1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
|
/*
#
# 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;
}
|
