bef34093aa
Made all STL usages explicit to be able automatically find all usages of
particular class or function.
(cherry picked from commit 2a6fb2867e)
(only cherry picked "apps/trancascade")
90 lines
2.9 KiB
C++
90 lines
2.9 KiB
C++
#ifndef _OPENCV_HAARFEATURES_H_
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#define _OPENCV_HAARFEATURES_H_
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#include "traincascade_features.h"
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#define CV_HAAR_FEATURE_MAX 3
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#define HFP_NAME "haarFeatureParams"
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class CvHaarFeatureParams : public CvFeatureParams
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{
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public:
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enum { BASIC = 0, CORE = 1, ALL = 2 };
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/* 0 - BASIC = Viola
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* 1 - CORE = All upright
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* 2 - ALL = All features */
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CvHaarFeatureParams();
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CvHaarFeatureParams( int _mode );
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virtual void init( const CvFeatureParams& fp );
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virtual void write( FileStorage &fs ) const;
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virtual bool read( const FileNode &node );
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virtual void printDefaults() const;
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virtual void printAttrs() const;
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virtual bool scanAttr( const std::string prm, const std::string val);
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int mode;
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};
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class CvHaarEvaluator : public CvFeatureEvaluator
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{
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public:
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virtual void init(const CvFeatureParams *_featureParams,
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int _maxSampleCount, Size _winSize );
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virtual void setImage(const Mat& img, uchar clsLabel, int idx);
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virtual float operator()(int featureIdx, int sampleIdx) const;
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virtual void writeFeatures( FileStorage &fs, const Mat& featureMap ) const;
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void writeFeature( FileStorage &fs, int fi ) const; // for old file fornat
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protected:
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virtual void generateFeatures();
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class Feature
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{
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public:
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Feature();
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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 = 0, int y2 = 0, int w2 = 0, int h2 = 0, float wt2 = 0.0F );
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float calc( const Mat &sum, const Mat &tilted, size_t y) const;
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void write( FileStorage &fs ) const;
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bool tilted;
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struct
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{
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Rect r;
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float weight;
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} rect[CV_HAAR_FEATURE_MAX];
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struct
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{
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int p0, p1, p2, p3;
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} fastRect[CV_HAAR_FEATURE_MAX];
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};
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std::vector<Feature> features;
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Mat sum; /* sum images (each row represents image) */
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Mat tilted; /* tilted sum images (each row represents image) */
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Mat normfactor; /* normalization factor */
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};
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inline float CvHaarEvaluator::operator()(int featureIdx, int sampleIdx) const
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{
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float nf = normfactor.at<float>(0, sampleIdx);
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return !nf ? 0.0f : (features[featureIdx].calc( sum, tilted, sampleIdx)/nf);
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}
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inline float CvHaarEvaluator::Feature::calc( const Mat &_sum, const Mat &_tilted, size_t y) const
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{
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const int* img = tilted ? _tilted.ptr<int>((int)y) : _sum.ptr<int>((int)y);
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float ret = rect[0].weight * (img[fastRect[0].p0] - img[fastRect[0].p1] - img[fastRect[0].p2] + img[fastRect[0].p3] ) +
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rect[1].weight * (img[fastRect[1].p0] - img[fastRect[1].p1] - img[fastRect[1].p2] + img[fastRect[1].p3] );
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if( rect[2].weight != 0.0f )
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ret += rect[2].weight * (img[fastRect[2].p0] - img[fastRect[2].p1] - img[fastRect[2].p2] + img[fastRect[2].p3] );
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return ret;
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}
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#endif
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