future.graph
Module: future.graph
skimage.future.graph.cut_normalized (labels, rag) | Perform Normalized Graph cut on the Region Adjacency Graph. |
skimage.future.graph.cut_threshold (labels, ...) | Combine regions separated by weight less than threshold. |
skimage.future.graph.draw_rag (labels, rag, img) | Draw a Region Adjacency Graph on an image. |
skimage.future.graph.merge_hierarchical (...) | Perform hierarchical merging of a RAG. |
skimage.future.graph.ncut (labels, rag[, ...]) | Perform Normalized Graph cut on the Region Adjacency Graph. |
skimage.future.graph.rag_boundary (labels, ...) | Comouter RAG based on region boundaries |
skimage.future.graph.rag_mean_color (image, ...) | Compute the Region Adjacency Graph using mean colors. |
skimage.future.graph.RAG ([label_image, ...]) | The Region Adjacency Graph (RAG) of an image, subclasses |
cut_normalized
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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 noden
the function will set a new attributerag.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
[R223] 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, compactness=30, n_segments=400) >>> rag = graph.rag_mean_color(img, labels, mode='similarity') >>> new_labels = graph.cut_normalized(labels, rag)
cut_threshold
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skimage.future.graph.cut_threshold(labels, rag, thresh, in_place=True)
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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 thatthresh
. If set toFalse
the function makes a copy ofrag
before proceeding.Returns: out : ndarray
The new labelled array.
References
[R224] 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)
draw_rag
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skimage.future.graph.draw_rag(labels, rag, img, border_color=None, node_color='#ffff00', edge_color='#00ff00', colormap=None, thresh=inf, desaturate=False, in_place=True)
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Draw a Region Adjacency Graph on an image.
Given a labelled image and its corresponding RAG, draw the nodes and edges of the RAG on the image with the specified colors. Nodes are marked by the centroids of the corresponding regions.
Parameters: labels : ndarray, shape (M, N)
The labelled image.
rag : RAG
The Region Adjacency Graph.
img : ndarray, shape (M, N, 3)
Input image.
border_color : colorspec, optional
Any matplotlib colorspec.
node_color : colorspec, optional
Any matplotlib colorspec. Yellow by default.
edge_color : colorspec, optional
Any matplotlib colorspec. Green by default.
colormap : colormap, optional
Any matplotlib colormap. If specified the edges are colormapped with the specified color map.
thresh : float, optional
Edges with weight below
thresh
are not drawn, or considered for color mapping.desaturate : bool, optional
Convert the image to grayscale before displaying. Particularly helps visualization when using the
colormap
option.in_place : bool, optional
If set, the RAG is modified in place. For each node
n
the function will set a new attributerag.node[n]['centroid']
.Returns: out : ndarray, shape (M, N, 3)
The image with the RAG drawn.
Examples
>>> from skimage import data, segmentation >>> from skimage.future import graph >>> img = data.coffee() >>> labels = segmentation.slic(img) >>> g = graph.rag_mean_color(img, labels) >>> out = graph.draw_rag(labels, g, img)
merge_hierarchical
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skimage.future.graph.merge_hierarchical(labels, rag, thresh, rag_copy, in_place_merge, merge_func, weight_func)
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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 mergingsrc
anddst
, it is called as followsmerge_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
tomerge_nodes
.Returns: out : ndarray
The new labeled array.
ncut
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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 noden
the function will set a new attributerag.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
[R225] 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, compactness=30, n_segments=400) >>> rag = graph.rag_mean_color(img, labels, mode='similarity') >>> new_labels = graph.cut_normalized(labels, rag)
rag_boundary
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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 inedge_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 inedge_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 tolabels.ndim
. Its behavior is the same asconnectivity
parameter inscipy.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)
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rag_mean_color
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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 inlabels
. The weight between two adjacent regions represents how similar or dissimilar two regions are depending on themode
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
. Ifimage
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 tolabels.ndim
. Its behavior is the same asconnectivity
parameter inscipy.ndimage.generate_binary_structure
.mode : {‘distance’, ‘similarity’}, optional
The strategy to assign edge weights.
‘distance’ : The weight between two adjacent regions is the , where and are the mean colors of the two regions. It represents the Euclidean distance in their average color.
‘similarity’ : The weight between two adjacent is where , where and 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 ofsigma
could make any two colors behave as though they were similar.Returns: out : RAG
The region adjacency graph.
References
[R226] 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)
RAG
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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
}, optionalThe 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. Seescipy.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.
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__init__(label_image=None, connectivity=1, data=None, **attr)
[source]
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add_edge(u, v, attr_dict=None, **attr)
[source] -
Add an edge between
u
andv
while updating max node id.See also
networkx.Graph.add_edge()
.
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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()
.
-
merge_nodes(src, dst, weight_func=<function min_weight>, in_place=True, extra_arguments=[], extra_keywords={})
[source] -
Merge node
src
anddst
.The new combined node is adjacent to all the neighbors of
src
anddst
.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 edge weight of edges incident on the new node. For each neighbor
n
forsrc and `dst
,weight_func
will be called as follows:weight_func(src, dst, n, *extra_arguments, **extra_keywords)
.src
,dst
andn
are IDs of vertices in the RAG object which is in turn a subclass ofnetworkx.Graph
.in_place : bool, optional
If set to
True
, the merged node has the iddst
, 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
isFalse
the resulting node has a new id, rather thandst
.
-
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.
-
© 2011 the scikit-image team
Licensed under the BSD 3-clause License.
http://scikit-image.org/docs/0.12.x/api/skimage.future.graph.html