function C = Pruning(A, len) B = CreateEndpointSE(); X1 = A; for k = 1:len endpoints = false(size(A)); for m = 1:size(B,1) endpoints = endpoints | bwhitmiss(X1, B{m,1}, B{m,2}); end X1(endpoints) = 0; end X2 = false(size(A)); for m = 1:size(B,1) endpoints = bwhitmiss(X1, B{m,1}, B{m,2}); X2(endpoints) = 1; end se = strel(ones(3,3)); X3 = X2; for k = 1:len X3 = imdilate(X3, se) & A; end C = X3 | X1; end改成PYTHON代码

下面是转化为 Python 后的代码：

import numpy as np
from scipy import ndimage

def Pruning(A, len):
    B = CreateEndpointSE()
    X1 = A.copy()
    for k in range(len):
        endpoints = np.zeros_like(A, dtype=bool)
        for m in range(B.shape[0]):
            endpoints |= ndimage.binary_hit_or_miss(X1, B[m, 0], B[m, 1])
        X1[endpoints] = 0

    X2 = np.zeros_like(A, dtype=bool)
    for m in range(B.shape[0]):
        endpoints = ndimage.binary_hit_or_miss(X1, B[m, 0], B[m, 1])
        X2[endpoints] = 1

    se = np.ones((3, 3), dtype=bool)
    X3 = X2.copy()
    for k in range(len):
        X3 = ndimage.binary_dilation(X3, se) & A

    C = X3 | X1
    return C

需要注意的是，在 Python 中，需要使用 NumPy 和 SciPy 库中的函数来实现类似的图像处理操作。另外，函数中涉及的 CreateEndpointSE() 函数没有给出，需要根据具体应用场景自行实现。