mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 05:17:50 +08:00
Emmanuelle Gouillart
6635cf16db
* when returning the full probability instead of the segmentation
* for three or more labels (two was OK)
175 lines
5.9 KiB
Python
175 lines
5.9 KiB
Python
import numpy as np
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from skimage.segmentation import random_walker
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try:
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import pyamg
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amg_loaded = True
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except ImportError:
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amg_loaded = False
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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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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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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, ly / 2 - small_l / 4, 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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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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# 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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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data2 = data.copy()
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data.shape += (1,)
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data = data.repeat(2, axis=2) # Result should be identical
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multi_labels = random_walker(data, labels, mode='cg', multichannel=True)
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single_labels = random_walker(data2, labels, mode='cg')
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assert (multi_labels.reshape(labels.shape)[25:45, 40:60] == 2).all()
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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.shape += (1,)
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data = data.repeat(2, axis=3) # Result should be identical
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multi_labels = random_walker(data, labels, mode='cg', multichannel=True)
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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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return data, multi_labels, single_labels, labels
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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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