def estimate_center_by_correlation(params):
"""Use correlation to estimate center of rotation for tomography."""
def flat_correct(flat, radio):
nonzero = np.where(radio != 0)
result = np.zeros_like(radio)
result[nonzero] = flat[nonzero] / radio[nonzero]
# log(1) = 0
result[result <= 0] = 1
return np.log(result)
first = read_image(get_filenames(params.projections)[0]).astype(np.float)
last_index = params.start + params.number if params.number else -1
last = read_image(get_filenames(params.projections)[last_index]).astype(np.float)
if params.darks and params.flats:
dark = read_image(get_filenames(params.darks)[0]).astype(np.float)
flat = read_image(get_filenames(params.flats)[0]) - dark
first = flat_correct(flat, first - dark)
last = flat_correct(flat, last - dark)
height = params.height if params.height else -1
y_region = slice(params.y, min(params.y + height, first.shape[0]), params.y_step)
first = first[y_region, :]
last = last[y_region, :]
return compute_rotation_axis(first, last)
def estimate_center_by_correlation(params):
"""Use correlation to estimate center of rotation for tomography."""
def flat_correct(flat, radio):
nonzero = np.where(radio != 0)
result = np.zeros_like(radio)
result[nonzero] = flat[nonzero] / radio[nonzero]
# log(1) = 0
result[result <= 0] = 1
return np.log(result)
first = read_image(get_filenames(params.projections)[0]).astype(np.float)
last_index = params.start + params.number if params.number else -1
last = read_image(get_filenames(params.projections)[last_index]).astype(np.float)
if params.darks and params.flats:
dark = read_image(get_filenames(params.darks)[0]).astype(np.float)
flat = read_image(get_filenames(params.flats)[0]) - dark
first = flat_correct(flat, first - dark)
last = flat_correct(flat, last - dark)
height = params.height if params.height else -1
y_region = slice(params.y, min(params.y + height, first.shape[0]), params.y_step)
first = first[y_region, :]
last = last[y_region, :]
return compute_rotation_axis(first, last)
def find_axis_corr(self, ctset, vcrop, y, height, multipage):
indir = self.make_inpaths(ctset[0], ctset[1])
"""Use correlation to estimate center of rotation for tomography."""
from scipy.signal import fftconvolve
def flat_correct(flat, radio):
nonzero = np.where(radio != 0)
result = np.zeros_like(radio)
result[nonzero] = flat[nonzero] / radio[nonzero]
# log(1) = 0
result[result <= 0] = 1
return np.log(result)
if multipage:
with tifffile.TiffFile(get_filenames(indir[2])[0]) as tif:
first = tif.pages[0].asarray().astype(np.float)
with tifffile.TiffFile(get_filenames(indir[2])[-1]) as tif:
last = tif.pages[-1].asarray().astype(np.float)
with tifffile.TiffFile(get_filenames(indir[0])[-1]) as tif:
dark = tif.pages[-1].asarray().astype(np.float)
with tifffile.TiffFile(get_filenames(indir[1])[0]) as tif:
flat1 = tif.pages[-1].asarray().astype(np.float) - dark
else:
first = read_image(get_filenames(indir[2])[0]).astype(np.float)
last = read_image(get_filenames(indir[2])[-1]).astype(np.float)
dark = read_image(get_filenames(indir[0])[-1]).astype(np.float)
flat1 = read_image(get_filenames(indir[1])[-1]) - dark
first = flat_correct(flat1, first - dark)
if ctset[1] == 4:
if multipage:
with tifffile.TiffFile(get_filenames(indir[3])[0]) as tif:
flat2 = tif.pages[-1].asarray().astype(np.float) - dark
else:
flat2 = read_image(get_filenames(indir[3])[-1]) - dark
last = flat_correct(flat2, last - dark)
else:
last = flat_correct(flat1, last - dark)
if vcrop:
y_region = slice(y, min(y + height, first.shape[0]), 1)
first = first[y_region, :]
last = last[y_region, :]
width = first.shape[1]
first = first - first.mean()
last = last - last.mean()
conv = fftconvolve(first, last[::-1, :], mode='same')
center = np.unravel_index(conv.argmax(), conv.shape)[1]
return (width / 2.0 + center) / 2.0
def bad_vert_ROI(multipage, path2proj, y, height):
if multipage:
with tifffile.TiffFile(get_filenames(path2proj)[0]) as tif:
proj = tif.pages[0].asarray().astype(np.float)
else:
proj = read_image(get_filenames(path2proj)[0]).astype(np.float)
y_region = slice(y, min(y + height, proj.shape[0]), 1)
proj = proj[y_region, :]
if proj.shape[0] == 0:
return True
else:
return False
def find_axis_std(self, ctset, tmpdir, ax_range, p_width, search_row,
nviews):
indir = self.make_inpaths(ctset[0], ctset[1])
print indir[2]
image = read_image(get_filenames(indir[2])[0])
cmd = 'tofu lamino --absorptivity --fix-nan-and-inf --overall-angle 180'\
' --lamino-angle 90 --height 2'
cmd += ' --darks {} --flats {} --projections {}'.\
format(indir[0], indir[1], enquote(indir[2]))
cmd += ' --number {}'.format(nviews)
cmd += ' --angle {:0.5f}'.format(np.radians(180.0 / float(nviews)))
if ctset[1] == 4:
cmd += ' --flats2 {}'.format(indir[3])
out_pattern = os.path.join(tmpdir, "axis-search/slice")
cmd += ' --output {}'.format(enquote(out_pattern))
cmd += ' --x-region={},{},{}'.format(-p_width / 2, p_width / 2, 1)
cmd += ' --y-region={},{},{}'.format(-p_width / 2, p_width / 2, 1)
cmd += ' --y {} --height 2'.format(search_row)
cmd += ' --z-parameter x-center'
cmd += ' --region={}'.format(enquote(ax_range))
cmd += ' --z 0'
res = [float(num) for num in ax_range.split(',')]
cmd += ' --axis {},{}'.format((res[0] + res[1]) / 2.,
1.0) #middle of ax search range?
cmd += " --output-bytes-per-file 0"
# cmd += ' --delete-slice-dir'
print cmd
os.system(cmd)
points, maximum = evaluate_images_simp(out_pattern + '*.tif', "msag")
return res[0] + res[2] * maximum
def lamino(params):
"""Laminographic reconstruction utilizing all GPUs."""
LOG.info('Z parameter: {}'.format(params.z_parameter))
if not params.overall_angle:
params.overall_angle = 360.
LOG.info('Overall angle not specified, using 360 deg')
if not params.angle:
if params.dry_run:
if not params.number:
raise ValueError('--number must be specified by --dry-run')
num_files = params.number
else:
num_files = len(get_filenames(params.projections))
if not num_files:
raise RuntimeError("No files found in `{}'".format(
params.projections))
params.angle = params.overall_angle / num_files * params.step
LOG.info('Angle not specified, calculating from ' +
'{} projections and step {}: {} deg'.format(
num_files, params.step, params.angle))
if not (params.width and params.height):
proj_width, proj_height = determine_shape(params, params.projections)
if not proj_width:
raise RuntimeError("Could not determine width from the input")
if not params.number:
params.number = int(
np.round(np.abs(params.overall_angle / params.angle)))
if not params.width:
params.width = proj_width
if not params.height:
params.height = proj_height - params.y
if params.dry_run:
LOG.info('Dummy data W x H x N: {} x {} x {}'.format(
params.width, params.height, params.number))
params.projection_filter_scale = np.sin(np.deg2rad(params.lamino_angle))
# For now we need to make a workaround for the memory leak, which means we need to execute
# the passes in separate processes to clean up the low level code. For that we also need to
# call the region-splitting in a separate function.
# TODO: Simplify after the memory leak fix!
queue = Queue()
proc = Process(target=_create_runs, args=(
params,
queue,
))
proc.start()
proc.join()
x_region, y_region, regions, num_gpus = queue.get()
for i in range(0, len(regions), num_gpus):
z_subregion = regions[i:min(i + num_gpus, len(regions))]
LOG.info('Computing slices {}..{}'.format(z_subregion[0][0],
z_subregion[-1][1]))
proc = Process(target=_run,
args=(params, x_region, y_region, z_subregion,
i / num_gpus))
proc.start()
proc.join()
def make_copy_of_flat(flatdir, flat_copy_name, dryrun):
first_flat_file = get_first_filename(flatdir)
try:
shape = get_image_shape(first_flat_file)
except:
raise ValueError('Failed to determine size and number of flats in {}'.format(flatdir))
cmd = ""
if len(shape) == 2:
last_flat_file = get_filenames(flatdir)[-1]
cmd = "cp {} {}".format(last_flat_file, flat_copy_name)
else:
flat = read_image(get_filenames(flatdir)[-1])[-1]
if dryrun:
cmd = "echo Will save a copy of flat into \"{}\"".format(flat_copy_name)
else:
tifffile.imsave(flat_copy_name, flat)
return cmd
def get_dims(pth):
# get number of projections and projections dimensions
first_proj = get_first_filename(pth)
multipage = False
try:
shape = get_image_shape(first_proj)
except:
raise ValueError('Failed to determine size and number of projections in {}'.format(pth))
if len(shape) == 2: # single page input
return len(get_filenames(pth)), [shape[-2], shape[-1]], multipage
elif len(shape) == 3: # multipage input
nviews = 0
for i in get_filenames(pth):
nviews += get_image_shape(i)[0]
multipage = True
return nviews, [shape[-2], shape[-1]], multipage
return -6, [-6, -6]
def get_dims(pth, args): #must be changed if multipage tiff input!!!
# get number of projections and projections dimensions
if not args.bigtif_inp:
tomos = get_filenames(pth)
image = read_image(tomos[0])
return len(tomos), image.shape
else:
return args.nviews, [args.H, args.W]
def main():
args = parse_args()
sinos = get_filenames(os.path.join(args.sinos, '*.tif'))
#create output directory
wdir = os.path.split(args.sinos)[0]
odir = os.path.join(wdir, 'sinos-filt')
if not os.path.exists(odir):
os.makedirs(odir)
pool = mp.Pool(processes=mp.cpu_count())
exec_func = partial(RR, args.mws, odir)
pool.map(exec_func, sinos)
def lamino(params):
"""Laminographic reconstruction utilizing all GPUs."""
LOG.info('Z parameter: {}'.format(params.z_parameter))
if not params.overall_angle:
params.overall_angle = 360.
LOG.info('Overall angle not specified, using 360 deg')
if not params.angle:
if params.dry_run:
if not params.number:
raise ValueError('--number must be specified by --dry-run')
num_files = params.number
else:
num_files = len(get_filenames(params.projections))
if not num_files:
raise RuntimeError("No files found in `{}'".format(params.projections))
params.angle = params.overall_angle / num_files * params.step
LOG.info('Angle not specified, calculating from ' +
'{} projections and step {}: {} deg'.format(num_files, params.step,
params.angle))
if not (params.width and params.height):
proj_width, proj_height = determine_shape(params, params.projections)
if not proj_width:
raise RuntimeError("Could not determine width from the input")
if not params.number:
params.number = int(np.round(np.abs(params.overall_angle / params.angle)))
if not params.width:
params.width = proj_width
if not params.height:
params.height = proj_height - params.y
if params.dry_run:
LOG.info('Dummy data W x H x N: {} x {} x {}'.format(params.width,
params.height,
params.number))
# For now we need to make a workaround for the memory leak, which means we need to execute
# the passes in separate processes to clean up the low level code. For that we also need to
# call the region-splitting in a separate function.
# TODO: Simplify after the memory leak fix!
queue = Queue()
proc = Process(target=_create_runs, args=(params, queue,))
proc.start()
proc.join()
x_region, y_region, regions, num_gpus = queue.get()
for i in range(0, len(regions), num_gpus):
z_subregion = regions[i:min(i + num_gpus, len(regions))]
LOG.info('Computing slices {}..{}'.format(z_subregion[0][0], z_subregion[-1][1]))
proc = Process(target=_run, args=(params, x_region, y_region, z_subregion, i / num_gpus))
proc.start()
proc.join()
def create_sinogram_pipeline(args, graph):
"""Create sinogram generating pipeline based on arguments from *args*."""
pm = Ufo.PluginManager()
sinos = pm.get_task('transpose-projections')
if args.number:
region = (args.start, args.start + args.number, args.step)
num_projections = len(range(*region))
else:
num_projections = len(get_filenames(args.projections))
sinos.props.number = num_projections
if args.darks and args.flats:
start = create_flat_correct_pipeline(args, graph)
else:
start = pm.get_task('read')
start.props.path = args.projections
set_node_props(start, args)
graph.connect_nodes(start, sinos)
return sinos
def create_sinogram_pipeline(args, graph):
"""Create sinogram generating pipeline based on arguments from *args*."""
pm = Ufo.PluginManager()
sinos = pm.get_task('transpose-projections')
if args.number:
region = (args.start, args.start + args.number, args.step)
num_projections = len(range(*region))
else:
num_projections = len(get_filenames(args.projections))
sinos.props.number = num_projections
if args.darks and args.flats:
start = create_flat_correct_pipeline(args, graph)
else:
start = pm.get_task('read')
start.props.path = args.projections
set_node_props(start, args)
graph.connect_nodes(start, sinos)
return sinos
def prepare_angular_arguments(params):
if not params.overall_angle:
params.overall_angle = 360.
LOG.info('Overall angle not specified, using 360 deg')
if not params.angle:
if params.dry_run:
if not params.number:
raise ValueError('--number must be specified by --dry-run')
num_files = params.number
else:
num_files = len(get_filenames(params.projections))
if not num_files:
raise RuntimeError("No files found in `{}'".format(params.projections))
params.angle = params.overall_angle / num_files * params.step
LOG.info('Angle not specified, calculating from ' +
'{} projections and step {}: {} deg'.format(num_files, params.step,
params.angle))
determine_shape(params, params.projections, store=True)
if not params.number:
params.number = int(np.round(np.abs(params.overall_angle / params.angle)))
if params.dry_run:
LOG.info('Dummy data W x H x N: {} x {} x {}'.format(params.width,
params.height,
params.number))
def create_flat_correct_pipeline(args, graph):
"""
Create flat field correction pipeline. All the settings are provided in
*args*. *graph* is used for making the connections. Returns the flat field
correction task which can be used for further pipelining.
"""
pm = Ufo.PluginManager()
if args.projections is None or args.flats is None or args.darks is None:
raise RuntimeError("You must specify --projections, --flats and --darks.")
def get_task(name, **kwargs):
"""Get task *name* with properties *kwargs*."""
task = pm.get_task(name)
task.set_properties(**kwargs)
return task
reader = get_task('read')
dark_reader = get_task('read')
flat_before_reader = get_task('read')
ffc = get_task('flat-field-correct', dark_scale=args.dark_scale,
absorption_correct=args.absorptivity,
fix_nan_and_inf=args.fix_nan_and_inf)
mode = args.reduction_mode.lower()
roi_args = make_subargs(args, ['y', 'height', 'y_step'])
set_node_props(reader, args)
set_node_props(dark_reader, roi_args)
set_node_props(flat_before_reader, roi_args)
for r, path in ((reader, args.projections), (dark_reader, args.darks), (flat_before_reader, args.flats)):
setup_read_task(r, path, args)
LOG.debug("Doing flat field correction using reduction mode `{}'".format(mode))
if args.flats2:
flat_after_reader = get_task('read')
setup_read_task(flat_after_reader, args.flats2, args)
set_node_props(flat_after_reader, roi_args)
num_files = len(get_filenames(args.projections))
can_read = len(range(args.start, num_files, args.step))
number = args.number if args.number else num_files
num_read = min(can_read, number)
flat_interpolate = get_task('interpolate', number=num_read)
if args.resize:
LOG.debug("Resize input data by factor of {}".format(args.resize))
proj_bin = get_task('bin', size=args.resize)
dark_bin = get_task('bin', size=args.resize)
flat_bin = get_task('bin', size=args.resize)
graph.connect_nodes(reader, proj_bin)
graph.connect_nodes(dark_reader, dark_bin)
graph.connect_nodes(flat_before_reader, flat_bin)
reader, dark_reader, flat_before_reader = proj_bin, dark_bin, flat_bin
if args.flats2:
flat_bin = get_task('bin', size=args.resize)
graph.connect_nodes(flat_after_reader, flat_bin)
flat_after_reader = flat_bin
if mode == 'median':
dark_stack = get_task('stack', number=len(get_filenames(args.darks)))
dark_reduced = get_task('flatten', mode='median')
flat_before_stack = get_task('stack', number=len(get_filenames(args.flats)))
flat_before_reduced = get_task('flatten', mode='median')
graph.connect_nodes(dark_reader, dark_stack)
graph.connect_nodes(dark_stack, dark_reduced)
graph.connect_nodes(flat_before_reader, flat_before_stack)
graph.connect_nodes(flat_before_stack, flat_before_reduced)
if args.flats2:
flat_after_stack = get_task('stack', number=len(get_filenames(args.flats2)))
flat_after_reduced = get_task('flatten', mode='median')
graph.connect_nodes(flat_after_reader, flat_after_stack)
graph.connect_nodes(flat_after_stack, flat_after_reduced)
elif mode == 'average':
dark_reduced = get_task('average')
flat_before_reduced = get_task('average')
graph.connect_nodes(dark_reader, dark_reduced)
graph.connect_nodes(flat_before_reader, flat_before_reduced)
if args.flats2:
flat_after_reduced = get_task('average')
graph.connect_nodes(flat_after_reader, flat_after_reduced)
else:
raise ValueError('Invalid reduction mode')
graph.connect_nodes_full(reader, ffc, 0)
graph.connect_nodes_full(dark_reduced, ffc, 1)
if args.flats2:
graph.connect_nodes_full(flat_before_reduced, flat_interpolate, 0)
graph.connect_nodes_full(flat_after_reduced, flat_interpolate, 1)
graph.connect_nodes_full(flat_interpolate, ffc, 2)
else:
graph.connect_nodes_full(flat_before_reduced, ffc, 2)
return ffc
def tomo(params):
# Create reader and writer
if params.projections and params.sinograms:
raise RuntimeError("Cannot specify both --projections and --sinograms.")
if params.projections is None and params.sinograms is None:
reader, width, height = get_dummy_reader(params)
else:
if params.projections:
reader, width, height = get_projection_reader(params)
else:
reader, width, height = get_sinogram_reader(params)
axis = params.axis or width / 2.0
if params.projections and params.resize:
width /= params.resize
height /= params.resize
axis /= params.resize
LOG.debug("Input dimensions: {}x{} pixels".format(width, height))
writer = get_writer(pm, params)
# Setup graph depending on the chosen method and input data
g = Ufo.TaskGraph()
if params.projections is not None:
if params.number:
count = len(range(params.start, params.start + params.number, params.step))
else:
count = len(get_filenames(params.projections))
LOG.debug("Number of projections: {}".format(count))
sino_output = get_task('transpose-projections', number=count)
if params.darks and params.flats:
g.connect_nodes(create_flat_correct_pipeline(params, g), sino_output)
else:
g.connect_nodes(reader, sino_output)
if height:
# Sinogram height is the one needed for further padding
height = count
else:
sino_output = reader
if params.method == 'fbp':
fft = get_task('fft', dimensions=1)
ifft = get_task('ifft', dimensions=1)
fltr = get_task('filter', filter=params.projection_filter)
bp = get_task('backproject', axis_pos=axis)
if params.angle:
bp.props.angle_step = params.angle
if params.offset:
bp.props.angle_offset = params.offset
if width and height:
# Pad the image with its extent to prevent reconstuction ring
pad = get_task('pad')
crop = get_task('crop')
setup_padding(pad, crop, width, height, params.projection_padding_mode)
LOG.debug("Padding input to: {}x{} pixels".format(pad.props.width, pad.props.height))
g.connect_nodes(sino_output, pad)
g.connect_nodes(pad, fft)
g.connect_nodes(fft, fltr)
g.connect_nodes(fltr, ifft)
g.connect_nodes(ifft, crop)
g.connect_nodes(crop, bp)
else:
if params.crop_width:
ifft.props.crop_width = int(params.crop_width)
LOG.debug("Cropping to {} pixels".format(ifft.props.crop_width))
g.connect_nodes(sino_output, fft)
g.connect_nodes(fft, fltr)
g.connect_nodes(fltr, ifft)
g.connect_nodes(ifft, bp)
g.connect_nodes(bp, writer)
if params.method in ('sart', 'sirt', 'sbtv', 'asdpocs'):
projector = pm.get_task_from_package('ir', 'parallel-projector')
projector.set_properties(model='joseph', is_forward=False)
projector.set_properties(axis_position=axis)
projector.set_properties(step=params.angle if params.angle else np.pi / 180.0)
method = pm.get_task_from_package('ir', params.method)
method.set_properties(projector=projector, num_iterations=params.num_iterations)
if params.method in ('sart', 'sirt'):
method.set_properties(relaxation_factor=params.relaxation_factor)
if params.method == 'asdpocs':
minimizer = pm.get_task_from_package('ir', 'sirt')
method.set_properties(df_minimizer=minimizer)
if params.method == 'sbtv':
# FIXME: the lambda keyword is preventing from the following
# assignment ...
# method.props.lambda = params.lambda
method.set_properties(mu=params.mu)
g.connect_nodes(sino_output, method)
g.connect_nodes(method, writer)
if params.method == 'dfi':
oversampling = params.oversampling or 1
pad = get_task('zeropad', center_of_rotation=axis, oversampling=oversampling)
fft = get_task('fft', dimensions=1, auto_zeropadding=0)
dfi = get_task('dfi-sinc')
ifft = get_task('ifft', dimensions=2)
swap_forward = get_task('swap-quadrants')
swap_backward = get_task('swap-quadrants')
if params.angle:
dfi.props.angle_step = params.angle
g.connect_nodes(sino_output, pad)
g.connect_nodes(pad, fft)
g.connect_nodes(fft, dfi)
g.connect_nodes(dfi, swap_forward)
g.connect_nodes(swap_forward, ifft)
g.connect_nodes(ifft, swap_backward)
if width:
crop = get_task('crop')
crop.set_properties(from_center=True, width=width, height=width)
g.connect_nodes(swap_backward, crop)
g.connect_nodes(crop, writer)
else:
g.connect_nodes(swap_backward, writer)
scheduler = Ufo.Scheduler()
if hasattr(scheduler.props, 'enable_tracing'):
LOG.debug("Use tracing: {}".format(params.enable_tracing))
scheduler.props.enable_tracing = params.enable_tracing
scheduler.run(g)
duration = scheduler.props.time
LOG.info("Execution time: {} s".format(duration))
return duration
def create_flat_correct_pipeline(args, graph):
"""
Create flat field correction pipeline. All the settings are provided in
*args*. *graph* is used for making the connections. Returns the flat field
correction task which can be used for further pipelining.
"""
pm = Ufo.PluginManager()
if args.projections is None or args.flats is None or args.darks is None:
raise RuntimeError(
"You must specify --projections, --flats and --darks.")
def get_task(name, **kwargs):
"""Get task *name* with properties *kwargs*."""
task = pm.get_task(name)
task.set_properties(**kwargs)
return task
reader = get_task('read')
dark_reader = get_task('read')
flat_before_reader = get_task('read')
ffc = get_task('flat-field-correct',
dark_scale=args.dark_scale,
absorption_correct=args.absorptivity,
fix_nan_and_inf=args.fix_nan_and_inf)
mode = args.reduction_mode.lower()
roi_args = make_subargs(args, ['y', 'height', 'y_step'])
set_node_props(reader, args)
set_node_props(dark_reader, roi_args)
set_node_props(flat_before_reader, roi_args)
for r, path in ((reader, args.projections), (dark_reader, args.darks),
(flat_before_reader, args.flats)):
setup_read_task(r, path, args)
LOG.debug(
"Doing flat field correction using reduction mode `{}'".format(mode))
if args.flats2:
flat_after_reader = get_task('read')
setup_read_task(flat_after_reader, args.flats2, args)
set_node_props(flat_after_reader, roi_args)
num_files = len(get_filenames(args.projections))
can_read = len(range(args.start, num_files, args.step))
number = args.number if args.number else num_files
num_read = min(can_read, number)
flat_interpolate = get_task('interpolate', number=num_read)
if args.resize:
LOG.debug("Resize input data by factor of {}".format(args.resize))
proj_bin = get_task('bin', size=args.resize)
dark_bin = get_task('bin', size=args.resize)
flat_bin = get_task('bin', size=args.resize)
graph.connect_nodes(reader, proj_bin)
graph.connect_nodes(dark_reader, dark_bin)
graph.connect_nodes(flat_before_reader, flat_bin)
reader, dark_reader, flat_before_reader = proj_bin, dark_bin, flat_bin
if args.flats2:
flat_bin = get_task('bin', size=args.resize)
graph.connect_nodes(flat_after_reader, flat_bin)
flat_after_reader = flat_bin
if mode == 'median':
dark_stack = get_task('stack', number=len(get_filenames(args.darks)))
dark_reduced = get_task('flatten', mode='median')
flat_before_stack = get_task('stack',
number=len(get_filenames(args.flats)))
flat_before_reduced = get_task('flatten', mode='median')
graph.connect_nodes(dark_reader, dark_stack)
graph.connect_nodes(dark_stack, dark_reduced)
graph.connect_nodes(flat_before_reader, flat_before_stack)
graph.connect_nodes(flat_before_stack, flat_before_reduced)
if args.flats2:
flat_after_stack = get_task('stack',
number=len(get_filenames(args.flats2)))
flat_after_reduced = get_task('flatten', mode='median')
graph.connect_nodes(flat_after_reader, flat_after_stack)
graph.connect_nodes(flat_after_stack, flat_after_reduced)
elif mode == 'average':
dark_reduced = get_task('average')
flat_before_reduced = get_task('average')
graph.connect_nodes(dark_reader, dark_reduced)
graph.connect_nodes(flat_before_reader, flat_before_reduced)
if args.flats2:
flat_after_reduced = get_task('average')
graph.connect_nodes(flat_after_reader, flat_after_reduced)
else:
raise ValueError('Invalid reduction mode')
graph.connect_nodes_full(reader, ffc, 0)
graph.connect_nodes_full(dark_reduced, ffc, 1)
if args.flats2:
graph.connect_nodes_full(flat_before_reduced, flat_interpolate, 0)
graph.connect_nodes_full(flat_after_reduced, flat_interpolate, 1)
graph.connect_nodes_full(flat_interpolate, ffc, 2)
else:
graph.connect_nodes_full(flat_before_reduced, ffc, 2)
return ffc
def frmt_ufo_cmds(cmds, ctset, out_pattern, ax, args, Tofu, Ufo, FindCOR,
nviews, WH):
'''formats list of processing commands for a CT set'''
############ Preprocessing commands: working on projections
#must be made compatible with multipage tiffs
#any_flat = get_filenames(os.path.join(ctset[0],Tofu._fdt_names[1]))[1]
any_flat = get_filenames(os.path.join(ctset[0], Tofu._fdt_names[1]))[0]
# make a copy of original flat-field file in case if inp is enabled
if args.inp and args.pre:
any_flat_copy = os.path.join(args.tmpdir, 'flat.tif')
cmds.append("cp {} {}".format(any_flat, any_flat_copy))
any_flat = any_flat_copy
if args.pre:
cmds.append("echo \" - Preprocessing \"")
cmds_prepro = Ufo.get_pre_cmd(ctset, args.pre_cmd, args.tmpdir)
cmds.extend(cmds_prepro)
# reset location of input data
ctset = (args.tmpdir, ctset[1])
###################################################
if args.inp: # generate commands to "inpaint" projections
cmds.append("echo \" - Inpainting\"")
cmds_inpaint = Ufo.get_inp_cmd(ctset, args.tmpdir, args, WH[0], nviews,
any_flat)
# reset location of input data
ctset = (args.tmpdir, ctset[1])
cmds.extend(cmds_inpaint)
######### Projections ready #############
######### If RR is not enabled we do not need sinograms ########
if (not args.RR):
if args.inp and (not args.PR):
cmds.append(
"echo \" - CT with axis {}; no ffc, no PR\"".format(ax))
cmds.append(
Tofu.get_reco_cmd(ctset, out_pattern, ax, args, nviews, WH,
False, False))
elif args.inp and args.PR:
cmds.append(
"echo \" - Phase retrieval from inpainted projections\"")
cmds.extend(
Ufo.get_pr_ufo_cmd(ctset, args, args.tmpdir, nviews, WH))
cmds.append(
"echo \" - CT with axis {}; no ffc, no PR\"".format(ax))
cmds.append(
Tofu.get_reco_cmd(ctset, out_pattern, ax, args, nviews, WH,
False, False))
elif (not args.inp) and args.PR:
cmds.append("echo \" - CT with axis {}; ffc and PR\"".format(ax))
cmds.append(
Tofu.get_reco_cmd(ctset, out_pattern, ax, args, nviews, WH,
True, True))
elif (not args.inp) and (not args.PR):
cmds.append("echo \" - CT with axis {}; ffc, no PR\"".format(ax))
cmds.append(
Tofu.get_reco_cmd(ctset, out_pattern, ax, args, nviews, WH,
True, False))
################# RING REMOVAL #######################
if args.RR:
# Generate sinograms
if args.PR: # we need to do phase retrieval
if args.inp: # if inp was requested flat field correction has been done already
cmds.append(
"echo \" - Phase retrieval from inpainted projections\"")
cmds.extend(
Ufo.get_pr_ufo_cmd(ctset, args, args.tmpdir, nviews, WH))
else:
cmds.append("echo \" - Phase retrieval with flat-correction\"")
cmds.append(Tofu.get_pr_tofu_cmd(ctset, args, nviews, WH[0]))
cmds.append(
"echo \" - Make sinograms from phase-retrieved projections\"")
cmds.append(Tofu.get_sinos_noffc_cmd(args.tmpdir, args, nviews,
WH))
else: # we do not need to do phase retrieval
if args.inp: # if inp was requested flat field correction has been done already
cmds.append(
"echo \" - Make sinograms from inpainted projections\"")
cmds.append(
Tofu.get_sinos_noffc_cmd(args.tmpdir, args, nviews, WH))
else:
cmds.append("echo \" - Make sinograms with flat-correction\"")
cmds.append(
Tofu.get_sinos_ffc_cmd(ctset, args.tmpdir, args, nviews,
WH))
# Filter sinograms
if (args.RR_par >= 1) and (args.RR_par <= 3):
cmds.append("echo \" - Ring removal - ufo 1d stripes filter\"")
cmds.append(
Ufo.get_filter_sinos_cmd(ctset[0], args.tmpdir, args.RR_par,
nviews, WH[1]))
elif args.RR_par > 5:
cmds.append("echo \" - Ring removal - median filter\"")
#note - calling an external program, not ufo-kit script
tmp = os.path.dirname(os.path.abspath(__file__))
path_to_filt = os.path.join(tmp, 'ez_RR_simple.py')
if os.path.isfile(path_to_filt):
tmp = os.path.join(args.tmpdir, "sinos")
cmdtmp = '{} --sinos {} --mws {}'\
.format(path_to_filt, tmp, args.RR_par)
cmds.append(cmdtmp)
else:
cmds.append(
"echo \"Omitting RR because file with filter does not exist\""
)
if not args.keep_tmp:
cmds.append('rm -rf {}'.format(os.path.join(args.tmpdir, 'sinos')))
# Preparation for final CT command
cmds.append("echo \" - Generating proj from filtered sinograms\"")
cmds.append(Tofu.get_sinos2proj_cmd(args, WH[0]))
# reset location of input data
ctset = (args.tmpdir, ctset[1])
#Finally tofu reco without ffc and PR
cmds.append("echo \" - CT with axis {}\"".format(ax))
cmds.append(
Tofu.get_reco_cmd(ctset, out_pattern, ax, args, nviews, WH, False,
False))
return nviews, WH
def tomo(params):
# Create reader and writer
if params.projections and params.sinograms:
raise RuntimeError("Cannot specify both --projections and --sinograms.")
if params.projections is None and params.sinograms is None:
reader, width, height = get_dummy_reader(params)
else:
if params.projections:
reader, width, height = get_projection_reader(params)
else:
reader, width, height = get_sinogram_reader(params)
axis = params.axis or width / 2.0
if params.projections and params.resize:
width /= params.resize
height /= params.resize
axis /= params.resize
LOG.debug("Input dimensions: {}x{} pixels".format(width, height))
writer = get_writer(params)
# Setup graph depending on the chosen method and input data
g = Ufo.TaskGraph()
if params.projections is not None:
if params.number:
count = len(range(params.start, params.start + params.number, params.step))
else:
count = len(get_filenames(params.projections))
LOG.debug("Number of projections: {}".format(count))
sino_output = get_task('transpose-projections', number=count)
if params.darks and params.flats:
g.connect_nodes(create_flat_correct_pipeline(params, g), sino_output)
else:
g.connect_nodes(reader, sino_output)
if height:
# Sinogram height is the one needed for further padding
height = count
else:
sino_output = reader
if params.method == 'fbp':
fft = get_task('fft', dimensions=1)
ifft = get_task('ifft', dimensions=1)
fltr = get_task('filter', filter=params.projection_filter)
bp = get_task('backproject', axis_pos=axis)
if params.angle:
bp.props.angle_step = params.angle
if params.offset:
bp.props.angle_offset = params.offset
if width and height:
# Pad the image with its extent to prevent reconstuction ring
pad = get_task('pad')
crop = get_task('crop')
setup_padding(pad, crop, width, height)
LOG.debug("Padding input to: {}x{} pixels".format(pad.props.width, pad.props.height))
g.connect_nodes(sino_output, pad)
g.connect_nodes(pad, fft)
g.connect_nodes(fft, fltr)
g.connect_nodes(fltr, ifft)
g.connect_nodes(ifft, crop)
g.connect_nodes(crop, bp)
else:
if params.crop_width:
ifft.props.crop_width = int(params.crop_width)
LOG.debug("Cropping to {} pixels".format(ifft.props.crop_width))
g.connect_nodes(sino_output, fft)
g.connect_nodes(fft, fltr)
g.connect_nodes(fltr, ifft)
g.connect_nodes(ifft, bp)
g.connect_nodes(bp, writer)
if params.method in ('sart', 'sirt', 'sbtv', 'asdpocs'):
projector = pm.get_task_from_package('ir', 'parallel-projector')
projector.set_properties(model='joseph', is_forward=False)
projector.set_properties(axis_position=axis)
projector.set_properties(step=params.angle if params.angle else np.pi / 180.0)
method = pm.get_task_from_package('ir', params.method)
method.set_properties(projector=projector, num_iterations=params.num_iterations)
if params.method in ('sart', 'sirt'):
method.set_properties(relaxation_factor=params.relaxation_factor)
if params.method == 'asdpocs':
minimizer = pm.get_task_from_package('ir', 'sirt')
method.set_properties(df_minimizer=minimizer)
if params.method == 'sbtv':
# FIXME: the lambda keyword is preventing from the following
# assignment ...
# method.props.lambda = params.lambda
method.set_properties(mu=params.mu)
g.connect_nodes(sino_output, method)
g.connect_nodes(method, writer)
if params.method == 'dfi':
oversampling = params.oversampling or 1
pad = get_task('zeropad', center_of_rotation=axis, oversampling=oversampling)
fft = get_task('fft', dimensions=1, auto_zeropadding=0)
dfi = get_task('dfi-sinc')
ifft = get_task('ifft', dimensions=2)
swap_forward = get_task('swap-quadrants')
swap_backward = get_task('swap-quadrants')
if params.angle:
dfi.props.angle_step = params.angle
g.connect_nodes(sino_output, pad)
g.connect_nodes(pad, fft)
g.connect_nodes(fft, dfi)
g.connect_nodes(dfi, swap_forward)
g.connect_nodes(swap_forward, ifft)
g.connect_nodes(ifft, swap_backward)
if width:
crop = get_task('crop')
crop.set_properties(from_center=True, width=width, height=width)
g.connect_nodes(swap_backward, crop)
g.connect_nodes(crop, writer)
else:
g.connect_nodes(swap_backward, writer)
scheduler = Ufo.Scheduler()
if hasattr(scheduler.props, 'enable_tracing'):
LOG.debug("Use tracing: {}".format(params.enable_tracing))
scheduler.props.enable_tracing = params.enable_tracing
scheduler.run(g)
duration = scheduler.props.time
LOG.info("Execution time: {} s".format(duration))
return duration
