def reconstruct(sname, rot_center, ovlpfind, s_start, s_end):
fname = dfolder + sname + '.h5'
print(fname)
start = s_start
end = s_end
chunks = 24
num_sino = (end - start) // chunks
for m in range(chunks):
sino_start = start + num_sino * m
sino_end = start + num_sino * (m + 1)
start_read_time = time.time()
proj, flat, dark, thetat = dxchange.read_aps_2bm(fname,
sino=(sino_start,
sino_end))
print(' done read in %0.1f min' %
((time.time() - start_read_time) / 60))
dark = proj[9001:9002]
flat = proj[0:1]
proj = proj[1:9000]
theta = tomopy.angles(proj.shape[0], 0., 360.)
proj = tomopy.sino_360_to_180(proj, overlap=ovlpfind, rotation='right')
proj = tomopy.remove_outlier(proj, dif=0.4)
proj = tomopy.normalize_bg(proj, air=10)
proj = tomopy.minus_log(proj)
center = rot_center
start_ring_time = time.time()
proj = tomopy.remove_stripe_fw(proj, wname='sym5', sigma=4, pad=False)
proj = tomopy.remove_stripe_sf(proj, size=3)
print(' done pre-process in %0.1f min' %
((time.time() - start_ring_time) / 60))
start_phase_time = time.time()
proj = tomopy.retrieve_phase(proj,
pixel_size=detector_pixel_size_x,
dist=sample_detector_distance,
energy=energy,
alpha=alpha,
pad=True,
ncore=None,
nchunk=None)
print(' done phase retrieval in %0.1f min' %
((time.time() - start_phase_time) / 60))
start_recon_time = time.time()
rec = tomopy.recon(proj,
theta,
center=center,
algorithm='gridrec',
filter_name='ramalk')
tomopy.circ_mask(rec, axis=0, ratio=0.95)
print("Reconstructed", rec.shape)
dxchange.write_tiff_stack(rec,
fname=dfolder + '/' + sname + '/' + sname,
overwrite=True,
start=sino_start)
print(' Chunk reconstruction done in %0.1f min' %
((time.time() - start_recon_time) / 60))
print("Done!")
def rec_try(h5fname, nsino, rot_center, center_search_width, algorithm, binning):
zinger_level = 800 # Zinger level for projections
zinger_level_w = 1000 # Zinger level for white
data_shape = get_dx_dims(h5fname, 'data')
print(data_shape)
ssino = int(data_shape[1] * nsino)
center_range = (rot_center-center_search_width, rot_center+center_search_width, 0.5)
#print(sino,ssino, center_range)
#print(center_range[0], center_range[1], center_range[2])
# Select sinogram range to reconstruct
sino = None
start = ssino
end = start + 1
sino = (start, end)
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
# zinger_removal
proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0)
flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0)
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
# remove stripes
data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True)
print("Raw data: ", h5fname)
print("Center: ", rot_center)
data = tomopy.minus_log(data)
stack = np.empty((len(np.arange(*center_range)), data_shape[0], data_shape[2]))
index = 0
for axis in np.arange(*center_range):
stack[index] = data[:, 0, :]
index = index + 1
# Reconstruct the same slice with a range of centers.
rec = tomopy.recon(stack, theta, center=np.arange(*center_range), sinogram_order=True, algorithm='gridrec', filter_name='parzen', nchunk=1)
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
index = 0
# Save images to a temporary folder.
fname = os.path.dirname(h5fname) + '/' + 'try_rec/' + 'recon_' + os.path.splitext(os.path.basename(h5fname))[0]
for axis in np.arange(*center_range):
rfname = fname + '_' + str('{0:.2f}'.format(axis) + '.tiff')
dxchange.write_tiff(rec[index], fname=rfname, overwrite=True)
index = index + 1
print("Reconstructions: ", fname)
def main(arg):
fname = '/local/dataraid/elettra/Oak_16bit_slice343_all_repack.h5'
# Read the hdf raw data.
sino, sflat, sdark, th = dxchange.read_aps_32id(fname)
slider(sino)
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(sino.shape[1], ang1=0.0, ang2=180.0)
print(sino.shape, sdark.shape, sflat.shape, theta.shape)
# Quick normalization just to see something ....
ndata = sino / float(np.amax(sino))
slider(ndata)
# Find rotation center.
rot_center = 962
binning = 1
ndata = tomopy.downsample(ndata, level=int(binning))
rot_center = rot_center/np.power(2, float(binning))
ndata = tomopy.minus_log(ndata)
# Reconstruct object using Gridrec algorithm.
rec = tomopy.recon(ndata, theta, center=rot_center, sinogram_order=True, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
# Write data as stack of TIFs.
dxchange.write_tiff_stack(rec, fname='recon_dir/recon')
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'):
sample_detector_distance = 3 # Propagation distance of the wavefront in cm
detector_pixel_size_x = 1.17e-4 # Detector pixel size in cm (5x: 1.17e-4, 2X: 2.93e-4)
monochromator_energy = 22.7 # Energy of incident wave in keV
alpha = 1e-02 # Phase retrieval coeff.
zinger_level = 800 # Zinger level for projections
zinger_level_w = 1000 # Zinger level for white
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
# zinger_removal
proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0)
flat = tomopy.misc.corr.remove_outlier(flat,
zinger_level_w,
size=15,
axis=0)
# Flat-field correction of raw data.
##data = tomopy.normalize(proj, flat, dark, cutoff=0.8)
data = tomopy.normalize(proj, flat, dark)
# remove stripes
## data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True)
##data = tomopy.remove_stripe_ti(data, alpha=1.5)
data = tomopy.remove_stripe_sf(data, size=150)
# phase retrieval
##data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=alpha,pad=True)
print("Raw data: ", h5fname)
print("Center: ", rot_center)
data = tomopy.minus_log(data)
data = tomopy.remove_nan(data, val=0.0)
data = tomopy.remove_neg(data, val=0.00)
data[np.where(data == np.inf)] = 0.00
rot_center = rot_center / np.power(2, float(binning))
data = tomopy.downsample(data, level=binning)
data = tomopy.downsample(data, level=binning, axis=1)
# Reconstruct object.
if algorithm == 'sirtfbp':
rec = rec_sirtfbp(data, theta, rot_center)
else:
rec = tomopy.recon(data,
theta,
center=rot_center,
algorithm=algorithm,
filter_name='parzen')
print("Algorithm: ", algorithm)
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
return rec
def recon_gridrec(sinogram, center, angles=None, ratio=1.0):
"""
Wrapper of the gridrec method implemented in the tomopy package.
https://tomopy.readthedocs.io/en/latest/api/tomopy.recon.algorithm.html
Parameters
----------
sinogram : array_like
2D tomographic data.
center : float
Center of rotation.
angles : float
1D array. Tomographic angles in radian.
ratio : float
To apply a circle mask to the reconstructed image.
Returns
-------
ndarray
Square array.
"""
(nrow, _) = sinogram.shape
if angles is None:
angles = np.linspace(0.0, 180.0, nrow) * np.pi / 180.0
sinogram = np.expand_dims(sinogram, 1)
recont = tomopy.recon(sinogram, angles, center=center, algorithm='gridrec')
recont = tomopy.circ_mask(recont, axis=0, ratio=ratio)
return recont[0]
def _adjust_hist_limits(rec, mask, ratio):
# Apply circular mask.
if mask is True:
rec = tomopy.circ_mask(rec, axis=0, ratio=ratio)
# Adjust histogram boundaries according to reconstruction.
return _adjust_hist_min(rec.min()), _adjust_hist_max(rec.max())
def reconstruct_tomcat(proj, angle_offset, rot_center):
theta = tomopy.angles(proj.shape[0]) + angle_offset
recon = tomopy.recon(proj, theta, center=rot_center, algorithm='gridrec')
# rot_center = tomopy.find_center(proj, theta, init=882, ind=0, tol=0.5)
print(f"Reconstruction shape: {recon.shape}")
recon = tomopy.circ_mask(recon, axis=0, ratio=0.90)
return recon
def rec_try(h5fname, nsino, rot_center, center_search_width, algorithm, binning):
data_shape = get_dx_dims(h5fname, 'data')
print(data_shape)
ssino = int(data_shape[1] * nsino)
center_range = (rot_center-center_search_width, rot_center+center_search_width, 0.5)
#print(sino,ssino, center_range)
#print(center_range[0], center_range[1], center_range[2])
# Select sinogram range to reconstruct
sino = None
start = ssino
end = start + 1
sino = (start, end)
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
# remove stripes
# data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True)
print("Raw data: ", h5fname)
print("Center: ", rot_center)
data = tomopy.minus_log(data)
data = tomopy.remove_nan(data, val=0.0)
data = tomopy.remove_neg(data, val=0.00)
data[np.where(data == np.inf)] = 0.00
stack = np.empty((len(np.arange(*center_range)), data_shape[0], data_shape[2]))
index = 0
for axis in np.arange(*center_range):
stack[index] = data[:, 0, :]
index = index + 1
# Reconstruct the same slice with a range of centers.
rec = tomopy.recon(stack, theta, center=np.arange(*center_range), sinogram_order=True, algorithm='gridrec', filter_name='parzen', nchunk=1)
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
index = 0
# Save images to a temporary folder.
fname = os.path.dirname(h5fname) + os.sep + 'try_rec/' + path_base_name(h5fname) + os.sep + 'recon_' + os.path.splitext(os.path.basename(h5fname))[0]
for axis in np.arange(*center_range):
rfname = fname + '_' + str('{0:.2f}'.format(axis) + '.tiff')
dxchange.write_tiff(rec[index], fname=rfname, overwrite=True)
index = index + 1
print("Reconstructions: ", fname)
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'):
sample_detector_distance = 8 # Propagation distance of the wavefront in cm
detector_pixel_size_x = 2.247e-4 # Detector pixel size in cm (5x: 1.17e-4, 2X: 2.93e-4)
monochromator_energy = 24.9 # Energy of incident wave in keV
alpha = 1e-02 # Phase retrieval coeff.
zinger_level = 800 # Zinger level for projections
zinger_level_w = 1000 # Zinger level for white
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
# zinger_removal
# proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0)
# flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0)
# Flat-field correction of raw data.
##data = tomopy.normalize(proj, flat, dark, cutoff=0.8)
data = tomopy.normalize(proj, flat, dark)
# remove stripes
data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True)
# data = tomopy.remove_stripe_ti(data, alpha=1.5)
# data = tomopy.remove_stripe_sf(data, size=150)
# phase retrieval
#data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=alpha,pad=True)
print("Raw data: ", h5fname)
print("Center: ", rot_center)
data = tomopy.minus_log(data)
data = tomopy.remove_nan(data, val=0.0)
data = tomopy.remove_neg(data, val=0.00)
data[np.where(data == np.inf)] = 0.00
rot_center = rot_center/np.power(2, float(binning))
data = tomopy.downsample(data, level=binning)
data = tomopy.downsample(data, level=binning, axis=1)
# Reconstruct object.
if algorithm == 'sirtfbp':
rec = rec_sirtfbp(data, theta, rot_center)
elif algorithm == 'astrasirt':
extra_options ={'MinConstraint':0}
options = {'proj_type':'cuda', 'method':'SIRT_CUDA', 'num_iter':200, 'extra_options':extra_options}
rec = tomopy.recon(data, theta, center=rot_center, algorithm=tomopy.astra, options=options)
else:
rec = tomopy.recon(data, theta, center=rot_center, algorithm=algorithm, filter_name='parzen')
print("Algorithm: ", algorithm)
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
return rec
def main():
# file_name = '/local/data/2020-02/Stock/100_B949_81_84_B2.h5'
file_name = '/local/data/tomo_00001.h5'
data_size = get_dx_dims(file_name)
ssino = int(data_size[1] * 0.5)
detector_center = int(data_size[2] * 0.5)
# Select sinogram range to reconstruct
sino_start = ssino
sino_end = sino_start + 1
sino = (int(sino_start), int(sino_end))
# Read APS 2-BM raw data
proj, flat, dark, theta = dxchange.read_aps_32id(file_name, sino=sino)
tomo_ind = tomopy.normalize(proj, flat, dark)
# data = tomopy.normalize_bg(proj, air=10)
tomo_ind = tomopy.minus_log(tomo_ind)
rec = recon(tomo_ind, theta, center=detector_center, sinogram_order=False,
algorithm='gridrec', filter_name='shepp')
rec = circ_mask(rec, axis=0)
# tomopy score, simplified rescaling of histogram bounds
hmin, hmax = _adjust_hist_limits(
tomo_ind, theta, mask=True, sinogram_order=False)
print(hmin, hmax)
print(find_center(tomo_ind, theta))
centers = np.linspace(-25, 25, 101) + detector_center
print(centers)
tpscore = []
blur = []
for center in centers:
val, bval = _find_center_cost(
center, tomo_ind, theta, hmin, hmax, mask=True, ratio=1.,
sinogram_order=False)
tpscore.append(val)
blur.append(bval)
plt.plot(centers, rescale(tpscore), label='tomopy score')
plt.plot(centers, rescale(blur), label='blurriness')
plt.legend()
plt.title('centering scores')
plt.xlabel('center')
plt.show()
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'):
sample_detector_distance = 8 # Propagation distance of the wavefront in cm
detector_pixel_size_x = 2.247e-4 # Detector pixel size in cm (5x: 1.17e-4, 2X: 2.93e-4)
monochromator_energy = 24.9 # Energy of incident wave in keV
alpha = 1e-02 # Phase retrieval coeff.
zinger_level = 800 # Zinger level for projections
zinger_level_w = 1000 # Zinger level for white
# h5fname_norm = '/local/data/2019-02/Burke/C47M_0015.h5'
h5fname_norm = '/local/data/2019-02/Burke/kc78_Menardii_0003.h5'
proj1, flat, dark, theta1 = dxchange.read_aps_32id(h5fname_norm, sino=sino)
proj, dummy, dummy1, theta = dxchange.read_aps_32id(h5fname, sino=sino)
# zinger_removal
proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0)
flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0)
# Flat-field correction of raw data.
##data = tomopy.normalize(proj, flat, dark, cutoff=0.8)
data = tomopy.normalize(proj, flat, dark)
# remove stripes
data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True)
#data = tomopy.remove_stripe_ti(data, alpha=1.5)
data = tomopy.remove_stripe_sf(data, size=20)
# phase retrieval
#data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=alpha,pad=True)
print("Raw data: ", h5fname)
print("Center: ", rot_center)
data = tomopy.minus_log(data)
data = tomopy.remove_nan(data, val=0.0)
data = tomopy.remove_neg(data, val=0.00)
data[np.where(data == np.inf)] = 0.00
rot_center = rot_center/np.power(2, float(binning))
data = tomopy.downsample(data, level=binning)
data = tomopy.downsample(data, level=binning, axis=1)
# Reconstruct object.
if algorithm == 'sirtfbp':
rec = rec_sirtfbp(data, theta, rot_center)
else:
rec = tomopy.recon(data, theta, center=rot_center, algorithm=algorithm, filter_name='parzen')
print("Algorithm: ", algorithm)
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
return rec
def reconstruct(h5fname, sino, rot_center, args, blocked_views=None):
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
# Manage the missing angles:
if blocked_views is not None:
print("Blocked Views: ", blocked_views)
proj = np.concatenate((proj[0:blocked_views[0], :, :],
proj[blocked_views[1] + 1:-1, :, :]),
axis=0)
theta = np.concatenate(
(theta[0:blocked_views[0]], theta[blocked_views[1] + 1:-1]))
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
# remove stripes
data = tomopy.remove_stripe_fw(data,
level=7,
wname='sym16',
sigma=1,
pad=True)
print("Raw data: ", h5fname)
print("Center: ", rot_center)
data = tomopy.minus_log(data)
data = tomopy.remove_nan(data, val=0.0)
data = tomopy.remove_neg(data, val=0.00)
data[np.where(data == np.inf)] = 0.00
algorithm = args.algorithm
ncores = args.ncores
nitr = args.num_iter
# always add algorithm
_kwargs = {"algorithm": algorithm}
# assign number of cores
_kwargs["ncore"] = ncores
# don't assign "num_iter" if gridrec or fbp
if algorithm not in ["fbp", "gridrec"]:
_kwargs["num_iter"] = nitr
# Reconstruct object.
with timemory.util.auto_timer(
"[tomopy.recon(algorithm='{}')]".format(algorithm)):
rec = tomopy.recon(proj, theta, **_kwargs)
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
return rec
def main(arg):
parser = argparse.ArgumentParser()
parser.add_argument("top", help="top directory where the tiff images are located: /data/")
parser.add_argument("start", nargs='?', const=1, type=int, default=1, help="index of the first image: 1000 (default 1)")
args = parser.parse_args()
top = args.top
index_start = int(args.start)
template = os.listdir(top)[0]
nfile = len(fnmatch.filter(os.listdir(top), '*.tif'))
index_end = index_start + nfile
ind_tomo = range(index_start, index_end)
fname = top + template
print (nfile, index_start, index_end, fname)
# Select the sinogram range to reconstruct.
start = 0
end = 512
sino=(start, end)
# Read the tiff raw data.
ndata = dxchange.read_tiff_stack(fname, ind=ind_tomo, slc=(sino, None))
print(ndata.shape)
binning = 8
ndata = tomopy.downsample(ndata, level=binning, axis=1)
print(ndata.shape)
# Normalize to 1 using the air counts
ndata = tomopy.normalize_bg(ndata, air=5)
## slider(ndata)
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(ndata.shape[0])
rot_center = 960
print("Center of rotation: ", rot_center)
ndata = tomopy.minus_log(ndata)
# Reconstruct object using Gridrec algorithm.
rec = tomopy.recon(ndata, theta, center=rot_center, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
# Write data as stack of TIFs.
dxchange.write_tiff_stack(rec, fname='/local/dataraid/mark/rec/recon')
def reconstruct(self, sino, cors, angles, vol_shape, init):
recon = tomopy.recon(sino,
np.deg2rad(angles),
center=cors[0],
ncore=1,
algorithm=self.alg,
init_recon=init,
**self.kwargs)
recon = tomopy.circ_mask(recon, axis=0, ratio=0.95)
return np.transpose(recon, (1, 0, 2))
def save_reconstruction(self, recon, savedir = None, index=-1):
try:
if savedir == "":
raise IOError
if savedir == None:
savedir = QtGui.QFileDialog.getSaveFileName()[0]
if index == -1:
recon = tomopy.circ_mask(recon, axis=0)
dxchange.writer.write_tiff_stack(recon, fname=savedir)
if index != -1:
recon = tomopy.circ_mask(recon, axis=0)
indx = "0000"
recon_index = indx[:-len(str(index))]+str(index)
io.imsave(savedir+"_"+str(recon_index)+".tif", recon[0])
return
except IOError:
print("type the header name")
except:
print("Something went horribly wrong.")
def recon_wrapper(projs,
beam,
theta,
pad_frac=0.8,
mask_ratio=0.95,
contrast_s=0.001):
'''
Do a reconstruction with gridrec.
Parameters
----------
projs : np.array
a stack of radiographs (sinogram order input to tomopy recon is False)
theta : tuple
The tuple must be defined as (starting_theta, ending_theta, number_projections). The angle is intepreted as degrees.
beam : np.array
The flat-field (beam array) must be provided with shape (1, nrows, ncols).
pad_frac : float
Fraction of padding applied to rows (e.g. pad_frac = 0.8 on 1000 rows adds 800 pixels to either side.
mask_ratio : float
ratio between (0,1) for applying circular mask on reconstruction (typical value 0.9 to 1.0).
'''
# make theta array in radians
theta = np.linspace(*theta, endpoint=True)
theta = np.radians(theta)
projs = normalize(projs, beam, 1.0e-6 * np.zeros(beam.shape))
projs = minus_log(projs)
pad_w = int(pad_frac * projs.shape[-1])
projs = np.pad(projs, ((0, 0), (0, 0), (pad_w, pad_w)),
mode="constant",
constant_values=0.0)
rec = recon(projs, theta = theta, \
center = projs.shape[-1]//2, \
algorithm = 'gridrec', \
sinogram_order = False)
rec = rec[:, pad_w:-pad_w, pad_w:-pad_w]
rec = circ_mask(rec, 0, ratio=mask_ratio)
mask_val = rec[int(rec.shape[0] // 2), 0, 0]
vcrop = int(rec.shape[0] * (1 - mask_ratio))
rec[0:vcrop, ...] = mask_val
rec[-vcrop:, ...] = mask_val
if contrast_s > 0.0:
h = modified_autocontrast(rec, s=contrast_s)
rec = np.clip(rec, *h)
return rec
def save_reconstruction(self, recon):
try:
savedir = QtGui.QFileDialog.getSaveFileName()[0]
if savedir == "":
raise IndexError
recon = tomopy.circ_mask(recon, axis=0)
dxchange.writer.write_tiff_stack(recon, fname=savedir)
except IndexError:
print("type the header name")
return
def reconstruct(h5fname, sino, rot_center, args, blocked_views=None):
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
# Manage the missing angles:
if blocked_views is not None:
print("Blocked Views: ", blocked_views)
proj = np.concatenate((proj[0:blocked_views[0], :, :],
proj[blocked_views[1]+1:-1, :, :]), axis=0)
theta = np.concatenate((theta[0:blocked_views[0]],
theta[blocked_views[1]+1: -1]))
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
# remove stripes
data = tomopy.remove_stripe_fw(data, level=7, wname='sym16', sigma=1,
pad=True)
print("Raw data: ", h5fname)
print("Center: ", rot_center)
data = tomopy.minus_log(data)
data = tomopy.remove_nan(data, val=0.0)
data = tomopy.remove_neg(data, val=0.00)
data[np.where(data == np.inf)] = 0.00
algorithm = args.algorithm
ncores = args.ncores
nitr = args.num_iter
# always add algorithm
_kwargs = {"algorithm": algorithm}
# assign number of cores
_kwargs["ncore"] = ncores
# don't assign "num_iter" if gridrec or fbp
if algorithm not in ["fbp", "gridrec"]:
_kwargs["num_iter"] = nitr
# Reconstruct object.
with timemory.util.auto_timer(
"[tomopy.recon(algorithm='{}')]".format(algorithm)):
rec = tomopy.recon(proj, theta, **_kwargs)
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
return rec
def save_reconstruction(self, recon, savedir=None):
try:
if savedir == "":
raise IOError
if savedir == None:
savedir = QtGui.QFileDialog.getSaveFileName()[0]
recon = tomopy.circ_mask(recon, axis=0)
dxchange.writer.write_tiff_stack(recon, fname=savedir)
return
except IOError:
print("type the header name")
except:
print("Something went horribly wrong.")
def _adjust_hist_limits(tomo_ind, theta, mask, sinogram_order):
# Make an initial reconstruction to adjust histogram limits.
rec = recon(tomo_ind,
theta,
sinogram_order=sinogram_order,
algorithm='gridrec')
# Apply circular mask.
if mask is True:
rec = circ_mask(rec, axis=0)
# Adjust histogram boundaries according to reconstruction.
return _adjust_hist_min(rec.min()), _adjust_hist_max(rec.max())
def mask(data, params):
log.info(" *** mask")
if(params.reconstruction_mask):
log.info(' *** *** ON')
if 0 < params.reconstruction_mask_ratio <= 1:
log.warning(" *** mask ratio: %f " % params.reconstruction_mask_ratio)
data = tomopy.circ_mask(data, axis=0, ratio=params.reconstruction_mask_ratio)
log.info(' *** masking finished')
else:
log.error(" *** mask ratio must be between 0-1: %f is ignored" % params.reconstruction_mask_ratio)
else:
log.warning(' *** *** OFF')
return data
def main(arg):
parser = argparse.ArgumentParser()
parser.add_argument(
"top", help="top directory where the tiff images are located: /data/")
parser.add_argument("start",
nargs='?',
const=1,
type=int,
default=1,
help="index of the first image: 1000 (default 1)")
args = parser.parse_args()
top = args.top
index_start = int(args.start)
template = os.listdir(top)[0]
nfile = len(fnmatch.filter(os.listdir(top), '*.tiff'))
index_end = index_start + nfile
ind_tomo = range(index_start, index_end)
fname = top + template
print(nfile, index_start, index_end, fname)
# Select the sinogram range to reconstruct.
start = 70
end = 72
sino = (start, end)
# Read the tiff raw data.
ndata = dxchange.read_tiff_stack(fname, ind=ind_tomo, slc=(sino, None))
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(ndata.shape[0])
rot_center = 251
print("Center of rotation: ", rot_center)
#ndata = tomopy.minus_log(ndata)
# Reconstruct object using Gridrec algorithm.
rec = tomopy.recon(ndata, theta, center=rot_center, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
# Write data as stack of TIFs.
dxchange.write_tiff_stack(rec, fname='/local/dataraid/mark/rec/recon')
def save_recon_2npy(self,recon, savedir=None, index=-1):
try:
if savedir == "":
raise IOError
if savedir == None:
savedir = QtGui.QFileDialog.getSaveFileName()[0]
if index == -1:
recon = tomopy.circ_mask(recon, axis=0)
np.save(savedir, recon)
return
except IOError:
print("type the header name")
except:
print("Something went horribly wrong.")
def _center_resid_blur(center, sino, omega, rmin, rmax, sinogram_order=True):
"""
Cost function used for the ``find_center`` routine.
"""
rec = tomopy.recon(sino,
omega,
center,
sinogram_order=sinogram_order,
algorithm='gridrec',
filter_name='shepp')
rec = tomopy.circ_mask(rec, axis=0)
score = -((rec - rec.mean())**2).sum()
logger.info("blur center = %.4f %.4f" % (center, score))
return score
def reconstruct(sname, rot_center, ovlpfind, s_start, s_end):
fname = dfolder + sname + '.h5'
print (fname)
start = s_start
end = s_end
chunks = 24
num_sino = (end - start) // chunks
for m in range(chunks):
sino_start = start + num_sino * m
sino_end = start + num_sino * (m + 1)
start_read_time = time.time()
proj, flat, dark, thetat = dxchange.read_aps_2bm(fname, sino=(sino_start, sino_end))
print(' done read in %0.1f min' % ((time.time() - start_read_time)/60))
dark = proj[9001:9002]
flat = proj[0:1]
proj = proj[1:9000]
theta = tomopy.angles(proj.shape[0], 0., 360.)
proj = tomopy.sino_360_to_180(proj, overlap=ovlpfind, rotation='right')
proj = tomopy.remove_outlier(proj, dif=0.4)
proj = tomopy.normalize_bg(proj, air=10)
proj = tomopy.minus_log(proj)
center = rot_center
start_ring_time = time.time()
proj = tomopy.remove_stripe_fw(proj, wname='sym5', sigma=4, pad=False)
proj = tomopy.remove_stripe_sf(proj, size=3)
print(' done pre-process in %0.1f min' % ((time.time() - start_ring_time)/60))
start_phase_time = time.time()
proj = tomopy.retrieve_phase(proj, pixel_size=detector_pixel_size_x, dist=sample_detector_distance, energy=energy, alpha=alpha, pad=True, ncore=None, nchunk=None)
print(' done phase retrieval in %0.1f min' % ((time.time() - start_phase_time)/60))
start_recon_time = time.time()
rec = tomopy.recon(proj, theta, center=center, algorithm='gridrec', filter_name='ramalk')
tomopy.circ_mask(rec, axis=0, ratio=0.95)
print ("Reconstructed", rec.shape)
dxchange.write_tiff_stack(rec, fname = dfolder + '/' + sname + '/' + sname, overwrite=True, start=sino_start)
print(' Chunk reconstruction done in %0.1f min' % ((time.time() - start_recon_time)/60))
print ("Done!")
def img_variance(img):
import tomopy
s = img.shape
variance = np.zeros(s[0])
img = tomopy.circ_mask(img, axis=0, ratio=0.8)
for i in range(s[0]):
img[i] = medfilt2d(img[i], 5)
img_ = img[i].flatten()
t = img_ > 0
img_ = img_[t]
t = np.mean(img_)
variance[i] = np.sqrt(
np.sum(np.power(np.abs(img_ - t), 2)) / len(img_ - 1))
return variance
def recon_hdf5_mpi(src_fanme, dest_folder, sino_range, sino_step, center_vec, shift_grid, dtype='float32',
algorithm='gridrec', tolerance=1, save_sino=False, sino_blur=None, **kwargs):
"""
Reconstruct a single tile, or fused HDF5 created using util/total_fusion. MPI supported.
"""
raise DeprecationWarning
if rank == 0:
if not os.path.exists(dest_folder):
os.mkdir(dest_folder)
sino_ini = int(sino_range[0])
sino_end = int(sino_range[1])
f = h5py.File(src_fanme)
dset = f['exchange/data']
full_shape = dset.shape
theta = tomopy.angles(full_shape[0])
center_vec = np.asarray(center_vec)
sino_ls = np.arange(sino_ini, sino_end, sino_step, dtype='int')
grid_bins = np.ceil(shift_grid[:, 0, 0])
t0 = time.time()
alloc_set = allocate_mpi_subsets(sino_ls.size, size, task_list=sino_ls)
for slice in alloc_set[rank]:
print(' Rank {:d}: reconstructing {:d}'.format(rank, slice))
grid_line = np.digitize(slice, grid_bins)
grid_line = grid_line - 1
center = center_vec[grid_line]
data = dset[:, slice, :]
if sino_blur is not None:
data = gaussian_filter(data, sino_blur)
data = data.reshape([full_shape[0], 1, full_shape[2]])
data[np.isnan(data)] = 0
data = data.astype('float32')
if save_sino:
dxchange.write_tiff(data[:, slice, :], fname=os.path.join(dest_folder, 'sino/recon_{:05d}_{:d}.tiff').format(slice, center))
# data = tomopy.remove_stripe_ti(data)
rec = tomopy.recon(data, theta, center=center, algorithm=algorithm, **kwargs)
# rec = tomopy.remove_ring(rec)
rec = tomopy.remove_outlier(rec, tolerance)
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
dxchange.write_tiff(rec, fname='{:s}/recon/recon_{:05d}_{:d}'.format(dest_folder, slice, center), dtype=dtype)
print('Rank {:d} finished in {:.2f} s.'.format(rank, time.time()-t0))
return
def rec(data, rot_center):
"""
Reconstruct with Gridrec.
"""
[nframes, nproj, ns, n] = data.shape
theta = np.linspace(0, np.pi * nframes, nproj * nframes, endpoint=False)
# Reconstruct object. FBP.
rec = np.zeros([nframes, ns, n, n], dtype='float32')
for time_frame in range(0, nframes):
rec0 = tomopy.recon(data[time_frame],
theta[time_frame * nproj:(time_frame + 1) * nproj],
center=rot_center,
algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec[time_frame] = tomopy.circ_mask(rec0, axis=0, ratio=0.95)
return rec
def _find_center_cost(center, recon_function, hmin, hmax, mask, ratio, cache):
"""
Cost function used for the ``find_center`` routine.
"""
logger.info('Trying rotation center: %s', center)
center = np.array(center, dtype='float32')
if float(center) in cache:
logger.info("Using cached value for center: %s", center)
return cache[float(center)]
rec = recon_function(center)
if mask is True:
rec = tomopy.circ_mask(rec, axis=0, ratio=ratio)
hist, _ = np.histogram(rec, bins=64, range=[hmin, hmax])
hist = hist.astype('float32') / rec.size + 1e-12
val = -np.dot(hist, np.log2(hist))
logger.info("Function value = %f", val)
cache[float(center)] = val
return val
def _center_resid_negent(center, sino, omega, rmin, rmax, sinogram_order=True):
"""
Cost function used for the ``find_center`` routine.
"""
_, nang, nx = sino.shape
if center < 1:
return 10 * (1 - center)
if center > nx - 2:
return 10 * (center - nx + 2)
n1 = int(nx / 4.0)
n2 = int(3 * nx / 4.0)
rec = tomopy.recon(sino,
omega,
center,
algorithm='gridrec',
sinogram_order=sinogram_order)
rec = tomopy.circ_mask(rec, axis=0)[:, n1:n2, n1:n2]
hist, e = np.histogram(rec, bins=64, range=[rmin, rmax])
hist = hist / rec.size
score = -np.dot(hist, np.log(1.e-12 + hist))
logger.info("negent center = %.4f %.4f" % (center, score))
return score
def main(arg):
fname = '/local/dataraid/elettra/Oak_16bit_slice343_all_repack.h5'
# Read the hdf raw data.
sino, sflat, sdark, th = dxchange.read_aps_32id(fname)
slider(sino)
proj = np.swapaxes(sino, 0, 1)
flat = np.swapaxes(sflat, 0, 1)
dark = np.swapaxes(sdark, 0, 1)
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(proj.shape[0], ang1=0.0, ang2=180.0)
print(proj.shape, dark.shape, flat.shape, theta.shape)
# Flat-field correction of raw data.
ndata = tomopy.normalize(proj, flat, dark)
#slider(ndata)
# Find rotation center.
rot_center = 962
binning = 1
ndata = tomopy.downsample(ndata, level=int(binning))
rot_center = rot_center / np.power(2, float(binning))
ndata = tomopy.minus_log(ndata)
# Reconstruct object using Gridrec algorithm.
rec = tomopy.recon(ndata, theta, center=rot_center, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
# Write data as stack of TIFs.
dxchange.write_tiff_stack(rec, fname='recon_dir/recon')
def rec_tv(data, m, nsp, rot_center, lambda0, lambda1, niters, ngpus):
"""
Reconstruct. Time-domain decomposition + regularization.
"""
[nframes, nproj, ns, n] = data.shape
# cut data according to the rotation center
if (rot_center < n // 2):
data = data[:, :, :, :n // 2 + rot_center - 1]
if (rot_center > n // 2):
data = data[:, :, :, rot_center - n // 2:]
n = data.shape[3]
# reorder input data for compatibility
data = np.reshape(data, [nframes * nproj, ns, n])
data = np.ndarray.flatten(data.swapaxes(0, 1))
# memory for result
rec = np.zeros([n * n * ns * m], dtype='float32')
# Make a class for tv
cl = rectv.rectv(n, nframes * nproj, m, nframes, ns, ns, ngpus, lambda0,
lambda1)
# Run iterations
cl.itertvR_wrap(rec, data, niters)
# reorder result for compatibility with tomopy
rec = np.rot90(np.reshape(rec, [ns, m, n, n]).swapaxes(0, 1),
axes=(2, 3)) / nproj * 2
# take slices corresponding to angles k\pi
rec = rec[::m // nframes]
for time_frame in range(0, nframes):
# Mask each reconstructed slice with a circle.
rec[time_frame] = tomopy.circ_mask(rec[time_frame], axis=0, ratio=0.95)
return rec
def main(arg):
fname = '/local/dataraid/elettra/Oak_16bit_slice343_all_repack.h5'
# Read the hdf raw data.
sino, sflat, sdark, th = dxchange.read_aps_32id(fname)
slider(sino)
proj = np.swapaxes(sino,0,1)
flat = np.swapaxes(sflat,0,1)
dark = np.swapaxes(sdark,0,1)
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(proj.shape[0], ang1=0.0, ang2=180.0)
print(proj.shape, dark.shape, flat.shape, theta.shape)
# Flat-field correction of raw data.
ndata = tomopy.normalize(proj, flat, dark)
#slider(ndata)
# Find rotation center.
rot_center = 962
binning = 1
ndata = tomopy.downsample(ndata, level=int(binning))
rot_center = rot_center/np.power(2, float(binning))
ndata = tomopy.minus_log(ndata)
# Reconstruct object using Gridrec algorithm.
rec = tomopy.recon(ndata, theta, center=rot_center, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
# Write data as stack of TIFs.
dxchange.write_tiff_stack(rec, fname='recon_dir/recon')
def find_tomo_center(sino, omega, center=None, sinogram_order=True):
xmax = sino.shape[0]
if sinogram_order:
xmax = sino.shape[1]
if center is None:
center = xmax / 2.0
# init center to scale recon
rec = tomopy.recon(sino,
omega,
center=center,
sinogram_order=sinogram_order,
algorithm='gridrec',
filter_name='shepp')
rec = tomopy.circ_mask(rec, axis=0)
# tomopy score, tweaked slightly
rmin, rmax = rec.min(), rec.max()
rmin -= 0.5 * (rmax - rmin)
rmax += 0.5 * (rmax - rmin)
out = minimize(_center_resid_negent,
center,
args=(sino, omega, rmin, rmax, sinogram_order),
method='Nelder-Mead',
tol=1.5)
cen = out.x
if cen > 0 and cen < xmax:
out = minimize(_center_resid_blur,
cen,
args=(sino, omega, rmin, rmax, sinogram_order),
method='Nelder-Mead',
tol=0.5)
cen = out.x
return cen
def reconstruct(h5fname, sino, rot_center, blocked_views=None):
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
# Manage the missing angles:
if blocked_views is not None:
print("Blocked Views: ", blocked_views)
proj = np.concatenate((proj[0:blocked_views[0],:,:], proj[blocked_views[1]+1:-1,:,:]), axis=0)
theta = np.concatenate((theta[0:blocked_views[0]], theta[blocked_views[1]+1:-1]))
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
# remove stripes
data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True)
print("Raw data: ", h5fname)
print("Center: ", rot_center)
# # phase retrieval
# data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=8e-3,pad=True)
data = tomopy.minus_log(data)
data = tomopy.remove_nan(data, val=0.0)
data = tomopy.remove_neg(data, val=0.00)
data[np.where(data == np.inf)] = 0.00
# Reconstruct object.
rec = tomopy.recon(data, theta, center=rot_center, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
return rec
def _find_center_cost(
center, tomo_ind, theta, hmin, hmax, mask, ratio,
sinogram_order=False):
"""
Cost function used for the ``find_center`` routine.
"""
center = np.array(center, dtype='float32')
rec = recon(
tomo_ind, theta, center,
sinogram_order=sinogram_order,
algorithm='gridrec',
filter_name='shepp')
if mask is True:
rec = circ_mask(rec, axis=0)
hist, e = np.histogram(rec, bins=64, range=[hmin, hmax])
hist = hist.astype('float32') / rec.size + 1e-12
val = -np.dot(hist, np.log2(hist))
# from Matt
bval = -((rec - rec.mean())**2).sum()
return val, bval
def recon_hdf5(src_fanme, dest_folder, sino_range, sino_step, shift_grid, center_vec=None, center_eq=None, dtype='float32',
algorithm='gridrec', tolerance=1, chunk_size=20, save_sino=False, sino_blur=None, flattened_radius=120,
mode='180', test_mode=False, phase_retrieval=None, ring_removal=True, **kwargs):
"""
center_eq: a and b parameters in fitted center position equation center = a*slice + b.
"""
if not os.path.exists(dest_folder):
try:
os.mkdir(dest_folder)
except:
pass
sino_ini = int(sino_range[0])
sino_end = int(sino_range[1])
sino_ls_all = np.arange(sino_ini, sino_end, sino_step, dtype='int')
alloc_set = allocate_mpi_subsets(sino_ls_all.size, size, task_list=sino_ls_all)
sino_ls = alloc_set[rank]
# prepare metadata
f = h5py.File(src_fanme)
dset = f['exchange/data']
full_shape = dset.shape
theta = tomopy.angles(full_shape[0])
if center_eq is not None:
a, b = center_eq
center_ls = sino_ls * a + b
center_ls = np.round(center_ls)
for iblock in range(int(sino_ls.size/chunk_size)+1):
print('Beginning block {:d}.'.format(iblock))
t0 = time.time()
istart = iblock*chunk_size
iend = np.min([(iblock+1)*chunk_size, sino_ls.size])
fstart = sino_ls[istart]
fend = sino_ls[iend]
center = center_ls[istart:iend]
data = dset[:, fstart:fend:sino_step, :]
data[np.isnan(data)] = 0
data = data.astype('float32')
data = tomopy.remove_stripe_ti(data, alpha=4)
if sino_blur is not None:
for i in range(data.shape[1]):
data[:, i, :] = gaussian_filter(data[:, i, :], sino_blur)
rec = tomopy.recon(data, theta, center=center, algorithm=algorithm, **kwargs)
rec = tomopy.remove_ring(rec)
rec = tomopy.remove_outlier(rec, tolerance)
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
for i in range(rec.shape[0]):
slice = fstart + i*sino_step
dxchange.write_tiff(rec[i, :, :], fname=os.path.join(dest_folder, 'recon/recon_{:05d}_{:05d}.tiff').format(slice, sino_ini))
if save_sino:
dxchange.write_tiff(data[:, i, :], fname=os.path.join(dest_folder, 'sino/recon_{:05d}_{:d}.tiff').format(slice, int(center[i])))
iblock += 1
print('Block {:d} finished in {:.2f} s.'.format(iblock, time.time()-t0))
else:
# divide chunks
grid_bins = np.append(np.ceil(shift_grid[:, 0, 0]), full_shape[1])
chunks = []
center_ls = []
istart = 0
counter = 0
# irow should be 0 for slice 0
irow = np.searchsorted(grid_bins, sino_ls[0], side='right')-1
for i in range(sino_ls.size):
counter += 1
sino_next = i+1 if i != sino_ls.size-1 else i
if counter >= chunk_size or sino_ls[sino_next] >= grid_bins[irow+1] or sino_next == i:
iend = i+1
chunks.append((istart, iend))
istart = iend
center_ls.append(center_vec[irow])
if sino_ls[sino_next] >= grid_bins[irow+1]:
irow += 1
counter = 0
# reconstruct chunks
iblock = 1
for (istart, iend), center in izip(chunks, center_ls):
print('Beginning block {:d}.'.format(iblock))
t0 = time.time()
fstart = sino_ls[istart]
fend = sino_ls[iend-1]
print('Reading data...')
data = dset[:, fstart:fend+1:sino_step, :]
if mode == '360':
overlap = 2 * (dset.shape[2] - center)
data = tomosaic.morph.sino_360_to_180(data, overlap=overlap, rotation='right')
theta = tomopy.angles(data.shape[0])
data[np.isnan(data)] = 0
data = data.astype('float32')
if sino_blur is not None:
for i in range(data.shape[1]):
data[:, i, :] = gaussian_filter(data[:, i, :], sino_blur)
if ring_removal:
data = tomopy.remove_stripe_ti(data, alpha=4)
if phase_retrieval:
data = tomopy.retrieve_phase(data, kwargs['pixel_size'], kwargs['dist'], kwargs['energy'],
kwargs['alpha'])
rec0 = tomopy.recon(data, theta, center=center, algorithm=algorithm, **kwargs)
rec = tomopy.remove_ring(np.copy(rec0))
cent = int((rec.shape[1]-1) / 2)
xx, yy = np.meshgrid(np.arange(rec.shape[2]), np.arange(rec.shape[1]))
mask0 = ((xx-cent)**2+(yy-cent)**2 <= flattened_radius**2)
mask = np.zeros(rec.shape, dtype='bool')
for i in range(mask.shape[0]):
mask[i, :, :] = mask0
rec[mask] = (rec[mask] + rec0[mask])/2
else:
rec = tomopy.recon(data, theta, center=center, algorithm=algorithm, **kwargs)
rec = tomopy.remove_outlier(rec, tolerance)
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
for i in range(rec.shape[0]):
slice = fstart + i*sino_step
if test_mode:
dxchange.write_tiff(rec[i, :, :], fname=os.path.join(dest_folder, 'recon/recon_{:05d}_{:d}.tiff').format(slice, center), dtype=dtype)
else:
dxchange.write_tiff(rec[i, :, :], fname=os.path.join(dest_folder, 'recon/recon_{:05d}.tiff').format(slice), dtype=dtype)
if save_sino:
dxchange.write_tiff(data[:, i, :], fname=os.path.join(dest_folder, 'sino/recon_{:05d}_{:d}.tiff').format(slice, center), dtype=dtype)
print('Block {:d} finished in {:.2f} s.'.format(iblock, time.time()-t0))
iblock += 1
return
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'):
sample_detector_distance = 31 # Propagation distance of the wavefront in cm
detector_pixel_size_x = 1.17e-4 # Detector pixel size in cm (5x: 1.17e-4, 2X: 2.93e-4)
monochromator_energy = 65 # Energy of incident wave in keV
# used pink beam
alpha = 4*1e-4 # Phase retrieval coeff.
zinger_level = 800 # Zinger level for projections
zinger_level_w = 1000 # Zinger level for white
# Read APS 2-BM raw data.
# DIMAX saves 3 files: proj, flat, dark
# when loading the data set select the proj file (larger size)
fname = os.path.splitext(h5fname)[0]
fbase = fname.rsplit('_', 1)[0]
fnum = fname.rsplit('_', 1)[1]
fext = os.path.splitext(h5fname)[1]
fnum_flat = str("%4.4d" % (int(fnum)+1))
fnum_dark = str("%4.4d" % (int(fnum)+2))
fnproj = fbase + '_' + fnum + fext
fnflat = fbase + '_' + fnum_flat + fext
fndark = fbase + '_' + fnum_dark + fext
print('proj', fnproj)
print('flat', fnflat)
print('dark', fndark)
# Read APS 2-BM DIMAX raw data.
proj, dum, dum2, theta = dxchange.read_aps_32id(fnproj, sino=sino)
dum3, flat, dum4, dum5 = dxchange.read_aps_32id(fnflat, sino=sino)
#flat, dum3, dum4, dum5 = dxchange.read_aps_32id(fnflat, sino=sino)
dum6, dum7, dark, dum8 = dxchange.read_aps_32id(fndark, sino=sino)
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
# remove stripes
data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True)
# zinger_removal
proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0)
flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0)
# Flat-field correction of raw data.
##data = tomopy.normalize(proj, flat, dark, cutoff=0.8)
data = tomopy.normalize(proj, flat, dark)
# remove stripes
#data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True)
#data = tomopy.remove_stripe_ti(data, alpha=1.5)
data = tomopy.remove_stripe_sf(data, size=150)
# phase retrieval
#data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=alpha,pad=True)
print("Raw data: ", h5fname)
print("Center: ", rot_center)
data = tomopy.minus_log(data)
data = tomopy.remove_nan(data, val=0.0)
data = tomopy.remove_neg(data, val=0.00)
data[np.where(data == np.inf)] = 0.00
rot_center = rot_center/np.power(2, float(binning))
data = tomopy.downsample(data, level=binning)
data = tomopy.downsample(data, level=binning, axis=1)
# padding
N = data.shape[2]
data_pad = np.zeros([data.shape[0],data.shape[1],3*N//2],dtype = "float32")
data_pad[:,:,N//4:5*N//4] = data
data_pad[:,:,0:N//4] = np.tile(np.reshape(data[:,:,0],[data.shape[0],data.shape[1],1]),(1,1,N//4))
data_pad[:,:,5*N//4:] = np.tile(np.reshape(data[:,:,-1],[data.shape[0],data.shape[1],1]),(1,1,N//4))
data = data_pad
rot_center = rot_center+N//4
nframes = 8
nproj = 1500
theta = np.linspace(0, np.pi*nframes, nproj*nframes, endpoint=False)
rec = np.zeros(
(nframes, data.shape[1], data.shape[2], data.shape[2]), dtype='float32')
for time_frame in range(0, nframes):
rec0 = tomopy.recon(data[time_frame*nproj:(time_frame+1)*nproj], theta[time_frame*nproj:(
time_frame+1)*nproj], center=rot_center, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec[time_frame] = tomopy.circ_mask(rec0, axis=0, ratio=0.95)
rec = rec[:,:,N//4:5*N//4,N//4:5*N//4]
print("Algorithm: ", algorithm)
return rec
inputPath = '{}_{:d}{}'.format(fn,y,fileextension)
tomo[y] = dxchange.reader.read_tiff(inputPath,slc = (sinoused, raysused))
print('loading flat images')
for y in range(0,len(floc)):
inputPath = '{}{}_{:d}{}'.format(fn,flatextension,floc[y],fileextension)
flat[y] = dxchange.reader.read_tiff(inputPath,slc = (sinoused, raysused))
print('loading dark images')
for y in range(0,numdrk):
inputPath = '{}{}_{:d}{}'.format(fn,darkextension,y,fileextension)
dark[y] = dxchange.reader.read_tiff(inputPath,slc = (sinoused, raysused))
print('normalizing')
tomo = tomo.astype(np.float32)
tomopy.normalize_nf(tomo, flat, dark, floc, out=tomo)
tomopy.minus_log(tomo, out=tomo)
tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge')
rec = tomopy.recon(tomo, tomopy.angles(numangles, angle_offset, angle_offset-angularrange), center=cor+npad, algorithm='gridrec', filter_name='butterworth', filter_par=[.25, 2])
rec = rec[:, npad:-npad, npad:-npad]
rec /= pxsize # convert reconstructed voxel values from 1/pixel to 1/cm
rec = tomopy.circ_mask(rec, 0)
print('writing recon')
dxchange.write_tiff_stack(rec, fname='rec/'+fn, start=sinoused[0])
def reconstruct(filename,inputPath="", outputPath="", COR=COR, doOutliers=doOutliers, outlier_diff=outlier_diff, outlier_size=outlier_size, doFWringremoval=doFWringremoval, ringSigma=ringSigma,ringLevel=ringLevel, ringWavelet=ringWavelet,pad_sino=pad_sino, doPhaseRetrieval=doPhaseRetrieval, propagation_dist=propagation_dist, kev=kev,alphaReg=alphaReg, butterworthpars=butterworthpars, doPolarRing=doPolarRing,Rarc=Rarc, Rmaxwidth=Rmaxwidth, Rtmax=Rtmax, Rthr=Rthr, Rtmin=Rtmin, useAutoCOR=useAutoCOR, use360to180=use360to180, num_substacks=num_substacks,recon_slice=recon_slice):
# Convert filename to list type if only one file name is given
if type(filename) != list:
filename=[filename]
# If useAutoCor == true, a list of COR will be automatically calculated for all files
# If a list of COR is given, only entries with boolean False will use automatic COR calculation
if useAutoCOR==True or (len(COR) != len(filename)):
logging.info('using auto COR for all input files')
COR = [False]*len(filename)
for x in range(len(filename)):
logging.info('opening data set, checking metadata')
fdata, gdata = read_als_832h5_metadata(inputPath[x]+filename[x]+'.h5')
pxsize = float(gdata['pxsize'])/10.0 # convert from metadata (mm) to this script (cm)
numslices = int(gdata['nslices'])
# recon_slice == True, only center slice will be reconstructed
# if integer is given, a specific
if recon_slice != False:
if (type(recon_slice) == int) and (recon_slice <= numslices):
sinorange [recon_slice-1, recon_slice]
else:
sinorange = [numslices//2-1, numslices//2]
else:
sinorange = [0, numslices]
# Calculate number of substacks (chunks)
substacks = num_substacks #(sinorange[1]-sinorange[0]-1)//num_sino_per_substack+1
if (sinorange[1]-sinorange[0]) >= substacks:
num_sino_per_substack = (sinorange[1]-sinorange[0])//num_substacks
else:
num_sino_per_substack = 1
firstcor, lastcor = 0, int(gdata['nangles'])-1
projs, flat, dark, floc = dxchange.read_als_832h5(inputPath[x]+filename[x]+'.h5', ind_tomo=(firstcor, lastcor))
projs = tomopy.normalize_nf(projs, flat, dark, floc)
autocor = tomopy.find_center_pc(projs[0], projs[1], tol=0.25)
if (type(COR[x]) == bool) or (COR[x]<0) or (COR[x]=='auto'):
firstcor, lastcor = 0, int(gdata['nangles'])-1
projs, flat, dark, floc = dxchange.read_als_832h5(inputPath[x]+filename[x]+'.h5', ind_tomo=(firstcor, lastcor))
projs = tomopy.normalize_nf(projs, flat, dark, floc)
cor = tomopy.find_center_pc(projs[0], projs[1], tol=0.25)
else:
cor = COR[x]
logging.info('Dataset %s, has %d total slices, reconstructing slices %d through %d in %d substack(s), using COR: %f',filename[x], int(gdata['nslices']), sinorange[0], sinorange[1]-1, substacks, cor)
for y in range(0, substacks):
logging.info('Starting dataset %s (%d of %d), substack %d of %d',filename[x], x+1, len(filename), y+1, substacks)
logging.info('Reading sinograms...')
projs, flat, dark, floc = dxchange.read_als_832h5(inputPath[x]+filename[x]+'.h5', sino=(sinorange[0]+y*num_sino_per_substack, sinorange[0]+(y+1)*num_sino_per_substack, 1))
logging.info('Doing remove outliers, norm (nearest flats), and -log...')
if doOutliers:
projs = tomopy.remove_outlier(projs, outlier_diff, size=outlier_size, axis=0)
flat = tomopy.remove_outlier(flat, outlier_diff, size=outlier_size, axis=0)
tomo = tomopy.normalize_nf(projs, flat, dark, floc)
tomo = tomopy.minus_log(tomo, out=tomo) # in place logarithm
# Use padding to remove halo in reconstruction if present
if pad_sino:
npad = int(np.ceil(tomo.shape[2] * np.sqrt(2)) - tomo.shape[2])//2
tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge')
cor_rec = cor + npad # account for padding
else:
cor_rec = cor
if doFWringremoval:
logging.info('Doing ring (Fourier-wavelet) function...')
tomo = tomopy.remove_stripe_fw(tomo, sigma=ringSigma, level=ringLevel, pad=True, wname=ringWavelet)
if doPhaseRetrieval:
logging.info('Doing Phase retrieval...')
#tomo = tomopy.retrieve_phase(tomo, pixel_size=pxsize, dist=propagation_dist, energy=kev, alpha=alphaReg, pad=True)
tomo = tomopy.retrieve_phase(tomo, pixel_size=pxsize, dist=propagation_dist, energy=kev, alpha=alphaReg, pad=True)
logging.info('Doing recon (gridrec) function and scaling/masking, with cor %f...',cor_rec)
rec = tomopy.recon(tomo, tomopy.angles(tomo.shape[0], 270, 90), center=cor_rec, algorithm='gridrec', filter_name='butterworth', filter_par=butterworthpars)
#rec = tomopy.recon(tomo, tomopy.angles(tomo.shape[0], 180+angularrange/2, 180-angularrange/2), center=cor_rec, algorithm='gridrec', filter_name='butterworth', filter_par=butterworthpars)
rec /= pxsize # intensity values in cm^-1
if pad_sino:
rec = tomopy.circ_mask(rec[:, npad:-npad, npad:-npad], 0)
else:
rec = tomopy.circ_mask(rec, 0, ratio=1.0, val=0.0)
if doPolarRing:
logging.info('Doing ring (polar mean filter) function...')
rec = tomopy.remove_ring(rec, theta_min=Rarc, rwidth=Rmaxwidth, thresh_max=Rtmax, thresh=Rthr, thresh_min=Rtmin)
logging.info('Writing reconstruction slices to %s', filename[x])
#dxchange.write_tiff_stack(rec, fname=outputPath+'alpha'+str(alphaReg)+'/rec'+filename[x]+'/rec'+filename[x], start=sinorange[0]+y*num_sino_per_substack)
dxchange.write_tiff_stack(rec, fname=outputPath + 'recon_'+filename[x]+'/recon_'+filename[x], start=sinorange[0]+y*num_sino_per_substack)
logging.info('Reconstruction Complete: '+ filename[x])
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'):
sample_detector_distance = 8 # Propagation distance of the wavefront in cm
detector_pixel_size_x = 2.247e-4 # Detector pixel size in cm (5x: 1.17e-4, 2X: 2.93e-4)
monochromator_energy = 24.9 # Energy of incident wave in keV
alpha = 1e-02 # Phase retrieval coeff.
zinger_level = 800 # Zinger level for projections
zinger_level_w = 1000 # Zinger level for white
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
# zinger_removal
# proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0)
# flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0)
# Flat-field correction of raw data.
##data = tomopy.normalize(proj, flat, dark, cutoff=0.8)
data = tomopy.normalize(proj, flat, dark)
# remove stripes
data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=2,pad=True)
#data = tomopy.remove_stripe_ti(data, alpha=1.5)
data = tomopy.remove_stripe_sf(data, size=150)
# phase retrieval
#data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=alpha,pad=True)
print("Raw data: ", h5fname)
print("Center: ", rot_center)
data = tomopy.minus_log(data)
data = tomopy.remove_nan(data, val=0.0)
data = tomopy.remove_neg(data, val=0.00)
data[np.where(data == np.inf)] = 0.00
rot_center = rot_center/np.power(2, float(binning))
data = tomopy.downsample(data, level=binning)
data = tomopy.downsample(data, level=binning, axis=1)
# padding
N = data.shape[2]
data_pad = np.zeros([data.shape[0],data.shape[1],3*N//2],dtype = "float32")
data_pad[:,:,N//4:5*N//4] = data
data_pad[:,:,0:N//4] = np.tile(np.reshape(data[:,:,0],[data.shape[0],data.shape[1],1]),(1,1,N//4))
data_pad[:,:,5*N//4:] = np.tile(np.reshape(data[:,:,-1],[data.shape[0],data.shape[1],1]),(1,1,N//4))
data = data_pad
rot_center = rot_center+N//4
# Reconstruct object.
if algorithm == 'sirtfbp':
rec = rec_sirtfbp(data, theta, rot_center)
else:
rec = tomopy.recon(data, theta, center=rot_center, algorithm=algorithm, filter_name='parzen')
rec = rec[:,N//4:5*N//4,N//4:5*N//4]
print("Algorithm: ", algorithm)
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
return rec
# Set path to the micro-CT data to reconstruct.
fname = '../../../tomopy/data/tooth.h5'
# Select the sinogram range to reconstruct.
start = 0
end = 2
# Read the APS 2-BM 0r 32-ID raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(fname, sino=(start, end))
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(proj.shape[0])
# Set data collection angles as equally spaced between 0-180 degrees.
proj = tomopy.normalize(proj, flat, dark)
# Set data collection angles as equally spaced between 0-180 degrees.
rot_center = tomopy.find_center(proj, theta, init=290, ind=0, tol=0.5)
proj = tomopy.minus_log(proj)
# Reconstruct object using Gridrec algorithm.
recon = tomopy.recon(proj, theta, center=rot_center, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
recon = tomopy.circ_mask(recon, axis=0, ratio=0.95)
# Write data as stack of TIFs.
dxchange.write_tiff_stack(recon, fname='recon_dir/recon')
logging.info('...with center of rotation shifted %f',k)
rec = tomopy.recon(tomo, tomopy.angles(tomo.shape[0], 270, 270-angularrange), center=cor_rec+k, algorithm='gridrec', filter_name='butterworth', filter_par=butterworthpars)
rec /= pxsize # intensity values in cm^-1
CORtoWrite =cor_rec+k
if doPolarRing:
logging.info('Doing ring removal (polar mean filter)')
rec = tomopy.remove_ring(rec, theta_min=Rarc, rwidth=Rmaxwidth, thresh_max=Rtmax, thresh=Rthr, thresh_min=Rtmin)
if pad_sino:
logging.info('Unpadding...')
rec = tomopy.circ_mask(rec[:, npad:-npad, npad:-npad], 0)
CORtoWrite = CORtoWrite - npad
else:
rec = tomopy.circ_mask(rec, 0, ratio=1.0, val=0.0)
logging.info('Writing reconstruction slices to %s', iname[x])
if testCOR_insteps:
filenametowrite = odirectory+'/rec'+iname[x]+'/'+'cor'+str(CORtoWrite)+'_'+iname[x]
else:
filenametowrite = odirectory+'/rec'+iname[x]+'/'+iname[x]
if castTo8bit:
rec = convert8bit(rec,data_min,data_max)
def reconstruct(h5fname, sino, rot_center, args, blocked_views=None):
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
# Manage the missing angles:
if blocked_views is not None:
print("Blocked Views: ", blocked_views)
proj = np.concatenate((proj[0:blocked_views[0], :, :],
proj[blocked_views[1]+1:-1, :, :]), axis=0)
theta = np.concatenate((theta[0:blocked_views[0]],
theta[blocked_views[1]+1: -1]))
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
# remove stripes
data = tomopy.remove_stripe_fw(data, level=7, wname='sym16', sigma=1,
pad=True)
print("Raw data: ", h5fname)
print("Center: ", rot_center)
data = tomopy.minus_log(data)
data = tomopy.remove_nan(data, val=0.0)
data = tomopy.remove_neg(data, val=0.00)
data[np.where(data == np.inf)] = 0.00
algorithm = args.algorithm
ncores = args.ncores
nitr = args.num_iter
# always add algorithm
_kwargs = {"algorithm": algorithm}
# assign number of cores
_kwargs["ncore"] = ncores
# use the accelerated version
if algorithm in ["mlem", "sirt"]:
_kwargs["accelerated"] = True
# don't assign "num_iter" if gridrec or fbp
if algorithm not in ["fbp", "gridrec"]:
_kwargs["num_iter"] = nitr
sname = os.path.join(args.output_dir, 'proj_{}'.format(args.algorithm))
print(proj.shape)
tmp = np.zeros((proj.shape[0], proj.shape[2]))
tmp[:,:] = proj[:,0,:]
output_image(tmp, sname + "." + args.format)
# Reconstruct object.
with timemory.util.auto_timer(
"[tomopy.recon(algorithm='{}')]".format(algorithm)):
print("Starting reconstruction with kwargs={}...".format(_kwargs))
rec = tomopy.recon(data, theta, **_kwargs)
print("Completed reconstruction...")
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
obj = np.zeros(rec.shape, dtype=rec.dtype)
label = "{} @ {}".format(algorithm.upper(), h5fname)
quantify_difference(label, obj, rec)
return rec
def _apply_mask(self, recon):
ratio = self.parameters['ratio']
if ratio:
recon = tomopy.circ_mask(recon, axis=0, ratio=ratio)
return self._transpose(recon)
def recon(
filename,
inputPath = './',
outputPath = None,
outputFilename = None,
doOutliers1D = False, # outlier removal in 1d (along sinogram columns)
outlier_diff1D = 750, # difference between good data and outlier data (outlier removal)
outlier_size1D = 3, # radius around each pixel to look for outliers (outlier removal)
doOutliers2D = False, # outlier removal, standard 2d on each projection
outlier_diff2D = 750, # difference between good data and outlier data (outlier removal)
outlier_size2D = 3, # radius around each pixel to look for outliers (outlier removal)
doFWringremoval = True, # Fourier-wavelet ring removal
doTIringremoval = False, # Titarenko ring removal
doSFringremoval = False, # Smoothing filter ring removal
ringSigma = 3, # damping parameter in Fourier space (Fourier-wavelet ring removal)
ringLevel = 8, # number of wavelet transform levels (Fourier-wavelet ring removal)
ringWavelet = 'db5', # type of wavelet filter (Fourier-wavelet ring removal)
ringNBlock = 0, # used in Titarenko ring removal (doTIringremoval)
ringAlpha = 1.5, # used in Titarenko ring removal (doTIringremoval)
ringSize = 5, # used in smoothing filter ring removal (doSFringremoval)
doPhaseRetrieval = False, # phase retrieval
alphaReg = 0.0002, # smaller = smoother (used for phase retrieval)
propagation_dist = 75, # sample-to-scintillator distance (phase retrieval)
kev = 24, # energy level (phase retrieval)
butterworth_cutoff = 0.25, #0.1 would be very smooth, 0.4 would be very grainy (reconstruction)
butterworth_order = 2, # for reconstruction
doTranslationCorrection = False, # correct for linear drift during scan
xshift = 0, # undesired dx transation correction (from 0 degree to 180 degree proj)
yshift = 0, # undesired dy transation correction (from 0 degree to 180 degree proj)
doPolarRing = False, # ring removal
Rarc=30, # min angle needed to be considered ring artifact (ring removal)
Rmaxwidth=100, # max width of rings to be filtered (ring removal)
Rtmax=3000.0, # max portion of image to filter (ring removal)
Rthr=3000.0, # max value of offset due to ring artifact (ring removal)
Rtmin=-3000.0, # min value of image to filter (ring removal)
cor=None, # center of rotation (float). If not used then cor will be detected automatically
corFunction = 'pc', # center of rotation function to use - can be 'pc', 'vo', or 'nm'
voInd = None, # index of slice to use for cor search (vo)
voSMin = -40, # min radius for searching in sinogram (vo)
voSMax = 40, # max radius for searching in sinogram (vo)
voSRad = 10, # search radius (vo)
voStep = 0.5, # search step (vo)
voRatio = 2.0, # ratio of field-of-view and object size (vo)
voDrop = 20, # drop lines around vertical center of mask (vo)
nmInd = None, # index of slice to use for cor search (nm)
nmInit = None, # initial guess for center (nm)
nmTol = 0.5, # desired sub-pixel accuracy (nm)
nmMask = True, # if True, limits analysis to circular region (nm)
nmRatio = 1.0, # ratio of radius of circular mask to edge of reconstructed image (nm)
nmSinoOrder = False, # if True, analyzes in sinogram space. If False, analyzes in radiograph space
use360to180 = False, # use 360 to 180 conversion
doBilateralFilter = False, # if True, uses bilateral filter on image just before write step # NOTE: image will be converted to 8bit if it is not already
bilateral_srad = 3, # spatial radius for bilateral filter (image will be converted to 8bit if not already)
bilateral_rrad = 30, # range radius for bilateral filter (image will be converted to 8bit if not already)
castTo8bit = False, # convert data to 8bit before writing
cast8bit_min=-10, # min value if converting to 8bit
cast8bit_max=30, # max value if converting to 8bit
useNormalize_nf = False, # normalize based on background intensity (nf)
chunk_proj = 100, # chunk size in projection direction
chunk_sino = 100, # chunk size in sinogram direction
npad = None, # amount to pad data before reconstruction
projused = None, #should be slicing in projection dimension (start,end,step)
sinoused = None, #should be sliceing in sinogram dimension (start,end,step). If first value is negative, it takes the number of slices from the second value in the middle of the stack.
correcttilt = 0, #tilt dataset
tiltcenter_slice = None, # tilt center (x direction)
tiltcenter_det = None, # tilt center (y direction)
angle_offset = 0, #this is the angle offset from our default (270) so that tomopy yields output in the same orientation as previous software (Octopus)
anglelist = None, #if not set, will assume evenly spaced angles which will be calculated by the angular range and number of angles found in the file. if set to -1, will read individual angles from each image. alternatively, a list of angles can be passed.
doBeamHardening = False, #turn on beam hardening correction, based on "Correction for beam hardening in computed tomography", Gabor Herman, 1979 Phys. Med. Biol. 24 81
BeamHardeningCoefficients = None, #6 values, tomo = a0 + a1*tomo + a2*tomo^2 + a3*tomo^3 + a4*tomo^4 + a5*tomo^5
projIgnoreList = None, #projections to be ignored in the reconstruction (for simplicity in the code, they will not be removed and will be processed as all other projections but will be set to zero absorption right before reconstruction.
*args, **kwargs):
start_time = time.time()
print("Start {} at:".format(filename)+time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime()))
outputPath = inputPath if outputPath is None else outputPath
outputFilename = filename if outputFilename is None else outputFilename
outputFilename = outputFilename.replace('.h5','')
tempfilenames = [outputPath+'tmp0.h5',outputPath+'tmp1.h5']
filenametowrite = outputPath+'/rec'+filename.strip(".h5")+'/'+outputFilename
#filenametowrite = outputPath+'/rec'+filename+'/'+outputFilename
print("cleaning up previous temp files", end="")
for tmpfile in tempfilenames:
try:
os.remove(tmpfile)
except OSError:
pass
print(", reading metadata")
datafile = h5py.File(inputPath+filename, 'r')
gdata = dict(dxchange.reader._find_dataset_group(datafile).attrs)
pxsize = float(gdata['pxsize'])/10 # /10 to convert units from mm to cm
numslices = int(gdata['nslices'])
numangles = int(gdata['nangles'])
angularrange = float(gdata['arange'])
numrays = int(gdata['nrays'])
npad = int(np.ceil(numrays * np.sqrt(2)) - numrays)//2 if npad is None else npad
projused = (0,numangles-1,1) if projused is None else projused
# ndark = int(gdata['num_dark_fields'])
# ind_dark = list(range(0, ndark))
# group_dark = [numangles - 1]
inter_bright = int(gdata['i0cycle'])
nflat = int(gdata['num_bright_field'])
ind_flat = list(range(0, nflat))
if inter_bright > 0:
group_flat = list(range(0, numangles, inter_bright))
if group_flat[-1] != numangles - 1:
group_flat.append(numangles - 1)
elif inter_bright == 0:
group_flat = [0, numangles - 1]
else:
group_flat = None
ind_tomo = list(range(0, numangles))
floc_independent = dxchange.reader._map_loc(ind_tomo, group_flat)
#figure out the angle list (a list of angles, one per projection image)
dtemp = datafile[list(datafile.keys())[0]]
fltemp = list(dtemp.keys())
firstangle = float(dtemp[fltemp[0]].attrs.get('rot_angle',0))
if anglelist is None:
#the offset angle should offset from the angle of the first image, which is usually 0, but in the case of timbir data may not be.
#we add the 270 to be inte same orientation as previous software used at bl832
angle_offset = 270 + angle_offset - firstangle
anglelist = tomopy.angles(numangles, angle_offset, angle_offset-angularrange)
elif anglelist==-1:
anglelist = np.zeros(shape=numangles)
for icount in range(0,numangles):
anglelist[icount] = np.pi/180*(270 + angle_offset - float(dtemp[fltemp[icount]].attrs['rot_angle']))
#if projused is different than default, need to chnage numangles and angularrange
#can't do useNormalize_nf and doOutliers2D at the same time, or doOutliers2D and doOutliers1D at the same time, b/c of the way we chunk, for now just disable that
if useNormalize_nf==True and doOutliers2D==True:
useNormalize_nf = False
print("we cannot currently do useNormalize_nf and doOutliers2D at the same time, turning off useNormalize_nf")
if doOutliers2D==True and doOutliers1D==True:
doOutliers1D = False
print("we cannot currently do doOutliers1D and doOutliers2D at the same time, turning off doOutliers1D")
#figure out how user can pass to do central x number of slices, or set of slices dispersed throughout (without knowing a priori the value of numslices)
if sinoused is None:
sinoused = (0,numslices,1)
elif sinoused[0]<0:
sinoused=(int(np.floor(numslices/2.0)-np.ceil(sinoused[1]/2.0)),int(np.floor(numslices/2.0)+np.floor(sinoused[1]/2.0)),1)
num_proj_per_chunk = np.minimum(chunk_proj,projused[1]-projused[0])
numprojchunks = (projused[1]-projused[0]-1)//num_proj_per_chunk+1
num_sino_per_chunk = np.minimum(chunk_sino,sinoused[1]-sinoused[0])
numsinochunks = (sinoused[1]-sinoused[0]-1)//num_sino_per_chunk+1
numprojused = (projused[1]-projused[0])//projused[2]
numsinoused = (sinoused[1]-sinoused[0])//sinoused[2]
BeamHardeningCoefficients = (0, 1, 0, 0, 0, .1) if BeamHardeningCoefficients is None else BeamHardeningCoefficients
if cor is None:
print("Detecting center of rotation", end="")
if angularrange>300:
lastcor = int(np.floor(numangles/2)-1)
else:
lastcor = numangles-1
#I don't want to see the warnings about the reader using a deprecated variable in dxchange
with warnings.catch_warnings():
warnings.simplefilter("ignore")
tomo, flat, dark, floc = dxchange.read_als_832h5(inputPath+filename,ind_tomo=(0,lastcor))
tomo = tomo.astype(np.float32)
if useNormalize_nf:
tomopy.normalize_nf(tomo, flat, dark, floc, out=tomo)
else:
tomopy.normalize(tomo, flat, dark, out=tomo)
if corFunction == 'vo':
# same reason for catching warnings as above
with warnings.catch_warnings():
warnings.simplefilter("ignore")
cor = tomopy.find_center_vo(tomo, ind=voInd, smin=voSMin, smax=voSMax, srad=voSRad, step=voStep,
ratio=voRatio, drop=voDrop)
elif corFunction == 'nm':
cor = tomopy.find_center(tomo, tomopy.angles(numangles, angle_offset, angle_offset-angularrange),
ind=nmInd, init=nmInit, tol=nmTol, mask=nmMask, ratio=nmRatio,
sinogram_order=nmSinoOrder)
elif corFunction == 'pc':
cor = tomopy.find_center_pc(tomo[0], tomo[1], tol=0.25)
else:
raise ValueError("\'corFunction\' must be one of: [ pc, vo, nm ].")
print(", {}".format(cor))
else:
print("using user input center of {}".format(cor))
function_list = []
if doOutliers1D:
function_list.append('remove_outlier1d')
if doOutliers2D:
function_list.append('remove_outlier2d')
if useNormalize_nf:
function_list.append('normalize_nf')
else:
function_list.append('normalize')
function_list.append('minus_log')
if doBeamHardening:
function_list.append('beam_hardening')
if doFWringremoval:
function_list.append('remove_stripe_fw')
if doTIringremoval:
function_list.append('remove_stripe_ti')
if doSFringremoval:
function_list.append('remove_stripe_sf')
if correcttilt:
function_list.append('correcttilt')
if use360to180:
function_list.append('do_360_to_180')
if doPhaseRetrieval:
function_list.append('phase_retrieval')
function_list.append('recon_mask')
if doPolarRing:
function_list.append('polar_ring')
if castTo8bit:
function_list.append('castTo8bit')
if doBilateralFilter:
function_list.append('bilateral_filter')
function_list.append('write_output')
# Figure out first direction to slice
for func in function_list:
if slice_dir[func] != 'both':
axis = slice_dir[func]
break
done = False
curfunc = 0
curtemp = 0
while True: # Loop over reading data in certain chunking direction
if axis=='proj':
niter = numprojchunks
else:
niter = numsinochunks
for y in range(niter): # Loop over chunks
print("{} chunk {} of {}".format(axis, y+1, niter))
if curfunc==0:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
if axis=='proj':
tomo, flat, dark, floc = dxchange.read_als_832h5(inputPath+filename,ind_tomo=range(y*num_proj_per_chunk+projused[0],np.minimum((y + 1)*num_proj_per_chunk+projused[0],numangles)),sino=(sinoused[0],sinoused[1], sinoused[2]) )
else:
tomo, flat, dark, floc = dxchange.read_als_832h5(inputPath+filename,ind_tomo=range(projused[0],projused[1],projused[2]),sino=(y*num_sino_per_chunk+sinoused[0],np.minimum((y + 1)*num_sino_per_chunk+sinoused[0],numslices),1) )
else:
if axis=='proj':
start, end = y * num_proj_per_chunk, np.minimum((y + 1) * num_proj_per_chunk,numprojused)
tomo = dxchange.reader.read_hdf5(tempfilenames[curtemp],'/tmp/tmp',slc=((start,end,1),(0,numslices,1),(0,numrays,1))) #read in intermediate file
else:
start, end = y * num_sino_per_chunk, np.minimum((y + 1) * num_sino_per_chunk,numsinoused)
tomo = dxchange.reader.read_hdf5(tempfilenames[curtemp],'/tmp/tmp',slc=((0,numangles,1),(start,end,1),(0,numrays,1)))
dofunc = curfunc
keepvalues = None
while True: # Loop over operations to do in current chunking direction
func_name = function_list[dofunc]
newaxis = slice_dir[func_name]
if newaxis != 'both' and newaxis != axis:
# We have to switch axis, so flush to disk
if y==0:
try:
os.remove(tempfilenames[1-curtemp])
except OSError:
pass
appendaxis = 1 if axis=='sino' else 0
dxchange.writer.write_hdf5(tomo,fname=tempfilenames[1-curtemp],gname='tmp',dname='tmp',overwrite=False,appendaxis=appendaxis) #writing intermediate file...
break
print(func_name, end=" ")
curtime = time.time()
if func_name == 'remove_outlier1d':
tomo = tomo.astype(np.float32,copy=False)
remove_outlier1d(tomo, outlier_diff1D, size=outlier_size1D, out=tomo)
if func_name == 'remove_outlier2d':
tomo = tomo.astype(np.float32,copy=False)
tomopy.remove_outlier(tomo, outlier_diff2D, size=outlier_size2D, axis=0, out=tomo)
elif func_name == 'normalize_nf':
tomo = tomo.astype(np.float32,copy=False)
tomopy.normalize_nf(tomo, flat, dark, floc_independent, out=tomo) #use floc_independent b/c when you read file in proj chunks, you don't get the correct floc returned right now to use here.
elif func_name == 'normalize':
tomo = tomo.astype(np.float32,copy=False)
tomopy.normalize(tomo, flat, dark, out=tomo)
elif func_name == 'minus_log':
mx = np.float32(0.00000000000000000001)
ne.evaluate('where(tomo>mx, tomo, mx)', out=tomo)
tomopy.minus_log(tomo, out=tomo)
elif func_name == 'beam_hardening':
loc_dict = {'a{}'.format(i):np.float32(val) for i,val in enumerate(BeamHardeningCoefficients)}
tomo = ne.evaluate('a0 + a1*tomo + a2*tomo**2 + a3*tomo**3 + a4*tomo**4 + a5*tomo**5', local_dict=loc_dict, out=tomo)
elif func_name == 'remove_stripe_fw':
tomo = tomopy.remove_stripe_fw(tomo, sigma=ringSigma, level=ringLevel, pad=True, wname=ringWavelet)
elif func_name == 'remove_stripe_ti':
tomo = tomopy.remove_stripe_ti(tomo, nblock=ringNBlock, alpha=ringAlpha)
elif func_name == 'remove_stripe_sf':
tomo = tomopy.remove_stripe_sf(tomo, size=ringSize)
elif func_name == 'correcttilt':
if tiltcenter_slice is None:
tiltcenter_slice = numslices/2.
if tiltcenter_det is None:
tiltcenter_det = tomo.shape[2]/2
new_center = tiltcenter_slice - 0.5 - sinoused[0]
center_det = tiltcenter_det - 0.5
#add padding of 10 pixels, to be unpadded right after tilt correction. This makes the tilted image not have zeros at certain edges, which matters in cases where sample is bigger than the field of view. For the small amounts we are generally tilting the images, 10 pixels is sufficient.
# tomo = tomopy.pad(tomo, 2, npad=10, mode='edge')
# center_det = center_det + 10
cntr = (center_det, new_center)
for b in range(tomo.shape[0]):
tomo[b] = st.rotate(tomo[b], correcttilt, center=cntr, preserve_range=True, order=1, mode='edge', clip=True) #center=None means image is rotated around its center; order=1 is default, order of spline interpolation
# tomo = tomo[:, :, 10:-10]
elif func_name == 'do_360_to_180':
# Keep values around for processing the next chunk in the list
keepvalues = [angularrange, numangles, projused, num_proj_per_chunk, numprojchunks, numprojused, numrays, anglelist]
#why -.5 on one and not on the other?
if tomo.shape[0]%2>0:
tomo = sino_360_to_180(tomo[0:-1,:,:], overlap=int(np.round((tomo.shape[2]-cor-.5))*2), rotation='right')
angularrange = angularrange/2 - angularrange/(tomo.shape[0]-1)
else:
tomo = sino_360_to_180(tomo[:,:,:], overlap=int(np.round((tomo.shape[2]-cor))*2), rotation='right')
angularrange = angularrange/2
numangles = int(numangles/2)
projused = (0,numangles-1,1)
num_proj_per_chunk = np.minimum(chunk_proj,projused[1]-projused[0])
numprojchunks = (projused[1]-projused[0]-1)//num_proj_per_chunk+1
numprojused = (projused[1]-projused[0])//projused[2]
numrays = tomo.shape[2]
anglelist = anglelist[:numangles]
elif func_name == 'phase_retrieval':
tomo = tomopy.retrieve_phase(tomo, pixel_size=pxsize, dist=propagation_dist, energy=kev, alpha=alphaReg, pad=True)
elif func_name == 'translation_correction':
tomo = linear_translation_correction(tomo,dx=xshift,dy=yshift,interpolation=False):
elif func_name == 'recon_mask':
tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge')
if projIgnoreList is not None:
for badproj in projIgnoreList:
tomo[badproj] = 0
rec = tomopy.recon(tomo, anglelist, center=cor+npad, algorithm='gridrec', filter_name='butterworth', filter_par=[butterworth_cutoff, butterworth_order])
rec = rec[:, npad:-npad, npad:-npad]
rec /= pxsize # convert reconstructed voxel values from 1/pixel to 1/cm
rec = tomopy.circ_mask(rec, 0)
elif func_name == 'polar_ring':
rec = np.ascontiguousarray(rec, dtype=np.float32)
rec = tomopy.remove_ring(rec, theta_min=Rarc, rwidth=Rmaxwidth, thresh_max=Rtmax, thresh=Rthr, thresh_min=Rtmin,out=rec)
elif func_name == 'castTo8bit':
rec = convert8bit(rec, cast8bit_min, cast8bit_max)
elif func_name == 'bilateral_filter':
rec = pyF3D.run_BilateralFilter(rec, spatialRadius=bilateral_srad, rangeRadius=bilateral_rrad)
elif func_name == 'write_output':
dxchange.write_tiff_stack(rec, fname=filenametowrite, start=y*num_sino_per_chunk + sinoused[0])
print('(took {:.2f} seconds)'.format(time.time()-curtime))
dofunc+=1
if dofunc==len(function_list):
break
if y<niter-1 and keepvalues: # Reset original values for next chunk
angularrange, numangles, projused, num_proj_per_chunk, numprojchunks, numprojused, numrays, anglelist = keepvalues
curtemp = 1 - curtemp
curfunc = dofunc
if curfunc==len(function_list):
break
axis = slice_dir[function_list[curfunc]]
print("cleaning up temp files")
for tmpfile in tempfilenames:
try:
os.remove(tmpfile)
except OSError:
pass
print("End Time: "+time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime()))
print('It took {:.3f} s to process {}'.format(time.time()-start_time,inputPath+filename))
def main(arg):
parser = argparse.ArgumentParser()
parser.add_argument("fname", help="Full file name: /data/fname.raw")
parser.add_argument("--start", nargs='?', type=int, default=0, help="First image to read")
parser.add_argument("--nimg", nargs='?', type=int, default=1, help="Number of images to read")
parser.add_argument("--ndark", nargs='?', type=int, default=10, help="Number of dark images")
parser.add_argument("--nflat", nargs='?', type=int, default=10, help="Number of white images")
args = parser.parse_args()
fname = args.fname
start = args.start
end = args.start + args.nimg
nflat, ndark, nimg, height, width = read_adimec_header(fname)
print("Image Size:", width, height)
print("Dataset metadata (nflat, ndark, nimg:", nflat, ndark, nimg)
# override nflat and ndark from header with the passed parameter
# comment the two lines below if the meta data in the binary
# file for nflat and ndark is correct
nflat = args.nflat
ndark = args.ndark
proj = read_adimec_stack(fname, img=(start, end))
print("Projection:", proj.shape)
# slider(proj)
flat = read_adimec_stack(fname, img=(nimg-ndark-nflat, nimg-ndark))
print("Flat:", flat.shape)
# slider(flat)
dark = read_adimec_stack(fname, img=(nimg-ndark, nimg))
print("Dark:", dark.shape)
# slider(dark)
nproj = tomopy.normalize(proj, flat, dark)
print("Normalized projection:", nproj.shape)
# slider(proj)
proj = nproj[:,100:110, :]
print("Sino chunk:", proj.shape)
slider(proj)
theta = tomopy.angles(proj.shape[0])
print(theta.shape)
proj = tomopy.minus_log(proj)
proj = tomopy.remove_nan(proj, val=0.0)
proj = tomopy.remove_neg(proj, val=0.00)
proj[np.where(proj == np.inf)] = 0.00
rot_center = 1280
# Reconstruct object using Gridrec algorithm.
rec = tomopy.recon(proj, theta, center=rot_center, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
# Write data as stack of TIFs.
dxchange.write_tiff_stack(rec, fname='recon_dir/recon')
def fast_tomo_recon(argv):
"""
Reconstruct subset slices (sinograms) equally spaced within tomographic
dataset
"""
logger = logging.getLogger("fast_tomopy.fast_tomo_recon")
# Parse arguments passed to function
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input", type=str, help="path to input raw " "dataset", required=True)
parser.add_argument(
"-o",
"--output-file",
type=str,
help="full path to h5 output " "file",
default=os.path.join(os.getcwd(), "fast-tomopy.h5"),
)
parser.add_argument("-sn", "--sino-num", type=int, help="Number of slices " "to reconstruct", default=5)
parser.add_argument(
"-a",
"--algorithm",
type=str,
help="Reconstruction" " algorithm",
default="gridrec",
choices=[
"art",
"bart",
"fbp",
"gridrec",
"mlem",
"ospml_hybrid",
"ospml_quad",
"pml_hybrid",
"pml_quad",
"sirt",
],
)
parser.add_argument("-c", "--center", type=float, help="Center of rotation", default=None)
parser.add_argument(
"-fn",
"--filter-name",
type=str,
help="Name of filter" " used for reconstruction",
choices=["none", "shepp", "cosine", "hann", "hamming", "ramlak", "parzen", "butterworth"],
default="butterworth",
)
parser.add_argument(
"-rr",
"--ring-remove",
type=str,
help="Ring removal " "method",
choices=["Octopus", "Tomopy-FW", "Tomopy-T"],
default="Tomopy-T",
)
parser.add_argument("-lf", "--log-file", type=str, help="log file name", default="fast-tomopy.log")
args = parser.parse_args()
fh = logging.FileHandler(args.log_file)
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
logger.addHandler(fh)
if os.path.isdir(os.path.dirname(args.output_file)) is False:
raise IOError(2, "Directory of output file does not exist", args.output_file)
# Read file metadata
logger.info("Reading input file metadata")
fdata, gdata = read_als_832h5_metadata(args.input)
proj_total = int(gdata["nangles"])
last = proj_total - 1
sino_total = int(gdata["nslices"])
ray_total = int(gdata["nrays"])
px_size = float(gdata["pxsize"]) / 10 # cm
# Set parameters for sinograms to read
step = sino_total // (args.sino_num + 2)
start = step
end = step * (args.sino_num + 1)
sino = (start, end, step)
# Read full first and last projection to determine center of rotation
if args.center is None:
logger.info("Reading full first and last projection for COR")
first_last, flats, darks, floc = tomopy.read_als_832h5(args.input, ind_tomo=(0, last))
first_last = tomopy.normalize(first_last, flats, darks)
args.center = tomopy.find_center_pc(first_last[0, :, :], first_last[1, :, :], tol=0.1)
logger.info("Detected center: %f", args.center)
# Read and normalize raw sinograms
logger.info("Reading raw data")
tomo, flats, darks, floc = tomopy.read_als_832h5(args.input, sino=sino)
logger.info("Normalizing raw data")
tomo = tomopy.normalize_nf(tomo, flats, darks, floc)
# Remove stripes from sinograms (remove rings)
logger.info("Preprocessing normalized data")
if args.ring_remove == "Tomopy-FW":
logger.info("Removing stripes from sinograms with %s", args.ring_remove)
tomo = tomopy.remove_stripe_fw(tomo)
elif args.ring_remove == "Tomopy-T":
logger.info("Removing stripes from sinograms with %s", args.ring_remove)
tomo = tomopy.remove_stripe_ti(tomo)
# Pad sinograms with edge values
npad = int(np.ceil(ray_total * np.sqrt(2)) - ray_total) // 2
tomo = tomopy.pad(tomo, 2, npad=npad, mode="edge")
args.center += npad # account for padding
filter_name = np.array(args.filter_name, dtype=(str, 16))
theta = tomopy.angles(proj_total, 270, 90)
logger.info("Reconstructing normalized data")
# Reconstruct sinograms
# rec = tomopy.minus_log(tomo, out=tomo)
rec = tomopy.recon(
tomo, theta, center=args.center, emission=False, algorithm=args.algorithm, filter_name=filter_name
)
rec = tomopy.circ_mask(rec[:, npad:-npad, npad:-npad], 0)
rec = rec / px_size
# Remove rings from reconstruction
if args.ring_remove == "Octopus":
logger.info("Removing rings from reconstructions with %s", args.ring_remove)
thresh = float(gdata["ring_threshold"])
thresh_max = float(gdata["upp_ring_value"])
thresh_min = float(gdata["low_ring_value"])
theta_min = int(gdata["max_arc_length"])
rwidth = int(gdata["max_ring_size"])
rec = tomopy.remove_rings(
rec,
center_x=args.center,
thresh=thresh,
thresh_max=thresh_max,
thresh_min=thresh_min,
theta_min=theta_min,
rwidth=rwidth,
)
# Write reconstruction data to new hdf5 file
fdata["stage"] = "fast-tomopy"
fdata["stage_flow"] = "/raw/" + fdata["stage"]
fdata["stage_version"] = "fast-tomopy-0.1"
# Generate a new uuid based on host ID and current time
fdata["uuid"] = str(uuid.uuid1())
gdata["Reconstruction_Type"] = "tomopy-gridrec"
gdata["ring_removal_method"] = args.ring_remove
gdata["rfilter"] = args.filter_name
logger.info("Writing reconstructed data to h5 file")
write_als_832h5(rec, args.input, fdata, gdata, args.output_file, step)
return
def rec_try(h5fname, nsino, rot_center, center_search_width, algorithm, binning):
data_shape = get_dx_dims(h5fname, 'data')
print(data_shape)
ssino = int(data_shape[1] * nsino)
rot_center+=data_shape[2]//4
center_range = (rot_center-center_search_width, rot_center+center_search_width, 0.5)
#print(sino,ssino, center_range)
#print(center_range[0], center_range[1], center_range[2])
# Select sinogram range to reconstruct
sino = None
start = ssino
end = start + 1
sino = (start, end)
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=2,pad=True)
#data = tomopy.remove_stripe_ti(data, alpha=1.5)
data = tomopy.remove_stripe_sf(data, size=150)
# remove stripes
# data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True)
print("Raw data: ", h5fname)
print("Center: ", rot_center)
data = tomopy.minus_log(data)
# padding
N = data.shape[2]
data_pad = np.zeros([data.shape[0],data.shape[1],3*N//2],dtype = "float32")
data_pad[:,:,N//4:5*N//4] = data
data_pad[:,:,0:N//4] = np.tile(np.reshape(data[:,:,0],[data.shape[0],data.shape[1],1]),(1,1,N//4))
data_pad[:,:,5*N//4:] = np.tile(np.reshape(data[:,:,-1],[data.shape[0],data.shape[1],1]),(1,1,N//4))
data = data_pad
stack = np.empty((len(np.arange(*center_range)), data.shape[0], data.shape[2]))
print(stack.shape)
print(data.shape)
index = 0
for axis in np.arange(*center_range):
stack[index] = data[:, 0, :]
index = index + 1
# Reconstruct the same slice with a range of centers.
rec = tomopy.recon(stack, theta, center=np.arange(*center_range), sinogram_order=True, algorithm='gridrec', filter_name='parzen', nchunk=1)
rec = rec[:,N//4:5*N//4,N//4:5*N//4]
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
index = 0
# Save images to a temporary folder.
fname = os.path.dirname(h5fname) + '/' + 'try_rec/' + 'recon_' + os.path.splitext(os.path.basename(h5fname))[0]
for axis in np.arange(*center_range):
rfname = fname + '_' + str('{0:.2f}'.format(axis-N//4) + '.tiff')
dxchange.write_tiff(rec[index], fname=rfname, overwrite=True)
index = index + 1
print("Reconstructions: ", fname)
def recon_block(grid, shift_grid, src_folder, dest_folder, slice_range, sino_step, center_vec, ds_level=0, blend_method='max',
blend_options=None, tolerance=1, sinogram_order=False, algorithm='gridrec', init_recon=None, ncore=None, nchunk=None, dtype='float32',
crop=None, save_sino=False, assert_width=None, sino_blur=None, color_correction=False, flattened_radius=120, normalize=True,
test_mode=False, mode='180', phase_retrieval=None, **kwargs):
"""
Reconstruct dsicrete HDF5 tiles, blending sinograms only.
"""
raw_folder = os.getcwd()
os.chdir(src_folder)
sino_ini = int(slice_range[0])
sino_end = int(slice_range[1])
mod_start_slice = 0
center_vec = np.asarray(center_vec)
center_pos_cache = 0
sino_ls = np.arange(sino_ini, sino_end, sino_step, dtype='int')
pix_shift_grid = np.ceil(shift_grid)
pix_shift_grid[pix_shift_grid < 0] = 0
alloc_set = allocate_mpi_subsets(sino_ls.size, size, task_list=sino_ls)
for i_slice in alloc_set[rank]:
print('############################################')
print('Reconstructing ' + str(i_slice))
# judge from which tile to retrieve sinos
grid_lines = np.zeros(grid.shape[1], dtype=np.int)
slice_in_tile = np.zeros(grid.shape[1], dtype=np.int)
for col in range(grid.shape[1]):
bins = pix_shift_grid[:, col, 0]
grid_lines[col] = int(np.squeeze(np.digitize(i_slice, bins)) - 1)
if grid_lines[col] == -1:
print("WARNING: The specified starting slice number does not allow for full sinogram construction. Trying next slice...")
mod_start_slice = 1
break
else:
mod_start_slice = 0
slice_in_tile[col] = i_slice - bins[grid_lines[col]]
if mod_start_slice == 1:
continue
center_pos = int(np.round(center_vec[grid_lines].mean()))
if center_pos_cache == 0:
center_pos_cache = center_pos
center_diff = center_pos - center_pos_cache
center_pos_0 = center_pos
row_sino, center_pos = prepare_slice(grid, shift_grid, grid_lines, slice_in_tile, ds_level=ds_level,
method=blend_method, blend_options=blend_options, rot_center=center_pos,
assert_width=assert_width, sino_blur=sino_blur, color_correction=color_correction,
normalize=normalize, mode=mode, phase_retrieval=phase_retrieval)
rec0 = recon_slice(row_sino, center_pos, sinogram_order=sinogram_order, algorithm=algorithm,
init_recon=init_recon, ncore=ncore, nchunk=nchunk, **kwargs)
rec = tomopy.remove_ring(np.copy(rec0))
cent = int((rec.shape[1] - 1) / 2)
xx, yy = np.meshgrid(np.arange(rec.shape[2]), np.arange(rec.shape[1]))
mask0 = ((xx - cent) ** 2 + (yy - cent) ** 2 <= flattened_radius ** 2)
mask = np.zeros(rec.shape, dtype='bool')
for i in range(mask.shape[0]):
mask[i, :, :] = mask0
rec[mask] = (rec[mask] + rec0[mask]) / 2
rec = tomopy.remove_outlier(rec, tolerance)
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
print('Center: {:d}'.format(center_pos))
rec = np.squeeze(rec)
if center_diff != 0:
rec = np.roll(rec, -center_diff, axis=0)
if not crop is None:
crop = np.asarray(crop)
rec = rec[crop[0, 0]:crop[1, 0], crop[0, 1]:crop[1, 1]]
os.chdir(raw_folder)
if test_mode:
dxchange.write_tiff(rec, fname=os.path.join(dest_folder, 'recon/recon_{:05d}_{:04d}.tiff'.format(i_slice, center_pos)), dtype=dtype)
else:
dxchange.write_tiff(rec, fname=os.path.join(dest_folder, 'recon/recon_{:05d}.tiff'.format(i_slice)), dtype=dtype)
if save_sino:
dxchange.write_tiff(np.squeeze(row_sino), fname=os.path.join(dest_folder, 'sino/sino_{:05d}.tiff'.format(i_slice)), overwrite=True)
os.chdir(src_folder)
os.chdir(raw_folder)
return
if remove_stripe2: data = tomopy.remove_stripe_ti(data)
#d.downsample2d(level=level) # apply binning on the data
data = tomopy.downsample(data, level=level) # apply binning on the data
theta = tomopy.angles(data.shape[0])
if 1:
#if not best_center: d.optimize_center()
if not best_center: calc_center = tomopy.find_center(data, theta, emission=False, ind=0, tol=0.3)
else:
#d.center=best_center/pow(2,level) # Manage the rotation center
calc_center = best_center/pow(2,level) # Manage the rotation center
#d.gridrec(ringWidth=RingW) # Run the reconstruction
rec = tomopy.recon(data, theta, center=calc_center, algorithm='gridrec', emission=False)
#d.apply_mask(ratio=1)
rec = tomopy.circ_mask(rec, axis=0)
# Write data as stack of TIFs.
#tomopy.xtomo_writer(d.data_recon, output_name,
# axis=0,
# x_start=slice_first)
tomopy.io.writer.write_tiff_stack(rec, fname=output_name, axis=0, start=slice_first)
#### for the whole volume reconstruction
if 1:
f = h5py.File(file_name, "r"); nProj, nslices, nCol = f["/exchange3/data"].shape
nslices_per_chunk = nslices/chunk
for iChunk in range(0,chunk):
print '\n -- chunk # %i' % (iChunk+1)
slice_first = nslices_per_chunk*iChunk
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'):
sample_detector_distance = 25 # Propagation distance of the wavefront in cm
detector_pixel_size_x = 2.143e-4 # Detector pixel size in cm (5x: 1.17e-4, 2X: 2.93e-4)
#monochromator_energy = 24.9 # Energy of incident wave in keV
# used pink beam
alpha = 1e-02 # Phase retrieval coeff.
zinger_level = 800 # Zinger level for projections
zinger_level_w = 1000 # Zinger level for white
# Read APS 2-BM raw data.
# DIMAX saves 3 files: proj, flat, dark
# when loading the data set select the proj file (larger size)
fname = os.path.splitext(h5fname)[0]
fbase = fname.rsplit('_', 1)[0]
fnum = fname.rsplit('_', 1)[1]
fext = os.path.splitext(h5fname)[1]
fnum_flat = str("%4.4d" % (int(fnum)+1))
fnum_dark = str("%4.4d" % (int(fnum)+2))
fnproj = fbase + '_' + fnum + fext
fnflat = fbase + '_' + fnum_flat + fext
fndark = fbase + '_' + fnum_dark + fext
fnflat = '/local/data/2018-11/Chawla/1G_A/1G_A_0002.hdf'
fndark = '/local/data/2018-11/Chawla/1G_A/1G_A_0003.hdf'
print('proj', fnproj)
print('flat', fnflat)
print('dark', fndark)
# Read APS 2-BM DIMAX raw data.
proj, dum, dum2, theta = dxchange.read_aps_32id(fnproj, sino=sino)
dum3, flat, dum4, dum5 = dxchange.read_aps_32id(fnflat, sino=sino)
dum6, dum7, dark, dum8 = dxchange.read_aps_32id(fndark, sino=sino)
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
# remove stripes
data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True)
# zinger_removal
proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0)
flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0)
# Flat-field correction of raw data.
##data = tomopy.normalize(proj, flat, dark, cutoff=0.8)
data = tomopy.normalize(proj, flat, dark)
# remove stripes
#data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True)
#data = tomopy.remove_stripe_ti(data, alpha=1.5)
data = tomopy.remove_stripe_sf(data, size=150)
# phase retrieval
#data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=alpha,pad=True)
print("Raw data: ", h5fname)
print("Center: ", rot_center)
data = tomopy.minus_log(data)
data = tomopy.remove_nan(data, val=0.0)
data = tomopy.remove_neg(data, val=0.00)
data[np.where(data == np.inf)] = 0.00
rot_center = rot_center/np.power(2, float(binning))
data = tomopy.downsample(data, level=binning)
data = tomopy.downsample(data, level=binning, axis=1)
# Reconstruct object.
if algorithm == 'sirtfbp':
rec = rec_sirtfbp(data, theta, rot_center)
else:
rec = tomopy.recon(data, theta, center=rot_center, algorithm=algorithm, filter_name='parzen')
print("Algorithm: ", algorithm)
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
#rec = np.swapaxes(rec,0,2)
return rec
# Filters are saved in .mat files in "./ยจ
test_sirtfbp_iter = True
if test_sirtfbp_iter:
nCol = data.shape[2]
output_name = './test_iter/'
num_iter = [50,100,150]
filter_dict = sirtfilter.getfilter(nCol, theta, num_iter, filter_dir='./')
for its in num_iter:
tomopy_filter = sirtfilter.convert_to_tomopy_filter(filter_dict[its], nCol)
rec = tomopy.recon(data, theta, center=rot_center, algorithm='gridrec', filter_name='custom2d', filter_par=tomopy_filter)
output_name_2 = output_name + 'sirt_fbp_%iiter_slice_' % its
dxchange.write_tiff_stack(data, fname=output_name_2, start=start, dtype='float32')
# Reconstruct object using sirt-fbp algorithm:
num_iter = 100
nCol = data.shape[2]
sirtfbp_filter = sirtfilter.getfilter(nCol, theta, num_iter, filter_dir='./')
tomopy_filter = sirtfilter.convert_to_tomopy_filter(sirtfbp_filter, nCol)
rec = tomopy.recon(data, theta, center=rot_center, algorithm='gridrec', filter_name='custom2d', filter_par=tomopy_filter)
# Reconstruct object using Gridrec algorithm.
# rec = tomopy.recon(data, theta, center=rot_center, algorithm='gridrec', nchunk=1)
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
# Write data as stack of TIFs.
fname='/replace_this_with_data_dir_path/sirtfbp_recon'
dxchange.write_tiff_stack(rec, fname=fname)
def rec_try(h5fname, nsino, rot_center, center_search_width, algorithm, binning, dark_file):
data_shape = get_dx_dims(h5fname, 'data')
print(data_shape)
ssino = int(data_shape[1] * nsino)
center_range = (rot_center-center_search_width, rot_center+center_search_width, 0.5)
#print(sino,ssino, center_range)
#print(center_range[0], center_range[1], center_range[2])
# Select sinogram range to reconstruct
sino = None
start = ssino
end = start + 1
sino = (start, end)
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
if dark_file is not None:
print('Reading white/dark from {}'.format(dark_file))
proj_, flat, dark, theta_ = dxchange.read_aps_32id(dark_file, sino=sino)
del proj_, theta_
print(proj.shape, flat.shape, dark.shape)
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
# remove stripes
# data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True)
print("Raw data: ", h5fname)
print("Center: ", rot_center)
data = tomopy.minus_log(data)
data = tomopy.remove_nan(data, val=0.0)
data = tomopy.remove_neg(data, val=0.00)
data[np.where(data == np.inf)] = 0.00
stack = np.empty((len(np.arange(*center_range)), data_shape[0], data_shape[2]))
index = 0
for axis in np.arange(*center_range):
stack[index] = data[:, 0, :]
index = index + 1
# Reconstruct the same slice with a range of centers.
rec = tomopy.recon(stack, theta, center=np.arange(*center_range), sinogram_order=True, algorithm='gridrec', filter_name='parzen', nchunk=1)
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
index = 0
# Save images to a temporary folder.
#fname = os.path.dirname(h5fname) + os.sep + 'try_rec/' + path_base_name(h5fname) + os.sep + 'recon_' + os.path.splitext(os.path.basename(h5fname))[0]
fname = os.path.dirname(h5fname) + os.sep + 'centers/' + path_base_name(h5fname) + os.sep + 'recon_' + os.path.splitext(os.path.basename(h5fname))[0]
for axis in np.arange(*center_range):
rfname = fname + '_' + str('{0:.2f}'.format(axis) + '.tiff')
dxchange.write_tiff(rec[index], fname=rfname, overwrite=True)
index = index + 1
print("Reconstructions: ", fname)
import tomopy
import dxchange
if __name__ == '__main__':
# Set path to the micro-CT data to reconstruct.
fname = '../../../tomopy/data/tooth.h5'
# Select the sinogram range to reconstruct.
start = 0
end = 2
# Read the APS 2-BM 0r 32-ID raw data.
proj, flat, dark = dxchange.read_aps_32id(fname, sino=(start, end))
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(proj.shape[0])
# Set data collection angles as equally spaced between 0-180 degrees.
proj = tomopy.normalize(proj, flat, dark)
# Set data collection angles as equally spaced between 0-180 degrees.
rot_center = tomopy.find_center(proj, theta, init=290, ind=0, tol=0.5)
tomopy.minus_log(proj)
# Reconstruct object using Gridrec algorithm.
recon = tomopy.recon(proj, theta, center=rot_center, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
recon = tomopy.circ_mask(recon, axis=0, ratio=0.95)
def _apply_mask(self, recon):
ratio = self.parameters['ratio']
if ratio:
recon = tomopy.circ_mask(recon, axis=0, ratio=ratio)
return self._transpose(recon)
def fast_tomo_recon(argv):
"""
Reconstruct subset slices (sinograms) equally spaced within tomographic
dataset
"""
logger = logging.getLogger('fast_tomopy.fast_tomo_recon')
# Parse arguments passed to function
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, help='path to input raw '
'dataset', required=True)
parser.add_argument('-o', '--output-file', type=str, help='full path to h5 output '
'file', default=os.path.join(os.getcwd(), "fast-tomopy.h5"))
parser.add_argument('-sn', '--sino-num', type=int, help='Number of slices '
'to reconstruct', default=5)
parser.add_argument('-a', '--algorithm', type=str, help='Reconstruction'
' algorithm', default='gridrec',
choices=['art', 'bart', 'fbp', 'gridrec', 'mlem',
'ospml_hybrid', 'ospml_quad', 'pml_hybrid',
'pml_quad', 'sirt'])
parser.add_argument('-c', '--center', type=float, help='Center of rotation',
default=None)
parser.add_argument('-fn', '--filter-name', type=str, help='Name of filter'
' used for reconstruction',
choices=['none', 'shepp', 'cosine', 'hann', 'hamming',
'ramlak', 'parzen', 'butterworth'],
default='butterworth')
parser.add_argument('-rr', '--ring-remove', type=str, help='Ring removal '
'method', choices=['Octopus', 'Tomopy-FW', 'Tomopy-T'],
default='Tomopy-FW')
parser.add_argument('-lf', '--log-file', type=str, help='log file name',
default='fast-tomopy.log')
args = parser.parse_args()
fh = logging.FileHandler(args.log_file)
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
logger.addHandler(fh)
if os.path.isdir(os.path.dirname(args.output_file)) is False:
raise IOError(2, 'Directory of output file does not exist', args.output_file)
# Read file metadata
logger.info('Reading input file metadata')
fdata, gdata = tomopy.read_als_832h5_metadata(args.input)
proj_total = int(gdata['nangles'])
last = proj_total - 1
sino_total = int(gdata['nslices'])
ray_total = int(gdata['nrays'])
px_size = float(gdata['pxsize'])/10 # cm
# Set parameters for sinograms to read
step = sino_total // (args.sino_num + 2)
start = step
end = step*(args.sino_num + 1)
sino = (start, end, step)
# Read full first and last projection to determine center of rotation
if args.center is None:
logger.info('Reading full first and last projection for COR')
first_last, flats, darks, floc = tomopy.read_als_832h5(args.input,
ind_tomo=(0, last))
first_last = tomopy.normalize_nf(first_last, flats, darks, floc)
args.center = tomopy.find_center_pc(first_last[0, :, :],
first_last[1, :, :], tol=0.1)
logger.info('Detected center: %f', args.center)
# Read and normalize raw sinograms
logger.info('Reading raw data')
tomo, flats, darks, floc = tomopy.read_als_832h5(args.input, sino=sino)
logger.info('Normalizing raw data')
tomo = tomopy.normalize_nf(tomo, flats, darks, floc)
# Remove stripes from sinograms (remove rings)
logger.info('Preprocessing normalized data')
if args.ring_remove == 'Tomopy-FW':
logger.info('Removing stripes from sinograms with %s',
args.ring_remove)
tomo = tomopy.remove_stripe_fw(tomo)
elif args.ring_remove == 'Tomopy-T':
logger.info('Removing stripes from sinograms with %s',
args.ring_remove)
tomo = tomopy.remove_stripe_ti(tomo)
# Pad sinograms with edge values
npad = int(np.ceil(ray_total*np.sqrt(2)) - ray_total)//2
tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge')
args.center += npad # account for padding
filter_name = np.array(args.filter_name, dtype=(str, 16))
theta = tomopy.angles(tomo.shape[0], 270, 90)
logger.info('Reconstructing normalized data')
# Reconstruct sinograms
rec = tomopy.recon(tomo, theta, center=args.center, emission=False,
algorithm=args.algorithm, filter_name=filter_name)
rec = tomopy.circ_mask(rec[:, npad:-npad, npad:-npad], 0)
rec = rec/px_size
# Remove rings from reconstruction
if args.ring_remove == 'Octopus':
logger.info('Removing rings from reconstructions with %s',
args.ring_remove)
thresh = float(gdata['ring_threshold'])
thresh_max = float(gdata['upp_ring_value'])
thresh_min = float(gdata['low_ring_value'])
theta_min = int(gdata['max_arc_length'])
rwidth = int(gdata['max_ring_size'])
rec = tomopy.remove_rings(rec, center_x=args.center, thresh=thresh,
thresh_max=thresh_max, thresh_min=thresh_min,
theta_min=theta_min, rwidth=rwidth)
# Write reconstruction data to new hdf5 file
fdata['stage'] = 'fast-tomopy'
fdata['stage_flow'] = '/raw/' + fdata['stage']
fdata['stage_version'] = 'fast-tomopy-0.1'
# WHAT ABOUT uuid ????? Who asigns this???
del fdata['uuid'] # I'll get rid of it altogether then...
gdata['Reconstruction_Type'] = 'tomopy-gridrec'
gdata['ring_removal_method'] = args.ring_remove
gdata['rfilter'] = args.filter_name
logger.info('Writing reconstructed data to h5 file')
write_als_832h5(rec, args.input, fdata, gdata, args.output_file, step)
return
