import matplotlib.pyplot as plt
import corrct as cct
import corrct.utils_test
try:
import phantom
except ImportError:
cct.utils_test.download_phantom()
import phantom
vol_shape = [256, 256, 3]
data_type = np.float32
ph_or = np.squeeze(phantom.modified_shepp_logan(vol_shape).astype(data_type))
ph_or = ph_or[:, :, 1]
# psf = spsig.gaussian(11, 1)
psf = None
det_angles = [+np.pi / 2, -np.pi / 2]
(ph, vol_att_in,
vol_att_out) = cct.utils_test.phantom_assign_concentration(ph_or)
(sino, angles, expected_ph,
_) = cct.utils_test.create_sino(ph,
120,
detectors_pos_rad=det_angles,
vol_att_in=vol_att_in,
vol_att_out=vol_att_out,
psf=psf)
import scipy.ndimage.interpolation as spni
import phantom
import numpy as np
from skimage.transform import radon
import pylab as pyl
import tomopy_peri.tomopy
# SIMULATED TOMO DATA
#---------------------
p = 128
n_projections = 200
emission = True
iters = 1
simulate_misaligned_projections = False
theta = np.linspace(0,180,num=n_projections,endpoint=True)
msl = phantom.modified_shepp_logan((p,p,p))
data = np.zeros((1, n_projections, p, p))
for i in range(p):
data[0,:,i,:] = np.rollaxis(radon(msl[:,i,:], theta=theta, circle=True),1,0)
if simulate_misaligned_projections:
for i in range(n_projections):
sx, sy = 3*(np.random.random()-0.5), 3*(np.random.random()-0.5)
data[0,i,:,:]=spni.shift(data[0,i,:,:], (sx, sy))
d = tomopy.xftomo_dataset(data=data, theta=theta, channel_names=['Modified Shepp-Logan'], log='debug')
tomopy.xftomo_writer(d.data, channel=0, output_file='/tmp/projections/projection_{:}_{:}.tif')
if simulate_misaligned_projections:
d.align_projections(output_gifs=True, output_filename='/tmp/projections.gif')
d.align_projections(output_gifs=True, output_filename='/tmp/projections.gif')
except ImportError:
cct.utils_test.download_phantom()
import phantom
try:
import skimage.transform
__have_skimage__ = True
except ImportError:
print(
'Scikit image not available, no comparison against other filters will be possible'
)
__have_skimage__ = False
vol_shape_xy = [256, 256]
ph = np.squeeze(
phantom.modified_shepp_logan([*vol_shape_xy, 3]).astype(np.float32))
ph = ph[:, :, 1]
fbp_filter = 'Shepp-Logan'
(sino, angles_rad, expected_ph,
_) = cct.utils_test.create_sino(ph, 30, add_poisson=True, photon_flux=1e4)
print('Reconstructing sino:')
with cct.projectors.ProjectorUncorrected(vol_shape_xy, angles_rad) as p:
print('- Filter: "%s"' % fbp_filter)
vol_sl = p.fbp(sino, fbp_filter=fbp_filter)
print('- Phantom power: %g, noise power: %g' %
cct.utils_test.compute_error_power(expected_ph, vol_sl))
print('- Filter: "MR"')
filter_mr = cct.projectors.FilterMR()
vol_mr = p.fbp(sino, filter_mr)
try:
import phantom
except ImportError:
cct.utils_test.download_phantom()
import phantom
def cm2inch(x):
return np.array(x) / 2.54
data_type = np.float32
# Create the phantom shape
ph_or = np.squeeze(
phantom.modified_shepp_logan([256, 256, 3]).astype(data_type))
ph_or = ph_or[:, :, 1]
# Simulate the XRF-CT acquisition data (sinogram)
(ph, vol_att_in,
vol_att_out) = cct.utils_test.phantom_assign_concentration(ph_or)
(sinogram, angles, expected_ph,
background_avg) = cct.utils_test.create_sino(ph,
30,
add_poisson=True,
dwell_time_s=2e-2,
background_avg=1e-2,
background_std=1e-4)
prec = True
iterations = 200
import ipdb
import scipy.ndimage.interpolation as spni
import phantom
import numpy as np
from skimage.transform import radon
import pylab as pyl
# SIMULATED TOMO DATA
#---------------------
p = 128
n_projections = 200
emission = True
iters = 1
simulate_misaligned_projections = False
theta = np.linspace(0, 180, num=n_projections, endpoint=True)
msl = phantom.modified_shepp_logan((p, p, p))
data = np.zeros((1, n_projections, p, p))
for i in range(p):
data[0, :,
i, :] = np.rollaxis(radon(msl[:, i, :], theta=theta, circle=True), 1,
0)
if simulate_misaligned_projections:
for i in range(n_projections):
sx, sy = 3 * (np.random.random() - 0.5), 3 * (np.random.random() - 0.5)
data[0, i, :, :] = spni.shift(data[0, i, :, :], (sx, sy))
d = tomopy.xftomo_dataset(data=data,
theta=theta,
channel_names=['Modified Shepp-Logan'],
log='debug')