# 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:: ring_removal_normalization
:platform: Unix
:synopsis: Method working in the sinogram space to remove ring artifacts.
.. moduleauthor:: Nghia Vo <scientificsoftware@diamond.ac.uk>
"""
from savu.plugins.plugin import Plugin
from savu.plugins.driver.cpu_plugin import CpuPlugin
from savu.plugins.utils import register_plugin
import numpy as np
from scipy.ndimage import gaussian_filter
[docs]@register_plugin
class RingRemovalNormalization(Plugin, CpuPlugin):
def __init__(self):
super(RingRemovalNormalization, self).__init__(
"RingRemovalNormalization")
[docs] def setup(self):
in_dataset, out_dataset = self.get_datasets()
out_dataset[0].create_dataset(in_dataset[0])
in_pData, out_pData = self.get_plugin_datasets()
in_pData[0].plugin_data_setup('SINOGRAM', 'single')
out_pData[0].plugin_data_setup('SINOGRAM', 'single')
[docs] def pre_process(self):
in_pData = self.get_plugin_in_datasets()
width_dim = \
in_pData[0].get_data_dimension_by_axis_label('detector_x')
height_dim = \
in_pData[0].get_data_dimension_by_axis_label('rotation_angle')
sino_shape = list(in_pData[0].get_shape())
self.width1 = sino_shape[width_dim]
self.height1 = sino_shape[height_dim]
[docs] def process_frames(self, data):
sinogram = np.copy(data[0])
radius = np.clip(np.int16(self.parameters['radius']), 0, self.width1)
num_chunks = np.clip(np.int16(
self.parameters['number_of_chunks']), 1, self.height1)
list_pos = np.array_split(np.arange(self.height1), num_chunks)
for pos in list_pos:
bindex = pos[0]
eindex = pos[-1] + 1
list_mean = np.mean(sinogram[bindex:eindex, :], axis=0)
list_mean_filtered = gaussian_filter(list_mean, radius)
list_coe = list_mean_filtered - list_mean
mat_coe = \
np.zeros((eindex - bindex, self.width1), dtype=np.float32)
mat_coe[:] = list_coe
sinogram[bindex:eindex, :] = sinogram[bindex:eindex, :] + mat_coe
return sinogram