/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // Intel License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of Intel Corporation may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "test_precomp.hpp" #include using namespace cv; using namespace cv::gpu; using namespace std; const string FEATURES2D_DIR = "features2d"; const string IMAGE_FILENAME = "aloe.png"; class CV_GPU_SURFTest : public cvtest::BaseTest { public: CV_GPU_SURFTest() { } protected: bool isSimilarKeypoints(const KeyPoint& p1, const KeyPoint& p2); int getValidCount(const vector& keypoints1, const vector& keypoints2, const vector& matches); void compareKeypointSets(const vector& validKeypoints, const vector& calcKeypoints, const Mat& validDescriptors, const Mat& calcDescriptors); void emptyDataTest(); void accuracyTest(); virtual void run(int); }; void CV_GPU_SURFTest::emptyDataTest() { SURF_GPU fdetector; GpuMat image; vector keypoints; vector descriptors; try { fdetector(image, GpuMat(), keypoints, descriptors); } catch(...) { ts->printf( cvtest::TS::LOG, "detect() on empty image must not generate exception (1).\n" ); ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); } if( !keypoints.empty() ) { ts->printf( cvtest::TS::LOG, "detect() on empty image must return empty keypoints vector (1).\n" ); ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); return; } if( !descriptors.empty() ) { ts->printf( cvtest::TS::LOG, "detect() on empty image must return empty descriptors vector (1).\n" ); ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT ); return; } } bool CV_GPU_SURFTest::isSimilarKeypoints(const KeyPoint& p1, const KeyPoint& p2) { const float maxPtDif = 1.f; const float maxSizeDif = 1.f; const float maxAngleDif = 2.f; const float maxResponseDif = 0.1f; float dist = (float)norm( p1.pt - p2.pt ); return (dist < maxPtDif && fabs(p1.size - p2.size) < maxSizeDif && abs(p1.angle - p2.angle) < maxAngleDif && abs(p1.response - p2.response) < maxResponseDif && p1.octave == p2.octave && p1.class_id == p2.class_id ); } int CV_GPU_SURFTest::getValidCount(const vector& keypoints1, const vector& keypoints2, const vector& matches) { int count = 0; for (size_t i = 0; i < matches.size(); ++i) { const DMatch& m = matches[i]; const KeyPoint& kp1 = keypoints1[m.queryIdx]; const KeyPoint& kp2 = keypoints2[m.trainIdx]; if (isSimilarKeypoints(kp1, kp2)) ++count; } return count; } void CV_GPU_SURFTest::compareKeypointSets(const vector& validKeypoints, const vector& calcKeypoints, const Mat& validDescriptors, const Mat& calcDescriptors) { BruteForceMatcher< L2 > matcher; vector matches; matcher.match(validDescriptors, calcDescriptors, matches); int validCount = getValidCount(validKeypoints, calcKeypoints, matches); float validRatio = (float)validCount / matches.size(); if (validRatio < 0.5f) { ts->printf(cvtest::TS::LOG, "Bad accuracy - %f.\n", validRatio); ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY ); return; } } void CV_GPU_SURFTest::accuracyTest() { string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME; // Read the test image. Mat image = imread(imgFilename, 0); if (image.empty()) { ts->printf( cvtest::TS::LOG, "Image %s can not be read.\n", imgFilename.c_str() ); ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); return; } Mat mask(image.size(), CV_8UC1, Scalar::all(1)); mask(Range(0, image.rows / 2), Range(0, image.cols / 2)).setTo(Scalar::all(0)); // Compute keypoints. vector calcKeypoints; GpuMat calcDescriptors; SURF_GPU fdetector; fdetector.extended = false; fdetector(GpuMat(image), GpuMat(mask), calcKeypoints, calcDescriptors); // Calc validation keypoints set. vector validKeypoints; vector validDescriptors; SURF fdetector_gold; fdetector_gold.extended = false; fdetector_gold(image, mask, validKeypoints, validDescriptors); compareKeypointSets(validKeypoints, calcKeypoints, Mat(validKeypoints.size(), fdetector_gold.descriptorSize(), CV_32F, &validDescriptors[0]), calcDescriptors); } void CV_GPU_SURFTest::run( int /*start_from*/ ) { emptyDataTest(); accuracyTest(); } TEST(SURF, empty_data_and_accuracy) { CV_GPU_SURFTest test; test.safe_run(); }