mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 11:34:37 +08:00
Jostein Bø Fløystad
178 lines
6.2 KiB
Python
178 lines
6.2 KiB
Python
from __future__ import print_function
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from __future__ import division
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import numpy as np
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from numpy.testing import *
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import itertools
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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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debug = False
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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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if debug:
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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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size = 20
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image = np.tri(size) + np.tri(size)[::-1]
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reconstructed = iradon(radon(image), filter="ramp", interpolation="nearest")
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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) -
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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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def test_radon_minimal():
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"""
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Test for small images for various angles
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"""
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thetas = [np.arange(180)]
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for theta in thetas:
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a = np.zeros((3, 3))
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a[1, 1] = 1
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p = radon(a, theta)
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reconstructed = iradon(p, theta)
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reconstructed /= np.max(reconstructed)
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assert np.all(abs(a - reconstructed) < 0.4)
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b = np.zeros((4, 4))
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b[1:3, 1:3] = 1
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p = radon(b, theta)
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reconstructed = iradon(p, theta)
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reconstructed /= np.max(reconstructed)
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assert np.all(abs(b - reconstructed) < 0.4)
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c = np.zeros((5, 5))
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c[1:3, 1:3] = 1
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p = radon(c, theta)
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reconstructed = iradon(p, theta)
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reconstructed /= np.max(reconstructed)
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assert np.all(abs(c - reconstructed) < 0.4)
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def test_reconstruct_with_wrong_angles():
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a = np.zeros((3, 3))
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p = radon(a, theta=[0, 1, 2])
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iradon(p, theta=[0, 1, 2])
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assert_raises(ValueError, iradon, p, theta=[0, 1, 2, 3])
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def test_radon_circle():
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a = np.ones((10, 10))
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assert_raises(ValueError, radon, a, circle=True)
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# Synthetic data, circular symmetry
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shape = (61, 79)
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c0, c1 = np.ogrid[0:shape[0], 0:shape[1]]
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r = np.sqrt((c0 - shape[0] // 2)**2 + (c1 - shape[1] // 2)**2)
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radius = min(shape) // 2
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image = np.clip(radius - r, 0, np.inf)
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image = rescale(image)
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angles = np.linspace(0, 180, min(shape), endpoint=False)
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sinogram = radon(image, theta=angles, circle=True)
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assert np.all(sinogram.std(axis=1) < 1e-2)
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# Synthetic data, random
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np.random.seed(98312871)
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image = np.random.rand(*shape)
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image[r >= radius] = 0.
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sinogram = radon(image, theta=angles, circle=True)
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mass = sinogram.sum(axis=0)
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average_mass = mass.mean()
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relative_error = np.abs(mass - average_mass) / average_mass
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print(relative_error.max(), relative_error.mean())
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assert np.all(relative_error < 3e-3)
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def test_radon_iradon_circle():
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shape = (61, 79)
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# Synthetic random data, zero outside reconstruction circle
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image = np.random.rand(*shape)
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interpolations = ('nearest', 'linear')
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output_sizes = (None, min(shape), max(shape), 97)
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for interpolation, output_size in itertools.product(interpolations,
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output_sizes):
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print('interpolation =', interpolation)
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print('output_size =', output_size)
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c0, c1 = np.ogrid[0:shape[0], 0:shape[1]]
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r = np.sqrt((c0 - shape[0] // 2)**2 + (c1 - shape[1] // 2)**2)
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radius = min(shape) // 2
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image[r >= radius] = 0.
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# Forward and inverse radon on synthetic data
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sinogram_rectangle = radon(image, circle=False)
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reconstruction_rectangle = iradon(sinogram_rectangle,
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output_size=output_size,
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interpolation=interpolation,
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circle=False)
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sinogram_circle = radon(image, circle=True)
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reconstruction_circle = iradon(sinogram_circle,
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output_size=output_size,
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interpolation=interpolation,
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circle=True)
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# Crop rectangular reconstruction to match circle=True reconstruction
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width = reconstruction_circle.shape[0]
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excess = int(np.ceil((reconstruction_rectangle.shape[0] - width) / 2))
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s = np.s_[excess:width + excess, excess:width + excess]
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reconstruction_rectangle = reconstruction_rectangle[s]
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# Find the reconstruction circle, set reconstruction to zero outside
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c0, c1 = np.ogrid[0:width, 0:width]
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r = np.sqrt((c0 - width // 2)**2 + (c1 - width // 2)**2)
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reconstruction_rectangle[r >= radius] = 0.
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print(reconstruction_circle.shape)
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print(reconstruction_rectangle.shape)
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np.allclose(reconstruction_rectangle, reconstruction_circle)
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if __name__ == "__main__":
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run_module_suite()
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