summaryrefslogtreecommitdiffstats
path: root/python/astra/optomo.py
blob: 5ecff8feecdcadd1d9979e865b2e260f5b469593 (plain)
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
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
# -----------------------------------------------------------------------
# Copyright: 2010-2018, imec Vision Lab, University of Antwerp
#            2013-2018, CWI, Amsterdam
#
# Contact: astra@astra-toolbox.com
# Website: http://www.astra-toolbox.com/
#
# This file is part of the ASTRA Toolbox.
#
#
# The ASTRA Toolbox is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# The ASTRA Toolbox is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with the ASTRA Toolbox. If not, see <http://www.gnu.org/licenses/>.
#
# -----------------------------------------------------------------------

from . import data2d
from . import data3d
from . import projector
from . import projector3d
from . import creators
from . import algorithm
from . import functions
import numpy as np
from six.moves import reduce
try:
    from six.moves import range
except ImportError:
    # six 1.3.0
    from six.moves import xrange as range

import operator
import scipy.sparse.linalg

class OpTomo(scipy.sparse.linalg.LinearOperator):
    """Object that imitates a projection matrix with a given projector.

    This object can do forward projection by using the ``*`` operator::

        W = astra.OpTomo(proj_id)
        fp = W*image
        bp = W.T*sinogram

    It can also be used in minimization methods of the :mod:`scipy.sparse.linalg` module::

        W = astra.OpTomo(proj_id)
        output = scipy.sparse.linalg.lsqr(W,sinogram)

    :param proj_id: ID to a projector.
    :type proj_id: :class:`int`
    """

    def __init__(self,proj_id):
        self.dtype = np.float32
        try:
            self.vg = projector.volume_geometry(proj_id)
            self.pg = projector.projection_geometry(proj_id)
            self.data_mod = data2d
            self.appendString = ""
            if projector.is_cuda(proj_id):
                self.appendString += "_CUDA"
        except Exception:
            self.vg = projector3d.volume_geometry(proj_id)
            self.pg = projector3d.projection_geometry(proj_id)
            self.data_mod = data3d
            self.appendString = "3D"
            if projector3d.is_cuda(proj_id):
                self.appendString += "_CUDA"

        self.vshape = functions.geom_size(self.vg)
        self.vsize = reduce(operator.mul,self.vshape)
        self.sshape = functions.geom_size(self.pg)
        self.ssize = reduce(operator.mul,self.sshape)

        self.shape = (self.ssize, self.vsize)

        self.proj_id = proj_id

        self.transposeOpTomo = OpTomoTranspose(self)
        try:
            self.T = self.transposeOpTomo
        except AttributeError:
            # Scipy >= 0.16 defines self.T using self._transpose()
            pass

    def _transpose(self):
        return self.transposeOpTomo

    # real operator
    _adjoint = _transpose

    def __checkArray(self, arr, shp):
        if len(arr.shape)==1:
            arr = arr.reshape(shp)
        if arr.dtype != np.float32:
            arr = arr.astype(np.float32)
        if arr.flags['C_CONTIGUOUS']==False:
            arr = np.ascontiguousarray(arr)
        return arr

    def _matvec(self,v):
        """Implements the forward operator.

        :param v: Volume to forward project.
        :type v: :class:`numpy.ndarray`
        """
        return self.FP(v, out=None).ravel()

    def rmatvec(self,s):
        """Implements the transpose operator.

        :param s: The projection data.
        :type s: :class:`numpy.ndarray`
        """
        return self.BP(s, out=None).ravel()

    def __mul__(self,v):
        """Provides easy forward operator by *.

        :param v: Volume to forward project.
        :type v: :class:`numpy.ndarray`
        """
        # Catch the case of a forward projection of a 2D/3D image
        if isinstance(v, np.ndarray) and v.shape==self.vshape:
            return self._matvec(v)
        return scipy.sparse.linalg.LinearOperator.__mul__(self, v)

    def reconstruct(self, method, s, iterations=1, extraOptions = None):
        """Reconstruct an object.

        :param method: Method to use for reconstruction.
        :type method: :class:`string`
        :param s: The projection data.
        :type s: :class:`numpy.ndarray`
        :param iterations: Number of iterations to use.
        :type iterations: :class:`int`
        :param extraOptions: Extra options to use during reconstruction (i.e. for cfg['option']).
        :type extraOptions: :class:`dict`
        """
        if extraOptions is None:
            extraOptions={}
        s = self.__checkArray(s, self.sshape)
        sid = self.data_mod.link('-sino',self.pg,s)
        v = np.zeros(self.vshape,dtype=np.float32)
        vid = self.data_mod.link('-vol',self.vg,v)
        cfg = creators.astra_dict(method)
        cfg['ProjectionDataId'] = sid
        cfg['ReconstructionDataId'] = vid
        cfg['ProjectorId'] = self.proj_id
        cfg['option'] = extraOptions
        alg_id = algorithm.create(cfg)
        algorithm.run(alg_id,iterations)
        algorithm.delete(alg_id)
        self.data_mod.delete([vid,sid])
        return v

    def FP(self,v,out=None):
        """Perform forward projection.

        Output must have the right 2D/3D shape. Input may also be flattened.

        Output must also be contiguous and float32. This isn't required for the
        input, but it is more efficient if it is.

        :param v: Volume to forward project.
        :type v: :class:`numpy.ndarray`
        :param out: Array to store result in.
        :type out: :class:`numpy.ndarray`
        """

        v = self.__checkArray(v, self.vshape)
        vid = self.data_mod.link('-vol',self.vg,v)
        if out is None:
            out = np.zeros(self.sshape,dtype=np.float32)
        sid = self.data_mod.link('-sino',self.pg,out)

        cfg = creators.astra_dict('FP'+self.appendString)
        cfg['ProjectionDataId'] = sid
        cfg['VolumeDataId'] = vid
        cfg['ProjectorId'] = self.proj_id
        fp_id = algorithm.create(cfg)
        algorithm.run(fp_id)

        algorithm.delete(fp_id)
        self.data_mod.delete([vid,sid])
        return out

    def BP(self,s,out=None):
        """Perform backprojection.

        Output must have the right 2D/3D shape. Input may also be flattened.

        Output must also be contiguous and float32. This isn't required for the
        input, but it is more efficient if it is.

        :param : The projection data.
        :type s: :class:`numpy.ndarray`
        :param out: Array to store result in.
        :type out: :class:`numpy.ndarray`
        """
        s = self.__checkArray(s, self.sshape)
        sid = self.data_mod.link('-sino',self.pg,s)
        if out is None:
            out = np.zeros(self.vshape,dtype=np.float32)
        vid = self.data_mod.link('-vol',self.vg,out)

        cfg = creators.astra_dict('BP'+self.appendString)
        cfg['ProjectionDataId'] = sid
        cfg['ReconstructionDataId'] = vid
        cfg['ProjectorId'] = self.proj_id
        bp_id = algorithm.create(cfg)
        algorithm.run(bp_id)

        algorithm.delete(bp_id)
        self.data_mod.delete([vid,sid])
        return out




class OpTomoTranspose(scipy.sparse.linalg.LinearOperator):
    """This object provides the transpose operation (``.T``) of the OpTomo object.

    Do not use directly, since it can be accessed as member ``.T`` of
    an :class:`OpTomo` object.
    """
    def __init__(self,parent):
        self.parent = parent
        self.dtype = np.float32
        self.shape = (parent.shape[1], parent.shape[0])
        try:
            self.T = self.parent
        except AttributeError:
            # Scipy >= 0.16 defines self.T using self._transpose()
            pass

    def _matvec(self, s):
        return self.parent.rmatvec(s)

    def rmatvec(self, v):
        return self.parent.matvec(v)

    def _transpose(self):
        return self.parent

    # real operator
    _adjoint = _transpose

    def __mul__(self,s):
        # Catch the case of a backprojection of 2D/3D data
        if isinstance(s, np.ndarray) and s.shape==self.parent.sshape:
            return self._matvec(s)
        return scipy.sparse.linalg.LinearOperator.__mul__(self, s)