scikit-image/skimage/graph/rag.py

import networkx as nx
import numpy as np
from scipy.ndimage import filters
from scipy import ndimage as nd


class RAG(nx.Graph):

    """
    The Region Adjacency Graph (RAG) of an image.
    """

    def merge_nodes(self, src, dst, weight_func=None, extra_arguments=[],
                    extra_keywords={}):
        """Merge two nodes.

        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
        ----------
        i, j : int
            Nodes to be merged. The resulting node will have ID `dst`.
        weight_func : callable, optional
            Function to decide edge weight of edges incident on the new node.
            The arguments passed to the function are, the graph, `src`, `dst`
            and the node which is adjacent to the new node.
        extra_arguments : sequence, optional
            The sequence of extra positional arguments passed to
            `weight_func`.
        extra_keywords :
            The dict of keyword arguments passed to the `weight_func`.

        """
        for neighbor in self.neighbors(src):
            if neighbor == dst:
                continue
            w1 = self.get_edge_data(neighbor, src)['weight']
            w2 = None
            if self.has_edge(neighbor, dst):
                w2 = self.get_edge_data(neighbor, dst)['weight']
            if not weight_func:
                if w2 is None:
                    w = w1
                else:
                    w = min(w1, w2)
            else:
                w = weight_func(self, src, dst, neighbor,
                                *extra_arguments, **extra_keywords)
            self.add_edge(neighbor, dst, weight=w)

        self.node[dst]['labels'] += self.node[src]['labels']
        self.remove_node(src)


def _add_edge_filter(values, g):
    """Create and edge between the first and the remaining
    values in an array.

    Add an edge between first element in `values` and
    all other elements of `values` in the graph `g`. `values[0]`
    is expected to be the central value of the footprint used.

    Parameters
    ----------
    values : array
        The array to process.
    g : RAG
        The graph to add edges in.

    Returns
    -------
    0 : int
        Always returns 0.

    """
    values = values.astype(int)
    current = values[0]
    for value in values[1:]:
        g.add_edge(current, value)

    return 0


def rag_meancolor(image, labels, connectivity=2):
    """Compute the Region Adjacency Graph using mean colors.

    Given an image and its segmentation, this method constructs the
    corresponsing Region Adjacency Graph (RAG). Each node in the RAG
    represents a contiguous set pixels within `image` with the same
    label in `labels`. The weight between two adjacent regions is the 
    difference int their mean color.

    Parameters
    ----------
    image : ndarray
        Input image.
    labels : ndarray
        The array with labels. This should have one dimention less than
        `image`. If `image` has dimensions `(M, N, 3)` `labels` should have
         dimensions `(M, N)`.
    connectivity : float, optional
        Pixels with a squared distance less than `connectivity` from each other
        are considered adjacent. It can range from 1 to `labels.ndim`. It's
        behaviour is the same as `connectivity` parameter in
        `scipy.ndimage.filters.generate_binary_structure`.

    Returns
    -------
    out : RAG
        The region adjacency graph.

    Examples
    --------
    >>> from skimage import data, graph, segmentation
    >>> img = data.lena()
    >>> labels = segmentation.slic(img)
    >>> rag = graph.rag_meancolor(img, labels)

    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

    """
    g = RAG()

    # The footprint is constructed in such a way that the first
    # element in the array being passed to _add_edge_filter is
    # the central value.
    fp = nd.generate_binary_structure(labels.ndim, connectivity)
    for d in range(fp.ndim):
        fp = fp.swapaxes(0, d)
        fp[0, ...] = 0
        fp = fp.swapaxes(0, d)

    filters.generic_filter(
        labels,
        function=_add_edge_filter,
        footprint=fp,
        mode='nearest',
        output=np.zeros(labels.shape, dtype=np.uint8),
        extra_arguments=(g,))

    for n in g:
        g.node[n].update({'labels': [n],
                          'pixel count': 0,
                          'total color': np.array([0, 0, 0], dtype=np.double)})

    for index in np.ndindex(labels.shape):
        current = labels[index]
        g.node[current]['pixel count'] += 1
        g.node[current]['total color'] += image[index]

    for n in g:
        g.node[n]['mean color'] = (g.node[n]['total color'] /
                                   g.node[n]['pixel count'])

    for x, y in g.edges_iter():
        diff = g.node[x]['mean color'] - g.node[y]['mean color']
        g[x][y]['weight'] = np.linalg.norm(diff)

    return g