48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
import numpy as np
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from skimage.transform import integral_image, integrate
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from skimage._shared.testing import assert_equal
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np.random.seed(0)
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x = (np.random.rand(50, 50) * 255).astype(np.uint8)
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s = integral_image(x)
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def test_validity():
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y = np.arange(12).reshape((4, 3))
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y = (np.random.rand(50, 50) * 255).astype(np.uint8)
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assert_equal(integral_image(y)[-1, -1],
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y.sum())
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def test_basic():
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assert_equal(x[12:24, 10:20].sum(), integrate(s, (12, 10), (23, 19)))
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assert_equal(x[:20, :20].sum(), integrate(s, (0, 0), (19, 19)))
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assert_equal(x[:20, 10:20].sum(), integrate(s, (0, 10), (19, 19)))
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assert_equal(x[10:20, :20].sum(), integrate(s, (10, 0), (19, 19)))
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def test_single():
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assert_equal(x[0, 0], integrate(s, (0, 0), (0, 0)))
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assert_equal(x[10, 10], integrate(s, (10, 10), (10, 10)))
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def test_vectorized_integrate():
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r0 = np.array([12, 0, 0, 10, 0, 10, 30])
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c0 = np.array([10, 0, 10, 0, 0, 10, 31])
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r1 = np.array([23, 19, 19, 19, 0, 10, 49])
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c1 = np.array([19, 19, 19, 19, 0, 10, 49])
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expected = np.array([x[12:24, 10:20].sum(),
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x[:20, :20].sum(),
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x[:20, 10:20].sum(),
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x[10:20, :20].sum(),
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x[0, 0],
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x[10, 10],
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x[30:, 31:].sum()])
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start_pts = [(r0[i], c0[i]) for i in range(len(r0))]
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end_pts = [(r1[i], c1[i]) for i in range(len(r0))]
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assert_equal(expected, integrate(s, start_pts, end_pts))
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