127 lines
4.4 KiB
Python
127 lines
4.4 KiB
Python
"""Tests for `_util`."""
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import pytest
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import numpy as np
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from numpy.testing import assert_array_equal
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from skimage.morphology import _util
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@pytest.mark.parametrize("image_shape", [
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(111,), (33, 44), (22, 55, 11), (6, 5, 4, 3)
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])
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@pytest.mark.parametrize("order", ["C", "F"])
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def test_offsets_to_raveled_neighbors_highest_connectivity(image_shape, order):
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"""
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Check scenarios where selem is always of the highest connectivity
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and all dimensions are > 2.
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"""
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selem = np.ones((3,) * len(image_shape), dtype=bool)
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center = (1,) * len(image_shape)
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offsets = _util._offsets_to_raveled_neighbors(
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image_shape, selem, center, order
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)
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# Assert only neighbors are present, center was removed
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assert len(offsets) == selem.sum() - 1
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assert 0 not in offsets
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# Assert uniqueness
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assert len(set(offsets)) == offsets.size
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# offsets form pairs of with same value but different signs
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# if selem is symmetric around center
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assert all(-x in offsets for x in offsets)
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# Construct image whose values are the Manhattan distance to its center
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image_center = tuple(s // 2 for s in image_shape)
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coords = [
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np.abs(np.arange(s, dtype=np.intp) - c)
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for s, c in zip(image_shape, image_center)
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]
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grid = np.meshgrid(*coords, indexing="ij")
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image = np.sum(grid, axis=0)
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image_raveled = image.ravel(order)
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image_center_raveled = np.ravel_multi_index(
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image_center, image_shape, order=order
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)
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# Sample raveled image around its center
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samples = []
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for offset in offsets:
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index = image_center_raveled + offset
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samples.append(image_raveled[index])
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# Assert that center with value 0 wasn't selected
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assert np.min(samples) == 1
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# Assert that only neighbors where selected
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# (highest value == connectivity)
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assert np.max(samples) == len(image_shape)
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# Assert that nearest neighbors are selected first
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assert list(sorted(samples)) == samples
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@pytest.mark.parametrize("image_shape", [
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(2,), (2, 2), (2, 1, 2), (2, 2, 1, 2), (0, 2, 1, 2)
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])
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@pytest.mark.parametrize("order", ["C", "F"])
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def test_offsets_to_raveled_neighbors_selem_smaller_image(image_shape, order):
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"""
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Test if a dimension indicated by `image_shape` is smaller than in
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`selem`.
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"""
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selem = np.ones((3,) * len(image_shape), dtype=bool)
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center = (1,) * len(image_shape)
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offsets = _util._offsets_to_raveled_neighbors(
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image_shape, selem, center, order
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)
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# Assert only neighbors are present, center and duplicates (possible
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# for this scenario) where removed
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assert len(offsets) <= selem.sum() - 1
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assert 0 not in offsets
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# Assert uniqueness
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assert len(set(offsets)) == offsets.size
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# offsets form pairs of with same value but different signs
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# if selem is symmetric around center
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assert all(-x in offsets for x in offsets)
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def test_offsets_to_raveled_neighbors_explicit_0():
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"""Check reviewed example."""
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image_shape = (100, 200, 3)
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selem = np.ones((3, 3, 3), dtype=bool)
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center = (1, 1, 1)
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offsets = _util._offsets_to_raveled_neighbors(
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image_shape, selem, center
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)
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desired = np.array([
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3, -600, 1, -1, 600, -3, 4, 2, 603, -2, -4,
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-597, 601, -599, -601, -603, 599, 597, 602, -604, 596, -596,
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-598, -602, 598, 604
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])
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assert_array_equal(offsets, desired)
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def test_offsets_to_raveled_neighbors_explicit_1():
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"""Check reviewed example where selem is larger in last dimension."""
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image_shape = (10, 9, 8, 3)
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selem = np.ones((3, 3, 3, 4), dtype=bool)
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center = (1, 1, 1, 1)
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offsets = _util._offsets_to_raveled_neighbors(
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image_shape, selem, center
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)
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desired = np.array([
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24, 3, 1, -1, -3, -24, -216, 216, -192, 215, -2,
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-21, -23, 2, -25, -27, 4, 217, 21, 219, -4, 23,
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25, -240, 240, 192, 27, -213, -219, 213, -215, -217, -243,
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191, -241, 195, 189, 212, 26, 5, 20, 28, 22, 214,
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243, -237, -22, 241, -214, -212, 237, -218, -195, -20, 220,
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-193, -191, 218, -189, -28, -26, 193, -239, -220, 239, 196,
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221, 242, 236, 238, 194, -244, -188, -238, -211, -196, -194,
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-190, -236, -19, 244, 29, 188, -242, 190, -187, 197, -235,
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245
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])
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assert_array_equal(offsets, desired)
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