skimage2.graph.central_pixel#
- skimage2.graph.central_pixel(graph, nodes=None, shape=None, partition_size=100)[source]#
Find the pixel with the highest closeness centrality.
Closeness centrality is the inverse of the total sum of shortest distances from a node to every other node.
- Parameters:
- graphscipy.sparse.csr_array
The sparse representation of the graph.
- nodesarray of int, optional
The raveled index of each node in graph in the image. If not provided, the returned value will be the index in the input graph.
- shapetuple of int, optional
The shape of the image in which the nodes are embedded. If provided, the returned coordinates are a NumPy multi-index of the same dimensionality as the input shape. Otherwise, the returned coordinate is the raveled index provided in
nodes.- partition_sizeint, optional
This function computes the shortest path distance between every pair of nodes in the graph. This can result in a very large (N*N) matrix. As a simple performance tweak, the distance values are computed in lots of
partition_size, resulting in a memory requirement of only partition_size*N.
- Returns:
- positionint or tuple of int
If shape is given, the coordinate of the central pixel in the image. Otherwise, the raveled index of that pixel.
- distancesarray of float
The total sum of distances from each node to each other reachable node.
