162 lines
6.1 KiB
Python
162 lines
6.1 KiB
Python
import numpy as np
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import skimage.graph.mcp as mcp
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from skimage._shared.testing import (assert_array_equal, assert_almost_equal,
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parametrize)
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from skimage._shared._warnings import expected_warnings
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np.random.seed(0)
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a = np.ones((8, 8), dtype=np.float32)
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a[1:-1, 1] = 0
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a[1, 1:-1] = 0
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warning_optional = r'|\A\Z'
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def test_basic():
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with expected_warnings(['Upgrading NumPy' + warning_optional]):
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m = mcp.MCP(a, fully_connected=True)
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costs, traceback = m.find_costs([(1, 6)])
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return_path = m.traceback((7, 2))
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assert_array_equal(costs,
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[[1., 1., 1., 1., 1., 1., 1., 1.],
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[1., 0., 0., 0., 0., 0., 0., 1.],
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[1., 0., 1., 1., 1., 1., 1., 1.],
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[1., 0., 1., 2., 2., 2., 2., 2.],
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[1., 0., 1., 2., 3., 3., 3., 3.],
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[1., 0., 1., 2., 3., 4., 4., 4.],
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[1., 0., 1., 2., 3., 4., 5., 5.],
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[1., 1., 1., 2., 3., 4., 5., 6.]])
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assert_array_equal(return_path,
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[(1, 6),
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(1, 5),
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(1, 4),
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(1, 3),
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(1, 2),
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(2, 1),
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(3, 1),
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(4, 1),
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(5, 1),
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(6, 1),
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(7, 2)])
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def test_neg_inf():
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expected_costs = np.where(a == 1, np.inf, 0)
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expected_path = [(1, 6),
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(1, 5),
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(1, 4),
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(1, 3),
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(1, 2),
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(2, 1),
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(3, 1),
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(4, 1),
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(5, 1),
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(6, 1)]
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test_neg = np.where(a == 1, -1, 0)
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test_inf = np.where(a == 1, np.inf, 0)
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with expected_warnings(['Upgrading NumPy' + warning_optional]):
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m = mcp.MCP(test_neg, fully_connected=True)
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costs, traceback = m.find_costs([(1, 6)])
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return_path = m.traceback((6, 1))
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assert_array_equal(costs, expected_costs)
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assert_array_equal(return_path, expected_path)
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with expected_warnings(['Upgrading NumPy' + warning_optional]):
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m = mcp.MCP(test_inf, fully_connected=True)
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costs, traceback = m.find_costs([(1, 6)])
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return_path = m.traceback((6, 1))
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assert_array_equal(costs, expected_costs)
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assert_array_equal(return_path, expected_path)
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def test_route():
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with expected_warnings(['Upgrading NumPy' + warning_optional]):
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return_path, cost = mcp.route_through_array(a, (1, 6), (7, 2),
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geometric=True)
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assert_almost_equal(cost, np.sqrt(2) / 2)
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assert_array_equal(return_path,
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[(1, 6),
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(1, 5),
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(1, 4),
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(1, 3),
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(1, 2),
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(2, 1),
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(3, 1),
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(4, 1),
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(5, 1),
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(6, 1),
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(7, 2)])
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def test_no_diagonal():
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with expected_warnings(['Upgrading NumPy' + warning_optional]):
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m = mcp.MCP(a, fully_connected=False)
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costs, traceback = m.find_costs([(1, 6)])
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return_path = m.traceback((7, 2))
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assert_array_equal(costs,
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[[2., 1., 1., 1., 1., 1., 1., 2.],
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[1., 0., 0., 0., 0., 0., 0., 1.],
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[1., 0., 1., 1., 1., 1., 1., 2.],
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[1., 0., 1., 2., 2., 2., 2., 3.],
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[1., 0., 1., 2., 3., 3., 3., 4.],
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[1., 0., 1., 2., 3., 4., 4., 5.],
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[1., 0., 1., 2., 3., 4., 5., 6.],
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[2., 1., 2., 3., 4., 5., 6., 7.]])
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assert_array_equal(return_path,
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[(1, 6),
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(1, 5),
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(1, 4),
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(1, 3),
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(1, 2),
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(1, 1),
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(2, 1),
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(3, 1),
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(4, 1),
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(5, 1),
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(6, 1),
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(7, 1),
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(7, 2)])
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def test_offsets():
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offsets = [(1, i) for i in range(10)] + [(1, -i) for i in range(1, 10)]
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with expected_warnings(['Upgrading NumPy' + warning_optional]):
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m = mcp.MCP(a, offsets=offsets)
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costs, traceback = m.find_costs([(1, 6)])
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assert_array_equal(traceback,
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[[-2, -2, -2, -2, -2, -2, -2, -2],
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[-2, -2, -2, -2, -2, -2, -1, -2],
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[15, 14, 13, 12, 11, 10, 0, 1],
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[10, 0, 1, 2, 3, 4, 5, 6],
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[10, 0, 1, 2, 3, 4, 5, 6],
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[10, 0, 1, 2, 3, 4, 5, 6],
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[10, 0, 1, 2, 3, 4, 5, 6],
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[10, 0, 1, 2, 3, 4, 5, 6]])
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@parametrize("shape", [(100, 100), (5, 8, 13, 17)] * 5)
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def test_crashing(shape):
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_test_random(shape)
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def _test_random(shape):
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# Just tests for crashing -- not for correctness.
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a = np.random.rand(*shape).astype(np.float32)
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starts = [[0] * len(shape), [-1] * len(shape),
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(np.random.rand(len(shape)) * shape).astype(int)]
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ends = [(np.random.rand(len(shape)) * shape).astype(int)
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for i in range(4)]
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with expected_warnings(['Upgrading NumPy' + warning_optional]):
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m = mcp.MCP(a, fully_connected=True)
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costs, offsets = m.find_costs(starts)
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for point in [(np.random.rand(len(shape)) * shape).astype(int)
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for i in range(4)]:
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m.traceback(point)
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m._reset()
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m.find_costs(starts, ends)
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for end in ends:
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m.traceback(end)
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return a, costs, offsets
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