In this script, we build a synthetic sinogram to be used with PyHST as well as
with the demo_tv_reconstruction.py script. This will help to compare the
different methods.
"""
import numpy as np
from reconstruction.util import generate_synthetic_data
from reconstruction.projections import build_projection_operator
from EdfFile import EdfFile
# Synthetic data
l = 256
x = generate_synthetic_data(l)
# Projection operator and projections data, with noise
H = build_projection_operator(l, l / 5)
y = H * x.ravel()[:, np.newaxis]
y += 2 * np.random.randn(*y.shape)
y[y < 0] = 0
y = y.reshape((l / 5, l))
y = y.astype(np.float32)
# Write EdfFile with float32 data
phantom = EdfFile("phantom_float.edf")
for line in y:
lines = np.tile(line, (3, 1))
print "writing", lines.shape
phantom._WriteImage({}, lines, DataType="FloatValue", Append=1)
#phantom.WriteImage({}, lines)
import numpy as np
from reconstruction.util import generate_synthetic_data
from reconstruction.projections import build_projection_operator
from reconstruction.forward_backward_tv import fista_tv
from joblib import Parallel, delayed
# Synthetic data
l = 256
x = generate_synthetic_data(l, n_pts=100)
n_dir = 80
# Projection operator and projections data, with 2 realizations of the noise
H = build_projection_operator(l, n_dir)
y = H * x.ravel()[:, np.newaxis]
np.random.seed(0)
y1 = y + 2*np.random.randn(*y.shape) # 1st realization
y2 = y + 2*np.random.randn(*y.shape) # 2nd realization
# Range of beta parameter
betas = 2**np.arange(2, 6, 0.25)
def rec_error(beta):
"""
cross-validation
"""
res, energies = fista_tv(y1, beta, 400, H)
yres = H * res[-1].ravel()[:, np.newaxis]
with the demo_tv_reconstruction.py script. This will help to compare the
different methods.
"""
import numpy as np
from reconstruction.util import generate_synthetic_data
from reconstruction.projections import build_projection_operator
from EdfFile import EdfFile
# Synthetic data
l = 256
x = generate_synthetic_data(l)
# Projection operator and projections data, with noise
H = build_projection_operator(l, l/5)
y = H * x.ravel()[:, np.newaxis]
y += 2*np.random.randn(*y.shape)
y[y<0] = 0
y = y.reshape((l/5, l))
y = y.astype(np.float32)
# Write EdfFile with float32 data
phantom = EdfFile("phantom_float.edf")
for line in y:
lines = np.tile(line, (3, 1))
print "writing", lines.shape
phantom._WriteImage({}, lines, DataType="FloatValue", Append=1)
#phantom.WriteImage({}, lines)
