mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-15 11:25:53 +08:00
Josh Warner (Mac)
de42ba831a
The Poissson generator now works. The improved Poisson generator now infers the bit depth of the image after conversion to a floating point image, by analyzing the unique values present and finding the next power of two. This value is then used to scale the floating point image up, after which Poisson noise is generated, and then image is then scaled back down.
104 lines
3.2 KiB
Python
104 lines
3.2 KiB
Python
from numpy.testing import assert_array_equal, assert_allclose, assert_raises
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import numpy as np
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from skimage.data import camera
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from skimage.util import random_noise, img_as_float
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def test_set_seed():
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seed = 42
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cam = camera()
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test = random_noise(cam, seed=seed)
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assert_array_equal(test, random_noise(cam, seed=seed))
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def test_salt():
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seed = 42
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cam = img_as_float(camera())
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cam_noisy = random_noise(cam, seed=seed, mode='salt', amount=0.15)
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saltmask = cam != cam_noisy
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# Ensure all changes are to 1.0
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assert_allclose(cam_noisy[saltmask], np.ones(saltmask.sum()))
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# Ensure approximately correct amount of noise was added
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proportion = float(saltmask.sum()) / (cam.shape[0] * cam.shape[1])
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assert 0.11 < proportion <= 0.18
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def test_pepper():
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seed = 42
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cam = img_as_float(camera())
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cam_noisy = random_noise(cam, seed=seed, mode='pepper', amount=0.15)
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peppermask = cam != cam_noisy
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# Ensure all changes are to 1.0
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assert_allclose(cam_noisy[peppermask], np.zeros(peppermask.sum()))
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# Ensure approximately correct amount of noise was added
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proportion = float(peppermask.sum()) / (cam.shape[0] * cam.shape[1])
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assert 0.11 < proportion <= 0.18
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def test_salt_and_pepper():
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seed = 42
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cam = img_as_float(camera())
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cam_noisy = random_noise(cam, seed=seed, mode='s&p', amount=0.15,
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salt_vs_pepper=0.25)
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saltmask = np.logical_and(cam != cam_noisy, cam_noisy == 1.)
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peppermask = np.logical_and(cam != cam_noisy, cam_noisy == 0.)
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# Ensure all changes are to 0. or 1.
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assert_allclose(cam_noisy[saltmask], np.ones(saltmask.sum()))
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assert_allclose(cam_noisy[peppermask], np.zeros(peppermask.sum()))
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# Ensure approximately correct amount of noise was added
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proportion = float(
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saltmask.sum() + peppermask.sum()) / (cam.shape[0] * cam.shape[1])
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assert 0.11 < proportion <= 0.18
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# Verify the relative amount of salt vs. pepper is close to expected
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assert 0.18 < saltmask.sum() / float(peppermask.sum()) < 0.32
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def test_gaussian():
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seed = 42
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data = np.zeros((128, 128)) + 0.5
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data_gaussian = random_noise(data, seed=seed, var=0.01)
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assert 0.008 < data_gaussian.var() < 0.012
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data_gaussian = random_noise(data, seed=seed, mean=0.3, var=0.015)
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assert 0.28 < data_gaussian.mean() - 0.5 < 0.32
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assert 0.012 < data_gaussian.var() < 0.018
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def test_speckle():
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seed = 42
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data = np.zeros((128, 128)) + 0.1
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np.random.seed(seed=seed)
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noise = np.random.normal(0.1, 0.02 ** 0.5, (128, 128))
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expected = np.clip(data + data * noise, 0, 1)
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data_speckle = random_noise(data, mode='speckle', seed=seed, mean=0.1,
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var=0.02)
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assert_allclose(expected, data_speckle)
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def test_poisson():
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seed = 42
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data = camera() # 512x512 grayscale uint8
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cam_noisy = random_noise(data, mode='poisson', seed=seed)
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np.random.seed(seed=seed)
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expected = np.random.poisson(img_as_float(data) * 256) / 256.
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assert_allclose(cam_noisy, expected)
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def test_bad_mode():
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data = np.zeros((64, 64))
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assert_raises(KeyError, random_noise, data, 'perlin')
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if __name__ == '__main__':
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np.testing.run_module_suite()
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