darknet_2_5_python/include/opencv2/tracking/feature.hpp

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#ifndef __OPENCV_FEATURE_HPP__
#define __OPENCV_FEATURE_HPP__

#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>
#include <string>
#include <time.h>

/*
 * TODO This implementation is based on apps/traincascade/
 * TODO Changed CvHaarEvaluator based on ADABOOSTING implementation (Grabner et al.)
 */

namespace cv
{

//! @addtogroup tracking
//! @{

#define FEATURES "features"

#define CC_FEATURES       FEATURES
#define CC_FEATURE_PARAMS "featureParams"
#define CC_MAX_CAT_COUNT  "maxCatCount"
#define CC_FEATURE_SIZE   "featSize"
#define CC_NUM_FEATURES   "numFeat"
#define CC_ISINTEGRAL "isIntegral"
#define CC_RECTS       "rects"
#define CC_TILTED      "tilted"
#define CC_RECT "rect"

#define LBPF_NAME "lbpFeatureParams"
#define HOGF_NAME "HOGFeatureParams"
#define HFP_NAME "haarFeatureParams"

#define CV_HAAR_FEATURE_MAX 3
#define N_BINS 9
#define N_CELLS 4

#define CV_SUM_OFFSETS( p0, p1, p2, p3, rect, step )                      \
    /* (x, y) */                                                          \
    (p0) = (rect).x + (step) * (rect).y;                                  \
    /* (x + w, y) */                                                      \
    (p1) = (rect).x + (rect).width + (step) * (rect).y;                   \
    /* (x + w, y) */                                                      \
    (p2) = (rect).x + (step) * ((rect).y + (rect).height);                \
    /* (x + w, y + h) */                                                  \
    (p3) = (rect).x + (rect).width + (step) * ((rect).y + (rect).height);

#define CV_TILTED_OFFSETS( p0, p1, p2, p3, rect, step )                   \
    /* (x, y) */                                                          \
    (p0) = (rect).x + (step) * (rect).y;                                  \
    /* (x - h, y + h) */                                                  \
    (p1) = (rect).x - (rect).height + (step) * ((rect).y + (rect).height);\
    /* (x + w, y + w) */                                                  \
    (p2) = (rect).x + (rect).width + (step) * ((rect).y + (rect).width);  \
    /* (x + w - h, y + w + h) */                                          \
    (p3) = (rect).x + (rect).width - (rect).height                        \
           + (step) * ((rect).y + (rect).width + (rect).height);

float calcNormFactor( const Mat& sum, const Mat& sqSum );

template<class Feature>
void _writeFeatures( const std::vector<Feature> features, FileStorage &fs, const Mat& featureMap )
{
  fs << FEATURES << "[";
  const Mat_<int>& featureMap_ = (const Mat_<int>&) featureMap;
  for ( int fi = 0; fi < featureMap.cols; fi++ )
    if( featureMap_( 0, fi ) >= 0 )
    {
      fs << "{";
      features[fi].write( fs );
      fs << "}";
    }
  fs << "]";
}

class CvParams
{
 public:
  CvParams();
  virtual ~CvParams()
  {
  }
  // from|to file
  virtual void write( FileStorage &fs ) const = 0;
  virtual bool read( const FileNode &node ) = 0;
  // from|to screen
  virtual void printDefaults() const;
  virtual void printAttrs() const;
  virtual bool scanAttr( const std::string prmName, const std::string val );
  std::string name;
};

class CvFeatureParams : public CvParams
{
 public:
  enum
  {
    HAAR = 0,
    LBP = 1,
    HOG = 2
  };
  CvFeatureParams();
  virtual void init( const CvFeatureParams& fp );
  virtual void write( FileStorage &fs ) const CV_OVERRIDE;
  virtual bool read( const FileNode &node ) CV_OVERRIDE;
  static Ptr<CvFeatureParams> create( int featureType );
  int maxCatCount;  // 0 in case of numerical features
  int featSize;  // 1 in case of simple features (HAAR, LBP) and N_BINS(9)*N_CELLS(4) in case of Dalal's HOG features
  int numFeatures;
};

class CvFeatureEvaluator
{
 public:
  virtual ~CvFeatureEvaluator()
  {
  }
  virtual void init( const CvFeatureParams *_featureParams, int _maxSampleCount, Size _winSize );
  virtual void setImage( const Mat& img, uchar clsLabel, int idx );
  virtual void writeFeatures( FileStorage &fs, const Mat& featureMap ) const = 0;
  virtual float operator()( int featureIdx, int sampleIdx ) = 0;
  static Ptr<CvFeatureEvaluator> create( int type );

  int getNumFeatures() const
  {
    return numFeatures;
  }
  int getMaxCatCount() const
  {
    return featureParams->maxCatCount;
  }
  int getFeatureSize() const
  {
    return featureParams->featSize;
  }
  const Mat& getCls() const
  {
    return cls;
  }
  float getCls( int si ) const
  {
    return cls.at<float>( si, 0 );
  }
 protected:
  virtual void generateFeatures() = 0;

  int npos, nneg;
  int numFeatures;
  Size winSize;
  CvFeatureParams *featureParams;
  Mat cls;
};

class CvHaarFeatureParams : public CvFeatureParams
{
 public:

  CvHaarFeatureParams();

  virtual void init( const CvFeatureParams& fp ) CV_OVERRIDE;
  virtual void write( FileStorage &fs ) const CV_OVERRIDE;
  virtual bool read( const FileNode &node ) CV_OVERRIDE;

  virtual void printDefaults() const CV_OVERRIDE;
  virtual void printAttrs() const CV_OVERRIDE;
  virtual bool scanAttr( const std::string prm, const std::string val ) CV_OVERRIDE;

  bool isIntegral;
};

class CvHaarEvaluator : public CvFeatureEvaluator
{
 public:

  class FeatureHaar
  {

   public:

    FeatureHaar( Size patchSize );
    bool eval( const Mat& image, Rect ROI, float* result ) const;
    int getNumAreas();
    const std::vector<float>& getWeights() const;
    const std::vector<Rect>& getAreas() const;
    void write( FileStorage ) const
    {
    }
    ;
    float getInitMean() const;
    float getInitSigma() const;

   private:
    int m_type;
    int m_numAreas;
    std::vector<float> m_weights;
    float m_initMean;
    float m_initSigma;
    void generateRandomFeature( Size imageSize );
    float getSum( const Mat& image, Rect imgROI ) const;
    std::vector<Rect> m_areas;  // areas within the patch over which to compute the feature
    cv::Size m_initSize;  // size of the patch used during training
    cv::Size m_curSize;  // size of the patches currently under investigation
    float m_scaleFactorHeight;  // scaling factor in vertical direction
    float m_scaleFactorWidth;  // scaling factor in horizontal direction
    std::vector<Rect> m_scaleAreas;  // areas after scaling
    std::vector<float> m_scaleWeights;  // weights after scaling

  };

  virtual void init( const CvFeatureParams *_featureParams, int _maxSampleCount, Size _winSize ) CV_OVERRIDE;
  virtual void setImage( const Mat& img, uchar clsLabel = 0, int idx = 1 ) CV_OVERRIDE;
  virtual float operator()( int featureIdx, int sampleIdx ) CV_OVERRIDE;
  virtual void writeFeatures( FileStorage &fs, const Mat& featureMap ) const CV_OVERRIDE;
  void writeFeature( FileStorage &fs ) const;  // for old file format
  const std::vector<CvHaarEvaluator::FeatureHaar>& getFeatures() const;
  inline CvHaarEvaluator::FeatureHaar& getFeatures( int idx )
  {
    return features[idx];
  }
  void setWinSize( Size patchSize );
  Size setWinSize() const;
  virtual void generateFeatures() CV_OVERRIDE;

  /**
   * TODO new method
   * \brief Overload the original generateFeatures in order to limit the number of the features
   * @param numFeatures Number of the features
   */

  virtual void generateFeatures( int numFeatures );

 protected:
  bool isIntegral;

  /* TODO Added from MIL implementation */
  Mat _ii_img;
  void compute_integral( const cv::Mat & img, std::vector<cv::Mat_<float> > & ii_imgs )
  {
    Mat ii_img;
    integral( img, ii_img, CV_32F );
    split( ii_img, ii_imgs );
  }

  std::vector<FeatureHaar> features;
  Mat sum; /* sum images (each row represents image) */
};

struct CvHOGFeatureParams : public CvFeatureParams
{
  CvHOGFeatureParams();
};

class CvHOGEvaluator : public CvFeatureEvaluator
{
 public:
  virtual ~CvHOGEvaluator()
  {
  }
  virtual void init( const CvFeatureParams *_featureParams, int _maxSampleCount, Size _winSize ) CV_OVERRIDE;
  virtual void setImage( const Mat& img, uchar clsLabel, int idx ) CV_OVERRIDE;
  virtual float operator()( int varIdx, int sampleIdx ) CV_OVERRIDE;
  virtual void writeFeatures( FileStorage &fs, const Mat& featureMap ) const CV_OVERRIDE;
 protected:
  virtual void generateFeatures() CV_OVERRIDE;
  virtual void integralHistogram( const Mat &img, std::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 std::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];
  };
  std::vector<Feature> features;

  Mat normSum;  //for nomalization calculation (L1 or L2)
  std::vector<Mat> hist;
};

inline float CvHOGEvaluator::operator()( int varIdx, int sampleIdx )
{
  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 std::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 *phist = _hists[binIdx].ptr<float>( (int) y );
  res = phist[fastRect[cellIdx].p0] - phist[fastRect[cellIdx].p1] - phist[fastRect[cellIdx].p2] + phist[fastRect[cellIdx].p3];

  const float *pnormSum = _normSum.ptr<float>( (int) y );
  normFactor = (float) ( pnormSum[fastRect[0].p0] - pnormSum[fastRect[1].p1] - pnormSum[fastRect[2].p2] + pnormSum[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;
}

struct CvLBPFeatureParams : CvFeatureParams
{
  CvLBPFeatureParams();

};

class CvLBPEvaluator : public CvFeatureEvaluator
{
 public:
  virtual ~CvLBPEvaluator() CV_OVERRIDE
  {
  }
  virtual void init( const CvFeatureParams *_featureParams, int _maxSampleCount, Size _winSize ) CV_OVERRIDE;
  virtual void setImage( const Mat& img, uchar clsLabel, int idx ) CV_OVERRIDE;
  virtual float operator()( int featureIdx, int sampleIdx ) CV_OVERRIDE
  {
    return (float) features[featureIdx].calc( sum, sampleIdx );
  }
  virtual void writeFeatures( FileStorage &fs, const Mat& featureMap ) const CV_OVERRIDE;
 protected:
  virtual void generateFeatures() CV_OVERRIDE;

  class Feature
  {
   public:
    Feature();
    Feature( int offset, int x, int y, int _block_w, int _block_h );
    uchar calc( const Mat& _sum, size_t y ) const;
    void write( FileStorage &fs ) const;

    Rect rect;
    int p[16];
  };
  std::vector<Feature> features;

  Mat sum;
};

inline uchar CvLBPEvaluator::Feature::calc( const Mat &_sum, size_t y ) const
{
  const int* psum = _sum.ptr<int>( (int) y );
  int cval = psum[p[5]] - psum[p[6]] - psum[p[9]] + psum[p[10]];

  return (uchar) ( ( psum[p[0]] - psum[p[1]] - psum[p[4]] + psum[p[5]] >= cval ? 128 : 0 ) |   // 0
      ( psum[p[1]] - psum[p[2]] - psum[p[5]] + psum[p[6]] >= cval ? 64 : 0 ) |    // 1
      ( psum[p[2]] - psum[p[3]] - psum[p[6]] + psum[p[7]] >= cval ? 32 : 0 ) |    // 2
      ( psum[p[6]] - psum[p[7]] - psum[p[10]] + psum[p[11]] >= cval ? 16 : 0 ) |  // 5
      ( psum[p[10]] - psum[p[11]] - psum[p[14]] + psum[p[15]] >= cval ? 8 : 0 ) |  // 8
      ( psum[p[9]] - psum[p[10]] - psum[p[13]] + psum[p[14]] >= cval ? 4 : 0 ) |  // 7
      ( psum[p[8]] - psum[p[9]] - psum[p[12]] + psum[p[13]] >= cval ? 2 : 0 ) |   // 6
      ( psum[p[4]] - psum[p[5]] - psum[p[8]] + psum[p[9]] >= cval ? 1 : 0 ) );     // 3
}

//! @}

} /* namespace cv */

#endif