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haarfeatures.cpp
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#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>((int)idx));
Mat innTilted(winSize.height + 1, winSize.width + 1, tilted.type(), tilted.ptr<int>((int)idx));
Mat innSqSum;
integral(img, innSum, innSqSum, innTilted);
normfactor.ptr<float>(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;
}