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# Copyright 2014 Diamond Light Source Ltd. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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""" |
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.. module:: forward_projector_cpu |
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:platform: Unix |
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:synopsis: A forward data projector using ToMoBAR software |
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.. moduleauthor:: Daniil Kazantsev <[email protected]> |
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""" |
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from savu.plugins.plugin import Plugin |
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from savu.plugins.driver.cpu_plugin import CpuPlugin |
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from savu.plugins.utils import register_plugin |
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from tomobar.methodsDIR import RecToolsDIR |
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import numpy as np |
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@register_plugin |
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class ForwardProjectorCpu(Plugin, CpuPlugin): |
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""" |
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This plugin uses ToMoBAR software and CPU Astra projector underneath to generate projection data. |
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The plugin will project the given object using the metadata OR user-provided parallel-beam geometry. |
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In case when angles set to None, the metadata projection geometry will be used. |
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:param angles_deg: Projection angles in degrees in a format [start, stop, step]. Default: None. |
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:param det_horiz: The size of the _horizontal_ detector. Default: 300. |
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:param centre_of_rotation: The centre of rotation. Default: 85.5. |
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:param out_datasets: Default out dataset names. Default: ['forw_proj'] |
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""" |
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def __init__(self): |
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super(ForwardProjectorCpu, self).__init__('ForwardProjectorCpu') |
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def pre_process(self): |
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#getting metadata |
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in_meta_data = self.get_in_meta_data()[0] |
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self.cor = in_meta_data.get('centre_of_rotation') |
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self.cor=self.cor[0] |
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def setup(self): |
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in_dataset, out_dataset = self.get_datasets() |
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in_pData, out_pData = self.get_plugin_datasets() |
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in_pData[0].plugin_data_setup('VOLUME_XZ', 'single') |
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if (self.parameters['angles_deg'] is None): |
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# data extracted geometry parameters |
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in_meta_data=self.get_in_meta_data()[0] |
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angles_meta_deg = in_meta_data.get('rotation_angle') |
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self.angles_rad = np.deg2rad(angles_meta_deg) |
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self.detectors_horiz = in_meta_data.get('detector_x') |
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else: |
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# user-set parameters |
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angles_list=self.parameters['angles_deg'] |
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self.cor=self.parameters['centre_of_rotation'] |
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self.angles_rad = np.deg2rad(np.arange(angles_list[0], angles_list[1], angles_list[2], dtype=np.float)) |
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self.detectors_horiz = self.parameters['det_horiz'] |
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self.det_horiz_half=0.5*self.detectors_horiz |
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self.angles_total = len(self.angles_rad) |
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out_shape_sino = self.new_shape(in_dataset[0].get_shape(), in_dataset[0]) |
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labels = ['rotation_angle.degrees', 'detector_y.pixel', 'detector_x.pixel'] |
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#labels = ['x.pixels', 'proj.angles', 'y.pixels'] |
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pattern = {'name': 'SINOGRAM', 'slice_dims': (1,), |
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'core_dims': (2,0)} |
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out_dataset[0].create_dataset(axis_labels=labels, shape=out_shape_sino) |
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out_dataset[0].add_pattern(pattern['name'], |
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slice_dims=pattern['slice_dims'], |
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core_dims=pattern['core_dims']) |
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out_pData[0].plugin_data_setup(pattern['name'], self.get_max_frames()) |
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out_dataset[0].meta_data.set('rotation_angle', self.angles_rad) |
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def process_frames(self, data): |
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image = data[0].astype(np.float32) |
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image = np.where(np.isfinite(image), image, 0) |
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objsize_image = np.shape(image)[0] |
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RectoolsDIR = RecToolsDIR(DetectorsDimH = self.detectors_horiz, # DetectorsDimH # detector dimension (horizontal) |
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DetectorsDimV = None, # DetectorsDimV # detector dimension (vertical) for 3D case only |
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CenterRotOffset = -self.cor+self.det_horiz_half, # Center of Rotation (CoR) scalar |
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AnglesVec = self.angles_rad, # array of angles in radians |
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ObjSize = objsize_image, # a scalar to define reconstructed object dimensions |
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device_projector='cpu') |
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sinogram_new = RectoolsDIR.FORWPROJ(image) |
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return sinogram_new |
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def new_shape(self, full_shape, data): |
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# calculate a new output data shape based on the input data shape |
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new_shape_sino_orig = list(full_shape) |
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new_shape_sino= (self.angles_total, new_shape_sino_orig[1], self.detectors_horiz) |
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return tuple(new_shape_sino) |
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def get_max_frames(self): |
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return 'single' |
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def nInput_datasets(self): |
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return 1 |
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def nOutput_datasets(self): |
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return 1 |
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DiamondLightSource /
Savu
