def featureExtractOneFile(loc,clusterData,doRandom=True,randomPixno=1000):
size=(128,128)
im=(resize(rgb2grey(imread(loc)),size)*255) #resize
rp=extractPatch(im)
print "patch extraction done"
#for each pixel that has been converted into a 36 pixel patch, we are first going to find out the nearest codebook patch. Then,
#we will create an one hot vector. Finally we will sum up the vectors belonging to the same quadrant and concatenate them.
patchDict={}
quadrant=0
#this is too time consuming, we are choosing 5000 pixels randomly
#randomPixelIndices=sorted(range((128-(PS/2))*(128-(PS/2))), key=os.urandom)[0:5000]
#print len(randomPixelIndices)
randomPixelIndices=range((128-(PS/2))*(128-(PS/2)))
if doRandom:
random.shuffle(randomPixelIndices)
print "shuffling done"
else:
randomPixno=len(randomPixelIndices)
#for i,patch in enumerate(rp):
for i in randomPixelIndices[:randomPixno]:
patch=rp[i]
divFactor=128-(PS/2)
x=i/divFactor
y=i%divFactor
if x<65 and y<65:
quadrant=1
elif x>65 and y<65:
quadrant=2
elif x>65 and y>65:
quadrant=3
else:
quadrant=4
if patch.shape[0]*patch.shape[1]==36:
patch=standardizePatch(patch)
if quadrant not in patchDict:
patchDict[quadrant]=nearestOneHot(patch,clusterData)
else:
patchDict[quadrant]=np.sum((patchDict[quadrant],nearestOneHot(patch,clusterData)),axis=0)
#pprint(patchDict)
return np.hstack((patchDict[1],patchDict[2],patchDict[3],patchDict[4]))
def extractPatch1(img):
return filter(lambda y: y.shape[0]*y.shape[1]==PS*PS, [standardizePatch(img[x/64:x/64+PS,x%64:x%64+PS]) for x in range(64*64) \
if ndimage.variance(img[x/64:x/64+PS,x%64:x%64+PS]) > VR*ndimage.variance(img)])