libptouch: PTdecode/CImg-1.3.0/examples/pde_TschumperleDeriche2d.cpp@b7fe751ea60d (annotated)

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PTdecode/CImg-1.3.0/examples/pde_TschumperleDeriche2d.cpp@b7fe751ea60d (annotated)

PTdecode/CImg-1.3.0/examples/pde_TschumperleDeriche2d.cpp

Thu, 24 Sep 2009 17:18:28 +0100

author: Philip Pemberton <philpem@philpem.me.uk>
date: Thu, 24 Sep 2009 17:18:28 +0100
changeset 19: b7fe751ea60d
parent 5: 1204ebf9340d
permissions: -rwxr-xr-x

Added support for separator ticks between labels

 /*
  #
  #  File        : pde_TschumperleDeriche2D.cpp
  #                ( C++ source file )
  #
  #  Description : Implementation of the Tschumperle-Deriche's Regularization
  #                PDE, for 2D multivalued images, as described in the articles below.
  #                This file is a part of the CImg Library project.
  #                ( http://cimg.sourceforge.net )
  #
  #  (1) PDE-Based Regularization of Multivalued Images and Applications.
  #               (D. Tschumperle). PhD Thesis. University of Nice-Sophia Antipolis, December 2002.
  #  (2) Diffusion PDE's on Vector-valued Images : Local Approach and Geometric Viewpoint.
  #               (D. Tschumperle and R. Deriche). IEEE Signal Processing Magazine, October 2002.
  #  (3) Vector-Valued Image Regularization with PDE's : A Common Framework for Different Applications.
  #               (D. Tschumperle and R. Deriche). CVPR'2003, Computer Vision and Pattern Recognition, Madison, United States, June 2003.
  #
  #  This code can be used to perform image restoration, inpainting, magnification or flow visualization.
  #
  #  NOTE : THIS SOURCE IS DISTRIBUTED FOR EDUCATIONAL PURPOSES ONLY. A BETTER ANISOTROPIC SMOOTHING ALGORITHM CAN BE FOUND
  #  IN THE FILE 'greycstoration.cpp' WHICH IS THE RESULT OF MORE RECENT WORK.
  #
  #  Copyright   : David Tschumperle
  #                ( http://www.greyc.ensicaen.fr/~dtschump/ )
  #
  #  License     : CeCILL v2.0
  #                ( http://www.cecill.info/licences/Licence_CeCILL_V2-en.html )
  #
  #  This software is governed by the CeCILL  license under French law and
  #  abiding by the rules of distribution of free software.  You can  use,
  #  modify and/ or redistribute the software under the terms of the CeCILL
  #  license as circulated by CEA, CNRS and INRIA at the following URL
  #  "http://www.cecill.info".
  #
  #  As a counterpart to the access to the source code and  rights to copy,
  #  modify and redistribute granted by the license, users are provided only
  #  with a limited warranty  and the software's author,  the holder of the
  #  economic rights,  and the successive licensors  have only  limited
  #  liability.
  #
  #  In this respect, the user's attention is drawn to the risks associated
  #  with loading,  using,  modifying and/or developing or reproducing the
  #  software by the user in light of its specific status of free software,
  #  that may mean  that it is complicated to manipulate,  and  that  also
  #  therefore means  that it is reserved for developers  and  experienced
  #  professionals having in-depth computer knowledge. Users are therefore
  #  encouraged to load and test the software's suitability as regards their
  #  requirements in conditions enabling the security of their systems and/or
  #  data to be ensured and,  more generally, to use and operate it in the
  #  same conditions as regards security.
  #
  #  The fact that you are presently reading this means that you have had
  #  knowledge of the CeCILL license and that you accept its terms.
  #
 */
 #include "CImg.h"
 using namespace cimg_library;
 // The lines below are necessary when using a non-standard compiler as visualcpp6.
 #ifdef cimg_use_visualcpp6
 #define std
 #endif
 #ifdef min
 #undef min
 #undef max
 #endif
 #ifndef cimg_imagepath
 #define cimg_imagepath "img/"
 #endif
 int main(int argc,char **argv) {
   // Read command line arguments
   //-----------------------------
   cimg_usage("Tschumperle-Deriche's flow for 2D Image Restoration, Inpainting, Magnification or Flow visualization");
   const char *file_i  = cimg_option("-i",cimg_imagepath "milla.bmp","Input image");
   const char *file_m  = cimg_option("-m",(char*)NULL,"Mask image (if Inpainting)");
   const char *file_f  = cimg_option("-f",(char*)NULL,"Flow image (if Flow visualization)");
   const char *file_o  = cimg_option("-o",(char*)NULL,"Output file");
   const double zoom   = cimg_option("-zoom",1.0,"Image magnification");
   const unsigned int nb_iter  = cimg_option("-iter",100000,"Number of iterations");
   const double dt     = cimg_option("-dt",20.0,"Adapting time step");
   const double alpha  = cimg_option("-alpha",0.0,"Gradient smoothing");
   const double sigma  = cimg_option("-sigma",0.5,"Structure tensor smoothing");
   const float a1      = cimg_option("-a1",0.5f,"Diffusion limiter along minimal variations");
   const float a2      = cimg_option("-a2",0.9f,"Diffusion limiter along maximal variations");
   const double noiseg = cimg_option("-ng",0.0,"Add gauss noise before aplying the algorithm");
   const double noiseu = cimg_option("-nu",0.0,"Add uniform noise before applying the algorithm");
   const double noises = cimg_option("-ns",0.0,"Add salt&pepper noise before applying the algorithm");
   const bool stflag   = cimg_option("-stats",false,"Display image statistics at each iteration");
   const unsigned int save = cimg_option("-save",0,"Iteration saving step");
   const unsigned int visu = cimg_option("-visu",10,"Visualization step (0=no visualization)");
   const unsigned int init = cimg_option("-init",3,"Inpainting initialization (0=black, 1=white, 2=noise, 3=unchanged)");
   const unsigned int skip = cimg_option("-skip",1,"Step of image geometry computation");
   bool view_t         = cimg_option("-d",false,"View tensor directions (useful for debug)");
   double xdt = 0;
   // Variable initialization
   //-------------------------
   CImg<> img, flow;
   CImg<int> mask;
   if (file_i) {
     img = CImg<>(file_i).resize(-100,-100,1,-100);
     if (file_m) mask = CImg<unsigned char>(file_m).resize(img.dimx(),img.dimy(),1,1);
     else if (zoom>1) {
       mask = CImg<int>(img.dimx(),img.dimy(),1,1,-1).resize((int)(img.dimx()*zoom),(int)(img.dimy()*zoom),1,1,4)+1;
       img.resize((int)(img.dimx()*zoom),(int)(img.dimy()*zoom),1,-100,3);
     }
   } else {
     if (file_f) {
       flow = CImg<>(file_f);
       img = CImg<>((int)(flow.dimx()*zoom),(int)(flow.dimy()*zoom),1,1,0).noise(100,2);
       flow.resize(img.dimx(),img.dimy(),1,2,3);
     } else throw CImgException("You need to specify at least one input image (option -i), or one flow image (option -f)");
   }
   img.noise(noiseg,0).noise(noiseu,1).noise(noises,2);
   float initial_min, initial_max = img.maxmin(initial_min);
   if (mask.data && init!=3)
     cimg_forXYV(img,x,y,k) if (mask(x,y))
       img(x,y,k)=(float)((init?
                           (init==1?initial_max:((initial_max-initial_min)*cimg::rand())):
                           initial_min));
   CImgDisplay disp;
   if (visu) disp.assign(img,"Iterated Image");
   CImg<> G(img.dimx(),img.dimy(),1,3,0), T(G), veloc(img), val(2), vec(2,2);
   // PDE main iteration loop
   //-------------------------
   for (unsigned int iter=0; iter<nb_iter && (!disp || (!disp.is_closed && !disp.is_keyQ && !disp.is_keyESC)); iter++) {
     std::printf("\riter %u , xdt = %g               ",iter,xdt); std::fflush(stdout);
     if (stflag) img.print();
     if (disp && disp.key==cimg::keySPACE) { view_t = !view_t; disp.key=0; }
     if (!(iter%skip)) {
       // Compute the tensor field T, used to drive the diffusion
       //---------------------------------------------------------
       // When using PDE for flow visualization
       if (flow.data) cimg_forXY(flow,x,y) {
         const float
           u = flow(x,y,0,0),
           v = flow(x,y,0,1),
           n = (float)std::sqrt((double)(u*u+v*v)),
           nn = (n!=0)?n:1;
         T(x,y,0) = u*u/nn;
         T(x,y,1) = u*v/nn;
         T(x,y,2) = v*v/nn;
       } else {
         // Compute structure tensor field G
         CImgList<> grad = img.get_gradient();
         if (alpha!=0) cimglist_for(grad,l) grad[l].blur((float)alpha);
         G.fill(0);
         cimg_forXYV(img,x,y,k) {
           const float ix = grad[0](x,y,k), iy = grad[1](x,y,k);
           G(x,y,0) += ix*ix;
           G(x,y,1) += ix*iy;
           G(x,y,2) += iy*iy;
         }
         if (sigma!=0) G.blur((float)sigma);
         // When using PDE for image restoration, inpainting or zooming
         T.fill(0);
         if (!mask.data) cimg_forXY(G,x,y) {
           G.get_tensor_at(x,y).symmetric_eigen(val,vec);
           const float
             l1 = (float)std::pow(1.0f+val[0]+val[1],-a1),
             l2 = (float)std::pow(1.0f+val[0]+val[1],-a2),
             ux = vec(1,0),
             uy = vec(1,1);
           T(x,y,0) = l1*ux*ux + l2*uy*uy;
           T(x,y,1) = l1*ux*uy - l2*ux*uy;
           T(x,y,2) = l1*uy*uy + l2*ux*ux;
         }
         else cimg_forXY(G,x,y) if (mask(x,y)) {
           G.get_tensor_at(x,y).symmetric_eigen(val,vec);
           const float
             ux = vec(1,0),
             uy = vec(1,1);
           T(x,y,0) = ux*ux;
           T(x,y,1) = ux*uy;
           T(x,y,2) = uy*uy;
         }
       }
     }
     // Compute the PDE velocity and update the iterated image
     //--------------------------------------------------------
     CImg_3x3(I,float);
     veloc.fill(0);
     cimg_forV(img,k) cimg_for3x3(img,x,y,0,k,I) {
       const float
         a = T(x,y,0),
         b = T(x,y,1),
         c = T(x,y,2),
         ixx = Inc+Ipc-2*Icc,
         iyy = Icn+Icp-2*Icc,
         ixy = 0.25f*(Ipp+Inn-Ipn-Inp);
       veloc(x,y,k) = a*ixx + 2*b*ixy + c*iyy;
     }
     if (dt>0) {
       float m, M = veloc.maxmin(m);
       xdt = dt/cimg::max(cimg::abs(m),cimg::abs(M));
     } else xdt=-dt;
     img+=veloc*xdt;
     img.cut((float)initial_min,(float)initial_max);
     // Display and save iterations
     if (disp && !(iter%visu)) {
       if (!view_t) img.display(disp);
       else {
         const unsigned char white[3] = {255,255,255};
         CImg<unsigned char> visu = img.get_resize(disp.dimx(),disp.dimy()).normalize(0,255);
         CImg<> isophotes(img.dimx(),img.dimy(),1,2,0);
         cimg_forXY(img,x,y) if (!mask.data || mask(x,y)) {
           T.get_tensor_at(x,y).symmetric_eigen(val,vec);
           isophotes(x,y,0) = vec(0,0);
           isophotes(x,y,1) = vec(0,1);
         }
         visu.draw_quiver(isophotes,white,0.5f,10,9,0).display(disp);
       }
     }
     if (save && file_o && !(iter%save)) img.save(file_o,iter);
     if (disp) disp.resize().display(img);
   }
   // Save result and exit.
   if (file_o) img.save(file_o);
   return 0;
 }

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PTdecode/CImg-1.3.0/examples/pde_TschumperleDeriche2d.cpp@b7fe751ea60d (annotated)

PTdecode/CImg-1.3.0/examples/pde_TschumperleDeriche2d.cpp