opencv/apps/traincascade/HOGfeatures.h

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#ifndef _OPENCV_HOGFEATURES_H_
#define _OPENCV_HOGFEATURES_H_
#include "traincascade_features.h"
//#define TEST_INTHIST_BUILD
//#define TEST_FEAT_CALC
#define N_BINS 9
#define N_CELLS 4
#define HOGF_NAME "HOGFeatureParams"
struct CvHOGFeatureParams : public CvFeatureParams
{
CvHOGFeatureParams();
};
class CvHOGEvaluator : public CvFeatureEvaluator
{
public:
virtual ~CvHOGEvaluator() {}
virtual void init(const CvFeatureParams *_featureParams,
int _maxSampleCount, Size _winSize );
virtual void setImage(const Mat& img, uchar clsLabel, int idx);
virtual float operator()(int varIdx, int sampleIdx) const;
virtual void writeFeatures( FileStorage &fs, const Mat& featureMap ) const;
protected:
virtual void generateFeatures();
virtual void integralHistogram(const Mat &img, vector<Mat> &histogram, Mat &norm, int nbins) const;
class Feature
{
public:
Feature();
Feature( int offset, int x, int y, int cellW, int cellH );
float calc( const vector<Mat> &_hists, const Mat &_normSum, size_t y, int featComponent ) const;
void write( FileStorage &fs ) const;
void write( FileStorage &fs, int varIdx ) const;
Rect rect[N_CELLS]; //cells
struct
{
int p0, p1, p2, p3;
} fastRect[N_CELLS];
};
vector<Feature> features;
Mat normSum; //for nomalization calculation (L1 or L2)
vector<Mat> hist;
};
inline float CvHOGEvaluator::operator()(int varIdx, int sampleIdx) const
{
int featureIdx = varIdx / (N_BINS * N_CELLS);
int componentIdx = varIdx % (N_BINS * N_CELLS);
//return features[featureIdx].calc( hist, sampleIdx, componentIdx);
return features[featureIdx].calc( hist, normSum, sampleIdx, componentIdx);
}
inline float CvHOGEvaluator::Feature::calc( const vector<Mat>& _hists, const Mat& _normSum, size_t y, int featComponent ) const
{
float normFactor;
float res;
int binIdx = featComponent % N_BINS;
int cellIdx = featComponent / N_BINS;
const float *hist = _hists[binIdx].ptr<float>((int)y);
res = hist[fastRect[cellIdx].p0] - hist[fastRect[cellIdx].p1] - hist[fastRect[cellIdx].p2] + hist[fastRect[cellIdx].p3];
const float *normSum = _normSum.ptr<float>((int)y);
normFactor = (float)(normSum[fastRect[0].p0] - normSum[fastRect[1].p1] - normSum[fastRect[2].p2] + normSum[fastRect[3].p3]);
res = (res > 0.001f) ? ( res / (normFactor + 0.001f) ) : 0.f; //for cutting negative values, which apper due to floating precision
return res;
}
#endif // _OPENCV_HOGFEATURES_H_