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# Copyright 2014 Diamond Light Source Ltd. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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# $Id: dezing_filter.py 467 2016-02-16 11:40:42Z kny48981 $ |
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""" |
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.. module:: dezinger_simple_deprecated |
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:platform: Unix |
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:synopsis: A plugin to remove zingers |
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.. moduleauthor:: Mark Basham <[email protected]> |
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""" |
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import numpy as np |
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import scipy.signal.signaltools as sig |
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from savu.plugins.filters.base_filter import BaseFilter |
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from savu.plugins.driver.cpu_plugin import CpuPlugin |
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from savu.plugins.utils import register_plugin |
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class DezingerSimpleDeprecated(BaseFilter, CpuPlugin): |
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""" |
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""" |
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def __init__(self): |
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super(DezingerSimpleDeprecated, self).__init__("DezingerSimpleDeprecated") |
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self.zinger_proportion = 0.0 |
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self.frame_limit = 8 |
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def pre_process(self): |
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inData = self.get_in_datasets()[0] |
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self.proj_dim = inData.data.proj_dim |
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self._kernel = [1]*3 |
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self._kernel[self.proj_dim] = self.kernel_size |
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pad_list = [(0, 0)]*3 |
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pad_list[self.proj_dim] = (self.pad, self.pad) |
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dark = inData.data.dark() |
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flat = inData.data.flat() |
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if dark.size: |
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dark = np.pad(inData.data.dark(), pad_list, mode='edge') |
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dark = self._process_calibration_frames(dark) |
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inData.data.update_dark(dark[self.pad:-self.pad]) |
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if flat.size: |
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flat = np.pad(inData.data.flat(), pad_list, mode='edge') |
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flat = self._process_calibration_frames(flat) |
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inData.data.update_flat(flat[self.pad:-self.pad]) |
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def _process_calibration_frames(self, data): |
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nSlices = data.shape[self.proj_dim] - 2*self.pad |
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nSublists = int(np.ceil(nSlices/float(self.frame_limit))) |
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idx = np.array_split(np.arange(self.pad, nSlices+self.pad), nSublists) |
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idx = [np.arange(a[0]-self.pad, a[-1]+self.pad+1) for a in idx] |
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out_sl = np.tile([slice(None)]*3, [len(idx), 1]) |
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out_sl[:, self.proj_dim] = idx |
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result = np.empty_like(data) |
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for sl in out_sl: |
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result[tuple(sl)] = self._dezing(data[tuple(sl)]) |
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return result |
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def _dezing(self, data): |
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result = data[...] |
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median_result = sig.medfilt(data, self._kernel) |
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differrence = np.abs(data-median_result) |
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replace_mask = differrence > self.parameters['outlier_mu'] |
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self.zinger_proportion = \ |
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max(self.zinger_proportion, np.sum(replace_mask)/( |
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np.size(replace_mask)*1.0)) |
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result[replace_mask] = median_result[replace_mask] |
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return result |
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def process_frames(self, data): |
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return self._dezing(data[0]) |
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def get_max_frames(self): |
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""" Setting nFrames to multiple with an upper limit of 4 frames. """ |
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return ['multiple', self.frame_limit] |
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def raw_data(self): |
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return True |
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def set_filter_padding(self, in_data, out_data): |
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# kernel size must be odd |
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ksize = self.parameters['kernel_size'] |
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self.kernel_size = ksize+1 if ksize % 2 == 0 else ksize |
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in_data = in_data[0] |
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self.pad = (self.kernel_size - 1) // 2 |
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self.data_size = in_data.get_shape() |
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in_data.padding = {'pad_multi_frames': self.pad} |
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out_data[0].padding = {'pad_multi_frames': self.pad} |
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def executive_summary(self): |
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if self.zinger_proportion > 0.5: |
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return ["Over 50% of the pixels were treated as zingers!!"] |
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if self.zinger_proportion > 0.05: |
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return ["Over 5% of the pixels were treated as zingers"] |
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return ["Nothing to Report"] |
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DiamondLightSource /
Savu
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