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
232
233
234
235
236
237
238
|
import numpy as np
import unittest
import astra
import math
import pylab
DISPLAY=False
def VolumeGeometries(is3D,noncube):
if not is3D:
for s in [0.8, 1.0, 1.25]:
yield astra.create_vol_geom(128, 128, -64*s, 64*s, -64*s, 64*s)
elif noncube:
for sx in [0.8, 1.0]:
for sy in [0.8, 1.0]:
for sz in [0.8, 1.0]:
yield astra.create_vol_geom(64, 64, 64, -32*sx, 32*sx, -32*sy, 32*sy, -32*sz, 32*sz)
else:
for s in [0.8, 1.0]:
yield astra.create_vol_geom(64, 64, 64, -32*s, 32*s, -32*s, 32*s, -32*s, 32*s)
def ProjectionGeometries(type,shortscan):
if type == 'parallel':
for dU in [0.8, 1.0, 1.25]:
yield astra.create_proj_geom('parallel', dU, 256, np.linspace(0,np.pi,180,False))
elif type == 'fanflat':
for dU in [0.8, 1.0, 1.25]:
for src in [500, 1000]:
for det in [0, 250, 500]:
yield astra.create_proj_geom('fanflat', dU, 256, np.linspace(0,2*np.pi,180,False), src, det)
elif type == 'parallel3d':
for dU in [0.8, 1.0]:
for dV in [0.8, 1.0]:
yield astra.create_proj_geom('parallel3d', dU, dV, 128, 128, np.linspace(0,np.pi,180,False))
elif type == 'parallel3d_vec':
for j in range(10):
Vectors = np.zeros([180,12])
wu = 0.6 + 0.8 * np.random.random()
wv = 0.6 + 0.8 * np.random.random()
for i in range(Vectors.shape[0]):
l = 0.6 + 0.8 * np.random.random()
angle1 = 2*np.pi*np.random.random()
angle2 = angle1 + 0.5 * np.random.random()
angle3 = 0.1*np.pi*np.random.random()
detc = 10 * np.random.random(size=3)
detu = [ math.cos(angle1) * wu, math.sin(angle1) * wu, 0 ]
detv = [ -math.sin(angle1) * math.sin(angle3) * wv, math.cos(angle1) * math.sin(angle3) * wv, math.cos(angle3) * wv ]
ray = [ math.sin(angle2) * l, -math.cos(angle2) * l, 0 ]
Vectors[i, :] = [ ray[0], ray[1], ray[2], detc[0], detc[1], detc[2], detu[0], detu[1], detu[2], detv[0], detv[1], detv[2] ]
pg = astra.create_proj_geom('parallel3d_vec', 128, 128, Vectors)
yield pg
elif type == 'cone':
A = [1.5, 2] if shortscan else [ 2 ]
for dU in [0.8, 1.0]:
for dV in [0.8, 1.0]:
for src in [500, 1000]:
for det in [0, 250]:
for a in A:
yield astra.create_proj_geom('cone', dU, dV, 128, 128, np.linspace(0,a*np.pi,180,False), src, det)
elif type == 'cone_vec':
for j in range(10):
Vectors = np.zeros([180,12])
wu = 0.6 + 0.8 * np.random.random()
wv = 0.6 + 0.8 * np.random.random()
for i in range(Vectors.shape[0]):
l = 256 * (0.5 * np.random.random())
angle1 = 2*np.pi*np.random.random()
angle2 = angle1 + 0.5 * np.random.random()
angle3 = 0.1*np.pi*np.random.random()
detc = 10 * np.random.random(size=3)
detu = [ math.cos(angle1) * wu, math.sin(angle1) * wu, 0 ]
detv = [ -math.sin(angle1) * math.sin(angle3) * wv, math.cos(angle1) * math.sin(angle3) * wv, math.cos(angle3) * wv ]
src = [ math.sin(angle2) * l, -math.cos(angle2) * l, 0 ]
Vectors[i, :] = [ src[0], src[1], src[2], detc[0], detc[1], detc[2], detu[0], detu[1], detu[2], detv[0], detv[1], detv[2] ]
pg = astra.create_proj_geom('parallel3d_vec', 128, 128, Vectors)
yield pg
class TestRecScale(unittest.TestCase):
def single_test(self, geom_type, proj_type, alg, iters, vss, dss):
if alg == 'FBP' and 'fanflat' in geom_type:
self.skipTest('CPU FBP is parallel-beam only')
is3D = (geom_type in ['parallel3d', 'cone'])
for vg in VolumeGeometries(is3D, 'FDK' not in alg):
for pg in ProjectionGeometries(geom_type, 'FDK' in alg):
if not is3D:
vol = np.zeros((128,128),dtype=np.float32)
vol[50:70,50:70] = 1
else:
vol = np.zeros((64,64,64),dtype=np.float32)
vol[25:35,25:35,25:35] = 1
options = {}
if vss > 1:
options["VoxelSuperSampling"] = vss
if dss > 1:
options["DetectorSuperSampling"] = vss
proj_id = astra.create_projector(proj_type, pg, vg, options=options)
if not is3D:
sino_id, sinogram = astra.create_sino(vol, proj_id)
else:
sino_id, sinogram = astra.create_sino3d_gpu(vol, pg, vg)
if not is3D:
DATA = astra.data2d
else:
DATA = astra.data3d
rec_id = DATA.create('-vol', vg, 0.0 if 'EM' not in alg else 1.0)
cfg = astra.astra_dict(alg)
cfg['ReconstructionDataId'] = rec_id
cfg['ProjectionDataId'] = sino_id
cfg['ProjectorId'] = proj_id
if 'FDK' in alg and geom_type == "cone" and pg["ProjectionAngles"][-1] < 1.8*np.pi:
cfg['option'] = { 'ShortScan': True }
alg_id = astra.algorithm.create(cfg)
for i in range(iters):
astra.algorithm.run(alg_id, 1)
rec = DATA.get(rec_id)
astra.astra.delete([sino_id, alg_id, alg_id, proj_id])
if not is3D:
val = np.sum(rec[55:65,55:65]) / 100.
else:
val = np.sum(rec[27:32,27:32,27:32]) / 125.
TOL = 5e-2
if DISPLAY and abs(val-1.0) >= TOL:
print(geom_type, proj_type, alg, vg, pg)
print(val)
pylab.gray()
if not is3D:
pylab.imshow(rec)
else:
pylab.imshow(rec[:,32,:])
pylab.show()
self.assertTrue(abs(val-1.0) < TOL)
def single_test_adjoint3D(self, geom_type, proj_type):
for vg in VolumeGeometries(True, True):
for pg in ProjectionGeometries(geom_type):
for i in range(5):
X = np.random.random(astra.geom_size(vg))
Y = np.random.random(astra.geom_size(pg))
proj_id, fX = astra.create_sino3d_gpu(X, pg, vg)
bp_id, fTY = astra.create_backprojection3d_gpu(Y, pg, vg)
astra.data3d.delete([proj_id, bp_id])
da = np.dot(fX.ravel(), Y.ravel())
db = np.dot(X.ravel(), fTY.ravel())
m = np.abs(da - db)
TOL = 1e-1
if m / da >= TOL:
print(vg)
print(pg)
print(m/da, da/db, da, db)
self.assertTrue(m / da < TOL)
__combinations = {
'parallel': [ 'line', 'linear', 'distance_driven', 'strip', 'cuda' ],
'fanflat': [ 'line_fanflat', 'strip_fanflat', 'cuda' ],
'parallel3d': [ 'cuda3d' ],
'cone': [ 'cuda3d' ],
}
__combinations_adjoint = {
'parallel3d': [ 'cuda3d' ],
'cone': [ 'cuda3d' ],
'parallel3d_vec': [ 'cuda3d' ],
'cone_vec': [ 'cuda3d' ],
}
__algs = {
'SIRT': 50, 'SART': 10*180, 'CGLS': 30,
'FBP': 1
}
__algs_CUDA = {
'SIRT_CUDA': 50, 'SART_CUDA': 10*180, 'CGLS_CUDA': 30, 'EM_CUDA': 50,
'FBP_CUDA': 1
}
__algs_parallel3d = {
'SIRT3D_CUDA': 200, 'CGLS3D_CUDA': 20,
}
__algs_cone = {
'SIRT3D_CUDA': 200, 'CGLS3D_CUDA': 20,
'FDK_CUDA': 1
}
__combinations_ss = {
'parallel': [ { 'projector': 'cuda', 'alg': 'SIRT_CUDA', 'iters': 50 } ],
'fanflat': [ { 'projector': 'cuda', 'alg': 'SIRT_CUDA', 'iters': 50 } ],
'parallel3d': [ { 'projector': 'cuda3d', 'alg': 'SIRT3D_CUDA', 'iters': 200 } ],
'cone': [ { 'projector': 'cuda3d', 'alg': 'SIRT3D_CUDA', 'iters': 200 } ]
}
for k, l in __combinations.items():
for v in l:
is3D = (k in ['parallel3d', 'cone'])
if k == 'parallel3d':
A = __algs_parallel3d
elif k == 'cone':
A = __algs_cone
elif v == 'cuda':
A = __algs_CUDA
else:
A = __algs
for a, i in A.items():
def f(k, v, a, i):
return lambda self: self.single_test(k, v, a, i, 1, 1)
setattr(TestRecScale, 'test_' + a + '_' + k + '_' + v, f(k,v,a,i))
for k, l in __combinations_adjoint.items():
for v in l:
def g(k, v):
return lambda self: self.single_test_adjoint3D(k, v)
setattr(TestRecScale, 'test_adjoint_' + k + '_' + v, g(k,v))
for k, l in __combinations_ss.items():
for A in l:
for vss in [1, 2]:
for dss in [1, 2]:
def h(k, v, a, i, vss, dss):
return lambda self: self.single_test(k, v, a, i, vss, dss)
setattr(TestRecScale, 'test_ss_' + a + '_' + k + '_' + v + '_' + str(vss) + '_' + str(dss), h(k, A['projector'], A['alg'], A['iters'], vss, dss))
if __name__ == '__main__':
unittest.main()
|
