Docs for 0.15.0
 All versions

Module: future.graph

skimage.future.graph.rag_mean_color(image, …) Compute the Region Adjacency Graph using mean colors.
skimage.future.graph.cut_threshold(labels, …) Combine regions separated by weight less than threshold.
skimage.future.graph.cut_normalized(labels, rag) Perform Normalized Graph cut on the Region Adjacency Graph.
skimage.future.graph.ncut(labels, rag[, …]) Perform Normalized Graph cut on the Region Adjacency Graph.
skimage.future.graph.show_rag(labels, rag, image) Show a Region Adjacency Graph on an image.
skimage.future.graph.merge_hierarchical(…) Perform hierarchical merging of a RAG.
skimage.future.graph.rag_boundary(labels, …) Comouter RAG based on region boundaries
skimage.future.graph.RAG([label_image, …]) The Region Adjacency Graph (RAG) of an image, subclasses networx.Graph

rag_mean_color

skimage.future.graph.rag_mean_color(image, labels, connectivity=2, mode='distance', sigma=255.0)[source]

Compute the Region Adjacency Graph using mean colors.

Given an image and its initial segmentation, this method constructs the corresponding Region Adjacency Graph (RAG). Each node in the RAG represents a set of pixels within image with the same label in labels. The weight between two adjacent regions represents how similar or dissimilar two regions are depending on the mode parameter.

Parameters:
image : ndarray, shape(M, N, […, P,] 3)

Input image.

labels : ndarray, shape(M, N, […, P])

The labelled image. This should have one dimension less than image. If image has dimensions (M, N, 3) labels should have dimensions (M, N).

connectivity : int, optional

Pixels with a squared distance less than connectivity from each other are considered adjacent. It can range from 1 to labels.ndim. Its behavior is the same as connectivity parameter in scipy.ndimage.generate_binary_structure.

mode : {‘distance’, ‘similarity’}, optional

The strategy to assign edge weights.

‘distance’ : The weight between two adjacent regions is the \(|c_1 - c_2|\), where \(c_1\) and \(c_2\) are the mean colors of the two regions. It represents the Euclidean distance in their average color.

‘similarity’ : The weight between two adjacent is \(e^{-d^2/sigma}\) where \(d=|c_1 - c_2|\), where \(c_1\) and \(c_2\) are the mean colors of the two regions. It represents how similar two regions are.

sigma : float, optional

Used for computation when mode is “similarity”. It governs how close to each other two colors should be, for their corresponding edge weight to be significant. A very large value of sigma could make any two colors behave as though they were similar.
Returns:
out : RAG

The region adjacency graph.

References

[1]Alain Tremeau and Philippe Colantoni “Regions Adjacency Graph Applied To Color Image Segmentation” DOI:10.1109/83.841950

Examples

>>> from skimage import data, segmentation
>>> from skimage.future import graph
>>> img = data.astronaut()
>>> labels = segmentation.slic(img)
>>> rag = graph.rag_mean_color(img, labels)

Examples using skimage.future.graph.rag_mean_color

cut_threshold

skimage.future.graph.cut_threshold(labels, rag, thresh, in_place=True)[source]

Combine regions separated by weight less than threshold.

Given an image’s labels and its RAG, output new labels by combining regions whose nodes are separated by a weight less than the given threshold.

Parameters:
labels : ndarray

The array of labels.

rag : RAG

The region adjacency graph.

thresh : float

The threshold. Regions connected by edges with smaller weights are combined.

in_place : bool

If set, modifies rag in place. The function will remove the edges with weights less that thresh. If set to False the function makes a copy of rag before proceeding.
Returns:
out : ndarray

The new labelled array.

References

[1]Alain Tremeau and Philippe Colantoni “Regions Adjacency Graph Applied To Color Image Segmentation” DOI:10.1109/83.841950

Examples

>>> from skimage import data, segmentation
>>> from skimage.future import graph
>>> img = data.astronaut()
>>> labels = segmentation.slic(img)
>>> rag = graph.rag_mean_color(img, labels)
>>> new_labels = graph.cut_threshold(labels, rag, 10)

Examples using skimage.future.graph.cut_threshold

cut_normalized

skimage.future.graph.cut_normalized(labels, rag, thresh=0.001, num_cuts=10, in_place=True, max_edge=1.0)[source]

Perform Normalized Graph cut on the Region Adjacency Graph.

Given an image’s labels and its similarity RAG, recursively perform a 2-way normalized cut on it. All nodes belonging to a subgraph that cannot be cut further are assigned a unique label in the output.

Parameters:
labels : ndarray

The array of labels.

rag : RAG

The region adjacency graph.

thresh : float

The threshold. A subgraph won’t be further subdivided if the value of the N-cut exceeds thresh.

num_cuts : int

The number or N-cuts to perform before determining the optimal one.

in_place : bool

If set, modifies rag in place. For each node n the function will set a new attribute rag.node[n]['ncut label'].

max_edge : float, optional

The maximum possible value of an edge in the RAG. This corresponds to an edge between identical regions. This is used to put self edges in the RAG.
Returns:
out : ndarray

The new labeled array.

References

[1]Shi, J.; Malik, J., “Normalized cuts and image segmentation”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 8, pp. 888-905, August 2000.

Examples

>>> from skimage import data, segmentation
>>> from skimage.future import graph
>>> img = data.astronaut()
>>> labels = segmentation.slic(img)
>>> rag = graph.rag_mean_color(img, labels, mode='similarity')
>>> new_labels = graph.cut_normalized(labels, rag)

Examples using skimage.future.graph.cut_normalized

ncut

skimage.future.graph.ncut(labels, rag, thresh=0.001, num_cuts=10, in_place=True, max_edge=1.0)[source]

Perform Normalized Graph cut on the Region Adjacency Graph.

Given an image’s labels and its similarity RAG, recursively perform a 2-way normalized cut on it. All nodes belonging to a subgraph that cannot be cut further are assigned a unique label in the output.

Parameters:
labels : ndarray

The array of labels.

rag : RAG

The region adjacency graph.

thresh : float

The threshold. A subgraph won’t be further subdivided if the value of the N-cut exceeds thresh.

num_cuts : int

The number or N-cuts to perform before determining the optimal one.

in_place : bool

If set, modifies rag in place. For each node n the function will set a new attribute rag.node[n]['ncut label'].

max_edge : float, optional

The maximum possible value of an edge in the RAG. This corresponds to an edge between identical regions. This is used to put self edges in the RAG.
Returns:
out : ndarray

The new labeled array.

References

[1]Shi, J.; Malik, J., “Normalized cuts and image segmentation”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 8, pp. 888-905, August 2000.

Examples

>>> from skimage import data, segmentation
>>> from skimage.future import graph
>>> img = data.astronaut()
>>> labels = segmentation.slic(img)
>>> rag = graph.rag_mean_color(img, labels, mode='similarity')
>>> new_labels = graph.cut_normalized(labels, rag)

show_rag

skimage.future.graph.show_rag(labels, rag, image, border_color='black', edge_width=1.5, edge_cmap='magma', img_cmap='bone', in_place=True, ax=None)[source]

Show a Region Adjacency Graph on an image.

Given a labelled image and its corresponding RAG, show the nodes and edges of the RAG on the image with the specified colors. Edges are displayed between the centroid of the 2 adjacent regions in the image.

Parameters:
labels : ndarray, shape (M, N)

The labelled image.

rag : RAG

The Region Adjacency Graph.

image : ndarray, shape (M, N[, 3])

Input image. If colormap is None, the image should be in RGB format.

border_color : color spec, optional

Color with which the borders between regions are drawn.

edge_width : float, optional

The thickness with which the RAG edges are drawn.

edge_cmap : matplotlib.colors.Colormap, optional

Any matplotlib colormap with which the edges are drawn.

img_cmap : matplotlib.colors.Colormap, optional

Any matplotlib colormap with which the image is draw. If set to None the image is drawn as it is.

in_place : bool, optional

If set, the RAG is modified in place. For each node n the function will set a new attribute rag.node[n]['centroid'].

ax : matplotlib.axes.Axes, optional

The axes to draw on. If not specified, new axes are created and drawn on.
Returns:
lc : matplotlib.collections.LineCollection

A colection of lines that represent the edges of the graph. It can be passed to the matplotlib.figure.Figure.colorbar() function.

Examples

>>> from skimage import data, segmentation
>>> from skimage.future import graph
>>> import matplotlib.pyplot as plt
>>>
>>> img = data.coffee()
>>> labels = segmentation.slic(img)
>>> g =  graph.rag_mean_color(img, labels)
>>> lc = graph.show_rag(labels, g, img)
>>> cbar = plt.colorbar(lc)

merge_hierarchical

skimage.future.graph.merge_hierarchical(labels, rag, thresh, rag_copy, in_place_merge, merge_func, weight_func)[source]

Perform hierarchical merging of a RAG.

Greedily merges the most similar pair of nodes until no edges lower than thresh remain.

Parameters:
labels : ndarray

The array of labels.

rag : RAG

The Region Adjacency Graph.

thresh : float

Regions connected by an edge with weight smaller than thresh are merged.

rag_copy : bool

If set, the RAG copied before modifying.

in_place_merge : bool

If set, the nodes are merged in place. Otherwise, a new node is created for each merge..

merge_func : callable

This function is called before merging two nodes. For the RAG graph while merging src and dst, it is called as follows merge_func(graph, src, dst).

weight_func : callable

The function to compute the new weights of the nodes adjacent to the merged node. This is directly supplied as the argument weight_func to merge_nodes.
Returns:
out : ndarray

The new labeled array.

Examples using skimage.future.graph.merge_hierarchical

rag_boundary

skimage.future.graph.rag_boundary(labels, edge_map, connectivity=2)[source]

Comouter RAG based on region boundaries

Given an image’s initial segmentation and its edge map this method constructs the corresponding Region Adjacency Graph (RAG). Each node in the RAG represents a set of pixels within the image with the same label in labels. The weight between two adjacent regions is the average value in edge_map along their boundary.

labels : ndarray
The labelled image.
edge_map : ndarray
This should have the same shape as that of labels. For all pixels along the boundary between 2 adjacent regions, the average value of the corresponding pixels in edge_map is the edge weight between them.
connectivity : int, optional
Pixels with a squared distance less than connectivity from each other are considered adjacent. It can range from 1 to labels.ndim. Its behavior is the same as connectivity parameter in scipy.ndimage.filters.generate_binary_structure.

Examples

>>> from skimage import data, segmentation, filters, color
>>> from skimage.future import graph
>>> img = data.chelsea()
>>> labels = segmentation.slic(img)
>>> edge_map = filters.sobel(color.rgb2gray(img))
>>> rag = graph.rag_boundary(labels, edge_map)

Examples using skimage.future.graph.rag_boundary

RAG

class skimage.future.graph.RAG(label_image=None, connectivity=1, data=None, **attr)[source]

Bases: networkx.classes.graph.Graph

The Region Adjacency Graph (RAG) of an image, subclasses networx.Graph

Parameters:
label_image : array of int

An initial segmentation, with each region labeled as a different integer. Every unique value in label_image will correspond to a node in the graph.

connectivity : int in {1, …, label_image.ndim}, optional

The connectivity between pixels in label_image. For a 2D image, a connectivity of 1 corresponds to immediate neighbors up, down, left, and right, while a connectivity of 2 also includes diagonal neighbors. See scipy.ndimage.generate_binary_structure.

data : networkx Graph specification, optional

Initial or additional edges to pass to the NetworkX Graph constructor. See networkx.Graph. Valid edge specifications include edge list (list of tuples), NumPy arrays, and SciPy sparse matrices.

**attr : keyword arguments, optional

Additional attributes to add to the graph.
__init__(label_image=None, connectivity=1, data=None, **attr)[source]

Initialize a graph with edges, name, or graph attributes.

Parameters:
incoming_graph_data : input graph (optional, default: None)

Data to initialize graph. If None (default) an empty graph is created. The data can be an edge list, or any NetworkX graph object. If the corresponding optional Python packages are installed the data can also be a NumPy matrix or 2d ndarray, a SciPy sparse matrix, or a PyGraphviz graph.

attr : keyword arguments, optional (default= no attributes)

Attributes to add to graph as key=value pairs.

See also

convert

Examples

>>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G = nx.Graph(name='my graph')
>>> e = [(1, 2), (2, 3), (3, 4)]  # list of edges
>>> G = nx.Graph(e)

Arbitrary graph attribute pairs (key=value) may be assigned

>>> G = nx.Graph(e, day="Friday")
>>> G.graph
{'day': 'Friday'}
add_edge(u, v, attr_dict=None, **attr)[source]

Add an edge between u and v while updating max node id.

See also

networkx.Graph.add_edge().

add_node(n, attr_dict=None, **attr)[source]

Add node n while updating the maximum node id.

See also

networkx.Graph.add_node().

copy()[source]

Copy the graph with its max node id.

See also

networkx.Graph.copy().

fresh_copy()[source]

Return a fresh copy graph with the same data structure.

A fresh copy has no nodes, edges or graph attributes. It is the same data structure as the current graph. This method is typically used to create an empty version of the graph.

This is required when subclassing Graph with networkx v2 and does not cause problems for v1. Here is more detail from the network migrating from 1.x to 2.x document:

With the new GraphViews (SubGraph, ReversedGraph, etc)
you can't assume that ``G.__class__()`` will create a new
instance of the same graph type as ``G``. In fact, the
call signature for ``__class__`` differs depending on
whether ``G`` is a view or a base class. For v2.x you
should use ``G.fresh_copy()`` to create a null graph of
the correct type---ready to fill with nodes and edges.
merge_nodes(src, dst, weight_func=<function min_weight>, in_place=True, extra_arguments=[], extra_keywords={})[source]

Merge node src and dst.

The new combined node is adjacent to all the neighbors of src and dst. weight_func is called to decide the weight of edges incident on the new node.

Parameters:
src, dst : int

Nodes to be merged.

weight_func : callable, optional

Function to decide the attributes of edges incident on the new node. For each neighbor n for src and `dst, weight_func will be called as follows: weight_func(src, dst, n, *extra_arguments, **extra_keywords). src, dst and n are IDs of vertices in the RAG object which is in turn a subclass of networkx.Graph. It is expected to return a dict of attributes of the resulting edge.

in_place : bool, optional

If set to True, the merged node has the id dst, else merged node has a new id which is returned.

extra_arguments : sequence, optional

The sequence of extra positional arguments passed to weight_func.

extra_keywords : dictionary, optional

The dict of keyword arguments passed to the weight_func.
Returns:
id : int

The id of the new node.

Notes

If in_place is False the resulting node has a new id, rather than dst.

next_id()[source]

Returns the id for the new node to be inserted.

The current implementation returns one more than the maximum id.

Returns:
id : int

The id of the new node to be inserted.
© Copyright the scikit-image development team. Created using Bootstrap and Sphinx.