mirror of
https://github.com/opencv/opencv.git
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2a6fb2867e
Made all STL usages explicit to be able automatically find all usages of particular class or function.
307 lines
11 KiB
C++
307 lines
11 KiB
C++
#include "opencv2/core/core.hpp"
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#include "opencv2/core/internal.hpp"
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#include "haarfeatures.h"
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#include "cascadeclassifier.h"
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using namespace std;
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CvHaarFeatureParams::CvHaarFeatureParams() : mode(BASIC)
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{
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name = HFP_NAME;
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}
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CvHaarFeatureParams::CvHaarFeatureParams( int _mode ) : mode( _mode )
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{
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name = HFP_NAME;
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}
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void CvHaarFeatureParams::init( const CvFeatureParams& fp )
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{
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CvFeatureParams::init( fp );
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mode = ((const CvHaarFeatureParams&)fp).mode;
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}
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void CvHaarFeatureParams::write( FileStorage &fs ) const
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{
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CvFeatureParams::write( fs );
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string modeStr = mode == BASIC ? CC_MODE_BASIC :
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mode == CORE ? CC_MODE_CORE :
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mode == ALL ? CC_MODE_ALL : string();
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CV_Assert( !modeStr.empty() );
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fs << CC_MODE << modeStr;
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}
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bool CvHaarFeatureParams::read( const FileNode &node )
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{
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if( !CvFeatureParams::read( node ) )
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return false;
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FileNode rnode = node[CC_MODE];
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if( !rnode.isString() )
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return false;
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string modeStr;
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rnode >> modeStr;
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mode = !modeStr.compare( CC_MODE_BASIC ) ? BASIC :
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!modeStr.compare( CC_MODE_CORE ) ? CORE :
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!modeStr.compare( CC_MODE_ALL ) ? ALL : -1;
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return (mode >= 0);
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}
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void CvHaarFeatureParams::printDefaults() const
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{
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CvFeatureParams::printDefaults();
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cout << " [-mode <" CC_MODE_BASIC << "(default) | "
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<< CC_MODE_CORE <<" | " << CC_MODE_ALL << endl;
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}
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void CvHaarFeatureParams::printAttrs() const
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{
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CvFeatureParams::printAttrs();
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string mode_str = mode == BASIC ? CC_MODE_BASIC :
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mode == CORE ? CC_MODE_CORE :
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mode == ALL ? CC_MODE_ALL : 0;
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cout << "mode: " << mode_str << endl;
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}
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bool CvHaarFeatureParams::scanAttr( const string prmName, const string val)
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{
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if ( !CvFeatureParams::scanAttr( prmName, val ) )
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{
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if( !prmName.compare("-mode") )
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{
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mode = !val.compare( CC_MODE_CORE ) ? CORE :
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!val.compare( CC_MODE_ALL ) ? ALL :
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!val.compare( CC_MODE_BASIC ) ? BASIC : -1;
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if (mode == -1)
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return false;
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}
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return false;
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}
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return true;
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}
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//--------------------- HaarFeatureEvaluator ----------------
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void CvHaarEvaluator::init(const CvFeatureParams *_featureParams,
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int _maxSampleCount, Size _winSize )
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{
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CV_Assert(_maxSampleCount > 0);
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int cols = (_winSize.width + 1) * (_winSize.height + 1);
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sum.create((int)_maxSampleCount, cols, CV_32SC1);
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tilted.create((int)_maxSampleCount, cols, CV_32SC1);
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normfactor.create(1, (int)_maxSampleCount, CV_32FC1);
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CvFeatureEvaluator::init( _featureParams, _maxSampleCount, _winSize );
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}
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void CvHaarEvaluator::setImage(const Mat& img, uchar clsLabel, int idx)
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{
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CV_DbgAssert( !sum.empty() && !tilted.empty() && !normfactor.empty() );
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CvFeatureEvaluator::setImage( img, clsLabel, idx);
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Mat innSum(winSize.height + 1, winSize.width + 1, sum.type(), sum.ptr<int>((int)idx));
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Mat innTilted(winSize.height + 1, winSize.width + 1, tilted.type(), tilted.ptr<int>((int)idx));
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Mat innSqSum;
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integral(img, innSum, innSqSum, innTilted);
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normfactor.ptr<float>(0)[idx] = calcNormFactor( innSum, innSqSum );
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}
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void CvHaarEvaluator::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
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{
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_writeFeatures( features, fs, featureMap );
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}
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void CvHaarEvaluator::writeFeature(FileStorage &fs, int fi) const
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{
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CV_DbgAssert( fi < (int)features.size() );
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features[fi].write(fs);
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}
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void CvHaarEvaluator::generateFeatures()
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{
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int mode = ((const CvHaarFeatureParams*)((CvFeatureParams*)featureParams))->mode;
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int offset = winSize.width + 1;
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for( int x = 0; x < winSize.width; x++ )
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{
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for( int y = 0; y < winSize.height; y++ )
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{
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for( int dx = 1; dx <= winSize.width; dx++ )
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{
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for( int dy = 1; dy <= winSize.height; dy++ )
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{
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// haar_x2
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if ( (x+dx*2 <= winSize.width) && (y+dy <= winSize.height) )
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{
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features.push_back( Feature( offset, false,
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x, y, dx*2, dy, -1,
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x+dx, y, dx , dy, +2 ) );
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}
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// haar_y2
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if ( (x+dx <= winSize.width) && (y+dy*2 <= winSize.height) )
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{
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features.push_back( Feature( offset, false,
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x, y, dx, dy*2, -1,
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x, y+dy, dx, dy, +2 ) );
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}
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// haar_x3
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if ( (x+dx*3 <= winSize.width) && (y+dy <= winSize.height) )
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{
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features.push_back( Feature( offset, false,
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x, y, dx*3, dy, -1,
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x+dx, y, dx , dy, +3 ) );
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}
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// haar_y3
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if ( (x+dx <= winSize.width) && (y+dy*3 <= winSize.height) )
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{
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features.push_back( Feature( offset, false,
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x, y, dx, dy*3, -1,
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x, y+dy, dx, dy, +3 ) );
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}
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if( mode != CvHaarFeatureParams::BASIC )
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{
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// haar_x4
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if ( (x+dx*4 <= winSize.width) && (y+dy <= winSize.height) )
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{
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features.push_back( Feature( offset, false,
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x, y, dx*4, dy, -1,
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x+dx, y, dx*2, dy, +2 ) );
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}
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// haar_y4
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if ( (x+dx <= winSize.width ) && (y+dy*4 <= winSize.height) )
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{
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features.push_back( Feature( offset, false,
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x, y, dx, dy*4, -1,
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x, y+dy, dx, dy*2, +2 ) );
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}
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}
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// x2_y2
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if ( (x+dx*2 <= winSize.width) && (y+dy*2 <= winSize.height) )
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{
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features.push_back( Feature( offset, false,
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x, y, dx*2, dy*2, -1,
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x, y, dx, dy, +2,
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x+dx, y+dy, dx, dy, +2 ) );
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}
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if (mode != CvHaarFeatureParams::BASIC)
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{
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if ( (x+dx*3 <= winSize.width) && (y+dy*3 <= winSize.height) )
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{
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features.push_back( Feature( offset, false,
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x , y , dx*3, dy*3, -1,
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x+dx, y+dy, dx , dy , +9) );
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}
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}
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if (mode == CvHaarFeatureParams::ALL)
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{
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// tilted haar_x2
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if ( (x+2*dx <= winSize.width) && (y+2*dx+dy <= winSize.height) && (x-dy>= 0) )
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{
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features.push_back( Feature( offset, true,
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x, y, dx*2, dy, -1,
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x, y, dx, dy, +2 ) );
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}
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// tilted haar_y2
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if ( (x+dx <= winSize.width) && (y+dx+2*dy <= winSize.height) && (x-2*dy>= 0) )
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{
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features.push_back( Feature( offset, true,
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x, y, dx, 2*dy, -1,
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x, y, dx, dy, +2 ) );
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}
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// tilted haar_x3
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if ( (x+3*dx <= winSize.width) && (y+3*dx+dy <= winSize.height) && (x-dy>= 0) )
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{
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features.push_back( Feature( offset, true,
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x, y, dx*3, dy, -1,
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x+dx, y+dx, dx, dy, +3 ) );
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}
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// tilted haar_y3
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if ( (x+dx <= winSize.width) && (y+dx+3*dy <= winSize.height) && (x-3*dy>= 0) )
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{
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features.push_back( Feature( offset, true,
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x, y, dx, 3*dy, -1,
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x-dy, y+dy, dx, dy, +3 ) );
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}
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// tilted haar_x4
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if ( (x+4*dx <= winSize.width) && (y+4*dx+dy <= winSize.height) && (x-dy>= 0) )
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{
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features.push_back( Feature( offset, true,
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x, y, dx*4, dy, -1,
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x+dx, y+dx, dx*2, dy, +2 ) );
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}
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// tilted haar_y4
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if ( (x+dx <= winSize.width) && (y+dx+4*dy <= winSize.height) && (x-4*dy>= 0) )
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{
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features.push_back( Feature( offset, true,
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x, y, dx, 4*dy, -1,
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x-dy, y+dy, dx, 2*dy, +2 ) );
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}
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}
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}
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}
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}
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}
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numFeatures = (int)features.size();
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}
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CvHaarEvaluator::Feature::Feature()
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{
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tilted = false;
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rect[0].r = rect[1].r = rect[2].r = Rect(0,0,0,0);
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rect[0].weight = rect[1].weight = rect[2].weight = 0;
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}
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CvHaarEvaluator::Feature::Feature( int offset, bool _tilted,
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int x0, int y0, int w0, int h0, float wt0,
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int x1, int y1, int w1, int h1, float wt1,
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int x2, int y2, int w2, int h2, float wt2 )
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{
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tilted = _tilted;
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rect[0].r.x = x0;
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rect[0].r.y = y0;
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rect[0].r.width = w0;
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rect[0].r.height = h0;
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rect[0].weight = wt0;
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rect[1].r.x = x1;
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rect[1].r.y = y1;
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rect[1].r.width = w1;
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rect[1].r.height = h1;
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rect[1].weight = wt1;
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rect[2].r.x = x2;
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rect[2].r.y = y2;
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rect[2].r.width = w2;
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rect[2].r.height = h2;
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rect[2].weight = wt2;
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if( !tilted )
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{
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for( int j = 0; j < CV_HAAR_FEATURE_MAX; j++ )
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{
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if( rect[j].weight == 0.0F )
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break;
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CV_SUM_OFFSETS( fastRect[j].p0, fastRect[j].p1, fastRect[j].p2, fastRect[j].p3, rect[j].r, offset )
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}
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}
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else
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{
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for( int j = 0; j < CV_HAAR_FEATURE_MAX; j++ )
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{
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if( rect[j].weight == 0.0F )
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break;
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CV_TILTED_OFFSETS( fastRect[j].p0, fastRect[j].p1, fastRect[j].p2, fastRect[j].p3, rect[j].r, offset )
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}
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}
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}
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void CvHaarEvaluator::Feature::write( FileStorage &fs ) const
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{
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fs << CC_RECTS << "[";
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for( int ri = 0; ri < CV_HAAR_FEATURE_MAX && rect[ri].r.width != 0; ++ri )
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{
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fs << "[:" << rect[ri].r.x << rect[ri].r.y <<
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rect[ri].r.width << rect[ri].r.height << rect[ri].weight << "]";
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}
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fs << "]" << CC_TILTED << tilted;
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}
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