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# -*- coding: utf-8 -*-
# This work is part of the Core Imaging Library developed by
# Visual Analytics and Imaging System Group of the Science Technology
# Facilities Council, STFC
# Copyright 2018 Edoardo Pasca
# 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
from ccpi.framework import DataProcessor, AcquisitionData,\
AcquisitionGeometry, ImageGeometry, ImageData
from ccpi.reconstruction.parallelbeam import alg as pbalg
import numpy
def setupCCPiGeometries(voxel_num_x, voxel_num_y, voxel_num_z, angles, counter):
vg = ImageGeometry(voxel_num_x=voxel_num_x,voxel_num_y=voxel_num_y, voxel_num_z=voxel_num_z)
Phantom_ccpi = ImageData(geometry=vg,
dimension_labels=['horizontal_x','horizontal_y','vertical'])
#.subset(['horizontal_x','horizontal_y','vertical'])
# ask the ccpi code what dimensions it would like
voxel_per_pixel = 1
geoms = pbalg.pb_setup_geometry_from_image(Phantom_ccpi.as_array(),
angles,
voxel_per_pixel )
pg = AcquisitionGeometry('parallel',
'3D',
angles,
geoms['n_h'], 1.0,
geoms['n_v'], 1.0 #2D in 3D is a slice 1 pixel thick
)
center_of_rotation = Phantom_ccpi.get_dimension_size('horizontal_x') / 2
ad = AcquisitionData(geometry=pg,dimension_labels=['angle','vertical','horizontal'])
geoms_i = pbalg.pb_setup_geometry_from_acquisition(ad.as_array(),
angles,
center_of_rotation,
voxel_per_pixel )
counter+=1
if counter < 4:
if (not ( geoms_i == geoms )):
print ("not equal and {0}".format(counter))
X = max(geoms['output_volume_x'], geoms_i['output_volume_x'])
Y = max(geoms['output_volume_y'], geoms_i['output_volume_y'])
Z = max(geoms['output_volume_z'], geoms_i['output_volume_z'])
return setupCCPiGeometries(X,Y,Z,angles, counter)
else:
return geoms
else:
return geoms_i
class CCPiForwardProjector(DataProcessor):
'''Normalization based on flat and dark
This processor read in a AcquisitionData and normalises it based on
the instrument reading with and without incident photons or neutrons.
Input: AcquisitionData
Parameter: 2D projection with flat field (or stack)
2D projection with dark field (or stack)
Output: AcquisitionDataSetn
'''
def __init__(self,
image_geometry = None,
acquisition_geometry = None,
output_axes_order = None):
if output_axes_order is None:
# default ccpi projector image storing order
output_axes_order = ['angle','vertical','horizontal']
kwargs = {
'image_geometry' : image_geometry,
'acquisition_geometry' : acquisition_geometry,
'output_axes_order' : output_axes_order,
'default_image_axes_order' : ['horizontal_x','horizontal_y','vertical'],
'default_acquisition_axes_order' : ['angle','vertical','horizontal']
}
super(CCPiForwardProjector, self).__init__(**kwargs)
def check_input(self, dataset):
if dataset.number_of_dimensions == 3 or dataset.number_of_dimensions == 2:
# sort in the order that this projector needs it
return True
else:
raise ValueError("Expected input dimensions is 2 or 3, got {0}"\
.format(dataset.number_of_dimensions))
def process(self):
volume = self.get_input()
volume_axes = volume.get_data_axes_order(new_order=self.default_image_axes_order)
if not volume_axes == [0,1,2]:
volume.array = numpy.transpose(volume.array, volume_axes)
pixel_per_voxel = 1 # should be estimated from image_geometry and
# acquisition_geometry
if self.acquisition_geometry.geom_type == 'parallel':
pixels = pbalg.pb_forward_project(volume.as_array(),
self.acquisition_geometry.angles,
pixel_per_voxel)
out = AcquisitionData(geometry=self.acquisition_geometry,
label_dimensions=self.default_acquisition_axes_order)
out.fill(pixels)
out_axes = out.get_data_axes_order(new_order=self.output_axes_order)
if not out_axes == [0,1,2]:
out.array = numpy.transpose(out.array, out_axes)
return out
else:
raise ValueError('Cannot process cone beam')
class CCPiBackwardProjector(DataProcessor):
'''Backward projector
This processor reads in a AcquisitionData and performs a backward projection,
i.e. project to reconstruction space.
Notice that it assumes that the center of rotation is in the middle
of the horizontal axis: in case when that's not the case it can be chained
with the AcquisitionDataPadder.
Input: AcquisitionData
Parameter: 2D projection with flat field (or stack)
2D projection with dark field (or stack)
Output: AcquisitionDataSetn
'''
def __init__(self,
image_geometry = None,
acquisition_geometry = None,
output_axes_order=None):
if output_axes_order is None:
# default ccpi projector image storing order
output_axes_order = ['horizontal_x','horizontal_y','vertical']
kwargs = {
'image_geometry' : image_geometry,
'acquisition_geometry' : acquisition_geometry,
'output_axes_order' : output_axes_order,
'default_image_axes_order' : ['horizontal_x','horizontal_y','vertical'],
'default_acquisition_axes_order' : ['angle','vertical','horizontal']
}
super(CCPiBackwardProjector, self).__init__(**kwargs)
def check_input(self, dataset):
if dataset.number_of_dimensions == 3 or dataset.number_of_dimensions == 2:
return True
else:
raise ValueError("Expected input dimensions is 2 or 3, got {0}"\
.format(dataset.number_of_dimensions))
def process(self):
projections = self.get_input()
projections_axes = projections.get_data_axes_order(new_order=self.default_acquisition_axes_order)
if not projections_axes == [0,1,2]:
projections.array = numpy.transpose(projections.array, projections_axes)
pixel_per_voxel = 1 # should be estimated from image_geometry and acquisition_geometry
image_geometry = ImageGeometry(voxel_num_x = self.acquisition_geometry.pixel_num_h,
voxel_num_y = self.acquisition_geometry.pixel_num_h,
voxel_num_z = self.acquisition_geometry.pixel_num_v)
# input centered/padded acquisitiondata
center_of_rotation = projections.get_dimension_size('horizontal') / 2
if self.acquisition_geometry.geom_type == 'parallel':
back = pbalg.pb_backward_project(
projections.as_array(),
self.acquisition_geometry.angles,
center_of_rotation,
pixel_per_voxel
)
out = ImageData(geometry=self.image_geometry,
dimension_labels=self.default_image_axes_order)
out_axes = out.get_data_axes_order(new_order=self.output_axes_order)
if not out_axes == [0,1,2]:
back = numpy.transpose(back, out_axes)
out.fill(back)
return out
else:
raise ValueError('Cannot process cone beam')
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