Python wrappers for GCO alpha-expansion and alpha-beta-swaps. These wrappers provide a high level interface for graph cut inference for multi-label problems.
See my blog for examples and comments: peekaboo-vision.blogspot.com
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Download gco from http://vision.csd.uwo.ca/code/ and compile.
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Download and install Cython (use your package manager).
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Set the path in setup.py to point to the path of gco.
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run
python setup.py build -
Install or add the build path (build/lib.XXX) to your
PYTHONPATH -
Run example.py for a simple example.
GCO implements alpha expansion and alpha beta swaps using graphcuts. These can be used to efficiently find low energy configurations of certain energy functions. Note that from a probabilistic viewpoint, GCO works in log-space.
Note that all input arrays are assumed to be in int32. This means that float potentials must be rounded!
These algorithms can only deal with certain energies. Unfortunately I have not figured out yet how to convert C++ errors to Python. If an unknown error is raised, it probably means that you used an invalid energy function. Look at the gco README for details.
This package gives a high level interface to gco, providing the following functions:
cut_simple:
Graph cut on a 2D grid using a global label affinity-matrix.
cut_VH:
NOT DONE YET
Graph cut on a 2D grid using a global label affinity-matrix and edge-weights.
V contains the weights for vertical edges, H for horizontal ones.
cut_from_graph:
Graph cut on an arbitrary graph with global label affinity-matrix.
cut_from_graph_weighted:
NOT DONE YET
Graph cut on an arbitrary graph with global label affinity-matrix and
edgeweights.
See example.py and example_middlebury.py for examples and the gco README
for more details.