mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-18 12:40:14 +08:00
94 lines
3.3 KiB
Python
94 lines
3.3 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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from scipy import ndimage as nd
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import skimage
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from skimage.data import camera
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from skimage import restoration
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from skimage.restoration import uft
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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 = restoration.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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_, laplacian = uft.laplacian(2, data.shape)
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otf = uft.ir2tf(psf, data.shape, is_real=False)
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deconvolved = restoration.wiener(data, otf, 0.05,
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reg=laplacian,
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is_real=False)
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np.testing.assert_allclose(np.real(deconvolved),
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np.load(path),
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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, _ = restoration.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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_, laplacian = uft.laplacian(2, data.shape)
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otf = uft.ir2tf(psf, data.shape, is_real=False)
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np.random.seed(0)
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deconvolved = restoration.unsupervised_wiener(
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data, otf, reg=laplacian, is_real=False,
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user_params={"callback": lambda x: None})[0]
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path = pjoin(dirname(abspath(__file__)), 'camera_unsup2.npy')
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np.testing.assert_allclose(np.real(deconvolved),
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np.load(path),
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rtol=1e-3)
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def test_image_shape():
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"""Test that shape of output image in deconvolution is same as input.
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This addresses issue #1172.
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"""
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point = np.zeros((5, 5), np.float)
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point[2, 2] = 1.
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psf = nd.gaussian_filter(point, sigma=1.)
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# image shape: (45, 45), as reported in #1172
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image = skimage.img_as_float(camera()[110:155, 225:270]) # just the face
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image_conv = nd.convolve(image, psf)
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deconv_sup = restoration.wiener(image_conv, psf, 1)
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deconv_un = restoration.unsupervised_wiener(image_conv, psf)[0]
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# test the shape
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np.testing.assert_equal(image.shape, deconv_sup.shape)
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np.testing.assert_equal(image.shape, deconv_un.shape)
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# test the reconstruction error
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sup_relative_error = np.abs(deconv_sup - image) / image
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un_relative_error = np.abs(deconv_un - image) / image
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np.testing.assert_array_less(np.median(sup_relative_error), 0.1)
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np.testing.assert_array_less(np.median(un_relative_error), 0.1)
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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 = restoration.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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if __name__ == '__main__':
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from numpy import testing
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testing.run_module_suite()
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