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1367 lines
46 KiB
C++
1367 lines
46 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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namespace opencv_test { namespace {
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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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FileNode fn_names = fn[DETECTOR_NAMES];
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if( fn_names.size() != 0 )
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{
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FileNodeIterator it = fn_names.begin(), it_end = fn_names.end();
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for( ; it != it_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].size() != 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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CV_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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//String buf = cv::format("img_%d", 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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printf("detector %s\n", detectorFilename.c_str());
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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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String msg = cv::format("image %d is empty", ii);
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ts->printf( cvtest::TS::LOG, msg.c_str() );
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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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String imageIdxStr = cv::format("img_%d", ii);
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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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static 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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CV_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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String imageIdxStr = cv::format("img_%d", imageIdx);
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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.size() != 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)cv::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)cv::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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/*if( noPair > cvRound(valRects.size()*eps.noPair)+1 )
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{
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printf("Problem discovered: imageIdx = %d, cascade=%s: %d vs %d rects\n", imageIdx, detectorNames[detectorIdx].c_str(), (int)it->size(), (int)valRects.size());
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Mat image = images[imageIdx].clone();
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for( int k = 0; k < 2; k++ )
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{
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const std::vector<Rect>& imgObjects = k == 0 ? *it : valRects;
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Scalar color = k == 0 ? Scalar(0, 255, 0) : Scalar(0, 0, 255);
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for( size_t i = 0; i < imgObjects.size(); i++ )
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{
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Rect r = imgObjects[i];
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rectangle(image, r, color, 3);
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if( k == 1 )
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putText(image, format("%d", (int)i), Point(r.x + r.width/4, r.y + r.height*3/4), FONT_HERSHEY_PLAIN, 2, Scalar(0, 0, 255), 3);
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}
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}
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imshow("results", image);
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waitKey();
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}*/
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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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<< "In total, detector " << detectorNames[detectorIdx] << " has overrated count of rectangles without pair on the whole image 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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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( CASCADE_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] & CASCADE_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( 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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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, COLOR_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].size() != 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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CV_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");
|
|
FileStorage fs(tempfilename, FileStorage::WRITE);
|
|
hog.write(fs, "myHOG");
|
|
|
|
fs.open(tempfilename, FileStorage::READ);
|
|
remove(tempfilename.c_str());
|
|
|
|
FileNode n = fs["myHOG"];
|
|
|
|
ASSERT_NO_THROW(hog.read(n));
|
|
}
|
|
|
|
|
|
|
|
TEST(Objdetect_CascadeDetector, regression) { CV_CascadeDetectorTest test; test.safe_run(); }
|
|
TEST(Objdetect_HOGDetector, regression) { CV_HOGDetectorTest test; test.safe_run(); }
|
|
|
|
|
|
//----------------------------------------------- HOG SSE2 compatible test -----------------------------------
|
|
|
|
class HOGDescriptorTester :
|
|
public cv::HOGDescriptor
|
|
{
|
|
HOGDescriptor* actual_hog;
|
|
cvtest::TS* ts;
|
|
mutable bool failed;
|
|
|
|
public:
|
|
HOGDescriptorTester(HOGDescriptor& instance) :
|
|
cv::HOGDescriptor(instance), actual_hog(&instance),
|
|
ts(cvtest::TS::ptr()), failed(false)
|
|
{ }
|
|
|
|
virtual void computeGradient(InputArray img, InputOutputArray grad, InputOutputArray qangle,
|
|
Size paddingTL, Size paddingBR) const;
|
|
|
|
virtual void detect(InputArray img,
|
|
vector<Point>& hits, vector<double>& weights, double hitThreshold = 0.0,
|
|
Size winStride = Size(), Size padding = Size(),
|
|
const vector<Point>& locations = vector<Point>()) const;
|
|
|
|
virtual void detect(InputArray img, vector<Point>& hits, double hitThreshold = 0.0,
|
|
Size winStride = Size(), Size padding = Size(),
|
|
const vector<Point>& locations = vector<Point>()) const;
|
|
|
|
virtual void compute(InputArray img, vector<float>& descriptors,
|
|
Size winStride = Size(), Size padding = Size(),
|
|
const vector<Point>& locations = vector<Point>()) const;
|
|
|
|
bool is_failed() const;
|
|
};
|
|
|
|
struct HOGCacheTester
|
|
{
|
|
struct BlockData
|
|
{
|
|
BlockData() : histOfs(0), imgOffset() {}
|
|
int histOfs;
|
|
Point imgOffset;
|
|
};
|
|
|
|
struct PixData
|
|
{
|
|
size_t gradOfs, qangleOfs;
|
|
int histOfs[4];
|
|
float histWeights[4];
|
|
float gradWeight;
|
|
};
|
|
|
|
HOGCacheTester(const HOGDescriptorTester* descriptor,
|
|
const Mat& img, Size paddingTL, Size paddingBR,
|
|
bool useCache, Size cacheStride);
|
|
virtual ~HOGCacheTester() { }
|
|
virtual void init(const HOGDescriptorTester* descriptor,
|
|
const Mat& img, Size paddingTL, Size paddingBR,
|
|
bool useCache, Size cacheStride);
|
|
|
|
Size windowsInImage(Size imageSize, Size winStride) const;
|
|
Rect getWindow(Size imageSize, Size winStride, int idx) const;
|
|
|
|
const float* getBlock(Point pt, float* buf);
|
|
virtual void normalizeBlockHistogram(float* histogram) const;
|
|
|
|
vector<PixData> pixData;
|
|
vector<BlockData> blockData;
|
|
|
|
bool useCache;
|
|
vector<int> ymaxCached;
|
|
Size winSize, cacheStride;
|
|
Size nblocks, ncells;
|
|
int blockHistogramSize;
|
|
int count1, count2, count4;
|
|
Point imgoffset;
|
|
Mat_<float> blockCache;
|
|
Mat_<uchar> blockCacheFlags;
|
|
|
|
Mat grad, qangle;
|
|
const HOGDescriptorTester* descriptor;
|
|
|
|
private:
|
|
HOGCacheTester(); //= delete
|
|
};
|
|
|
|
HOGCacheTester::HOGCacheTester(const HOGDescriptorTester* _descriptor,
|
|
const Mat& _img, Size _paddingTL, Size _paddingBR,
|
|
bool _useCache, Size _cacheStride)
|
|
{
|
|
init(_descriptor, _img, _paddingTL, _paddingBR, _useCache, _cacheStride);
|
|
}
|
|
|
|
void HOGCacheTester::init(const HOGDescriptorTester* _descriptor,
|
|
const Mat& _img, Size _paddingTL, Size _paddingBR,
|
|
bool _useCache, Size _cacheStride)
|
|
{
|
|
descriptor = _descriptor;
|
|
cacheStride = _cacheStride;
|
|
useCache = _useCache;
|
|
|
|
descriptor->computeGradient(_img, grad, qangle, _paddingTL, _paddingBR);
|
|
imgoffset = _paddingTL;
|
|
|
|
winSize = descriptor->winSize;
|
|
Size blockSize = descriptor->blockSize;
|
|
Size blockStride = descriptor->blockStride;
|
|
Size cellSize = descriptor->cellSize;
|
|
int i, j, nbins = descriptor->nbins;
|
|
int rawBlockSize = blockSize.width*blockSize.height;
|
|
|
|
nblocks = Size((winSize.width - blockSize.width)/blockStride.width + 1,
|
|
(winSize.height - blockSize.height)/blockStride.height + 1);
|
|
ncells = Size(blockSize.width/cellSize.width, blockSize.height/cellSize.height);
|
|
blockHistogramSize = ncells.width*ncells.height*nbins;
|
|
|
|
if( useCache )
|
|
{
|
|
Size cacheSize((grad.cols - blockSize.width)/cacheStride.width+1,
|
|
(winSize.height/cacheStride.height)+1);
|
|
blockCache.create(cacheSize.height, cacheSize.width*blockHistogramSize);
|
|
blockCacheFlags.create(cacheSize);
|
|
size_t cacheRows = blockCache.rows;
|
|
ymaxCached.resize(cacheRows);
|
|
for(size_t ii = 0; ii < cacheRows; ii++ )
|
|
ymaxCached[ii] = -1;
|
|
}
|
|
|
|
Mat_<float> weights(blockSize);
|
|
float sigma = (float)descriptor->getWinSigma();
|
|
float scale = 1.f/(sigma*sigma*2);
|
|
|
|
for(i = 0; i < blockSize.height; i++)
|
|
for(j = 0; j < blockSize.width; j++)
|
|
{
|
|
float di = i - blockSize.height*0.5f;
|
|
float dj = j - blockSize.width*0.5f;
|
|
weights(i,j) = std::exp(-(di*di + dj*dj)*scale);
|
|
}
|
|
|
|
blockData.resize(nblocks.width*nblocks.height);
|
|
pixData.resize(rawBlockSize*3);
|
|
|
|
// Initialize 2 lookup tables, pixData & blockData.
|
|
// Here is why:
|
|
//
|
|
// The detection algorithm runs in 4 nested loops (at each pyramid layer):
|
|
// loop over the windows within the input image
|
|
// loop over the blocks within each window
|
|
// loop over the cells within each block
|
|
// loop over the pixels in each cell
|
|
//
|
|
// As each of the loops runs over a 2-dimensional array,
|
|
// we could get 8(!) nested loops in total, which is very-very slow.
|
|
//
|
|
// To speed the things up, we do the following:
|
|
// 1. loop over windows is unrolled in the HOGDescriptor::{compute|detect} methods;
|
|
// inside we compute the current search window using getWindow() method.
|
|
// Yes, it involves some overhead (function call + couple of divisions),
|
|
// but it's tiny in fact.
|
|
// 2. loop over the blocks is also unrolled. Inside we use pre-computed blockData[j]
|
|
// to set up gradient and histogram pointers.
|
|
// 3. loops over cells and pixels in each cell are merged
|
|
// (since there is no overlap between cells, each pixel in the block is processed once)
|
|
// and also unrolled. Inside we use PixData[k] to access the gradient values and
|
|
// update the histogram
|
|
//
|
|
count1 = count2 = count4 = 0;
|
|
for( j = 0; j < blockSize.width; j++ )
|
|
for( i = 0; i < blockSize.height; i++ )
|
|
{
|
|
PixData* data = 0;
|
|
float cellX = (j+0.5f)/cellSize.width - 0.5f;
|
|
float cellY = (i+0.5f)/cellSize.height - 0.5f;
|
|
int icellX0 = cvFloor(cellX);
|
|
int icellY0 = cvFloor(cellY);
|
|
int icellX1 = icellX0 + 1, icellY1 = icellY0 + 1;
|
|
cellX -= icellX0;
|
|
cellY -= icellY0;
|
|
|
|
if( (unsigned)icellX0 < (unsigned)ncells.width &&
|
|
(unsigned)icellX1 < (unsigned)ncells.width )
|
|
{
|
|
if( (unsigned)icellY0 < (unsigned)ncells.height &&
|
|
(unsigned)icellY1 < (unsigned)ncells.height )
|
|
{
|
|
data = &pixData[rawBlockSize*2 + (count4++)];
|
|
data->histOfs[0] = (icellX0*ncells.height + icellY0)*nbins;
|
|
data->histWeights[0] = (1.f - cellX)*(1.f - cellY);
|
|
data->histOfs[1] = (icellX1*ncells.height + icellY0)*nbins;
|
|
data->histWeights[1] = cellX*(1.f - cellY);
|
|
data->histOfs[2] = (icellX0*ncells.height + icellY1)*nbins;
|
|
data->histWeights[2] = (1.f - cellX)*cellY;
|
|
data->histOfs[3] = (icellX1*ncells.height + icellY1)*nbins;
|
|
data->histWeights[3] = cellX*cellY;
|
|
}
|
|
else
|
|
{
|
|
data = &pixData[rawBlockSize + (count2++)];
|
|
if( (unsigned)icellY0 < (unsigned)ncells.height )
|
|
{
|
|
icellY1 = icellY0;
|
|
cellY = 1.f - cellY;
|
|
}
|
|
data->histOfs[0] = (icellX0*ncells.height + icellY1)*nbins;
|
|
data->histWeights[0] = (1.f - cellX)*cellY;
|
|
data->histOfs[1] = (icellX1*ncells.height + icellY1)*nbins;
|
|
data->histWeights[1] = cellX*cellY;
|
|
data->histOfs[2] = data->histOfs[3] = 0;
|
|
data->histWeights[2] = data->histWeights[3] = 0;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
if( (unsigned)icellX0 < (unsigned)ncells.width )
|
|
{
|
|
icellX1 = icellX0;
|
|
cellX = 1.f - cellX;
|
|
}
|
|
|
|
if( (unsigned)icellY0 < (unsigned)ncells.height &&
|
|
(unsigned)icellY1 < (unsigned)ncells.height )
|
|
{
|
|
data = &pixData[rawBlockSize + (count2++)];
|
|
data->histOfs[0] = (icellX1*ncells.height + icellY0)*nbins;
|
|
data->histWeights[0] = cellX*(1.f - cellY);
|
|
data->histOfs[1] = (icellX1*ncells.height + icellY1)*nbins;
|
|
data->histWeights[1] = cellX*cellY;
|
|
data->histOfs[2] = data->histOfs[3] = 0;
|
|
data->histWeights[2] = data->histWeights[3] = 0;
|
|
}
|
|
else
|
|
{
|
|
data = &pixData[count1++];
|
|
if( (unsigned)icellY0 < (unsigned)ncells.height )
|
|
{
|
|
icellY1 = icellY0;
|
|
cellY = 1.f - cellY;
|
|
}
|
|
data->histOfs[0] = (icellX1*ncells.height + icellY1)*nbins;
|
|
data->histWeights[0] = cellX*cellY;
|
|
data->histOfs[1] = data->histOfs[2] = data->histOfs[3] = 0;
|
|
data->histWeights[1] = data->histWeights[2] = data->histWeights[3] = 0;
|
|
}
|
|
}
|
|
data->gradOfs = (grad.cols*i + j)*2;
|
|
data->qangleOfs = (qangle.cols*i + j)*2;
|
|
data->gradWeight = weights(i,j);
|
|
}
|
|
|
|
CV_Assert( count1 + count2 + count4 == rawBlockSize );
|
|
// defragment pixData
|
|
for( j = 0; j < count2; j++ )
|
|
pixData[j + count1] = pixData[j + rawBlockSize];
|
|
for( j = 0; j < count4; j++ )
|
|
pixData[j + count1 + count2] = pixData[j + rawBlockSize*2];
|
|
count2 += count1;
|
|
count4 += count2;
|
|
|
|
// initialize blockData
|
|
for( j = 0; j < nblocks.width; j++ )
|
|
for( i = 0; i < nblocks.height; i++ )
|
|
{
|
|
BlockData& data = blockData[j*nblocks.height + i];
|
|
data.histOfs = (j*nblocks.height + i)*blockHistogramSize;
|
|
data.imgOffset = Point(j*blockStride.width,i*blockStride.height);
|
|
}
|
|
}
|
|
|
|
const float* HOGCacheTester::getBlock(Point pt, float* buf)
|
|
{
|
|
float* blockHist = buf;
|
|
CV_Assert(descriptor != 0);
|
|
|
|
Size blockSize = descriptor->blockSize;
|
|
pt += imgoffset;
|
|
|
|
CV_Assert( (unsigned)pt.x <= (unsigned)(grad.cols - blockSize.width) &&
|
|
(unsigned)pt.y <= (unsigned)(grad.rows - blockSize.height) );
|
|
|
|
if( useCache )
|
|
{
|
|
CV_Assert( pt.x % cacheStride.width == 0 &&
|
|
pt.y % cacheStride.height == 0 );
|
|
Point cacheIdx(pt.x/cacheStride.width,
|
|
(pt.y/cacheStride.height) % blockCache.rows);
|
|
if( pt.y != ymaxCached[cacheIdx.y] )
|
|
{
|
|
Mat_<uchar> cacheRow = blockCacheFlags.row(cacheIdx.y);
|
|
cacheRow = (uchar)0;
|
|
ymaxCached[cacheIdx.y] = pt.y;
|
|
}
|
|
|
|
blockHist = &blockCache[cacheIdx.y][cacheIdx.x*blockHistogramSize];
|
|
uchar& computedFlag = blockCacheFlags(cacheIdx.y, cacheIdx.x);
|
|
if( computedFlag != 0 )
|
|
return blockHist;
|
|
computedFlag = (uchar)1; // set it at once, before actual computing
|
|
}
|
|
|
|
int k, C1 = count1, C2 = count2, C4 = count4;
|
|
const float* gradPtr = grad.ptr<float>(pt.y) + pt.x*2;
|
|
const uchar* qanglePtr = qangle.ptr(pt.y) + pt.x*2;
|
|
|
|
CV_Assert( blockHist != 0 );
|
|
for( k = 0; k < blockHistogramSize; k++ )
|
|
blockHist[k] = 0.f;
|
|
|
|
const PixData* _pixData = &pixData[0];
|
|
|
|
for( k = 0; k < C1; k++ )
|
|
{
|
|
const PixData& pk = _pixData[k];
|
|
const float* a = gradPtr + pk.gradOfs;
|
|
float w = pk.gradWeight*pk.histWeights[0];
|
|
const uchar* h = qanglePtr + pk.qangleOfs;
|
|
int h0 = h[0], h1 = h[1];
|
|
float* hist = blockHist + pk.histOfs[0];
|
|
float t0 = hist[h0] + a[0]*w;
|
|
float t1 = hist[h1] + a[1]*w;
|
|
hist[h0] = t0; hist[h1] = t1;
|
|
}
|
|
|
|
for( ; k < C2; k++ )
|
|
{
|
|
const PixData& pk = _pixData[k];
|
|
const float* a = gradPtr + pk.gradOfs;
|
|
float w, t0, t1, a0 = a[0], a1 = a[1];
|
|
const uchar* h = qanglePtr + pk.qangleOfs;
|
|
int h0 = h[0], h1 = h[1];
|
|
|
|
float* hist = blockHist + pk.histOfs[0];
|
|
w = pk.gradWeight*pk.histWeights[0];
|
|
t0 = hist[h0] + a0*w;
|
|
t1 = hist[h1] + a1*w;
|
|
hist[h0] = t0; hist[h1] = t1;
|
|
|
|
hist = blockHist + pk.histOfs[1];
|
|
w = pk.gradWeight*pk.histWeights[1];
|
|
t0 = hist[h0] + a0*w;
|
|
t1 = hist[h1] + a1*w;
|
|
hist[h0] = t0; hist[h1] = t1;
|
|
}
|
|
|
|
for( ; k < C4; k++ )
|
|
{
|
|
const PixData& pk = _pixData[k];
|
|
const float* a = gradPtr + pk.gradOfs;
|
|
float w, t0, t1, a0 = a[0], a1 = a[1];
|
|
const uchar* h = qanglePtr + pk.qangleOfs;
|
|
int h0 = h[0], h1 = h[1];
|
|
|
|
float* hist = blockHist + pk.histOfs[0];
|
|
w = pk.gradWeight*pk.histWeights[0];
|
|
t0 = hist[h0] + a0*w;
|
|
t1 = hist[h1] + a1*w;
|
|
hist[h0] = t0; hist[h1] = t1;
|
|
|
|
hist = blockHist + pk.histOfs[1];
|
|
w = pk.gradWeight*pk.histWeights[1];
|
|
t0 = hist[h0] + a0*w;
|
|
t1 = hist[h1] + a1*w;
|
|
hist[h0] = t0; hist[h1] = t1;
|
|
|
|
hist = blockHist + pk.histOfs[2];
|
|
w = pk.gradWeight*pk.histWeights[2];
|
|
t0 = hist[h0] + a0*w;
|
|
t1 = hist[h1] + a1*w;
|
|
hist[h0] = t0; hist[h1] = t1;
|
|
|
|
hist = blockHist + pk.histOfs[3];
|
|
w = pk.gradWeight*pk.histWeights[3];
|
|
t0 = hist[h0] + a0*w;
|
|
t1 = hist[h1] + a1*w;
|
|
hist[h0] = t0; hist[h1] = t1;
|
|
}
|
|
|
|
normalizeBlockHistogram(blockHist);
|
|
|
|
return blockHist;
|
|
}
|
|
|
|
void HOGCacheTester::normalizeBlockHistogram(float* _hist) const
|
|
{
|
|
float* hist = &_hist[0], partSum[4] = { 0.0f, 0.0f, 0.0f, 0.0f };
|
|
size_t i, sz = blockHistogramSize;
|
|
|
|
for (i = 0; i <= sz - 4; i += 4)
|
|
{
|
|
partSum[0] += hist[i] * hist[i];
|
|
partSum[1] += hist[i+1] * hist[i+1];
|
|
partSum[2] += hist[i+2] * hist[i+2];
|
|
partSum[3] += hist[i+3] * hist[i+3];
|
|
}
|
|
float t0 = partSum[0] + partSum[1];
|
|
float t1 = partSum[2] + partSum[3];
|
|
float sum = t0 + t1;
|
|
for( ; i < sz; i++ )
|
|
sum += hist[i]*hist[i];
|
|
|
|
float scale = 1.f/(std::sqrt(sum)+sz*0.1f), thresh = (float)descriptor->L2HysThreshold;
|
|
partSum[0] = partSum[1] = partSum[2] = partSum[3] = 0.0f;
|
|
for(i = 0; i <= sz - 4; i += 4)
|
|
{
|
|
hist[i] = std::min(hist[i]*scale, thresh);
|
|
hist[i+1] = std::min(hist[i+1]*scale, thresh);
|
|
hist[i+2] = std::min(hist[i+2]*scale, thresh);
|
|
hist[i+3] = std::min(hist[i+3]*scale, thresh);
|
|
partSum[0] += hist[i]*hist[i];
|
|
partSum[1] += hist[i+1]*hist[i+1];
|
|
partSum[2] += hist[i+2]*hist[i+2];
|
|
partSum[3] += hist[i+3]*hist[i+3];
|
|
}
|
|
t0 = partSum[0] + partSum[1];
|
|
t1 = partSum[2] + partSum[3];
|
|
sum = t0 + t1;
|
|
for( ; i < sz; i++ )
|
|
{
|
|
hist[i] = std::min(hist[i]*scale, thresh);
|
|
sum += hist[i]*hist[i];
|
|
}
|
|
|
|
scale = 1.f/(std::sqrt(sum)+1e-3f);
|
|
for( i = 0; i < sz; i++ )
|
|
hist[i] *= scale;
|
|
}
|
|
|
|
Size HOGCacheTester::windowsInImage(Size imageSize, Size winStride) const
|
|
{
|
|
return Size((imageSize.width - winSize.width)/winStride.width + 1,
|
|
(imageSize.height - winSize.height)/winStride.height + 1);
|
|
}
|
|
|
|
Rect HOGCacheTester::getWindow(Size imageSize, Size winStride, int idx) const
|
|
{
|
|
int nwindowsX = (imageSize.width - winSize.width)/winStride.width + 1;
|
|
int y = idx / nwindowsX;
|
|
int x = idx - nwindowsX*y;
|
|
return Rect( x*winStride.width, y*winStride.height, winSize.width, winSize.height );
|
|
}
|
|
|
|
inline bool HOGDescriptorTester::is_failed() const
|
|
{
|
|
return failed;
|
|
}
|
|
|
|
static inline int gcd(int a, int b) { return (a % b == 0) ? b : gcd (b, a % b); }
|
|
|
|
void HOGDescriptorTester::detect(InputArray _img,
|
|
vector<Point>& hits, vector<double>& weights, double hitThreshold,
|
|
Size winStride, Size padding, const vector<Point>& locations) const
|
|
{
|
|
if (failed)
|
|
return;
|
|
|
|
hits.clear();
|
|
if( svmDetector.empty() )
|
|
return;
|
|
|
|
Mat img = _img.getMat();
|
|
if( winStride == Size() )
|
|
winStride = cellSize;
|
|
Size cacheStride(gcd(winStride.width, blockStride.width),
|
|
gcd(winStride.height, blockStride.height));
|
|
size_t nwindows = locations.size();
|
|
padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
|
|
padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
|
|
Size paddedImgSize(img.cols + padding.width*2, img.rows + padding.height*2);
|
|
|
|
HOGCacheTester cache(this, img, padding, padding, nwindows == 0, cacheStride);
|
|
|
|
if( !nwindows )
|
|
nwindows = cache.windowsInImage(paddedImgSize, winStride).area();
|
|
|
|
const HOGCacheTester::BlockData* blockData = &cache.blockData[0];
|
|
|
|
int nblocks = cache.nblocks.area();
|
|
int blockHistogramSize = cache.blockHistogramSize;
|
|
size_t dsize = getDescriptorSize();
|
|
|
|
double rho = svmDetector.size() > dsize ? svmDetector[dsize] : 0;
|
|
vector<float> blockHist(blockHistogramSize);
|
|
|
|
for( size_t i = 0; i < nwindows; i++ )
|
|
{
|
|
Point pt0;
|
|
if( !locations.empty() )
|
|
{
|
|
pt0 = locations[i];
|
|
if( pt0.x < -padding.width || pt0.x > img.cols + padding.width - winSize.width ||
|
|
pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height )
|
|
continue;
|
|
}
|
|
else
|
|
{
|
|
pt0 = cache.getWindow(paddedImgSize, winStride, (int)i).tl() - Point(padding);
|
|
CV_Assert(pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0);
|
|
}
|
|
double s = rho;
|
|
const float* svmVec = &svmDetector[0];
|
|
int j, k;
|
|
for( j = 0; j < nblocks; j++, svmVec += blockHistogramSize )
|
|
{
|
|
const HOGCacheTester::BlockData& bj = blockData[j];
|
|
Point pt = pt0 + bj.imgOffset;
|
|
|
|
const float* vec = cache.getBlock(pt, &blockHist[0]);
|
|
for( k = 0; k <= blockHistogramSize - 4; k += 4 )
|
|
s += vec[k]*svmVec[k] + vec[k+1]*svmVec[k+1] +
|
|
vec[k+2]*svmVec[k+2] + vec[k+3]*svmVec[k+3];
|
|
for( ; k < blockHistogramSize; k++ )
|
|
s += vec[k]*svmVec[k];
|
|
}
|
|
if( s >= hitThreshold )
|
|
{
|
|
hits.push_back(pt0);
|
|
weights.push_back(s);
|
|
}
|
|
}
|
|
|
|
// validation
|
|
std::vector<Point> actual_find_locations;
|
|
std::vector<double> actual_weights;
|
|
actual_hog->detect(img, actual_find_locations, actual_weights,
|
|
hitThreshold, winStride, padding, locations);
|
|
|
|
if (!std::equal(hits.begin(), hits.end(),
|
|
actual_find_locations.begin()))
|
|
{
|
|
ts->printf(cvtest::TS::SUMMARY, "Found locations are not equal (see detect function)\n");
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
|
ts->set_gtest_status();
|
|
failed = true;
|
|
return;
|
|
}
|
|
|
|
const double eps = FLT_EPSILON * 100;
|
|
double diff_norm = cvtest::norm(actual_weights, weights, NORM_L2 + NORM_RELATIVE);
|
|
if (diff_norm > eps)
|
|
{
|
|
ts->printf(cvtest::TS::SUMMARY, "Weights for found locations aren't equal.\n"
|
|
"Norm of the difference is %lf\n", diff_norm);
|
|
ts->printf(cvtest::TS::LOG, "Channels: %d\n", img.channels());
|
|
failed = true;
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
|
ts->set_gtest_status();
|
|
}
|
|
}
|
|
|
|
void HOGDescriptorTester::detect(InputArray img, vector<Point>& hits, double hitThreshold,
|
|
Size winStride, Size padding, const vector<Point>& locations) const
|
|
{
|
|
vector<double> weightsV;
|
|
detect(img, hits, weightsV, hitThreshold, winStride, padding, locations);
|
|
}
|
|
|
|
void HOGDescriptorTester::compute(InputArray _img, vector<float>& descriptors,
|
|
Size winStride, Size padding, const vector<Point>& locations) const
|
|
{
|
|
Mat img = _img.getMat();
|
|
|
|
if( winStride == Size() )
|
|
winStride = cellSize;
|
|
Size cacheStride(gcd(winStride.width, blockStride.width),
|
|
gcd(winStride.height, blockStride.height));
|
|
size_t nwindows = locations.size();
|
|
padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
|
|
padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
|
|
Size paddedImgSize(img.cols + padding.width*2, img.rows + padding.height*2);
|
|
|
|
HOGCacheTester cache(this, img, padding, padding, nwindows == 0, cacheStride);
|
|
|
|
if( !nwindows )
|
|
nwindows = cache.windowsInImage(paddedImgSize, winStride).area();
|
|
|
|
const HOGCacheTester::BlockData* blockData = &cache.blockData[0];
|
|
|
|
int nblocks = cache.nblocks.area();
|
|
int blockHistogramSize = cache.blockHistogramSize;
|
|
size_t dsize = getDescriptorSize();
|
|
descriptors.resize(dsize*nwindows);
|
|
|
|
for( size_t i = 0; i < nwindows; i++ )
|
|
{
|
|
float* descriptor = &descriptors[i*dsize];
|
|
|
|
Point pt0;
|
|
if( !locations.empty() )
|
|
{
|
|
pt0 = locations[i];
|
|
if( pt0.x < -padding.width || pt0.x > img.cols + padding.width - winSize.width ||
|
|
pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height )
|
|
continue;
|
|
}
|
|
else
|
|
{
|
|
pt0 = cache.getWindow(paddedImgSize, winStride, (int)i).tl() - Point(padding);
|
|
CV_Assert(pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0);
|
|
}
|
|
|
|
for( int j = 0; j < nblocks; j++ )
|
|
{
|
|
const HOGCacheTester::BlockData& bj = blockData[j];
|
|
Point pt = pt0 + bj.imgOffset;
|
|
|
|
float* dst = descriptor + bj.histOfs;
|
|
const float* src = cache.getBlock(pt, dst);
|
|
if( src != dst )
|
|
for( int k = 0; k < blockHistogramSize; k++ )
|
|
dst[k] = src[k];
|
|
}
|
|
}
|
|
|
|
// validation
|
|
std::vector<float> actual_descriptors;
|
|
actual_hog->compute(img, actual_descriptors, winStride, padding, locations);
|
|
|
|
double diff_norm = cvtest::norm(actual_descriptors, descriptors, NORM_L2 + NORM_RELATIVE);
|
|
const double eps = 2.0e-3;
|
|
if (diff_norm > eps)
|
|
{
|
|
ts->printf(cvtest::TS::SUMMARY, "Norm of the difference: %lf\n", diff_norm);
|
|
ts->printf(cvtest::TS::SUMMARY, "Found descriptors are not equal (see compute function)\n");
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
|
ts->printf(cvtest::TS::LOG, "Channels: %d\n", img.channels());
|
|
ts->set_gtest_status();
|
|
failed = true;
|
|
}
|
|
}
|
|
|
|
void HOGDescriptorTester::computeGradient(InputArray _img, InputOutputArray _grad, InputOutputArray _qangle,
|
|
Size paddingTL, Size paddingBR) const
|
|
{
|
|
Mat img = _img.getMat();
|
|
CV_Assert( img.type() == CV_8U || img.type() == CV_8UC3 );
|
|
|
|
Size gradsize(img.cols + paddingTL.width + paddingBR.width,
|
|
img.rows + paddingTL.height + paddingBR.height);
|
|
_grad.create(gradsize, CV_32FC2); // <magnitude*(1-alpha), magnitude*alpha>
|
|
_qangle.create(gradsize, CV_8UC2); // [0..nbins-1] - quantized gradient orientation
|
|
Mat grad = _grad.getMat();
|
|
Mat qangle = _qangle.getMat();
|
|
|
|
Size wholeSize;
|
|
Point roiofs;
|
|
img.locateROI(wholeSize, roiofs);
|
|
|
|
int i, x, y;
|
|
int cn = img.channels();
|
|
|
|
Mat_<float> _lut(1, 256);
|
|
const float* lut = &_lut(0,0);
|
|
|
|
if( gammaCorrection )
|
|
for( i = 0; i < 256; i++ )
|
|
_lut(0,i) = std::sqrt((float)i);
|
|
else
|
|
for( i = 0; i < 256; i++ )
|
|
_lut(0,i) = (float)i;
|
|
|
|
AutoBuffer<int> mapbuf(gradsize.width + gradsize.height + 4);
|
|
int* xmap = mapbuf.data() + 1;
|
|
int* ymap = xmap + gradsize.width + 2;
|
|
|
|
const int borderType = (int)BORDER_REFLECT_101;
|
|
|
|
for( x = -1; x < gradsize.width + 1; x++ )
|
|
xmap[x] = borderInterpolate(x - paddingTL.width + roiofs.x,
|
|
wholeSize.width, borderType) - roiofs.x;
|
|
for( y = -1; y < gradsize.height + 1; y++ )
|
|
ymap[y] = borderInterpolate(y - paddingTL.height + roiofs.y,
|
|
wholeSize.height, borderType) - roiofs.y;
|
|
|
|
// x- & y- derivatives for the whole row
|
|
int width = gradsize.width;
|
|
AutoBuffer<float> _dbuf(width*4);
|
|
float* dbuf = _dbuf.data();
|
|
Mat Dx(1, width, CV_32F, dbuf);
|
|
Mat Dy(1, width, CV_32F, dbuf + width);
|
|
Mat Mag(1, width, CV_32F, dbuf + width*2);
|
|
Mat Angle(1, width, CV_32F, dbuf + width*3);
|
|
|
|
int _nbins = nbins;
|
|
float angleScale = (float)(_nbins/CV_PI);
|
|
for( y = 0; y < gradsize.height; y++ )
|
|
{
|
|
const uchar* imgPtr = img.ptr(ymap[y]);
|
|
const uchar* prevPtr = img.ptr(ymap[y-1]);
|
|
const uchar* nextPtr = img.ptr(ymap[y+1]);
|
|
float* gradPtr = (float*)grad.ptr(y);
|
|
uchar* qanglePtr = (uchar*)qangle.ptr(y);
|
|
|
|
if( cn == 1 )
|
|
{
|
|
for( x = 0; x < width; x++ )
|
|
{
|
|
int x1 = xmap[x];
|
|
dbuf[x] = (float)(lut[imgPtr[xmap[x+1]]] - lut[imgPtr[xmap[x-1]]]);
|
|
dbuf[width + x] = (float)(lut[nextPtr[x1]] - lut[prevPtr[x1]]);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
for( x = 0; x < width; x++ )
|
|
{
|
|
int x1 = xmap[x]*3;
|
|
float dx0, dy0, dx, dy, mag0, mag;
|
|
const uchar* p2 = imgPtr + xmap[x+1]*3;
|
|
const uchar* p0 = imgPtr + xmap[x-1]*3;
|
|
|
|
dx0 = lut[p2[2]] - lut[p0[2]];
|
|
dy0 = lut[nextPtr[x1+2]] - lut[prevPtr[x1+2]];
|
|
mag0 = dx0*dx0 + dy0*dy0;
|
|
|
|
dx = lut[p2[1]] - lut[p0[1]];
|
|
dy = lut[nextPtr[x1+1]] - lut[prevPtr[x1+1]];
|
|
mag = dx*dx + dy*dy;
|
|
|
|
if( mag0 < mag )
|
|
{
|
|
dx0 = dx;
|
|
dy0 = dy;
|
|
mag0 = mag;
|
|
}
|
|
|
|
dx = lut[p2[0]] - lut[p0[0]];
|
|
dy = lut[nextPtr[x1]] - lut[prevPtr[x1]];
|
|
mag = dx*dx + dy*dy;
|
|
|
|
if( mag0 < mag )
|
|
{
|
|
dx0 = dx;
|
|
dy0 = dy;
|
|
mag0 = mag;
|
|
}
|
|
|
|
dbuf[x] = dx0;
|
|
dbuf[x+width] = dy0;
|
|
}
|
|
}
|
|
|
|
cartToPolar( Dx, Dy, Mag, Angle, false );
|
|
for( x = 0; x < width; x++ )
|
|
{
|
|
float mag = dbuf[x+width*2], angle = dbuf[x+width*3]*angleScale - 0.5f;
|
|
int hidx = cvFloor(angle);
|
|
angle -= hidx;
|
|
gradPtr[x*2] = mag*(1.f - angle);
|
|
gradPtr[x*2+1] = mag*angle;
|
|
if( hidx < 0 )
|
|
hidx += _nbins;
|
|
else if( hidx >= _nbins )
|
|
hidx -= _nbins;
|
|
CV_Assert( (unsigned)hidx < (unsigned)_nbins );
|
|
|
|
qanglePtr[x*2] = (uchar)hidx;
|
|
hidx++;
|
|
hidx &= hidx < _nbins ? -1 : 0;
|
|
qanglePtr[x*2+1] = (uchar)hidx;
|
|
}
|
|
}
|
|
|
|
// validation
|
|
Mat actual_mats[2], reference_mats[2] = { grad, qangle };
|
|
const char* args[] = { "Gradient's", "Qangles's" };
|
|
actual_hog->computeGradient(img, actual_mats[0], actual_mats[1], paddingTL, paddingBR);
|
|
|
|
const double eps = 8.0e-3;
|
|
for (i = 0; i < 2; ++i)
|
|
{
|
|
double diff_norm = cvtest::norm(actual_mats[i], reference_mats[i], NORM_L2 + NORM_RELATIVE);
|
|
if (diff_norm > eps)
|
|
{
|
|
ts->printf(cvtest::TS::LOG, "%s matrices are not equal\n"
|
|
"Norm of the difference is %lf\n", args[i], diff_norm);
|
|
ts->printf(cvtest::TS::LOG, "Channels: %d\n", img.channels());
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
|
ts->set_gtest_status();
|
|
failed = true;
|
|
}
|
|
}
|
|
}
|
|
|
|
TEST(Objdetect_HOGDetector_Strict, accuracy)
|
|
{
|
|
cvtest::TS* ts = cvtest::TS::ptr();
|
|
RNG& rng = ts->get_rng();
|
|
|
|
HOGDescriptor actual_hog;
|
|
actual_hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());
|
|
HOGDescriptorTester reference_hog(actual_hog);
|
|
|
|
const unsigned int test_case_count = 5;
|
|
for (unsigned int i = 0; i < test_case_count && !reference_hog.is_failed(); ++i)
|
|
{
|
|
// creating a matrix
|
|
Size ssize(rng.uniform(1, 10) * actual_hog.winSize.width,
|
|
rng.uniform(1, 10) * actual_hog.winSize.height);
|
|
int type = rng.uniform(0, 1) > 0 ? CV_8UC1 : CV_8UC3;
|
|
Mat image(ssize, type);
|
|
rng.fill(image, RNG::UNIFORM, 0, 256, true);
|
|
|
|
// checking detect
|
|
std::vector<Point> hits;
|
|
std::vector<double> weights;
|
|
reference_hog.detect(image, hits, weights);
|
|
|
|
// checking compute
|
|
std::vector<float> descriptors;
|
|
reference_hog.compute(image, descriptors);
|
|
}
|
|
}
|
|
|
|
TEST(Objdetect_CascadeDetector, small_img)
|
|
{
|
|
String root = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/cascades/";
|
|
String cascades[] =
|
|
{
|
|
root + "haarcascade_frontalface_alt.xml",
|
|
root + "lbpcascade_frontalface.xml",
|
|
String()
|
|
};
|
|
|
|
vector<Rect> objects;
|
|
RNG rng((uint64)-1);
|
|
|
|
for( int i = 0; !cascades[i].empty(); i++ )
|
|
{
|
|
printf("%d. %s\n", i, cascades[i].c_str());
|
|
CascadeClassifier cascade(cascades[i]);
|
|
for( int j = 0; j < 100; j++ )
|
|
{
|
|
int width = rng.uniform(1, 100);
|
|
int height = rng.uniform(1, 100);
|
|
Mat img(height, width, CV_8U);
|
|
randu(img, 0, 256);
|
|
cascade.detectMultiScale(img, objects);
|
|
}
|
|
}
|
|
}
|
|
|
|
}} // namespace
|