Extracting value regions/clusters from numpy array

You can use skimage.measure which has functions to label these "clusters", which can be described as connected components, and obtain the coordinates using skimage.measure.regionprops's coords method:

from skimage.measure import label, regionprops

l = label(a, connectivity=1)
clusters = [i.coords for i in regionprops(l)]

Different numbers will imply different regions. But to limit neighbouring points to a single orthogonal position, we must set connectivity=1 in skimage.measure.label, otherwise the two last clusters would be considered the same, as both have 2s.

For instance, the last component you also shared would be:

clusters[-1]
array([[ 9, 14],
       [ 9, 15],
       [ 9, 16],
       [10, 14],
       [10, 15],
       [10, 16]], dtype=int64)

Numpy array construction:

from io import StringIO
import numpy as np

s = StringIO("""
01110000000000000000000000
00111110000222222220000000
01110000000222222200000000
00000000000222000000000000
00000000000000000000000000
00000000000000000000000000
00003333300000000000000000
00003333322222000000000000
00000000022222000000000000
00000000000000222000000000
00000000000000222000000000
00000000000000000000000000
00000000000000000000000000
00000000000000000000000000
00000000000000000000000000
00000000000000000000000000""")
a = np.genfromtxt(s, delimiter=1)