|
1
|
|
|
# Copyright 2014 Diamond Light Source Ltd. |
|
2
|
|
|
# |
|
3
|
|
|
# Licensed under the Apache License, Version 2.0 (the "License"); |
|
4
|
|
|
# you may not use this file except in compliance with the License. |
|
5
|
|
|
# You may obtain a copy of the License at |
|
6
|
|
|
# |
|
7
|
|
|
# http://www.apache.org/licenses/LICENSE-2.0 |
|
8
|
|
|
# |
|
9
|
|
|
# Unless required by applicable law or agreed to in writing, software |
|
10
|
|
|
# distributed under the License is distributed on an "AS IS" BASIS, |
|
11
|
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
|
12
|
|
|
# See the License for the specific language governing permissions and |
|
13
|
|
|
# limitations under the License. |
|
14
|
|
|
|
|
15
|
|
|
""" |
|
16
|
|
|
.. module:: forward_projector_tomobar |
|
17
|
|
|
:platform: Unix |
|
18
|
|
|
:synopsis: A forward data projector using ToMoBAR software |
|
19
|
|
|
|
|
20
|
|
|
.. moduleauthor:: Daniil Kazantsev <[email protected]> |
|
21
|
|
|
""" |
|
22
|
|
|
|
|
23
|
|
|
from savu.plugins.reconstructions.base_recon import BaseRecon |
|
24
|
|
|
from savu.plugins.driver.cpu_plugin import CpuPlugin |
|
25
|
|
|
from savu.plugins.utils import register_plugin |
|
26
|
|
|
|
|
27
|
|
|
from tomobar.methodsDIR import RecToolsDIR |
|
28
|
|
|
import numpy as np |
|
29
|
|
|
|
|
30
|
|
|
@register_plugin |
|
31
|
|
|
class ForwardProjectorTomobar(BaseRecon, CpuPlugin): |
|
32
|
|
|
""" |
|
33
|
|
|
This plugin uses ToMoBAR software and CPU Astra projector to generate projection data, |
|
34
|
|
|
one needs to provide 2 inputs [original projection data, object to project]. |
|
35
|
|
|
The plugin will project the given object using geometry of the provided projection data |
|
36
|
|
|
|
|
37
|
|
|
:param out_datasets: Default out dataset names. Default: ['forw_proj'] |
|
38
|
|
|
""" |
|
39
|
|
|
|
|
40
|
|
|
def __init__(self): |
|
41
|
|
|
super(ForwardProjectorTomobar, self).__init__('ForwardProjectorTomobar') |
|
42
|
|
|
|
|
43
|
|
|
def setup(self): |
|
44
|
|
|
in_dataset, out_dataset = self.get_datasets() |
|
45
|
|
|
in_pData, out_pData = self.get_plugin_datasets() |
|
46
|
|
|
in_pData[0].plugin_data_setup('SINOGRAM', self.get_max_frames()) |
|
47
|
|
|
in_pData[1].plugin_data_setup('VOLUME_XZ', 'single') |
|
48
|
|
|
|
|
49
|
|
|
out_shape_sino = in_dataset[0].get_shape() |
|
50
|
|
|
out_dataset[0].create_dataset(patterns=in_dataset[0], |
|
51
|
|
|
axis_labels=in_dataset[0], |
|
52
|
|
|
shape=out_shape_sino) |
|
53
|
|
|
out_pData[0].plugin_data_setup('SINOGRAM',self.get_max_frames()) |
|
54
|
|
|
|
|
55
|
|
|
def process_frames(self, data): |
|
56
|
|
|
cor, angles, vol_shape, init = self.get_frame_params() |
|
57
|
|
|
sinogram = data[0].astype(np.float32) |
|
58
|
|
|
image = data[1].astype(np.float32) |
|
59
|
|
|
objsize_image = np.shape(image)[0] |
|
60
|
|
|
proj_number, self.DetectorsDimH = np.shape(sinogram) |
|
61
|
|
|
self.anglesRAD = np.deg2rad(angles.astype(np.float32)) |
|
62
|
|
|
half_det_width = 0.5*self.DetectorsDimH |
|
63
|
|
|
cor_astra = half_det_width - cor |
|
64
|
|
|
RectoolsDIR = RecToolsDIR(DetectorsDimH = self.DetectorsDimH+1, # DetectorsDimH # detector dimension (horizontal) |
|
65
|
|
|
DetectorsDimV = None, # DetectorsDimV # detector dimension (vertical) for 3D case only |
|
66
|
|
|
CenterRotOffset = cor_astra.item() - 0.5, # Center of Rotation (CoR) scalar (for 3D case only) |
|
67
|
|
|
AnglesVec = self.anglesRAD, # array of angles in radians |
|
68
|
|
|
ObjSize = objsize_image, # a scalar to define reconstructed object dimensions |
|
69
|
|
|
device_projector='cpu') |
|
70
|
|
|
sinogram_new = RectoolsDIR.FORWPROJ(image) |
|
71
|
|
|
return sinogram_new |
|
72
|
|
|
|
|
73
|
|
|
def get_max_frames(self): |
|
74
|
|
|
return 'single' |
|
75
|
|
|
|
|
76
|
|
|
def nInput_datasets(self): |
|
77
|
|
|
return 2 |
|
78
|
|
|
|
|
79
|
|
|
def nOutput_datasets(self): |
|
80
|
|
|
return 1 |
|
81
|
|
|
|
DiamondLightSource /
Savu
