54 lines
1.7 KiB
Python
54 lines
1.7 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
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a = np.ones((8, 8), dtype=np.float32)
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a[1::2] *= 2.0
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class FlexibleMCP(mcp.MCP_Flexible):
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""" Simple MCP subclass that allows the front to travel
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a certain distance from the seed point, and uses a constant
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cost factor that is independent of the cost array.
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"""
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def _reset(self):
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mcp.MCP_Flexible._reset(self)
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self._distance = np.zeros((8, 8), dtype=np.float32).ravel()
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def goal_reached(self, index, cumcost):
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if self._distance[index] > 4:
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return 2
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else:
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return 0
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def travel_cost(self, index, new_index, offset_length):
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return 1.0 # fixed cost
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def examine_neighbor(self, index, new_index, offset_length):
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pass # We do not test this
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def update_node(self, index, new_index, offset_length):
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self._distance[new_index] = self._distance[index] + 1
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def test_flexible():
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# Create MCP and do a traceback
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mcp = FlexibleMCP(a)
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costs, traceback = mcp.find_costs([(0, 0)])
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# Check that inner part is correct. This basically
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# tests whether travel_cost works.
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assert_array_equal(costs[:4, :4], [[1, 2, 3, 4],
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[2, 2, 3, 4],
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[3, 3, 3, 4],
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[4, 4, 4, 4]])
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# Test that the algorithm stopped at the right distance.
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# Note that some of the costs are filled in but not yet frozen,
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# so we take a bit of margin
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assert np.all(costs[-2:, :] == np.inf)
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assert np.all(costs[:, -2:] == np.inf)
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