# code1
def filter(img, func, ksize, strides=1):
    height,width = img.shape
    f_height,f_width = ksize
    new_height = height - f_height + 1
    new_width = width - f_width + 1

    new_img = np.zeros((new_height,new_width))

    for i in range(new_height):
        for j in range(new_width):
            patch = img[i:i+f_height,j:j+f_width]
            new_img[i][j] = func(patch)

    return new_img

# code2
def relative_median_and_center_diff(patch, in_the_boundary, rectangle, center_point):
        mask = patch == 255
        mask[center_point] = True
        masked_patch = np.ma.array(patch, mask=mask)
        count = masked_patch.count()
        if count <= 1:
            return 0
        else:
            return patch[center_point]/(np.ma.median(masked_patch)+1)

# code3
def filter(img, func, ksize, strides=1):
    height,width = img.shape
    f_height,f_width = ksize
    new_height = height - f_height + 1
    new_width = width - f_width + 1

    new_img = np.zeros((new_height,new_width))

    from skimage.util.shape import view_as_windows
    patches = view_as_windows(img, (f_height,f_width))

    for i in range(new_height):
        for j in range(new_width):
            patch = patches[i,j]
            new_img[i][j] = func(patch)

    return new_img