python计算机视觉实验

    from pygraph.classes.digraph import digraph
    from pygraph.algorithms.minmax import maximum_flow
    
    gr = digraph()
    gr.add_nodes([0,1,2,3])
    gr.add_edge((0,1), wt=4)
    gr.add_edge((1,2), wt=3)
    gr.add_edge((2,3), wt=5)
    gr.add_edge((0,2), wt=3)
    gr.add_edge((1,3), wt=4)
    flows,cuts = maximum_flow(gr, 0, 3)
    print 'flow is:' , flows
    print 'cut is:' , cuts

    # -*- coding: utf-8 -*-
    
    from scipy.misc import imresize
    from PCV.tools import graphcut
    from PIL import Image
    from numpy import *
    from pylab import *
    
    im = array(Image.open("empire.jpg"))
    im = imresize(im, 0.07)
    size = im.shape[:2]
    print "OK!!"
    
    # add two rectangular training regions
    labels = zeros(size)
    labels[3:18, 3:18] = -1
    labels[-18:-3, -18:-3] = 1
    print "OK!!"
    
    
    # create graph
    g = graphcut.build_bayes_graph(im, labels, kappa=1)
    
    # cut the graph
    res = graphcut.cut_graph(g, size)
    print "OK!!"
    
    
    figure()
    graphcut.show_labeling(im, labels)
    
    figure()
    imshow(res)
    gray()
    axis('off')
    
    show()