340 lines
13 KiB
C++
340 lines
13 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 FEATURES2D_DIR = "features2d";
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const string IMAGE_FILENAME = "tsukuba.png";
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const string DESCRIPTOR_DIR = FEATURES2D_DIR + "/descriptor_extractors";
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/****************************************************************************************\
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* Regression tests for descriptor extractors. *
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\****************************************************************************************/
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static void writeMatInBin( const Mat& mat, const string& filename )
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{
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FILE* f = fopen( filename.c_str(), "wb");
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if( f )
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{
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int type = mat.type();
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fwrite( (void*)&mat.rows, sizeof(int), 1, f );
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fwrite( (void*)&mat.cols, sizeof(int), 1, f );
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fwrite( (void*)&type, sizeof(int), 1, f );
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int dataSize = (int)(mat.step * mat.rows * mat.channels());
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fwrite( (void*)&dataSize, sizeof(int), 1, f );
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fwrite( (void*)mat.data, 1, dataSize, f );
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fclose(f);
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}
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}
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static Mat readMatFromBin( const string& filename )
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{
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FILE* f = fopen( filename.c_str(), "rb" );
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if( f )
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{
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int rows, cols, type, dataSize;
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size_t elements_read1 = fread( (void*)&rows, sizeof(int), 1, f );
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size_t elements_read2 = fread( (void*)&cols, sizeof(int), 1, f );
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size_t elements_read3 = fread( (void*)&type, sizeof(int), 1, f );
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size_t elements_read4 = fread( (void*)&dataSize, sizeof(int), 1, f );
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CV_Assert(elements_read1 == 1 && elements_read2 == 1 && elements_read3 == 1 && elements_read4 == 1);
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uchar* data = (uchar*)cvAlloc(dataSize);
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size_t elements_read = fread( (void*)data, 1, dataSize, f );
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CV_Assert(elements_read == (size_t)(dataSize));
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fclose(f);
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return Mat( rows, cols, type, data );
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}
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return Mat();
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}
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template<class Distance>
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class CV_DescriptorExtractorTest : public cvtest::BaseTest
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{
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public:
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typedef typename Distance::ValueType ValueType;
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typedef typename Distance::ResultType DistanceType;
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CV_DescriptorExtractorTest( const string _name, DistanceType _maxDist, const Ptr<DescriptorExtractor>& _dextractor,
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Distance d = Distance() ):
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name(_name), maxDist(_maxDist), dextractor(_dextractor), distance(d) {}
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protected:
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virtual void createDescriptorExtractor() {}
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void compareDescriptors( const Mat& validDescriptors, const Mat& calcDescriptors )
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{
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if( validDescriptors.size != calcDescriptors.size || validDescriptors.type() != calcDescriptors.type() )
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{
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ts->printf(cvtest::TS::LOG, "Valid and computed descriptors matrices must have the same size and type.\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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CV_Assert( DataType<ValueType>::type == validDescriptors.type() );
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int dimension = validDescriptors.cols;
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DistanceType curMaxDist = std::numeric_limits<DistanceType>::min();
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for( int y = 0; y < validDescriptors.rows; y++ )
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{
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DistanceType dist = distance( validDescriptors.ptr<ValueType>(y), calcDescriptors.ptr<ValueType>(y), dimension );
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if( dist > curMaxDist )
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curMaxDist = dist;
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}
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stringstream ss;
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ss << "Max distance between valid and computed descriptors " << curMaxDist;
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if( curMaxDist < maxDist )
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ss << "." << endl;
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else
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{
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ss << ">" << maxDist << " - bad accuracy!"<< endl;
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ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
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}
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ts->printf(cvtest::TS::LOG, ss.str().c_str() );
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}
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void emptyDataTest()
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{
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assert( !dextractor.empty() );
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// One image.
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Mat image;
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vector<KeyPoint> keypoints;
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Mat descriptors;
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try
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{
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dextractor->compute( image, keypoints, descriptors );
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}
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catch(...)
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{
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ts->printf( cvtest::TS::LOG, "compute() on empty image and empty keypoints must not generate exception (1).\n");
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
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}
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image.create( 50, 50, CV_8UC3 );
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try
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{
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dextractor->compute( image, keypoints, descriptors );
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}
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catch(...)
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{
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ts->printf( cvtest::TS::LOG, "compute() on nonempty image and empty keypoints must not generate exception (1).\n");
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
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}
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// Several images.
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vector<Mat> images;
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vector<vector<KeyPoint> > keypointsCollection;
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vector<Mat> descriptorsCollection;
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try
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{
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dextractor->compute( images, keypointsCollection, descriptorsCollection );
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}
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catch(...)
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{
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ts->printf( cvtest::TS::LOG, "compute() on empty images and empty keypoints collection must not generate exception (2).\n");
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
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}
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}
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void regressionTest()
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{
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assert( !dextractor.empty() );
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// Read the test image.
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string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
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Mat img = imread( imgFilename );
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if( img.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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vector<KeyPoint> keypoints;
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FileStorage fs( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::READ );
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if( fs.isOpened() )
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{
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read( fs.getFirstTopLevelNode(), keypoints );
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Mat calcDescriptors;
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double t = (double)getTickCount();
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dextractor->compute( img, keypoints, calcDescriptors );
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t = getTickCount() - t;
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ts->printf(cvtest::TS::LOG, "\nAverage time of computing one descriptor = %g ms.\n", t/((double)cvGetTickFrequency()*1000.)/calcDescriptors.rows);
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if( calcDescriptors.rows != (int)keypoints.size() )
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{
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ts->printf( cvtest::TS::LOG, "Count of computed descriptors and keypoints count must be equal.\n" );
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ts->printf( cvtest::TS::LOG, "Count of keypoints is %d.\n", (int)keypoints.size() );
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ts->printf( cvtest::TS::LOG, "Count of computed descriptors is %d.\n", calcDescriptors.rows );
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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( calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType() )
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{
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ts->printf( cvtest::TS::LOG, "Incorrect descriptor size or descriptor type.\n" );
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ts->printf( cvtest::TS::LOG, "Expected size is %d.\n", dextractor->descriptorSize() );
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ts->printf( cvtest::TS::LOG, "Calculated size is %d.\n", calcDescriptors.cols );
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ts->printf( cvtest::TS::LOG, "Expected type is %d.\n", dextractor->descriptorType() );
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ts->printf( cvtest::TS::LOG, "Calculated type is %d.\n", calcDescriptors.type() );
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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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// TODO read and write descriptor extractor parameters and check them
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Mat validDescriptors = readDescriptors();
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if( !validDescriptors.empty() )
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compareDescriptors( validDescriptors, calcDescriptors );
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else
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{
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if( !writeDescriptors( calcDescriptors ) )
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{
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ts->printf( cvtest::TS::LOG, "Descriptors can not be written.\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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}
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}
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else
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{
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ts->printf( cvtest::TS::LOG, "Compute and write keypoints.\n" );
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fs.open( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::WRITE );
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if( fs.isOpened() )
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{
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ORB fd;
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fd.detect(img, keypoints);
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write( fs, "keypoints", keypoints );
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}
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else
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{
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ts->printf(cvtest::TS::LOG, "File for writting keypoints can not be opened.\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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}
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}
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void run(int)
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{
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createDescriptorExtractor();
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if( dextractor.empty() )
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{
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ts->printf(cvtest::TS::LOG, "Descriptor extractor is empty.\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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emptyDataTest();
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regressionTest();
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ts->set_failed_test_info( cvtest::TS::OK );
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}
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virtual Mat readDescriptors()
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{
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Mat res = readMatFromBin( string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) );
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return res;
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}
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virtual bool writeDescriptors( Mat& descs )
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{
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writeMatInBin( descs, string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) );
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return true;
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}
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string name;
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const DistanceType maxDist;
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Ptr<DescriptorExtractor> dextractor;
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Distance distance;
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private:
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CV_DescriptorExtractorTest& operator=(const CV_DescriptorExtractorTest&) { return *this; }
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};
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/****************************************************************************************\
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* Tests registrations *
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\****************************************************************************************/
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TEST( Features2d_DescriptorExtractor_BRISK, regression )
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{
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CV_DescriptorExtractorTest<Hamming> test( "descriptor-brisk", (CV_DescriptorExtractorTest<Hamming>::DistanceType)2.f,
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DescriptorExtractor::create("BRISK") );
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_ORB, regression )
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{
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// TODO adjust the parameters below
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CV_DescriptorExtractorTest<Hamming> test( "descriptor-orb", (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
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DescriptorExtractor::create("ORB") );
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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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// TODO adjust the parameters below
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CV_DescriptorExtractorTest<Hamming> test( "descriptor-freak", (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
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DescriptorExtractor::create("FREAK") );
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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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DescriptorExtractor::create("BRIEF") );
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test.safe_run();
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}
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TEST( Features2d_DescriptorExtractor_OpponentBRIEF, regression )
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{
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CV_DescriptorExtractorTest<Hamming> test( "descriptor-opponent-brief", 1,
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DescriptorExtractor::create("OpponentBRIEF") );
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test.safe_run();
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}
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