scikit-image/skimage/graph/_construct.pyx

import rag
cimport numpy as cnp
import numpy as np


def construct_rag_meancolor_3d(img, arr):
    """Computes the Region Adjacency Graph of a 3D color image using
    difference in mean color of regions as edge weights.

    Given an image and its segmentation, this method constructs the
    corresponsing Region Adjacency Graph (RAG).Each node in the RAG
    represents a contiguous pixels with in `img` the same label in
    `arr`

    Parameters
    ----------
    img : (width, height, depth, 3) ndarray
        Input image.
    arr : (width, height, depth) ndarray
        The array with labels.

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

    cdef Py_ssize_t l, b, h, i, j, k
    cdef cnp.int32_t current, next
    l = arr.shape[0]
    b = arr.shape[1]
    h = arr.shape[2]

    g = rag.RAG()

    i = 0
    while i < l - 1:
        j = 0
        while j < b - 1:
            k = 0
            while k < h - 1:
                current = arr[i, j, k]

                try:
                    g.node[current]['pixel_count'] += 1
                    g.node[current]['total_color'] += img[i, j]
                except KeyError:
                    g.add_node(current)
                    g.node[current]['pixel_count'] = 1
                    g.node[current]['total_color'] = img[i, j].astype(np.long)
                    g.node[current]['labels'] = [arr[i, j]]

                next = arr[i + 1, j, k]
                if current != next:
                    g.add_edge(current, next)

                next = arr[i, j + 1, k]
                if current != next:
                    g.add_edge(current, next)

                next = arr[i + 1, j + 1, k]
                if current != next:
                    g.add_edge(current, next)

                next = arr[i + 1, j, k + 1]
                if current != next:
                    g.add_edge(current, next)

                next = arr[i, j + 1, k + 1]
                if current != next:
                    g.add_edge(current, next)

                next = arr[i + 1, j + 1, k + 1]
                if current != next:
                    g.add_edge(current, next)

                next = arr[i, j, k + 1]
                if current != next:
                    g.add_edge(current, next)

                k += 1

            j += 1

        i += 1

    for n in g.nodes():
        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.sqrt(diff.dot(diff))

    return g


def construct_rag_meancolor_2d(img, arr):
    """Computes the Region Adjacency Graph of a 2D color image using
    difference in mean color of regions as edge weights.

    Given an image and its segmentation, this method constructs the
    corresponsing Region Adjacency Graph (RAG).Each node in the RAG
    represents a contiguous pixels with in `img` the same label in
    `arr`

    Parameters
    ----------
    img : (width, height, 3) ndarray
        Input image.
    arr : (width, height) ndarray
        The array with labels.

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

    cdef Py_ssize_t l, b, h, i, j, k
    cdef cnp.int32_t current, next
    l = arr.shape[0]
    b = arr.shape[1]

    g = rag.RAG()

    i = 0
    while i < l - 1:
        j = 0
        while j < b - 1:
            current = arr[i, j]

            try:
                g.node[current]['pixel_count'] += 1
                g.node[current]['total_color'] += img[i, j]
            except KeyError:
                g.add_node(current)
                g.node[current]['pixel_count'] = 1
                g.node[current]['total_color'] = img[i, j].astype(np.long)
                g.node[current]['labels'] = [arr[i, j]]

            next = arr[i + 1, j]
            if current != next:
                g.add_edge(current, next)

            next = arr[i, j + 1]
            if current != next:
                g.add_edge(current, next)

            next = arr[i + 1, j + 1]
            if current != next:
                g.add_edge(current, next)

            j += 1

        i += 1

    for n in g.nodes():
        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.sqrt(diff.dot(diff))

    return g