mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 07:34:41 +08:00
Steven Silvester
a4e4e57ba5
Add new helper function for expected warnings during test Indentation cleanups and avoid skipping doctests if possible.
331 lines
11 KiB
Python
331 lines
11 KiB
Python
import numpy as np
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from skimage.segmentation import random_walker
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from skimage.transform import resize
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from skimage._shared.utils import all_warnings
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def make_2d_syntheticdata(lx, ly=None):
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if ly is None:
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ly = lx
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np.random.seed(1234)
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data = np.zeros((lx, ly)) + 0.1 * np.random.randn(lx, ly)
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small_l = int(lx // 5)
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data[lx // 2 - small_l:lx // 2 + small_l,
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ly // 2 - small_l:ly // 2 + small_l] = 1
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data[lx // 2 - small_l + 1:lx // 2 + small_l - 1,
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ly // 2 - small_l + 1:ly // 2 + small_l - 1] = (
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0.1 * np.random.randn(2 * small_l - 2, 2 * small_l - 2))
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data[lx // 2 - small_l, ly // 2 - small_l // 8:ly // 2 + small_l // 8] = 0
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seeds = np.zeros_like(data)
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seeds[lx // 5, ly // 5] = 1
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seeds[lx // 2 + small_l // 4, ly // 2 - small_l // 4] = 2
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return data, seeds
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def make_3d_syntheticdata(lx, ly=None, lz=None):
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if ly is None:
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ly = lx
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if lz is None:
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lz = lx
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np.random.seed(1234)
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data = np.zeros((lx, ly, lz)) + 0.1 * np.random.randn(lx, ly, lz)
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small_l = int(lx // 5)
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data[lx // 2 - small_l:lx // 2 + small_l,
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ly // 2 - small_l:ly // 2 + small_l,
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lz // 2 - small_l:lz // 2 + small_l] = 1
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data[lx // 2 - small_l + 1:lx // 2 + small_l - 1,
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ly // 2 - small_l + 1:ly // 2 + small_l - 1,
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lz // 2 - small_l + 1:lz // 2 + small_l - 1] = 0
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# make a hole
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hole_size = np.max([1, small_l // 8])
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data[lx // 2 - small_l,
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ly // 2 - hole_size:ly // 2 + hole_size,
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lz // 2 - hole_size:lz // 2 + hole_size] = 0
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seeds = np.zeros_like(data)
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seeds[lx // 5, ly // 5, lz // 5] = 1
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seeds[lx // 2 + small_l // 4,
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ly // 2 - small_l // 4,
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lz // 2 - small_l // 4] = 2
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return data, seeds
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def test_2d_bf():
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lx = 70
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ly = 100
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data, labels = make_2d_syntheticdata(lx, ly)
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labels_bf = random_walker(data, labels, beta=90, mode='bf')
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assert (labels_bf[25:45, 40:60] == 2).all()
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assert data.shape == labels.shape
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full_prob_bf = random_walker(data, labels, beta=90, mode='bf',
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return_full_prob=True)
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assert (full_prob_bf[1, 25:45, 40:60] >=
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full_prob_bf[0, 25:45, 40:60]).all()
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assert data.shape == labels.shape
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# Now test with more than two labels
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labels[55, 80] = 3
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full_prob_bf = random_walker(data, labels, beta=90, mode='bf',
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return_full_prob=True)
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assert (full_prob_bf[1, 25:45, 40:60] >=
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full_prob_bf[0, 25:45, 40:60]).all()
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assert len(full_prob_bf) == 3
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assert data.shape == labels.shape
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def test_2d_cg():
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lx = 70
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ly = 100
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data, labels = make_2d_syntheticdata(lx, ly)
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with all_warnings(): # cg mode
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labels_cg = random_walker(data, labels, beta=90, mode='cg')
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assert (labels_cg[25:45, 40:60] == 2).all()
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assert data.shape == labels.shape
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with all_warnings(): # cg mode
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full_prob = random_walker(data, labels, beta=90, mode='cg',
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return_full_prob=True)
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assert (full_prob[1, 25:45, 40:60] >=
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full_prob[0, 25:45, 40:60]).all()
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assert data.shape == labels.shape
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return data, labels_cg
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def test_2d_cg_mg():
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lx = 70
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ly = 100
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data, labels = make_2d_syntheticdata(lx, ly)
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with all_warnings(): # pyamg optional
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labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg')
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assert (labels_cg_mg[25:45, 40:60] == 2).all()
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assert data.shape == labels.shape
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with all_warnings(): # pyamg optional
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full_prob = random_walker(data, labels, beta=90, mode='cg_mg',
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return_full_prob=True)
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assert (full_prob[1, 25:45, 40:60] >=
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full_prob[0, 25:45, 40:60]).all()
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assert data.shape == labels.shape
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return data, labels_cg_mg
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def test_types():
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lx = 70
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ly = 100
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data, labels = make_2d_syntheticdata(lx, ly)
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data = 255 * (data - data.min()) // (data.max() - data.min())
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data = data.astype(np.uint8)
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with all_warnings(): # pyamg optional
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labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg')
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assert (labels_cg_mg[25:45, 40:60] == 2).all()
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assert data.shape == labels.shape
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return data, labels_cg_mg
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def test_reorder_labels():
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lx = 70
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ly = 100
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data, labels = make_2d_syntheticdata(lx, ly)
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labels[labels == 2] = 4
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labels_bf = random_walker(data, labels, beta=90, mode='bf')
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assert (labels_bf[25:45, 40:60] == 2).all()
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assert data.shape == labels.shape
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return data, labels_bf
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def test_2d_inactive():
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lx = 70
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ly = 100
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data, labels = make_2d_syntheticdata(lx, ly)
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labels[10:20, 10:20] = -1
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labels[46:50, 33:38] = -2
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labels = random_walker(data, labels, beta=90)
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assert (labels.reshape((lx, ly))[25:45, 40:60] == 2).all()
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assert data.shape == labels.shape
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return data, labels
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def test_3d():
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n = 30
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lx, ly, lz = n, n, n
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data, labels = make_3d_syntheticdata(lx, ly, lz)
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with all_warnings(): # cg mode
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labels = random_walker(data, labels, mode='cg')
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assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all()
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assert data.shape == labels.shape
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return data, labels
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def test_3d_inactive():
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n = 30
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lx, ly, lz = n, n, n
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data, labels = make_3d_syntheticdata(lx, ly, lz)
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old_labels = np.copy(labels)
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labels[5:25, 26:29, 26:29] = -1
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after_labels = np.copy(labels)
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with all_warnings(): # cg mode
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labels = random_walker(data, labels, mode='cg')
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assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all()
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assert data.shape == labels.shape
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return data, labels, old_labels, after_labels
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def test_multispectral_2d():
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lx, ly = 70, 100
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data, labels = make_2d_syntheticdata(lx, ly)
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data = data[..., np.newaxis].repeat(2, axis=-1) # Expect identical output
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with all_warnings(): # cg mode
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multi_labels = random_walker(data, labels, mode='cg',
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multichannel=True)
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assert data[..., 0].shape == labels.shape
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with all_warnings(): # cg mode
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single_labels = random_walker(data[..., 0], labels, mode='cg')
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assert (multi_labels.reshape(labels.shape)[25:45, 40:60] == 2).all()
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assert data[..., 0].shape == labels.shape
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return data, multi_labels, single_labels, labels
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def test_multispectral_3d():
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n = 30
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lx, ly, lz = n, n, n
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data, labels = make_3d_syntheticdata(lx, ly, lz)
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data = data[..., np.newaxis].repeat(2, axis=-1) # Expect identical output
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with all_warnings(): # cg mode
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multi_labels = random_walker(data, labels, mode='cg',
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multichannel=True)
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assert data[..., 0].shape == labels.shape
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with all_warnings(): # cg mode
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single_labels = random_walker(data[..., 0], labels, mode='cg')
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assert (multi_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all()
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assert (single_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all()
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assert data[..., 0].shape == labels.shape
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return data, multi_labels, single_labels, labels
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def test_spacing_0():
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n = 30
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lx, ly, lz = n, n, n
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data, _ = make_3d_syntheticdata(lx, ly, lz)
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# Rescale `data` along Z axis
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data_aniso = np.zeros((n, n, n // 2))
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for i, yz in enumerate(data):
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data_aniso[i, :, :] = resize(yz, (n, n // 2))
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# Generate new labels
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small_l = int(lx // 5)
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labels_aniso = np.zeros_like(data_aniso)
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labels_aniso[lx // 5, ly // 5, lz // 5] = 1
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labels_aniso[lx // 2 + small_l // 4,
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ly // 2 - small_l // 4,
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lz // 4 - small_l // 8] = 2
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# Test with `spacing` kwarg
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with all_warnings(): # cg mode
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labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg',
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spacing=(1., 1., 0.5))
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assert (labels_aniso[13:17, 13:17, 7:9] == 2).all()
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def test_spacing_1():
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n = 30
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lx, ly, lz = n, n, n
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data, _ = make_3d_syntheticdata(lx, ly, lz)
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# Rescale `data` along Y axis
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# `resize` is not yet 3D capable, so this must be done by looping in 2D.
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data_aniso = np.zeros((n, n * 2, n))
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for i, yz in enumerate(data):
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data_aniso[i, :, :] = resize(yz, (n * 2, n))
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# Generate new labels
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small_l = int(lx // 5)
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labels_aniso = np.zeros_like(data_aniso)
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labels_aniso[lx // 5, ly // 5, lz // 5] = 1
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labels_aniso[lx // 2 + small_l // 4,
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ly - small_l // 2,
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lz // 2 - small_l // 4] = 2
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# Test with `spacing` kwarg
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# First, anisotropic along Y
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with all_warnings(): # using cg mode
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labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg',
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spacing=(1., 2., 1.))
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assert (labels_aniso[13:17, 26:34, 13:17] == 2).all()
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# Rescale `data` along X axis
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# `resize` is not yet 3D capable, so this must be done by looping in 2D.
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data_aniso = np.zeros((n, n * 2, n))
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for i in range(data.shape[1]):
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data_aniso[i, :, :] = resize(data[:, 1, :], (n * 2, n))
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# Generate new labels
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small_l = int(lx // 5)
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labels_aniso2 = np.zeros_like(data_aniso)
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labels_aniso2[lx // 5, ly // 5, lz // 5] = 1
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labels_aniso2[lx - small_l // 2,
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ly // 2 + small_l // 4,
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lz // 2 - small_l // 4] = 2
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# Anisotropic along X
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with all_warnings(): # cg mode
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labels_aniso2 = random_walker(data_aniso,
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labels_aniso2,
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mode='cg', spacing=(2., 1., 1.))
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assert (labels_aniso2[26:34, 13:17, 13:17] == 2).all()
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def test_trivial_cases():
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# When all voxels are labeled
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img = np.ones((10, 10))
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labels = np.ones((10, 10))
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with all_warnings(): # using provided labels
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pass_through = random_walker(img, labels)
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np.testing.assert_array_equal(pass_through, labels)
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# When all voxels are labeled AND return_full_prob is True
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labels[:, :5] = 3
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expected = np.concatenate(((labels == 1)[..., np.newaxis],
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(labels == 3)[..., np.newaxis]), axis=2)
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with all_warnings(): # using provided labels
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test = random_walker(img, labels, return_full_prob=True)
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np.testing.assert_array_equal(test, expected)
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def test_length2_spacing():
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# If this passes without raising an exception (warnings OK), the new
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# spacing code is working properly.
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np.random.seed(42)
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img = np.ones((10, 10)) + 0.2 * np.random.normal(size=(10, 10))
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labels = np.zeros((10, 10), dtype=np.uint8)
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labels[2, 4] = 1
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labels[6, 8] = 4
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random_walker(img, labels, spacing=(1., 2.))
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def test_bad_inputs():
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# Too few dimensions
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img = np.ones(10)
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labels = np.arange(10)
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np.testing.assert_raises(ValueError, random_walker, img, labels)
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np.testing.assert_raises(ValueError,
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random_walker, img, labels, multichannel=True)
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# Too many dimensions
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np.random.seed(42)
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img = np.random.normal(size=(3, 3, 3, 3, 3))
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labels = np.arange(3 ** 5).reshape(img.shape)
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np.testing.assert_raises(ValueError, random_walker, img, labels)
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np.testing.assert_raises(ValueError,
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random_walker, img, labels, multichannel=True)
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# Spacing incorrect length
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img = np.random.normal(size=(10, 10))
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labels = np.zeros((10, 10))
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labels[2, 4] = 2
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labels[6, 8] = 5
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np.testing.assert_raises(ValueError,
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random_walker, img, labels, spacing=(1,))
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if __name__ == '__main__':
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np.testing.run_module_suite()
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