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 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)
Please login to continue.