def generate_data(validation=False):
"""Generate a set of random data."""
n_iter = 1 if validation else n_data
x_arr = np.empty((n_iter, space.shape[0], space.shape[1], 1),
dtype='float32')
y_arr = np.empty(
(n_iter, operator.range.shape[0], operator.range.shape[1], 1),
dtype='float32')
x_true_arr = np.empty((n_iter, space.shape[0], space.shape[1], 1),
dtype='float32')
for i in range(n_iter):
if validation:
phantom = odl.phantom.shepp_logan(space, True)
else:
phantom = random_phantom(space)
data = operator(phantom)
noisy_data = data + odl.phantom.white_noise(operator.range) * np.mean(
np.abs(data)) * 0.05
fbp = pseudoinverse(noisy_data)
x_arr[i, ..., 0] = fbp
x_true_arr[i, ..., 0] = phantom
y_arr[i, ..., 0] = noisy_data
return x_arr, y_arr, x_true_arr
def generate_data(validation=False, noiseLev = noiseLev):
"""Generate a set of random data."""
n_generate = val_size if validation else n_data
x_arr = np.empty((n_generate, 1, ray_trafo.range.shape[0], ray_trafo.range.shape[1]), dtype='float32')
x_true_arr = np.empty((n_generate, 1, space.shape[0], space.shape[1]), dtype='float32')
for i in range(n_generate):
if validation:
phantom = odl.phantom.shepp_logan(space, True)
else:
phantom = (random_phantom(space,n_ellipse=30))
data = ray_trafo(phantom)
noisy_data = data + odl.phantom.white_noise(ray_trafo.range) * np.mean(np.abs(data)) * noiseLev
x_arr[i, 0] = noisy_data
x_true_arr[i, 0] = phantom
return x_arr, x_true_arr
