def getEdgeNeighborTest():
from algorithm import getEdge,getNeighborMatrix,getNeighbor
labelMap = getSlic(img,200)
show(mark_boundaries(img,labelMap),0)
edge = getEdge(labelMap)
neighborMatrix = getNeighborMatrix(labelMap)
m, n = labelMap.shape
dic = getNeighbor(0,labelMap,neighborMatrix,4)
print dic
imgg = np.zeros((m,n))
for k in dic:
imgg[labelMap==k]=dic[k]
show(imgg)
imgg = np.zeros((m,n))
for k in edge:
imgg[labelMap==k]=1
show(imgg)
edge = getEdge(labelMap,0.06)
imgg = np.zeros((m,n))
for k in edge:
imgg[labelMap==k]=1
print 'edge width 0.06'
show(imgg)
def my4test():
#if 1:
from algorithm import *
labelMap = getSlic(rgbImg,200)
maxLabel = labelMap.max()+1
m, n = labelMap.shape
coarseMethods = ['MEAN']
coarseDic = getCoarseDic(imgName,coarseMethods)
# show(coarseDic)
sumCoarseImg = getSumCoarseImg(coarseDic)
coarseImgs=coarseDic.values()
img = sk.color.rgb2lab(rgbImg)
# 获得4+4维 distance
degreeVectors, Ws = getVectors(img, labelMap)
weightSumVectors = getWeightSum(labelMap, degreeVectors, Ws)
diffEdges,diffNeighbors = getAllDiffEdgeAndNeighbor(labelMap,Ws)
vectors = np.append(weightSumVectors,diffEdges,1)
vectors = np.append(vectors,diffNeighbors,1)
vectorsTrains = []
coarseTrains = []
for coarseImg in coarseImgs:
coarseTrain, vectorsTrainTag = getCoarseTrain(coarseImg, labelMap)
vectorsTrains += list(vectors[vectorsTrainTag])
coarseTrains += list(coarseTrain)
elm = getElm(np.array(vectorsTrains), np.array(coarseTrains))
refined = elm.predict(vectors)[:,0]
refinedImg = valueToLabelMap(labelMap,normalizing(refined))
show(mark_boundaries(sk.color.lab2rgb(img),labelMap))
print diffEdges.shape
show(valueToLabelMap(labelMap,diffEdges.sum(1)))
show(valueToLabelMap(labelMap,diffNeighbors[:,:4].sum(1)))
# show(valueToLabelMap(labelMap,diffNeighbors[:,4:].sum(1)))
# show(valueToLabelMap(labelMap,diffNeighbors.sum(1)))
show(valueToLabelMap(labelMap,vectors[:,4:8].sum(1)))