# Copyright 2021 United Kingdom Research and Innovation
# Copyright 2021 The University of Manchester
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Authors:
# CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt
import logging
import numpy as np
from cil.framework import ImageData, AcquisitionData, BlockGeometry
from cil.framework.labels import AcquisitionDimension, ImageDimension
from cil.optimisation.operators import BlockOperator, LinearOperator
from cil.plugins.tigre import CIL2TIGREGeometry
log = logging.getLogger(__name__)
try:
from _Atb import _Atb_ext as Atb
from _Ax import _Ax_ext as Ax
except ModuleNotFoundError:
raise ModuleNotFoundError(
"This plugin requires the additional package TIGRE\n" +
"Please install it via conda as tigre from the ccpi channel")
try:
from tigre.utilities.gpu import GpuIds
has_gpu_sel = True
except ModuleNotFoundError:
has_gpu_sel = False
[docs]
class ProjectionOperator(LinearOperator):
"""
ProjectionOperator configures and calls TIGRE Projectors for your dataset.
Please refer to the TIGRE documentation for further descriptions
https://github.com/CERN/TIGRE
https://iopscience.iop.org/article/10.1088/2057-1976/2/5/055010
Parameters
----------
image_geometry : `ImageGeometry`, default used if None
A description of the area/volume to reconstruct
acquisition_geometry :`AcquisitionGeometry`, `BlockGeometry`
A description of the acquisition data. If passed a BlockGeometry it will return a BlockOperator.
direct_method : str, default 'interpolated'
The method used by the forward projector, 'Siddon' for ray-voxel intersection, 'interpolated' for interpolated projection
adjoint_weights : str, default 'matched'
The weighting method used by the cone-beam backward projector, 'matched' for weights to approximately match the 'interpolated' forward projector, 'FDK' for FDK weights
Example
-------
>>> from cil.plugins.tigre import ProjectionOperator
>>> PO = ProjectionOperator(image.geometry, data.geometry)
>>> forward_projection = PO.direct(image)
>>> backward_projection = PO.adjoint(data)
"""
def __new__(cls, image_geometry=None, acquisition_geometry=None, \
direct_method='interpolated',adjoint_weights='matched', **kwargs):
if isinstance(acquisition_geometry, BlockGeometry):
log.info("BlockOperator is returned.")
K = []
for ag in acquisition_geometry:
K.append(
ProjectionOperator_ag(image_geometry=image_geometry, acquisition_geometry=ag, \
direct_method=direct_method, adjoint_weights=adjoint_weights, **kwargs)
)
return BlockOperator(*K)
else:
log.info("Standard Operator is returned.")
return super(ProjectionOperator,
cls).__new__(ProjectionOperator_ag)
class ProjectionOperator_ag(ProjectionOperator):
'''TIGRE Projection Operator'''
def __init__(self,
image_geometry=None,
acquisition_geometry=None,
direct_method='interpolated',
adjoint_weights='matched',
**kwargs):
"""
ProjectionOperator configures and calls TIGRE Projectors for your dataset.
Please refer to the TIGRE documentation for further descriptions
https://github.com/CERN/TIGRE
https://iopscience.iop.org/article/10.1088/2057-1976/2/5/055010
Parameters
----------
image_geometry : ImageGeometry, default used if None
A description of the area/volume to reconstruct
acquisition_geometry : AcquisitionGeometry
A description of the acquisition data
direct_method : str, default 'interpolated'
The method used by the forward projector, 'Siddon' for ray-voxel intersection, 'interpolated' for interpolated projection
adjoint_weights : str, default 'matched'
The weighting method used by the cone-beam backward projector, 'matched' for weights to approximately match the 'interpolated' forward projector, 'FDK' for FDK weights
Example
-------
>>> from cil.plugins.tigre import ProjectionOperator
>>> PO = ProjectionOperator(image.geometry, data.geometry)
>>> forward_projection = PO.direct(image)
>>> backward_projection = PO.adjoint(data)
"""
if acquisition_geometry is None:
raise TypeError("Please specify an acquisition_geometry to configure this operator")
if image_geometry == None:
image_geometry = acquisition_geometry.get_ImageGeometry()
device = kwargs.get('device', 'gpu')
if device != 'gpu':
raise ValueError(
"TIGRE projectors are GPU only. Got device = {}".format(
device))
ImageDimension.check_order_for_engine('tigre', image_geometry)
AcquisitionDimension.check_order_for_engine('tigre', acquisition_geometry)
super(ProjectionOperator,self).__init__(domain_geometry=image_geometry,\
range_geometry=acquisition_geometry)
if direct_method not in ['interpolated', 'Siddon']:
raise ValueError(
"direct_method expected 'interpolated' or 'Siddon' got {}".
format(direct_method))
if adjoint_weights not in ['matched', 'FDK']:
raise ValueError(
"adjoint_weights expected 'matched' or 'FDK' got {}".format(
adjoint_weights))
self.method = {'direct': direct_method, 'adjoint': adjoint_weights}
#set up TIGRE geometry
tigre_geom, tigre_angles = CIL2TIGREGeometry.getTIGREGeometry(
image_geometry, acquisition_geometry)
tigre_geom.check_geo(tigre_angles)
tigre_geom.cast_to_single()
self.tigre_geom = tigre_geom
#set up TIGRE GPU targets (from 2.2)
if has_gpu_sel:
self.gpuids = GpuIds()
def __call_Ax(self, data):
if has_gpu_sel:
return Ax(data, self.tigre_geom, self.tigre_geom.angles,
self.method['direct'], self.tigre_geom.mode, self.gpuids)
else:
return Ax(data, self.tigre_geom, self.tigre_geom.angles,
self.method['direct'], self.tigre_geom.mode)
def direct(self, x, out=None):
data = x.as_array()
if self.tigre_geom.is2D:
data_temp = np.expand_dims(data, axis=0)
arr_out = self.__call_Ax(data_temp)
arr_out = np.squeeze(arr_out, axis=1)
else:
arr_out = self.__call_Ax(data)
#if single angle projection remove the dimension for CIL
if arr_out.shape[0] == 1:
arr_out = np.squeeze(arr_out, axis=0)
if out is None:
out = AcquisitionData(arr_out,
deep_copy=False,
geometry=self._range_geometry.copy())
else:
out.fill(arr_out)
return out
def __call_Atb(self, data):
if has_gpu_sel:
return Atb(data, self.tigre_geom, self.tigre_geom.angles,
self.method['adjoint'], self.tigre_geom.mode,
self.gpuids)
else:
return Atb(data, self.tigre_geom, self.tigre_geom.angles,
self.method['adjoint'], self.tigre_geom.mode)
def adjoint(self, x, out=None):
data = x.as_array()
#if single angle projection add the dimension in for TIGRE
if x.dimension_labels[0] != AcquisitionDimension.ANGLE:
data = np.expand_dims(data, axis=0)
if self.tigre_geom.is2D:
data = np.expand_dims(data, axis=1)
arr_out = self.__call_Atb(data)
arr_out = np.squeeze(arr_out, axis=0)
else:
arr_out = self.__call_Atb(data)
if out is None:
out = ImageData(arr_out,
deep_copy=False,
geometry=self._domain_geometry.copy())
else:
out.fill(arr_out)
return out
def domain_geometry(self):
return self._domain_geometry
def range_geometry(self):
return self._range_geometry
