def main():
normalized = i3.normalize(ct_series,
dfs,
obs,
workdir=os.path.join(workdir, 'normalization'))
tilt_corrected = i3.correct_tilt(normalized,
workdir=os.path.join(
workdir, 'tilt-correction'))
if_corrected = i3.correct_intensity_fluctuation(
tilt_corrected,
workdir=os.path.join(workdir, 'intensity-fluctuation-correction'))
angles, sinograms = i3.build_sinograms(if_corrected,
workdir=os.path.join(
workdir, 'sinogram'))
# take the middle part to calculate the center of rotation
sino = [s.data for s in sinograms[900:1100]]
sino = np.array(sino)
proj = np.swapaxes(sino, 0, 1)
rot_center = tomopy.find_center(proj,
theta,
emission=False,
init=1024,
tol=0.5)
rot_center = rot_center[0]
# reconstruct
recon = i3.reconstruct(angles,
sinograms,
workdir=outdir,
center=rot_center)
return
def test():
normalized = i3.normalize(ct_series, dfs, obs, workdir=workdir)
tilt_corrected = i3.correct_tilt(normalized, workdir=workdir)
if_corrected = i3.correct_intensity_fluctuation(tilt_corrected,
workdir=workdir)
angles, sinograms = i3.build_sinograms(if_corrected, workdir=workdir)
recon = i3.reconstruct(angles, sinograms, workdir=outdir)
return
def main():
normalized = i3.normalize(ct_series, dfs, obs, workdir=os.path.join(workdir, 'normalization'))
tilt_corrected = i3.correct_tilt(normalized, workdir=os.path.join(workdir, 'tilt-correction'))
if_corrected = i3.correct_intensity_fluctuation(tilt_corrected, workdir=os.path.join(workdir, 'intensity-fluctuation-correction'))
angles, sinograms = i3.build_sinograms(if_corrected, workdir=os.path.join(workdir, 'sinogram'))
# take the middle part to calculate the center of rotation
sino = [s.data for s in sinograms[900:1100]]
sino= np.array(sino)
proj = np.swapaxes(sino, 0, 1)
rot_center = tomopy.find_center(proj, theta, emission=False, init=1024, tol=0.5)
rot_center = rot_center[0]
# reconstruct
recon = i3.reconstruct(angles, sinograms, workdir=outdir, center=rot_center)
return
def preprocess(self, workdir=None, outdir=None):
workdir = workdir or self.workdir
outdir = outdir or self.outdir
# get image objs
dfs = self.dfs; obs = self.obs
ct_series = self.ct_series
theta = self.theta
# preprocess
gamma_filtered = i3.gamma_filter(
ct_series, workdir=os.path.join(workdir, 'gamma-filter'),
parallel = self.parallel_preprocessing)
normalized = i3.normalize(gamma_filtered, dfs, obs, workdir=os.path.join(workdir, 'normalization'))
if self.clean_on_the_fly:
gamma_filtered.removeAll()
if_corrected = i3.correct_intensity_fluctuation(normalized, workdir=os.path.join(workdir, 'intensity-fluctuation-correction'))
if self.clean_on_the_fly:
normalized.removeAll()
return if_corrected
def recon(self,
workdir=None, outdir=None, outfilename_template=None,
tilt=None, crop_window=None,
smooth_projection=None, remove_rings_at_sinograms=None,
smooth_recon=None, remove_rings=None,
**kwds):
"""Run CT reconstruction workflow
Parameters:
* workdir: fast work dir
* outdir: output dir. can be at a slower disk
* crop_window: (xmin, ymin, xmax, ymax)
* remove_rings_at_sinograms: remove ring artifacts by filtering sinograms
- if ==False, no filtering
- if is True, filtering with default parameters
- if is a dictionary, will be used as kwd arguments for filtering component
e.g. dict(average_window_size=20, Nsubsets=10, correction_range=(0.9, 1.1))
* smooth_recon: smooth the reconstruction result
- if ==False, no smoothing
- if is True, smoothing with default parameters
- if is a dictionary, will be used as kwd arguments for smoothing component
e.g. dict(algorithm='bilateral', sigma_color=0.0005, sigma_spatial=5)
* smooth_projection: extra smoothing of projection
- if ==False, no smoothing
- if is True, smoothing with default parameters
- if is a dictionary, will be used as kwd arguments for smoothing component
e.g. dict(algorithm='bilateral', sigma_color=0.02, sigma_spatial=5)
Default processing chain:
* preprocess
- gamma filtering
- normalization
* crop
* median-filter (size 3)
* (optional) smooth: by default, use bilateral filter
* intensity fluctuation correction
* tilt correction
* create sinograms
* (optional) apply ring-artifact-removal filter to sinograms
* find center of rotation
* reconstruction
* (optional) smooth reconstruction
"""
workdir = workdir or self.workdir; outdir = outdir or self.outdir
# preprocess
pre = self.preprocess(workdir=workdir, outdir=outdir)
# crop
if crop_window is None:
# auto-cropping
cropped = self.autoCrop(pre)
else:
xmin, ymin, xmax, ymax = crop_window
cropped = self.crop(
pre,
left=xmin, right=xmax, top=ymin, bottom=ymax)
if self.clean_intermediate_files == 'on_the_fly':
pre.removeAll()
# median filter
self.r.median_filtered = median_filtered = self.smooth(
cropped, outname='median_filtered', algorithm='median', size=3)
if smooth_projection:
if smooth_projection is True:
# default smooth
smooth_projection = dict(algorithm='bilateral', sigma_color=0.02, sigma_spatial=5)
pre = smoothed = self.smooth(median_filtered, outname='smoothed', **smooth_projection)
self.r.smoothed_projection = smoothed
else:
pre = median_filtered
# correct intensity fluctuation
if_corrected = i3.correct_intensity_fluctuation(
pre, workdir=os.path.join(workdir, 'intensity-fluctuation-correction'))
if self.clean_intermediate_files == 'on_the_fly':
pre.removeAll()
# correct tilt
pre = if_corrected
if tilt is None:
tilt_corrected, tilt = self.correctTilt_loop(
pre, workdir=workdir)
else:
tilt_corrected, tilt = i3.correct_tilt(
pre, tilt=tilt,
workdir=os.path.join(workdir, 'tilt-correction' ),
max_npairs=None, parallel=self.parallel_preprocessing)
if self.clean_intermediate_files == 'on_the_fly':
pre.removeAll()
#
self.r.cropped = cropped
self.r.if_corrected = if_corrected
self.r.tilt_corrected = tilt_corrected
# reconstruct
outfilename_template = outfilename_template or "recon_%04d.tiff"
recon = self.reconstruct(
tilt_corrected,
workdir=workdir, outdir=outdir,
remove_rings_at_sinograms=remove_rings_at_sinograms,
outfilename_template=outfilename_template,
**kwds)
if smooth_recon:
if smooth_recon is True:
smooth_recon = dict(algorithm='bilateral', sigma_color=0.0005, sigma_spatial=5)
from . import smooth
recon = self.r.sm_recon = smooth(
recon, workdir=os.path.join(self.outdir, 'smoothed'),
parallel = self.parallel_preprocessing,
filename_template='sm_' + outfilename_template,
**smooth_recon)
if remove_rings:
self.removeRings(recon)
# clean up
import shutil
if self.clean_intermediate_files == 'archive':
archive_bg(workdir, outdir)
elif self.clean_intermediate_files == 'on_the_fly':
shutil.rmtree(workdir)
return
