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Added example for region boundary merging

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This commit is contained in:
Vighnesh Birodkar
2016-04-27 01:03:09 -04:00
parent 2a87c07800
commit b3f6b545cb
4 changed files with 82 additions and 36 deletions
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doc/examples/segmentation/plot_boundary_merge.py
+60 -18
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@@ -1,36 +1,78 @@
from skimage import data, io, segmentation, color
"""
============================================
Hierarchical Merging of Region Boundary RAGs
============================================
TODO: Description
"""
from skimage import data, segmentation, filters, color
from skimage.future import graph
import numpy as np
from matplotlib import pyplot as plt
def weight_boundary(graph, src, dst, n):
if graph.has_edge(src, n) and graph.has_edge(dst, n):
count_src = graph[src][n]['count']
count_dst = graph[dst][n]['count']
"""
Callback to handle merging of nodes of a region boundary RAG.
weight_src = graph[src][n]['weight']
weight_dst = graph[dst][n]['weight']
This function computes the `"weight"` and the count `"count"`
attributes of the edge between `n` and the node formed after
merging `src` and `dst`.
count = count_src + count_dst
return {
'count': count,
'weight': (count_src*weight_src + count_dst*weight_dst)/count
}
elif graph.has_edge(src, n):
return graph[src][n]
elif graph.has_edge(dst, n):
return graph[dst][n]
Parameters
----------
graph : RAG
The graph under consideration.
src, dst : int
The vertices in `graph` to be merged.
n : int
A neighbor of `src` or `dst` or both.
Returns
-------
data : dict
A dictionary with the `"weight"` and `"count"` attributes to be
assigned for the merged node.
"""
default = {'weight': 0.0, 'count': 0}
count_src = graph[src].get(n, default)['count']
count_dst = graph[dst].get(n, default)['count']
weight_src = graph[src].get(n, default)['weight']
weight_dst = graph[dst].get(n, default)['weight']
count = count_src + count_dst
return {
'count': count,
'weight': (count_src*weight_src + count_dst*weight_dst)/count
}
def merge_boundary(graph, src, dst):
pass
img = data.coffee()
edges = filters.sobel(color.rgb2gray(img))
labels = segmentation.slic(img, compactness=30, n_segments=400)
g = graph.rag_mean_color(img, labels)
g = graph.rag_boundary(labels, edges)
labels2 = graph.merge_hierarchical(labels, g, thresh=40, rag_copy=False,
graph.show_rag(labels, g, img)
plt.title('Initial RAG')
labels2 = graph.merge_hierarchical(labels, g, thresh=0.08, rag_copy=False,
in_place_merge=True,
merge_func=merge_boundary,
weight_func=weight_boundary)
graph.show_rag(labels, g, img)
plt.title('RAG after hierarchical merging')
plt.figure()
out = color.label2rgb(labels2, img, kind='avg')
plt.imshow(out)
plt.title('Final segmentation')
plt.show()
doc/examples/segmentation/plot_rag.py
+10 -9
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@@ -23,29 +23,30 @@ import numpy as np
def max_edge(g, src, dst, n):
"""Callback to handle merging nodes by choosing maximum weight.
Returns either the weight between (`src`, `n`) or (`dst`, `n`)
in `g` or the maximum of the two when both exist.
Returns a dictionary with `"weight"` set as either the weight between
(`src`, `n`) or (`dst`, `n`) in `graph` or the maximum of the two when
both exist.
Parameters
----------
g : RAG
graph : RAG
The graph under consideration.
src, dst : int
The vertices in `g` to be merged.
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 `g` or the
maximum of the two when both exist.
data : dict
A dict with the `"weight"` attribute set the weight between
(`src`, `n`) or (`dst`, `n`) in `graph` or the maximum of the two when
both exist.
"""
w1 = g[n].get(src, {'weight': -np.inf})['weight']
w2 = g[n].get(dst, {'weight': -np.inf})['weight']
return max(w1, w2)
return {'weight': max(w1, w2)}
def display(g, title):
doc/examples/segmentation/plot_rag_merge.py
+5 -4
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@@ -31,13 +31,14 @@ def _weight_mean_color(graph, src, dst, n):
Returns
-------
weight : float
The absolute difference of the mean color between node `dst` and `n`.
data : dict
A dictionary with the `"weight"` attribute set as the absolute
difference of the mean color between node `dst` and `n`.
"""
diff = graph.node[dst]['mean color'] - graph.node[n]['mean color']
diff = np.linalg.norm(diff)
return diff
return {'weight': diff}
def merge_mean_color(graph, src, dst):
@@ -62,7 +63,7 @@ img = data.coffee()
labels = segmentation.slic(img, compactness=30, n_segments=400)
g = graph.rag_mean_color(img, labels)
labels2 = graph.merge_hierarchical(labels, g, thresh=40, rag_copy=False,
labels2 = graph.merge_hierarchical(labels, g, thresh=35, rag_copy=False,
in_place_merge=True,
merge_func=merge_mean_color,
weight_func=_weight_mean_color)
skimage/future/graph/rag.py
+7 -5
View File
@@ -50,8 +50,9 @@ def _edge_generator_from_csr(csr_matrix):
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 minimum of the two when both exist.
Returns a dictionary with `"weight"` set as either the weight between
(`src`, `n`) or (`dst`, `n`) in `graph` or the minimum of the two when
both exist.
Parameters
----------
@@ -64,9 +65,10 @@ def min_weight(graph, src, dst, n):
Returns
-------
weight : float
The weight between (`src`, `n`) or (`dst`, `n`) in `graph` or the
minimum of the two when both exist.
data : dict
A dict with the `"weight"` attribute set the weight between
(`src`, `n`) or (`dst`, `n`) in `graph` or the minimum of the two when
both exist.
"""