[FIXED] Creating boundaries on numpy arrays

Issue

I am looking for an algorithm that will help me create a boundary around the points on a 2-dim array that are not zero. Let me give an example.

I have the following array:]

I want an algorithm that will give me the most efficient way I can create a "barricade" around each of the little "islands". For context, each group of 1s represent a source of resources in this resource management game, and I want to build walls around it to guard it from my opponent.

Essentially, this algorithm should give the value of the 2s in the following figure:

There are a few points to this:

• The array is always square but the size of each dimension can vary from 12 to 40;
• Units cannot move on diagonals;
• There can be various islands (groups of 1s), and can have all sorts of irregular shapes;
• Sometimes it might be more efficient to barricade two close by "islands" using the same barricade, if they are super close by.

Any ideas on this? I don't even know how to google this problem, so I ended up not finding anything. Thank you!

EDIT1: Code to create the initial array EDIT2: Fixed the answer

import numpy as np

size = 15
test_mat = np.zeros((size, size))
test_mat[(4, 5, 5, 6), (5, 4, 6, 5)] = 1
test_mat[(9, 9, 9, 10, 10, 11, 11), (8, 9, 10, 8, 10, 8, 10)] = 1

Solution

How about this:

import numpy as np
from scipy.ndimage import binary_dilation, binary_erosion

size = 15
test_mat = np.zeros((size, size))
test_mat[(4, 5, 5, 6), (5, 4, 6, 5)] = 1
test_mat[(9, 9, 9, 10, 10, 11, 11), (8, 9, 10, 8, 10, 8, 10)] = 1

dilated = binary_dilation(test_mat).astype(int)
eroded = binary_erosion(dilated)
borders = dilated-eroded

which gives:

[[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 1 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 1 0 1 0 0 0 0 0 0 0 0]
 [0 0 0 1 0 0 0 1 0 0 0 0 0 0 0]
 [0 0 0 0 1 0 1 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 1 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 1 1 1 0 0 0 0]
 [0 0 0 0 0 0 0 1 0 0 0 1 0 0 0]
 [0 0 0 0 0 0 0 1 0 0 0 1 0 0 0]
 [0 0 0 0 0 0 0 1 0 1 0 1 0 0 0]
 [0 0 0 0 0 0 0 0 1 0 1 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]

Answered By - alex