main.py 文件源码

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