# Copyright 2020 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:: dezinger_sinogram
:platform: Unix
:synopsis: A 2D/3D median-based dezinger plugin to apply to sinogram data
.. moduleauthor::Daniil Kazantsev <scientificsoftware@diamond.ac.uk>
"""
from savu.plugins.filters.dezingers.base_dezinger_sinogram import BaseDezingerSinogram
from savu.plugins.driver.cpu_plugin import CpuPlugin
from savu.plugins.utils import register_plugin
import numpy as np
from larix.methods.misc import MEDIAN_DEZING
[docs]@register_plugin
class DezingerSinogram(BaseDezingerSinogram, CpuPlugin):
def __init__(self):
super(DezingerSinogram, self).__init__("DezingerSinogram")
[docs] def process_frames(self, data):
input_temp = data[0]
indices = np.where(np.isnan(input_temp))
input_temp[indices] = 0.0
if self.pcount == 0:
try:
in_dataset = self.get_in_datasets()[0]
self.std_dev = self.stats_obj.get_stats_from_dataset(in_dataset, "median_std_dev")
except KeyError:
pass
if self.std_dev:
std_dev = self.std_dev
else:
std_dev = np.std(input_temp)
input_temp =np.swapaxes(input_temp,0,1)
result = MEDIAN_DEZING(input_temp.copy(order='C'), self.parameters['kernel_size'], std_dev*self.parameters['outlier_mu'])
return np.swapaxes(result,0,1)