487 lines
16 KiB
C++
487 lines
16 KiB
C++
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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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//
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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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namespace opencv_test { namespace {
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const string FEATURES2D_DIR = "features2d";
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const string DETECTOR_DIR = FEATURES2D_DIR + "/feature_detectors";
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const string DESCRIPTOR_DIR = FEATURES2D_DIR + "/descriptor_extractors";
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const string IMAGE_FILENAME = "tsukuba.png";
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}} // namespace
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#include "features2d/test/test_detectors_regression.impl.hpp"
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#include "features2d/test/test_descriptors_regression.impl.hpp"
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namespace opencv_test { namespace {
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#ifdef OPENCV_ENABLE_NONFREE
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TEST( Features2d_Detector_SIFT, regression)
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{
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CV_FeatureDetectorTest test( "detector-sift", SIFT::create() );
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test.safe_run();
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}
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TEST( Features2d_Detector_SURF, regression )
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{
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CV_FeatureDetectorTest test( "detector-surf", SURF::create() );
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test.safe_run();
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}
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#endif
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TEST( Features2d_Detector_STAR, regression )
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{
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CV_FeatureDetectorTest test( "detector-star", StarDetector::create() );
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test.safe_run();
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}
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TEST( Features2d_Detector_Harris_Laplace, regression )
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{
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CV_FeatureDetectorTest test( "detector-harris-laplace", HarrisLaplaceFeatureDetector::create() );
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test.safe_run();
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}
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TEST( Features2d_Detector_Harris_Laplace_Affine_Keypoint_Invariance, regression )
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{
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CV_FeatureDetectorTest test( "detector-harris-laplace", AffineFeature2D::create(HarrisLaplaceFeatureDetector::create()));
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test.safe_run();
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}
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TEST( Features2d_Detector_Harris_Laplace_Affine, regression )
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{
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CV_FeatureDetectorTest test( "detector-harris-laplace-affine", AffineFeature2D::create(HarrisLaplaceFeatureDetector::create()));
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test.safe_run();
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}
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/*
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* Descriptors
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*/
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#ifdef OPENCV_ENABLE_NONFREE
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TEST( Features2d_DescriptorExtractor_SIFT, regression )
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{
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CV_DescriptorExtractorTest<L1<float> > test( "descriptor-sift", 1.0f,
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SIFT::create() );
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_SURF, regression )
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{
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#ifdef HAVE_OPENCL
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bool useOCL = cv::ocl::useOpenCL();
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cv::ocl::setUseOpenCL(false);
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#endif
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CV_DescriptorExtractorTest<L2<float> > test( "descriptor-surf", 0.05f,
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SURF::create() );
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test.safe_run();
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#ifdef HAVE_OPENCL
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cv::ocl::setUseOpenCL(useOCL);
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#endif
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}
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#ifdef HAVE_OPENCL
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TEST( Features2d_DescriptorExtractor_SURF_OCL, regression )
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{
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bool useOCL = cv::ocl::useOpenCL();
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cv::ocl::setUseOpenCL(true);
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if(cv::ocl::useOpenCL())
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{
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CV_DescriptorExtractorTest<L2<float> > test( "descriptor-surf_ocl", 0.05f,
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SURF::create() );
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test.safe_run();
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}
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cv::ocl::setUseOpenCL(useOCL);
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}
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#endif
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#endif // NONFREE
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TEST( Features2d_DescriptorExtractor_DAISY, regression )
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{
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CV_DescriptorExtractorTest<L2<float> > test( "descriptor-daisy", 0.05f,
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DAISY::create() );
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_FREAK, regression )
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{
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CV_DescriptorExtractorTest<Hamming> test("descriptor-freak", (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
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FREAK::create());
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_BRIEF, regression )
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{
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CV_DescriptorExtractorTest<Hamming> test( "descriptor-brief", 1,
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BriefDescriptorExtractor::create() );
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test.safe_run();
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}
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template <int threshold = 0>
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struct LUCIDEqualityDistance
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{
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typedef unsigned char ValueType;
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typedef int ResultType;
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ResultType operator()( const unsigned char* a, const unsigned char* b, int size ) const
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{
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int res = 0;
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for (int i = 0; i < size; i++)
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{
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if (threshold == 0)
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res += (a[i] != b[i]) ? 1 : 0;
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else
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res += abs(a[i] - b[i]) > threshold ? 1 : 0;
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}
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return res;
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}
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};
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TEST( Features2d_DescriptorExtractor_LUCID, regression )
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{
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CV_DescriptorExtractorTest< LUCIDEqualityDistance<1/*used blur is not bit-exact*/> > test(
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"descriptor-lucid", 2,
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LUCID::create(1, 2)
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);
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_LATCH, regression )
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{
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CV_DescriptorExtractorTest<Hamming> test( "descriptor-latch", 1,
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LATCH::create(32, true, 3, 0) );
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_VGG, regression )
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{
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CV_DescriptorExtractorTest<L2<float> > test( "descriptor-vgg", 0.03f,
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VGG::create() );
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_BGM, regression )
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{
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CV_DescriptorExtractorTest<Hamming> test( "descriptor-boostdesc-bgm",
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(CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
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BoostDesc::create(BoostDesc::BGM) );
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_BGM_HARD, regression )
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{
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CV_DescriptorExtractorTest<Hamming> test( "descriptor-boostdesc-bgm_hard",
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(CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
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BoostDesc::create(BoostDesc::BGM_HARD) );
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_BGM_BILINEAR, regression )
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{
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CV_DescriptorExtractorTest<Hamming> test( "descriptor-boostdesc-bgm_bilinear",
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(CV_DescriptorExtractorTest<Hamming>::DistanceType)15.f,
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BoostDesc::create(BoostDesc::BGM_BILINEAR) );
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_LBGM, regression )
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{
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CV_DescriptorExtractorTest<L2<float> > test( "descriptor-boostdesc-lbgm",
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1.0f,
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BoostDesc::create(BoostDesc::LBGM) );
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_BINBOOST_64, regression )
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{
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CV_DescriptorExtractorTest<Hamming> test( "descriptor-boostdesc-binboost_64",
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(CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
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BoostDesc::create(BoostDesc::BINBOOST_64) );
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_BINBOOST_128, regression )
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{
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CV_DescriptorExtractorTest<Hamming> test( "descriptor-boostdesc-binboost_128",
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(CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
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BoostDesc::create(BoostDesc::BINBOOST_128) );
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_BINBOOST_256, regression )
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{
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CV_DescriptorExtractorTest<Hamming> test( "descriptor-boostdesc-binboost_256",
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(CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
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BoostDesc::create(BoostDesc::BINBOOST_256) );
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test.safe_run();
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}
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#ifdef OPENCV_ENABLE_NONFREE
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TEST(Features2d_BruteForceDescriptorMatcher_knnMatch, regression)
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{
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const int sz = 100;
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const int k = 3;
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Ptr<DescriptorExtractor> ext = SURF::create();
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ASSERT_TRUE(ext);
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Ptr<FeatureDetector> det = SURF::create();
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//"%YAML:1.0\nhessianThreshold: 8000.\noctaves: 3\noctaveLayers: 4\nupright: 0\n"
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ASSERT_TRUE(det);
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Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce");
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ASSERT_TRUE(matcher);
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Mat imgT(256, 256, CV_8U, Scalar(255));
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line(imgT, Point(20, sz/2), Point(sz-21, sz/2), Scalar(100), 2);
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line(imgT, Point(sz/2, 20), Point(sz/2, sz-21), Scalar(100), 2);
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vector<KeyPoint> kpT;
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kpT.push_back( KeyPoint(50, 50, 16, 0, 20000, 1, -1) );
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kpT.push_back( KeyPoint(42, 42, 16, 160, 10000, 1, -1) );
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Mat descT;
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ext->compute(imgT, kpT, descT);
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Mat imgQ(256, 256, CV_8U, Scalar(255));
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line(imgQ, Point(30, sz/2), Point(sz-31, sz/2), Scalar(100), 3);
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line(imgQ, Point(sz/2, 30), Point(sz/2, sz-31), Scalar(100), 3);
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vector<KeyPoint> kpQ;
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det->detect(imgQ, kpQ);
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Mat descQ;
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ext->compute(imgQ, kpQ, descQ);
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vector<vector<DMatch> > matches;
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matcher->knnMatch(descQ, descT, matches, k);
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//cout << "\nBest " << k << " matches to " << descT.rows << " train desc-s." << endl;
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ASSERT_EQ(descQ.rows, static_cast<int>(matches.size()));
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for(size_t i = 0; i<matches.size(); i++)
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{
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//cout << "\nmatches[" << i << "].size()==" << matches[i].size() << endl;
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ASSERT_GE(min(k, descT.rows), static_cast<int>(matches[i].size()));
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for(size_t j = 0; j<matches[i].size(); j++)
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{
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//cout << "\t" << matches[i][j].queryIdx << " -> " << matches[i][j].trainIdx << endl;
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ASSERT_EQ(matches[i][j].queryIdx, static_cast<int>(i));
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}
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}
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}
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#endif
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class CV_DetectPlanarTest : public cvtest::BaseTest
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{
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public:
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CV_DetectPlanarTest(const string& _fname, int _min_ninliers, const Ptr<Feature2D>& _f2d)
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: fname(_fname), min_ninliers(_min_ninliers), f2d(_f2d) {}
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protected:
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void run(int)
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{
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if(f2d.empty())
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return;
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string path = string(ts->get_data_path()) + "detectors_descriptors_evaluation/planar/";
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string imgname1 = path + "box.png";
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string imgname2 = path + "box_in_scene.png";
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Mat img1 = imread(imgname1, 0);
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Mat img2 = imread(imgname2, 0);
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if( img1.empty() || img2.empty() )
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{
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ts->printf( cvtest::TS::LOG, "missing %s and/or %s\n", imgname1.c_str(), imgname2.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> kpt1, kpt2;
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Mat d1, d2;
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#ifdef HAVE_OPENCL
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if (cv::ocl::useOpenCL())
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{
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cv::UMat uimg1;
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img1.copyTo(uimg1);
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f2d->detectAndCompute(uimg1, Mat(), kpt1, d1);
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f2d->detectAndCompute(uimg1, Mat(), kpt2, d2);
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}
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else
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#endif
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{
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f2d->detectAndCompute(img1, Mat(), kpt1, d1);
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f2d->detectAndCompute(img1, Mat(), kpt2, d2);
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}
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for( size_t i = 0; i < kpt1.size(); i++ )
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CV_Assert(kpt1[i].response > 0 );
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for( size_t i = 0; i < kpt2.size(); i++ )
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CV_Assert(kpt2[i].response > 0 );
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vector<DMatch> matches;
|
||
|
|
BFMatcher(f2d->defaultNorm(), true).match(d1, d2, matches);
|
||
|
|
|
||
|
|
vector<Point2f> pt1, pt2;
|
||
|
|
for( size_t i = 0; i < matches.size(); i++ ) {
|
||
|
|
pt1.push_back(kpt1[matches[i].queryIdx].pt);
|
||
|
|
pt2.push_back(kpt2[matches[i].trainIdx].pt);
|
||
|
|
}
|
||
|
|
|
||
|
|
Mat inliers, H = findHomography(pt1, pt2, RANSAC, 10, inliers);
|
||
|
|
int ninliers = countNonZero(inliers);
|
||
|
|
|
||
|
|
if( ninliers < min_ninliers )
|
||
|
|
{
|
||
|
|
ts->printf( cvtest::TS::LOG, "too little inliers (%d) vs expected %d\n", ninliers, min_ninliers);
|
||
|
|
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||
|
|
return;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
string fname;
|
||
|
|
int min_ninliers;
|
||
|
|
Ptr<Feature2D> f2d;
|
||
|
|
};
|
||
|
|
|
||
|
|
#ifdef OPENCV_ENABLE_NONFREE
|
||
|
|
TEST(Features2d_SIFTHomographyTest, regression) { CV_DetectPlanarTest test("SIFT", 80, SIFT::create()); test.safe_run(); }
|
||
|
|
TEST(Features2d_SURFHomographyTest, regression) { CV_DetectPlanarTest test("SURF", 80, SURF::create()); test.safe_run(); }
|
||
|
|
#endif
|
||
|
|
|
||
|
|
class FeatureDetectorUsingMaskTest : public cvtest::BaseTest
|
||
|
|
{
|
||
|
|
public:
|
||
|
|
FeatureDetectorUsingMaskTest(const Ptr<FeatureDetector>& featureDetector) :
|
||
|
|
featureDetector_(featureDetector)
|
||
|
|
{
|
||
|
|
CV_Assert(featureDetector_);
|
||
|
|
}
|
||
|
|
|
||
|
|
protected:
|
||
|
|
|
||
|
|
void run(int)
|
||
|
|
{
|
||
|
|
const int nStepX = 2;
|
||
|
|
const int nStepY = 2;
|
||
|
|
|
||
|
|
const string imageFilename = string(ts->get_data_path()) + "/features2d/tsukuba.png";
|
||
|
|
|
||
|
|
Mat image = imread(imageFilename);
|
||
|
|
if(image.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;
|
||
|
|
}
|
||
|
|
|
||
|
|
Mat mask(image.size(), CV_8U);
|
||
|
|
|
||
|
|
const int stepX = image.size().width / nStepX;
|
||
|
|
const int stepY = image.size().height / nStepY;
|
||
|
|
|
||
|
|
vector<KeyPoint> keyPoints;
|
||
|
|
vector<Point2f> points;
|
||
|
|
for(int i=0; i<nStepX; ++i)
|
||
|
|
for(int j=0; j<nStepY; ++j)
|
||
|
|
{
|
||
|
|
|
||
|
|
mask.setTo(0);
|
||
|
|
Rect whiteArea(i * stepX, j * stepY, stepX, stepY);
|
||
|
|
mask(whiteArea).setTo(255);
|
||
|
|
|
||
|
|
featureDetector_->detect(image, keyPoints, mask);
|
||
|
|
KeyPoint::convert(keyPoints, points);
|
||
|
|
|
||
|
|
for(size_t k=0; k<points.size(); ++k)
|
||
|
|
{
|
||
|
|
if ( !whiteArea.contains(points[k]) )
|
||
|
|
{
|
||
|
|
ts->printf(cvtest::TS::LOG, "The feature point is outside of the mask.");
|
||
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
||
|
|
return;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
ts->set_failed_test_info( cvtest::TS::OK );
|
||
|
|
}
|
||
|
|
|
||
|
|
Ptr<FeatureDetector> featureDetector_;
|
||
|
|
};
|
||
|
|
|
||
|
|
#ifdef OPENCV_ENABLE_NONFREE
|
||
|
|
TEST(Features2d_SIFT_using_mask, regression)
|
||
|
|
{
|
||
|
|
FeatureDetectorUsingMaskTest test(SIFT::create());
|
||
|
|
test.safe_run();
|
||
|
|
}
|
||
|
|
|
||
|
|
TEST(DISABLED_Features2d_SURF_using_mask, regression)
|
||
|
|
{
|
||
|
|
FeatureDetectorUsingMaskTest test(SURF::create());
|
||
|
|
test.safe_run();
|
||
|
|
}
|
||
|
|
|
||
|
|
TEST( XFeatures2d_DescriptorExtractor, batch )
|
||
|
|
{
|
||
|
|
string path = string(cvtest::TS::ptr()->get_data_path() + "detectors_descriptors_evaluation/images_datasets/graf");
|
||
|
|
vector<Mat> imgs, descriptors;
|
||
|
|
vector<vector<KeyPoint> > keypoints;
|
||
|
|
int i, n = 6;
|
||
|
|
Ptr<SIFT> sift = SIFT::create();
|
||
|
|
|
||
|
|
for( i = 0; i < n; i++ )
|
||
|
|
{
|
||
|
|
string imgname = format("%s/img%d.png", path.c_str(), i+1);
|
||
|
|
Mat img = imread(imgname, 0);
|
||
|
|
imgs.push_back(img);
|
||
|
|
}
|
||
|
|
|
||
|
|
sift->detect(imgs, keypoints);
|
||
|
|
sift->compute(imgs, keypoints, descriptors);
|
||
|
|
|
||
|
|
ASSERT_EQ((int)keypoints.size(), n);
|
||
|
|
ASSERT_EQ((int)descriptors.size(), n);
|
||
|
|
|
||
|
|
for( i = 0; i < n; i++ )
|
||
|
|
{
|
||
|
|
EXPECT_GT((int)keypoints[i].size(), 100);
|
||
|
|
EXPECT_GT(descriptors[i].rows, 100);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
#endif // NONFREE
|
||
|
|
|
||
|
|
}} // namespace
|