try: import networkx as nx except ImportError: msg = "Graph functions require networkx, which is not installed" class nx: class Graph: def __init__(self, *args, **kwargs): raise ImportError(msg) import warnings warnings.warn(msg) import numpy as np from scipy.ndimage import filters from scipy import ndimage as nd import math from .. import draw, measure, segmentation, util, color try: from matplotlib import colors from matplotlib import cm except ImportError: pass def min_weight(graph, src, dst, n): """Callback to handle merging nodes by choosing minimum weight. Returns either the weight between (`src`, `n`) or (`dst`, `n`) in `graph` or the minumum of the two when both exist. Parameters ---------- graph : RAG The graph under consideration. src, dst : int The verices in `graph` to be merged. n : int A neighbor of `src` or `dst` or both. Returns ------- weight : float The weight between (`src`, `n`) or (`dst`, `n`) in `graph` or the minumum of the two when both exist. """ # cover the cases where n only has edge to either `src` or `dst` default = {'weight': np.inf} w1 = graph[n].get(src, default)['weight'] w2 = graph[n].get(dst, default)['weight'] return min(w1, w2) class RAG(nx.Graph): """ The Region Adjacency Graph (RAG) of an image, subclasses `networx.Graph `_ """ def merge_nodes(self, src, dst, weight_func=min_weight, extra_arguments=[], extra_keywords={}): """Merge node `src` into `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 edge weight of edges incident on the new node. For each neighbor `n` for `src and `dst`, `weight_func` will be called as follows: `weight_func(src, dst, n, *extra_arguments, **extra_keywords)`. `src`, `dst` and `n` are IDs of vertices in the RAG object which is in turn a subclass of `networkx.Graph`. 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`. """ src_nbrs = set(self.neighbors(src)) dst_nbrs = set(self.neighbors(dst)) neighbors = (src_nbrs & dst_nbrs) - set([src, dst]) for neighbor in neighbors: 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, graph): """Create edge in `g` between the first element of `values` and the rest. Add an edge between the 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. graph : RAG The graph to add edges in. Returns ------- 0 : int Always returns 0. The return value is required so that `generic_filter` can put it in the output array. """ values = values.astype(int) current = values[0] for value in values[1:]: if value != current: graph.add_edge(current, value) return 0 def rag_mean_color(image, labels, connectivity=2, mode='distance', sigma=255.0): """Compute the Region Adjacency Graph using mean colors. Given an image and its initial segmentation, this method constructs the corresponsing 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 `image`. If `image` 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 to `labels.ndim`. Its behavior is the same as `connectivity` parameter in `scipy.ndimage.filters.generate_binary_structure`. mode : {'distance', 'similarity'}, optional The strategy to assign edge weights. 'distance' : The weight between two adjacent regions is the :math:`|c_1 - c_2|`, where :math:`c_1` and :math:`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 :math:`e^{-d^2/sigma}` where :math:`d=|c_1 - c_2|`, where :math:`c_1` and :math:`c_2` 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 of `sigma` could make any two colors behave as though they were similar. Returns ------- out : RAG The region adjacency graph. Examples -------- >>> from skimage import data, graph, segmentation >>> img = data.lena() >>> labels = segmentation.slic(img) >>> rag = graph.rag_mean_color(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 """ graph = 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) # For example # if labels.ndim = 2 and connectivity = 1 # fp = [[0,0,0], # [0,1,1], # [0,1,0]] # # if labels.ndim = 2 and connectivity = 2 # fp = [[0,0,0], # [0,1,1], # [0,1,1]] filters.generic_filter( labels, function=_add_edge_filter, footprint=fp, mode='nearest', output=np.zeros(labels.shape, dtype=np.uint8), extra_arguments=(graph,)) for n in graph: graph.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] graph.node[current]['pixel count'] += 1 graph.node[current]['total color'] += image[index] for n in graph: graph.node[n]['mean color'] = (graph.node[n]['total color'] / graph.node[n]['pixel count']) for x, y, d in graph.edges_iter(data=True): diff = graph.node[x]['mean color'] - graph.node[y]['mean color'] diff = np.linalg.norm(diff) if mode == 'similarity': d['weight'] = math.e ** (-(diff ** 2) / sigma) elif mode == 'distance': d['weight'] = diff else: raise ValueError("The mode '%s' is not recognised" % mode) return graph def draw_rag(labels, rag, img, border_color=None, node_color='#ffff00', edge_color='#00ff00', colormap=None, thresh=np.inf, desaturate=False, in_place=True): """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 attribute ``rag.node[n]['centroid']``. Returns ------- out : ndarray, shape (M, N, 3) The image with the RAG drawn. Examples -------- >>> from skimage import data, graph, segmentation >>> img = data.coffee() >>> labels = segmentation.slic(img) >>> g = graph.rag_mean_color(img, labels) >>> out = graph.draw_rag(labels, g, img) """ if not in_place: rag = rag.copy() if desaturate: img = color.rgb2gray(img) img = color.gray2rgb(img) out = util.img_as_float(img, force_copy=True) cc = colors.ColorConverter() edge_color = cc.to_rgb(edge_color) node_color = cc.to_rgb(node_color) # Handling the case where one node has multiple labels # offset is 1 so that regionprops does not ignore 0 offset = 1 map_array = np.arange(labels.max() + 1) for n, d in rag.nodes_iter(data=True): for label in d['labels']: map_array[label] = offset offset += 1 rag_labels = map_array[labels] regions = measure.regionprops(rag_labels) for (n, data), region in zip(rag.nodes_iter(data=True), regions): data['centroid'] = region['centroid'] if border_color is not None: border_color = cc.to_rgb(border_color) out = segmentation.mark_boundaries(out, rag_labels, color=border_color) if colormap is not None: edge_weight_list = [d['weight'] for x, y, d in rag.edges_iter(data=True) if d['weight'] < thresh] norm = colors.Normalize() norm.autoscale(edge_weight_list) smap = cm.ScalarMappable(norm, colormap) for n1, n2, data in rag.edges_iter(data=True): if data['weight'] >= thresh: continue r1, c1 = map(int, rag.node[n1]['centroid']) r2, c2 = map(int, rag.node[n2]['centroid']) line = draw.line(r1, c1, r2, c2) if colormap is not None: out[line] = smap.to_rgba([data['weight']])[0][:-1] else: out[line] = edge_color circle = draw.circle(r1, c1, 2) out[circle] = node_color return out