mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 12:10:54 +08:00
Jonathan Helmus
78a0eb6b98
Implement Appveyor builds initial test Updated appveyor.yml New nosetest call With conda update conda with pillow without pillow TST: Change case sensitivive JPG extension Revert "TST: Change case sensitivive JPG extension" This reverts commit 2deed7cc63736f7c6f0387bd37df4c6643c32847. Trying with Python 2.7 Trying with version env Trying again with variables Testing on all Python versions don't allow failures Allow failure Do not actually use tests Ignore failing tests Removed Python 2.6 test Testing only Python 2.6 Testing out more sklearn like AppVeyor CI Added install to pip Add artifacts Enabled all permutations Disable 2.6, add PIL Python 2.6, 2.7 only with PIL Testing with variable dependencies Allow failure with IF ELSE With Python 3.4 Scikit-learn like appveyor CI Fixing paths Undo path fix path fix single line path fix single line 2 Using Miniconda More path fixes New wheelhouse link Added pillow to requirements.txt Added networkx to requirements.txt Add testing of 32/64-bit Python 2.7 and 3.4 to matrix Debugging Cython compile Retry with all 4 builds Updated install.ps1 file Updated based on latest python-appveyor-demo Debugging pip install Specify numpy 1.8.1 until whl is uploaded to rackspace Use skimage-wide requirements.txt file Minor comment change to trigger build Install wheel and then install from WHEELHOUSE Install six from pip Install networkx from pip Install pyparsing from pip Install pytz from pip Try using just find-links Install the binary dependencies first, then the rest Add pillow to the install list Fix appveyor.yml syntax Fix requirements.txt syntax Fix requirements.txt syntax again Fix appveyor call to initial install Fix appveyor call to initial install again Fix appveyor call to initial install yet again Install wheel Install wheel first Install wheel and nose in the appveyor requirements.txt Fix Python3 version to match python ftp site Only use cleanup decorator if available Add debug info to multiimage test More debugging information Fix handling of path separators on Windows Add another warning guard Fix warning handling for non-windows Do not use TkAgg as it may be causing alloc error Clean up echo command Allow for unclosed file warning Fix spacing in echo command
334 lines
12 KiB
Python
334 lines
12 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._warnings import expected_warnings
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PYAMG_EXPECTED_WARNING = 'pyamg|\A\Z'
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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 expected_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 expected_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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expected = 'scipy.sparse.sparsetools|%s' % PYAMG_EXPECTED_WARNING
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with expected_warnings([expected]):
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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 expected_warnings([expected]):
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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 expected_warnings([PYAMG_EXPECTED_WARNING]):
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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 expected_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 expected_warnings(['"cg" mode|CObject type']):
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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 expected_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 expected_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 expected_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 expected_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 expected_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 expected_warnings(['"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 expected_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 expected_warnings(["Returning 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 expected_warnings(["Returning 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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