graph——cut 及其实现
文章目录
- 图割法原理
- 实现过程
图割法原理
（）
实现过程
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
from PCV.tools import ncut
from scipy.misc import imresize
from pylab import *
from PIL import Image
im = array(Image.open('C-uniform03.ppm'))
m, n = im.shape[:2]
# resize image to (wid,wid)
wid = 50
rim = imresize(im, (wid, wid), interp='bilinear')
rim = array(rim, 'f')
# create normalized cut matrix
A = ncut.ncut_graph_matrix(rim, sigma_d=1, sigma_g=1e-2)
# cluster
code, V = ncut.cluster(A, k=3, ndim=3)
codeim = imresize(code.reshape(wid,wid),(m,n),interp='nearest')
figure('203')
imshow(codeim)
gray()
show()
#imshow(imresize(V[i].reshape(wid,wid),(m,n),interp='bilinear'))
# -*- 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('92-1')
graphcut.show_labeling(im, labels)
figure('98_2')
imshow(res)
gray()
axis('off')
show()
from PCV.tools import ncut
from scipy.misc import imresize
from pylab import *
from PIL import Image
im = array(Image.open('C-uniform03.ppm'))
m, n = im.shape[:2]
# resize image to (wid,wid)
wid = 50
rim = imresize(im, (wid, wid), interp='bilinear')
rim = array(rim, 'f')
# create normalized cut matrix
A = ncut.ncut_graph_matrix(rim, sigma_d=1, sigma_g=1e-2)
# cluster
code, V = ncut.cluster(A, k=3, ndim=3)
codeim = imresize(code.reshape(wid,wid),(m,n),interp='nearest')
figure('203')
imshow(codeim)
gray()
show()
#imshow(imresize(V[i].reshape(wid,wid),(m,n),interp='bilinear'))
