faster way to erode/dilate images

I'm making a script thats does some mathemagical morphology on images (mainly gis rasters). Now, I've implemented erosion and dilation, with opening/closing with reconstruction still on the TODO but thats not the subject here.

My implementation is very simple with nested loops, which I tried on a 10900x10900 raster and it took an absurdly long amount of time to finish, obviously.

Before I continue with other operations, I'd like to know if theres a faster way to do this?

My implementation:

def erode(image, S):
    (m, n) = image.shape
    buffer = np.full((m, n), 0).astype(np.float64)
    
    for i in range(S, m - S):
        for j in range(S, n - S):
            buffer[i, j] = np.min(image[i - S: i + S + 1, j - S: j + S + 1]) #dilation is just np.max()

    return buffer

I've heard about vectorization but I'm not quite sure I understand it too well. Any advice or pointers are appreciated. Also I am aware that opencv has these morphological operations, but I want to implement my own to learn about them.

source https://stackoverflow.com/questions/73027495/faster-way-to-erode-dilate-images

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