working code

doc/examples/plot_rag_merge.py

@@ -0,0 +1,13 @@
+from skimage import graph, data, io, segmentation, color
+
+
+img = data.coffee()
+labels = segmentation.slic(img, compactness=30, n_segments=400)
+g = graph.rag_mean_color(img, labels)
+labels2 = graph.merge_hierarchical(g, labels, 40)
+g2 = graph.rag_mean_color(img, labels2)
+
+out = color.label2rgb(labels2, img, kind='avg')
+out = segmentation.mark_boundaries(out, labels2, (0, 0, 0))
+io.imshow(out)
+io.show()

skimage/graph/__init__.py

@@ -1,7 +1,11 @@
 from .spath import shortest_path
 from .mcp import MCP, MCP_Geometric, MCP_Connect, MCP_Flexible, route_through_array
 from .graph_cut import cut_threshold, cut_normalized
+<<<<<<< HEAD
 from .rag import rag_mean_color, RAG, draw_rag
+=======
+from .graph_merge import merge_hierarchical
+>>>>>>> working code
 ncut = cut_normalized
 
 
@@ -16,4 +20,5 @@ __all__ = ['shortest_path',
            'cut_normalized',
            'ncut',
            'draw_rag',
+           'merge_hierarchical',
            'RAG']

skimage/graph/graph_merge.py

@@ -0,0 +1,37 @@
+import numpy as np
+
+
+def _hmerge(rag, x, y, n):
+    diff = rag.node[y]['mean color'] - rag.node[n]['mean color']
+    diff = np.linalg.norm(diff)
+    return diff
+
+
+def merge_hierarchical(rag, labels, thresh):
+
+    min_wt = 0
+    while min_wt < thresh:
+
+        valid_edges = ((x, y, d)
+                       for x, y, d in rag.edges(data=True) if x != y)
+        x, y, d = min(valid_edges, key=lambda x: x[2]['weight'])
+        min_wt = d['weight']
+
+        if min_wt < thresh:
+            total_color = (rag.node[y]['total color'] +
+                           rag.node[x]['total color'])
+            n_pixels = rag.node[x]['pixel count'] + rag.node[y]['pixel count']
+            rag.node[y]['total color'] = total_color
+            rag.node[y]['pixel count'] = n_pixels
+            rag.node[y]['mean color'] = total_color / n_pixels
+
+            rag.merge_nodes(x, y, _hmerge)
+
+    count = 0
+    arr = np.arange(labels.max() + 1)
+    for n, d in rag.nodes_iter(data=True):
+        for l in d['labels']:
+            arr[l] = count
+        count += 1
+
+    return arr[labels]