source: trunk/src/ATrous/atrous_2d_reconstruct.cc @ 884

// -----------------------------------------------------------------------
// atrous_2d_reconstruct.cc: 2-dimensional wavelet reconstruction.
// -----------------------------------------------------------------------
// Copyright (C) 2006, Matthew Whiting, ATNF
//
// This program is free software; you can redistribute it and/or modify it
// under the terms of the GNU General Public License as published by the
// Free Software Foundation; either version 2 of the License, or (at your
// option) any later version.
//
// Duchamp is distributed in the hope that it will be useful, but WITHOUT
// ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
// FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License
// for more details.
//
// You should have received a copy of the GNU General Public License
// along with Duchamp; if not, write to the Free Software Foundation,
// Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA
//
// Correspondence concerning Duchamp may be directed to:
//    Internet email: Matthew.Whiting [at] atnf.csiro.au
//    Postal address: Dr. Matthew Whiting
//                    Australia Telescope National Facility, CSIRO
//                    PO Box 76
//                    Epping NSW 1710
//                    AUSTRALIA
// -----------------------------------------------------------------------
#include <iostream>
#include <iomanip>
#include <math.h>
#include <duchamp/duchamp.hh>
#include <duchamp/param.hh>
#include <duchamp/ATrous/atrous.hh>
#include <duchamp/ATrous/filter.hh>
#include <duchamp/Utils/utils.hh>
#include <duchamp/Utils/feedback.hh>
#include <duchamp/Utils/Statistics.hh>
using Statistics::madfmToSigma;

namespace duchamp
{

  void atrous2DReconstruct(unsigned long &xdim, unsigned long &ydim, float *&input, float *&output, Param &par)
  {
    ///  A routine that uses the a trous wavelet method to reconstruct a 
    ///   2-dimensional image.
    /// 
    ///  If there are no non-BLANK pixels (and we are testing for
    ///  BLANKs), the reconstruction cannot be done, so we return the
    ///  input array as the output array and give a warning message.
    /// 
    ///  \param xdim The length of the x-axis of the image.
    ///  \param ydim The length of the y-axis of the image.
    ///  \param input The input spectrum.
    ///  \param output The returned reconstructed spectrum. This array
    ///  needs to be declared beforehand.
    ///  \param par The Param set:contains all necessary info about the
    ///  filter and reconstruction parameters.

    size_t size = xdim * ydim;
    unsigned long mindim = xdim;
    if (ydim<mindim) mindim = ydim;
    unsigned int numScales = par.filter().getNumScales(mindim);
    double *sigmaFactors = new double[numScales+1];
    for(size_t i=0;i<=numScales;i++){
      if(i<=par.filter().maxFactor(2)) 
        sigmaFactors[i] = par.filter().sigmaFactor(2,i);
      else sigmaFactors[i] = sigmaFactors[i-1] / 2.;
    }

    float mean,sigma,originalSigma,originalMean,oldsigma,newsigma;
    size_t goodSize=0;
    bool *isGood = new bool[size];
    for(size_t pos=0;pos<size;pos++){
      isGood[pos] = !par.isBlank(input[pos]);
      if(isGood[pos]) goodSize++;
    }

    if(goodSize == 0){
      // There are no good pixels -- everything is BLANK for some reason.
      // Return the input array as the output, and give a warning message.

      for(size_t pos=0;pos<size; pos++) output[pos] = input[pos];

      duchampWarning("2D Reconstruction","\
There are no good pixels to be reconstructed -- all are BLANK.\n\
Returning input array.\n");
    }
    else{
      // Otherwise, all is good, and we continue.

      //      findMedianStats(input,goodSize,isGood,originalMean,originalSigma);
      // originalSigma = madfmToSigma(originalSigma);
      if(par.getFlagRobustStats())
        originalSigma = madfmToSigma(findMADFM(input,isGood,size));
      else
        originalSigma = findStddev(input,isGood,size);
  
      float *coeffs    = new float[size];
      float *wavelet   = new float[size];
      // float *residual  = new float[size];

      for(size_t pos=0;pos<size;pos++) output[pos]=0.;

      unsigned int filterwidth = par.filter().width();
      int filterHW = filterwidth/2;
      double *filter = new double[filterwidth*filterwidth];
      for(size_t i=0;i<filterwidth;i++){
        for(size_t j=0;j<filterwidth;j++){
          filter[i*filterwidth+j] = par.filter().coeff(i) * par.filter().coeff(j);
        }
      }

      long *xLim1 = new long[ydim];
      for(size_t i=0;i<ydim;i++) xLim1[i] = 0;
      long *yLim1 = new long[xdim];
      for(size_t i=0;i<xdim;i++) yLim1[i] = 0;
      long *xLim2 = new long[ydim];
      for(size_t i=0;i<ydim;i++) xLim2[i] = xdim-1;
      long *yLim2 = new long[xdim];
      for(size_t i=0;i<xdim;i++) yLim2[i] = ydim-1;

      if(par.getFlagBlankPix()){
        float avGapX = 0, avGapY = 0;
        for(size_t row=0;row<ydim;row++){
          size_t ct1 = 0;
          size_t ct2 = xdim - 1;
          while((ct1<ct2)&&(par.isBlank(input[row*xdim+ct1]))) ct1++;
          while((ct2>ct1)&&(par.isBlank(input[row*xdim+ct2]))) ct2--;
          xLim1[row] = ct1;
          xLim2[row] = ct2;
          avGapX += ct2 - ct1;
        }
        avGapX /= float(ydim);
    
        for(size_t col=0;col<xdim;col++){
          size_t ct1=0;
          size_t ct2=ydim-1;
          while((ct1<ct2)&&(par.isBlank(input[col+xdim*ct1]))) ct1++;
          while((ct2>ct1)&&(par.isBlank(input[col+xdim*ct2]))) ct2--;
          yLim1[col] = ct1;
          yLim2[col] = ct2;
          avGapY += ct2 - ct1;
        }
        avGapY /= float(xdim);
    
        // if(avGapX < mindim) mindim = int(avGapX);
        // if(avGapY < mindim) mindim = int(avGapY);
        // numScales = par.filter().getNumScales(mindim);
      }

      float threshold;
      int iteration=0;
      newsigma = 1.e9;
      for(size_t i=0;i<size;i++) output[i] = 0;
      do{
        if(par.isVerbose()) {
          std::cout << "Iteration #"<<std::setw(2)<<++iteration<<":";
          printBackSpace(13);
        }

        // first, get the value of oldsigma and set it to the previous 
        //   newsigma value
        oldsigma = newsigma;
        // we are transforming the residual array
        for(size_t i=0;i<size;i++)  coeffs[i] = input[i] - output[i];  

        int spacing = 1;
        for(unsigned int scale = 1; scale<numScales; scale++){

          if(par.isVerbose()){
            std::cout << "Scale ";
            std::cout << std::setw(2)<<scale<<" / "<<std::setw(2)<<numScales;
            printBackSpace(13);
            std::cout <<std::flush;
          }

          for(unsigned long ypos = 0; ypos<ydim; ypos++){
            for(unsigned long xpos = 0; xpos<xdim; xpos++){
              // loops over each pixel in the image
              size_t pos = ypos*xdim + xpos;
          
              wavelet[pos] = coeffs[pos];

              if(!isGood[pos]) wavelet[pos] = 0.;
              else{

                size_t filterpos = -1;
                for(int yoffset=-filterHW; yoffset<=filterHW; yoffset++){
                  long y = long(ypos) + spacing*yoffset;
                  // Boundary conditions -- assume reflection at boundaries.
                  // Use limits as calculated above
                  //          if(yLim1[xpos]!=yLim2[xpos]){ 
                  //            // if these are equal we will get into an infinite loop here 
                  //            while((y<yLim1[xpos])||(y>yLim2[xpos])){
                  //              if(y<yLim1[xpos]) y = 2*yLim1[xpos] - y;      
                  //              else if(y>yLim2[xpos]) y = 2*yLim2[xpos] - y;      
                  //            }
                  //          }
                  size_t oldrow = y * xdim;
          
                  for(int xoffset=-filterHW; xoffset<=filterHW; xoffset++){
                    long x = long(xpos) + spacing*xoffset;
                    // Boundary conditions -- assume reflection at boundaries.
                    // Use limits as calculated above
                    //          if(xLim1[ypos]!=xLim2[ypos]){
                    //            // if these are equal we will get into an infinite loop here 
                    //            while((x<xLim1[ypos])||(x>xLim2[ypos])){
                    //              if(x<xLim1[ypos]) x = 2*xLim1[ypos] - x;      
                    //              else if(x>xLim2[ypos]) x = 2*xLim2[ypos] - x;      
                    //            }
                    //          }

                    size_t oldpos = oldrow + x;

                    float oldCoeff;
                    if((y>=yLim1[xpos])&&(y<=yLim2[xpos])&&
                       (x>=xLim1[ypos])&&(x<=xLim2[ypos])  )
                      oldCoeff = coeffs[oldpos];
                    else oldCoeff = 0.;

                    filterpos++;

                    if(isGood[pos]) wavelet[pos] -= filter[filterpos] * oldCoeff;
                    //            wavelet[pos] -= filter[filterpos] * coeffs[oldpos];

                  } //-> end of xoffset loop
                } //-> end of yoffset loop
              } //-> end of else{ ( from if(!isGood[pos])  )
        
            } //-> end of xpos loop
          } //-> end of ypos loop

          // Need to do this after we've done *all* the convolving
          for(size_t pos=0;pos<size;pos++) coeffs[pos] = coeffs[pos] - wavelet[pos];

          // Have found wavelet coeffs for this scale -- now threshold    
          if(scale>=par.getMinScale()){
            //      findMedianStats(wavelet,goodSize,isGood,mean,sigma);
            if(par.getFlagRobustStats())
              mean = findMedian(wavelet,isGood,size);
            else
              mean= findMean(wavelet,isGood,size);

            threshold = mean + 
              par.getAtrousCut() * originalSigma * sigmaFactors[scale];
            for(size_t pos=0;pos<size;pos++){
              if(!isGood[pos]) output[pos] = input[pos];
              // preserve the Blank pixel values in the output.
              else if( fabs(wavelet[pos]) > threshold ) 
                output[pos] += wavelet[pos];
            }
          }
          spacing *= 2;

        } // END OF LOOP OVER SCALES

        for(size_t pos=0;pos<size;pos++) if(isGood[pos]) output[pos] += coeffs[pos];

        // for(size_t i=0;i<size;i++) residual[i] = input[i] - output[i];
        // findMedianStats(residual,goodSize,isGood,mean,newsigma);
        // findMedianStatsDiff(input,output,size,isGood,mean,newsigma);
        // newsigma = madfmToSigma(newsigma); 
        if(par.getFlagRobustStats())
          newsigma = madfmToSigma(findMADFMDiff(input,output,isGood,size));
        else
          newsigma = findStddevDiff(input,output,isGood,size);

        if(par.isVerbose()) printBackSpace(15);

      } while( (iteration==1) || 
               (fabs(oldsigma-newsigma)/newsigma > reconTolerance) );

      if(par.isVerbose()) std::cout << "Completed "<<iteration<<" iterations. ";

      delete [] xLim1;
      delete [] xLim2;
      delete [] yLim1;
      delete [] yLim2;
      delete [] filter;
      delete [] coeffs;
      delete [] wavelet;
      // delete [] residual;

    }

    delete [] isGood;
    delete [] sigmaFactors;
  }
    
}