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https://github.com/wassname/scikit-image.git
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emmanuelle
55f5103dd8
Modified travis_script.sh to account for the new structure of the gallery Added README.txt files in directories of gallery examples Fixed references to gallery images in user guide pages Fixed broken links
82 lines
2.1 KiB
Python
82 lines
2.1 KiB
Python
"""
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=======================
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Region Adjacency Graphs
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=======================
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This example demonstrates the use of the `merge_nodes` function of a Region
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Adjacency Graph (RAG). The `RAG` class represents a undirected weighted graph
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which inherits from `networkx.graph` class. When a new node is formed by
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merging two nodes, the edge weight of all the edges incident on the resulting
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node can be updated by a user defined function `weight_func`.
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The default behaviour is to use the smaller edge weight in case of a conflict.
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The example below also shows how to use a custom function to select the larger
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weight instead.
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"""
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from skimage.future.graph import rag
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import networkx as nx
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from matplotlib import pyplot as plt
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import numpy as np
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def max_edge(g, src, dst, n):
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"""Callback to handle merging nodes by choosing maximum weight.
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Returns either the weight between (`src`, `n`) or (`dst`, `n`)
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in `g` or the maximum of the two when both exist.
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Parameters
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----------
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g : RAG
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The graph under consideration.
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src, dst : int
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The vertices in `g` to be merged.
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n : int
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A neighbor of `src` or `dst` or both.
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Returns
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-------
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weight : float
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The weight between (`src`, `n`) or (`dst`, `n`) in `g` or the
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maximum of the two when both exist.
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"""
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w1 = g[n].get(src, {'weight': -np.inf})['weight']
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w2 = g[n].get(dst, {'weight': -np.inf})['weight']
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return max(w1, w2)
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def display(g, title):
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"""Displays a graph with the given title."""
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pos = nx.circular_layout(g)
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plt.figure()
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plt.title(title)
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nx.draw(g, pos)
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nx.draw_networkx_edge_labels(g, pos, font_size=20)
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g = rag.RAG()
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g.add_edge(1, 2, weight=10)
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g.add_edge(2, 3, weight=20)
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g.add_edge(3, 4, weight=30)
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g.add_edge(4, 1, weight=40)
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g.add_edge(1, 3, weight=50)
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# Assigning dummy labels.
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for n in g.nodes():
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g.node[n]['labels'] = [n]
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gc = g.copy()
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display(g, "Original Graph")
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g.merge_nodes(1, 3)
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display(g, "Merged with default (min)")
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gc.merge_nodes(1, 3, weight_func=max_edge, in_place=False)
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display(gc, "Merged with max without in_place")
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plt.show()
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