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
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Demonstration of CPU inpainters
@authors: Daniil Kazantsev, Edoardo Pasca
"""
import matplotlib.pyplot as plt
import numpy as np
import os
import timeit
from scipy import io
from ccpi.filters.regularisers import NDF_INP, NVM_INP
from ccpi.supp.qualitymetrics import QualityTools
###############################################################################
def printParametersToString(pars):
txt = r''
for key, value in pars.items():
if key== 'algorithm' :
txt += "{0} = {1}".format(key, value.__name__)
elif key == 'input':
txt += "{0} = {1}".format(key, np.shape(value))
elif key == 'maskData':
txt += "{0} = {1}".format(key, np.shape(value))
else:
txt += "{0} = {1}".format(key, value)
txt += '\n'
return txt
###############################################################################
# read sinogram and the mask
filename = os.path.join("data" ,"SinoInpaint.mat")
sino = io.loadmat(filename)
sino_full = sino.get('Sinogram')
Mask = sino.get('Mask')
[angles_dim,detectors_dim] = sino_full.shape
sino_full = sino_full/np.max(sino_full)
#apply mask to sinogram
sino_cut = sino_full*(1-Mask)
#sino_cut_new = np.zeros((angles_dim,detectors_dim),'float32')
#sino_cut_new = sino_cut.copy(order='c')
#sino_cut_new[:] = sino_cut[:]
sino_cut_new = np.ascontiguousarray(sino_cut, dtype=np.float32);
#mask = np.zeros((angles_dim,detectors_dim),'uint8')
#mask =Mask.copy(order='c')
#mask[:] = Mask[:]
mask = np.ascontiguousarray(Mask, dtype=np.uint8);
plt.figure(1)
plt.subplot(121)
plt.imshow(sino_cut_new,vmin=0.0, vmax=1)
plt.title('Missing Data sinogram')
plt.subplot(122)
plt.imshow(mask)
plt.title('Mask')
plt.show()
#%%
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("___Inpainting using linear diffusion (2D)__")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
## plot
fig = plt.figure(2)
plt.suptitle('Performance of linear inpainting using the CPU')
a=fig.add_subplot(1,2,1)
a.set_title('Missing data sinogram')
imgplot = plt.imshow(sino_cut_new,cmap="gray")
# set parameters
pars = {'algorithm' : NDF_INP, \
'input' : sino_cut_new,\
'maskData' : mask,\
'regularisation_parameter':5000,\
'edge_parameter':0,\
'number_of_iterations' :5000 ,\
'time_marching_parameter':0.000075,\
'penalty_type':0
}
start_time = timeit.default_timer()
ndf_inp_linear = NDF_INP(pars['input'],
pars['maskData'],
pars['regularisation_parameter'],
pars['edge_parameter'],
pars['number_of_iterations'],
pars['time_marching_parameter'],
pars['penalty_type'])
Qtools = QualityTools(sino_full, ndf_inp_linear)
pars['rmse'] = Qtools.rmse()
txtstr = printParametersToString(pars)
txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
print (txtstr)
a=fig.add_subplot(1,2,2)
# these are matplotlib.patch.Patch properties
props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
# place a text box in upper left in axes coords
a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
imgplot = plt.imshow(ndf_inp_linear, cmap="gray")
plt.title('{}'.format('Linear diffusion inpainting results'))
#%%
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("_Inpainting using nonlinear diffusion (2D)_")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
## plot
fig = plt.figure(3)
plt.suptitle('Performance of nonlinear diffusion inpainting using the CPU')
a=fig.add_subplot(1,2,1)
a.set_title('Missing data sinogram')
imgplot = plt.imshow(sino_cut_new,cmap="gray")
# set parameters
pars = {'algorithm' : NDF_INP, \
'input' : sino_cut_new,\
'maskData' : mask,\
'regularisation_parameter':80,\
'edge_parameter':0.00009,\
'number_of_iterations' :1500 ,\
'time_marching_parameter':0.000008,\
'penalty_type':1
}
start_time = timeit.default_timer()
ndf_inp_nonlinear = NDF_INP(pars['input'],
pars['maskData'],
pars['regularisation_parameter'],
pars['edge_parameter'],
pars['number_of_iterations'],
pars['time_marching_parameter'],
pars['penalty_type'])
Qtools = QualityTools(sino_full, ndf_inp_nonlinear)
pars['rmse'] = Qtools.rmse()
txtstr = printParametersToString(pars)
txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
print (txtstr)
a=fig.add_subplot(1,2,2)
# these are matplotlib.patch.Patch properties
props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
# place a text box in upper left in axes coords
a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
imgplot = plt.imshow(ndf_inp_nonlinear, cmap="gray")
plt.title('{}'.format('Nonlinear diffusion inpainting results'))
#%%
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("Inpainting using nonlocal vertical marching")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
## plot
fig = plt.figure(4)
plt.suptitle('Performance of NVM inpainting using the CPU')
a=fig.add_subplot(1,2,1)
a.set_title('Missing data sinogram')
imgplot = plt.imshow(sino_cut,cmap="gray")
# set parameters
pars = {'algorithm' : NVM_INP, \
'input' : sino_cut_new,\
'maskData' : mask,\
'SW_increment': 1,\
'number_of_iterations' : 150
}
start_time = timeit.default_timer()
(nvm_inp, mask_upd) = NVM_INP(pars['input'],
pars['maskData'],
pars['SW_increment'],
pars['number_of_iterations'])
Qtools = QualityTools(sino_full, nvm_inp)
pars['rmse'] = Qtools.rmse()
txtstr = printParametersToString(pars)
txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
print (txtstr)
a=fig.add_subplot(1,2,2)
# these are matplotlib.patch.Patch properties
props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
# place a text box in upper left in axes coords
a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
imgplot = plt.imshow(nvm_inp, cmap="gray")
plt.title('{}'.format('Nonlocal Vertical Marching inpainting results'))
#%%
|
