python - 在 2D 或 3D 数组中查找相邻数字的快速方法

Question

我有以下二维数组

regions = array([[3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4],
                 [3, 3, 3, 3, 8, 8, 8, 8, 8, 4, 4, 4, 4],
                 [3, 3, 3, 3, 8, 8, 8, 8, 8, 4, 4, 4, 4],
                 [3, 3, 3, 3, 8, 8, 8, 8, 8, 4, 4, 4, 4],
                 [3, 6, 6, 6, 8, 8, 8, 8, 8, 7, 7, 7, 4],
                 [3, 6, 6, 6, 8, 8, 8, 8, 8, 7, 7, 7, 4],
                 [3, 6, 6, 6, 6, 8, 8, 8, 7, 7, 7, 7, 4],
                 [3, 6, 6, 6, 6, 2, 2, 2, 7, 7, 7, 7, 4],
                 [5, 6, 6, 6, 6, 2, 2, 2, 7, 7, 7, 7, 1],
                 [5, 6, 6, 6, 6, 2, 2, 2, 7, 7, 7, 7, 1],
                 [5, 6, 6, 6, 6, 2, 2, 2, 7, 7, 7, 7, 1],
                 [5, 5, 5, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1],
                 [5, 5, 5, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1]])

我想为所有单个号码查找相邻号码。例如 3 是 4,5,6,8 的否定。目前，我正在按照下面提到的代码使用 for 循环 进行此练习。

numbers = scipy.unique(regions)
for i in numbers:
    index = i-1
    slices = scipy.ndimage.find_objects(regions)
    sub_im = regions[slices[index]]
    im = sub_im == i
    neighbors = scipy.ndimage.binary_dilation(input=im, structure=disk(1))
    neighbors = neighbors*sub_im
    neighbors_list = scipy.unique(neighbors)[1:] - 1
    print (neighbors_list)

问题是我不想使用 for 循环，因为我的区域数组有数百万个。有没有不用for循环的快速解决方法？

Answer 1

您可以使用 numpy、np.roll()、np.where() 和 np.独特的()。这个想法是将每个条目附加到它的四个邻居，然后从这五个列表中提取唯一成员(该条目及其四个邻居)。这是一个应该澄清的实现:

# make a 3d array with the matrix entry and its four neighbors
neighbor_array = np.array([regions,
                          np.roll(regions,+1,axis=0),
                          np.roll(regions,-1,axis=0),
                          np.roll(regions,+1,axis=1),
                          np.roll(regions,-1,axis=1),
                          ])
# if you want neighbors to include wraparounds, use above; if not, prune
neighbor_array_pruned = neighbor_array[:,1:-1,1:-1]
# reshape to a 2d array entries x neighbors
neighbor_list = np.reshape(neighbor_array_pruned,[5,-1]).T
# get uniques into a dictionary 
neighbor_dict = {}
for num in np.unique(regions):
    neighbor_dict[num] = np.unique(neighbor_list[np.where(neighbor_list[:,0]==num)])

这会产生 neighbor_dict:

{1: array([1, 2, 7]),
 2: array([1, 2, 5, 6, 7, 8]),
 3: array([3, 6, 8]),
 4: array([4, 7, 8]),
 5: array([2, 5, 6]),
 6: array([2, 3, 5, 6, 8]),
 7: array([1, 2, 4, 7, 8]),
 8: array([2, 3, 4, 6, 7, 8])}

注意我修剪了边缘；如果你想包括环绕式邻居或做一些更细微的事情，你可以详细说明修剪线。