feat: very slow region graph function

README.md

@@ -36,17 +36,22 @@ python setup.py develop
 ## Python Use
 
 ```python
-from cc3d import connected_components
+import cc3d
 import numpy as np
 
 labels_in = np.ones((512, 512, 512), dtype=np.int32)
-labels_out = connected_components(labels_in)
+labels_out = cc3d.connected_components(labels_in)
 
 # You can extract individual components like so:
 N = np.max(labels_out)
 for segid in range(1, N+1):
   extracted_image = labels_out * (labels_out == segid)
   process(extracted_image)
+
+# We also include a 26-connected region adjacency graph function 
+# that returns a set of undirected edges. It is not optimized 
+# (100x slower than connected_components) but it could be improved.
+graph = cc3d.region_graph(labels_out) 
 ```
 
 If you know approximately how many labels you are going to generate, you can save substantial memory by specifying a number a safety factor above that range. The max label ID in your input labels must be less than `max_labels`.

automated_test.py

@@ -388,3 +388,19 @@ def test_compare_scipy():
 # def test_sixty_four_bit():
 #   input_labels = np.ones((1626,1626,1626), dtype=np.uint8)
 #   cc3d.connected_components(input_labels, max_labels=0)  
+
+def test_region_grap():
+  labels = np.zeros( (10, 10, 10), dtype=np.uint32 )
+
+  labels[1,1,1] = 1
+  labels[2,2,2] = 2
+  labels[3,3,3] = 3
+
+  labels[4,3,3] = 4
+  labels[2,3,3] = 5
+
+  # not connected to anything else
+  labels[6,:,:] = 6
+
+  res = cc3d.region_graph(labels)
+  assert res == set([ (1,2), (2,3), (2,5), (3,4), (3,5) ])