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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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""" |
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.. module:: Point-spread-function correction |
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:platform: Unix |
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:synopsis: A plugin for MTF (modulation transfer function) deconvolution\ |
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or PSF (point spread function) correction in the Fourier domain. |
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.. moduleauthor:: Nghia Vo <[email protected]> |
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""" |
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import os |
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import logging |
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import numpy as np |
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from PIL import Image |
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import pyfftw.interfaces.scipy_fftpack as fft |
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from savu.plugins.plugin import Plugin |
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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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from savu.data.plugin_list import CitationInformation |
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import savu.test.test_utils as tu |
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import savu.core.utils as cu |
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@register_plugin |
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class MtfDeconvolution(Plugin, CpuPlugin): |
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""" |
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Method to correct the point-spread-function effect. \ |
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Working on raw projections and flats. |
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:u*param file_path: Path to file containing a 2D array of a MTF function. \ |
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File formats: 'npy', or 'tif'. Default: None. |
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:param pad_width: Pad the image before the deconvolution. Default: 128. |
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""" |
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def __init__(self): |
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super(MtfDeconvolution, self).__init__("MtfDeconvolution") |
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def load_image(self, file_path): |
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""" |
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Load data from an image. |
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""" |
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mat = None |
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try: |
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mat = np.asarray(Image.open(file_path), dtype = np.float32) |
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except IOError: |
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raise ValueError("No such file or directory {}".format(file_path)) |
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if len(mat.shape) > 2: |
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axis_m = np.argmin(mat.shape) |
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mat = np.mean(mat, axis=axis_m) |
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return mat |
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def check_file_path(self, file_path): |
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file_ext = ".tif" |
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if file_path is None: |
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msg = "!!! Please provide a file path to the MTF !!!" |
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logging.warning(msg) |
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cu.user_message(msg) |
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raise ValueError(msg) |
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else: |
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if not os.path.isfile(file_path): |
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msg = "!!! No such file: %s !!!"\ |
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" Please check the file path" %str(file_path) |
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logging.warning(msg) |
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cu.user_message(msg) |
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raise ValueError(msg) |
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else: |
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_, file_ext = os.path.splitext(file_path) |
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return file_ext |
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def psf_correction(self, mat, win, pad_width): |
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(nrow, ncol) = mat.shape |
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mat_pad = np.pad(mat, pad_width, mode = "reflect") |
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win_pad = np.pad(win, pad_width, mode = "constant", constant_values=1.0) |
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mat_dec = fft.ifft2(fft.fft2(mat_pad) / fft.ifftshift(win_pad)) |
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return np.abs(mat_dec)[pad_width:pad_width+nrow,pad_width:pad_width+ncol] |
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def setup(self): |
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in_dataset, out_dataset = self.get_datasets() |
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out_dataset[0].create_dataset(in_dataset[0], raw=True) |
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in_pData, out_pData = self.get_plugin_datasets() |
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in_pData[0].plugin_data_setup('PROJECTION','single') |
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out_pData[0].plugin_data_setup('PROJECTION','single') |
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def pre_process(self): |
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inData = self.get_in_datasets()[0] |
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dark = inData.data.dark() |
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flat = inData.data.flat() |
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self.data_size = inData.get_shape() |
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(self.depth, self.height, self.width) = flat.shape |
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file_path = self.get_conf_path() |
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file_ext = self.check_file_path(file_path) |
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if file_ext==".npy": |
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try: |
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self.mtf_array = 1.0*np.load(file_path) |
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except IOError: |
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msg = "\n*****************************************\n"\ |
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"!!! ERROR !!! -> Can't open this file: %s \n"\ |
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"*****************************************\n\ |
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" % str(file_path) |
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logging.warning(msg) |
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cu.user_message(msg) |
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raise ValueError(msg) |
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else: |
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try: |
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self.mtf_array = 1.0*np.float32(self.load_image(file_path)) |
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except IOError: |
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msg = "\n*****************************************\n"\ |
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"!!! ERROR !!! -> Can't open this file: %s \n"\ |
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"*****************************************\n\ |
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" % str(file_path) |
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logging.warning(msg) |
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cu.user_message(msg) |
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raise ValueError(msg) |
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self.mtf_array[self.mtf_array<=0.0] = 1.0 |
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self.mtf_array = self.mtf_array/np.max(self.mtf_array) |
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(height_mtf, width_mtf) = self.mtf_array.shape |
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if (self.height != height_mtf) or (self.width != width_mtf): |
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msg = "\n*****************************************\n"\ |
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"!!! ERROR !!!-> Projection shape: ({0},{1}) is not the same as "\ |
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"the mtf shape: ({2},{3})".format( |
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self.height, self.width, height_mtf, width_mtf) |
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logging.warning(msg) |
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cu.user_message(msg) |
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raise ValueError(msg) |
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self.pad_width = np.clip(int(self.parameters["pad_width"]), 0, None) |
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if flat.size: |
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flat_updated = np.ones_like(flat, dtype=np.float32) |
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for i in np.arange(self.depth): |
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flat_updated[i] = self.psf_correction( |
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flat[i], self.mtf_array, self.pad_width) |
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inData.data.update_flat(flat_updated) |
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def process_frames(self, data): |
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return self.psf_correction(data[0], self.mtf_array, self.pad_width) |
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def get_conf_path(self): |
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path = self.parameters["file_path"] |
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if path.split(os.sep)[0] == 'Savu': |
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path = tu.get_test_data_path(path.split('/test_data/data')[1]) |
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return path |
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def get_citation_information(self): |
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cite_info = CitationInformation() |
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cite_info.description = \ |
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("The PSF correction used in this plugin is taken\ |
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from this work.") |
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cite_info.bibtex = ("@inproceedings{10.1117/12.2530324,\n"\ |
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"author = {Nghia T. Vo and Robert C. Atwood "\ |
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"and Michael Drakopoulos},\n"\ |
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"title = {{Preprocessing techniques for removing artifacts in "\ |
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"synchrotron-based tomographic images}},\n"\ |
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"volume = {11113},\n"\ |
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"booktitle = {Developments in X-Ray Tomography XII},\n"\ |
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"editor = {Bert Muller and Ge Wang},\n"\ |
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"organization = {International Society for Optics and Photonics},\n"\ |
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"publisher = {SPIE},\n"\ |
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"pages = {309 -- 328},\n"\ |
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"year = {2019},\n"\ |
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"doi = {10.1117/12.2530324},\n"\ |
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"URL = {https://doi.org/10.1117/12.2530324}\n"\ |
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"}") |
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cite_info.doi = "doi: DOI: 10.1117/12.2530324" |
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return cite_info |
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DiamondLightSource /
Savu
