mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-15 11:25:53 +08:00
Juan Nunez-Iglesias
ea1566fffb
`np.atleast_3d` will add a singleton dimension at the end of an array if needed. This is not the correct thing to do if `multichannel=False` based on the subsequent lines. If the input image was 2D with shape `(40, 50)` and `multichannel=False`, then `np.atleast_3d` gives it shape `(40, 50, 1)`, and then, because `multichannel=False`, the rest of the code gives it shape `(40, 50, 1, 1)`. This results in the final returned array having shape `(40, 50, 1)` instead of the desired `(40, 50)`. This commit fixes that and updates the test to detect this failure.
96 lines
2.8 KiB
Python
96 lines
2.8 KiB
Python
import itertools as it
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import warnings
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import numpy as np
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from numpy.testing import assert_equal, assert_array_equal
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from skimage.segmentation import slic
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def test_color_2d():
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rnd = np.random.RandomState(0)
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img = np.zeros((20, 21, 3))
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img[:10, :10, 0] = 1
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img[10:, :10, 1] = 1
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img[10:, 10:, 2] = 1
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img += 0.01 * rnd.normal(size=img.shape)
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img[img > 1] = 1
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img[img < 0] = 0
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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seg = slic(img, n_segments=4, sigma=0)
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# we expect 4 segments
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assert_equal(len(np.unique(seg)), 4)
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assert_equal(seg.shape, img.shape[:-1])
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assert_array_equal(seg[:10, :10], 0)
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assert_array_equal(seg[10:, :10], 2)
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assert_array_equal(seg[:10, 10:], 1)
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assert_array_equal(seg[10:, 10:], 3)
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def test_gray_2d():
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rnd = np.random.RandomState(0)
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img = np.zeros((20, 21))
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img[:10, :10] = 0.33
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img[10:, :10] = 0.67
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img[10:, 10:] = 1.00
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img += 0.0033 * rnd.normal(size=img.shape)
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img[img > 1] = 1
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img[img < 0] = 0
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seg = slic(img, sigma=0, n_segments=4, compactness=1,
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multichannel=False, convert2lab=False)
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assert_equal(len(np.unique(seg)), 4)
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assert_equal(seg.shape, img.shape)
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assert_array_equal(seg[:10, :10], 0)
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assert_array_equal(seg[10:, :10], 2)
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assert_array_equal(seg[:10, 10:], 1)
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assert_array_equal(seg[10:, 10:], 3)
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def test_color_3d():
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rnd = np.random.RandomState(0)
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img = np.zeros((20, 21, 22, 3))
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slices = []
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for dim_size in img.shape[:-1]:
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midpoint = dim_size // 2
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slices.append((slice(None, midpoint), slice(midpoint, None)))
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slices = list(it.product(*slices))
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colors = list(it.product(*(([0, 1],) * 3)))
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for s, c in zip(slices, colors):
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img[s] = c
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img += 0.01 * rnd.normal(size=img.shape)
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img[img > 1] = 1
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img[img < 0] = 0
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seg = slic(img, sigma=0, n_segments=8)
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assert_equal(len(np.unique(seg)), 8)
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for s, c in zip(slices, range(8)):
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assert_array_equal(seg[s], c)
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def test_gray_3d():
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rnd = np.random.RandomState(0)
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img = np.zeros((20, 21, 22))
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slices = []
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for dim_size in img.shape:
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midpoint = dim_size // 2
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slices.append((slice(None, midpoint), slice(midpoint, None)))
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slices = list(it.product(*slices))
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shades = np.arange(0, 1.000001, 1.0/7)
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for s, sh in zip(slices, shades):
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img[s] = sh
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img += 0.001 * rnd.normal(size=img.shape)
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img[img > 1] = 1
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img[img < 0] = 0
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seg = slic(img, sigma=0, n_segments=8, compactness=1,
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multichannel=False, convert2lab=False)
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assert_equal(len(np.unique(seg)), 8)
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for s, c in zip(slices, range(8)):
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assert_array_equal(seg[s], c)
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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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