def getWeightSumTest():
from algorithm import getLbp, getVectors, getWeightSum
labelMap = getSlic(img, 200)
maxLabel = labelMap.max() + 1
im = sk.color.rgb2lab(img)
degreeVectors, Ws = getVectors(im, labelMap)
vectors = getWeightSum(labelMap, degreeVectors, Ws)
m, n = labelMap.shape
imgg = np.zeros((m, n))
imgg2 = np.zeros((m, n))
order = [
'lab', 'l', 'a', 'b', 'lab-texture', 'l-texture', 'a-texture',
'b-texture'
]
labs = [im] + [im[:, :, i] for i in range(3)]
lbps = map(lambda c: getLbp(c, labelMap, 1)[1], labs)
labLbp = labs + lbps
for color in range(vectors.shape[1]):
for k in range(maxLabel):
imgg[labelMap == k] = vectors[k][color]
imgg2[labelMap == k] = degreeVectors[k][color]
print order[color], 'raw | scatter | weight sum'
# show(sk.exposure.equalize_hist(imgg))
show([labLbp[color], imgg2, imgg], 1)
loga(degreeVectors)
loga(vectors)
def getRefindImgsTest():
IMG_DIR = r'E:\3-experiment\SalBenchmark-master\Data\DataSet1\Imgs/'
COARSE_DIR =r'E:\3-experiment\SalBenchmark-master\Data\DataSet1\Saliency/'
IMG_DIR = 'test/'
COARSE_DIR ='test/'
IMG_NAME_LIST = filter(lambda x:x[-3:]=='jpg',listdir(IMG_DIR))
coarseMethods = ['QCUT','DRFI']
coarseMethods = ['GT']
imgInd = 0
n_segments,compactness = 200,10
imgName = IMG_NAME_LIST[imgInd]
img,imgGt = readImg(imgName)
coarseDic = getCoarseDic(imgName,coarseMethods)
#show(coarsesDic)
sumCoarseImg = getSumCoarseImg(coarseDic)
coarseImgs=coarseDic.values()
labelMap = getSlic(img,n_segments,compactness)
rgb = img
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))
vectorsImg = valueToLabelMap(labelMap,normalizing(vectors.sum(1)))
show([rgb,refinedImg])
show([rgb,vectorsImg])
show(vectorsImg-refinedImg)
loga(vectorsImg-refinedImg)
g()
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)))