def my5diffEdgeAndNeighbor(img, coarseImgs, labelMap):
'''MY5
add different from Edge And Neighbor
'''
img = sk.color.rgb2lab(img)
# 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))
return refinedImg
def getRefindImgsOneElmAddLabAndLbp(img, coarseImgs, labelMap):
'''MY3
use all coarse imgs to train one elm, than, predict one refinedImg
add Lab
'''
img = sk.color.rgb2lab(img)
#show([mark_boundaries(img,labelMap),imgGt])
# 获得4+4维 distance
degreeVectors, Ws = getVectors(img, labelMap)
weightSumVectors = getWeightSum(labelMap, degreeVectors, Ws)
'''add lab lbp'''
labVectors = getColorVector(img, labelMap)
lbpVectors = getLbp(img, labelMap)
vectors = np.c_[weightSumVectors, labVectors, lbpVectors]
# print 'weightSumVectors labVectors vectors.shape',weightSumVectors.shape,labVectors.shape,vectors.shape
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))
return refinedImg
def getBgNoElm(img, coarseImgs=None, labelMap=None):
'''BG1
'''
img = sk.color.rgb2lab(img)
#show([mark_boundaries(img,labelMap),imgGt])
# 获得4+4维 distance
degreeVectors, Ws = getVectors(img, labelMap)
vectors = getWeightSum(labelMap, degreeVectors, Ws)
if not coarseImgs:
bg = norma(vectors2labelMap(vectors, labelMap).sum(-1))
return bg
show(bg)
pblue('befor Classifier!')
coarseImgs = [bg]
vectorsTrains = []
coarseTrains = []
for coarseImg in coarseImgs:
coarseTrain, vectorsTrainTag = getCoarseTrain(coarseImg, labelMap)
vectorsTrains += list(vectors[vectorsTrainTag])
coarseTrains += list(coarseTrain)
g()
elm = getElm(np.array(vectorsTrains), np.array(coarseTrains))
refined = elm.predict(vectors)[:, 0]
refinedImg = valueToLabelMap(labelMap, normalizing(refined))
return refinedImg
def getRefindImgsOneElm(img, coarseImgs, labelMap):
'''MY1
use all coarse imgs to train one elm, than, predict one refinedImg
'''
img = sk.color.rgb2lab(img)
#show([mark_boundaries(img,labelMap),imgGt])
# 获得4+4维 distance
degreeVectors, Ws = getVectors(img, labelMap)
vectors = getWeightSum(labelMap, degreeVectors, Ws)
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))
return refinedImg
def getRefindImgsManyElm(img, coarseImgs, labelMap):
'''MY2
for each coarse img train a elm, each elm predict a refinedImg
merge refinedImgs and normalizing to one refinedImg
'''
img = sk.color.rgb2lab(img)
#show([mark_boundaries(img,labelMap),imgGt])
# 获得4+4维 color and texture scatter degree
degreeVectors, Ws = getVectors(img, labelMap)
#
vectors = getWeightSum(labelMap, degreeVectors, Ws)
refinedImgs = []
for coarseImg in coarseImgs:
coarseTrain, vectorsTrainTag = getCoarseTrain(coarseImg, labelMap)
elm = getElm(vectors[vectorsTrainTag], coarseTrain)
refined = elm.predict(vectors)[:, 0]
refinedImg = valueToLabelMap(labelMap, normalizing(refined))
refinedImgs += [refinedImg]
# 合并 归一
refinedImgSum = integratImgsBy3way(refinedImgs)
return refinedImgSum
