def getForegroundMask(self):
        '''
        @return: A mask image indicating which pixels are considered foreground.
          Depending on whether soft-thresholding is used, this may be a binary image
          with values of [0 or 255], or image of weights [0.0-255.0], which will
          have to be divided by 255 to get weights [0.0-1.0].         
        @note: One may wish to perform additional morphological operations
            on the foreground mask prior to use.
        '''
        diff = self._computeBGDiff()
        if self._softThreshold:
            mask = 1 - (math.e)**(-(1.0*diff)/self._threshold)  #element-wise exp weighting
            #mask = (diff > self._threshold)   
        else:
            mask = (self._threshold > sp.absolute(diff))    
            #mu = sp.mean(diff)
            #sigma = sp.std(diff)
            #mask = sp.absolute((diff-mu)/sigma) > self._threshold
        return pv.Image(mask*255.0)
dataset/ETHPy150Open bolme/pyvision/src/pyvision/surveillance/BackgroundSubtraction.py/AbstractBGModel.getForegroundMask/swapped operands of `>`