mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-02 00:29:00 +08:00
Nelle Varoquaux
64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
import numpy as np
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from numpy.testing import *
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from skimage.transform import *
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def rescale(x):
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x = x.astype(float)
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x -= x.min()
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x /= x.max()
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return x
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def test_radon_iradon():
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size = 100
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image = np.tri(size) + np.tri(size)[::-1]
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for filter_type in ["ramp", "shepp-logan", "cosine", "hamming", "hann"]:
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reconstructed = iradon(radon(image), filter=filter_type)
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image = rescale(image)
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reconstructed = rescale(reconstructed)
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delta = np.mean(np.abs(image - reconstructed))
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## print delta
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## import matplotlib.pyplot as plt
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## f, (ax1, ax2) = plt.subplots(1, 2)
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## ax1.imshow(image, cmap=plt.cm.gray)
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## ax2.imshow(reconstructed, cmap=plt.cm.gray)
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## plt.show()
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assert delta < 0.05
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reconstructed = iradon(radon(image), filter="ramp", interpolation="nearest")
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delta = np.mean(abs(image - reconstructed))
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assert delta < 0.05
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def test_iradon_angles():
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"""
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Test with different number of projections
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"""
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size = 100
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# Synthetic data
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image = np.tri(size) + np.tri(size)[::-1]
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# Large number of projections: a good quality is expected
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nb_angles = 200
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radon_image_200 = radon(image, theta=np.linspace(0, 180, nb_angles,
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endpoint=False))
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reconstructed = iradon(radon_image_200)
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delta_200 = np.mean(abs(rescale(image) - rescale(reconstructed)))
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assert delta_200 < 0.03
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# Lower number of projections
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nb_angles = 80
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radon_image_80 = radon(image, theta=np.linspace(0, 180, nb_angles,
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endpoint=False))
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# Test whether the sum of all projections is approximately the same
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s = radon_image_80.sum(axis=0)
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assert np.allclose(s, s[0], rtol=0.01)
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reconstructed = iradon(radon_image_80)
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delta_80 = np.mean(abs(image/np.max(image) - reconstructed/np.max(reconstructed)))
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# Loss of quality when the number of projections is reduced
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assert delta_80 > delta_200
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if __name__ == "__main__":
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run_module_suite()
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