# Copyright 2014 Diamond Light Source Ltd.
#
# 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.
"""
.. module:: sinogram_clean
:platform: Unix
:synopsis: A plugin to clean the sinogram.
.. moduleauthor:: Nicola Wadeson <scientificsoftware@diamond.ac.uk>
"""
from savu.plugins.driver.cpu_plugin import CpuPlugin
import math
import numpy as np
import pyfftw.interfaces.scipy_fftpack as fft
from savu.plugins.utils import register_plugin
from savu.plugins.filters.base_filter import BaseFilter
[docs]@register_plugin
class SinogramClean(BaseFilter, CpuPlugin):
def __init__(self):
super(SinogramClean, self).__init__("SinogramClean")
def _create_mask(self, Nrow, Ncol, obj_radius):
du = 1.0 / Ncol
dv = (Nrow-1.0)/(Nrow*2.0*math.pi)
cen_row = np.ceil(Nrow / 2.0)-1
cen_col = np.ceil(Ncol / 2.0)-1
drop = self.parameters['row_drop']
mask = np.zeros((Nrow, Ncol), dtype=np.float32)
# do I need to have this loop?
for i in range(Nrow):
num1 = np.round(((i-cen_row)*dv/obj_radius)/du)
(p1, p2) = np.clip(np.sort((-num1+cen_col, num1+cen_col))
, 0, Ncol-1).astype(int)
mask[i, p1:p2+1] = np.ones(p2-p1+1, dtype=np.float32)
if drop < cen_row:
mask[cen_row-drop:cen_row+drop+1, :] = np.zeros((2*drop + 1, Ncol),
dtype=np.float32)
return mask
[docs] def pre_process(self):
(self.Nrow, Ncol) = self.get_plugin_in_datasets()[0].get_shape()
self.mask = self._create_mask(
2*self.Nrow-1, Ncol, 0.5*self.parameters['ratio']*Ncol)
[docs] def process_frames(self, data):
sino = data[0]
sino2 = np.fliplr(sino[1:])
FT1 = fft.fftshift(fft.fft2(np.vstack((sino, sino2))))
sino = fft.ifft2(fft.ifftshift(FT1 - FT1*self.mask))
return sino[0:self.Nrow].real
[docs] def get_max_frames(self):
return 1