mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 19:32:47 +08:00
Egor Panfilov
289 lines
10 KiB
Python
289 lines
10 KiB
Python
import numpy as np
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from numpy.testing import run_module_suite, assert_raises, assert_equal
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from skimage import restoration, data, color, img_as_float, measure
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from skimage._shared._warnings import expected_warnings
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np.random.seed(1234)
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astro = img_as_float(data.astronaut()[:128, :128])
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astro_gray = color.rgb2gray(astro)
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checkerboard_gray = img_as_float(data.checkerboard())
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checkerboard = color.gray2rgb(checkerboard_gray)
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def test_denoise_tv_chambolle_2d():
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# astronaut image
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img = astro_gray.copy()
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# add noise to astronaut
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img += 0.5 * img.std() * np.random.rand(*img.shape)
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# clip noise so that it does not exceed allowed range for float images.
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img = np.clip(img, 0, 1)
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# denoise
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denoised_astro = restoration.denoise_tv_chambolle(img, weight=0.1)
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# which dtype?
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assert denoised_astro.dtype in [np.float, np.float32, np.float64]
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from scipy import ndimage as ndi
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grad = ndi.morphological_gradient(img, size=((3, 3)))
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grad_denoised = ndi.morphological_gradient(denoised_astro, size=((3, 3)))
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# test if the total variation has decreased
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assert grad_denoised.dtype == np.float
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assert (np.sqrt((grad_denoised**2).sum())
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< np.sqrt((grad**2).sum()))
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def test_denoise_tv_chambolle_multichannel():
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denoised0 = restoration.denoise_tv_chambolle(astro[..., 0], weight=0.1)
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denoised = restoration.denoise_tv_chambolle(astro, weight=0.1,
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multichannel=True)
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assert_equal(denoised[..., 0], denoised0)
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# tile astronaut subset to generate 3D+channels data
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astro3 = np.tile(astro[:64, :64, np.newaxis, :], [1, 1, 2, 1])
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# modify along tiled dimension to give non-zero gradient on 3rd axis
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astro3[:, :, 0, :] = 2*astro3[:, :, 0, :]
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denoised0 = restoration.denoise_tv_chambolle(astro3[..., 0], weight=0.1)
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denoised = restoration.denoise_tv_chambolle(astro3, weight=0.1,
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multichannel=True)
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assert_equal(denoised[..., 0], denoised0)
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def test_denoise_tv_chambolle_float_result_range():
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# astronaut image
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img = astro_gray
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int_astro = np.multiply(img, 255).astype(np.uint8)
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assert np.max(int_astro) > 1
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denoised_int_astro = restoration.denoise_tv_chambolle(int_astro,
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weight=0.1)
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# test if the value range of output float data is within [0.0:1.0]
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assert denoised_int_astro.dtype == np.float
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assert np.max(denoised_int_astro) <= 1.0
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assert np.min(denoised_int_astro) >= 0.0
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def test_denoise_tv_chambolle_3d():
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"""Apply the TV denoising algorithm on a 3D image representing a sphere."""
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x, y, z = np.ogrid[0:40, 0:40, 0:40]
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mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2
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mask = 100 * mask.astype(np.float)
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mask += 60
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mask += 20 * np.random.rand(*mask.shape)
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mask[mask < 0] = 0
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mask[mask > 255] = 255
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res = restoration.denoise_tv_chambolle(mask.astype(np.uint8), weight=0.1)
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assert res.dtype == np.float
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assert res.std() * 255 < mask.std()
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def test_denoise_tv_chambolle_1d():
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"""Apply the TV denoising algorithm on a 1D sinusoid."""
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x = 125 + 100*np.sin(np.linspace(0, 8*np.pi, 1000))
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x += 20 * np.random.rand(x.size)
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x = np.clip(x, 0, 255)
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res = restoration.denoise_tv_chambolle(x.astype(np.uint8), weight=0.1)
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assert res.dtype == np.float
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assert res.std() * 255 < x.std()
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def test_denoise_tv_chambolle_4d():
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""" TV denoising for a 4D input."""
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im = 255 * np.random.rand(8, 8, 8, 8)
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res = restoration.denoise_tv_chambolle(im.astype(np.uint8), weight=0.1)
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assert res.dtype == np.float
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assert res.std() * 255 < im.std()
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def test_denoise_tv_chambolle_weighting():
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# make sure a specified weight gives consistent results regardless of
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# the number of input image dimensions
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rstate = np.random.RandomState(1234)
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img2d = astro_gray.copy()
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img2d += 0.15 * rstate.standard_normal(img2d.shape)
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img2d = np.clip(img2d, 0, 1)
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# generate 4D image by tiling
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img4d = np.tile(img2d[..., None, None], (1, 1, 2, 2))
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w = 0.2
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denoised_2d = restoration.denoise_tv_chambolle(img2d, weight=w)
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denoised_4d = restoration.denoise_tv_chambolle(img4d, weight=w)
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assert measure.compare_ssim(denoised_2d,
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denoised_4d[:, :, 0, 0]) > 0.99
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def test_denoise_tv_bregman_2d():
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img = checkerboard_gray.copy()
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# add some random noise
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img += 0.5 * img.std() * np.random.rand(*img.shape)
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img = np.clip(img, 0, 1)
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out1 = restoration.denoise_tv_bregman(img, weight=10)
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out2 = restoration.denoise_tv_bregman(img, weight=5)
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# make sure noise is reduced in the checkerboard cells
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assert img[30:45, 5:15].std() > out1[30:45, 5:15].std()
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assert out1[30:45, 5:15].std() > out2[30:45, 5:15].std()
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def test_denoise_tv_bregman_float_result_range():
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# astronaut image
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img = astro_gray.copy()
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int_astro = np.multiply(img, 255).astype(np.uint8)
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assert np.max(int_astro) > 1
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denoised_int_astro = restoration.denoise_tv_bregman(int_astro, weight=60.0)
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# test if the value range of output float data is within [0.0:1.0]
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assert denoised_int_astro.dtype == np.float
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assert np.max(denoised_int_astro) <= 1.0
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assert np.min(denoised_int_astro) >= 0.0
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def test_denoise_tv_bregman_3d():
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img = checkerboard.copy()
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# add some random noise
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img += 0.5 * img.std() * np.random.rand(*img.shape)
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img = np.clip(img, 0, 1)
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out1 = restoration.denoise_tv_bregman(img, weight=10)
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out2 = restoration.denoise_tv_bregman(img, weight=5)
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# make sure noise is reduced in the checkerboard cells
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assert img[30:45, 5:15].std() > out1[30:45, 5:15].std()
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assert out1[30:45, 5:15].std() > out2[30:45, 5:15].std()
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def test_denoise_bilateral_2d():
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img = checkerboard_gray.copy()
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# add some random noise
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img += 0.5 * img.std() * np.random.rand(*img.shape)
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img = np.clip(img, 0, 1)
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out1 = restoration.denoise_bilateral(img, sigma_range=0.1,
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sigma_spatial=20, multichannel=False)
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out2 = restoration.denoise_bilateral(img, sigma_range=0.2,
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sigma_spatial=30, multichannel=False)
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# make sure noise is reduced in the checkerboard cells
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assert img[30:45, 5:15].std() > out1[30:45, 5:15].std()
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assert out1[30:45, 5:15].std() > out2[30:45, 5:15].std()
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def test_denoise_bilateral_color():
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img = checkerboard.copy()
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# add some random noise
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img += 0.5 * img.std() * np.random.rand(*img.shape)
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img = np.clip(img, 0, 1)
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out1 = restoration.denoise_bilateral(img, sigma_range=0.1, sigma_spatial=20)
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out2 = restoration.denoise_bilateral(img, sigma_range=0.2, sigma_spatial=30)
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# make sure noise is reduced in the checkerboard cells
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assert img[30:45, 5:15].std() > out1[30:45, 5:15].std()
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assert out1[30:45, 5:15].std() > out2[30:45, 5:15].std()
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def test_denoise_bilateral_3d_grayscale():
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img = np.ones((50, 50, 3))
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assert_raises(ValueError, restoration.denoise_bilateral, img,
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multichannel=False)
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def test_denoise_bilateral_3d_multichannel():
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img = np.ones((50, 50, 50))
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with expected_warnings(["grayscale"]):
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result = restoration.denoise_bilateral(img)
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expected = np.empty_like(img)
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expected.fill(np.nan)
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assert_equal(result, expected)
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def test_denoise_bilateral_multidimensional():
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img = np.ones((10, 10, 10, 10))
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assert_raises(ValueError, restoration.denoise_bilateral, img)
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assert_raises(ValueError, restoration.denoise_bilateral, img,
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multichannel=True)
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def test_denoise_bilateral_nan():
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img = np.NaN + np.empty((50, 50))
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out = restoration.denoise_bilateral(img, multichannel=False)
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assert_equal(img, out)
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def test_nl_means_denoising_2d():
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img = np.zeros((40, 40))
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img[10:-10, 10:-10] = 1.
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img += 0.3*np.random.randn(*img.shape)
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denoised = restoration.denoise_nl_means(img, 7, 5, 0.2, fast_mode=True)
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# make sure noise is reduced
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assert img.std() > denoised.std()
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denoised = restoration.denoise_nl_means(img, 7, 5, 0.2, fast_mode=False)
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# make sure noise is reduced
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assert img.std() > denoised.std()
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def test_denoise_nl_means_2drgb():
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# reduce image size because nl means is very slow
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img = np.copy(astro[:50, :50])
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# add some random noise
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img += 0.5 * img.std() * np.random.random(img.shape)
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img = np.clip(img, 0, 1)
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denoised = restoration.denoise_nl_means(img, 7, 9, 0.3, fast_mode=True)
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# make sure noise is reduced
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assert img.std() > denoised.std()
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denoised = restoration.denoise_nl_means(img, 7, 9, 0.3, fast_mode=False)
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# make sure noise is reduced
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assert img.std() > denoised.std()
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def test_denoise_nl_means_3d():
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img = np.zeros((20, 20, 10))
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img[5:-5, 5:-5, 3:-3] = 1.
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img += 0.3*np.random.randn(*img.shape)
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denoised = restoration.denoise_nl_means(img, 5, 4, 0.2, fast_mode=True,
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multichannel=False)
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# make sure noise is reduced
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assert img.std() > denoised.std()
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denoised = restoration.denoise_nl_means(img, 5, 4, 0.2, fast_mode=False,
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multichannel=False)
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# make sure noise is reduced
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assert img.std() > denoised.std()
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def test_denoise_nl_means_multichannel():
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img = np.zeros((21, 20, 10))
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img[10, 9:11, 2:-2] = 1.
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img += 0.3*np.random.randn(*img.shape)
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denoised_wrong_multichannel = restoration.denoise_nl_means(img,
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5, 4, 0.1, fast_mode=True, multichannel=True)
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denoised_ok_multichannel = restoration.denoise_nl_means(img,
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5, 4, 0.1, fast_mode=True, multichannel=False)
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snr_wrong = 10 * np.log10(1. /
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((denoised_wrong_multichannel - img)**2).mean())
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snr_ok = 10 * np.log10(1. /
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((denoised_ok_multichannel - img)**2).mean())
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assert snr_ok > snr_wrong
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def test_denoise_nl_means_wrong_dimension():
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img = np.zeros((5, 5, 5, 5))
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assert_raises(NotImplementedError, restoration.denoise_nl_means, img)
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def test_no_denoising_for_small_h():
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img = np.zeros((40, 40))
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img[10:-10, 10:-10] = 1.
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img += 0.3*np.random.randn(*img.shape)
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# very small h should result in no averaging with other patches
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denoised = restoration.denoise_nl_means(img, 7, 5, 0.01, fast_mode=True)
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assert np.allclose(denoised, img)
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denoised = restoration.denoise_nl_means(img, 7, 5, 0.01, fast_mode=False)
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assert np.allclose(denoised, img)
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if __name__ == "__main__":
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run_module_suite()
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