Numpy 2d Array: Get Indices Of All Entries That Are Connected And Share The Same Value

I have a 2D numpy Array filled with integer-values from 0 to N, how can i get the indices of all entries that are directly connected and share the same value. Addition: Most of the

Solution 1:

You can use skimage.measure.label (install it with pip install scikit-image, if needed) for this:

import numpy as np
from skimage import measure

# Setup some data
np.random.seed(42)
img = np.random.choice([0, 1, 2], (5, 5), [0.7, 0.2, 0.1])
# [[2 0 2 2 0]#  [0 2 1 2 2]#  [2 2 0 2 1]#  [0 1 1 1 1]#  [0 0 1 1 0]]# Label each region, considering only directly adjacent pixels connected
img_labeled = measure.label(img, connectivity=1)
# [[1 0 2 2 0]#  [0 3 4 2 2]#  [3 3 0 2 5]#  [0 5 5 5 5]#  [0 0 5 5 0]]# Get the indices for each region, excluding zeros
idx = [np.where(img_labeled == label)
       for label in np.unique(img_labeled)
       if label]
# [(array([0]), array([0])),#  (array([0, 0, 1, 1, 2]), array([2, 3, 3, 4, 3])),#  (array([1, 2, 2]), array([1, 0, 1])),#  (array([1]), array([2])),#  (array([2, 3, 3, 3, 3, 4, 4]), array([4, 1, 2, 3, 4, 2, 3]))]# Get the bounding boxes of each region (ignoring zeros)
bboxes = [area.bbox for area in measure.regionprops(img_labeled)]
# [(0, 0, 1, 1),#  (0, 2, 3, 5),#  (1, 0, 3, 2),#  (1, 2, 2, 3),#  (2, 1, 5, 5)]

The bounding boxes can be found using the very helpful function skimage.measure.regionprops, which contains a plethora of information on the regions. For the bounding box it returns a tuple of (min_row, min_col, max_row, max_col), where pixels belonging to the bounding box are in the half-open interval [min_row; max_row) and [min_col; max_col).