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