source: trunk/src/Cubes/CubicSearch.cc @ 269

#include <iostream>
#include <iomanip>
#include <fstream>
#include <vector>
#include <param.hh>
#include <PixelMap/Object3D.hh>
#include <Cubes/cubes.hh>
#include <Utils/utils.hh>
#include <Utils/feedback.hh>
#include <Utils/Statistics.hh>

using std::vector;
using namespace PixelInfo;

void Cube::CubicSearch()
{
  /**
   *  A front end to the cubic searching routine that does not
   *  involve any wavelet reconstruction. 
   *  The statistics of the cube are calculated first of all.
   *  If baseline-removal is required that is done prior to searching.
   *  Once searching is complete, the detection map is updated and
   *  the intermediate detections are logged in the log file.
   */

  if(this->par.isVerbose()) std::cout << "  ";

  this->setCubeStats();
    
  if(this->par.isVerbose()) std::cout << "  Searching... " << std::flush;
  
//   this->objectList = search3DArray(this->axisDim,this->array,
//                                 this->par,this->Stats);
  this->objectList = search3DArraySimple(this->axisDim,this->array,
                                         this->par,this->Stats);

  if(this->par.isVerbose()) std::cout << "  Updating detection map... " 
                                      << std::flush;
  this->updateDetectMap();
  if(this->par.isVerbose()) std::cout << "Done.\n";

  if(this->par.getFlagLog()){
    if(this->par.isVerbose()) 
      std::cout << "  Logging intermediate detections... " << std::flush;
    this->logDetectionList();
    if(this->par.isVerbose()) std::cout << "Done.\n";
  }

}
//---------------------------------------------------------------


vector <Detection> search3DArray(long *dim, float *Array, Param &par,
                                 StatsContainer<float> &stats)
{
  /**
   *  Takes a dimension array and data array as input (and Parameter set)
   *  and searches for detections in a combination of 1D and 2D searches.
   *  Returns a vector list of Detections.
   *  No reconstruction is assumed to have taken place, so statistics are
   *  calculated (using robust methods) from the data array itself.
   * \param dim Array of dimension sizes for the data array.
   * \param Array Array of data.
   * \param par Param set defining how to do detection, and what a
   * BLANK pixel is etc.
   * \param stats The statistics that define what a detection is.
   * \return Vector of detected objects.
   */

  vector <Detection> outputList;
  long zdim = dim[2];
  long xySize = dim[0] * dim[1];
  int num = 0;

  ProgressBar bar;
  // FIRST SEARCH --  IN EACH SPECTRUM.
  if(zdim>1){
    if(par.isVerbose()) {
      std::cout << "  1D: ";
      bar.init(xySize);
    }

    bool *doPixel = new bool[xySize];
    int goodSize=0;
    for(int npix=0; npix<xySize; npix++){
      doPixel[npix] = false;
      for(int z=0;z<zdim;z++){
        doPixel[npix] = doPixel[npix] || 
          (!par.isBlank(Array[npix]) && !par.isInMW(z));
      }
      // doPixel[i] is false only when there are no good pixels in spectrum
      //  of pixel #i.
    }

    long *specdim = new long[2];
    specdim[0] = zdim; specdim[1]=1;
    Image *spectrum = new Image(specdim);
    delete [] specdim;
    spectrum->saveParam(par);
    spectrum->saveStats(stats);
    spectrum->setMinSize(par.getMinChannels());
    spectrum->pars().setBeamSize(2.); 
    // beam size: for spectrum, only neighbouring channels correlated

    for(int y=0; y<dim[1]; y++){
      for(int x=0; x<dim[0]; x++){

        int npix = y*dim[0] + x;
        if( par.isVerbose() ) bar.update(npix+1);
        
        if(doPixel[npix]){
          spectrum->extractSpectrum(Array,dim,npix);
          spectrum->removeMW(); // only works if flagMW is true
          std::vector<Scan> objlist = spectrum->spectrumDetect();
          num += objlist.size();
          for(int obj=0;obj<objlist.size();obj++){
            Detection newObject;
            // Fix up coordinates of each pixel to match original array
            for(int z=objlist[obj].getX();z<=objlist[obj].getXmax();z++) {
              newObject.pixels().addPixel(x,y,z);
            }
            newObject.setOffsets(par);
            mergeIntoList(newObject,outputList,par);
          }
        }
      }
    }

    delete spectrum;
    delete [] doPixel;
  
    if(par.isVerbose()) {
      bar.fillSpace("Found ");
      std::cout << num <<";" << std::flush;
    }

  }

  // SECOND SEARCH --  IN EACH CHANNEL
  if(par.isVerbose()){
    std::cout << "  2D: ";
    bar.init(zdim);
  }
  
  num = 0;

  long *imdim = new long[2];
  imdim[0] = dim[0]; imdim[1] = dim[1];
  Image *channelImage = new Image(imdim);
  delete [] imdim;
  channelImage->saveParam(par);
  channelImage->saveStats(stats);
  //  channelImage->setMinSize(par.getMinPix());
  channelImage->setMinSize(1);

  for(int z=0; z<zdim; z++){

    if( par.isVerbose() ) bar.update(z+1);

    if(!par.isInMW(z)){

      channelImage->extractImage(Array,dim,z);
      std::vector<Object2D> objlist = channelImage->lutz_detect();
      num += objlist.size();
      for(int obj=0;obj<objlist.size();obj++){
        Detection newObject;
        newObject.pixels().addChannel(z,objlist[obj]);
        newObject.setOffsets(par);
        mergeIntoList(newObject,outputList,par);
      }
    }

  }

  delete channelImage;

  if(par.isVerbose()){
    bar.fillSpace("Found ");
    std::cout << num << ".\n";
  }

  return outputList;
}
//---------------------------------------------------------------

vector <Detection> search3DArraySimple(long *dim, float *Array, Param &par,
                                       StatsContainer<float> &stats)
{
  /**
   *  Takes a dimension array and data array as input (and Parameter
   *  set) and searches for detections just in the channel maps -- no
   *  1D searches are done.  * Returns a vector list of Detections.
   *  No reconstruction is assumed to have taken place, so statistics
   *  are calculated (using robust methods) from the data array
   *  itself.
   * \param dim Array of dimension sizes for the data array.
   * \param Array Array of data.
   * \param par Param set defining how to do detection, and what a
   *              BLANK pixel is etc.
   * \param stats The statistics that define what a detection is.
   * \return Vector of detected objects.
   */

  vector <Detection> outputList;
  long zdim = dim[2];
  long xySize = dim[0] * dim[1];
  int num = 0;

  ProgressBar bar;
  if(par.isVerbose()) bar.init(zdim);
  
  num = 0;

  long *imdim = new long[2];
  imdim[0] = dim[0]; imdim[1] = dim[1];
  Image *channelImage = new Image(imdim);
  delete [] imdim;
  channelImage->saveParam(par);
  channelImage->saveStats(stats);
  channelImage->setMinSize(1);

  for(int z=0; z<zdim; z++){

    if( par.isVerbose() ) bar.update(z+1);

    if(!par.isInMW(z)){

      channelImage->extractImage(Array,dim,z);
      std::vector<Object2D> objlist = channelImage->lutz_detect();
      num += objlist.size();
      for(int obj=0;obj<objlist.size();obj++){
        Detection newObject;
        newObject.pixels().addChannel(z,objlist[obj]);
        newObject.setOffsets(par);
        mergeIntoList(newObject,outputList,par);
      }
    }

  }

  delete channelImage;

  if(par.isVerbose()){
    bar.fillSpace("Found ");
    std::cout << num << ".\n";
  }

  return outputList;
}