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pde_TschumperleDeriche2d.cpp
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pde_TschumperleDeriche2d.cpp
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/*
#
# File : pde_TschumperleDeriche2d.cpp
# ( C++ source file )
#
# Description : Implementation of the Tschumperlé-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.eu )
#
# (1) PDE-Based Regularization of Multivalued Images and Applications.
# (D. Tschumperlé). PhD Thesis. University of Nice-Sophia Antipolis, December 2002.
# (2) Diffusion PDE's on Vector-valued Images : Local Approach and Geometric Viewpoint.
# (D. Tschumperlé and R. Deriche). IEEE Signal Processing Magazine, October 2002.
# (3) Vector-Valued Image Regularization with PDE's : A Common Framework for Different Applications.
# (D. Tschumperlé 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.
#
# Copyright : David Tschumperlé
# ( http://tschumperle.users.greyc.fr/ )
#
# 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;
#ifndef cimg_imagepath
#define cimg_imagepath "img/"
#endif
#undef min
#undef max
// Main procedure
//----------------
int main(int argc,char **argv) {
// Read command line arguments
//-----------------------------
cimg_usage("Tschumperlé-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.width(),img.height(),1,1);
else if (zoom>1) {
mask = CImg<int>(img.width(),img.height(),1,1,-1).
resize((int)(img.width()*zoom),(int)(img.height()*zoom),1,1,4) + 1;
img.resize((int)(img.width()*zoom),(int)(img.height()*zoom),1,-100,3);
}
} else {
if (file_f) {
flow = CImg<>(file_f);
img = CImg<>((int)(flow.width()*zoom),(int)(flow.height()*zoom),1,1,0).noise(100,2);
flow.resize(img.width(),img.height(),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.max_min(initial_min);
if (mask && init!=3)
cimg_forXYC(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.width(),img.height(),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.is_keySPACE()) { view_t = !view_t; disp.set_key(); }
if (!(iter%skip)) {
// Compute the tensor field T, used to drive the diffusion
//---------------------------------------------------------
// When using PDE for flow visualization
if (flow) 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_forXYC(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) 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_forC(img,k) cimg_for3x3(img,x,y,0,k,I,float) {
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.max_min(m);
xdt = dt/std::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> nvisu = img.get_resize(disp.width(),disp.height()).normalize(0,255);
CImg<> isophotes(img.width(),img.height(),1,2,0);
cimg_forXY(img,x,y) if (!mask || 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);
}
nvisu.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;
}