# 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:: raven_filter
:platform: Unix
:synopsis: FFT-based method for removing ring artifacts.
.. moduleauthor:: Nicola Wadeson <scientificsoftware@diamond.ac.uk>
"""
import logging
import numpy as np
import pyfftw
import pyfftw.interfaces.numpy_fft as fft
from savu.plugins.filters.base_filter import BaseFilter
from savu.plugins.driver.cpu_plugin import CpuPlugin
from savu.plugins.utils import register_plugin
[docs]@register_plugin
class RavenFilter(BaseFilter, CpuPlugin):
def __init__(self):
logging.debug("Starting Raven Filter")
super(RavenFilter, self).__init__("RavenFilter")
self.count = 0
[docs] def set_filter_padding(self, in_data, out_data):
self.pad = self.parameters['padFT']
in_data[0].padding = {'pad_frame_edges': self.pad}
out_data[0].padding = {'pad_frame_edges': self.pad}
[docs] def pre_process(self):
in_pData = self.get_plugin_in_datasets()[0]
sino_shape = list(in_pData.get_shape())
width1 = sino_shape[1] + 2 * self.pad
height1 = sino_shape[0] + 2 * self.pad
v0 = np.abs(self.parameters['vvalue'])
u0 = np.abs(self.parameters['uvalue'])
n = np.abs(self.parameters['nvalue'])
# Create filter
centerx = np.ceil(width1 / 2.0) - 1.0
centery = np.int16(np.ceil(height1 / 2.0) - 1)
self.row1 = centery - v0
self.row2 = centery + v0 + 1
listx = np.arange(width1) - centerx
filtershape = 1.0 / (1.0 + np.power(listx / u0, 2 * n))
filtershapepad2d = np.zeros((self.row2 - self.row1, filtershape.size))
filtershapepad2d[:] = np.float64(filtershape)
self.filtercomplex = filtershapepad2d + filtershapepad2d * 1j
a = pyfftw.empty_aligned((height1, width1), dtype='complex128', n=16)
b = pyfftw.empty_aligned((height1, width1), dtype='complex128', n=16)
c = pyfftw.empty_aligned((height1, width1), dtype='complex128', n=16)
d = pyfftw.empty_aligned((height1, width1), dtype='complex128', n=16)
self.fft_object = pyfftw.FFTW(a, b, axes=(0, 1))
self.ifft_object = pyfftw.FFTW(c, d, axes=(0, 1),
direction='FFTW_BACKWARD')
[docs] def process_frames(self, data):
sino = fft.fftshift(self.fft_object(data[0]))
sino[self.row1:self.row2] = \
sino[self.row1:self.row2] * self.filtercomplex
sino = fft.ifftshift(sino)
return self.ifft_object(sino).real
[docs] def get_plugin_pattern(self):
return 'SINOGRAM'
[docs] def get_max_frames(self):
return 'single'