546 lines
18 KiB
C++
546 lines
18 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/imgproc/imgproc.hpp"
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using namespace cv;
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using namespace std;
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//#define GET_STAT
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#define DIST_E "distE"
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#define S_E "sE"
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#define NO_PAIR_E "noPairE"
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//#define TOTAL_NO_PAIR_E "totalNoPairE"
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#define DETECTOR_NAMES "detector_names"
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#define DETECTORS "detectors"
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#define IMAGE_FILENAMES "image_filenames"
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#define VALIDATION "validation"
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#define FILENAME "fn"
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#define C_SCALE_CASCADE "scale_cascade"
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class CV_DetectorTest : public cvtest::BaseTest
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{
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public:
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CV_DetectorTest();
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protected:
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virtual int prepareData( FileStorage& fs );
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virtual void run( int startFrom );
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virtual string& getValidationFilename();
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virtual void readDetector( const FileNode& fn ) = 0;
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virtual void writeDetector( FileStorage& fs, int di ) = 0;
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int runTestCase( int detectorIdx, vector<vector<Rect> >& objects );
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virtual int detectMultiScale( int di, const Mat& img, vector<Rect>& objects ) = 0;
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int validate( int detectorIdx, vector<vector<Rect> >& objects );
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struct
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{
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float dist;
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float s;
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float noPair;
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//float totalNoPair;
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} eps;
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vector<string> detectorNames;
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vector<string> detectorFilenames;
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vector<string> imageFilenames;
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vector<Mat> images;
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string validationFilename;
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string configFilename;
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FileStorage validationFS;
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bool write_results;
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};
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CV_DetectorTest::CV_DetectorTest()
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{
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configFilename = "dummy";
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write_results = false;
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}
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string& CV_DetectorTest::getValidationFilename()
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{
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return validationFilename;
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}
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int CV_DetectorTest::prepareData( FileStorage& _fs )
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{
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if( !_fs.isOpened() )
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test_case_count = -1;
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else
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{
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FileNode fn = _fs.getFirstTopLevelNode();
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fn[DIST_E] >> eps.dist;
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fn[S_E] >> eps.s;
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fn[NO_PAIR_E] >> eps.noPair;
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// fn[TOTAL_NO_PAIR_E] >> eps.totalNoPair;
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// read detectors
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if( fn[DETECTOR_NAMES].node->data.seq != 0 )
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{
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FileNodeIterator it = fn[DETECTOR_NAMES].begin();
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for( ; it != fn[DETECTOR_NAMES].end(); )
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{
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string _name;
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it >> _name;
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detectorNames.push_back(_name);
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readDetector(fn[DETECTORS][_name]);
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}
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}
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test_case_count = (int)detectorNames.size();
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// read images filenames and images
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string dataPath = ts->get_data_path();
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if( fn[IMAGE_FILENAMES].node->data.seq != 0 )
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{
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for( FileNodeIterator it = fn[IMAGE_FILENAMES].begin(); it != fn[IMAGE_FILENAMES].end(); )
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{
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string filename;
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it >> filename;
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imageFilenames.push_back(filename);
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Mat img = imread( dataPath+filename, 1 );
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images.push_back( img );
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}
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}
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}
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return cvtest::TS::OK;
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}
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void CV_DetectorTest::run( int )
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{
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string dataPath = ts->get_data_path();
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string vs_filename = dataPath + getValidationFilename();
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write_results = !validationFS.open( vs_filename, FileStorage::READ );
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int code;
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if( !write_results )
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{
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code = prepareData( validationFS );
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}
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else
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{
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FileStorage fs0(dataPath + configFilename, FileStorage::READ );
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code = prepareData(fs0);
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}
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if( code < 0 )
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{
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ts->set_failed_test_info( code );
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return;
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}
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if( write_results )
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{
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validationFS.release();
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validationFS.open( vs_filename, FileStorage::WRITE );
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validationFS << FileStorage::getDefaultObjectName(validationFilename) << "{";
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validationFS << DIST_E << eps.dist;
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validationFS << S_E << eps.s;
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validationFS << NO_PAIR_E << eps.noPair;
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// validationFS << TOTAL_NO_PAIR_E << eps.totalNoPair;
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// write detector names
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validationFS << DETECTOR_NAMES << "[";
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vector<string>::const_iterator nit = detectorNames.begin();
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for( ; nit != detectorNames.end(); ++nit )
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{
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validationFS << *nit;
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}
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validationFS << "]"; // DETECTOR_NAMES
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// write detectors
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validationFS << DETECTORS << "{";
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assert( detectorNames.size() == detectorFilenames.size() );
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nit = detectorNames.begin();
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for( int di = 0; nit != detectorNames.end(); ++nit, di++ )
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{
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validationFS << *nit << "{";
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writeDetector( validationFS, di );
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validationFS << "}";
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}
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validationFS << "}";
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// write image filenames
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validationFS << IMAGE_FILENAMES << "[";
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vector<string>::const_iterator it = imageFilenames.begin();
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for( int ii = 0; it != imageFilenames.end(); ++it, ii++ )
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{
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char buf[10];
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sprintf( buf, "%s%d", "img_", ii );
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cvWriteComment( validationFS.fs, buf, 0 );
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validationFS << *it;
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}
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validationFS << "]"; // IMAGE_FILENAMES
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validationFS << VALIDATION << "{";
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}
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int progress = 0;
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for( int di = 0; di < test_case_count; di++ )
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{
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progress = update_progress( progress, di, test_case_count, 0 );
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if( write_results )
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validationFS << detectorNames[di] << "{";
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vector<vector<Rect> > objects;
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int temp_code = runTestCase( di, objects );
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if (!write_results && temp_code == cvtest::TS::OK)
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temp_code = validate( di, objects );
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if (temp_code != cvtest::TS::OK)
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code = temp_code;
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if( write_results )
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validationFS << "}"; // detectorNames[di]
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}
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if( write_results )
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{
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validationFS << "}"; // VALIDATION
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validationFS << "}"; // getDefaultObjectName
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}
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if ( test_case_count <= 0 || imageFilenames.size() <= 0 )
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{
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ts->printf( cvtest::TS::LOG, "validation file is not determined or not correct" );
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code = cvtest::TS::FAIL_INVALID_TEST_DATA;
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}
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ts->set_failed_test_info( code );
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}
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int CV_DetectorTest::runTestCase( int detectorIdx, vector<vector<Rect> >& objects )
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{
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string dataPath = ts->get_data_path(), detectorFilename;
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if( !detectorFilenames[detectorIdx].empty() )
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detectorFilename = dataPath + detectorFilenames[detectorIdx];
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for( int ii = 0; ii < (int)imageFilenames.size(); ++ii )
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{
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vector<Rect> imgObjects;
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Mat image = images[ii];
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if( image.empty() )
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{
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char msg[30];
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sprintf( msg, "%s %d %s", "image ", ii, " can not be read" );
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ts->printf( cvtest::TS::LOG, msg );
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return cvtest::TS::FAIL_INVALID_TEST_DATA;
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}
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int code = detectMultiScale( detectorIdx, image, imgObjects );
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if( code != cvtest::TS::OK )
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return code;
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objects.push_back( imgObjects );
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if( write_results )
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{
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char buf[10];
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sprintf( buf, "%s%d", "img_", ii );
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string imageIdxStr = buf;
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validationFS << imageIdxStr << "[:";
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for( vector<Rect>::const_iterator it = imgObjects.begin();
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it != imgObjects.end(); ++it )
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{
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validationFS << it->x << it->y << it->width << it->height;
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}
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validationFS << "]"; // imageIdxStr
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}
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}
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return cvtest::TS::OK;
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}
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bool isZero( uchar i ) {return i == 0;}
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int CV_DetectorTest::validate( int detectorIdx, vector<vector<Rect> >& objects )
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{
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assert( imageFilenames.size() == objects.size() );
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int imageIdx = 0;
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int totalNoPair = 0, totalValRectCount = 0;
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for( vector<vector<Rect> >::const_iterator it = objects.begin();
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it != objects.end(); ++it, imageIdx++ ) // for image
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{
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Size imgSize = images[imageIdx].size();
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float dist = min(imgSize.height, imgSize.width) * eps.dist;
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float wDiff = imgSize.width * eps.s;
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float hDiff = imgSize.height * eps.s;
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int noPair = 0;
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// read validation rectangles
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char buf[10];
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sprintf( buf, "%s%d", "img_", imageIdx );
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string imageIdxStr = buf;
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FileNode node = validationFS.getFirstTopLevelNode()[VALIDATION][detectorNames[detectorIdx]][imageIdxStr];
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vector<Rect> valRects;
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if( node.node->data.seq != 0 )
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{
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for( FileNodeIterator it2 = node.begin(); it2 != node.end(); )
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{
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Rect r;
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it2 >> r.x >> r.y >> r.width >> r.height;
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valRects.push_back(r);
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}
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}
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totalValRectCount += (int)valRects.size();
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// compare rectangles
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vector<uchar> map(valRects.size(), 0);
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for( vector<Rect>::const_iterator cr = it->begin();
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cr != it->end(); ++cr )
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{
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// find nearest rectangle
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Point2f cp1 = Point2f( cr->x + (float)cr->width/2.0f, cr->y + (float)cr->height/2.0f );
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int minIdx = -1, vi = 0;
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float minDist = (float)norm( Point(imgSize.width, imgSize.height) );
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for( vector<Rect>::const_iterator vr = valRects.begin();
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vr != valRects.end(); ++vr, vi++ )
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{
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Point2f cp2 = Point2f( vr->x + (float)vr->width/2.0f, vr->y + (float)vr->height/2.0f );
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float curDist = (float)norm(cp1-cp2);
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if( curDist < minDist )
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{
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minIdx = vi;
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minDist = curDist;
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}
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}
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if( minIdx == -1 )
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{
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noPair++;
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}
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else
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{
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Rect vr = valRects[minIdx];
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if( map[minIdx] != 0 || (minDist > dist) || (abs(cr->width - vr.width) > wDiff) ||
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(abs(cr->height - vr.height) > hDiff) )
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noPair++;
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else
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map[minIdx] = 1;
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}
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}
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noPair += (int)count_if( map.begin(), map.end(), isZero );
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totalNoPair += noPair;
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EXPECT_LE(noPair, cvRound(valRects.size()*eps.noPair)+1)
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<< "detector " << detectorNames[detectorIdx] << " has overrated count of rectangles without pair on "
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<< imageFilenames[imageIdx] << " image";
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if (::testing::Test::HasFailure())
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break;
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}
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EXPECT_LE(totalNoPair, cvRound(totalValRectCount*eps./*total*/noPair)+1)
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<< "detector " << detectorNames[detectorIdx] << " has overrated count of rectangles without pair on all images set";
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if (::testing::Test::HasFailure())
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return cvtest::TS::FAIL_BAD_ACCURACY;
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return cvtest::TS::OK;
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}
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//----------------------------------------------- CascadeDetectorTest -----------------------------------
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class CV_CascadeDetectorTest : public CV_DetectorTest
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{
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public:
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CV_CascadeDetectorTest();
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protected:
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virtual void readDetector( const FileNode& fn );
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virtual void writeDetector( FileStorage& fs, int di );
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virtual int detectMultiScale( int di, const Mat& img, vector<Rect>& objects );
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virtual int detectMultiScale_C( const string& filename, int di, const Mat& img, vector<Rect>& objects );
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vector<int> flags;
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};
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CV_CascadeDetectorTest::CV_CascadeDetectorTest()
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{
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validationFilename = "cascadeandhog/cascade.xml";
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configFilename = "cascadeandhog/_cascade.xml";
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}
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void CV_CascadeDetectorTest::readDetector( const FileNode& fn )
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{
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string filename;
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int flag;
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fn[FILENAME] >> filename;
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detectorFilenames.push_back(filename);
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fn[C_SCALE_CASCADE] >> flag;
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if( flag )
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flags.push_back( 0 );
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else
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flags.push_back( CV_HAAR_SCALE_IMAGE );
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}
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void CV_CascadeDetectorTest::writeDetector( FileStorage& fs, int di )
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{
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int sc = flags[di] & CV_HAAR_SCALE_IMAGE ? 0 : 1;
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fs << FILENAME << detectorFilenames[di];
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fs << C_SCALE_CASCADE << sc;
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}
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int CV_CascadeDetectorTest::detectMultiScale_C( const string& filename,
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int di, const Mat& img,
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vector<Rect>& objects )
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{
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Ptr<CvHaarClassifierCascade> c_cascade = cvLoadHaarClassifierCascade(filename.c_str(), cvSize(0,0));
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Ptr<CvMemStorage> storage = cvCreateMemStorage();
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if( c_cascade.empty() )
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{
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ts->printf( cvtest::TS::LOG, "cascade %s can not be opened");
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return cvtest::TS::FAIL_INVALID_TEST_DATA;
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}
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Mat grayImg;
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cvtColor( img, grayImg, CV_BGR2GRAY );
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equalizeHist( grayImg, grayImg );
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CvMat c_gray = grayImg;
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CvSeq* rs = cvHaarDetectObjects(&c_gray, c_cascade, storage, 1.1, 3, flags[di] );
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objects.clear();
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for( int i = 0; i < rs->total; i++ )
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{
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Rect r = *(Rect*)cvGetSeqElem(rs, i);
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objects.push_back(r);
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}
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return cvtest::TS::OK;
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}
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int CV_CascadeDetectorTest::detectMultiScale( int di, const Mat& img,
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vector<Rect>& objects)
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{
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string dataPath = ts->get_data_path(), filename;
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filename = dataPath + detectorFilenames[di];
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const string pattern = "haarcascade_frontalface_default.xml";
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if( filename.size() >= pattern.size() &&
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strcmp(filename.c_str() + (filename.size() - pattern.size()),
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pattern.c_str()) == 0 )
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return detectMultiScale_C(filename, di, img, objects);
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CascadeClassifier cascade( filename );
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if( cascade.empty() )
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{
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ts->printf( cvtest::TS::LOG, "cascade %s can not be opened");
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return cvtest::TS::FAIL_INVALID_TEST_DATA;
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}
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Mat grayImg;
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cvtColor( img, grayImg, CV_BGR2GRAY );
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equalizeHist( grayImg, grayImg );
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cascade.detectMultiScale( grayImg, objects, 1.1, 3, flags[di] );
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return cvtest::TS::OK;
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}
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//----------------------------------------------- HOGDetectorTest -----------------------------------
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class CV_HOGDetectorTest : public CV_DetectorTest
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{
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public:
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CV_HOGDetectorTest();
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protected:
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virtual void readDetector( const FileNode& fn );
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virtual void writeDetector( FileStorage& fs, int di );
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virtual int detectMultiScale( int di, const Mat& img, vector<Rect>& objects );
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};
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CV_HOGDetectorTest::CV_HOGDetectorTest()
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{
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validationFilename = "cascadeandhog/hog.xml";
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}
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void CV_HOGDetectorTest::readDetector( const FileNode& fn )
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{
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string filename;
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if( fn[FILENAME].node->data.seq != 0 )
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fn[FILENAME] >> filename;
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detectorFilenames.push_back( filename);
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}
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void CV_HOGDetectorTest::writeDetector( FileStorage& fs, int di )
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{
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fs << FILENAME << detectorFilenames[di];
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}
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int CV_HOGDetectorTest::detectMultiScale( int di, const Mat& img,
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vector<Rect>& objects)
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{
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HOGDescriptor hog;
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if( detectorFilenames[di].empty() )
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hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());
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else
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assert(0);
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hog.detectMultiScale(img, objects);
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return cvtest::TS::OK;
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}
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//----------------------------------------------- HOGDetectorReadWriteTest -----------------------------------
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TEST(Objdetect_HOGDetectorReadWrite, regression)
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{
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// Inspired by bug #2607
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Mat img;
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img = imread(cvtest::TS::ptr()->get_data_path() + "/cascadeandhog/images/karen-and-rob.png");
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ASSERT_FALSE(img.empty());
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HOGDescriptor hog;
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hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());
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string tempfilename = cv::tempfile(".xml");
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FileStorage fs(tempfilename, FileStorage::WRITE);
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hog.write(fs, "myHOG");
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fs.open(tempfilename, FileStorage::READ);
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remove(tempfilename.c_str());
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FileNode n = fs["opencv_storage"]["myHOG"];
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ASSERT_NO_THROW(hog.read(n));
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}
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|
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TEST(Objdetect_CascadeDetector, regression) { CV_CascadeDetectorTest test; test.safe_run(); }
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TEST(Objdetect_HOGDetector, regression) { CV_HOGDetectorTest test; test.safe_run(); }
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