def process(line,d):
line = line.strip().split(tab)
sample=line[1]
image=line[0]
featureimages=line[2:]
img=Image.open(image)
images=imageTransforms.normalizeImage(img) #Arjun's thing
features=[]
featurecoordinates=[]
labels=[]
for j in xrange(len(featureimages)):
c=getcoordinates.getcoordinates(featureimages[j],sample)
for i in xrange(len(c)):
featurecoordinates.append(c[i])
del c
for j in xrange(len(featurecoordinates)):
a=d.get(featurecoordinates[j][5],0)
m=max([a,featurecoordinates[j][2]-featurecoordinates[j][0],featurecoordinates[j][3]-featurecoordinates[j][1]])
d[featurecoordinates[j][5]]=m
for j in xrange(len(featurecoordinates)):
newfeature=runtraininganalysis(images[RGB],featurecoordinates[j])
if newfeature:
for i in xrange(len(newfeature)):
features.append(newfeature[i])
print("new feature",i)
labels.append(featurecoordinates[j][4]+"_"+featurecoordinates[j][5])
labels.append(featurecoordinates[j][4]+"_"+featurecoordinates[j][5])
labels.append(featurecoordinates[j][4]+"_"+featurecoordinates[j][5])
labels.append(featurecoordinates[j][4]+"_"+featurecoordinates[j][5])
return labels,features,d
def process(line):
line = line.strip().split(tab)
sample=line[1]
image=line[0]
featureimages=line[2:]
img=Image.open(image)
normimg=normalize(img) #Arjun's thing
features=[]
featurecoordinates=[]
for j in range(len(featureimages)):
inf = featureimages[j].split('.')
features.append(inf[1])
c=getcoordinates.getcoordinates(featureimages[j],sample)
featurecoordinates.append(c)
del c
