def _adjust_hist_limits(tomo, theta, ind, mask, emission):
# Make an initial reconstruction to adjust histogram limits.
rec = recon(tomo, theta, emission=emission, 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 _adjust_hist_limits(tomo, theta, ind, mask, emission):
# Make an initial reconstruction to adjust histogram limits.
rec = recon(tomo, theta, emission=emission, 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 _find_center_cost(
center, tomo, theta, ind, hmin, hmax, mask, ratio, emission):
"""
Cost function used for the ``find_center`` routine.
"""
logger.info('trying center: %s', center)
center = np.array(center, dtype='float32')
rec = recon(
tomo[:, ind:ind + 1, :], theta, center, emission=emission, algorithm='gridrec')
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
return -np.dot(hist, np.log2(hist))
def _find_center_cost(center, tomo, theta, ind, hmin, hmax, mask, ratio,
emission):
"""
Cost function used for the ``find_center`` routine.
"""
logger.info('trying center: %s', center)
center = np.array(center, dtype='float32')
rec = recon(tomo[:, ind:ind + 1, :],
theta,
center,
emission=emission,
algorithm='gridrec')
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
return -np.dot(hist, np.log2(hist))
def write_center(
tomo, theta, dpath='tmp/center', cen_range=None, ind=None,
emission=True, mask=False, ratio=1.):
"""
Save images reconstructed with a range of rotation centers.
Helps finding the rotation center manually by visual inspection of
images reconstructed with a set of different centers.The output
images are put into a specified folder and are named by the
center position corresponding to the image.
Parameters
----------
tomo : ndarray
3D tomographic data.
theta : array
Projection angles in radian.
dpath : str, optional
Folder name to save output images.
cen_range : list, optional
[start, end, step] Range of center values.
ind : int, optional
Index of the slice to be used for reconstruction.
emission : bool, optional
Determines whether data is emission or transmission type.
mask : bool, optional
If ``True``, apply a circular mask to the reconstructed image to
limit the analysis into a circular region.
ratio : float, optional
The ratio of the radius of the circular mask to the edge of the
reconstructed image.
"""
tomo = dtype.as_float32(tomo)
theta = dtype.as_float32(theta)
dx, dy, dz = tomo.shape
if ind is None:
ind = dy / 2
if cen_range is None:
center = np.arange(dz / 2 - 5, dz / 2 + 5, 0.5)
else:
center = np.arange(cen_range[0], cen_range[1], cen_range[2] / 2.)
stack = np.zeros((dx, len(center), dz))
for m in range(center.size):
stack[:, m, :] = tomo[:, ind, :]
# Reconstruct the same slice with a range of centers.
rec = recon(
stack, theta, center=center, emission=emission, algorithm='gridrec')
# Apply circular mask.
if mask is True:
rec = circ_mask(rec, axis=0)
# Save images to a temporary folder.
for m in range(len(center)):
if m % 2 == 0: # 2 slices same bec of gridrec.
fname = os.path.join(
dpath, str('{:.2f}'.format(center[m]) + '.tiff'))
write_tiff(rec[m:m + 1], fname=fname, overwrite=True)
def write_center(tomo,
theta,
dpath='tmp/center',
cen_range=None,
ind=None,
emission=True,
mask=False,
ratio=1.):
"""
Save images reconstructed with a range of rotation centers.
Helps finding the rotation center manually by visual inspection of
images reconstructed with a set of different centers.The output
images are put into a specified folder and are named by the
center position corresponding to the image.
Parameters
----------
tomo : ndarray
3D tomographic data.
theta : array
Projection angles in radian.
dpath : str, optional
Folder name to save output images.
cen_range : list, optional
[start, end, step] Range of center values.
ind : int, optional
Index of the slice to be used for reconstruction.
emission : bool, optional
Determines whether data is emission or transmission type.
mask : bool, optional
If ``True``, apply a circular mask to the reconstructed image to
limit the analysis into a circular region.
ratio : float, optional
The ratio of the radius of the circular mask to the edge of the
reconstructed image.
"""
tomo = dtype.as_float32(tomo)
theta = dtype.as_float32(theta)
dx, dy, dz = tomo.shape
if ind is None:
ind = dy / 2
if cen_range is None:
center = np.arange(dz / 2 - 5, dz / 2 + 5, 0.5)
else:
center = np.arange(cen_range[0], cen_range[1], cen_range[2] / 2.)
stack = np.zeros((dx, len(center), dz))
for m in range(center.size):
stack[:, m, :] = tomo[:, ind, :]
# Reconstruct the same slice with a range of centers.
rec = recon(stack,
theta,
center=center,
emission=emission,
algorithm='gridrec')
# Apply circular mask.
if mask is True:
rec = circ_mask(rec, axis=0)
# Save images to a temporary folder.
for m in range(len(center)):
if m % 2 == 0: # 2 slices same bec of gridrec.
fname = os.path.join(dpath,
str('{:.2f}'.format(center[m]) + '.tiff'))
write_tiff(rec[m:m + 1], fname=fname, overwrite=True)
