源码：

# 读取图片
img = cv2.imread("d:/img.jpg", 1)
height, width, channels = img.shape

# 灰度图
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 将图像分为8x8的多个小块，并统计小块每个像素的灰度值
# 划分灰度等级，例如0-255: 0-63, 64-127, ...
# 找出每个小块中，每个等级的个数，找出最多的
# 用最多的的平均值替换原像素

dst_img = np.zeros((height, width, channels), np.uint8)
for i in range(0, height):
    for j in range(0, width):
        max_level_arr = []
        levelMap = {} # k: 级别, v: 原始像素值的列表
        for m in range(-4, 4):
            for n in range(-4, 4):
                # 处理越界
                if i + m >= height or i + m < 0:
                    m = -m
                if j + n >= width or j + n < 0:
                    n = -n
               
                # 划分等级 0-31, 32-63, ...
                level = gray_img[i + m, j + n] // 32
                if not level in levelMap.keys():
                    levelMap[level] = [img[i + m, j + n],]
                else:
                    levelMap[level].append(img[i + m, j + n])
                # 个数最多的级别
                if len(levelMap[level]) > len(max_level_arr):
                    max_level_arr = levelMap[level]
        # 取均值
        size = len(max_level_arr)
        b_sum, g_sum, r_sum = 0, 0, 0
        for b, g, r in max_level_arr:
            b_sum += b
            g_sum += g
            r_sum += r
        dst_img[i, j] = [b_sum // size, g_sum // size, r_sum // size ]
   

cv2.imshow("dst_img", dst_img)
if cv2.waitKey() == ord("q"):
    cv2.destroyAllWindows()

效果：