def _correct_drift(self, air_pixels=20, num_cores=None, chunk_size=None, overwrite=True):
# Check input.
if air_pixels <= 0:
air_pixels = 0
if not isinstance(air_pixels, np.int32):
air_pixels = np.array(air_pixels, dtype="int32")
data = correct_drift(self.data, air_pixels)
# Update log.
self.logger.debug("correct_drift: air_pixels: " + str(air_pixels))
self.logger.info("correct_drift [ok]")
# Update returned values.
if overwrite:
self.data = data
else:
return data
def _correct_drift(self,
air_pixels=20,
num_cores=None,
chunk_size=None,
overwrite=True):
# Check input.
if air_pixels <= 0:
air_pixels = 0
if not isinstance(air_pixels, np.int32):
air_pixels = np.array(air_pixels, dtype='int32')
data = correct_drift(self.data, air_pixels)
# Update log.
self.logger.debug("correct_drift: air_pixels: " + str(air_pixels))
self.logger.info("correct_drift [ok]")
# Update returned values.
if overwrite: self.data = data
else: return data
def focus_region(data, xcoord, ycoord, diameter, center, padded=False, correction=True):
"""
Uses only a portion of the sinogram for reconstructing
a circular region of interest (ROI).
Parameters
----------
data : ndarray
3-D tomographic data with dimensions:
[projections, slices, pixels]
xcoord, ycoord : scalar
X- and Y-coordinates of the center location of the circular ROI.
diameter : scalar
Diameter of the circular ROI.
center : scalar
Center of rotation of the original dataset.
padded : bool, optional
True if the original sinogram size is preserved.
correction : bool, optional
True if the correct_drift is applied after ROI selection.
Returns
-------
roidata : ndarray
Modified ROI data.
"""
num_projections = data.shape[0]
num_pixels = data.shape[2]
rad = np.sqrt(xcoord*xcoord+ycoord*ycoord)
alpha = np.arctan2(xcoord, ycoord)
l1 = center-diameter/2
l2 = center-diameter/2+rad
if padded: roidata = np.ones((data.shape[0], data.shape[1], data.shape[2]), dtype='float32')
else: roidata = np.ones((data.shape[0], data.shape[1], diameter), dtype='float32')
delphi = np.pi/num_projections
for m in range(num_projections):
ind1 = np.ceil(np.cos(alpha-m*delphi)*(l2-l1)+l1)
ind2 = np.floor(np.cos(alpha-m*delphi)*(l2-l1)+l1+diameter)
if ind1 < 0:
ind1 = 0
if ind2 < 0:
ind2 = 0
if ind1 > num_pixels:
ind1 = num_pixels
if ind2 > num_pixels:
ind2 = num_pixels
if padded:
if correction: roidata[m, :, ind1:ind2] = correct_drift(np.expand_dims(data[m, :, ind1:ind2], axis=1), air_pixels=5)
else: roidata[m, :, ind1:ind2] = data[m, :, ind1:ind2]
else:
if correction: roidata[m, :,0:(ind2-ind1)] = correct_drift(np.expand_dims(data[m, :, ind1:ind2], axis=1), air_pixels=5)
else: roidata[m, :, 0:(ind2-ind1)] = data[m, :, ind1:ind2]
return roidata
def focus_region(data, xcoord, ycoord, diameter, center, padded=False, correction=True):
"""
Uses only a portion of the sinogram for reconstructing
a circular region of interest (ROI).
Parameters
----------
data : ndarray
3-D tomographic data with dimensions:
[projections, slices, pixels]
xcoord, ycoord : scalar
X- and Y-coordinates of the center location of the circular ROI.
diameter : scalar
Diameter of the circular ROI.
center : scalar
Center of rotation of the original dataset.
padded : bool, optional
True if the original sinogram size is preserved.
correction : bool, optional
True if the correct_drift is applied after ROI selection.
Returns
-------
roidata : ndarray
Modified ROI data.
"""
num_projections = data.shape[0]
num_pixels = data.shape[2]
rad = np.sqrt(xcoord * xcoord + ycoord * ycoord)
alpha = np.arctan2(xcoord, ycoord)
l1 = center - diameter / 2
l2 = center - diameter / 2 + rad
if padded:
roidata = np.ones((data.shape[0], data.shape[1], data.shape[2]), dtype='float32')
else:
roidata = np.ones((data.shape[0], data.shape[1], diameter), dtype='float32')
delphi = np.pi / num_projections
for m in range(num_projections):
ind1 = np.ceil(np.cos(alpha - m * delphi) * (l2 - l1) + l1)
ind2 = np.floor(np.cos(alpha - m * delphi) * (l2 - l1) + l1 + diameter)
if ind1 < 0:
ind1 = 0
if ind2 < 0:
ind2 = 0
if ind1 > num_pixels:
ind1 = num_pixels
if ind2 > num_pixels:
ind2 = num_pixels
arr = np.expand_dims(data[m, :, ind1:ind2], axis=0)
if padded:
if correction:
roidata[m, :, ind1:ind2] = correct_drift(arr, air_pixels=5)
else:
roidata[m, :, ind1:ind2] = arr
else:
if correction:
roidata[m, :, 0:(ind2 - ind1)] = correct_drift(arr, air_pixels=5)
else:
roidata[m, :, 0:(ind2 - ind1)] = arr
return roidata