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