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 
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: 


Returns: 

References
[1]  Alain Tremeau and Philippe Colantoni “Regions Adjacency Graph Applied To Color Image Segmentation” http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.5274 
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)
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: 


Returns: 

References
[1]  Alain Tremeau and Philippe Colantoni “Regions Adjacency Graph Applied To Color Image Segmentation” http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.5274 
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)
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 2way normalized cut on it. All nodes belonging to a subgraph that cannot be cut further are assigned a unique label in the output.
Parameters: 


Returns: 

References
[1]  Shi, J.; Malik, J., “Normalized cuts and image segmentation”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 8, pp. 888905, 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)
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 2way normalized cut on it. All nodes belonging to a subgraph that cannot be cut further are assigned a unique label in the output.
Parameters: 


Returns: 

References
[1]  Shi, J.; Malik, J., “Normalized cuts and image segmentation”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 8, pp. 888905, 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)
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: 


Returns: 

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)
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: 


Returns: 

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.
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)
RAG
¶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: 


__init__
(label_image=None, connectivity=1, data=None, **attr)[source]¶Initialize a graph with edges, name, or graph attributes.
Parameters: 


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()
.
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 typeready 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: 


Returns: 

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