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iradon_sart: Also test accuracy with a missing wedge.

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Jostein Bø Fløystad
2013-06-16 14:25:23 +02:00
parent ace07a0b18
commit 116e1dd571
1 changed files with 47 additions and 44 deletions
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skimage/transform/tests/test_radon_transform.py
+47 -44
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@@ -320,54 +320,57 @@ def test_iradon_sart():
shepp_logan = imread(os.path.join(data_dir, "phantom.png"), as_grey=True)
image = rescale(shepp_logan, scale=0.4)
theta = np.linspace(0., 180., image.shape[0], endpoint=False)
sinogram = radon(image, theta, circle=True)
reconstructed = iradon_sart(sinogram, theta)
theta_ordered = np.linspace(0., 180., image.shape[0], endpoint=False)
theta_missing_wedge = np.linspace(0., 150., image.shape[0], endpoint=True)
for theta, error_factor in ((theta_ordered, 1.),
(theta_missing_wedge, 2.)):
sinogram = radon(image, theta, circle=True)
reconstructed = iradon_sart(sinogram, theta)
if debug:
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(221)
plt.imshow(image, interpolation='nearest')
plt.subplot(222)
plt.imshow(sinogram, interpolation='nearest')
plt.subplot(223)
plt.imshow(reconstructed, interpolation='nearest')
plt.subplot(224)
plt.imshow(reconstructed - image, interpolation='nearest')
plt.show()
if debug:
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(221)
plt.imshow(image, interpolation='nearest')
plt.subplot(222)
plt.imshow(sinogram, interpolation='nearest')
plt.subplot(223)
plt.imshow(reconstructed, interpolation='nearest')
plt.subplot(224)
plt.imshow(reconstructed - image, interpolation='nearest')
plt.show()
delta = np.mean(np.abs(reconstructed - image))
print('delta (1 iteration) =', delta)
assert delta < 0.025
reconstructed = iradon_sart(sinogram, theta, reconstructed)
delta = np.mean(np.abs(reconstructed - image))
print('delta (2 iterations) =', delta)
assert delta < 0.015
delta = np.mean(np.abs(reconstructed - image))
print('delta (1 iteration) =', delta)
assert delta < 0.016 * error_factor
reconstructed = iradon_sart(sinogram, theta, reconstructed)
delta = np.mean(np.abs(reconstructed - image))
print('delta (2 iterations) =', delta)
assert delta < 0.013 * error_factor
np.random.seed(1239867)
shifts = np.random.uniform(-3, 3, sinogram.shape[1])
x = np.arange(sinogram.shape[0])
sinogram_shifted = np.vstack(np.interp(x + shifts[i], x, sinogram[:, i])
for i in range(sinogram.shape[1])).T
reconstructed = iradon_sart(sinogram_shifted, theta,
projection_shifts=shifts)
if debug:
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(221)
plt.imshow(image, interpolation='nearest')
plt.subplot(222)
plt.imshow(sinogram_shifted, interpolation='nearest')
plt.subplot(223)
plt.imshow(reconstructed, interpolation='nearest')
plt.subplot(224)
plt.imshow(reconstructed - image, interpolation='nearest')
plt.show()
np.random.seed(1239867)
shifts = np.random.uniform(-3, 3, sinogram.shape[1])
x = np.arange(sinogram.shape[0])
sinogram_shifted = np.vstack(np.interp(x + shifts[i], x, sinogram[:, i])
for i in range(sinogram.shape[1])).T
reconstructed = iradon_sart(sinogram_shifted, theta,
projection_shifts=shifts)
if debug:
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(221)
plt.imshow(image, interpolation='nearest')
plt.subplot(222)
plt.imshow(sinogram_shifted, interpolation='nearest')
plt.subplot(223)
plt.imshow(reconstructed, interpolation='nearest')
plt.subplot(224)
plt.imshow(reconstructed - image, interpolation='nearest')
plt.show()
delta = np.mean(np.abs(reconstructed - image))
print('delta (1 iteration, shifted sinogram) =', delta)
assert delta < 0.025
delta = np.mean(np.abs(reconstructed - image))
print('delta (1 iteration, shifted sinogram) =', delta)
assert delta < 0.018 * error_factor
if __name__ == "__main__":
from numpy.testing import run_module_suite