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%matplotlib inline

Hierarchical Merging of Region Boundary RAGs

This example demonstrates how to perform hierarchical merging on region boundary Region Adjacency Graphs (RAGs). Region boundary RAGs can be constructed with the :py:func:skimage.future.graph.rag_boundary function. The regions with the lowest edge weights are successively merged until there is no edge with weight less than thresh. The hierarchical merging is done through the :py:func:skimage.future.graph.merge_hierarchical function. For an example of how to construct region boundary based RAGs, see :any:plot_rag_boundary.



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from skimage import data, segmentation, filters, color
from skimage.future import graph
from matplotlib import pyplot as plt


def weight_boundary(graph, src, dst, n):
    """
    Handle merging of nodes of a region boundary region adjacency graph.

    This function computes the `"weight"` and the count `"count"`
    attributes of the edge between `n` and the node formed after
    merging `src` and `dst`.


    Parameters
    ----------
    graph : RAG
        The graph under consideration.
    src, dst : int
        The vertices in `graph` to be merged.
    n : int
        A neighbor of `src` or `dst` or both.

    Returns
    -------
    data : dict
        A dictionary with the "weight" and "count" attributes to be
        assigned for the merged node.

    """
    default = {'weight': 0.0, 'count': 0}

    count_src = graph[src].get(n, default)['count']
    count_dst = graph[dst].get(n, default)['count']

    weight_src = graph[src].get(n, default)['weight']
    weight_dst = graph[dst].get(n, default)['weight']

    count = count_src + count_dst
    return {
        'count': count,
        'weight': (count_src * weight_src + count_dst * weight_dst)/count
    }


def merge_boundary(graph, src, dst):
    """Call back called before merging 2 nodes.

    In this case we don't need to do any computation here.
    """
    pass

img = data.coffee()
edges = filters.sobel(color.rgb2gray(img))
labels = segmentation.slic(img, compactness=30, n_segments=400)
g = graph.rag_boundary(labels, edges)

graph.show_rag(labels, g, img)
plt.title('Initial RAG')

labels2 = graph.merge_hierarchical(labels, g, thresh=0.08, rag_copy=False,
                                   in_place_merge=True,
                                   merge_func=merge_boundary,
                                   weight_func=weight_boundary)

graph.show_rag(labels, g, img)
plt.title('RAG after hierarchical merging')

plt.figure()
out = color.label2rgb(labels2, img, kind='avg')
plt.imshow(out)
plt.title('Final segmentation')

plt.show()



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