def _dist_recon(tomo, center, recon, algorithm, args, kwargs, ncore, nchunk):
axis_size = recon.shape[0]
ncore, nchunk = mproc.get_ncore_nchunk(axis_size, ncore, nchunk)
chnks = np.round(np.linspace(0, axis_size, ncore+1)).astype(np.int)
mulargs = []
for i in range(ncore):
mulargs.append(algorithm(tomo[chnks[i]:chnks[i+1]],
center[chnks[i]:chnks[i+1]], recon[chnks[i]:chnks[i+1]],
*args, **kwargs))
e = cf.ThreadPoolExecutor(ncore)
thrds = [e.submit(args[0], *args[1:]) for args in mulargs]
for t in thrds:
t.result()
return recon
def remove_ring(rec, center_x=None, center_y=None, thresh=300.0,
thresh_max=300.0, thresh_min=-100.0, theta_min=30,
rwidth=30, int_mode='WRAP', ncore=None, nchunk=None, out=None):
"""
Remove ring artifacts from images in the reconstructed domain.
Descriptions of parameters need to be more clear for sure.
Parameters
----------
arr : ndarray
Array of reconstruction data
center_x : float, optional
abscissa location of center of rotation
center_y : float, optional
ordinate location of center of rotation
thresh : float, optional
maximum value of an offset due to a ring artifact
thresh_max : float, optional
max value for portion of image to filter
thresh_min : float, optional
min value for portion of image to filer
theta_min : int, optional
Features larger than twice this angle (degrees) will be considered
a ring artifact. Must be less than 180 degrees.
rwidth : int, optional
Maximum width of the rings to be filtered in pixels
int_mode : str, optional
'WRAP' for wrapping at 0 and 360 degrees, 'REFLECT' for reflective
boundaries at 0 and 180 degrees.
ncore : int, optional
Number of cores that will be assigned to jobs.
nchunk : int, optional
Chunk size for each core.
out : ndarray, optional
Output array for result. If same as arr, process
will be done in-place.
Returns
-------
ndarray
Corrected reconstruction data
"""
rec = dtype.as_float32(rec)
if out is None:
out = rec.copy()
else:
out = dtype.as_float32(out)
dz, dy, dx = rec.shape
if center_x is None:
center_x = (dx - 1.0)/2.0
if center_y is None:
center_y = (dy - 1.0)/2.0
if int_mode.lower() == 'wrap':
int_mode = 0
elif int_mode.lower() == 'reflect':
int_mode = 1
else:
raise ValueError("int_mode should be WRAP or REFLECT")
if not 0 <= theta_min < 180:
raise ValueError("theta_min should be in the range [0 - 180)")
args = (center_x, center_y, dx, dy, dz, thresh_max, thresh_min,
thresh, theta_min, rwidth, int_mode)
axis_size = rec.shape[0]
ncore, nchunk = mproc.get_ncore_nchunk(axis_size, ncore, nchunk)
with cf.ThreadPoolExecutor(ncore) as e:
for offset in range(0, axis_size, nchunk):
slc = np.s_[offset:offset+nchunk]
e.submit(extern.c_remove_ring, out[slc], *args)
return out
def remove_ring(rec, center_x=None, center_y=None, thresh=300.0,
thresh_max=300.0, thresh_min=-100.0, theta_min=30,
rwidth=30, int_mode='WRAP', ncore=None, nchunk=None, out=None):
"""
Remove ring artifacts from images in the reconstructed domain.
Descriptions of parameters need to be more clear for sure.
Parameters
----------
arr : ndarray
Array of reconstruction data
center_x : float, optional
abscissa location of center of rotation
center_y : float, optional
ordinate location of center of rotation
thresh : float, optional
maximum value of an offset due to a ring artifact
thresh_max : float, optional
max value for portion of image to filter
thresh_min : float, optional
min value for portion of image to filer
theta_min : int, optional
minimum angle in degrees (int) to be considered ring artifact
rwidth : int, optional
Maximum width of the rings to be filtered in pixels
int_mode : str, optional
'WRAP' for wrapping at 0 and 360 degrees, 'REFLECT' for reflective
boundaries at 0 and 180 degrees.
ncore : int, optional
Number of cores that will be assigned to jobs.
nchunk : int, optional
Chunk size for each core.
out : ndarray, optional
Output array for result. If same as arr, process
will be done in-place.
Returns
-------
ndarray
Corrected reconstruction data
"""
rec = dtype.as_float32(rec)
if out is None:
out = rec.copy()
else:
out = dtype.as_float32(out)
dz, dy, dx = rec.shape
if center_x is None:
center_x = (dx - 1.0)/2.0
if center_y is None:
center_y = (dy - 1.0)/2.0
if int_mode.lower() == 'wrap':
int_mode = 0
elif int_mode.lower() == 'reflect':
int_mode = 1
else:
raise ValueError("int_mode should be WRAP or REFLECT")
args = (center_x, center_y, dx, dy, dz, thresh_max, thresh_min,
thresh, theta_min, rwidth, int_mode)
axis_size = rec.shape[0]
ncore, nchunk = mproc.get_ncore_nchunk(axis_size, ncore, nchunk)
with cf.ThreadPoolExecutor(ncore) as e:
for offset in range(0, axis_size, nchunk):
slc = np.s_[offset:offset+nchunk]
e.submit(extern.c_remove_ring, out[slc], *args)
return out
def remove_ring(rec, center_x=None, center_y=None, thresh=300.0,
thresh_max=300.0, thresh_min=-100.0, theta_min=30,
rwidth=30, ncore=None, nchunk=None, out=None):
"""
Remove ring artifacts from images in the reconstructed domain.
Descriptions of parameters need to be more clear for sure.
Parameters
----------
arr : ndarray
Array of reconstruction data
center_x : float, optional
abscissa location of center of rotation
center_y : float, optional
ordinate location of center of rotation
thresh : float, optional
maximum value of an offset due to a ring artifact
thresh_max : float, optional
max value for portion of image to filter
thresh_min : float, optional
min value for portion of image to filer
theta_min : int, optional
minimum angle in degrees (int) to be considered ring artifact
rwidth : int, optional
Maximum width of the rings to be filtered in pixels
ncore : int, optional
Number of cores that will be assigned to jobs.
nchunk : int, optional
Chunk size for each core.
out : ndarray, optional
Output array for result. If same as arr, process will be done in-place.
Returns
-------
ndarray
Corrected reconstruction data
"""
rec = dtype.as_float32(rec)
if out is None:
out = rec.copy()
else:
out = dtype.as_float32(out)
dz, dy, dx = rec.shape
if center_x is None:
center_x = (dx - 1.0)/2.0
if center_y is None:
center_y = (dy - 1.0)/2.0
args = (center_x, center_y, dx, dy, dz, thresh_max, thresh_min,
thresh, theta_min, rwidth)
axis_size = rec.shape[0]
ncore, nchunk = mproc.get_ncore_nchunk(axis_size, ncore, nchunk)
chnks = np.round(np.linspace(0, axis_size, ncore+1)).astype(np.int)
mulargs = []
for i in range(ncore):
mulargs.append(extern.c_remove_ring(out[chnks[i]:chnks[i+1]],
*args))
e = cf.ThreadPoolExecutor(ncore)
thrds = [e.submit(args[0], *args[1:]) for args in mulargs]
for t in thrds:
t.result()
return out