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https://github.com/wassname/scikit-image.git
synced 2026-08-17 11:26:12 +08:00
code cleanup and removed test
This commit is contained in:
Vighnesh Birodkar
@@ -11,12 +11,91 @@ until no highly similar region pairs remain.
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"""
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from skimage import graph, data, io, segmentation, color
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import numpy as np
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def _weight_mean_color(graph, src, dst, 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 `dst` 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 vertices 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 `dst` and `n`.
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"""
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#print 'merging
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diff = graph.node[dst]['mean color'] - graph.node[n]['mean color']
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diff = np.linalg.norm(diff)
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return diff
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def _pre_merge_mean_color(graph, src, dst):
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"""Callback called before merging two nodes of a mean color distance graph.
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This method computes the mean color of `dst`.
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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 vertices in `graph` to be merged.
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"""
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graph.node[dst]['total color'] += graph.node[src]['total color']
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graph.node[dst]['pixel count'] += graph.node[src]['pixel count']
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graph.node[dst]['mean color'] = (graph.node[dst]['total color'] /
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graph.node[dst]['pixel count'])
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def merge_hierarchical_mean_color(labels, rag, thresh, rag_copy=True,
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in_place_merge=False):
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"""Perform hierarchical merging of a color distance RAG.
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Greedily merges the most similar pair of nodes until no edges lower than
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`thresh` remain.
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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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Regions connected by an edge with weight smaller than `thresh` are
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merged.
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rag_copy : bool, optional
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If set, the RAG copied before modifying.
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in_place_merge : bool, optional
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If set, the nodes are merged in place. Otherwise, a new node is
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created for each merge.
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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_mean_color(labels, rag, 40)
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"""
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return graph.merge_hierarchical(labels, rag, thresh, rag_copy,
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in_place_merge, _pre_merge_mean_color,
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_weight_mean_color)
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img = data.coffee()
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labels = segmentation.slic(img, compactness=30, n_segments=400)
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g = graph.rag_mean_color(img, labels)
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labels2 = graph.merge_hierarchical_mean_color(labels, g, 40)
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labels2 = merge_hierarchical_mean_color(labels, g, 40)
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g2 = graph.rag_mean_color(img, labels2)
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out = color.label2rgb(labels2, img, kind='avg')
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@@ -2,7 +2,7 @@ from .spath import shortest_path
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from .mcp import MCP, MCP_Geometric, MCP_Connect, MCP_Flexible, route_through_array
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from .graph_cut import cut_threshold, cut_normalized
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from .rag import rag_mean_color, RAG, draw_rag
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from .graph_merge import merge_hierarchical, merge_hierarchical_mean_color
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from .graph_merge import merge_hierarchical
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ncut = cut_normalized
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@@ -18,5 +18,4 @@ __all__ = ['shortest_path',
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'ncut',
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'draw_rag',
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'merge_hierarchical',
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'merge_hierarchical_mean_color',
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'RAG']
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@@ -2,50 +2,6 @@ import numpy as np
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import heapq
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def _weight_mean_color(graph, src, dst, 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 `dst` 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 vertices 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 `dst` and `n`.
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"""
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#print 'merging
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diff = graph.node[dst]['mean color'] - graph.node[n]['mean color']
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diff = np.linalg.norm(diff)
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return diff
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def _pre_merge_mean_color(graph, src, dst):
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"""Callback called before merging two nodes of a mean color distance graph.
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This method computes the mean color of `dst`.
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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 vertices in `graph` to be merged.
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"""
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graph.node[dst]['total color'] += graph.node[src]['total color']
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graph.node[dst]['pixel count'] += graph.node[src]['pixel count']
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graph.node[dst]['mean color'] = (graph.node[dst]['total color'] /
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graph.node[dst]['pixel count'])
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def _revalidate_node_edges(rag, node, heap_list):
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"""Handles validation and invalidation of edges incident to a node.
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@@ -81,38 +37,21 @@ def _revalidate_node_edges(rag, node, heap_list):
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heapq.heappush(heap_list, heap_item)
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def merge_hierarchical_mean_color(labels, rag, thresh, in_place=True, merge_in_place=False):
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"""Perform hierarchical merging of a color distance RAG.
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def _copy_node(graph, node_id, copy_id):
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""" Copies `node_id` into `copy_id` along with all its edges. """
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Greedily merges the most similar pair of nodes until no edges lower than
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`thresh` remain.
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graph._add_node(copy_id)
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graph.node[copy_id] = graph.node[node_id]
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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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Regions connected by an edge with weight smaller than `thresh` are
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merged.
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in_place : bool, optional
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If set, the RAG is modified in place.
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for nbr in graph.neighbors(node_id):
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wt = graph[node_id][nbr]['weight']
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graph.add_edge(nbr, copy_id, {'weight': wt})
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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_mean_color(labels, rag, 40)
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"""
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return merge_hierarchical(labels, rag, thresh, in_place, merge_in_place,
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_pre_merge_mean_color, _weight_mean_color)
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graph.remove_node(node_id)
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def merge_hierarchical(labels, rag, thresh, in_place, merge_in_place,pre_merge_func,
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weight_func):
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def merge_hierarchical(labels, rag, thresh, rag_copy, in_place_merge,
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merge_func, weight_func):
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"""Perform hierarchical merging of a RAG.
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Greedily merges the most similar pair of nodes until no edges lower than
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@@ -127,12 +66,15 @@ def merge_hierarchical(labels, rag, thresh, in_place, merge_in_place,pre_merge_f
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thresh : float
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Regions connected by an edge with weight smaller than `thresh` are
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merged.
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in_place : bool, optional
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If set, the RAG is modified in place.
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pre_merge_func : callable
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rag_copy : bool, optional
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If set, the RAG copied before modifying.
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in_place_merge : bool, optional
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If set, the nodes are merged in place. Otherwise, a new node is
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created for each merge..
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merge_func : callable
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This function is called before merging two nodes. For the RAG `graph`
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while merging `src` and `dst`, it is called as follows
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``pre_merge_func(graph, src, dst)``.
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``merge_func(graph, src, dst)``.
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weight_func : callable
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The function to compute the new weights of the nodes adjacent to the
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merged node. This is directly supplied as the argument `weight_func`
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@@ -144,7 +86,7 @@ def merge_hierarchical(labels, rag, thresh, in_place, merge_in_place,pre_merge_f
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The new labeled array.
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"""
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if not in_place:
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if rag_copy:
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rag = rag.copy()
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edge_heap = []
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@@ -162,34 +104,23 @@ def merge_hierarchical(labels, rag, thresh, in_place, merge_in_place,pre_merge_f
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# Ensure popped edge is valid, if not, the edge is discarded
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if valid:
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pre_merge_func(rag, n1, n2)
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# Invalidate all neigbors of `src` before its deleted
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for n in rag.neighbors(n1):
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rag[n1][n]['heap item'][3] = False
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if not merge_in_place:
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if not in_place_merge:
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for n in rag.neighbors(n2):
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rag[n2][n]['heap item'][3] = False
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if not merge_in_place:
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#print 'added',next_id
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if not in_place_merge:
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next_id = rag.next_id()
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rag._add_node(next_id)
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rag.node[next_id] = rag.node[n2]
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for nbr in rag.neighbors(n2):
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rag.add_edge(nbr, next_id, {'weight':rag[n][n2]['weight']})
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rag.remove_node(n2)
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_copy_node(rag, n2, next_id)
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src, dst = n1, next_id
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else:
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src, dst = n1, n2
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merge_func(rag, src, dst)
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new_id = rag.merge_nodes(src, dst, weight_func)
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_revalidate_node_edges(rag, new_id, edge_heap)
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arr = np.arange(labels.max() + 1)
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@@ -108,23 +108,3 @@ def test_rag_error():
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labels[5:, :] = 1
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testing.assert_raises(ValueError, graph.rag_mean_color, img, labels,
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2, 'non existant mode')
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@skipif(not is_installed('networkx'))
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def test_merge_hierarchical():
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img = np.zeros((100, 100, 3), dtype='uint8')
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img[:50, :50] = 255, 255, 255
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img[:50, 50:] = 254, 254, 254
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img[50:, :50] = 2, 2, 2
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img[50:, 50:] = 1, 1, 1
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labels = np.zeros((100, 100), dtype='uint8')
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labels[:50, :50] = 0
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labels[:50, 50:] = 1
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labels[50:, :50] = 2
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labels[50:, 50:] = 3
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rag = graph.rag_mean_color(img, labels)
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new_labels = graph.merge_hierarchical_mean_color(labels, rag, 10)
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# Two labels
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assert new_labels.max() == 1
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