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#include "opencv2/core.hpp"
#include "traincascade_features.h"
#include "cascadeclassifier.h"
using namespace std;
using namespace cv;
float calcNormFactor( const Mat& sum, const Mat& sqSum )
{
CV_DbgAssert( sum.cols > 3 && sqSum.rows > 3 );
Rect normrect( 1, 1, sum.cols - 3, sum.rows - 3 );
size_t p0, p1, p2, p3;
CV_SUM_OFFSETS( p0, p1, p2, p3, normrect, sum.step1() )
double area = normrect.width * normrect.height;
const int *sp = sum.ptr<int>();
int valSum = sp[p0] - sp[p1] - sp[p2] + sp[p3];
const double *sqp = sqSum.ptr<double>();
double valSqSum = sqp[p0] - sqp[p1] - sqp[p2] + sqp[p3];
return (float) sqrt( (double) (area * valSqSum - (double)valSum * valSum) );
}
CvParams::CvParams() : name( "params" ) {}
void CvParams::printDefaults() const
{ cout << "--" << name << "--" << endl; }
void CvParams::printAttrs() const {}
bool CvParams::scanAttr( const string, const string ) { return false; }
//---------------------------- FeatureParams --------------------------------------
CvFeatureParams::CvFeatureParams() : maxCatCount( 0 ), featSize( 1 )
{
name = CC_FEATURE_PARAMS;
}
void CvFeatureParams::init( const CvFeatureParams& fp )
{
maxCatCount = fp.maxCatCount;
featSize = fp.featSize;
}
void CvFeatureParams::write( FileStorage &fs ) const
{
fs << CC_MAX_CAT_COUNT << maxCatCount;
fs << CC_FEATURE_SIZE << featSize;
}
bool CvFeatureParams::read( const FileNode &node )
{
if ( node.empty() )
return false;
maxCatCount = node[CC_MAX_CAT_COUNT];
featSize = node[CC_FEATURE_SIZE];
return ( maxCatCount >= 0 && featSize >= 1 );
}
Ptr<CvFeatureParams> CvFeatureParams::create( int featureType )
{
return featureType == HAAR ? Ptr<CvFeatureParams>(new CvHaarFeatureParams) :
featureType == LBP ? Ptr<CvFeatureParams>(new CvLBPFeatureParams) :
featureType == HOG ? Ptr<CvFeatureParams>(new CvHOGFeatureParams) :
Ptr<CvFeatureParams>();
}
//------------------------------------- FeatureEvaluator ---------------------------------------
void CvFeatureEvaluator::init(const CvFeatureParams *_featureParams,
int _maxSampleCount, Size _winSize )
{
CV_Assert(_maxSampleCount > 0);
featureParams = (CvFeatureParams *)_featureParams;
winSize = _winSize;
numFeatures = 0;
cls.create( (int)_maxSampleCount, 1, CV_32FC1 );
generateFeatures();
}
void CvFeatureEvaluator::setImage(const Mat &img, uchar clsLabel, int idx)
{
CV_Assert(img.cols == winSize.width);
CV_Assert(img.rows == winSize.height);
CV_Assert(idx < cls.rows);
cls.ptr<float>(idx)[0] = clsLabel;
}
Ptr<CvFeatureEvaluator> CvFeatureEvaluator::create(int type)
{
return type == CvFeatureParams::HAAR ? Ptr<CvFeatureEvaluator>(new CvHaarEvaluator) :
type == CvFeatureParams::LBP ? Ptr<CvFeatureEvaluator>(new CvLBPEvaluator) :
type == CvFeatureParams::HOG ? Ptr<CvFeatureEvaluator>(new CvHOGEvaluator) :
Ptr<CvFeatureEvaluator>();
}
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