Metadata-Version: 2.1
Name: thinmaxflow
Version: 0.1.5
Summary: A thin Maxflow wrapper for Python
Home-page: https://github.com/Skielex/thinmaxflow
Author: Niels Jeppesen
Author-email: niejep@dtu.dk
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Description-Content-Type: text/markdown
License-File: LICENSE

# Thin wrapper for Maxflow
Thin Python wrapper for a modified version of the Maxflow algorithm by Yuri Boykov and Vladimir Kolmogorov. The original source code by Vladimir Kolmogorov availbable at http://pub.ist.ac.at/~vnk/software.html. This wrapper uses a modified version with support for larger graphs and slightly lower memory usage. See [submodule repository](https://github.com/Skielex/maxflow) for more details.

## Maxflow vs. QPBO
A more advanced alternative to the Maxflow algorithm is (quadratic pseudo-Boolean optimization) [QPBO](https://github.com/Skielex/thinqpbo), which also uses *s-t* graph cut. Unlike Maxflow, it allows for non-submodular energy terms, which Maxflow doesn't (unless you construct the graph in a specific way, which is what QPBO does). Amongst other things, this allows QPBO to solve optimization problems with exclusions terms, which can be very usefull. QPBO uses more memory and is slightly slower than Maxflow.

## Installation
Install package using `pip install thinmaxflow` or clone this repository (including [submodule](https://github.com/Skielex/maxflow)). Building the package requires Cython.

## Graph types
Currently, there are four different types of graphs: `GraphInt`, `GraphShort`, `GraphFloat` and `GraphDouble`. The only difference is the underlying datatypes used for the edge capacities in the graph. For stability, it is recommended to use `GraphInt` for integer capacities and `GraphDouble` for floating point capacities. However, in some cases, it maybe be favourable to use `GraphShort` or `GraphFloat` to reduce memory consumption.

## Tiny example
```python
import thinmaxflow as tf

# Create graph object.
graph = tf.GraphInt()

# Number of nodes to add.
nodes_to_add = 2

# Add two nodes.
first_node_id = graph.add_node(nodes_to_add)

# Add edges.
graph.add_tweights(0, 5, 0) # s     --5->   n(0)
graph.add_tweights(0, 0, 1) # n(0)  --1->   t
graph.add_tweights(1, 0, 3) # n(1)  --3->   t
graph.add_edge(0, 1, 2, 1)  # n(0)  --2->   n(1)
                            # n(1)  --1->   n(0)

# Find maxflow/cut graph.
flow = graph.maxflow()

for n in range(nodes_to_add):
    segment = graph.what_segment(n)
    print('Node %d belongs to segment %d.' % (n, segment))
# Node 0 belongs to segment 0.
# Node 1 belongs to segment 1.
    
print('Maximum flow: %s' % flow)
# Maximum flow: 3
```

## License
As the Maxflow implementation is distributed under the GPLv3 license, so is this package.
