mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-22 13:00:09 +08:00
JDWarner
61320957eb
In this new version, all instances of 'spectrum' have been replaced with 'channel'. The documentation also reflects this change, and the new multichannel kwarg used to indicate multichannel input is named appropriately. New boolean multichannel kwarg added, which controls if the input has multiple channels or not. Input 'data' is now array_like for both gray-level and multichannel. This kwarg is needed mainly because a 3-D array could be either 3 spatial dimensions or a set of different 2-D channels. New scaling kwarg added (may be removed in future), controlling if data scaling is applied to ALL channels or each channel individually, if multichannel=True. No effect for gray-level data. Removed np.sqrt(gradients) in _compute_weights_3d(), which was a bug. Tests now pass consistently. New method for maintaining shape from input to output, where dims = data.shape prior to np.atleast_3d(). A theoretical (70,100,1) array passed should now result in a (70,100,1) shaped output, for example. Updated and fixed multispectral test script to work with new version. TODO: Additional test(s) likely needed to cover code branches from new kwargs.
157 lines
5.2 KiB
Python
157 lines
5.2 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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return data, labels_bf, full_prob_bf
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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():
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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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