/*  skeletonapp.cc

    Mark Jenkinson, FMRIB Image Analysis Group

    Copyright (C) 2003 University of Oxford  */

/*  Part of FSL - FMRIB's Software Library
    http://www.fmrib.ox.ac.uk/fsl
    fsl@fmrib.ox.ac.uk
    
    Developed at FMRIB (Oxford Centre for Functional Magnetic Resonance
    Imaging of the Brain), Department of Clinical Neurology, Oxford
    University, Oxford, UK
    
    
    LICENCE
    
    FMRIB Software Library, Release 5.0 (c) 2012, The University of
    Oxford (the "Software")
    
    The Software remains the property of the University of Oxford ("the
    University").
    
    The Software is distributed "AS IS" under this Licence solely for
    non-commercial use in the hope that it will be useful, but in order
    that the University as a charitable foundation protects its assets for
    the benefit of its educational and research purposes, the University
    makes clear that no condition is made or to be implied, nor is any
    warranty given or to be implied, as to the accuracy of the Software,
    or that it will be suitable for any particular purpose or for use
    under any specific conditions. Furthermore, the University disclaims
    all responsibility for the use which is made of the Software. It
    further disclaims any liability for the outcomes arising from using
    the Software.
    
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    University harmless from and against any and all claims, damages and
    liabilities asserted by third parties (including claims for
    negligence) which arise directly or indirectly from the use of the
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    No part of the Software may be reproduced, modified, transmitted or
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    without the express permission of the University. The permission of
    the University is not required if the said reproduction, modification,
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    You are not permitted under this Licence to use this Software
    commercially. Use for which any financial return is received shall be
    defined as commercial use, and includes (1) integration of all or part
    of the source code or the Software into a product for sale or license
    by or on behalf of Licensee to third parties or (2) use of the
    Software or any derivative of it for research with the final aim of
    developing software products for sale or license to a third party or
    (3) use of the Software or any derivative of it for research with the
    final aim of developing non-software products for sale or license to a
    third party, or (4) use of the Software to provide any service to an
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    interested in using the Software commercially, please contact Isis
    Innovation Limited ("Isis"), the technology transfer company of the
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    innovation@isis.ox.ac.uk quoting reference DE/9564. */

// Skeleton application framework for using newimage

#define _GNU_SOURCE 1
#define POSIX_SOURCE 1

#include "newimage/newimageall.h"
#include "miscmaths/miscmaths.h"
#include "utils/options.h"

using namespace MISCMATHS;
using namespace NEWIMAGE;
using namespace Utilities;

// The two strings below specify the title and example usage that is
//  printed out as the help or usage message

string title="lesion_filling \nCopyright(c) 2012, University of Oxford (Mark Jenkinson)";
string examples="lesion_filling [options] -i <intensity image> -l <lesion mask image> -o <output/filled image>";

// Each (global) object below specificies as option and can be accessed
//  anywhere in this file (since they are global).  The order of the
//  arguments needed is: name(s) of option, default value, help message,
//       whether it is compulsory, whether it requires arguments
// Note that they must also be included in the main() function or they
//  will not be active.

Option<bool> verbose(string("-v,--verbose"), false, 
		     string("switch on diagnostic messages"), 
		     false, no_argument);
Option<bool> help(string("-h,--help"), false,
		  string("display this message"),
		  false, no_argument);
Option<string> lesmaskname(string("-l,--lesionmask"), string(""),
		  string("filename of lesion mask image"),
		  true, requires_argument);
Option<string> involname(string("-i,--in"), string(""),
		  string("input image filename (e.g. T1w image)"),
		  true, requires_argument);
Option<string> outname(string("-o,--out"), string(""),
		  string("output filename (lesion filled image)"),
		  true, requires_argument);
int nonoptarg;

////////////////////////////////////////////////////////////////////////////

// Local functions

int urand(int min, int max) 
{
  int MAXRAND=2147483647;  // = 2^31 - 1
  int mrand=Max(Min(ceil((double(random())/MAXRAND)*(max-min+1)),(max-min+1)),1)+min-1;  
  return mrand;
}


// for example ... print difference of COGs between 2 images ...
int do_work(int argc, char* argv[]) 
{
  // variables from the matlab code...
  // [im,dims,pixdims]=read_avw(structim);
  // compb=read_avw(outerborder_comp);
  // comp=read_avw(lesioncomp);
  // lesinb=read_avw(innerborder);
  // lesinreg=read_avw(innerregion);

  volume<int> tmpcomp;
  volume4D<float> invol;
  volume<float> lesionmask, comp;
  read_volume(lesionmask,lesmaskname.value());
  read_volume4D(invol,involname.value());
  if (verbose.value()) { cout << "Read in images" << endl; }
  // connectedcomp ../Lesion_mask_2struct_bin.nii.gz lesion_comp
  tmpcomp = connected_components(lesionmask);
  copyconvert(tmpcomp,comp);
  if (verbose.value()) { cout << "Counted lesions in mask : total = " << comp.max() << endl; }
  // fslmaths ../Lesion_mask_2struct_bin.nii.gz -dilF -sub ../Lesion_mask_2struct_bin.nii.gz ANCO_outerborder.nii.gz
  volume<float> box3kernel, outborder, compb;
  box3kernel=box_kernel(3,3,3);
  outborder = morphfilter(lesionmask,box3kernel,"dilate") - lesionmask;
  outborder.binarise(0.5f);
  if (verbose.value()) { cout << "Calculated outer border mask" << endl; }
  // fslmaths lesion_comp.nii.gz -dilD lesion_comp_dil
  // fslmaths lesion_comp_dil.nii.gz -mul ANCO_outerborder.nii.gz ANCO_outerborder_comp
  compb = morphfilter(comp,box3kernel,"dilateD");
  compb *= outborder;
  if (verbose.value()) { cout << "Calculated outer border components" << endl; }
  volume<float> lesinb, lesinreg;
  // fslmaths ../Lesion_mask_2struct_bin.nii.gz -ero -sub ../Lesion_mask_2struct_bin.nii.gz -abs -thr 0.5 -bin ANCO_innerborder
  lesinb = morphfilter(lesionmask,box3kernel,"erodeS");
  lesinb = binarise(lesionmask - lesinb,0.5f);  // this is the inner border mask
  // fslmaths ../Lesion_mask_2struct_bin.nii.gz -sub ANCO_innerborder ANCO_innerregion
  lesinreg = lesionmask - lesinb;
  if (verbose.value()) { cout << "Calculated inner border and region masks" << endl; }
  
  int maxcomp = comp.max();
  
  // In the matlab code there was:
  // % check if any points in the inner boundary are more than
  // % one voxel from any other points, and if so, make them part
  // % of the inner region instead
  // but this seems pointless given the definition above
  
  // for each lesion fill the interior region
  for (int compnum=1; compnum<=maxcomp; compnum++) {
    if (verbose.value()) { cout << "Component " << compnum << " of " << maxcomp << endl; }
    volume<float> mask, maskinb, maskinr;
    mask=binarise(compb, compnum-0.5f, compnum+0.5f);
    maskinb=binarise(comp, compnum-0.5f, compnum+0.5f);
    maskinb*=lesinb;
    maskinr=binarise(comp, compnum-0.5f, compnum+0.5f);
    maskinr*=lesinreg;
    int nin=MISCMATHS::round(maskinr.sum());
    if (nin>0) {
      // extra intensity values from only within the mask
      Matrix vals, sval;
      vals = invol.matrix(mask);
      vals=vals.t();
      if (verbose.value()) { cout << "Cols = " << vals.Ncols() << " and rows = " << vals.Nrows() << endl; }
      int nvox;
      nvox = vals.Nrows();
      if (vals.Ncols()>vals.Nrows()) { cerr << "WRONG ASSUMPTION ABOUT MATRIX METHOD IN NEWIMAGE!!!!!!" << endl << "Cols = " << vals.Ncols() << " and rows = " << vals.Nrows() << endl; }
      sval=vals;
      SortAscending(sval);
      if (verbose.value()) { cout << "Calculating cummulative distribution" << endl; }
      ColumnVector borders(nvox), cumnb(nvox);  // cumnb is cummulative distribution
      float stot=sval.Sum(), cumsum=0.0;
      int cidx=1, prevnb=0;
      for (int nb=1; nb<=nvox; nb+=5) {  // sample cummulative dist at every 5th sample for a bit of smoothing
	for (int midx=prevnb+1; midx<=nb; midx++) { cumsum+=sval(midx,1); }
	prevnb=nb;
	borders(cidx)=sval(nb,1);
	cumnb(cidx++)=cumsum/stot;
      }
      int maxcidx=cidx-1;
      
      if (verbose.value()) { cout << "Random sampling..." << endl; }
      ColumnVector idx(nin), newvals(nin);
      for (int midx=1; midx<=nin; midx++) { idx(midx)=urand(1,nin)/((float) nin); }  // set of random values
      newvals=0.0f;
      for (int mm=1; mm<=nin; mm++) {
	int binno=0;
	// look up cummulative dist and interp
	for (int midx=maxcidx; midx>=1; midx--) { if (cumnb(midx)>idx(mm)) binno=midx; }  // find least index
	if (binno==0) { binno=nin; }
	float minv=borders(Max(1,binno-1));
	float maxv=borders(binno);
	newvals(mm)=(urand(0,100)/100.0f)*(maxv-minv) + minv;  
      }
      
      if (verbose.value()) { cout << "Putting samples back into image" << endl; }
      if (verbose.value()) { cout << "Values are : " << newvals.t() <<  endl; }
      {
	volume4D<float> newinvol(invol);
	newinvol.setmatrix(newvals.t(),maskinr);  // put values back into image
	invol *= (1.0f - maskinr);
	invol += newinvol;
      }
    }
  }

  save_volume(invol[0],fslbasename(outname.value())+"_inneronly");
  
  // set the (inner) border of the lesion to a smoothed version of its neighbours (outside + filled interior)
  ColumnVector ker1d(3);
  ker1d << 0.7 << 1.0 << 0.7;
  volume<float> mask_inreg_outb, imn, maskn, maskbinv, imall, maskall, wall, wn;
  mask_inreg_outb = binarise(lesinreg + compb,0.5f);
  imn=convolve_separable(invol[0]*mask_inreg_outb,ker1d,ker1d,ker1d);
  maskn=convolve_separable(mask_inreg_outb,ker1d,ker1d,ker1d);
  maskbinv=binarise(1.0f-lesinb,0.5f);
  
  // simple alternative
  //invol[0]=invol[0]*maskbinv + lesinb*divide(imn,maskn,maskn);

  imall=convolve_separable(invol[0]*maskbinv,ker1d,ker1d,ker1d);
  maskall=convolve_separable(maskbinv,ker1d,ker1d,ker1d);
  
  //wall=0.5*(maskall>0) + 0.5*(maskall>0).*(maskn==0);
  //wn=0.5*(maskn>0) + 0.5*(maskn>0).*(maskall==0);
  float eps=1e-8;
  wall=0.5f*binarise(maskall,eps)*(binarise(maskn,-eps,eps)+1.0f);
  wn=0.5f*binarise(maskn,eps)*(binarise(maskall,-eps,eps)+1.0f);

  invol[0] = invol[0]*maskbinv + wall*lesinb*divide(imall,maskall,maskall) + wn*lesinb*divide(imn,maskn,maskn);

  save_volume(invol[0],outname.value());
  
  return 0;
}


//////////////////////////////////////////////////////////////////////////////////////////////////////////////////

// ORIGINAL MATLAB CODE

/*
masks=convn((1-lesinb),ones([3,3,3]),'same');
newinreg=(masks==0).*lesinb;
lesinb=lesinb-newinreg;
lesinreg=lesinreg+newinreg;
    
for compnum=1:maxcomp,

  disp(['  Component ',num2str(compnum)]);
  
  mask=(compb==compnum);
  maskinb=(comp==compnum).*lesinb;
  maskinr=(comp==compnum).*lesinreg;
  nin=sum3(maskinr);

  if (nin>0),
    vals=im(mask);    
    nvox=length(vals);
    
    nb=1:5:nvox;
    sval=sort(vals);
    cumnb=cumsum(nb)/sum(nb);
    borders=sval(nb);
    
    disp('Made histogram');
    
    % sample from histogram for inner region
    idx=randperm(nin)/nin;  % make this truly random  - uniform [0,1]
    newvals=zeros([nin 1]);
    for mm=1:nin,
      binno=min(find(cumnb>idx(mm)));
      if (length(binno)==0),  binno=length(cumnb); end
      minv=borders(max(1,binno-1));
      maxv=borders(binno);
      newvals(mm)=rand(1)*(maxv-minv)+minv;
    end
    
    im(maskinr>0)=newvals;
    
    disp('Filled interior region');
  
  end
end

% average nearest neighbours of each voxel in the inner border

kernel=ones([3,3,3]);
k1d=[0.7 1 0.7];
kernel(:,:,1)=k1d(1)*k1d'*k1d;
kernel(:,:,2)=k1d(2)*k1d'*k1d;
kernel(:,:,3)=k1d(3)*k1d'*k1d;
disp('Averaging interior and boundary neighbourhood voxel for image');
imn=convn(im.*((lesinreg+compb)>0),kernel,'same');
disp('Averaging interior/boundary for mask');
maskn=convn(((lesinreg+compb)>0),kernel,'same');
disp('Filled in inner border');
%% CAN I REPLACE EVERYTHING BELOW WITH JUST: im=im.*(1-lesinb) + lesinb.*imn./max(0.00001,maskn)

disp('Averaging all neighbourhood voxels for image');
imall=convn(im.*(1-lesinb),kernel,'same');
disp('Averaging all neighbourhood for mask');
maskall=convn((1-lesinb),kernel,'same');
% calculate weights so that if both are valid it averages otherwise
% it takes just one of them
wall=0.5*(maskall>0) + 0.5*(maskall>0).*(maskn==0);
wn=0.5*(maskn>0) + 0.5*(maskn>0).*(maskall==0);
im=im.*(1-lesinb) + wall.*lesinb.*imall./max(0.00001,maskall) ...
   + wn.*lesinb.*imn./max(0.00001,maskn);


save_avw(im,outputname,'f',pixdims)

*/


////////////////////////////////////////////////////////////////////////////

int main(int argc,char *argv[])
{

  Tracer tr("main");
  OptionParser options(title, examples);

  try {
    // must include all wanted options here (the order determines how
    //  the help message is printed)
    options.add(involname);
    options.add(outname);
    options.add(lesmaskname);
    options.add(verbose);
    options.add(help);
    
    nonoptarg = options.parse_command_line(argc, argv);

    // line below stops the program if the help was requested or 
    //  a compulsory option was not set
    if ( (help.value()) || (!options.check_compulsory_arguments(true)) )
      {
	options.usage();
	exit(EXIT_FAILURE);
      }
    
  }  catch(X_OptionError& e) {
    options.usage();
    cerr << endl << e.what() << endl;
    exit(EXIT_FAILURE);
  } catch(std::exception &e) {
    cerr << e.what() << endl;
  } 

  // Call the local functions

  return do_work(argc,argv);
}