90 lines
3.1 KiB
Python
90 lines
3.1 KiB
Python
import numpy as np
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from skimage.segmentation import chan_vese
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from skimage._shared import testing
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from skimage._shared.testing import assert_array_equal
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def test_chan_vese_flat_level_set():
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# because the algorithm evolves the level set around the
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# zero-level, it the level-set has no zero level, the algorithm
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# will not produce results in theory. However, since a continuous
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# approximation of the delta function is used, the algorithm
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# still affects the entirety of the level-set. Therefore with
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# infinite time, the segmentation will still converge.
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img = np.zeros((10, 10))
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img[3:6, 3:6] = np.ones((3, 3))
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ls = np.full((10, 10), 1000)
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result = chan_vese(img, mu=0.0, tol=1e-3, init_level_set=ls)
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assert_array_equal(result.astype(np.float), np.ones((10, 10)))
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result = chan_vese(img, mu=0.0, tol=1e-3, init_level_set=-ls)
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assert_array_equal(result.astype(np.float), np.zeros((10, 10)))
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def test_chan_vese_small_disk_level_set():
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img = np.zeros((10, 10))
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img[3:6, 3:6] = np.ones((3, 3))
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result = chan_vese(img, mu=0.0, tol=1e-3, init_level_set="small disk")
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assert_array_equal(result.astype(np.float), img)
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def test_chan_vese_simple_shape():
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img = np.zeros((10, 10))
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img[3:6, 3:6] = np.ones((3, 3))
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result = chan_vese(img, mu=0.0, tol=1e-8).astype(np.float)
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assert_array_equal(result, img)
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def test_chan_vese_extended_output():
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img = np.zeros((10, 10))
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img[3:6, 3:6] = np.ones((3, 3))
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result = chan_vese(img, mu=0.0, tol=1e-8, extended_output=True)
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assert_array_equal(len(result), 3)
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def test_chan_vese_remove_noise():
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ref = np.zeros((10, 10))
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ref[1:6, 1:6] = np.array([[0, 1, 1, 1, 0],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1],
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[0, 1, 1, 1, 0]])
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img = ref.copy()
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img[8, 3] = 1
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result = chan_vese(img, mu=0.3, tol=1e-3, max_iter=100, dt=10,
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init_level_set="disk").astype(np.float)
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assert_array_equal(result, ref)
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def test_chan_vese_incorrect_image_type():
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img = np.zeros((10, 10, 3))
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ls = np.zeros((10, 9))
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with testing.raises(ValueError):
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chan_vese(img, mu=0.0, init_level_set=ls)
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def test_chan_vese_gap_closing():
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ref = np.zeros((20, 20))
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ref[8:15, :] = np.ones((7, 20))
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img = ref.copy()
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img[:, 6] = np.zeros((20))
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result = chan_vese(img, mu=0.7, tol=1e-3, max_iter=1000, dt=1000,
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init_level_set="disk").astype(np.float)
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assert_array_equal(result, ref)
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def test_chan_vese_incorrect_level_set():
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img = np.zeros((10, 10))
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ls = np.zeros((10, 9))
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with testing.raises(ValueError):
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chan_vese(img, mu=0.0, init_level_set=ls)
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with testing.raises(ValueError):
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chan_vese(img, mu=0.0, init_level_set="a")
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def test_chan_vese_blank_image():
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img = np.zeros((10, 10))
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level_set = np.random.rand(10, 10)
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ref = level_set > 0
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result = chan_vese(img, mu=0.0, tol=0.0, init_level_set=level_set)
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assert_array_equal(result, ref)
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