route-through-array

route_through_array

skimage.graph.route_through_array(array, start, end, fully_connected=True, geometric=True) [source]

Simple example of how to use the MCP and MCP_Geometric classes.

See the MCP and MCP_Geometric class documentation for explanation of the path-finding algorithm.

Parameters:

array : ndarray Array of costs. start : iterable n-d index into array defining the starting point end : iterable n-d index into array defining the end point fully_connected : bool (optional) If True, diagonal moves are permitted, if False, only axial moves. geometric : bool (optional) If True, the MCP_Geometric class is used to calculate costs, if False, the MCP base class is used. See the class documentation for an explanation of the differences between MCP and MCP_Geometric.

Returns:

path : list List of n-d index tuples defining the path from start to end. cost : float Cost of the path. If geometric is False, the cost of the path is the sum of the values of array along the path. If geometric is True, a finer computation is made (see the documentation of the MCP_Geometric class).

See also

MCP, MCP_Geometric

Examples

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>>> import numpy as np >>> from skimage.graph import route_through_array >>> >>> image = np.array([[1, 3], [10, 12]]) >>> image array([[ 1,  3],        [10, 12]]) >>> # Forbid diagonal steps >>> route_through_array(image, [0, 0], [1, 1], fully_connected=False) ([(0, 0), (0, 1), (1, 1)], 9.5) >>> # Now allow diagonal steps: the path goes directly from start to end >>> route_through_array(image, [0, 0], [1, 1]) ([(0, 0), (1, 1)], 9.1923881554251192) >>> # Cost is the sum of array values along the path (16 = 1 + 3 + 12) >>> route_through_array(image, [0, 0], [1, 1], fully_connected=False, ... geometric=False) ([(0, 0), (0, 1), (1, 1)], 16.0) >>> # Larger array where we display the path that is selected >>> image = np.arange((36)).reshape((6, 6)) >>> image array([[ 0,  1,  2,  3,  4,  5],        [ 6,  7,  8,  9, 10, 11],        [12, 13, 14, 15, 16, 17],        [18, 19, 20, 21, 22, 23],        [24, 25, 26, 27, 28, 29],        [30, 31, 32, 33, 34, 35]]) >>> # Find the path with lowest cost >>> indices, weight = route_through_array(image, (0, 0), (5, 5)) >>> indices = np.array(indices).T >>> path = np.zeros_like(image) >>> path[indices[0], indices[1]] = 1 >>> path array([[1, 1, 1, 1, 1, 0],        [0, 0, 0, 0, 0, 1],        [0, 0, 0, 0, 0, 1],        [0, 0, 0, 0, 0, 1],        [0, 0, 0, 0, 0, 1],        [0, 0, 0, 0, 0, 1]])