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split file of features2d tests
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Maria Dimashova
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modules/features2d/test/test_descriptors_regression.cpp
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332
modules/features2d/test/test_descriptors_regression.cpp
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@ -0,0 +1,332 @@
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/*M///////////////////////////////////////////////////////////////////////////////////////
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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.
|
||||
//
|
||||
//
|
||||
// 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.
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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_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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296
modules/features2d/test/test_detectors_regression.cpp
Normal file
296
modules/features2d/test/test_detectors_regression.cpp
Normal file
@ -0,0 +1,296 @@
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/*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"
|
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#include "opencv2/highgui/highgui.hpp"
|
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|
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using namespace std;
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using namespace cv;
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|
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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 DETECTOR_DIR = FEATURES2D_DIR + "/feature_detectors";
|
||||
|
||||
/****************************************************************************************\
|
||||
* Regression tests for feature detectors comparing keypoints. *
|
||||
\****************************************************************************************/
|
||||
|
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class CV_FeatureDetectorTest : public cvtest::BaseTest
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{
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public:
|
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CV_FeatureDetectorTest( const string& _name, const Ptr<FeatureDetector>& _fdetector ) :
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name(_name), fdetector(_fdetector) {}
|
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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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||||
|
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void emptyDataTest();
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void regressionTest(); // TODO test of detect() with mask
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||||
|
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virtual void run( int );
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||||
|
||||
string name;
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||||
Ptr<FeatureDetector> fdetector;
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};
|
||||
|
||||
void CV_FeatureDetectorTest::emptyDataTest()
|
||||
{
|
||||
// One image.
|
||||
Mat image;
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||||
vector<KeyPoint> keypoints;
|
||||
try
|
||||
{
|
||||
fdetector->detect( image, keypoints );
|
||||
}
|
||||
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 );
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||||
return;
|
||||
}
|
||||
|
||||
// Several images.
|
||||
vector<Mat> images;
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||||
vector<vector<KeyPoint> > keypointCollection;
|
||||
try
|
||||
{
|
||||
fdetector->detect( images, keypointCollection );
|
||||
}
|
||||
catch(...)
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "detect() on empty image vector must not generate exception (2).\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||||
}
|
||||
}
|
||||
|
||||
bool CV_FeatureDetectorTest::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 );
|
||||
}
|
||||
|
||||
void CV_FeatureDetectorTest::compareKeypointSets( const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints )
|
||||
{
|
||||
const float maxCountRatioDif = 0.01f;
|
||||
|
||||
// Compare counts of validation and calculated keypoints.
|
||||
float countRatio = (float)validKeypoints.size() / (float)calcKeypoints.size();
|
||||
if( countRatio < 1 - maxCountRatioDif || countRatio > 1.f + maxCountRatioDif )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Bad keypoints count ratio (validCount = %d, calcCount = %d).\n",
|
||||
validKeypoints.size(), calcKeypoints.size() );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||||
return;
|
||||
}
|
||||
|
||||
int progress = 0, progressCount = (int)(validKeypoints.size() * calcKeypoints.size());
|
||||
int badPointCount = 0, commonPointCount = max((int)validKeypoints.size(), (int)calcKeypoints.size());
|
||||
for( size_t v = 0; v < validKeypoints.size(); v++ )
|
||||
{
|
||||
int nearestIdx = -1;
|
||||
float minDist = std::numeric_limits<float>::max();
|
||||
|
||||
for( size_t c = 0; c < calcKeypoints.size(); c++ )
|
||||
{
|
||||
progress = update_progress( progress, (int)(v*calcKeypoints.size() + c), progressCount, 0 );
|
||||
float curDist = (float)norm( calcKeypoints[c].pt - validKeypoints[v].pt );
|
||||
if( curDist < minDist )
|
||||
{
|
||||
minDist = curDist;
|
||||
nearestIdx = (int)c;
|
||||
}
|
||||
}
|
||||
|
||||
assert( minDist >= 0 );
|
||||
if( !isSimilarKeypoints( validKeypoints[v], calcKeypoints[nearestIdx] ) )
|
||||
badPointCount++;
|
||||
}
|
||||
ts->printf( cvtest::TS::LOG, "badPointCount = %d; validPointCount = %d; calcPointCount = %d\n",
|
||||
badPointCount, validKeypoints.size(), calcKeypoints.size() );
|
||||
if( badPointCount > 0.9 * commonPointCount )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, " - Bad accuracy!\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
return;
|
||||
}
|
||||
ts->printf( cvtest::TS::LOG, " - OK\n" );
|
||||
}
|
||||
|
||||
void CV_FeatureDetectorTest::regressionTest()
|
||||
{
|
||||
assert( !fdetector.empty() );
|
||||
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
|
||||
string resFilename = string(ts->get_data_path()) + DETECTOR_DIR + "/" + string(name) + ".xml.gz";
|
||||
|
||||
// Read the test image.
|
||||
Mat image = imread( imgFilename );
|
||||
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;
|
||||
}
|
||||
|
||||
FileStorage fs( resFilename, FileStorage::READ );
|
||||
|
||||
// Compute keypoints.
|
||||
vector<KeyPoint> calcKeypoints;
|
||||
fdetector->detect( image, calcKeypoints );
|
||||
|
||||
if( fs.isOpened() ) // Compare computed and valid keypoints.
|
||||
{
|
||||
// TODO compare saved feature detector params with current ones
|
||||
|
||||
// Read validation keypoints set.
|
||||
vector<KeyPoint> validKeypoints;
|
||||
read( fs["keypoints"], validKeypoints );
|
||||
if( validKeypoints.empty() )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Keypoints can not be read.\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
|
||||
compareKeypointSets( validKeypoints, calcKeypoints );
|
||||
}
|
||||
else // Write detector parameters and computed keypoints as validation data.
|
||||
{
|
||||
fs.open( resFilename, FileStorage::WRITE );
|
||||
if( !fs.isOpened() )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "File %s can not be opened to write.\n", resFilename.c_str() );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
else
|
||||
{
|
||||
fs << "detector_params" << "{";
|
||||
fdetector->write( fs );
|
||||
fs << "}";
|
||||
|
||||
write( fs, "keypoints", calcKeypoints );
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void CV_FeatureDetectorTest::run( int /*start_from*/ )
|
||||
{
|
||||
if( fdetector.empty() )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Feature detector is empty.\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
|
||||
emptyDataTest();
|
||||
regressionTest();
|
||||
|
||||
ts->set_failed_test_info( cvtest::TS::OK );
|
||||
}
|
||||
|
||||
/****************************************************************************************\
|
||||
* Tests registrations *
|
||||
\****************************************************************************************/
|
||||
|
||||
TEST( Features2d_Detector_FAST, regression )
|
||||
{
|
||||
CV_FeatureDetectorTest test( "detector-fast", FeatureDetector::create("FAST") );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST( Features2d_Detector_GFTT, regression )
|
||||
{
|
||||
CV_FeatureDetectorTest test( "detector-gftt", FeatureDetector::create("GFTT") );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST( Features2d_Detector_Harris, regression )
|
||||
{
|
||||
CV_FeatureDetectorTest test( "detector-harris", FeatureDetector::create("HARRIS") );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST( Features2d_Detector_MSER, DISABLED_regression )
|
||||
{
|
||||
CV_FeatureDetectorTest test( "detector-mser", FeatureDetector::create("MSER") );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST( Features2d_Detector_STAR, regression )
|
||||
{
|
||||
CV_FeatureDetectorTest test( "detector-star", FeatureDetector::create("STAR") );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST( Features2d_Detector_ORB, regression )
|
||||
{
|
||||
CV_FeatureDetectorTest test( "detector-orb", FeatureDetector::create("ORB") );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST( Features2d_Detector_GridFAST, regression )
|
||||
{
|
||||
CV_FeatureDetectorTest test( "detector-grid-fast", FeatureDetector::create("GridFAST") );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST( Features2d_Detector_PyramidFAST, regression )
|
||||
{
|
||||
CV_FeatureDetectorTest test( "detector-pyramid-fast", FeatureDetector::create("PyramidFAST") );
|
||||
test.safe_run();
|
||||
}
|
||||
@ -46,452 +46,8 @@ using namespace std;
|
||||
using namespace cv;
|
||||
|
||||
const string FEATURES2D_DIR = "features2d";
|
||||
const string DETECTOR_DIR = FEATURES2D_DIR + "/feature_detectors";
|
||||
const string DESCRIPTOR_DIR = FEATURES2D_DIR + "/descriptor_extractors";
|
||||
const string IMAGE_FILENAME = "tsukuba.png";
|
||||
|
||||
/****************************************************************************************\
|
||||
* Regression tests for feature detectors comparing keypoints. *
|
||||
\****************************************************************************************/
|
||||
|
||||
class CV_FeatureDetectorTest : public cvtest::BaseTest
|
||||
{
|
||||
public:
|
||||
CV_FeatureDetectorTest( const string& _name, const Ptr<FeatureDetector>& _fdetector ) :
|
||||
name(_name), fdetector(_fdetector) {}
|
||||
|
||||
protected:
|
||||
bool isSimilarKeypoints( const KeyPoint& p1, const KeyPoint& p2 );
|
||||
void compareKeypointSets( const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints );
|
||||
|
||||
void emptyDataTest();
|
||||
void regressionTest(); // TODO test of detect() with mask
|
||||
|
||||
virtual void run( int );
|
||||
|
||||
string name;
|
||||
Ptr<FeatureDetector> fdetector;
|
||||
};
|
||||
|
||||
void CV_FeatureDetectorTest::emptyDataTest()
|
||||
{
|
||||
// One image.
|
||||
Mat image;
|
||||
vector<KeyPoint> keypoints;
|
||||
try
|
||||
{
|
||||
fdetector->detect( image, keypoints );
|
||||
}
|
||||
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;
|
||||
}
|
||||
|
||||
// Several images.
|
||||
vector<Mat> images;
|
||||
vector<vector<KeyPoint> > keypointCollection;
|
||||
try
|
||||
{
|
||||
fdetector->detect( images, keypointCollection );
|
||||
}
|
||||
catch(...)
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "detect() on empty image vector must not generate exception (2).\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||||
}
|
||||
}
|
||||
|
||||
bool CV_FeatureDetectorTest::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 );
|
||||
}
|
||||
|
||||
void CV_FeatureDetectorTest::compareKeypointSets( const vector<KeyPoint>& validKeypoints, const vector<KeyPoint>& calcKeypoints )
|
||||
{
|
||||
const float maxCountRatioDif = 0.01f;
|
||||
|
||||
// Compare counts of validation and calculated keypoints.
|
||||
float countRatio = (float)validKeypoints.size() / (float)calcKeypoints.size();
|
||||
if( countRatio < 1 - maxCountRatioDif || countRatio > 1.f + maxCountRatioDif )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Bad keypoints count ratio (validCount = %d, calcCount = %d).\n",
|
||||
validKeypoints.size(), calcKeypoints.size() );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||||
return;
|
||||
}
|
||||
|
||||
int progress = 0, progressCount = (int)(validKeypoints.size() * calcKeypoints.size());
|
||||
int badPointCount = 0, commonPointCount = max((int)validKeypoints.size(), (int)calcKeypoints.size());
|
||||
for( size_t v = 0; v < validKeypoints.size(); v++ )
|
||||
{
|
||||
int nearestIdx = -1;
|
||||
float minDist = std::numeric_limits<float>::max();
|
||||
|
||||
for( size_t c = 0; c < calcKeypoints.size(); c++ )
|
||||
{
|
||||
progress = update_progress( progress, (int)(v*calcKeypoints.size() + c), progressCount, 0 );
|
||||
float curDist = (float)norm( calcKeypoints[c].pt - validKeypoints[v].pt );
|
||||
if( curDist < minDist )
|
||||
{
|
||||
minDist = curDist;
|
||||
nearestIdx = (int)c;
|
||||
}
|
||||
}
|
||||
|
||||
assert( minDist >= 0 );
|
||||
if( !isSimilarKeypoints( validKeypoints[v], calcKeypoints[nearestIdx] ) )
|
||||
badPointCount++;
|
||||
}
|
||||
ts->printf( cvtest::TS::LOG, "badPointCount = %d; validPointCount = %d; calcPointCount = %d\n",
|
||||
badPointCount, validKeypoints.size(), calcKeypoints.size() );
|
||||
if( badPointCount > 0.9 * commonPointCount )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, " - Bad accuracy!\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
return;
|
||||
}
|
||||
ts->printf( cvtest::TS::LOG, " - OK\n" );
|
||||
}
|
||||
|
||||
void CV_FeatureDetectorTest::regressionTest()
|
||||
{
|
||||
assert( !fdetector.empty() );
|
||||
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
|
||||
string resFilename = string(ts->get_data_path()) + DETECTOR_DIR + "/" + string(name) + ".xml.gz";
|
||||
|
||||
// Read the test image.
|
||||
Mat image = imread( imgFilename );
|
||||
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;
|
||||
}
|
||||
|
||||
FileStorage fs( resFilename, FileStorage::READ );
|
||||
|
||||
// Compute keypoints.
|
||||
vector<KeyPoint> calcKeypoints;
|
||||
fdetector->detect( image, calcKeypoints );
|
||||
|
||||
if( fs.isOpened() ) // Compare computed and valid keypoints.
|
||||
{
|
||||
// TODO compare saved feature detector params with current ones
|
||||
|
||||
// Read validation keypoints set.
|
||||
vector<KeyPoint> validKeypoints;
|
||||
read( fs["keypoints"], validKeypoints );
|
||||
if( validKeypoints.empty() )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Keypoints can not be read.\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
|
||||
compareKeypointSets( validKeypoints, calcKeypoints );
|
||||
}
|
||||
else // Write detector parameters and computed keypoints as validation data.
|
||||
{
|
||||
fs.open( resFilename, FileStorage::WRITE );
|
||||
if( !fs.isOpened() )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "File %s can not be opened to write.\n", resFilename.c_str() );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
else
|
||||
{
|
||||
fs << "detector_params" << "{";
|
||||
fdetector->write( fs );
|
||||
fs << "}";
|
||||
|
||||
write( fs, "keypoints", calcKeypoints );
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void CV_FeatureDetectorTest::run( int /*start_from*/ )
|
||||
{
|
||||
if( fdetector.empty() )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Feature detector is empty.\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
|
||||
emptyDataTest();
|
||||
regressionTest();
|
||||
|
||||
ts->set_failed_test_info( cvtest::TS::OK );
|
||||
}
|
||||
|
||||
/****************************************************************************************\
|
||||
* Regression tests for descriptor extractors. *
|
||||
\****************************************************************************************/
|
||||
static void writeMatInBin( const Mat& mat, const string& filename )
|
||||
{
|
||||
FILE* f = fopen( filename.c_str(), "wb");
|
||||
if( f )
|
||||
{
|
||||
int type = mat.type();
|
||||
fwrite( (void*)&mat.rows, sizeof(int), 1, f );
|
||||
fwrite( (void*)&mat.cols, sizeof(int), 1, f );
|
||||
fwrite( (void*)&type, sizeof(int), 1, f );
|
||||
int dataSize = (int)(mat.step * mat.rows * mat.channels());
|
||||
fwrite( (void*)&dataSize, sizeof(int), 1, f );
|
||||
fwrite( (void*)mat.data, 1, dataSize, f );
|
||||
fclose(f);
|
||||
}
|
||||
}
|
||||
|
||||
static Mat readMatFromBin( const string& filename )
|
||||
{
|
||||
FILE* f = fopen( filename.c_str(), "rb" );
|
||||
if( f )
|
||||
{
|
||||
int rows, cols, type, dataSize;
|
||||
size_t elements_read1 = fread( (void*)&rows, sizeof(int), 1, f );
|
||||
size_t elements_read2 = fread( (void*)&cols, sizeof(int), 1, f );
|
||||
size_t elements_read3 = fread( (void*)&type, sizeof(int), 1, f );
|
||||
size_t elements_read4 = fread( (void*)&dataSize, sizeof(int), 1, f );
|
||||
CV_Assert(elements_read1 == 1 && elements_read2 == 1 && elements_read3 == 1 && elements_read4 == 1);
|
||||
|
||||
uchar* data = (uchar*)cvAlloc(dataSize);
|
||||
size_t elements_read = fread( (void*)data, 1, dataSize, f );
|
||||
CV_Assert(elements_read == (size_t)(dataSize));
|
||||
fclose(f);
|
||||
|
||||
return Mat( rows, cols, type, data );
|
||||
}
|
||||
return Mat();
|
||||
}
|
||||
|
||||
template<class Distance>
|
||||
class CV_DescriptorExtractorTest : public cvtest::BaseTest
|
||||
{
|
||||
public:
|
||||
typedef typename Distance::ValueType ValueType;
|
||||
typedef typename Distance::ResultType DistanceType;
|
||||
|
||||
CV_DescriptorExtractorTest( const string _name, DistanceType _maxDist, const Ptr<DescriptorExtractor>& _dextractor,
|
||||
Distance d = Distance() ):
|
||||
name(_name), maxDist(_maxDist), dextractor(_dextractor), distance(d) {}
|
||||
protected:
|
||||
virtual void createDescriptorExtractor() {}
|
||||
|
||||
void compareDescriptors( const Mat& validDescriptors, const Mat& calcDescriptors )
|
||||
{
|
||||
if( validDescriptors.size != calcDescriptors.size || validDescriptors.type() != calcDescriptors.type() )
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Valid and computed descriptors matrices must have the same size and type.\n");
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
|
||||
CV_Assert( DataType<ValueType>::type == validDescriptors.type() );
|
||||
|
||||
int dimension = validDescriptors.cols;
|
||||
DistanceType curMaxDist = std::numeric_limits<DistanceType>::min();
|
||||
for( int y = 0; y < validDescriptors.rows; y++ )
|
||||
{
|
||||
DistanceType dist = distance( validDescriptors.ptr<ValueType>(y), calcDescriptors.ptr<ValueType>(y), dimension );
|
||||
if( dist > curMaxDist )
|
||||
curMaxDist = dist;
|
||||
}
|
||||
|
||||
stringstream ss;
|
||||
ss << "Max distance between valid and computed descriptors " << curMaxDist;
|
||||
if( curMaxDist < maxDist )
|
||||
ss << "." << endl;
|
||||
else
|
||||
{
|
||||
ss << ">" << maxDist << " - bad accuracy!"<< endl;
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
|
||||
}
|
||||
ts->printf(cvtest::TS::LOG, ss.str().c_str() );
|
||||
}
|
||||
|
||||
void emptyDataTest()
|
||||
{
|
||||
assert( !dextractor.empty() );
|
||||
|
||||
// One image.
|
||||
Mat image;
|
||||
vector<KeyPoint> keypoints;
|
||||
Mat descriptors;
|
||||
|
||||
try
|
||||
{
|
||||
dextractor->compute( image, keypoints, descriptors );
|
||||
}
|
||||
catch(...)
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "compute() on empty image and empty keypoints must not generate exception (1).\n");
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
}
|
||||
|
||||
image.create( 50, 50, CV_8UC3 );
|
||||
try
|
||||
{
|
||||
dextractor->compute( image, keypoints, descriptors );
|
||||
}
|
||||
catch(...)
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "compute() on nonempty image and empty keypoints must not generate exception (1).\n");
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
}
|
||||
|
||||
// Several images.
|
||||
vector<Mat> images;
|
||||
vector<vector<KeyPoint> > keypointsCollection;
|
||||
vector<Mat> descriptorsCollection;
|
||||
try
|
||||
{
|
||||
dextractor->compute( images, keypointsCollection, descriptorsCollection );
|
||||
}
|
||||
catch(...)
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "compute() on empty images and empty keypoints collection must not generate exception (2).\n");
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
}
|
||||
}
|
||||
|
||||
void regressionTest()
|
||||
{
|
||||
assert( !dextractor.empty() );
|
||||
|
||||
// Read the test image.
|
||||
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
|
||||
|
||||
Mat img = imread( imgFilename );
|
||||
if( img.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;
|
||||
}
|
||||
|
||||
vector<KeyPoint> keypoints;
|
||||
FileStorage fs( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::READ );
|
||||
if( fs.isOpened() )
|
||||
{
|
||||
read( fs.getFirstTopLevelNode(), keypoints );
|
||||
|
||||
Mat calcDescriptors;
|
||||
double t = (double)getTickCount();
|
||||
dextractor->compute( img, keypoints, calcDescriptors );
|
||||
t = getTickCount() - t;
|
||||
ts->printf(cvtest::TS::LOG, "\nAverage time of computing one descriptor = %g ms.\n", t/((double)cvGetTickFrequency()*1000.)/calcDescriptors.rows);
|
||||
|
||||
if( calcDescriptors.rows != (int)keypoints.size() )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Count of computed descriptors and keypoints count must be equal.\n" );
|
||||
ts->printf( cvtest::TS::LOG, "Count of keypoints is %d.\n", (int)keypoints.size() );
|
||||
ts->printf( cvtest::TS::LOG, "Count of computed descriptors is %d.\n", calcDescriptors.rows );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||||
return;
|
||||
}
|
||||
|
||||
if( calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType() )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Incorrect descriptor size or descriptor type.\n" );
|
||||
ts->printf( cvtest::TS::LOG, "Expected size is %d.\n", dextractor->descriptorSize() );
|
||||
ts->printf( cvtest::TS::LOG, "Calculated size is %d.\n", calcDescriptors.cols );
|
||||
ts->printf( cvtest::TS::LOG, "Expected type is %d.\n", dextractor->descriptorType() );
|
||||
ts->printf( cvtest::TS::LOG, "Calculated type is %d.\n", calcDescriptors.type() );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
|
||||
return;
|
||||
}
|
||||
|
||||
// TODO read and write descriptor extractor parameters and check them
|
||||
Mat validDescriptors = readDescriptors();
|
||||
if( !validDescriptors.empty() )
|
||||
compareDescriptors( validDescriptors, calcDescriptors );
|
||||
else
|
||||
{
|
||||
if( !writeDescriptors( calcDescriptors ) )
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Descriptors can not be written.\n" );
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
ts->printf( cvtest::TS::LOG, "Compute and write keypoints.\n" );
|
||||
fs.open( string(ts->get_data_path()) + FEATURES2D_DIR + "/keypoints.xml.gz", FileStorage::WRITE );
|
||||
if( fs.isOpened() )
|
||||
{
|
||||
ORB fd;
|
||||
fd.detect(img, keypoints);
|
||||
write( fs, "keypoints", keypoints );
|
||||
}
|
||||
else
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "File for writting keypoints can not be opened.\n");
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void run(int)
|
||||
{
|
||||
createDescriptorExtractor();
|
||||
if( dextractor.empty() )
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Descriptor extractor is empty.\n");
|
||||
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
|
||||
return;
|
||||
}
|
||||
|
||||
emptyDataTest();
|
||||
regressionTest();
|
||||
|
||||
ts->set_failed_test_info( cvtest::TS::OK );
|
||||
}
|
||||
|
||||
virtual Mat readDescriptors()
|
||||
{
|
||||
Mat res = readMatFromBin( string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) );
|
||||
return res;
|
||||
}
|
||||
|
||||
virtual bool writeDescriptors( Mat& descs )
|
||||
{
|
||||
writeMatInBin( descs, string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) );
|
||||
return true;
|
||||
}
|
||||
|
||||
string name;
|
||||
const DistanceType maxDist;
|
||||
Ptr<DescriptorExtractor> dextractor;
|
||||
Distance distance;
|
||||
|
||||
private:
|
||||
CV_DescriptorExtractorTest& operator=(const CV_DescriptorExtractorTest&) { return *this; }
|
||||
};
|
||||
|
||||
/****************************************************************************************\
|
||||
* Algorithmic tests for descriptor matchers *
|
||||
\****************************************************************************************/
|
||||
@ -974,95 +530,6 @@ void CV_DescriptorMatcherTest::run( int )
|
||||
* Tests registrations *
|
||||
\****************************************************************************************/
|
||||
|
||||
/*
|
||||
* Detectors
|
||||
*/
|
||||
|
||||
TEST( Features2d_Detector_FAST, regression )
|
||||
{
|
||||
CV_FeatureDetectorTest test( "detector-fast", FeatureDetector::create("FAST") );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST( Features2d_Detector_GFTT, regression )
|
||||
{
|
||||
CV_FeatureDetectorTest test( "detector-gftt", FeatureDetector::create("GFTT") );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST( Features2d_Detector_Harris, regression )
|
||||
{
|
||||
CV_FeatureDetectorTest test( "detector-harris", FeatureDetector::create("HARRIS") );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST( Features2d_Detector_MSER, DISABLED_regression )
|
||||
{
|
||||
CV_FeatureDetectorTest test( "detector-mser", FeatureDetector::create("MSER") );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST( Features2d_Detector_STAR, regression )
|
||||
{
|
||||
CV_FeatureDetectorTest test( "detector-star", FeatureDetector::create("STAR") );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST( Features2d_Detector_ORB, regression )
|
||||
{
|
||||
CV_FeatureDetectorTest test( "detector-orb", FeatureDetector::create("ORB") );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST( Features2d_Detector_GridFAST, regression )
|
||||
{
|
||||
CV_FeatureDetectorTest test( "detector-grid-fast", FeatureDetector::create("GridFAST") );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST( Features2d_Detector_PyramidFAST, regression )
|
||||
{
|
||||
CV_FeatureDetectorTest test( "detector-pyramid-fast", FeatureDetector::create("PyramidFAST") );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
/*
|
||||
* Descriptors
|
||||
*/
|
||||
|
||||
TEST( Features2d_DescriptorExtractor_ORB, regression )
|
||||
{
|
||||
// TODO adjust the parameters below
|
||||
CV_DescriptorExtractorTest<Hamming> test( "descriptor-orb", (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
|
||||
DescriptorExtractor::create("ORB") );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST( Features2d_DescriptorExtractor_FREAK, regression )
|
||||
{
|
||||
// TODO adjust the parameters below
|
||||
CV_DescriptorExtractorTest<Hamming> test( "descriptor-freak", (CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
|
||||
DescriptorExtractor::create("FREAK") );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST( Features2d_DescriptorExtractor_BRIEF, regression )
|
||||
{
|
||||
CV_DescriptorExtractorTest<Hamming> test( "descriptor-brief", 1,
|
||||
DescriptorExtractor::create("BRIEF") );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST( Features2d_DescriptorExtractor_OpponentBRIEF, regression )
|
||||
{
|
||||
CV_DescriptorExtractorTest<Hamming> test( "descriptor-opponent-brief", 1,
|
||||
DescriptorExtractor::create("OpponentBRIEF") );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
/*
|
||||
* Matchers
|
||||
*/
|
||||
TEST( Features2d_DescriptorMatcher_BruteForce, regression )
|
||||
{
|
||||
CV_DescriptorMatcherTest test( "descriptor-matcher-brute-force", new BFMatcher(NORM_L2), 0.01f );
|
||||
@ -1074,51 +541,3 @@ TEST( Features2d_DescriptorMatcher_FlannBased, regression )
|
||||
CV_DescriptorMatcherTest test( "descriptor-matcher-flann-based", new FlannBasedMatcher, 0.04f );
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
|
||||
TEST(Features2D_ORB, _1996)
|
||||
{
|
||||
cv::Ptr<cv::FeatureDetector> fd = cv::FeatureDetector::create("ORB");
|
||||
cv::Ptr<cv::DescriptorExtractor> de = cv::DescriptorExtractor::create("ORB");
|
||||
|
||||
Mat image = cv::imread(string(cvtest::TS::ptr()->get_data_path()) + "shared/lena.jpg");
|
||||
ASSERT_FALSE(image.empty());
|
||||
|
||||
Mat roi(image.size(), CV_8UC1, Scalar(0));
|
||||
|
||||
Point poly[] = {Point(100, 20), Point(300, 50), Point(400, 200), Point(10, 500)};
|
||||
fillConvexPoly(roi, poly, int(sizeof(poly) / sizeof(poly[0])), Scalar(255));
|
||||
|
||||
std::vector<cv::KeyPoint> keypoints;
|
||||
fd->detect(image, keypoints, roi);
|
||||
cv::Mat descriptors;
|
||||
de->compute(image, keypoints, descriptors);
|
||||
|
||||
//image.setTo(Scalar(255,255,255), roi);
|
||||
|
||||
int roiViolations = 0;
|
||||
for(std::vector<cv::KeyPoint>::const_iterator kp = keypoints.begin(); kp != keypoints.end(); ++kp)
|
||||
{
|
||||
int x = cvRound(kp->pt.x);
|
||||
int y = cvRound(kp->pt.y);
|
||||
|
||||
ASSERT_LE(0, x);
|
||||
ASSERT_LE(0, y);
|
||||
ASSERT_GT(image.cols, x);
|
||||
ASSERT_GT(image.rows, y);
|
||||
|
||||
// if (!roi.at<uchar>(y,x))
|
||||
// {
|
||||
// roiViolations++;
|
||||
// circle(image, kp->pt, 3, Scalar(0,0,255));
|
||||
// }
|
||||
}
|
||||
|
||||
// if(roiViolations)
|
||||
// {
|
||||
// imshow("img", image);
|
||||
// waitKey();
|
||||
// }
|
||||
|
||||
ASSERT_EQ(0, roiViolations);
|
||||
}
|
||||
92
modules/features2d/test/test_orb.cpp
Normal file
92
modules/features2d/test/test_orb.cpp
Normal file
@ -0,0 +1,92 @@
|
||||
/*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 "opencv2/highgui/highgui.hpp"
|
||||
|
||||
using namespace cv;
|
||||
|
||||
TEST(Features2D_ORB, _1996)
|
||||
{
|
||||
Ptr<FeatureDetector> fd = FeatureDetector::create("ORB");
|
||||
Ptr<DescriptorExtractor> de = DescriptorExtractor::create("ORB");
|
||||
|
||||
Mat image = imread(string(cvtest::TS::ptr()->get_data_path()) + "shared/lena.jpg");
|
||||
ASSERT_FALSE(image.empty());
|
||||
|
||||
Mat roi(image.size(), CV_8UC1, Scalar(0));
|
||||
|
||||
Point poly[] = {Point(100, 20), Point(300, 50), Point(400, 200), Point(10, 500)};
|
||||
fillConvexPoly(roi, poly, int(sizeof(poly) / sizeof(poly[0])), Scalar(255));
|
||||
|
||||
std::vector<KeyPoint> keypoints;
|
||||
fd->detect(image, keypoints, roi);
|
||||
Mat descriptors;
|
||||
de->compute(image, keypoints, descriptors);
|
||||
|
||||
//image.setTo(Scalar(255,255,255), roi);
|
||||
|
||||
int roiViolations = 0;
|
||||
for(std::vector<KeyPoint>::const_iterator kp = keypoints.begin(); kp != keypoints.end(); ++kp)
|
||||
{
|
||||
int x = cvRound(kp->pt.x);
|
||||
int y = cvRound(kp->pt.y);
|
||||
|
||||
ASSERT_LE(0, x);
|
||||
ASSERT_LE(0, y);
|
||||
ASSERT_GT(image.cols, x);
|
||||
ASSERT_GT(image.rows, y);
|
||||
|
||||
// if (!roi.at<uchar>(y,x))
|
||||
// {
|
||||
// roiViolations++;
|
||||
// circle(image, kp->pt, 3, Scalar(0,0,255));
|
||||
// }
|
||||
}
|
||||
|
||||
// if(roiViolations)
|
||||
// {
|
||||
// imshow("img", image);
|
||||
// waitKey();
|
||||
// }
|
||||
|
||||
ASSERT_EQ(0, roiViolations);
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user