如何使用 Python 堆实现图像增强算法？

import cv2
import heapq

def pixel_mean(pixel, im_gray):
    """计算像素的邻域平均值"""
    row, col = pixel
    pts = [(p, q) for p in range(row-1, row+2) for q in range(col-1, col+2) if 0 <= p < im_gray.shape[0] and 0 <= q < im_gray.shape[1]]
    return sum([im_gray[p, q] for p, q in pts])/len(pts)

def pixel_median(pixel, im_gray):
    """计算像素的邻域中值"""
    row, col = pixel
    pts = [(p, q) for p in range(row-1, row+2) for q in range(col-1, col+2) if 0 <= p < im_gray.shape[0] and 0 <= q < im_gray.shape[1]]
    vals = [im_gray[p, q] for p, q in pts]
    vals.sort()
    return vals[len(vals)//2]

def enhance_image(im_gray, radius, method='mean'):
    """图像增强算法"""
    # 计算像素邻域值
    pts = [(p, q) for p in range(radius, im_gray.shape[0]-radius) for q in range(radius, im_gray.shape[1]-radius)]
    if method == 'mean':
        vals = [pixel_mean(pixel, im_gray) for pixel in pts]
    elif method == 'median':
        vals = [pixel_median(pixel, im_gray) for pixel in pts]
    # 应用堆排序
    vals_heap = vals.copy()
    heapq.heapify(vals_heap)
    new_im_gray = im_gray.copy()
    for pixel, val in zip(pts, vals):
        row, col = pixel
        new_im_gray[row, col] = heapq.nlargest(len(vals)//2, vals_heap)[-1]
        vals_heap.remove(val)
        heapq.heapify(vals_heap)
    return new_im_gray

# 测试代码
im_gray = cv2.imread('test.png', cv2.IMREAD_GRAYSCALE)
new_im_gray = enhance_image(im_gray, radius=1, method='mean')
cv2.imshow('original', im_gray)
cv2.imshow('enhanced', new_im_gray)
cv2.waitKey(0)