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transform.iradon_sart: Clean up code for ordering projections.

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Jostein Bø Fløystad
2013-06-09 18:21:16 +02:00
parent b474804654
commit 372f0127f9
1 changed files with 25 additions and 21 deletions
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skimage/transform/radon_transform.py
+25 -21
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@@ -257,20 +257,30 @@ def iradon(radon_image, theta=None, output_size=None,
return reconstructed * np.pi / (2 * len(th))
def _sart_next_angle(remaining, used):
used = np.array(used)
used.shape = (-1, 1)
remaining = np.array(remaining)
remaining.shape = (1, -1)
time = np.arange(used.shape[0]) + 1
time.shape = (-1, 1)
tau = 3.
difference = used - remaining
distance = np.minimum(abs(difference % 180), abs(difference % -180))
#print distance
cost = np.exp(-distance * time / tau).sum(axis=0).squeeze()
next_angle_index = np.argmin(cost)
return remaining[0, next_angle_index]
def _sart_order_angles(theta, tau=3.):
"""
Order angles to reduce the amount of correlated information
in subsequent projections, i.e. make sure subsequent angles
are as far away from each other mod 180 degrees as possible.
Indices into the ``theta`` array are yielded.
"""
used_indices = [0]
remaining_indices = range(1, len(theta))
yield 0
while remaining_indices:
used = np.array(theta[used_indices])
used.shape = (-1, 1)
remaining = np.array(theta[remaining_indices])
remaining.shape = (1, -1)
time = (np.arange(used.shape[0]) + 1)[::-1]
time.shape = (-1, 1)
difference = used - remaining
distance = np.minimum(abs(difference % 180), abs(difference % -180))
cost = np.exp(-distance * time / tau).sum(axis=0).squeeze()
next_angle_remaining_index = np.argmin(cost)
next_angle_index = remaining_indices.pop(next_angle_remaining_index)
used_indices.append(next_angle_index)
yield next_angle_index
def iradon_sart(radon_image, theta=None, image=None, projection_shifts=None,
@@ -334,7 +344,6 @@ def iradon_sart(radon_image, theta=None, image=None, projection_shifts=None,
"""
if theta is None:
theta = np.linspace(0, 180, radon_image.shape[1], endpoint=False)
angle_indices = {theta[i]: i for i in range(theta.shape[0])}
reconstructed_shape = (radon_image.shape[0], radon_image.shape[0])
if image is None:
image = np.zeros(reconstructed_shape, dtype=np.float)
@@ -344,16 +353,11 @@ def iradon_sart(radon_image, theta=None, image=None, projection_shifts=None,
raise ValueError('Shape of image (%s) does not match first dimension '
'of radon_image (%s)'
% (image.shape, reconstructed_shape))
used_angles = []
while angle_indices:
angle_index = angle_indices.pop(_sart_next_angle(angle_indices.keys(),
used_angles))
for angle_index in _sart_order_angles(theta):
image_update = sart_projection_update(image, theta[angle_index],
radon_image[:, angle_index],
projection_shifts[angle_index])
image += relaxation * image_update
if not clip is None:
image = clip(image, clip[0], clip[1])
used_angles.append(theta[angle_index])
return image