contrast-sensitive Potts model for pairwise smoothing on a grid
Closed this issue · 1 comments
Is it currently possible to implement this commonly used term for image segmentation without building the graph manually in a python loop? Or is it only possible to use a single set of constant neighborhood weights across the whole grid?
No, you can definitely use different weights at each location. There are a couple of different ways of doing so, depending on your specific needs. The docstring of add_grid_edges
shows one possible way (this is one of the examples, check the docstring for more):
>>> g = maxflow.GraphFloat()
>>> nodeids = g.add_grid_nodes((3, 3))
>>> structure = np.array([[0, 0, 0],
[0, 0, 1],
[0, 1, 0]])
>>> weights = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
>>> g.add_grid_edges(nodeids, weights=weights, structure=structure,
symmetric=False)
::
XXX----1--->XXX----2--->XXX
XXX XXX XXX
| | |
| | |
1| 2| 3|
| | |
V V V
XXX----4--->XXX----5--->XXX
XXX XXX XXX
| | |
| | |
4| 5| 6|
| | |
V V V
XXX----7--->XXX----8--->XXX
XXX XXX XXX
If you need different weights per location AND orientation (e.g., horizontal edges having different capacities than vertical edges), you can call add_grid_edges
once per orientation.
Finally, if you need even more flexibility, note that the nodeids
in add_grid_edges
are not required to have the shape of the image. You can create your own nodeids
to match you requirements. For example, consider the 3x3 grid
0 1 2
3 4 5
6 7 8
where you would like to connect 0 to 5 with capacity 1.0, 0 to 8 with capacity 2.0 and 1 to 6 with capacity 3.0. You can create a custom array for nodeids
,
g = maxflow.GraphFloat()
g.add_grid_nodes((3, 3))
mynodeids = np.array([
[0, 5],
[0, 8],
[1, 6]])
weights = np.array([[1.0], [2.0], [3.0]])
# Note that nodeids and weights must be broadcastable according to NumPy rules.
# Each node will be connected to its right node (i.e., 0-->5, 0-->8, 1-->6)
structure = np.array([[0, 0, 1]])
g.add_grid_edges(mynodeids, weights, structure)
Let me know if none of this methods apply to your problem.