labels[m == 255] = 1
else:
labels[m == 128] = 1
return labels
# load image and annotation map
im = array(Image.open('376043.jpg'))
m = array(Image.open('376043.bmp'))
# resize
scale = 0.1
im = imresize(im, scale, interp='bilinear')
m = imresize(m, scale, interp='nearest')
# create training labels
labels = create_msr_labels(m, False)
# build graph using annotations
g = graphcut.build_bayes_graph(im, labels, kappa=2)
# cut graph
res = graphcut.cut_graph(g, im.shape[:2])
# remove parts in background
res[m == 0] = 1
res[m == 64] = 1
# plot the result
figure()
imshow(res)
gray()
xticks([])
yticks([])
savefig('labelplot.pdf')
from scipy.misc import imresize
from PCV.tools import graphcut
from PIL import Image
from pylab import *
im = array(Image.open("../data/empire.jpg"))
im = imresize(im, 0.07)
size = im.shape[:2]
# add two rectangular training regions
labels = zeros(size)
labels[3:18, 3:18] = -1
labels[-18:-3, -18:-3] = 1
# create graph
g = graphcut.build_bayes_graph(im, labels, kappa=1)
# cut the graph
res = graphcut.cut_graph(g, size)
figure()
graphcut.show_labeling(im, labels)
figure()
imshow(res)
gray()
axis('off')
show()
if lasso:
labels[m == 255] = 1
else:
labels[m == 128] = 1
return labels
# load image and annotation map
im = array(Image.open('376043.jpg'))
m = array(Image.open('376043.bmp'))
# resize
scale = 0.1
im = imresize(im, scale, interp='bilinear')
m = imresize(m, scale, interp='nearest')
# create training labels
labels = create_msr_labels(m, False)
# build graph using annotations
g = graphcut.build_bayes_graph(im, labels, kappa=2)
# cut graph
res = graphcut.cut_graph(g, im.shape[:2])
# remove parts in background
res[m == 0] = 1
res[m == 64] = 1
# plot the result
figure()
imshow(res)
gray()
xticks([])
yticks([])
savefig('labelplot.pdf')
