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Open Source Computer Vision Library
https://opencv.org/
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306 lines
11 KiB
306 lines
11 KiB
#include "opencv2/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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