints1);
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removeVerySmallKeypoints(keypoints1);
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if(keypoints1.size() < 15)
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CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
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if(keypoints1.size() > keypoints0.size())
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{
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ts->printf(cvtest::TS::LOG, "Strange behavior of the detector. "
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"It gives more points count in an image of the smaller size.\n"
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"original size (%d, %d), keypoints count = %d\n"
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"reduced size (%d, %d), keypoints count = %d\n",
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image0.cols, image0.rows, keypoints0.size(),
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image1.cols, image1.rows, keypoints1.size());
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
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return;
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}
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scaleKeyPoints(keypoints1, osiKeypoints1, scale);
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vector<DMatch> matches;
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// image1 is query image (it's reduced image0)
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// image0 is train image
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matchKeyPoints(osiKeypoints1, Mat(), keypoints0, matches);
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const float minIntersectRatio = 0.5f;
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int keyPointMatchesCount = 0;
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int scaleInliersCount = 0;
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for(size_t m = 0; m < matches.size(); m++)
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{
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|
if(matches[m].distance < minIntersectRatio)
|
|
continue;
|
|
|
|
keyPointMatchesCount++;
|
|
|
|
// Check does this inlier have consistent sizes
|
|
const float maxSizeDiff = 0.8f;//0.9f; // grad
|
|
float size0 = keypoints0[matches[m].trainIdx].size;
|
|
float size1 = osiKeypoints1[matches[m].queryIdx].size;
|
|
CV_Assert(size0 > 0 && size1 > 0);
|
|
if(std::min(size0, size1) > maxSizeDiff * std::max(size0, size1))
|
|
scaleInliersCount++;
|
|
}
|
|
|
|
float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints1.size();
|
|
if(keyPointMatchesRatio < minKeyPointMatchesRatio)
|
|
{
|
|
ts->printf(cvtest::TS::LOG, "Incorrect keyPointMatchesRatio: curr = %f, min = %f.\n",
|
|
keyPointMatchesRatio, minKeyPointMatchesRatio);
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
|
return;
|
|
}
|
|
|
|
if(keyPointMatchesCount)
|
|
{
|
|
float scaleInliersRatio = static_cast<float>(scaleInliersCount) / keyPointMatchesCount;
|
|
if(scaleInliersRatio < minScaleInliersRatio)
|
|
{
|
|
ts->printf(cvtest::TS::LOG, "Incorrect scaleInliersRatio: curr = %f, min = %f.\n",
|
|
scaleInliersRatio, minScaleInliersRatio);
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
|
return;
|
|
}
|
|
}
|
|
#if SHOW_DEBUG_LOG
|
|
std::cout << "keyPointMatchesRatio - " << keyPointMatchesRatio
|
|
<< " - scaleInliersRatio " << static_cast<float>(scaleInliersCount) / keyPointMatchesCount << std::endl;
|
|
#endif
|
|
}
|
|
ts->set_failed_test_info( cvtest::TS::OK );
|
|
}
|
|
|
|
Ptr<FeatureDetector> featureDetector;
|
|
float minKeyPointMatchesRatio;
|
|
float minScaleInliersRatio;
|
|
};
|
|
|
|
class DescriptorScaleInvarianceTest : public cvtest::BaseTest
|
|
{
|
|
public:
|
|
DescriptorScaleInvarianceTest(const Ptr<FeatureDetector>& _featureDetector,
|
|
const Ptr<DescriptorExtractor>& _descriptorExtractor,
|
|
int _normType,
|
|
float _minDescInliersRatio) :
|
|
featureDetector(_featureDetector),
|
|
descriptorExtractor(_descriptorExtractor),
|
|
normType(_normType),
|
|
minDescInliersRatio(_minDescInliersRatio)
|
|
{
|
|
CV_Assert(!featureDetector.empty());
|
|
CV_Assert(!descriptorExtractor.empty());
|
|
}
|
|
|
|
protected:
|
|
|
|
void run(int)
|
|
{
|
|
const string imageFilename = string(ts->get_data_path()) + IMAGE_BIKES;
|
|
|
|
// Read test data
|
|
Mat image0 = imread(imageFilename);
|
|
if(image0.empty())
|
|
{
|
|
ts->printf(cvtest::TS::LOG, "Image %s can not be read.\n", imageFilename.c_str());
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
|
|
return;
|
|
}
|
|
|
|
vector<KeyPoint> keypoints0;
|
|
featureDetector->detect(image0, keypoints0);
|
|
removeVerySmallKeypoints(keypoints0);
|
|
if(keypoints0.size() < 15)
|
|
CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
|
|
Mat descriptors0;
|
|
descriptorExtractor->compute(image0, keypoints0, descriptors0);
|
|
|
|
BFMatcher bfmatcher(normType);
|
|
for(int scaleIdx = 1; scaleIdx <= 3; scaleIdx++)
|
|
{
|
|
float scale = 1.f + scaleIdx * 0.5f;
|
|
|
|
Mat image1;
|
|
resize(image0, image1, Size(), 1./scale, 1./scale);
|
|
|
|
vector<KeyPoint> keypoints1;
|
|
scaleKeyPoints(keypoints0, keypoints1, 1.0f/scale);
|
|
Mat descriptors1;
|
|
descriptorExtractor->compute(image1, keypoints1, descriptors1);
|
|
|
|
vector<DMatch> descMatches;
|
|
bfmatcher.match(descriptors0, descriptors1, descMatches);
|
|
|
|
const float minIntersectRatio = 0.5f;
|
|
int descInliersCount = 0;
|
|
for(size_t m = 0; m < descMatches.size(); m++)
|
|
{
|
|
const KeyPoint& transformed_p0 = keypoints0[descMatches[m].queryIdx];
|
|
const KeyPoint& p1 = keypoints0[descMatches[m].trainIdx];
|
|
if(calcIntersectRatio(transformed_p0.pt, 0.5f * transformed_p0.size,
|
|
p1.pt, 0.5f * p1.size) >= minIntersectRatio)
|
|
{
|
|
descInliersCount++;
|
|
}
|
|
}
|
|
|
|
float descInliersRatio = static_cast<float>(descInliersCount) / keypoints0.size();
|
|
if(descInliersRatio < minDescInliersRatio)
|
|
{
|
|
ts->printf(cvtest::TS::LOG, "Incorrect descInliersRatio: curr = %f, min = %f.\n",
|
|
descInliersRatio, minDescInliersRatio);
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
|
return;
|
|
}
|
|
#if SHOW_DEBUG_LOG
|
|
std::cout << "descInliersRatio " << static_cast<float>(descInliersCount) / keypoints0.size() << std::endl;
|
|
#endif
|
|
}
|
|
ts->set_failed_test_info( cvtest::TS::OK );
|
|
}
|
|
|
|
Ptr<FeatureDetector> featureDetector;
|
|
Ptr<DescriptorExtractor> descriptorExtractor;
|
|
int normType;
|
|
float minKeyPointMatchesRatio;
|
|
float minDescInliersRatio;
|
|
};
|
|
|
|
// Tests registration
|
|
|
|
/*
|
|
* Detector's rotation invariance check
|
|
*/
|
|
TEST(Features2d_RotationInvariance_Detector_SURF, regression)
|
|
{
|
|
DetectorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.SURF"),
|
|
0.44f,
|
|
0.76f);
|
|
test.safe_run();
|
|
}
|
|
|
|
TEST(Features2d_RotationInvariance_Detector_SIFT, regression)
|
|
{
|
|
DetectorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.SIFT"),
|
|
0.45f,
|
|
0.70f);
|
|
test.safe_run();
|
|
}
|
|
|
|
/*
|
|
* Descriptors's rotation invariance check
|
|
*/
|
|
TEST(Features2d_RotationInvariance_Descriptor_SURF, regression)
|
|
{
|
|
DescriptorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.SURF"),
|
|
Algorithm::create<DescriptorExtractor>("Feature2D.SURF"),
|
|
NORM_L1,
|
|
0.83f);
|
|
test.safe_run();
|
|
}
|
|
|
|
TEST(Features2d_RotationInvariance_Descriptor_SIFT, regression)
|
|
{
|
|
DescriptorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.SIFT"),
|
|
Algorithm::create<DescriptorExtractor>("Feature2D.SIFT"),
|
|
NORM_L1,
|
|
0.98f);
|
|
test.safe_run();
|
|
}
|
|
|
|
/*
|
|
* Detector's scale invariance check
|
|
*/
|
|
TEST(Features2d_ScaleInvariance_Detector_SURF, regression)
|
|
{
|
|
DetectorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.SURF"),
|
|
0.64f,
|
|
0.84f);
|
|
test.safe_run();
|
|
}
|
|
|
|
TEST(Features2d_ScaleInvariance_Detector_SIFT, regression)
|
|
{
|
|
DetectorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.SIFT"),
|
|
0.69f,
|
|
0.99f);
|
|
test.safe_run();
|
|
}
|
|
|
|
/*
|
|
* Descriptor's scale invariance check
|
|
*/
|
|
TEST(Features2d_ScaleInvariance_Descriptor_SURF, regression)
|
|
{
|
|
DescriptorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.SURF"),
|
|
Algorithm::create<DescriptorExtractor>("Feature2D.SURF"),
|
|
NORM_L1,
|
|
0.61f);
|
|
test.safe_run();
|
|
}
|
|
|
|
TEST(Features2d_ScaleInvariance_Descriptor_SIFT, regression)
|
|
{
|
|
DescriptorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.SIFT"),
|
|
Algorithm::create<DescriptorExtractor>("Feature2D.SIFT"),
|
|
NORM_L1,
|
|
0.78f);
|
|
test.safe_run();
|
|
}
|
|
|
|
|
|
TEST(Features2d_RotationInvariance2_Detector_SURF, regression)
|
|
{
|
|
Mat cross(100, 100, CV_8UC1, Scalar(255));
|
|
line(cross, Point(30, 50), Point(69, 50), Scalar(100), 3);
|
|
line(cross, Point(50, 30), Point(50, 69), Scalar(100), 3);
|
|
|
|
SURF surf(8000., 3, 4, true, false);
|
|
|
|
vector<KeyPoint> keypoints;
|
|
|
|
surf(cross, noArray(), keypoints);
|
|
|
|
ASSERT_EQ(keypoints.size(), (vector<KeyPoint>::size_type) 5);
|
|
ASSERT_LT( fabs(keypoints[1].response - keypoints[2].response), 1e-6);
|
|
ASSERT_LT( fabs(keypoints[1].response - keypoints[3].response), 1e-6);
|
|
ASSERT_LT( fabs(keypoints[1].response - keypoints[4].response), 1e-6);
|
|
}
|