"""
I = Image.open('../../../data/a.jpg')
I=numpy.array(I)/255.
pylab.figure()
pylab.imshow(I)
print 'quickshift_0'
ratio = 0.5
kernelsize = 2
maxdist = 10
seg=vlfeat.vl_quickseg(I,ratio,kernelsize,maxdist)
pylab.figure()
pylab.imshow(seg[0])
print 'quickshift_1'
kernelsize = 2
maxdist = 20
seg=vlfeat.vl_quickseg(I,ratio,kernelsize,maxdist)
pylab.figure()
pylab.imshow(seg[0])
print 'quickshift_2'
maxdist = 50
ndists = 10
seg=vlfeat.vl_quickvis(I,ratio,kernelsize,maxdist,ndists)
pylab.figure()
pylab.gray()
pylab.imshow(seg[0])
#pylab.show()
import vlfeat as vl
import cv2
import numpy as np
'''
vl_quickvis(I, ratio, kernelsize, maxdist, maxcuts=None)
Create an edge image from a Quickshift segmentation.
IEDGE = VL_QUICKVIS(I, RATIO, KERNELSIZE, MAXDIST, MAXCUTS) creates an edge
stability image from a Quickshift segmentation. RATIO controls the tradeoff
between color consistency and spatial consistency (See VL_QUICKSEG) and
KERNELSIZE controls the bandwidth of the density estimator (See VL_QUICKSEG,
VL_QUICKSHIFT). MAXDIST is the maximum distance between neighbors which
increase the density.
'''
I = cv2.imread('/lena.png')
ratio = .5
kernelsize = 10
maxdist = 10.0
vl.vl_quickvis(I, ratio, kernelsize, maxdist)
import vlfeat as vl
import cv2
import numpy as np
'''
vl_quickvis(I, ratio, kernelsize, maxdist, maxcuts=None)
Create an edge image from a Quickshift segmentation.
IEDGE = VL_QUICKVIS(I, RATIO, KERNELSIZE, MAXDIST, MAXCUTS) creates an edge
stability image from a Quickshift segmentation. RATIO controls the tradeoff
between color consistency and spatial consistency (See VL_QUICKSEG) and
KERNELSIZE controls the bandwidth of the density estimator (See VL_QUICKSEG,
VL_QUICKSHIFT). MAXDIST is the maximum distance between neighbors which
increase the density.
'''
I = cv2.imread('/lena.png')
ratio = .5
kernelsize=10
maxdist=10.0
vl.vl_quickvis(I, ratio, kernelsize, maxdist)
