OpenCV-based image similarity analyzer

dedupe.py

import cv
from optparse import OptionParser

class Simple2DAnalyser(object):
    def __init__(self, imagename):
        im = cv.LoadImageM(imagename, cv.CV_LOAD_IMAGE_COLOR)
        cv.Smooth(im, im, cv.CV_GAUSSIAN, 3)
        # Now let's get four quadrants and our total
        halfX = int(round(im.cols/2))
        halfY = int(round(im.rows/2))
        self.total = self.sumArea(im)
        nw_rect = (0, 0, halfX, halfY)
        ne_rect = (halfX+1, 0, im.cols-(halfX+1), halfY)
        sw_rect = (0, halfY+1, halfX, im.rows-(halfY+1))
        se_rect = (halfX+1, halfY+1, im.cols-(halfX+1), im.rows-(halfY+1))
        self.nw = self.sumArea(cv.GetSubRect(im, nw_rect))
        self.ne = self.sumArea(cv.GetSubRect(im, ne_rect))
        self.sw = self.sumArea(cv.GetSubRect(im, sw_rect))
        self.se = self.sumArea(cv.GetSubRect(im, se_rect))

    def sumArea(self, im):
        sums = cv.Sum(im)
        self.r = (sums[0]/(im.rows*im.cols))*0.2126
        self.g = (sums[1]/(im.rows*im.cols))*0.7152
        self.b = (sums[2]/(im.rows*im.cols))*0.0772
        return ((self.b+self.g+self.r)/3)
    def getValues(self):
        return ("%f %f %f %f %f" %
                (self.total, self.ne, self.nw, self.se, self.sw))

usage = "usage: %prog filename"
parser = OptionParser(usage=usage)
(options, args) = parser.parse_args()
s = Simple2DAnalyser(args[0])
print(s.getValues())