Merge pull request #104 from vais-ral/add_processors

added processors.py

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
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+++ b/Wrappers/Python/ccpi/processors.py
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+# -*- 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