为什么不直接使用scipy.ndimage.binary_dilation？

import numpy as np
from scipy import ndimage

a = np.array([
    [False, False, False],
    [False,  True, False],
    [False, False, False],
])
b = ndimage.binary_dilation(a, [
    [False, True, False],
    [ True, True,  True],
    [False, True, False],
])

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结果如下：

>>> a
array([[False, False, False],
       [False,  True, False],
       [False, False, False]])
>>> b
array([[False,  True, False],
       [ True,  True,  True],
       [False,  True, False]])

型

使用scipy.signal.convolve2d的解决方案：

import numpy as np
from scipy.signal import convolve2d

# Example input
# [[False False False False False]
#  [False False  True  True False]
#  [False False False False False]
#  [False False False False False]
#  [False False False False  True]]
in_array = np.zeros((5, 5), dtype=bool)
in_array[1,2] = True
in_array[1,3] = True
in_array[4,4] = True

# Kernel: here you should define how much the True "dilates"

kernel = np.asarray([[False, True, False],
                     [True, True, True],
                     [False, True, False]])

# Convolution happens here
# Convolution is not possible for bool values though, so we convert to int and
# back. That works because bool(N) == True if N != 0.
result = convolve2d(in_array.astype(int), kernel.astype(int), mode='same').astype(bool)
print(result)

# Result:
# [[False False  True  True False]
#  [False  True  True  True  True]
#  [False False  True  True False]
#  [False False False False  True]
#  [False False False  True  True]]

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我建议的一个简单方法是创建一个更大的数组并将较小的数组嵌入其中，如中所述：
How to "embed" a small numpy array into a predefined block of a large numpy array?
我刚刚开始尝试在stackoverflow上回答问题，所以我是一个初学者。因此，请原谅我，如果我的回答太简单，或者不是你想要的。