mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 04:23:17 +08:00
Vighnesh Birodkar
219 lines
6.6 KiB
Python
219 lines
6.6 KiB
Python
import numpy as np
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from skimage.future 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 {'weight': 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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n = g.merge_nodes(3, 4, in_place=False)
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assert sorted(g.node[n]['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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def _weight_mean_color(graph, src, dst, n):
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diff = graph.node[dst]['mean color'] - graph.node[n]['mean color']
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diff = np.linalg.norm(diff)
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return {'weight': diff}
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def _pre_merge_mean_color(graph, src, dst):
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graph.node[dst]['total color'] += graph.node[src]['total color']
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graph.node[dst]['pixel count'] += graph.node[src]['pixel count']
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graph.node[dst]['mean color'] = (graph.node[dst]['total color'] /
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graph.node[dst]['pixel count'])
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def merge_hierarchical_mean_color(labels, rag, thresh, rag_copy=True,
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in_place_merge=False):
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return graph.merge_hierarchical(labels, rag, thresh, rag_copy,
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in_place_merge, _pre_merge_mean_color,
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_weight_mean_color)
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@skipif(not is_installed('networkx'))
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def test_rag_hierarchical():
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img = np.zeros((8, 8, 3), dtype='uint8')
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labels = np.zeros((8, 8), dtype='uint8')
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img[:, :, :] = 31
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labels[:, :] = 1
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img[0:4, 0:4, :] = 10, 10, 10
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labels[0:4, 0:4] = 2
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img[4:, 0:4, :] = 20, 20, 20
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labels[4:, 0:4] = 3
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g = graph.rag_mean_color(img, labels)
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g2 = g.copy()
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thresh = 20 # more than 11*sqrt(3) but less than
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result = merge_hierarchical_mean_color(labels, g, thresh)
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assert(np.all(result[:, :4] == result[0, 0]))
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assert(np.all(result[:, 4:] == result[-1, -1]))
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result = merge_hierarchical_mean_color(labels, g2, thresh,
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in_place_merge=True)
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assert(np.all(result[:, :4] == result[0, 0]))
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assert(np.all(result[:, 4:] == result[-1, -1]))
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result = graph.cut_threshold(labels, g, thresh)
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assert np.all(result == result[0, 0])
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@skipif(not is_installed('networkx'))
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def test_ncut_stable_subgraph():
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""" Test to catch an error thrown when subgraph has all equal edges. """
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img = np.zeros((100, 100, 3), dtype='uint8')
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labels = np.zeros((100, 100), dtype='uint8')
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labels[...] = 0
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labels[:50, :50] = 1
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labels[:50, 50:] = 2
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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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assert new_labels.max() == 0
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def test_generic_rag_2d():
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labels = np.array([[1, 2], [3, 4]], dtype=np.uint8)
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g = graph.RAG(labels)
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assert g.has_edge(1, 2) and g.has_edge(2, 4) and not g.has_edge(1, 4)
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h = graph.RAG(labels, connectivity=2)
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assert h.has_edge(1, 2) and h.has_edge(1, 4) and h.has_edge(2, 3)
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def test_generic_rag_3d():
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labels = np.arange(8, dtype=np.uint8).reshape((2, 2, 2))
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g = graph.RAG(labels)
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assert g.has_edge(0, 1) and g.has_edge(1, 3) and not g.has_edge(0, 3)
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h = graph.RAG(labels, connectivity=2)
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assert h.has_edge(0, 1) and h.has_edge(0, 3) and not h.has_edge(0, 7)
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k = graph.RAG(labels, connectivity=3)
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assert k.has_edge(0, 1) and k.has_edge(1, 2) and k.has_edge(2, 5)
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def test_rag_boundary():
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labels = np.zeros((16, 16), dtype='uint8')
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edge_map = np.zeros_like(labels, dtype=float)
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edge_map[8, :] = 0.5
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edge_map[:, 8] = 1.0
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labels[:8, :8] = 1
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labels[:8, 8:] = 2
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labels[8:, :8] = 3
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labels[8:, 8:] = 4
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g = graph.rag_boundary(labels, edge_map, connectivity=1)
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assert set(g.nodes()) == set([1, 2, 3, 4])
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assert set(g.edges()) == set([(1, 2), (1, 3), (2, 4), (3, 4)])
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assert g[1][3]['weight'] == 0.25
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assert g[2][4]['weight'] == 0.34375
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assert g[1][3]['count'] == 16
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