mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 02:30:48 +08:00
Andreas Mueller
60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
import numpy as np
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from numpy.testing import assert_equal, assert_array_equal
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from skimage._shared.testing import assert_greater
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from skimage.segmentation import felzenszwalb
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from skimage import data
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def test_grey():
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# very weak tests. This algorithm is pretty unstable.
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img = np.zeros((20, 21))
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img[:10, 10:] = 0.2
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img[10:, :10] = 0.4
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img[10:, 10:] = 0.6
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seg = felzenszwalb(img, 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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# that mostly respect the 4 regions:
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for i in range(4):
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hist = np.histogram(img[seg == i], bins=[0, 0.1, 0.3, 0.5, 1])[0]
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assert_greater(hist[i], 40)
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def test_minsize():
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# single-channel:
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img = data.coins()[20:168,0:128]
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for min_size in np.arange(10, 100, 10):
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segments = felzenszwalb(img, min_size=min_size, sigma=3)
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counts = np.bincount(segments.ravel())
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# actually want to test greater or equal.
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assert_greater(counts.min() + 1, min_size)
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# multi-channel:
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coffee = data.coffee()[::4, ::4]
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for min_size in np.arange(10, 100, 10):
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segments = felzenszwalb(coffee, min_size=min_size, sigma=3)
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counts = np.bincount(segments.ravel())
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# actually want to test greater or equal.
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# the construction doesn't guarantee min_size is respected
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# after intersecting the sementations for the colors
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assert_greater(np.mean(counts) + 1, min_size)
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def test_color():
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# very weak tests. This algorithm is pretty unstable.
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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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seg = felzenszwalb(img, 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_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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if __name__ == '__main__':
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from numpy import testing
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testing.run_module_suite()
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