def center(io_paras, data_paras, center_start, center_end, center_step, diag_cycle=0,
mode='diag', normalize=True, stripe_removal=10, phase_retrieval=False):
# Input and output
datafile = io_paras.get('datafile')
path2white = io_paras.get('path2white', datafile)
path2dark = io_paras.get('path2dark', path2white)
out_dir = io_paras.get('out_dir')
diag_cent_dir = io_paras.get('diag_cent_dir', out_dir+"/center_diagnose/")
recon_dir = io_paras.get('recon_dir', out_dir+"/recon/")
out_prefix = io_paras.get('out_prefix', "recon_")
# Parameters of dataset
NumCycles = data_paras.get('NumCycles', 1) # Number of cycles used for recon
ProjPerCycle = data_paras.get('ProjPerCycle') # Number of projections per cycle, N_theta
cycle_offset = data_paras.get('cycle_offset', 0) # Offset in output cycle number
proj_start = data_paras.get('proj_start', 0) # Starting projection of reconstruction
proj_step = data_paras.get('proj_step')
z_start = data_paras.get('z_start', 0)
z_end = data_paras.get('z_end', z_start+1)
z_step = data_paras.get('z_step')
x_start = data_paras.get('x_start')
x_end = data_paras.get('x_end', x_start+1)
x_step = data_paras.get('x_step')
white_start = data_paras.get('white_start')
white_end = data_paras.get('white_end')
dark_start = data_paras.get('dark_start')
dark_end = data_paras.get('dark_end')
# Set start and end of each subcycle
projections_start = diag_cycle * ProjPerCycle + proj_start
projections_end = projections_start + ProjPerCycle
slice1 = slice(projections_start, projections_end, proj_step)
slice2 = slice(z_start, z_end, z_step)
slice3 = slice(x_start, x_end, x_step)
slices = (slice1, slice2, slice3)
white_slices = (slice(white_start, white_end), slice2, slice3)
dark_slices = (slice(dark_start, dark_end), slice2, slice3)
print("Running center diagnosis (projs %s to %s)"
% (projections_start, projections_end))
# Read HDF5 file.
print("Reading datafile %s..." % datafile, end="")
sys.stdout.flush()
data, white, dark = reader.read_aps_2bm(datafile, slices, white_slices, dark_slices,
path2white=path2white, path2dark=path2dark)
theta = gen_theta(data.shape[0])
print("Done!")
print("Data shape = %s;\nwhite shape = %s;\ndark shape = %s."
% (data.shape, white.shape, dark.shape))
## Normalize dataset using data_white and data_dark
if normalize:
data = tomopy.normalize(data, white, dark, cutoff=None, ncore=_ncore, nchunk=None)
## Remove stripes caused by dead pixels in the detector
if stripe_removal:
data = tomopy.remove_stripe_fw(data, level=stripe_removal, wname='db5',
sigma=2, pad=True, ncore=None, nchunk=None)
# data = tomopy.remove_stripe_ti(data, nblock=0, alpha=1.5,
# ncore=None, nchunk=None)
# # Show preprocessed projection
# plt.figure("%s-prep" % projections_start)
# plt.imshow(d.data[0,:,:], cmap=cm.Greys_r)
# plt.savefig(out_dir+"/preprocess/%s-prep.jpg"
# % projections_start)
# # plt.show()
# continue
## Phase retrieval
if phase_retrieval:
data = tomopy.retrieve_phase(data,
pixel_size=6.5e-5, dist=33, energy=30,
alpha=1e-3, pad=True, ncore=_ncore, nchunk=None)
## Determine and set the center of rotation
### Using optimization method to automatically find the center
# d.optimize_center()
if 'opti' in mode:
print("Optimizing center ...", end="")
sys.stdout.flush()
rot_center = tomopy.find_center(data, theta, ind=None, emission=True, init=None,
tol=0.5, mask=True, ratio=1.)
print("Done!")
print("center = %s" % rot_center)
### Output the reconstruction results using a range of centers,
### and then manually find the optimal center.
if 'diag' in mode:
if not os.path.exists(diag_cent_dir):
os.makedirs(diag_cent_dir)
print("Testing centers ...", end="")
sys.stdout.flush()
tomopy.write_center(data, theta, dpath=diag_cent_dir,
cen_range=[center_start, center_end, center_step],
ind=None, emission=False, mask=False, ratio=1.)
print("Done!")
def recon(io_paras, data_paras, rot_center=None, normalize=True, stripe_removal=10, phase_retrieval=False,
opt_center=False, diag_center=False, output="tiff"):
# Input and output
datafile = io_paras.get('datafile')
path2white = io_paras.get('path2white', datafile)
path2dark = io_paras.get('path2dark', path2white)
out_dir = io_paras.get('out_dir')
diag_cent_dir = io_paras.get('diag_cent_dir', out_dir+"/center_diagnose/")
recon_dir = io_paras.get('recon_dir', out_dir+"/recon/")
out_prefix = io_paras.get('out_prefix', "recon_")
# Parameters of dataset
NumCycles = data_paras.get('NumCycles', 1) # Number of cycles used for recon
ProjPerCycle = data_paras.get('ProjPerCycle') # Number of projections per cycle, N_theta
cycle_offset = data_paras.get('cycle_offset', 0) # Offset in output cycle number
proj_start = data_paras.get('proj_start', 0) # Starting projection of reconstruction
proj_step = data_paras.get('proj_step')
z_start = data_paras.get('z_start', 0)
z_end = data_paras.get('z_end', z_start+1)
z_step = data_paras.get('z_step')
x_start = data_paras.get('x_start')
x_end = data_paras.get('x_end', x_start+1)
x_step = data_paras.get('x_step')
white_start = data_paras.get('white_start')
white_end = data_paras.get('white_end')
dark_start = data_paras.get('dark_start')
dark_end = data_paras.get('dark_end')
rot_center_copy = rot_center
for cycle in xrange(NumCycles):
# Set start and end of each cycle
projections_start = cycle * ProjPerCycle + proj_start
projections_end = projections_start + ProjPerCycle
slice1 = slice(projections_start, projections_end, proj_step)
slice2 = slice(z_start, z_end, z_step)
slice3 = slice(x_start, x_end, x_step)
slices = (slice1, slice2, slice3)
white_slices = (slice(white_start, white_end), slice2, slice3)
dark_slices = (slice(dark_start, dark_end), slice2, slice3)
print("Running cycle #%s (projs %s to %s)"
% (cycle, projections_start, projections_end))
# Read HDF5 file.
print("Reading datafile %s..." % datafile, end="")
sys.stdout.flush()
data, white, dark = reader.read_aps_2bm(datafile, slices, white_slices, dark_slices,
path2white=path2white, path2dark=path2dark)
theta = gen_theta(data.shape[0])
print("Done!")
print("Data shape = %s;\nwhite shape = %s;\ndark shape = %s."
% (data.shape, white.shape, dark.shape))
## Normalize dataset using data_white and data_dark
if normalize:
print("Normalizing data ...")
# white = white.mean(axis=0).reshape(-1, *data.shape[1:])
# dark = dark.mean(axis=0).reshape(-1, *data.shape[1:])
# data = (data - dark) / (white - dark)
data = tomopy.normalize(data, white, dark, cutoff=None, ncore=_ncore, nchunk=None)[...]
## Remove stripes caused by dead pixels in the detector
if stripe_removal:
print("Removing stripes ...")
data = tomopy.remove_stripe_fw(data, level=stripe_removal, wname='db5', sigma=2,
pad=True, ncore=_ncore, nchunk=None)
# data = tomopy.remove_stripe_ti(data, nblock=0, alpha=1.5,
# ncore=None, nchunk=None)
# # Show preprocessed projection
# plt.figure("%s-prep" % projections_start)
# plt.imshow(d.data[0,:,:], cmap=cm.Greys_r)
# plt.savefig(out_dir+"/preprocess/%s-prep.jpg"
# % projections_start)
# # plt.show()
# continue
## Phase retrieval
if phase_retrieval:
print("Retrieving phase ...")
data = tomopy.retrieve_phase(data,
pixel_size=1e-4, dist=50, energy=20,
alpha=1e-3, pad=True, ncore=_ncore, nchunk=None)
## Determine and set the center of rotation
if opt_center or (rot_center == None):
### Using optimization method to automatically find the center
# d.optimize_center()
print("Optimizing center ...", end="")
sys.stdout.flush()
rot_center = tomopy.find_center(data, theta, ind=None, emission=True, init=None,
tol=0.5, mask=True, ratio=1.)
print("Done!")
print("center = %s" % rot_center)
if diag_center:
### Output the reconstruction results using a range of centers,
### and then manually find the optimal center.
# d.diagnose_center()
if not os.path.exists(diag_cent_dir):
os.makedirs(diag_cent_dir)
print("Testing centers ...", end="")
sys.stdout.flush()
tomopy.write_center(data, theta, dpath=diag_cent_dir,
cen_range=[center_start, center_end, center_step],
ind=None, emission=False, mask=False, ratio=1.)
print("Done!")
## Flip odd frames
if (cycle % 2):
data[...] = data[...,::-1]
rot_center = data.shape[-1] - rot_center_copy
else:
rot_center = rot_center_copy
## Reconstruction using FBP
print("Running gridrec ...", end="")
sys.stdout.flush()
recon = tomopy.recon(data, theta, center=rot_center, emission=False, algorithm='gridrec',
# num_gridx=None, num_gridy=None, filter_name='shepp',
ncore=_ncore, nchunk=_nchunk)
print("Done!")
## Collect background
# if cycle == 0:
# bg = recon
# elif cycle < 4:
# bg += recon
# else:
# recon -= bg/4.
# Write to stack of TIFFs.
if not os.path.exists(recon_dir):
os.makedirs(recon_dir)
out_fname = recon_dir+"/"+out_prefix+"t_%d" % (cycle + cycle_offset)
if "hdf" in output:
hdf_fname = out_fname + ".hdf5"
print("Writing reconstruction output file %s..."
% hdf_fname, end="")
sys.stdout.flush()
tomopy.write_hdf5(recon, fname=hdf_fname, gname='exchange', overwrite=False)
print("Done!")
if "tif" in output:
tiff_fname = out_fname + ".tiff"
print("Writing reconstruction tiff files %s ..."
% tiff_fname, end="")
sys.stdout.flush()
tomopy.write_tiff_stack(recon, fname=tiff_fname, axis=0, digit=5, start=0, overwrite=False)
print("Done!")
if "bin" in output:
bin_fname = out_fname + ".bin"
print("Writing reconstruction to binary files %s..."
% bin_fname, end="")
sys.stdout.flush()
recon.tofile(bin_fname)
def recon3(io_paras,
data_paras,
rot_center=None,
normalize=True,
stripe_removal=10,
stripe_sigma=2,
phase_retrieval=False,
opt_center=False,
diag_center=False,
output="tiff",
z_recon_size=None):
# Input and output
datafile = io_paras.get('datafile')
path2white = io_paras.get('path2white', datafile)
path2dark = io_paras.get('path2dark', path2white)
out_dir = io_paras.get('out_dir')
diag_cent_dir = io_paras.get('diag_cent_dir',
out_dir + "/center_diagnose/")
recon_dir = io_paras.get('recon_dir', out_dir + "/recon/")
out_prefix = io_paras.get('out_prefix', "recon_")
# Parameters of dataset
NumCycles = data_paras.get('NumCycles',
1) # Number of cycles used for recon
ProjPerCycle = data_paras.get(
'ProjPerCycle') # Number of projections per cycle, N_theta
cycle_offset = data_paras.get('cycle_offset',
0) # Offset in output cycle number
proj_start = data_paras.get('proj_start',
0) # Starting projection of reconstruction
proj_step = data_paras.get('proj_step')
z_start = data_paras.get('z_start', 0)
z_end = data_paras.get('z_end', z_start + 1)
z_step = data_paras.get('z_step')
x_start = data_paras.get('x_start')
x_end = data_paras.get('x_end', x_start + 1)
x_step = data_paras.get('x_step')
white_start = data_paras.get('white_start')
white_end = data_paras.get('white_end')
dark_start = data_paras.get('dark_start')
dark_end = data_paras.get('dark_end')
# TIMBIR parameters
NumSubCycles = data_paras.get('NumSubCycles',
1) # Number of subcycles in one cycle, K
SlewSpeed = data_paras.get('SlewSpeed', 0) # In deg/s
MinAcqTime = data_paras.get('MinAcqTime', 0) # In s
TotalNumCycles = data_paras.get(
'TotalNumCycles', 1) # Total number of cycles in the full scan data
ProjPerRecon = data_paras.get(
'ProjPerRecon',
ProjPerCycle) # Number of projections per reconstruction
# Calculate thetas for interlaced scan
theta = gen_theta_timbir(NumSubCycles, ProjPerCycle, SlewSpeed, MinAcqTime,
TotalNumCycles)
if ProjPerRecon is None:
ProjPerCycle = theta.size // TotalNumCycles
else:
ProjPerCycle = ProjPerRecon
print("Will use %s projections per reconstruction." % ProjPerCycle)
# Distribute z slices to processes
if z_step is None:
z_step = 1
z_pool = get_pool(z_start,
z_end,
z_step,
z_chunk_size=z_recon_size,
fmt='slice')
slice3 = slice(x_start, x_end, x_step)
rot_center_copy = rot_center
for cycle in xrange(NumCycles):
# Set start and end of each cycle
projections_start = cycle * ProjPerCycle + proj_start
projections_end = projections_start + ProjPerCycle
slice1 = slice(projections_start, projections_end, proj_step)
# Setup continuous output
if "cont" in output:
if not os.path.exists(recon_dir):
os.makedirs(recon_dir)
cont_fname = recon_dir+"/"+out_prefix+"t_%d_z_%d_%d.bin" \
% (cycle + cycle_offset, z_start, z_end)
cont_file = file(cont_fname, 'wb')
# Distribute z slices to processes
for i in range(_rank, len(z_pool), _nprocs):
slice2 = z_pool[i]
slices = (slice1, slice2, slice3)
white_slices = (slice(white_start, white_end), slice2, slice3)
dark_slices = (slice(dark_start, dark_end), slice2, slice3)
print(
"Running cycle #%s (projs %s to %s, z = %s - %s) on process %s of %s"
% (cycle, projections_start, projections_end, slice2.start,
slice2.stop, _rank, _nprocs))
# Read HDF5 file.
print("Reading datafile %s..." % datafile, end="")
sys.stdout.flush()
data, white, dark = reader.read_aps_2bm(datafile,
slices,
white_slices,
dark_slices,
path2white=path2white,
path2dark=path2dark)
# data += 1
# theta = gen_theta(data.shape[0])
print("Done!")
print("Data shape = %s;\nwhite shape = %s;\ndark shape = %s." %
(data.shape, white.shape, dark.shape))
# data = tomopy.focus_region(data, dia=1560, xcoord=1150, ycoord=1080,
# center=rot_center, pad=False, corr=True)
# rot_center = None
# print("Data shape = %s;\nwhite shape = %s;\ndark shape = %s."
# % (data.shape, white.shape, dark.shape))
## Normalize dataset using data_white and data_dark
if normalize:
print("Normalizing data ...")
# white = white.mean(axis=0).reshape(-1, *data.shape[1:])
# dark = dark.mean(axis=0).reshape(-1, *data.shape[1:])
# data = (data - dark) / (white - dark)
data = tomopy.normalize(data,
white,
dark,
cutoff=None,
ncore=_ncore,
nchunk=_nchunk)[...]
## Remove stripes caused by dead pixels in the detector
if stripe_removal:
print("Removing stripes ...")
data = tomopy.remove_stripe_fw(data,
level=stripe_removal,
wname='db5',
sigma=stripe_sigma,
pad=True,
ncore=_ncore,
nchunk=_nchunk)
# data = tomopy.remove_stripe_ti(data, nblock=0, alpha=1.5,
# ncore=None, nchunk=None)
# # Show preprocessed projection
# plt.figure("%s-prep" % projections_start)
# plt.imshow(d.data[0,:,:], cmap=cm.Greys_r)
# plt.savefig(out_dir+"/preprocess/%s-prep.jpg"
# % projections_start)
# # plt.show()
# continue
## Phase retrieval
if phase_retrieval:
print("Retrieving phase ...")
data = tomopy.retrieve_phase(data,
pixel_size=1.1e-4,
dist=6,
energy=25.7,
alpha=1e-2,
pad=True,
ncore=_ncore,
nchunk=_nchunk)
## Determine and set the center of rotation
if opt_center: # or (rot_center == None):
### Using optimization method to automatically find the center
# d.optimize_center()
print("Optimizing center ...", end="")
sys.stdout.flush()
rot_center = tomopy.find_center(data,
theta,
ind=None,
emission=True,
init=None,
tol=0.5,
mask=True,
ratio=1.)
print("Done!")
print("center = %s" % rot_center)
if diag_center:
### Output the reconstruction results using a range of centers,
### and then manually find the optimal center.
# d.diagnose_center()
if not os.path.exists(diag_cent_dir):
os.makedirs(diag_cent_dir)
print("Testing centers ...", end="")
sys.stdout.flush()
tomopy.write_center(
data,
theta,
dpath=diag_cent_dir,
cen_range=[center_start, center_end, center_step],
ind=None,
emission=False,
mask=False,
ratio=1.)
print("Done!")
## Flip odd frames
# if (cycle % 2):
# data[...] = data[...,::-1]
# rot_center = data.shape[-1] - rot_center_copy
# else:
# rot_center = rot_center_copy
## Reconstruction using FBP
print("Running gridrec ...", end="")
sys.stdout.flush()
recon = tomopy.recon(
data,
theta[slice1],
center=rot_center,
emission=False,
algorithm='gridrec',
# num_gridx=None, num_gridy=None, filter_name='shepp',
ncore=_ncore,
nchunk=_nchunk)
print("Done!")
## Collect background
# if cycle == 0:
# bg = recon
# elif cycle < 4:
# bg += recon
# else:
# recon -= bg/4.
# Write to stack of TIFFs.
if not os.path.exists(recon_dir):
os.makedirs(recon_dir)
out_fname = recon_dir + "/" + out_prefix + "t_%d_z_" % (
cycle + cycle_offset)
if "hdf" in output:
hdf_fname = out_fname + "%d_%d.hdf5" % (slice2.start,
slice2.stop)
print("Writing reconstruction output file %s..." % hdf_fname,
end="")
sys.stdout.flush()
tomopy.write_hdf5(recon,
fname=hdf_fname,
gname='exchange',
overwrite=False)
print("Done!")
if "tif" in output:
if "stack" in output: # single stacked file for multiple z
tiff_fname = out_fname + "%d_%d.tiff" % (slice2.start,
slice2.stop)
print("Writing reconstruction tiff files %s ..." %
tiff_fname,
end="")
sys.stdout.flush()
tomopy.write_tiff(recon, fname=tiff_fname, overwrite=False)
print("Done!")
else: # separate files for different z
for iz, z in enumerate(
range(slice2.start, slice2.stop, slice2.step)):
tiff_fname = out_fname + "%d.tiff" % z
print("Writing reconstruction tiff files %s ..." %
tiff_fname,
end="")
sys.stdout.flush()
tomopy.write_tiff(recon[iz],
fname=tiff_fname,
overwrite=False)
print("Done!")
if "bin" in output:
bin_fname = out_fname + "%d_%d.bin" % (slice2.start,
slice2.stop)
print("Writing reconstruction to binary files %s..." %
bin_fname,
end="")
sys.stdout.flush()
recon.tofile(bin_fname)
if "cont" in output:
print("Writing reconstruction to binary files %s..." %
cont_fname,
end="")
sys.stdout.flush()
recon.tofile(cont_file)
print("Done!")
if "cont" in output:
cont_file.close()
if _usempi:
comm.Barrier()
if _rank == 0:
print("All done!")
