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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright 2015 Pierre Paleo <pierre.paleo@esrf.fr>
# License: BSD
#
# 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 ESRF 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 THE COPYRIGHT HOLDERS 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 THE COPYRIGHT HOLDER 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
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import numpy as np
import astra
class AstraToolbox:
"""
ASTRA toolbox wrapper for parallel beam geometry.
"""
def __init__(self, n_x, n_y, angles, dwidth=None, rot_center=None, fullscan=False, super_sampling=None):
"""
Initialize the ASTRA toolbox with a simple parallel configuration.
The image is assumed to be square, and the detector count is equal to the number of rows/columns.
n_x : integer
number of pixels of the X (columns) dimension
n_y : integer
nimber of pixels of the Y (rows) dimension
angles : integer or numpy.ndarray
number of projection angles (if integer), or custom series of angles
dwidth : integer
detector width (number of pixels). If not provided, max(n_x, n_y) is taken.
rot_center : float
user-defined rotation center
fullscan : boolean
if True, use a 360 scan configuration
super_sampling : integer
Detector and Pixel supersampling
"""
angle_max = np.pi
if fullscan: angle_max *= 2
if isinstance(angles, int):
angles = np.linspace(0, angle_max, angles, False)
n_angles = angles.shape[0]
if dwidth is None: dwidth = max(n_x, n_y)
self.vol_geom = astra.create_vol_geom(n_x, n_y)
self.proj_geom = astra.create_proj_geom('parallel', 1.0, dwidth, angles)
if rot_center:
o_angles = np.ones(n_angles) if isinstance(n_angles, int) else np.ones_like(n_angles)
self.proj_geom['option'] = {'ExtraDetectorOffset': (rot_center - n_x / 2.) * o_angles}
self.proj_id = astra.create_projector('cuda', self.proj_geom, self.vol_geom)
# vg : Volume geometry
self.vg = astra.projector.volume_geometry(self.proj_id)
# pg : projection geometry
self.pg = astra.projector.projection_geometry(self.proj_id)
# ---- Configure Projector ------
# sinogram shape
self.sshape = astra.functions.geom_size(self.pg)
# Configure projector
self.cfg_proj = astra.creators.astra_dict('FP_CUDA')
self.cfg_proj['ProjectorId'] = self.proj_id
if super_sampling:
self.cfg_proj['option'] = {'DetectorSuperSampling':super_sampling}
# ---- Configure Backprojector ------
# volume shape
self.vshape = astra.functions.geom_size(self.vg)
# Configure backprojector
self.cfg_backproj = astra.creators.astra_dict('BP_CUDA')
self.cfg_backproj['ProjectorId'] = self.proj_id
if super_sampling:
self.cfg_backproj['option'] = {'PixelSuperSampling':super_sampling}
# -------------------
self.n_x = n_x
self.n_y = n_y
self.dwidth = dwidth
self.n_a = angles.shape[0]
self.rot_center = rot_center if rot_center else dwidth//2
self.angles = angles
def __checkArray(self, arr):
if arr.dtype != np.float32:
arr = arr.astype(np.float32)
if arr.flags['C_CONTIGUOUS']==False:
arr = np.ascontiguousarray(arr)
return arr
def backproj(self, s, filt=False):
if filt is True:
sino = self.filter_projections(s)
else:
sino = s
sino = self.__checkArray(sino)
# In
sid = astra.data2d.link('-sino', self.pg, sino)
self.cfg_backproj['ProjectionDataId'] = sid
# Out
v = np.zeros(self.vshape, dtype=np.float32)
vid = astra.data2d.link('-vol', self.vg, v)
self.cfg_backproj['ReconstructionDataId'] = vid
bp_id = astra.algorithm.create(self.cfg_backproj)
astra.algorithm.run(bp_id)
astra.algorithm.delete(bp_id)
astra.data2d.delete([sid, vid])
return v
def proj(self, v):
v = self.__checkArray(v)
# In
vid = astra.data2d.link('-vol', self.vg, v)
self.cfg_proj['VolumeDataId'] = vid
# Out
s = np.zeros(self.sshape, dtype=np.float32)
sid = astra.data2d.link('-sino',self.pg, s)
self.cfg_proj['ProjectionDataId'] = sid
fp_id = astra.algorithm.create(self.cfg_proj)
astra.algorithm.run(fp_id)
astra.algorithm.delete(fp_id)
astra.data2d.delete([vid, sid])
return s
def filter_projections(self, sino):
nb_angles, l_x = sino.shape
ramp = 1./l_x * np.hstack((np.arange(l_x), np.arange(l_x, 0, -1)))
return np.fft.ifft(ramp * np.fft.fft(sino, 2*l_x, axis=1), axis=1)[:, :l_x].real
def run_algorithm(self, alg, n_it, data):
rec_id = astra.data2d.create('-vol', self.vol_geom)
sino_id = astra.data2d.create('-sino', self.proj_geom, data)
cfg = astra.astra_dict(alg)
cfg['ReconstructionDataId'] = rec_id
cfg['ProjectionDataId'] = sino_id
alg_id = astra.algorithm.create(cfg)
print(("Running %s" %alg))
astra.algorithm.run(alg_id, n_it)
rec = astra.data2d.get(rec_id)
astra.algorithm.delete(alg_id)
astra.data2d.delete(rec_id)
astra.data2d.delete(sino_id)
return rec
def fbp(self, sino):
"""
Runs the Filtered Back-Projection algorithm on the provided sinogram.
sino : numpy.ndarray
sinogram. Its shape must be consistent with the current tomography configuration
"""
return self.backproj(sino, filt=True)
def cleanup(self):
astra.data2d.delete(self.proj_id)
#~ def clipCircle(x): # in-place !
#~ return astra.extrautils.clipCircle(x)
def generate_coords(img_shp, center=None):
R, C = np.indices(img_shp, dtype=np.float64)
if center is None:
center0, center1 = img_shp[0] / 2., img_shp[1] / 2.
else:
center0, center1 = center
R += 0.5 - center0
C += 0.5 - center1
return R, C
def clipCircle(x): # out-of-place !
res = np.copy(x)
R, C = generate_coords(x.shape)
res[R**2 + C**2 > (min(x.shape)//2)**2] = 0
return res
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