83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
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import numpy as np
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from skimage import data
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from skimage.segmentation import felzenszwalb
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from skimage._shared import testing
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from skimage._shared.testing import (assert_greater, test_parallel,
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assert_equal, assert_array_equal,
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assert_warns, assert_no_warnings)
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@test_parallel()
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def test_grey():
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# very weak tests.
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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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assert_greater(counts.min() + 1, min_size)
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def test_3D():
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grey_img = np.zeros((10, 10))
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rgb_img = np.zeros((10, 10, 3))
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three_d_img = np.zeros((10, 10, 10))
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with assert_no_warnings():
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felzenszwalb(grey_img, multichannel=True)
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felzenszwalb(grey_img, multichannel=False)
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felzenszwalb(rgb_img, multichannel=True)
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with assert_warns(RuntimeWarning):
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felzenszwalb(three_d_img, multichannel=True)
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with testing.raises(ValueError):
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felzenszwalb(rgb_img, multichannel=False)
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felzenszwalb(three_d_img, multichannel=False)
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def test_color():
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# very weak tests.
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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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def test_merging():
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# test region merging in the post-processing step
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img = np.array([[0, 0.3], [0.7, 1]])
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# With scale=0, only the post-processing is performed.
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seg = felzenszwalb(img, scale=0, sigma=0, min_size=2)
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# we expect 2 segments:
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assert_equal(len(np.unique(seg)), 2)
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assert_array_equal(seg[0, :], 0)
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assert_array_equal(seg[1, :], 1)
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