Cleaned up rag.py

skimage/graph/rag.py

@@ -211,51 +211,12 @@ def rag_mean_color(image, labels, connectivity=2):
         graph[x][y]['weight'] = np.linalg.norm(diff)
 
     return graph
- 
-def rag_similarity(image, labels, connectivity=2, sigma = 30.0):
-    graph = RAG()
-
-    # The footprint is constructed in such a way that the first
-    # element in the array being passed to _add_edge_filter is
-    # the central value.
-    fp = nd.generate_binary_structure(labels.ndim, connectivity)
-    for d in range(fp.ndim):
-        fp = fp.swapaxes(0, d)
-        fp[0, ...] = 0
-        fp = fp.swapaxes(0, d)
 
 
-    filters.generic_filter(
-        labels,
-        function=_add_edge_filter,
-        footprint=fp,
-        mode='nearest',
-        output=np.zeros(labels.shape, dtype=np.uint8),
-        extra_arguments=(graph,))
+def rag_similarity(image, labels, connectivity=2, sigma=30.0):
+    graph = rag_mean_color(image, labels, connectivity)
 
-    for n in graph:
-        graph.node[n].update({'labels': [n],
-                              'pixel count': 0,
-                              'total color': np.array([0, 0, 0],
-                                                      dtype=np.double)})
+    for x, w, d in graph.edges_iter(data=True):
+        d['weight'] = math.e ** (-(d['weight'] ** 2) / sigma)
 
-    for index in np.ndindex(labels.shape):
-        current = labels[index]
-        graph.node[current]['pixel count'] += 1
-        graph.node[current]['total color'] += image[index]
-
-    for n in graph:
-        graph.node[n]['mean color'] = (graph.node[n]['total color'] /
-                                       graph.node[n]['pixel count'])
-
-    for x, y in graph.edges_iter():
-        diff = graph.node[x]['mean color'] - graph.node[y]['mean color']
-        diff = np.linalg.norm(diff)
-    
-        #if diff == 0:
-        #    graph[x][y]['weight'] = 99999
-        #else:
-        graph[x][y]['weight'] = math.e**(-(diff**2)/sigma)
-        #print graph[x][y]['weight']
-        #print "diff",diff,"weight",math.e**(-(diff**2)/sigma)
     return graph