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115 lines
3.3 KiB
Python
115 lines
3.3 KiB
Python
import numpy as np
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import heapq
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def _hmerge(rag, x, y, n):
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"""Callback to handle merging nodes by recomputing mean color.
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The method expects that the mean color of `y` is already computed.
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Parameters
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----------
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graph : RAG
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The graph under consideration.
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src, dst : int
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The verices in `graph` to be merged.
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n : int
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A neighbor of `src` or `dst` or both.
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Returns
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-------
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weight : float
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The absolute difference of the mean color between node `y` and `n`.
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"""
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diff = rag.node[y]['mean color'] - rag.node[n]['mean color']
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diff = np.linalg.norm(diff)
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#rag.add_edge(y,n)
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#rag[y][n]['valid'] = True
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#heapq.heappush(heap,(diff, y , n, data))
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return diff
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def merge_hierarchical(labels, rag, thresh):
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"""Perform hierarchical merging of a RAG.
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Given an image's labels and its RAG, the method merges the similar nodes
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until the weight between every two nodes is more than `thresh`.
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Parameters
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----------
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labels : ndarray
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The array of labels.
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rag : RAG
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The Region Adjacency Graph.
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thresh : float
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The threshold. Regions connected by an edges with smaller wegiht than
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`thresh` are merged. A high value of `thresh` would mean that a lot of
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regions are merged, and the output will contain fewer regions.
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Returns
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-------
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out : ndarray
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The new labelled array.
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Examples
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--------
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>>> from skimage import data, graph, segmentation
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>>> img = data.coffee()
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>>> labels = segmentation.slic(img)
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>>> rag = graph.rag_mean_color(img, labels)
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>>> new_labels = graph.merge_hierarchical(labels, rag, 40)
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"""
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min_wt = 0
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edge_heap = []
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for x,y,data in rag.edges_iter(data=True):
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if x != y:
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data['valid'] = True
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wt = data['weight']
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heapq.heappush(edge_heap, (wt, x, y, data))
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while min_wt < thresh:
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#valid_edges = ((x, y, d) for x, y, d in rag.edges(data=True) if x != y)
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#x, y, d = min(valid_edges, key=lambda x: x[2]['weight'])
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#min_wt = d['weight']
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min_wt,x,y,data = heapq.heappop(edge_heap)
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print min_wt
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if min_wt < thresh and data['valid']:
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total_color = (rag.node[y]['total color'] +
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rag.node[x]['total color'])
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n_pixels = rag.node[x]['pixel count'] + rag.node[y]['pixel count']
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rag.node[y]['total color'] = total_color
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rag.node[y]['pixel count'] = n_pixels
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rag.node[y]['mean color'] = total_color / n_pixels
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#print x,y
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for n in rag.neighbors(x):
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rag[x][n]['valid'] = False
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for n in rag.neighbors(y):
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rag[y][n]['valid'] = False
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rag.merge_nodes(x, y, _hmerge)
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for n in rag.neighbors(y):
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if n!= y:
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rag[y][n]['valid'] = True
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wt = rag[y][n]['weight']
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heapq.heappush(edge_heap, (wt, y, n, rag[y][n]))
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arr = np.arange(labels.max() + 1)
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for ix, (n, d) in enumerate(rag.nodes_iter(data=True)):
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for label in d['labels']:
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arr[label] = ix
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return arr[labels]
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