saliency.py 文件源码

def N(image):
    """
        Normalization parameter as per Itti et al. (1998).
        returns a normalized feature map image.
    """
    M = 8.  # an arbitrary global maximum to which the image is scaled.
            # (When saving saliency maps as images, pixel values may become
            # too large or too small for the chosen image format depending
            # on this constant)
    image = cv2.convertScaleAbs(image, alpha=M/image.max(), beta=0.)
    w,h = image.shape
    maxima = maximum_filter(image, size=(w/10,h/1))
    maxima = (image == maxima)
    mnum = maxima.sum()
    logger.debug("Found %d local maxima.", mnum)
    maxima = numpy.multiply(maxima, image)
    mbar = float(maxima.sum()) / mnum
    logger.debug("Average of local maxima: %f.  Global maximum: %f", mbar, M)
    return image * (M-mbar)**2