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from __future__ import print_function
import vigra
from vigra import graphs
from vigra import numpy
import pylab
# parameter
filepath = '12003.jpg' # input image path
sigmaGradMag = 2.0 # sigma Gaussian gradient
superpixelDiameter = 10 # super-pixel size
slicWeight = 10.0 # SLIC color - spatial weight
beta = 0.5 # node vs edge weight
nodeNumStop = 50 # desired num. nodes in result
# load image and convert to LAB
img = vigra.impex.readImage(filepath)
# get super-pixels with slic on LAB image
imgLab = vigra.colors.transform_RGB2Lab(img)
labels, nseg = vigra.analysis.slicSuperpixels(imgLab, slicWeight,
superpixelDiameter)
labels = vigra.analysis.labelImage(labels)
gridGraph = graphs.gridGraph(img.shape[0:2])
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
# get the merge graph
mg = graphs.mergeGraph(rag)
# do n runs where we erase k edges in each run
n = 3
k = 200
for r in range(n):
erased = 0
while(erased<k):
# get a random edge
randEdgeId = numpy.random.randint(rag.edgeNum)
print("random edge:",randEdgeId)
# edge could be gone
# -since we could have merged it already
# - or due to transitivity of other merges
if mg.hasEdgeId(randEdgeId):
mg.contractEdge(mg.edgeFromId(randEdgeId))
erased+=1
# get the segmentation
# (brand new c++ function for max speed)
labels = mg.graphLabels()
# view results
rag.show(img,labels)
vigra.show()
# get the result as pixels wise labeling
asImage = rag.projectLabelsToGridGraph(labels)
asImage = vigra.taggedView(asImage, "xy")
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