Cell of an ipython notebook containing a function to convert the image of a skeleton defined on a neighborhood of 8 pixels, into a graph (networkx). Depends on numpy, mahotas, networkx

C8skel_to_Graph.py

import mahotas as mh
import networkx as nx
import numpy as np

def C8skeleton_to_graph(skeletonC8):
    #images processing: extraction of branchedpoints, end-points, edges
    ep = endPoints(skeletonC8)
    bp = branchedPoints(skeletonC8, showSE = False)
    ## Label branched-points
    l_bp,_ = mh.label(bp)
    ## Label the edges
    l_edges =  edges_from_C8skel(skeletonC8)
    ##Make end-points with the same label than their edge
    l_ep = ep*l_edges 
    
    ##edges between branched-points
    endpoints_labels = np.where(mh.histogram.fullhistogram(np.uint16(l_ep))[:]==1)[0]
    edges_bp = np.copy(l_edges)
    for l in endpoints_labels:
        edges_bp[np.where(edges_bp == l)]=0
    #edges between end-points and branched points
    edges_ep_to_bp = l_edges * np.logical_not(edges_bp > 0)
    
    #Building graph
    ## Add branched points first
    G=nx.Graph()
    lab_bpTonode = add_bp_to_graph(G, l_bp)
    ## Add end-points
    lab_epTonode = add_ep_to_graph(G, l_ep)
    ##Link end-points to branched-points
    ###labels of bp
    branchedpoints_labels = np.where(mh.histogram.fullhistogram(np.uint16(l_bp))[:]==1)[0]
    for lab in branchedpoints_labels:
        pos = np.where(l_bp == lab)
        row = int(pos[0])
        col = int(pos[1])
        #search label(s) of edges in image containing edges between ep and bp
        ## first get the neighborhood of the curent bp
        neigh_epbp = edges_ep_to_bp[row-1:row+1+1,col-1:col+1+1]
        labels_in_neigh = np.where(mh.histogram.fullhistogram(np.uint16(neigh_epbp))[:]<>0)[0]
        #print neigh_epbp, labels_in_neigh[1:]
        #get node(s) of attribute label= labels_in_neigh ! may be more than one, think to a  list
        for lab_ep in labels_in_neigh[1:]:
            #search for nodes f attribute label= lab_ep
            w = np.sum(l_edges==lab_ep)
            print 'linking ',lab, lab_ep, ' weight ',w
            G.add_edge(lab_bpTonode[lab],lab_epTonode[lab_ep],weight=w)#     
    ##
    ##Now try to link branched points between them
    ##
    bps_neighborhood = {}
    branchedpoints_labels = np.where(mh.histogram.fullhistogram(np.uint16(l_bp))[:]==1)[0]
    for lab in branchedpoints_labels:
        pos = np.where(l_bp == lab)
        row = int(pos[0])
        col = int(pos[1])
        #search label(s) of edges in image containing edges between ep and bp
        ## first get the neighborhood of the curent bp
        neigh_epbp = edges_bp[row-1:row+1+1,col-1:col+1+1]
        labels_in_neigh = np.where(mh.histogram.fullhistogram(np.uint16(neigh_epbp))[:]<>0)[0]
        bps_neighborhood[lab]=labels_in_neigh[1:].tolist()
    print bps_neighborhood
    
    ## Build the dictionnary of edges see (http://stackoverflow.com/questions/21375146/pythonic-inverse-dict-non-unique-mappings)
    invert_is_edges = {item: [key for key in bps_neighborhood if item in bps_neighborhood[key]] for value in bps_neighborhood.values() for item in value}
    ## Addeges to graph
    for ed in invert_is_edges.keys():
        ## first get edge size -> its weight
        w = np.sum(edges_bp==ed)
        vertex1 = invert_is_edges[ed][0]
        vertex2 = invert_is_edges[ed][1]
        #print ed,w
        G.add_edge(vertex1,vertex2,weight=w)
    ## This is it !!
    return G

skeletonB = mh.thin(imB)
Bgraph = C8skeleton_to_graph(skeletonB)
skeletonH = mh.thin(imH)
Hgraph = C8skeleton_to_graph(skeletonH)

figsize(6,6)
subplot(221,xticks=[]
,yticks=[])
imshow(imB, interpolation='nearest')
subplot(222,xticks=[],yticks=[])
nx.draw(Bgraph)
subplot(223,xticks=[],yticks=[])
imshow(imH, interpolation='nearest')
subplot(224,xticks=[],yticks=[])
nx.draw(Hgraph)

Interesting !!
I have tried to find the branch points or Junction points of a flow chart but did not get. In parts by understanding the code I tried. Will you pls try on flow chart and chk how cAN i GET junction point of shape and edge to transform flowchart into graph.