saliency.py 文件源码

def makeNormalizedColorChannels(image, thresholdRatio=10.):
    """
        Creates a version of the (3-channel color) input image in which each of
        the (4) channels is normalized.  Implements color opponencies as per 
        Itti et al. (1998).
        Arguments:
            image           : input image (3 color channels)
            thresholdRatio  : the threshold below which to set all color values
                                to zero.
        Returns:
            an output image with four normalized color channels for red, green,
            blue and yellow.
    """
    intens = intensity(image)
    threshold = intens.max() / thresholdRatio
    logger.debug("Threshold: %d", threshold)
    r,g,b = cv2.split(image)
    cv2.threshold(src=r, dst=r, thresh=threshold, maxval=0.0, type=cv2.THRESH_TOZERO)
    cv2.threshold(src=g, dst=g, thresh=threshold, maxval=0.0, type=cv2.THRESH_TOZERO)
    cv2.threshold(src=b, dst=b, thresh=threshold, maxval=0.0, type=cv2.THRESH_TOZERO)
    R = r - (g + b) / 2
    G = g - (r + b) / 2
    B = b - (g + r) / 2
    Y = (r + g) / 2 - cv2.absdiff(r,g) / 2 - b

    # Negative values are set to zero.
    cv2.threshold(src=R, dst=R, thresh=0., maxval=0.0, type=cv2.THRESH_TOZERO)
    cv2.threshold(src=G, dst=G, thresh=0., maxval=0.0, type=cv2.THRESH_TOZERO)
    cv2.threshold(src=B, dst=B, thresh=0., maxval=0.0, type=cv2.THRESH_TOZERO)
    cv2.threshold(src=Y, dst=Y, thresh=0., maxval=0.0, type=cv2.THRESH_TOZERO)

    image = cv2.merge((R,G,B,Y))
    return image