[FIXED] how to change the particuler elements of an array

Issue

I have an array for an example:

import numpy as np

data=np.array([[4,4,4,0,1,1,1,1,1,1,0,0,0,0,1],
               [3,0,1,0,1,1,1,1,1,1,1,1,1,1,0],
               [6,0,0,0,1,1,1,1,1,1,1,1,1,1,0],
               [2,0,0,0,1,1,1,0,1,0,1,1,1,0,0],
               [2,0,1,0,1,1,1,0,1,0,1,0,1,0,0]])

Requirement : In the data array, if element 1's are consecutive as the square size of ((3,3)) and more than square size no changes. Otherwise, replace element value 1 with zero except the square size.

Expected output :

               [[4 4 4 0 1 1 1 1 1 1 0 0 0 0 0]
                [3 0 0 0 1 1 1 1 1 1 1 1 1 0 0]
                [6 0 0 0 1 1 1 1 1 1 1 1 1 0 0]
                [2 0 0 0 1 1 1 0 0 0 1 1 1 0 0]
                [2 0 0 0 1 1 1 0 0 0 0 0 0 0 0]]

current code:

k = np.ones((3,3))
print(k)

jk=binary_dilation(binary_erosion(data==1, k), k)
print(jk)

current output:

[[False False False False  True  True  True  True  True  True False 
 False False False False]
 [False False False False  True  True  True  True  True  True  True  
  True True False False]
 [False False False False  True  True  True  True  True  True  True  
  True True False False]
 [False False False False  True  True  True False False False  True  
 True True False False]
 [False False False False  True  True  True False False False False 
  False False False False]]

Solution

You can use a 2D convolution on the 1s with a 3x3 kernel of 1s to identify the centers of the 3x3 squares, then dilate them and restore the non 1 numbers

from scipy.signal import convolve2d
from scipy.ndimage import binary_dilation

# get 1s (as boolean)
m = data==1

kernel = np.ones((3, 3))

# get centers
conv = convolve2d(m, kernel, mode='same')

# dilate and restore the other numbers
out = np.where(m, binary_dilation(conv == 9, kernel).astype(int), data)

print(out)

Alternative, erosion and dilation, similarly to my previous answer:

from scipy.ndimage import binary_dilation, binary_erosion

m = data==1
kernel = np.ones((3, 3))

out = np.where(m, binary_dilation(binary_erosion(m, kernel), kernel), data)

Output:

[[4 4 4 0 1 1 1 1 1 1 0 0 0 0 0]
 [3 0 0 0 1 1 1 1 1 1 1 1 1 0 0]
 [6 0 0 0 1 1 1 1 1 1 1 1 1 0 0]
 [2 0 0 0 1 1 1 0 0 0 1 1 1 0 0]
 [2 0 0 0 1 1 1 0 0 0 0 0 0 0 0]]

Answered By - mozway