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.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.show_rag (labels, rag, img) | Show a Region Adjacency Graph on an image. |
skimage.future.graph.RAG ([label_image, …]) | The Region Adjacency Graph (RAG) of an image, subclasses |
skimage.future.graph.graph_cut | |
skimage.future.graph.graph_merge | |
skimage.future.graph.rag |
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 num_cuts : int The number or N-cuts to perform before determining the optimal one. in_place : bool If set, modifies 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. |
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Returns: |
out : ndarray The new labeled array. |
[R289] | 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. |
>>> 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)
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 |
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Returns: |
out : ndarray The new labelled array. |
[R290] | 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 |
>>> 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.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 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 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 |
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Returns: |
out : ndarray The new labeled array. |
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 num_cuts : int The number or N-cuts to perform before determining the optimal one. in_place : bool If set, modifies 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. |
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Returns: |
out : ndarray The new labeled array. |
[R291] | 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. |
>>> 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)
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
edge_map : ndarray
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
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
.>>> 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)
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 connectivity : int, optional Pixels with a squared distance less than 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 |
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Returns: |
out : RAG The region adjacency graph. |
[R292] | 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 |
>>> 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.show_rag(labels, rag, img, 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. img : ndarray, shape (M, N[, 3]) Input image. If 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 : Any matplotlib colormap with which the edges are drawn. img_cmap : Any matplotlib colormap with which the image is draw. If set to in_place : bool, optional If set, the RAG is modified in place. For each node ax : The axes to draw on. If not specified, new axes are created and drawn on. |
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Returns: |
lc : A colection of lines that represent the edges of the graph. It can be passed to the |
>>> from skimage import data, segmentation >>> from skimage.future import graph >>> 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)
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 connectivity : int in {1, …, The connectivity between pixels in data : networkx Graph specification, optional Initial or additional edges to pass to the NetworkX Graph constructor. See **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]
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 in_place : bool, optional If set to extra_arguments : sequence, optional The sequence of extra positional arguments passed to extra_keywords : dictionary, optional The dict of keyword arguments passed to the |
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Returns: |
id : int The id of the new node. |
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 |
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© 2011 the scikit-image team
Licensed under the BSD 3-clause License.
http://scikit-image.org/docs/0.13.x/api/skimage.future.graph.html