230 lines
8.4 KiB
C++
230 lines
8.4 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 <string>
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using namespace cv;
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using namespace cv::gpu;
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using namespace std;
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const string FEATURES2D_DIR = "features2d";
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const string IMAGE_FILENAME = "aloe.png";
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const string VALID_FILE_NAME = "surf.xml.gz";
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class CV_GPU_SURFTest : public cvtest::BaseTest
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{
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public:
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CV_GPU_SURFTest()
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{
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}
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protected:
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bool isSimilarKeypoints(const KeyPoint& p1, const KeyPoint& p2);
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void compareKeypointSets(const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints,
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const Mat& validDescriptors, const Mat& calcDescriptors);
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void emptyDataTest(SURF_GPU& fdetector);
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void regressionTest(SURF_GPU& fdetector);
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virtual void run(int);
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};
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void CV_GPU_SURFTest::emptyDataTest(SURF_GPU& fdetector)
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{
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GpuMat image;
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vector<KeyPoint> keypoints;
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vector<float> descriptors;
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try
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{
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fdetector(image, GpuMat(), keypoints, descriptors);
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}
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catch(...)
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{
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ts->printf( cvtest::TS::LOG, "detect() on empty image must not generate exception (1).\n" );
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
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}
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if( !keypoints.empty() )
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{
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ts->printf( cvtest::TS::LOG, "detect() on empty image must return empty keypoints vector (1).\n" );
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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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if( !descriptors.empty() )
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{
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ts->printf( cvtest::TS::LOG, "detect() on empty image must return empty descriptors vector (1).\n" );
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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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}
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bool CV_GPU_SURFTest::isSimilarKeypoints(const KeyPoint& p1, const KeyPoint& p2)
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{
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const float maxPtDif = 1.f;
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const float maxSizeDif = 1.f;
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const float maxAngleDif = 2.f;
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const float maxResponseDif = 0.1f;
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float dist = (float)norm( p1.pt - p2.pt );
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return (dist < maxPtDif &&
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fabs(p1.size - p2.size) < maxSizeDif &&
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abs(p1.angle - p2.angle) < maxAngleDif &&
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abs(p1.response - p2.response) < maxResponseDif &&
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p1.octave == p2.octave &&
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p1.class_id == p2.class_id );
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}
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void CV_GPU_SURFTest::compareKeypointSets(const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints,
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const Mat& validDescriptors, const Mat& calcDescriptors)
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{
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if (validKeypoints.size() != calcKeypoints.size())
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{
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ts->printf(cvtest::TS::LOG, "Keypoints sizes doesn't equal (validCount = %d, calcCount = %d).\n",
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validKeypoints.size(), calcKeypoints.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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if (validDescriptors.size() != calcDescriptors.size())
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{
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ts->printf(cvtest::TS::LOG, "Descriptors sizes doesn't equal.\n");
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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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for (size_t v = 0; v < validKeypoints.size(); v++)
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{
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int nearestIdx = -1;
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float minDist = std::numeric_limits<float>::max();
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for (size_t c = 0; c < calcKeypoints.size(); c++)
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{
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float curDist = (float)norm(calcKeypoints[c].pt - validKeypoints[v].pt);
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if (curDist < minDist)
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{
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minDist = curDist;
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nearestIdx = c;
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}
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}
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assert(minDist >= 0);
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if (!isSimilarKeypoints(validKeypoints[v], calcKeypoints[nearestIdx]))
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{
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ts->printf(cvtest::TS::LOG, "Bad keypoints accuracy.\n");
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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 (norm(validDescriptors.row(v), calcDescriptors.row(nearestIdx), NORM_L2) > 1.5f)
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{
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ts->printf(cvtest::TS::LOG, "Bad descriptors accuracy.\n");
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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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}
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void CV_GPU_SURFTest::regressionTest(SURF_GPU& fdetector)
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{
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string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
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string resFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + VALID_FILE_NAME;
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// Read the test image.
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GpuMat image(imread(imgFilename, 0));
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if (image.empty())
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{
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ts->printf( cvtest::TS::LOG, "Image %s can not be read.\n", imgFilename.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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FileStorage fs(resFilename, FileStorage::READ);
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// Compute keypoints.
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GpuMat mask(image.size(), CV_8UC1, Scalar::all(1));
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mask(Range(0, image.rows / 2), Range(0, image.cols / 2)).setTo(Scalar::all(0));
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vector<KeyPoint> calcKeypoints;
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GpuMat calcDespcriptors;
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fdetector(image, mask, calcKeypoints, calcDespcriptors);
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if (fs.isOpened()) // Compare computed and valid keypoints.
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{
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// Read validation keypoints set.
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vector<KeyPoint> validKeypoints;
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Mat validDespcriptors;
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read(fs["keypoints"], validKeypoints);
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read(fs["descriptors"], validDespcriptors);
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if (validKeypoints.empty() || validDespcriptors.empty())
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{
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ts->printf(cvtest::TS::LOG, "Validation file can not be read.\n");
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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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compareKeypointSets(validKeypoints, calcKeypoints, validDespcriptors, calcDespcriptors);
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}
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else // Write detector parameters and computed keypoints as validation data.
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{
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fs.open(resFilename, FileStorage::WRITE);
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if (!fs.isOpened())
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{
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ts->printf(cvtest::TS::LOG, "File %s can not be opened to write.\n", resFilename.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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else
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{
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write(fs, "keypoints", calcKeypoints);
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write(fs, "descriptors", (Mat)calcDespcriptors);
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}
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}
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}
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void CV_GPU_SURFTest::run( int /*start_from*/ )
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{
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SURF_GPU fdetector;
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emptyDataTest(fdetector);
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regressionTest(fdetector);
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}
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TEST(SURF, empty_data_and_regression) { CV_GPU_SURFTest test; test.safe_run(); }
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