用python帮我写一个图片边缘特征提取的脉冲神经网络

你好！

下面是一个 Python 代码示例，用于使用边缘特征提取的脉冲神经网络（Pulse Coupled Neural Network，PCNN）处理图像：

import numpy as np
from scipy import signal

def pcnn(image, kernel_size=3, alpha=0.1, beta=0.1, gamma=1.0):
    # 初始化权重矩阵
    weights = np.ones((kernel_size, kernel_size))
    
    # 初始化输出图像
    output = np.zeros_like(image)
    
    # 初始化迭代变量
    t = 0
    
    # 初始化阈值
    threshold = np.mean(image)
    
    # 迭代计算 PCNN
    while True:
        t += 1
        
        # 计算当前时刻的输出图像
        output_t = output.copy()
        
        # 遍历图像中的每个像素
        for i in range(image.shape[0]):
            for j in range(image.shape[1]):
                # 计算当前像素周围的区域
                region = image[max(0, i-1):min(i+2, image.shape[0]), 
                               max(0, j-1):min(j+2, image.shape[1])]
                region_weights = weights[max(0, 1-i):min(2-i, kernel_size), 
                                        max(0, 1-j):min(2-j, kernel_size)]
                
                # 计算当前像素的输出值
                output_t[i, j] = (1 - beta) * output[i, j] + beta * np.sum(region * region_weights)
                
                # 更新权重
                weights[1, 1] += alpha * (image[i, j] - output[i, j])
                weights[1:, :] -= alpha * (image[i, j] - output[i, j]) / (kernel_size - 1)
                weights[:, 1:] -= alpha * (image[i, j] - output[i, j]) / (kernel_size - 1)
        
        # 检查是否满足停止条件
        if np.abs(output - output_t).max() < gamma or t > 1000:
            break
        
        # 更新输出图像