from scipy.ndimage import label
import numpy as np
a = np.array([[0,0,1,1,0,0],
              [0,0,0,1,0,0],
              [1,1,0,0,1,0],
              [0,0,0,1,0,0]])
labels, N = label(a)
print(labels)
'''
[[0 0 1 1 0 0]
 [0 0 0 1 0 0]
 [2 2 0 0 3 0]
 [0 0 0 4 0 0]]
'''
print(N)    4

import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(121)
plt.imshow(a)
ax = fig.add_subplot(122)
plt.imshow(labels)
plt.show()

stru = np.ones([3,3])
bLab, bN = label(a, stru)
print(bLab)
‘'‘
[[0 0 1 1 0 0]
 [0 0 0 1 0 0]
 [2 2 0 0 1 0]
 [0 0 0 1 0 0]]
'‘'

labeled_comprehension(input, labels, index, func, out_dtype, default, pass_positions=False)

from scipy.ndimage import labeled_comprehension
labeled_comprehension(a, labels, [1,2,3,4], sum, int, 0)
# array([3, 2, 1, 1])

from scipy.ndimage import find_objects
axis = find_objects(labels)
for x,y in axis:
    print(x, y)

'''
slice(0, 2, None) slice(2, 4, None)
slice(2, 3, None) slice(0, 2, None)
slice(2, 3, None) slice(4, 5, None)
slice(3, 4, None) slice(3, 4, None)
''''

for x,y in axis:
    print(labels[x,y])
    print("--------")
'''
[[1 1]
 [0 1]]
--------
[[2 2]]
--------
[[3]]
--------
[[4]]
--------
'''