def predict(img):
'''??????????'''
if len(_histograms)==0:
print 'model is not build'
return
img=cv2.resize(img,(48,48))
lbP_img=recognition.CircularLBP(img,radius=_radius,neighbors=_neighbors)
lbph_pre=recognition.LBPH(lbP_img,int(math.pow(2,_neighbors)),grid_x=_grid_x,grid_y=_grid_y)
minDist=sys.float_info.max
minClass=-1
for index in range(len(_histograms)):
dist=cv2.compareHist(_histograms[index],lbph_pre,cv2.HISTCMP_CHISQR)
if dist<minDist:
minDist=dist
minClass=_labels[index]
print 'label:%d distance:%f'%(minClass,minDist)
return minClass,minDist,lbP_img,lbph_pre
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