Merge pull request #2058 from vighneshbirodkar/hmerge_boundary

[WIP] Hierarchical Merging of Region Boundary RAGs

skimage/future/graph/rag.py

@@ -50,8 +50,9 @@ def _edge_generator_from_csr(csr_matrix):
 def min_weight(graph, src, dst, n):
     """Callback to handle merging nodes by choosing minimum weight.
 
-    Returns either the weight between (`src`, `n`) or (`dst`, `n`)
-    in `graph` or the minimum of the two when both exist.
+    Returns a dictionary with `"weight"` set as either the weight between
+    (`src`, `n`) or (`dst`, `n`) in `graph` or the minimum of the two when
+    both exist.
 
     Parameters
     ----------
@@ -64,9 +65,10 @@ def min_weight(graph, src, dst, n):
 
     Returns
     -------
-    weight : float
-        The weight between (`src`, `n`) or (`dst`, `n`) in `graph` or the
-        minimum of the two when both exist.
+    data : dict
+        A dict with the `"weight"` attribute set the weight between
+        (`src`, `n`) or (`dst`, `n`) in `graph` or the minimum of the two when
+        both exist.
 
     """
 
@@ -74,7 +76,7 @@ def min_weight(graph, src, dst, n):
     default = {'weight': np.inf}
     w1 = graph[n].get(src, default)['weight']
     w2 = graph[n].get(dst, default)['weight']
-    return min(w1, w2)
+    return {'weight': min(w1, w2)}
 
 
 def _add_edge_filter(values, graph):
@@ -171,12 +173,12 @@ class RAG(nx.Graph):
         src, dst : int
             Nodes to be merged.
         weight_func : callable, optional
-            Function to decide edge weight of edges incident on the new node.
-            For each neighbor `n` for `src and `dst`, `weight_func` will be
-            called as follows: `weight_func(src, dst, n, *extra_arguments,
+            Function to decide the attributes of edges incident on the new
+            node. For each neighbor `n` for `src and `dst`, `weight_func` will
+            be called as follows: `weight_func(src, dst, n, *extra_arguments,
             **extra_keywords)`. `src`, `dst` and `n` are IDs of vertices in the
-            RAG object which is in turn a subclass of
-            `networkx.Graph`.
+            RAG object which is in turn a subclass of `networkx.Graph`. It is
+            expected to return a dict of attributes of the resulting edge.
         in_place : bool, optional
             If set to `True`, the merged node has the id `dst`, else merged
             node has a new id which is returned.
@@ -207,9 +209,9 @@ class RAG(nx.Graph):
             self.add_node(new)
 
         for neighbor in neighbors:
-            w = weight_func(self, src, new, neighbor, *extra_arguments,
-                            **extra_keywords)
-            self.add_edge(neighbor, new, weight=w)
+            data = weight_func(self, src, new, neighbor, *extra_arguments,
+                               **extra_keywords)
+            self.add_edge(neighbor, new, attr_dict=data)
 
         self.node[new]['labels'] = (self.node[src]['labels'] +
                                     self.node[dst]['labels'])

skimage/future/graph/tests/test_rag.py

@@ -10,7 +10,7 @@ def max_edge(g, src, dst, n):
     default = {'weight': -np.inf}
     w1 = g[n].get(src, default)['weight']
     w2 = g[n].get(dst, default)['weight']
-    return max(w1, w2)
+    return {'weight': max(w1, w2)}
 
 
 @skipif(not is_installed('networkx'))
@@ -113,7 +113,7 @@ def test_rag_error():
 def _weight_mean_color(graph, src, dst, n):
     diff = graph.node[dst]['mean color'] - graph.node[n]['mean color']
     diff = np.linalg.norm(diff)
-    return diff
+    return {'weight': diff}
 
 
 def _pre_merge_mean_color(graph, src, dst):