From 0dbe15e80c67e4b320886015752f650ad7728d7c Mon Sep 17 00:00:00 2001 From: Edoardo Pasca Date: Tue, 6 Mar 2018 13:18:49 +0000 Subject: New datasetprocessors (#36) * added simple test for SinogramData * initial version of processors This contains a version of a simple normalization algorithm. * added default pixel size = 1 (mm) * Added FindCenter of rotation for parallel beam geometry closes #5 --- Wrappers/Python/ccpi/processors.py | 318 ++++++++++++++++++++++++++- Wrappers/Python/ccpi/reconstruction/geoms.py | 4 +- 2 files changed, 312 insertions(+), 10 deletions(-) (limited to 'Wrappers/Python') diff --git a/Wrappers/Python/ccpi/processors.py b/Wrappers/Python/ccpi/processors.py index f7dcf4e..86d8b3a 100755 --- a/Wrappers/Python/ccpi/processors.py +++ b/Wrappers/Python/ccpi/processors.py @@ -18,10 +18,12 @@ # limitations under the License from ccpi.framework import DataSetProcessor, DataSet, VolumeData, SinogramData +from ccpi.reconstruction import geoms import numpy import h5py +from scipy import ndimage -class NormalizationDataSetProcessor(DataSetProcessor): +class Normalizer(DataSetProcessor): '''Normalization based on flat and dark This processor read in a SinogramDataSet and normalises it based on @@ -42,7 +44,7 @@ class NormalizationDataSetProcessor(DataSetProcessor): } #DataSetProcessor.__init__(self, **kwargs) - super(NormalizationDataSetProcessor, self).__init__(**kwargs) + super(Normalizer, self).__init__(**kwargs) def checkInput(self, dataset): if dataset.number_of_dimensions == 3: @@ -90,7 +92,7 @@ class NormalizationDataSetProcessor(DataSetProcessor): a = numpy.asarray( - [ NormalizationDataSetProcessor.normalizeProjection( + [ Normalizer.normalizeProjection( projection, flat, dark, self.tolerance) \ for projection in projections.as_array() ] ) @@ -98,7 +100,286 @@ class NormalizationDataSetProcessor(DataSetProcessor): dimension_labels=projections.dimension_labels ) return y + +class CenterOfRotationFinder(DataSetProcessor): + '''Processor to find the center of rotation in a parallel beam experiment + + This processor read in a SinogramDataSet and finds the center of rotation + based on Nghia Vo's method. https://doi.org/10.1364/OE.22.019078 + + Input: SinogramDataSet + + Output: float. center of rotation in pixel coordinate + ''' + + def __init__(self): + kwargs = { + + } + + #DataSetProcessor.__init__(self, **kwargs) + super(CenterOfRotationFinder, self).__init__(**kwargs) + + def checkInput(self, dataset): + if dataset.number_of_dimensions == 3: + if dataset.geometry.geom_type == 'parallel': + return True + else: + raise ValueError('This algorithm is suitable only for parallel beam geometry') + 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.getInput() + + cor = CenterOfRotationFinder.find_center_vo(projections.as_array()) + + return cor + def loadNexus(filename): '''Load a dataset stored in a NeXuS file (HDF5)''' ########################################################################### @@ -146,10 +427,14 @@ def loadNexus(filename): if __name__ == '__main__': angles, proj, dark, flat = loadNexus('../../../data/24737_fd.nxs') - sino = SinogramData( proj ) - + parallelbeam = geoms.SinogramGeometry('parallel', '3D' , + angles=angles, + pixel_num_h=numpy.shape(proj)[2], + pixel_num_v=numpy.shape(proj)[1], + ) + sino = SinogramData( proj , geometry=parallelbeam) - normalizer = NormalizationDataSetProcessor() + normalizer = Normalizer() normalizer.setInput(sino) normalizer.setFlatField(flat) normalizer.setDarkField(dark) @@ -157,8 +442,25 @@ if __name__ == '__main__': print ("Processor min {0} max {1}".format(norm.as_array().min(), norm.as_array().max())) norm1 = numpy.asarray( - [NormalizationDataSetProcessor.normalizeProjection( p, flat, dark, 1e-5 ) + [Normalizer.normalizeProjection( p, flat, dark, 1e-5 ) for p in proj] ) - print ("Numpy min {0} max {1}".format(norm1.min(), norm1.max())) \ No newline at end of file + print ("Numpy min {0} max {1}".format(norm1.min(), norm1.max())) + + cor_finder = CenterOfRotationFinder() + cor_finder.setInput(sino) + cor = cor_finder.getOutput() + print ("center of rotation {0} == 86.25?".format(cor)) + + conebeam = geoms.SinogramGeometry('cone', '3D' , + angles=angles, + pixel_num_h=numpy.shape(proj)[2], + pixel_num_v=numpy.shape(proj)[1], + ) + sino = SinogramData( proj , geometry=conebeam) + try: + cor_finder.setInput(sino) + cor = cor_finder.getOutput() + except ValueError as err: + print (err) \ No newline at end of file diff --git a/Wrappers/Python/ccpi/reconstruction/geoms.py b/Wrappers/Python/ccpi/reconstruction/geoms.py index f7f3329..edce3b3 100644 --- a/Wrappers/Python/ccpi/reconstruction/geoms.py +++ b/Wrappers/Python/ccpi/reconstruction/geoms.py @@ -48,9 +48,9 @@ class SinogramGeometry: dimension, \ angles, \ pixel_num_h=None, \ - pixel_size_h=None, \ + pixel_size_h=1, \ pixel_num_v=None, \ - pixel_size_v=None, \ + pixel_size_v=1, \ dist_source_center=None, \ dist_center_detector=None, \ ): -- cgit v1.2.3