From 70f31bf2b94f262d799486d693494a85040e3e9d Mon Sep 17 00:00:00 2001 From: Edoardo Pasca Date: Tue, 17 Apr 2018 14:17:38 +0100 Subject: removes generic processors which go in CCPi-Framework --- Wrappers/Python/ccpi/plugins/processors.py | 616 ----------------------------- 1 file changed, 616 deletions(-) diff --git a/Wrappers/Python/ccpi/plugins/processors.py b/Wrappers/Python/ccpi/plugins/processors.py index 2fd3bf1..e05c6ca 100755 --- a/Wrappers/Python/ccpi/plugins/processors.py +++ b/Wrappers/Python/ccpi/plugins/processors.py @@ -27,369 +27,6 @@ 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 CCPiForwardProjector(DataProcessor): '''Normalization based on flat and dark @@ -537,256 +174,3 @@ class CCPiBackwardProjector(DataProcessor): else: raise ValueError('Cannot process cone beam') -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 - -#class FiniteDifferentiator(DataProcessor): -# def __init__(self): -# kwargs = { -# -# } -# -# super(FiniteDifferentiator, self).__init__(**kwargs) -# -# def check_input(self, dataset): -# return True -# -# def process(self): -# axis = 0 -# d1 = numpy.diff(x,n=1,axis=axis) -# d1 = numpy.resize(d1, numpy.shape(x)) - - - -def loadNexus(filename): - '''Load a dataset stored in a NeXuS file (HDF5)''' - ########################################################################### - ## Load a dataset - nx = h5py.File(filename, "r") - - data = nx.get('entry1/tomo_entry/data/rotation_angle') - angles = numpy.zeros(data.shape) - data.read_direct(angles) - - data = nx.get('entry1/tomo_entry/data/data') - stack = numpy.zeros(data.shape) - data.read_direct(stack) - data = nx.get('entry1/tomo_entry/instrument/detector/image_key') - - itype = numpy.zeros(data.shape) - data.read_direct(itype) - # 2 is dark field - darks = [stack[i] for i in range(len(itype)) if itype[i] == 2 ] - dark = darks[0] - for i in range(1, len(darks)): - dark += darks[i] - dark = dark / len(darks) - #dark[0][0] = dark[0][1] - - # 1 is flat field - flats = [stack[i] for i in range(len(itype)) if itype[i] == 1 ] - flat = flats[0] - for i in range(1, len(flats)): - flat += flats[i] - flat = flat / len(flats) - #flat[0][0] = dark[0][1] - - - # 0 is projection data - proj = [stack[i] for i in range(len(itype)) if itype[i] == 0 ] - angle_proj = [angles[i] for i in range(len(itype)) if itype[i] == 0 ] - angle_proj = numpy.asarray (angle_proj) - angle_proj = angle_proj.astype(numpy.float32) - - return angle_proj , numpy.asarray(proj) , dark, flat - - - -if __name__ == '__main__': - angles, proj, dark, flat = loadNexus('../../../data/24737_fd.nxs') - - parallelbeam = AcquisitionGeometry('parallel', '3D' , - angles=angles, - pixel_num_h=numpy.shape(proj)[2], - pixel_num_v=numpy.shape(proj)[1], - ) - - dim_labels = ['angles' , 'vertical' , 'horizontal'] - sino = AcquisitionData( proj , geometry=parallelbeam, - dimension_labels=dim_labels) - - normalizer = Normalizer() - normalizer.set_input(sino) - normalizer.set_flat_field(flat) - normalizer.set_dark_field(dark) - norm = normalizer.get_output() - #print ("Processor min {0} max {1}".format(norm.as_array().min(), norm.as_array().max())) - - #norm1 = numpy.asarray( - # [Normalizer.normalize_projection( p, flat, dark, 1e-5 ) - # for p in proj] - # ) - - #print ("Numpy min {0} max {1}".format(norm1.min(), norm1.max())) - - cor_finder = CenterOfRotationFinder() - cor_finder.set_input(sino) - cor = cor_finder.get_output() - print ("center of rotation {0} == 86.25?".format(cor)) - - - padder = AcquisitionDataPadder(center_of_rotation=cor, - pad_value=0.75, - acquisition_geometry=normalizer.get_output().geometry) - #padder.set_input(normalizer.get_output()) - padder.set_input_processor(normalizer) - - #print ("padder ", padder.get_output()) - - volume_geometry = ImageGeometry() - - back = CCPiBackwardProjector(acquisition_geometry=parallelbeam, - image_geometry=volume_geometry) - back.set_input_processor(padder) - #back.set_input(padder.get_output()) - #print (back.image_geometry) - forw = CCPiForwardProjector(acquisition_geometry=parallelbeam, - image_geometry=volume_geometry) - forw.set_input_processor(back) - - - out = padder.get_output() - fig = plt.figure() - # projections row - a = fig.add_subplot(1,4,1) - a.imshow(norm.array[80]) - a.set_title('orig') - a = fig.add_subplot(1,4,2) - a.imshow(out.array[80]) - a.set_title('padded') - - a = fig.add_subplot(1,4,3) - a.imshow(back.get_output().as_array()[15]) - a.set_title('back') - - a = fig.add_subplot(1,4,4) - a.imshow(forw.get_output().as_array()[80]) - a.set_title('forw') - - - plt.show() - - - conebeam = AcquisitionGeometry('cone', '3D' , - angles=angles, - pixel_num_h=numpy.shape(proj)[2], - pixel_num_v=numpy.shape(proj)[1], - ) - - try: - sino2 = AcquisitionData( proj , geometry=conebeam) - cor_finder.set_input(sino2) - cor = cor_finder.get_output() - except ValueError as err: - print (err) \ No newline at end of file -- cgit v1.2.3