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Merge pull request #1826 from jni/rag-generic
Allow construction of simple RAGs from label images
This commit is contained in:
Emmanuelle Gouillart
+67
-77
@@ -1,16 +1,6 @@
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try:
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import networkx as nx
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except ImportError:
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msg = "Graph functions require networkx, which is not installed"
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class nx:
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class Graph:
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def __init__(self, *args, **kwargs):
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raise ImportError(msg)
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import warnings
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warnings.warn(msg)
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import networkx as nx
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import numpy as np
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from numpy.lib.stride_tricks import as_strided
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from scipy import ndimage as ndi
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import math
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from ... import draw, measure, segmentation, util, color
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@@ -51,21 +41,79 @@ def min_weight(graph, src, dst, n):
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return min(w1, w2)
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def _add_edge_filter(values, graph):
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"""Create edge in `graph` between central element of `values` and the rest.
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Add an edge between the middle element in `values` and
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all other elements of `values` into `graph`. ``values[len(values) // 2]``
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is expected to be the central value of the footprint used.
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Parameters
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----------
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values : array
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The array to process.
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graph : RAG
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The graph to add edges in.
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Returns
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-------
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0 : float
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Always returns 0. The return value is required so that `generic_filter`
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can put it in the output array, but it is ignored by this filter.
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"""
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values = values.astype(int)
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center = values[len(values) // 2]
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for value in values:
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if value != center and not graph.has_edge(center, value):
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graph.add_edge(center, value)
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return 0.
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class RAG(nx.Graph):
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"""
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The Region Adjacency Graph (RAG) of an image, subclasses
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`networx.Graph <http://networkx.github.io/documentation/latest/reference/classes.graph.html>`_
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Parameters
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----------
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label_image : array of int
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An initial segmentation, with each region labeled as a different
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integer. Every unique value in ``label_image`` will correspond to
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a node in the graph.
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connectivity : int in {1, ..., ``label_image.ndim``}, optional
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The connectivity between pixels in ``label_image``. For a 2D image,
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a connectivity of 1 corresponds to immediate neighbors up, down,
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left, and right, while a connectivity of 2 also includes diagonal
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neighbors. See `scipy.ndimage.generate_binary_structure`.
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data : networkx Graph specification, optional
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Initial or additional edges to pass to the NetworkX Graph
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constructor. See `networkx.Graph`. Valid edge specifications
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include edge list (list of tuples), NumPy arrays, and SciPy
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sparse matrices.
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**attr : keyword arguments, optional
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Additional attributes to add to the graph.
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"""
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def __init__(self, data=None, **attr):
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def __init__(self, label_image=None, connectivity=1, data=None, **attr):
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super(RAG, self).__init__(data, **attr)
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try:
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self.max_id = max(self.nodes_iter())
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except ValueError:
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# Empty sequence
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if self.number_of_nodes() == 0:
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self.max_id = 0
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else:
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self.max_id = max(self.nodes_iter())
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if label_image is not None:
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fp = ndi.generate_binary_structure(label_image.ndim, connectivity)
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ndi.generic_filter(
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label_image,
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function=_add_edge_filter,
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footprint=fp,
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mode='nearest',
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output=as_strided(np.empty((1,), dtype=np.float_),
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shape=label_image.shape,
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strides=((0,) * label_image.ndim)),
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extra_arguments=(self,))
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def merge_nodes(self, src, dst, weight_func=min_weight, in_place=True,
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extra_arguments=[], extra_keywords={}):
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@@ -172,36 +220,6 @@ class RAG(nx.Graph):
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super(RAG, self).add_node(n)
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def _add_edge_filter(values, graph):
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"""Create edge in `g` between the first element of `values` and the rest.
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Add an edge between the first element in `values` and
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all other elements of `values` in the graph `g`. `values[0]`
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is expected to be the central value of the footprint used.
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Parameters
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----------
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values : array
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The array to process.
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graph : RAG
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The graph to add edges in.
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Returns
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-------
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0 : int
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Always returns 0. The return value is required so that `generic_filter`
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can put it in the output array.
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"""
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values = values.astype(int)
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current = values[0]
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for value in values[1:]:
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if value != current:
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graph.add_edge(current, value)
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return 0
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def rag_mean_color(image, labels, connectivity=2, mode='distance',
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sigma=255.0):
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"""Compute the Region Adjacency Graph using mean colors.
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@@ -224,7 +242,7 @@ def rag_mean_color(image, labels, connectivity=2, mode='distance',
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Pixels with a squared distance less than `connectivity` from each other
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are considered adjacent. It can range from 1 to `labels.ndim`. Its
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behavior is the same as `connectivity` parameter in
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`scipy.ndimage.filters.generate_binary_structure`.
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`scipy.ndimage.generate_binary_structure`.
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mode : {'distance', 'similarity'}, optional
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The strategy to assign edge weights.
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@@ -263,35 +281,7 @@ def rag_mean_color(image, labels, connectivity=2, mode='distance',
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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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graph = RAG()
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# The footprint is constructed in such a way that the first
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# element in the array being passed to _add_edge_filter is
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# the central value.
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fp = ndi.generate_binary_structure(labels.ndim, connectivity)
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for d in range(fp.ndim):
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fp = fp.swapaxes(0, d)
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fp[0, ...] = 0
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fp = fp.swapaxes(0, d)
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# For example
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# if labels.ndim = 2 and connectivity = 1
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# fp = [[0,0,0],
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# [0,1,1],
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# [0,1,0]]
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#
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# if labels.ndim = 2 and connectivity = 2
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# fp = [[0,0,0],
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# [0,1,1],
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# [0,1,1]]
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ndi.generic_filter(
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labels,
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function=_add_edge_filter,
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footprint=fp,
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mode='nearest',
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output=np.zeros(labels.shape, dtype=np.uint8),
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extra_arguments=(graph,))
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graph = RAG(labels, connectivity=connectivity)
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for n in graph:
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graph.node[n].update({'labels': [n],
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@@ -178,3 +178,21 @@ def test_ncut_stable_subgraph():
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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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