mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 12:08:00 +08:00
Steven Silvester
63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
try:
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import networkx as nx
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except ImportError:
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import warnings
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warnings.warn('"cut_threshold" requires networkx')
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import numpy as np
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def cut_threshold(labels, rag, thresh):
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"""Combine regions seperated by weight less than threshold.
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Given an image's labels and its RAG, output new labels by
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combining regions whose nodes are seperated by a weight less
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than the given threshold.
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Parameters
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----------
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labels : ndarray
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The array of labels.
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rag : RAG
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The region adjacency graph.
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thresh : float
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The threshold. Regions connected by edges with smaller weights are
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combined.
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Returns
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-------
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out : ndarray
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The new labelled array.
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Examples
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--------
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>>> from skimage import data, graph, segmentation
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>>> img = data.lena()
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>>> labels = segmentation.slic(img)
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>>> rag = graph.rag_mean_color(img, labels)
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>>> new_labels = graph.cut_threshold(labels, rag, 10)
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References
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----------
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.. [1] Alain Tremeau and Philippe Colantoni
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"Regions Adjacency Graph Applied To Color Image Segmentation"
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.5274
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"""
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# Because deleting edges while iterating through them produces an error.
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to_remove = [(x, y) for x, y, d in rag.edges_iter(data=True)
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if d['weight'] >= thresh]
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rag.remove_edges_from(to_remove)
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comps = nx.connected_components(rag)
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# We construct an array which can map old labels to the new ones.
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# All the labels within a connected component are assigned to a single
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# label in the output.
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map_array = np.arange(labels.max() + 1, dtype=labels.dtype)
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for i, nodes in enumerate(comps):
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for node in nodes:
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for label in rag.node[node]['labels']:
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map_array[label] = i
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return map_array[labels]
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