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44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
from os.path import abspath, dirname, join as pjoin
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import numpy as np
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from scipy.signal import convolve2d
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import skimage
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from skimage.data import camera
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from skimage import deconvolution
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test_img = skimage.img_as_float(camera())
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def test_wiener():
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psf = np.ones((5, 5)) / 25
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data = convolve2d(test_img, psf, 'same')
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np.random.seed(0)
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data += 0.1 * data.std() * np.random.standard_normal(data.shape)
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deconvolved = deconvolution.wiener(data, psf, 0.05)
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path = pjoin(dirname(abspath(__file__)), 'camera_wiener.npy')
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np.testing.assert_allclose(deconvolved, np.load(path), rtol=1e-3)
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def test_unsupervised_wiener():
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psf = np.ones((5, 5)) / 25
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data = convolve2d(test_img, psf, 'same')
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np.random.seed(0)
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data += 0.1 * data.std() * np.random.standard_normal(data.shape)
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deconvolved, _ = deconvolution.unsupervised_wiener(data, psf)
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path = pjoin(dirname(abspath(__file__)), 'camera_unsup.npy')
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np.testing.assert_allclose(deconvolved, np.load(path), rtol=1e-3)
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def test_richardson_lucy():
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psf = np.ones((5, 5)) / 25
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data = convolve2d(test_img, psf, 'same')
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np.random.seed(0)
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data += 0.1 * data.std() * np.random.standard_normal(data.shape)
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deconvolved = deconvolution.richardson_lucy(data, psf, 5)
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path = pjoin(dirname(abspath(__file__)), 'camera_rl.npy')
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np.testing.assert_allclose(deconvolved, np.load(path), rtol=1e-3)
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