build net work from nd skeleton image
graph = sknw.build_sknw(ske， multi=False)
ske: should be a nd skeleton image
multi: if True，a multigraph is retured, which allows more than one edge between two nodes and self-self edge. default is False.
return: is a networkx Graph object
graph.node[id]['pts'] : Numpy(x, n), coordinates of nodes points
graph.node[id]['o']: Numpy(n), centried of the node
graph.edge(id1, id2)['pts']: Numpy(x, n), sequence of the edge point
graph.edge(id1, id2)['weight']: float, length of this edge
if it's a multigraph, you must add a index after two node id to get the edge, like: graph.edge(id1, id2).
build Graph by Skeleton, then plot as a vector Graph in matplotlib.
from skimage.morphology import skeletonize from skimage import data import sknw # open and skeletonize img = data.horse() ske = skeletonize(~img).astype(np.uint16) # build graph from skeleton graph = sknw.build_sknw(ske) # draw image plt.imshow(img, cmap='gray') # draw edges by pts for (s,e) in graph.edges(): ps = graph[s][e]['pts'] plt.plot(ps[:,1], ps[:,0], 'green') # draw node by o node, nodes = graph.node, graph.nodes() ps = np.array([node[i]['o'] for i in nodes]) plt.plot(ps[:,1], ps[:,0], 'r.') # title and show plt.title('Build Graph') plt.show()
ImagePy is my opensource image processihng framework. It is the ImageJ of Python, you can wrap any numpy based function esaily. And sknw is a sub module of ImagePy. You can use sknw without any code.