此外,有人可以解释并提供更深入的解释为什么绝对值的计算和focus_stack()函数中发生了什么？

#   Compute the gradient map of the image
def doLap(image):

    # YOU SHOULD TUNE THESE VALUES TO SUIT YOUR NEEDS
    kernel_size = 5         # Size of the laplacian window
    blur_size = 5           # How big of a kernal to use for the gaussian blur
                            # Generally,keeping these two values the same or very close works well
                            # Also,odd numbers,please...

    blurred = cv2.GaussianBlur(image,(blur_size,blur_size),0)
    return cv2.Laplacian(blurred,cv2.CV_64F,ksize=kernel_size)

#
#   This routine finds the points of best focus in all images and produces a merged result...
#
def focus_stack(unimages):
    images = align_images(unimages)

    print "Computing the laplacian of the blurred images"
    laps = []
    for i in range(len(images)):
        print "Lap {}".format(i)
        laps.append(doLap(cv2.cvtColor(images[i],cv2.COLOR_BGR2GRAY)))

    laps = np.asarray(laps)
    print "Shape of array of laplacians = {}".format(laps.shape)

    output = np.zeros(shape=images[0].shape,dtype=images[0].dtype)

    abs_laps = np.absolute(laps)
    maxima = abs_laps.max(axis=0)
    bool_mask = abs_laps == maxima
    mask = bool_mask.astype(np.uint8)
    for i in range(0,len(images)):
        output = cv2.bitwise_not(images[i],output,mask=mask[i])

    return 255-output