mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 03:37:34 +08:00
Vighnesh Birodkar
111 lines
3.1 KiB
Python
111 lines
3.1 KiB
Python
import numpy as np
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from skimage import graph
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from skimage._shared.version_requirements import is_installed
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from numpy.testing.decorators import skipif
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from skimage import segmentation
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from numpy import testing
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def max_edge(g, src, dst, n):
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default = {'weight': -np.inf}
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w1 = g[n].get(src, default)['weight']
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w2 = g[n].get(dst, default)['weight']
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return max(w1, w2)
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@skipif(not is_installed('networkx'))
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def test_rag_merge():
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g = graph.rag.RAG()
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for i in range(5):
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g.add_node(i, {'labels': [i]})
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g.add_edge(0, 1, {'weight': 10})
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g.add_edge(1, 2, {'weight': 20})
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g.add_edge(2, 3, {'weight': 30})
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g.add_edge(3, 0, {'weight': 40})
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g.add_edge(0, 2, {'weight': 50})
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g.add_edge(3, 4, {'weight': 60})
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gc = g.copy()
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# We merge nodes and ensure that the minimum weight is chosen
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# when there is a conflict.
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g.merge_nodes(0, 2)
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assert g.edge[1][2]['weight'] == 10
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assert g.edge[2][3]['weight'] == 30
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# We specify `max_edge` as `weight_func` as ensure that maximum
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# weight is chosen in case on conflict
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gc.merge_nodes(0, 2, weight_func=max_edge)
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assert gc.edge[1][2]['weight'] == 20
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assert gc.edge[2][3]['weight'] == 40
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g.merge_nodes(1, 4)
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g.merge_nodes(2, 3)
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g.merge_nodes(3, 4)
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assert sorted(g.node[4]['labels']) == list(range(5))
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assert g.edges() == []
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@skipif(not is_installed('networkx'))
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def test_threshold_cut():
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img = np.zeros((100, 100, 3), dtype='uint8')
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img[:50, :50] = 255, 255, 255
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img[:50, 50:] = 254, 254, 254
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img[50:, :50] = 2, 2, 2
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img[50:, 50:] = 1, 1, 1
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labels = np.zeros((100, 100), dtype='uint8')
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labels[:50, :50] = 0
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labels[:50, 50:] = 1
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labels[50:, :50] = 2
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labels[50:, 50:] = 3
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rag = graph.rag_mean_color(img, labels)
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new_labels = graph.cut_threshold(labels, rag, 10, in_place=False)
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# Two labels
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assert new_labels.max() == 1
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new_labels = graph.cut_threshold(labels, rag, 10)
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# Two labels
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assert new_labels.max() == 1
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@skipif(not is_installed('networkx'))
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def test_cut_normalized():
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img = np.zeros((100, 100, 3), dtype='uint8')
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img[:50, :50] = 255, 255, 255
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img[:50, 50:] = 254, 254, 254
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img[50:, :50] = 2, 2, 2
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img[50:, 50:] = 1, 1, 1
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labels = np.zeros((100, 100), dtype='uint8')
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labels[:50, :50] = 0
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labels[:50, 50:] = 1
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labels[50:, :50] = 2
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labels[50:, 50:] = 3
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rag = graph.rag_mean_color(img, labels, mode='similarity')
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new_labels = graph.cut_normalized(labels, rag, in_place=False)
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new_labels, _, _ = segmentation.relabel_sequential(new_labels)
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# Two labels
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assert new_labels.max() == 1
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new_labels = graph.cut_normalized(labels, rag)
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new_labels, _, _ = segmentation.relabel_sequential(new_labels)
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assert new_labels.max() == 1
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@skipif(not is_installed('networkx'))
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def test_rag_error():
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img = np.zeros((10, 10, 3), dtype='uint8')
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labels = np.zeros((10, 10), dtype='uint8')
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labels[:5, :] = 0
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labels[5:, :] = 1
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testing.assert_raises(ValueError, graph.rag_mean_color, img, labels,
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2, 'non existant mode')
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