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()