mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 00:39:44 +08:00
Steven Silvester
a4e4e57ba5
Add new helper function for expected warnings during test Indentation cleanups and avoid skipping doctests if possible.
290 lines
8.9 KiB
Python
290 lines
8.9 KiB
Python
import numpy as np
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from numpy.testing import assert_array_equal, run_module_suite
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from skimage.morphology import label as _label
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import skimage.measure._ccomp as ccomp
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from skimage._shared.utils import all_warnings
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np.random.seed(0)
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# The background label value
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# is supposed to be changed to 0 soon
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BG = -1
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def label(*args, **kwargs):
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"""Wrap the label function to avoid deprecation warning"""
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with all_warnings():
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return _label(*args, **kwargs)
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class TestConnectedComponents:
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def setup(self):
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self.x = np.array([[0, 0, 3, 2, 1, 9],
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[0, 1, 1, 9, 2, 9],
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[0, 0, 1, 9, 9, 9],
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[3, 1, 1, 5, 3, 0]])
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self.labels = np.array([[0, 0, 1, 2, 3, 4],
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[0, 5, 5, 4, 2, 4],
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[0, 0, 5, 4, 4, 4],
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[6, 5, 5, 7, 8, 9]])
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def test_basic(self):
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assert_array_equal(label(self.x), self.labels)
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# Make sure data wasn't modified
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assert self.x[0, 2] == 3
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def test_random(self):
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x = (np.random.rand(20, 30) * 5).astype(np.int)
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with all_warnings():
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labels = label(x)
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n = labels.max()
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for i in range(n):
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values = x[labels == i]
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assert np.all(values == values[0])
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def test_diag(self):
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x = np.array([[0, 0, 1],
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[0, 1, 0],
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[1, 0, 0]])
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with all_warnings():
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assert_array_equal(label(x), x)
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def test_4_vs_8(self):
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x = np.array([[0, 1],
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[1, 0]], dtype=int)
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with all_warnings():
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assert_array_equal(label(x, 4),
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[[0, 1],
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[2, 3]])
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assert_array_equal(label(x, 8),
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[[0, 1],
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[1, 0]])
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def test_background(self):
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x = np.array([[1, 0, 0],
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[1, 1, 5],
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[0, 0, 0]])
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with all_warnings():
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assert_array_equal(label(x), [[0, 1, 1],
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[0, 0, 2],
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[3, 3, 3]])
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assert_array_equal(label(x, background=0),
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[[0, -1, -1],
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[0, 0, 1],
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[-1, -1, -1]])
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def test_background_two_regions(self):
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x = np.array([[0, 0, 6],
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[0, 0, 6],
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[5, 5, 5]])
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res = label(x, background=0)
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assert_array_equal(res,
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[[-1, -1, 0],
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[-1, -1, 0],
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[+1, 1, 1]])
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def test_background_one_region_center(self):
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x = np.array([[0, 0, 0],
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[0, 1, 0],
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[0, 0, 0]])
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assert_array_equal(label(x, neighbors=4, background=0),
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[[-1, -1, -1],
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[-1, 0, -1],
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[-1, -1, -1]])
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def test_return_num(self):
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x = np.array([[1, 0, 6],
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[0, 0, 6],
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[5, 5, 5]])
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with all_warnings():
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assert_array_equal(label(x, return_num=True)[1], 4)
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assert_array_equal(label(x, background=0, return_num=True)[1], 3)
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class TestConnectedComponents3d:
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def setup(self):
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self.x = np.zeros((3, 4, 5), int)
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self.x[0] = np.array([[0, 3, 2, 1, 9],
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[0, 1, 9, 2, 9],
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[0, 1, 9, 9, 9],
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[3, 1, 5, 3, 0]])
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self.x[1] = np.array([[3, 3, 2, 1, 9],
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[0, 3, 9, 2, 1],
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[0, 3, 3, 1, 1],
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[3, 1, 3, 3, 0]])
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self.x[2] = np.array([[3, 3, 8, 8, 0],
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[2, 3, 9, 8, 8],
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[2, 3, 0, 8, 0],
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[2, 1, 0, 0, 0]])
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self.labels = np.zeros((3, 4, 5), int)
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self.labels[0] = np.array([[0, 1, 2, 3, 4],
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[0, 5, 4, 2, 4],
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[0, 5, 4, 4, 4],
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[1, 5, 6, 1, 7]])
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self.labels[1] = np.array([[1, 1, 2, 3, 4],
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[0, 1, 4, 2, 3],
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[0, 1, 1, 3, 3],
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[1, 5, 1, 1, 7]])
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self.labels[2] = np.array([[1, 1, 8, 8, 9],
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[10, 1, 4, 8, 8],
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[10, 1, 7, 8, 7],
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[10, 5, 7, 7, 7]])
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def test_basic(self):
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labels = label(self.x)
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assert_array_equal(labels, self.labels)
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assert self.x[0, 0, 2] == 2, \
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"Data was modified!"
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def test_random(self):
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x = (np.random.rand(20, 30) * 5).astype(np.int)
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with all_warnings():
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labels = label(x)
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n = labels.max()
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for i in range(n):
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values = x[labels == i]
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assert np.all(values == values[0])
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def test_diag(self):
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x = np.zeros((3, 3, 3), int)
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x[0, 2, 2] = 1
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x[1, 1, 1] = 1
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x[2, 0, 0] = 1
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with all_warnings():
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assert_array_equal(label(x), x)
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def test_4_vs_8(self):
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x = np.zeros((2, 2, 2), int)
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x[0, 1, 1] = 1
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x[1, 0, 0] = 1
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label4 = x.copy()
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label4[1, 0, 0] = 2
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with all_warnings():
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assert_array_equal(label(x, 4), label4)
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assert_array_equal(label(x, 8), x)
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def test_background(self):
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x = np.zeros((2, 3, 3), int)
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x[0] = np.array([[1, 0, 0],
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[1, 0, 0],
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[0, 0, 0]])
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x[1] = np.array([[0, 0, 0],
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[0, 1, 5],
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[0, 0, 0]])
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lnb = x.copy()
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lnb[0] = np.array([[0, 1, 1],
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[0, 1, 1],
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[1, 1, 1]])
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lnb[1] = np.array([[1, 1, 1],
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[1, 0, 2],
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[1, 1, 1]])
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lb = x.copy()
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lb[0] = np.array([[0, BG, BG],
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[0, BG, BG],
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[BG, BG, BG]])
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lb[1] = np.array([[BG, BG, BG],
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[BG, 0, 1],
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[BG, BG, BG]])
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with all_warnings():
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assert_array_equal(label(x), lnb)
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assert_array_equal(label(x, background=0), lb)
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def test_background_two_regions(self):
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x = np.zeros((2, 3, 3), int)
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x[0] = np.array([[0, 0, 6],
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[0, 0, 6],
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[5, 5, 5]])
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x[1] = np.array([[6, 6, 0],
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[5, 0, 0],
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[0, 0, 0]])
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lb = x.copy()
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lb[0] = np.array([[BG, BG, 0],
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[BG, BG, 0],
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[1, 1, 1]])
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lb[1] = np.array([[0, 0, BG],
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[1, BG, BG],
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[BG, BG, BG]])
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res = label(x, background=0)
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assert_array_equal(res, lb)
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def test_background_one_region_center(self):
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x = np.zeros((3, 3, 3), int)
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x[1, 1, 1] = 1
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lb = np.ones_like(x) * BG
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lb[1, 1, 1] = 0
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assert_array_equal(label(x, neighbors=4, background=0), lb)
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def test_return_num(self):
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x = np.array([[1, 0, 6],
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[0, 0, 6],
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[5, 5, 5]])
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with all_warnings():
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assert_array_equal(label(x, return_num=True)[1], 4)
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assert_array_equal(label(x, background=0, return_num=True)[1], 3)
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def test_1D(self):
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x = np.array((0, 1, 2, 2, 1, 1, 0, 0))
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xlen = len(x)
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y = np.array((0, 1, 2, 2, 3, 3, 4, 4))
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reshapes = ((xlen,),
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(1, xlen), (xlen, 1),
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(1, xlen, 1), (xlen, 1, 1), (1, 1, xlen))
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for reshape in reshapes:
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x2 = x.reshape(reshape)
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labelled = label(x2)
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assert_array_equal(y, labelled.flatten())
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def test_nd(self):
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x = np.ones((1, 2, 3, 4))
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np.testing.assert_raises(NotImplementedError, label, x)
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class TestSupport:
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def test_reshape(self):
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shapes_in = ((3, 1, 2), (1, 4, 5), (3, 1, 1), (2, 1), (1,))
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for shape in shapes_in:
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shape = np.array(shape)
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numones = sum(shape == 1)
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inp = np.random.random(shape)
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fixed, swaps = ccomp.reshape_array(inp)
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shape2 = fixed.shape
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# now check that all ones are at the beginning
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for i in range(numones):
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assert shape2[i] == 1
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back = ccomp.undo_reshape_array(fixed, swaps)
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# check that the undo works as expected
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assert_array_equal(inp, back)
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if __name__ == "__main__":
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run_module_suite()
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