Improved test case and removed duplicate test

doc/examples/plot_rag_merge.py

@@ -34,13 +34,12 @@ def _weight_mean_color(graph, src, dst, n):
         The absolute difference of the mean color between node `dst` and `n`.
     """
 
-    #print 'merging
     diff = graph.node[dst]['mean color'] - graph.node[n]['mean color']
     diff = np.linalg.norm(diff)
     return diff
 
 
-def _pre_merge_mean_color(graph, src, dst):
+def merge_mean_color(graph, src, dst):
     """Callback called before merging two nodes of a mean color distance graph.
 
     This method computes the mean color of `dst`.
@@ -58,44 +57,13 @@ def _pre_merge_mean_color(graph, src, dst):
                                      graph.node[dst]['pixel count'])
 
 
-def merge_hierarchical_mean_color(labels, rag, thresh, rag_copy=True,
-                                  in_place_merge=False):
-    """Perform hierarchical merging of a color distance RAG.
-
-    Greedily merges the most similar pair of nodes until no edges lower than
-    `thresh` remain.
-
-    Parameters
-    ----------
-    labels : ndarray
-        The array of labels.
-    rag : RAG
-        The Region Adjacency Graph.
-    thresh : float
-        Regions connected by an edge with weight smaller than `thresh` are
-        merged.
-    rag_copy : bool, optional
-        If set, the RAG copied before modifying.
-    in_place_merge : bool, optional
-        If set, the nodes are merged in place. Otherwise, a new node is
-        created for each merge.
-
-    Examples
-    --------
-    >>> from skimage import data, graph, segmentation
-    >>> img = data.coffee()
-    >>> labels = segmentation.slic(img)
-    >>> rag = graph.rag_mean_color(img, labels)
-    >>> new_labels = graph.merge_hierarchical_mean_color(labels, rag, 40)
-    """
-    return graph.merge_hierarchical(labels, rag, thresh, rag_copy,
-                                    in_place_merge, _pre_merge_mean_color,
-                                    _weight_mean_color)
-
 img = data.coffee()
 labels = segmentation.slic(img, compactness=30, n_segments=400)
 g = graph.rag_mean_color(img, labels)
-labels2 = merge_hierarchical_mean_color(labels, g, 40)
+
+labels2 = graph.merge_hierarchical(labels, g, 40, False, True,
+                                   merge_mean_color, _weight_mean_color)
+
 g2 = graph.rag_mean_color(img, labels2)
 
 out = color.label2rgb(labels2, img, kind='avg')