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| -rwxr-xr-x | Wrappers/Python/ccpi/processors.py | 495 |
1 files changed, 495 insertions, 0 deletions
diff --git a/Wrappers/Python/ccpi/processors.py b/Wrappers/Python/ccpi/processors.py new file mode 100755 index 0000000..b65722e --- /dev/null +++ b/Wrappers/Python/ccpi/processors.py @@ -0,0 +1,495 @@ +# -*- coding: utf-8 -*-
+# This work is part of the Core Imaging Library developed by
+# Visual Analytics and Imaging System Group of the Science Technology
+# Facilities Council, STFC
+
+# Copyright 2018 Edoardo Pasca
+
+# 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
+
+from ccpi.framework import DataProcessor, DataContainer, AcquisitionData,\
+ AcquisitionGeometry, ImageGeometry, ImageData
+from ccpi.reconstruction.parallelbeam import alg as pbalg
+import numpy
+from scipy import ndimage
+
+import matplotlib.pyplot as plt
+
+
+class Normalizer(DataProcessor):
+ '''Normalization based on flat and dark
+
+ This processor read in a AcquisitionData and normalises it based on
+ the instrument reading with and without incident photons or neutrons.
+
+ Input: AcquisitionData
+ Parameter: 2D projection with flat field (or stack)
+ 2D projection with dark field (or stack)
+ Output: AcquisitionDataSetn
+ '''
+
+ def __init__(self, flat_field = None, dark_field = None, tolerance = 1e-5):
+ kwargs = {
+ 'flat_field' : None,
+ 'dark_field' : None,
+ # very small number. Used when there is a division by zero
+ 'tolerance' : tolerance
+ }
+
+ #DataProcessor.__init__(self, **kwargs)
+ super(Normalizer, self).__init__(**kwargs)
+ if not flat_field is None:
+ self.set_flat_field(flat_field)
+ if not dark_field is None:
+ self.set_dark_field(dark_field)
+
+ def check_input(self, dataset):
+ if dataset.number_of_dimensions == 3:
+ return True
+ else:
+ raise ValueError("Expected input dimensions is 2 or 3, got {0}"\
+ .format(dataset.number_of_dimensions))
+
+ def set_dark_field(self, df):
+ if type(df) is numpy.ndarray:
+ if len(numpy.shape(df)) == 3:
+ raise ValueError('Dark Field should be 2D')
+ elif len(numpy.shape(df)) == 2:
+ self.dark_field = df
+ elif issubclass(type(df), DataSet):
+ self.dark_field = self.set_dark_field(df.as_array())
+
+ def set_flat_field(self, df):
+ if type(df) is numpy.ndarray:
+ if len(numpy.shape(df)) == 3:
+ raise ValueError('Flat Field should be 2D')
+ elif len(numpy.shape(df)) == 2:
+ self.flat_field = df
+ elif issubclass(type(df), DataSet):
+ self.flat_field = self.set_flat_field(df.as_array())
+
+ @staticmethod
+ def normalize_projection(projection, flat, dark, tolerance):
+ a = (projection - dark)
+ b = (flat-dark)
+ with numpy.errstate(divide='ignore', invalid='ignore'):
+ c = numpy.true_divide( a, b )
+ c[ ~ numpy.isfinite( c )] = tolerance # set to not zero if 0/0
+ return c
+
+ def process(self):
+
+ projections = self.get_input()
+ dark = self.dark_field
+ flat = self.flat_field
+
+ if not (projections.shape[1:] == dark.shape and \
+ projections.shape[1:] == flat.shape):
+ raise ValueError('Flats/Dark and projections size do not match.')
+
+
+ a = numpy.asarray(
+ [ Normalizer.normalize_projection(
+ projection, flat, dark, self.tolerance) \
+ for projection in projections.as_array() ]
+ )
+ y = type(projections)( a , True,
+ dimension_labels=projections.dimension_labels,
+ geometry=projections.geometry)
+ return y
+
+
+class CenterOfRotationFinder(DataProcessor):
+ '''Processor to find the center of rotation in a parallel beam experiment
+
+ This processor read in a AcquisitionDataSet and finds the center of rotation
+ based on Nghia Vo's method. https://doi.org/10.1364/OE.22.019078
+
+ Input: AcquisitionDataSet
+
+ Output: float. center of rotation in pixel coordinate
+ '''
+
+ def __init__(self):
+ kwargs = {
+
+ }
+
+ #DataProcessor.__init__(self, **kwargs)
+ super(CenterOfRotationFinder, self).__init__(**kwargs)
+
+ def check_input(self, dataset):
+ if dataset.number_of_dimensions == 3:
+ if dataset.geometry.geom_type == 'parallel':
+ return True
+ else:
+ raise ValueError('{0} is suitable only for parallel beam geometry'\
+ .format(self.__class__.__name__))
+ else:
+ raise ValueError("Expected input dimensions is 3, got {0}"\
+ .format(dataset.number_of_dimensions))
+
+
+ # #########################################################################
+ # Copyright (c) 2015, UChicago Argonne, LLC. All rights reserved. #
+ # #
+ # Copyright 2015. UChicago Argonne, LLC. This software was produced #
+ # under U.S. Government contract DE-AC02-06CH11357 for Argonne National #
+ # Laboratory (ANL), which is operated by UChicago Argonne, LLC for the #
+ # U.S. Department of Energy. The U.S. Government has rights to use, #
+ # reproduce, and distribute this software. NEITHER THE GOVERNMENT NOR #
+ # UChicago Argonne, LLC MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR #
+ # ASSUMES ANY LIABILITY FOR THE USE OF THIS SOFTWARE. If software is #
+ # modified to produce derivative works, such modified software should #
+ # be clearly marked, so as not to confuse it with the version available #
+ # from ANL. #
+ # #
+ # Additionally, redistribution and use in source and binary forms, with #
+ # or without modification, are permitted provided that the following #
+ # conditions are met: #
+ # #
+ # * Redistributions of source code must retain the above copyright #
+ # notice, this list of conditions and the following disclaimer. #
+ # #
+ # * Redistributions in binary form must reproduce the above copyright #
+ # notice, this list of conditions and the following disclaimer in #
+ # the documentation and/or other materials provided with the #
+ # distribution. #
+ # #
+ # * Neither the name of UChicago Argonne, LLC, Argonne National #
+ # Laboratory, ANL, the U.S. Government, nor the names of its #
+ # contributors may be used to endorse or promote products derived #
+ # from this software without specific prior written permission. #
+ # #
+ # THIS SOFTWARE IS PROVIDED BY UChicago Argonne, LLC AND CONTRIBUTORS #
+ # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT #
+ # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS #
+ # FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL UChicago #
+ # Argonne, LLC OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, #
+ # INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, #
+ # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; #
+ # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER #
+ # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT #
+ # LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN #
+ # ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE #
+ # POSSIBILITY OF SUCH DAMAGE. #
+ # #########################################################################
+
+ @staticmethod
+ def as_ndarray(arr, dtype=None, copy=False):
+ if not isinstance(arr, numpy.ndarray):
+ arr = numpy.array(arr, dtype=dtype, copy=copy)
+ return arr
+
+ @staticmethod
+ def as_dtype(arr, dtype, copy=False):
+ if not arr.dtype == dtype:
+ arr = numpy.array(arr, dtype=dtype, copy=copy)
+ return arr
+
+ @staticmethod
+ def as_float32(arr):
+ arr = CenterOfRotationFinder.as_ndarray(arr, numpy.float32)
+ return CenterOfRotationFinder.as_dtype(arr, numpy.float32)
+
+
+
+
+ @staticmethod
+ def find_center_vo(tomo, ind=None, smin=-40, smax=40, srad=10, step=0.5,
+ ratio=2., drop=20):
+ """
+ Find rotation axis location using Nghia Vo's method. :cite:`Vo:14`.
+
+ Parameters
+ ----------
+ tomo : ndarray
+ 3D tomographic data.
+ ind : int, optional
+ Index of the slice to be used for reconstruction.
+ smin, smax : int, optional
+ Reference to the horizontal center of the sinogram.
+ srad : float, optional
+ Fine search radius.
+ step : float, optional
+ Step of fine searching.
+ ratio : float, optional
+ The ratio between the FOV of the camera and the size of object.
+ It's used to generate the mask.
+ drop : int, optional
+ Drop lines around vertical center of the mask.
+
+ Returns
+ -------
+ float
+ Rotation axis location.
+
+ Notes
+ -----
+ The function may not yield a correct estimate, if:
+
+ - the sample size is bigger than the field of view of the camera.
+ In this case the ``ratio`` argument need to be set larger
+ than the default of 2.0.
+
+ - there is distortion in the imaging hardware. If there's
+ no correction applied, the center of the projection image may
+ yield a better estimate.
+
+ - the sample contrast is weak. Paganin's filter need to be applied
+ to overcome this.
+
+ - the sample was changed during the scan.
+ """
+ tomo = CenterOfRotationFinder.as_float32(tomo)
+
+ if ind is None:
+ ind = tomo.shape[1] // 2
+ _tomo = tomo[:, ind, :]
+
+
+
+ # Reduce noise by smooth filters. Use different filters for coarse and fine search
+ _tomo_cs = ndimage.filters.gaussian_filter(_tomo, (3, 1))
+ _tomo_fs = ndimage.filters.median_filter(_tomo, (2, 2))
+
+ # Coarse and fine searches for finding the rotation center.
+ if _tomo.shape[0] * _tomo.shape[1] > 4e6: # If data is large (>2kx2k)
+ #_tomo_coarse = downsample(numpy.expand_dims(_tomo_cs,1), level=2)[:, 0, :]
+ #init_cen = _search_coarse(_tomo_coarse, smin, smax, ratio, drop)
+ #fine_cen = _search_fine(_tomo_fs, srad, step, init_cen*4, ratio, drop)
+ init_cen = CenterOfRotationFinder._search_coarse(_tomo_cs, smin,
+ smax, ratio, drop)
+ fine_cen = CenterOfRotationFinder._search_fine(_tomo_fs, srad,
+ step, init_cen,
+ ratio, drop)
+ else:
+ init_cen = CenterOfRotationFinder._search_coarse(_tomo_cs,
+ smin, smax,
+ ratio, drop)
+ fine_cen = CenterOfRotationFinder._search_fine(_tomo_fs, srad,
+ step, init_cen,
+ ratio, drop)
+
+ #logger.debug('Rotation center search finished: %i', fine_cen)
+ return fine_cen
+
+
+ @staticmethod
+ def _search_coarse(sino, smin, smax, ratio, drop):
+ """
+ Coarse search for finding the rotation center.
+ """
+ (Nrow, Ncol) = sino.shape
+ centerfliplr = (Ncol - 1.0) / 2.0
+
+ # Copy the sinogram and flip left right, the purpose is to
+ # make a full [0;2Pi] sinogram
+ _copy_sino = numpy.fliplr(sino[1:])
+
+ # This image is used for compensating the shift of sinogram 2
+ temp_img = numpy.zeros((Nrow - 1, Ncol), dtype='float32')
+ temp_img[:] = sino[-1]
+
+ # Start coarse search in which the shift step is 1
+ listshift = numpy.arange(smin, smax + 1)
+ listmetric = numpy.zeros(len(listshift), dtype='float32')
+ mask = CenterOfRotationFinder._create_mask(2 * Nrow - 1, Ncol,
+ 0.5 * ratio * Ncol, drop)
+ for i in listshift:
+ _sino = numpy.roll(_copy_sino, i, axis=1)
+ if i >= 0:
+ _sino[:, 0:i] = temp_img[:, 0:i]
+ else:
+ _sino[:, i:] = temp_img[:, i:]
+ listmetric[i - smin] = numpy.sum(numpy.abs(numpy.fft.fftshift(
+ #pyfftw.interfaces.numpy_fft.fft2(
+ # numpy.vstack((sino, _sino)))
+ numpy.fft.fft2(numpy.vstack((sino, _sino)))
+ )) * mask)
+ minpos = numpy.argmin(listmetric)
+ return centerfliplr + listshift[minpos] / 2.0
+
+ @staticmethod
+ def _search_fine(sino, srad, step, init_cen, ratio, drop):
+ """
+ Fine search for finding the rotation center.
+ """
+ Nrow, Ncol = sino.shape
+ centerfliplr = (Ncol + 1.0) / 2.0 - 1.0
+ # Use to shift the sinogram 2 to the raw CoR.
+ shiftsino = numpy.int16(2 * (init_cen - centerfliplr))
+ _copy_sino = numpy.roll(numpy.fliplr(sino[1:]), shiftsino, axis=1)
+ if init_cen <= centerfliplr:
+ lefttake = numpy.int16(numpy.ceil(srad + 1))
+ righttake = numpy.int16(numpy.floor(2 * init_cen - srad - 1))
+ else:
+ lefttake = numpy.int16(numpy.ceil(
+ init_cen - (Ncol - 1 - init_cen) + srad + 1))
+ righttake = numpy.int16(numpy.floor(Ncol - 1 - srad - 1))
+ Ncol1 = righttake - lefttake + 1
+ mask = CenterOfRotationFinder._create_mask(2 * Nrow - 1, Ncol1,
+ 0.5 * ratio * Ncol, drop)
+ numshift = numpy.int16((2 * srad) / step) + 1
+ listshift = numpy.linspace(-srad, srad, num=numshift)
+ listmetric = numpy.zeros(len(listshift), dtype='float32')
+ factor1 = numpy.mean(sino[-1, lefttake:righttake])
+ num1 = 0
+ for i in listshift:
+ _sino = ndimage.interpolation.shift(
+ _copy_sino, (0, i), prefilter=False)
+ factor2 = numpy.mean(_sino[0,lefttake:righttake])
+ _sino = _sino * factor1 / factor2
+ sinojoin = numpy.vstack((sino, _sino))
+ listmetric[num1] = numpy.sum(numpy.abs(numpy.fft.fftshift(
+ #pyfftw.interfaces.numpy_fft.fft2(
+ # sinojoin[:, lefttake:righttake + 1])
+ numpy.fft.fft2(sinojoin[:, lefttake:righttake + 1])
+ )) * mask)
+ num1 = num1 + 1
+ minpos = numpy.argmin(listmetric)
+ return init_cen + listshift[minpos] / 2.0
+
+ @staticmethod
+ def _create_mask(nrow, ncol, radius, drop):
+ du = 1.0 / ncol
+ dv = (nrow - 1.0) / (nrow * 2.0 * numpy.pi)
+ centerrow = numpy.ceil(nrow / 2) - 1
+ centercol = numpy.ceil(ncol / 2) - 1
+ # added by Edoardo Pasca
+ centerrow = int(centerrow)
+ centercol = int(centercol)
+ mask = numpy.zeros((nrow, ncol), dtype='float32')
+ for i in range(nrow):
+ num1 = numpy.round(((i - centerrow) * dv / radius) / du)
+ (p1, p2) = numpy.int16(numpy.clip(numpy.sort(
+ (-num1 + centercol, num1 + centercol)), 0, ncol - 1))
+ mask[i, p1:p2 + 1] = numpy.ones(p2 - p1 + 1, dtype='float32')
+ if drop < centerrow:
+ mask[centerrow - drop:centerrow + drop + 1,
+ :] = numpy.zeros((2 * drop + 1, ncol), dtype='float32')
+ mask[:,centercol-1:centercol+2] = numpy.zeros((nrow, 3), dtype='float32')
+ return mask
+
+ def process(self):
+
+ projections = self.get_input()
+
+ cor = CenterOfRotationFinder.find_center_vo(projections.as_array())
+
+ return cor
+
+
+class AcquisitionDataPadder(DataProcessor):
+ '''Normalization based on flat and dark
+
+ This processor read in a AcquisitionData and normalises it based on
+ the instrument reading with and without incident photons or neutrons.
+
+ Input: AcquisitionData
+ Parameter: 2D projection with flat field (or stack)
+ 2D projection with dark field (or stack)
+ Output: AcquisitionDataSetn
+ '''
+
+ def __init__(self,
+ center_of_rotation = None,
+ acquisition_geometry = None,
+ pad_value = 1e-5):
+ kwargs = {
+ 'acquisition_geometry' : acquisition_geometry,
+ 'center_of_rotation' : center_of_rotation,
+ 'pad_value' : pad_value
+ }
+
+ super(AcquisitionDataPadder, self).__init__(**kwargs)
+
+ def check_input(self, dataset):
+ if self.acquisition_geometry is None:
+ self.acquisition_geometry = dataset.geometry
+ if dataset.number_of_dimensions == 3:
+ return True
+ else:
+ raise ValueError("Expected input dimensions is 2 or 3, got {0}"\
+ .format(dataset.number_of_dimensions))
+
+ def process(self):
+ projections = self.get_input()
+ w = projections.get_dimension_size('horizontal')
+ delta = w - 2 * self.center_of_rotation
+
+ padded_width = int (
+ numpy.ceil(abs(delta)) + w
+ )
+ delta_pix = padded_width - w
+
+ voxel_per_pixel = 1
+ geom = pbalg.pb_setup_geometry_from_acquisition(projections.as_array(),
+ self.acquisition_geometry.angles,
+ self.center_of_rotation,
+ voxel_per_pixel )
+
+ padded_geometry = self.acquisition_geometry.clone()
+
+ padded_geometry.pixel_num_h = geom['n_h']
+ padded_geometry.pixel_num_v = geom['n_v']
+
+ delta_pix_h = padded_geometry.pixel_num_h - self.acquisition_geometry.pixel_num_h
+ delta_pix_v = padded_geometry.pixel_num_v - self.acquisition_geometry.pixel_num_v
+
+ if delta_pix_h == 0:
+ delta_pix_h = delta_pix
+ padded_geometry.pixel_num_h = padded_width
+ #initialize a new AcquisitionData with values close to 0
+ out = AcquisitionData(geometry=padded_geometry)
+ out = out + self.pad_value
+
+
+ #pad in the horizontal-vertical plane -> slice on angles
+ if delta > 0:
+ #pad left of middle
+ command = "out.array["
+ for i in range(out.number_of_dimensions):
+ if out.dimension_labels[i] == 'horizontal':
+ value = '{0}:{1}'.format(delta_pix_h, delta_pix_h+w)
+ command = command + str(value)
+ else:
+ if out.dimension_labels[i] == 'vertical' :
+ value = '{0}:'.format(delta_pix_v)
+ command = command + str(value)
+ else:
+ command = command + ":"
+ if i < out.number_of_dimensions -1:
+ command = command + ','
+ command = command + '] = projections.array'
+ #print (command)
+ else:
+ #pad right of middle
+ command = "out.array["
+ for i in range(out.number_of_dimensions):
+ if out.dimension_labels[i] == 'horizontal':
+ value = '{0}:{1}'.format(0, w)
+ command = command + str(value)
+ else:
+ if out.dimension_labels[i] == 'vertical' :
+ value = '{0}:'.format(delta_pix_v)
+ command = command + str(value)
+ else:
+ command = command + ":"
+ if i < out.number_of_dimensions -1:
+ command = command + ','
+ command = command + '] = projections.array'
+ #print (command)
+ #cleaned = eval(command)
+ exec(command)
+ return out
\ No newline at end of file |