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/*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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// * 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,
|
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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 FEATURES2D_DIR = "features2d"; |
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const string IMAGE_FILENAME = "tsukuba.png"; |
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static |
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Mat generateHomography(float angle) |
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{ |
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float angleRadian = 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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int diag = std::sqrt(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) = -srcImage.cols/2; |
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LUShift.at<float>(1,2) = -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(diag, 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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static |
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float calcIntersectArea(const Point2f& p0, float r0, const Point2f& p1, float r1) |
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{ |
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float c = 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 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 0.5 * sqr_r0 * (A0 - sin(A0)) + |
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0.5 * 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 = calcIntersectArea(p0, r0, p1, r1); |
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float unionArea = CV_PI * (r0 * r0 + r1 * r1) - intersectArea; |
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return intersectArea / unionArea; |
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} |
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class DetectorRotatationInvarianceTest : public cvtest::BaseTest |
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{ |
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public: |
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DetectorRotatationInvarianceTest(const Ptr<FeatureDetector>& _featureDetector, |
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float _minInliersRatio, |
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float _minAngleInliersRatio) : |
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featureDetector(_featureDetector), minInliersRatio(_minInliersRatio), 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()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME; |
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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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CV_Assert(keypoints0.size() > 15); |
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const int maxAngle = 360, angleStep = 10; |
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for(int angle = 0; angle < maxAngle; angle += angleStep) |
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{ |
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Mat H = rotateImage(image0, angle, image1, mask1); |
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vector<KeyPoint> keypoints1; |
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featureDetector->detect(image1, keypoints1, mask1); |
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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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perspectiveTransform(Mat(points0), points0t, H); |
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int inliersCount = 0; |
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int angleInliersCount = 0; |
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for(size_t m0 = 0; m0 < points0t.total(); m0++) |
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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[m0].size; |
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for(size_t m1 = 0; m1 < keypoints1.size(); m1++) |
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{ |
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float r1 = 0.5f * keypoints1[m1].size; |
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float intersectRatio = calcIntersectRatio(points0t.at<Point2f>(m0), r0, |
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keypoints1[m1].pt, r1); |
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if(intersectRatio > maxIntersectRatio) |
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{ |
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maxIntersectRatio = intersectRatio; |
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nearestPointIndex = m1; |
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} |
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} |
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if(maxIntersectRatio > 0.5f) |
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{ |
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inliersCount++; |
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const float maxAngleDiff = 3.f; // grad
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float angle0 = keypoints0[m0].angle; |
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float angle1 = keypoints1[nearestPointIndex].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>(2. * CV_PI - angleDiff)); |
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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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} |
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float inliersRatio = static_cast<float>(inliersCount) / keypoints0.size(); |
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if(inliersRatio < minInliersRatio) |
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{ |
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ts->printf(cvtest::TS::LOG, "Incorrect inliersRatio: curr = %f, min = %f.\n", |
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inliersRatio, minInliersRatio); |
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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(inliersCount) |
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{ |
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float angleInliersRatio = static_cast<float>(angleInliersCount) / inliersCount; |
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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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// std::cout << "inliersRatio - " << inliersRatio
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// << " - angleInliersRatio " << static_cast<float>(angleInliersCount) / inliersCount << std::endl;
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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 minInliersRatio; |
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float minAngleInliersRatio; |
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}; |
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// Tests registration
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TEST(Features2d_RotationInvariance_Detector_MSER, regression) |
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{ |
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DetectorRotatationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.MSER"), 0.52f, 0.94f); |
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test.safe_run(); |
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} |
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TEST(Features2d_RotationInvariance_Detector_ORB, regression) |
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{ |
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DetectorRotatationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"), 0.90, 0.95); |
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test.safe_run(); |
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} |
@ -0,0 +1,255 @@ |
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
|
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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.
|
||||
//
|
||||
//
|
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// Intel License Agreement
|
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// For Open Source Computer Vision Library
|
||||
//
|
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
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// 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.
|
||||
//
|
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// * The name of Intel Corporation may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
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// 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.
|
||||
//
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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 FEATURES2D_DIR = "features2d"; |
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const string IMAGE_FILENAME = "tsukuba.png"; |
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static |
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Mat generateHomography(float angle) |
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{ |
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float angleRadian = 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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int diag = std::sqrt(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) = -srcImage.cols/2; |
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LUShift.at<float>(1,2) = -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(diag, 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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static |
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float calcIntersectArea(const Point2f& p0, float r0, const Point2f& p1, float r1) |
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{ |
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float c = 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 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 0.5 * sqr_r0 * (A0 - sin(A0)) + |
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0.5 * 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 = calcIntersectArea(p0, r0, p1, r1); |
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float unionArea = CV_PI * (r0 * r0 + r1 * r1) - intersectArea; |
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return intersectArea / unionArea; |
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} |
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class DetectorRotatationInvarianceTest : public cvtest::BaseTest |
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{ |
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public: |
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DetectorRotatationInvarianceTest(const Ptr<FeatureDetector>& _featureDetector, |
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float _minInliersRatio, |
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float _minAngleInliersRatio) : |
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featureDetector(_featureDetector), minInliersRatio(_minInliersRatio), 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()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME; |
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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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CV_Assert(keypoints0.size() > 15); |
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const int maxAngle = 360, angleStep = 10; |
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for(int angle = 0; angle < maxAngle; angle += angleStep) |
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{ |
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Mat H = rotateImage(image0, angle, image1, mask1); |
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vector<KeyPoint> keypoints1; |
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featureDetector->detect(image1, keypoints1, mask1); |
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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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perspectiveTransform(Mat(points0), points0t, H); |
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int inliersCount = 0; |
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int angleInliersCount = 0; |
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for(size_t m0 = 0; m0 < points0t.total(); m0++) |
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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[m0].size; |
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for(size_t m1 = 0; m1 < keypoints1.size(); m1++) |
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{ |
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float r1 = 0.5f * keypoints1[m1].size; |
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float intersectRatio = calcIntersectRatio(points0t.at<Point2f>(m0), r0, |
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keypoints1[m1].pt, r1); |
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if(intersectRatio > maxIntersectRatio) |
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{ |
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maxIntersectRatio = intersectRatio; |
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nearestPointIndex = m1; |
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} |
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} |
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if(maxIntersectRatio > 0.5f) |
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{ |
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inliersCount++; |
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const float maxAngleDiff = 3.f; // grad
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float angle0 = keypoints0[m0].angle; |
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float angle1 = keypoints1[nearestPointIndex].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>(2. * CV_PI - angleDiff)); |
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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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} |
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float inliersRatio = static_cast<float>(inliersCount) / keypoints0.size(); |
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if(inliersRatio < minInliersRatio) |
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{ |
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ts->printf(cvtest::TS::LOG, "Incorrect inliersRatio: curr = %f, min = %f.\n", |
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inliersRatio, minInliersRatio); |
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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(inliersCount) |
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{ |
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float angleInliersRatio = static_cast<float>(angleInliersCount) / inliersCount; |
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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); |
||||
return; |
||||
} |
||||
} |
||||
|
||||
// std::cout << "inliersRatio - " << inliersRatio
|
||||
// << " - angleInliersRatio " << static_cast<float>(angleInliersCount) / inliersCount << std::endl;
|
||||
} |
||||
ts->set_failed_test_info( cvtest::TS::OK ); |
||||
} |
||||
|
||||
Ptr<FeatureDetector> featureDetector; |
||||
float minInliersRatio; |
||||
float minAngleInliersRatio; |
||||
}; |
||||
|
||||
// Tests registration
|
||||
|
||||
TEST(Features2d_RotationInvariance_Detector_SURF, regression) |
||||
{ |
||||
DetectorRotatationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.SURF"), 0.60f, 0.94f); |
||||
test.safe_run(); |
||||
} |
||||
|
||||
TEST(Features2d_RotationInvariance_Detector_SIFT, regression) |
||||
{ |
||||
DetectorRotatationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.SIFT"), 0.76f, 0.99f); |
||||
test.safe_run(); |
||||
} |
||||
|
Loading…
Reference in new issue