mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-10 20:19:24 +08:00
Vighnesh Birodkar
284 lines
8.7 KiB
Python
284 lines
8.7 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('RAGs require networkx')
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import numpy as np
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from . import _ncut
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from . import _ncut_cy
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from scipy.sparse import linalg
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def cut_threshold(labels, rag, thresh, in_place=True):
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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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in_place : bool
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If set, modifies `rag` in place. The function will remove the edges
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with weights less that `thresh`. If set to `False` the function
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makes a copy of `rag` before proceeding.
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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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if not in_place:
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rag = rag.copy()
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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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def cut_normalized(labels, rag, thresh=0.001, num_cuts=10, in_place=True,
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max_edge=1.0):
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"""Perform Normalized Graph cut on the Region Adjacency Graph.
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Given an image's labels and its similarity RAG, recursively perform
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a 2-way normalized cut on it. All nodes belonging to a subgraph
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that cannot be cut further are assigned a unique label in the
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output.
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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. A subgraph won't be further subdivided if the
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value of the N-cut exceeds `thresh`.
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num_cuts : int
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The number or N-cuts to perform before determining the optimal one.
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in_place : bool
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If set, modifies `rag` in place. For each node `n` the function will
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set a new attribute ``rag.node[n]['ncut label']``.
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max_egde : float, optinal
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The maximum possible value of an edge in the RAG. This corresponds to
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an edge between regions which are identical. This is used to put self
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edges in the RAG.
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Returns
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-------
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out : ndarray
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The new labeled 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, compactness=30, n_segments=400)
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>>> rag = graph.rag_mean_color(img, labels, mode='similarity')
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>>> new_labels = graph.cut_normalized(labels, rag)
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References
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----------
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.. [1] Shi, J.; Malik, J., "Normalized cuts and image segmentation",
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Pattern Analysis and Machine Intelligence,
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IEEE Transactions on, vol. 22, no. 8, pp. 888-905, August 2000.
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"""
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if not in_place:
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rag = rag.copy()
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for node in rag.nodes_iter():
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rag.add_edge(node, node, weight=max_edge)
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_ncut_relabel(rag, thresh, num_cuts)
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map_array = np.zeros(labels.max() + 1)
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# Mapping from old labels to new
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for n, d in rag.nodes_iter(data=True):
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map_array[d['labels']] = d['ncut label']
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return map_array[labels]
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def partition_by_cut(cut, rag):
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"""Compute resulting subgraphs from given bi-parition.
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Parameters
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----------
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cut : array
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A array of booleans. Elements set to `True` belong to one
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set.
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rag : RAG
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The Region Adjacency Graph.
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Returns
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-------
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sub1, sub2 : RAG
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The two resulting subgraphs from the bi-partition.
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"""
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# `cut` is derived from `D` and `W` matrices, which also follow the
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# ordering returned by `rag.nodes()` because we use
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# nx.to_scipy_sparse_matrix.
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# Example
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# rag.nodes() = [3, 7, 9, 13]
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# cut = [True, False, True, False]
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# nodes1 = [3, 9]
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# nodes2 = [7, 10]
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nodes1 = [n for i, n in enumerate(rag.nodes()) if cut[i]]
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nodes2 = [n for i, n in enumerate(rag.nodes()) if not cut[i]]
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sub1 = rag.subgraph(nodes1)
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sub2 = rag.subgraph(nodes2)
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return sub1, sub2
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def get_min_ncut(ev, d, w, num_cuts):
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"""Threshold an eigenvector evenly, to determine minimum ncut.
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Parameters
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----------
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ev : array
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The eigenvector to threshold.
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d : ndarray
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The diagonal matrix of the graph.
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w : ndarray
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The weight matrix of the graph.
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num_cuts : int
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The number of evenly spaced thresholds to check for.
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Returns
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-------
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mask : array
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The array of booleans which denotes the bi-partition.
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mcut : float
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The value of the minimum ncut.
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"""
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mcut = np.inf
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# Refer Shi & Malik 2001, Section 3.1.3, Page 892
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# Perform evenly spaced n-cuts and determine the optimal one.
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for t in np.linspace(0, 1, num_cuts, endpoint=False):
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mask = ev > t
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cost = _ncut.ncut_cost(mask, d, w)
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if cost < mcut:
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min_mask = mask
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mcut = cost
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return min_mask, mcut
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def _label_all(rag, attr_name):
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"""Assign a unique integer to the given attribute in the RAG.
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This function assumes that all labels in `rag` are unique. It
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picks up a random label from them and assigns it to the `attr_name`
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attribute of all the nodes.
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rag : RAG
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The Region Adjacency Graph.
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attr_name : string
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The attribute to which a unique integer is assigned.
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"""
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node = rag.nodes()[0]
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new_label = rag.node[node]['labels'][0]
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for n, d in rag.nodes_iter(data=True):
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d[attr_name] = new_label
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def _ncut_relabel(rag, thresh, num_cuts):
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"""Perform Normalized Graph cut on the Region Adjacency Graph.
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Recursively partition the graph into 2, until further subdivision
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yields a cut greather than `thresh` or such a cut cannot be computed.
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For such a subgraph, indices to labels of all its nodes map to a single
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unique value.
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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. A subgraph won't be further subdivided if the
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value of the N-cut exceeds `thresh`.
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num_cuts : int
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The number or N-cuts to perform before determining the optimal one.
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map_array : array
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The array which maps old labels to new ones. This is modified inside
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the function.
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"""
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d, w = _ncut.DW_matrices(rag)
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m = w.shape[0]
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if m > 2:
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d2 = d.copy()
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# Since d is diagonal, we can directly operate on its data
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# the inverse of the square root
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d2.data = np.reciprocal(np.sqrt(d2.data, out=d2.data), out=d2.data)
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# Refer Shi & Malik 2001, Equation 7, Page 891
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vals, vectors = linalg.eigsh(d2 * (d - w) * d2, which='SM',
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k=min(100, m - 2))
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# Pick second smallest eigenvector.
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# Refer Shi & Malik 2001, Section 3.2.3, Page 893
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vals, vectors = np.real(vals), np.real(vectors)
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index2 = _ncut_cy.argmin2(vals)
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ev = _ncut.normalize(vectors[:, index2])
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cut_mask, mcut = get_min_ncut(ev, d, w, num_cuts)
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if (mcut < thresh):
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# Sub divide and perform N-cut again
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# Refer Shi & Malik 2001, Section 3.2.5, Page 893
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sub1, sub2 = partition_by_cut(cut_mask, rag)
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_ncut_relabel(sub1, thresh, num_cuts)
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_ncut_relabel(sub2, thresh, num_cuts)
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return
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# The N-cut wasn't small enough, or could not be computed.
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# The remaining graph is a region.
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# Assign `ncut label` by picking any label from the existing nodes, since
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# `labels` are unique, `new_label` is also unique.
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_label_all(rag, 'ncut label')
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