def cut_normalized(labels, rag, thresh=0.001, num_cuts=10, in_place=True, max_edge=1.0):
 if (not in_place):
  rag = rag.copy()
 for node in rag.nodes_iter():
  rag.add_edge(node, node, weight=max_edge)
 _ncut_relabel(rag, thresh, num_cuts)
 map_array = np.zeros((labels.max() + 1), dtype=labels.dtype)
 for (n, d) in rag.nodes_iter(data=True):
  map_array[d['labels']] = d['ncut label']
 return map_array[labels]

def cut_normalized ( labels , rag , thresh = 0.001 , num_cuts = 10 , in_place = True , max_edge = 1.0 ) : if ( not in_place ) : rag = rag . copy ( ) for node in rag . nodes_iter ( ) : rag . add_edge ( node , node , weight = max_edge ) _ncut_relabel ( rag , thresh , num_cuts ) map_array = np . zeros ( ( labels . max ( ) + 1 ) , dtype = labels . dtype ) for ( n , d ) in rag . nodes_iter ( data = True ) : map_array [ d [ 'labels' ] ] = d [ 'ncut label' ] return map_array [ labels ]
perform normalized graph cut on the region adjacency graph .