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"""
============================================
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.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.graph.merge_hierarchical` function.
For an example of how to construct region boundary based RAGs, see
:any:`plot_rag_boundary`.
"""
from skimage import data, segmentation, filters, color
from skimage 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, start_label=1)
g = graph.rag_boundary(labels, edges)
fig, axes = plt.subplots(nrows=2, ncols=2)
graph.show_rag(labels, g, img, ax=axes[0, 0])
axes[0, 0].set_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, ax=axes[0, 1])
axes[0, 1].set_title('RAG after hierarchical merging')
out = color.label2rgb(labels2, img, kind='avg', bg_label=0)
axes[1, 0].imshow(out)
axes[1, 0].set_title('Final segmentation')
axes[1, 1].axis('off')
plt.show()
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