opencv_contrib/modules/tracking/src/trackerFeature.cpp

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#include "precomp.hpp"

namespace cv
{

/*
 *  TrackerFeature
 */

TrackerFeature::~TrackerFeature()
{

}

void TrackerFeature::compute( const std::vector<Mat>& images, Mat& response )
{
  if( images.empty() )
    return;

  computeImpl( images, response );
}

Ptr<TrackerFeature> TrackerFeature::create( const String& trackerFeatureType )
{
  if( trackerFeatureType.find( "FEATURE2D" ) == 0 )
  {
    size_t firstSep = trackerFeatureType.find_first_of( "." );
    size_t secondSep = trackerFeatureType.find_last_of( "." );

    String detector = trackerFeatureType.substr( firstSep, secondSep - firstSep );
    String descriptor = trackerFeatureType.substr( secondSep, trackerFeatureType.length() - secondSep );

    return Ptr<TrackerFeatureFeature2d>( new TrackerFeatureFeature2d( detector, descriptor ) );
  }

  if( trackerFeatureType.find( "HOG" ) == 0 )
  {
    return Ptr<TrackerFeatureHOG>( new TrackerFeatureHOG() );
  }

  if( trackerFeatureType.find( "HAAR" ) == 0 )
  {
    return Ptr<TrackerFeatureHAAR>( new TrackerFeatureHAAR() );
  }

  if( trackerFeatureType.find( "LBP" ) == 0 )
  {
    return Ptr<TrackerFeatureLBP>( new TrackerFeatureLBP() );
  }

  CV_Error( -1, "Tracker feature type not supported" );
  return Ptr<TrackerFeature>();
}

String TrackerFeature::getClassName() const
{
  return className;
}

/**
 * TrackerFeatureFeature2d
 */
TrackerFeatureFeature2d::TrackerFeatureFeature2d( String /*detectorType*/, String /*descriptorType*/)
{
  className = "FEATURE2D";
}

TrackerFeatureFeature2d::~TrackerFeatureFeature2d()
{

}

bool TrackerFeatureFeature2d::computeImpl( const std::vector<Mat>& /*images*/, Mat& /*response*/)
{
  return false;
}

void TrackerFeatureFeature2d::selection( Mat& /*response*/, int /*npoints*/)
{

}

/**
 * TrackerFeatureHOG
 */
TrackerFeatureHOG::TrackerFeatureHOG()
{
  className = "HOG";
}

TrackerFeatureHOG::~TrackerFeatureHOG()
{

}

bool TrackerFeatureHOG::computeImpl( const std::vector<Mat>& /*images*/, Mat& /*response*/)
{
  return false;
}

void TrackerFeatureHOG::selection( Mat& /*response*/, int /*npoints*/)
{

}

/**
 * TrackerFeatureHAAR
 */

/**
 * Parameters
 */

TrackerFeatureHAAR::Params::Params()
{
  numFeatures = 250;
  rectSize = Size( 100, 100 );
  isIntegral = false;
}

TrackerFeatureHAAR::TrackerFeatureHAAR( const TrackerFeatureHAAR::Params &parameters ) :
    params( parameters )
{
  className = "HAAR";

  CvHaarFeatureParams haarParams;
  haarParams.numFeatures = params.numFeatures;
  haarParams.isIntegral = params.isIntegral;
  featureEvaluator = CvFeatureEvaluator::create( CvFeatureParams::HAAR ).staticCast<CvHaarEvaluator>();
  featureEvaluator->init( &haarParams, 1, params.rectSize );
}

TrackerFeatureHAAR::~TrackerFeatureHAAR()
{

}

CvHaarEvaluator::FeatureHaar& TrackerFeatureHAAR::getFeatureAt( int id )
{
  return featureEvaluator->getFeatures( id );
}

bool TrackerFeatureHAAR::swapFeature( int id, CvHaarEvaluator::FeatureHaar& feature )
{
  featureEvaluator->getFeatures( id ) = feature;
  return true;
}

bool TrackerFeatureHAAR::swapFeature( int source, int target )
{
  CvHaarEvaluator::FeatureHaar feature = featureEvaluator->getFeatures( source );
  featureEvaluator->getFeatures( source ) = featureEvaluator->getFeatures( target );
  featureEvaluator->getFeatures( target ) = feature;
  return true;
}

bool TrackerFeatureHAAR::extractSelected( const std::vector<int> selFeatures, const std::vector<Mat>& images, Mat& response )
{
  if( images.empty() )
  {
    return false;
  }

  int numFeatures = featureEvaluator->getNumFeatures();
  int numSelFeatures = (int)selFeatures.size();

  //response = Mat_<float>( Size( images.size(), numFeatures ) );
  response.create( Size( (int)images.size(), numFeatures ), CV_32F );
  response.setTo( 0 );

  //double t = getTickCount();
  //for each sample compute #n_feature -> put each feature (n Rect) in response
  for ( size_t i = 0; i < images.size(); i++ )
  {
    int c = images[i].cols;
    int r = images[i].rows;
    for ( int j = 0; j < numSelFeatures; j++ )
    {
      float res = 0;
      //const feat
      CvHaarEvaluator::FeatureHaar& feature = featureEvaluator->getFeatures( selFeatures[j] );
      feature.eval( images[i], Rect( 0, 0, c, r ), &res );
      //( Mat_<float>( response ) )( j, i ) = res;
      response.at<float>( selFeatures[j], (int)i ) = res;
    }
  }
  //t = ( (double) getTickCount() - t ) / getTickFrequency();
  //std::cout << "StrongClassifierDirectSelection time " << t << std::endl;

  return true;
}

class Parallel_compute : public cv::ParallelLoopBody
{
 private:
  Ptr<CvHaarEvaluator> featureEvaluator;
  std::vector<Mat> images;
  Mat response;
  //std::vector<CvHaarEvaluator::FeatureHaar> features;
 public:
  Parallel_compute( Ptr<CvHaarEvaluator>& fe, const std::vector<Mat>& img, Mat& resp ) :
      featureEvaluator( fe ),
      images( img ),
      response( resp )
  {

    //features = featureEvaluator->getFeatures();
  }

  virtual void operator()( const cv::Range &r ) const
  {
    for ( register int jf = r.start; jf != r.end; ++jf )
    {
      int cols = images[jf].cols;
      int rows = images[jf].rows;
      for ( int j = 0; j < featureEvaluator->getNumFeatures(); j++ )
      {
        float res = 0;
        featureEvaluator->getFeatures()[j].eval( images[jf], Rect( 0, 0, cols, rows ), &res );
        ( Mat_<float>( response ) )( j, jf ) = res;
      }
    }
  }
};

bool TrackerFeatureHAAR::computeImpl( const std::vector<Mat>& images, Mat& response )
{
  if( images.empty() )
  {
    return false;
  }

  int numFeatures = featureEvaluator->getNumFeatures();

  response = Mat_<float>( Size( (int)images.size(), numFeatures ) );

  std::vector<CvHaarEvaluator::FeatureHaar> f = featureEvaluator->getFeatures();
  //for each sample compute #n_feature -> put each feature (n Rect) in response
  parallel_for_( Range( 0, (int)images.size() ), Parallel_compute( featureEvaluator, images, response ) );

  /*for ( size_t i = 0; i < images.size(); i++ )
  {
    int c = images[i].cols;
    int r = images[i].rows;
    for ( int j = 0; j < numFeatures; j++ )
    {
      float res = 0;
      featureEvaluator->getFeatures( j ).eval( images[i], Rect( 0, 0, c, r ), &res );
      ( Mat_<float>( response ) )( j, i ) = res;
    }
  }*/

  return true;
}

void TrackerFeatureHAAR::selection( Mat& /*response*/, int /*npoints*/)
{

}

/**
 * TrackerFeatureLBP
 */
TrackerFeatureLBP::TrackerFeatureLBP()
{
  className = "LBP";
}

TrackerFeatureLBP::~TrackerFeatureLBP()
{

}

bool TrackerFeatureLBP::computeImpl( const std::vector<Mat>& /*images*/, Mat& /*response*/)
{
  return false;
}

void TrackerFeatureLBP::selection( Mat& /*response*/, int /*npoints*/)
{

}

} /* namespace cv */