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        "author": "Niels Jeppesen",
        "author_email": "niejep@dtu.dk",
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            "Development Status :: 3 - Alpha",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: C++",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Image Recognition",
            "Topic :: Scientific/Engineering :: Mathematics"
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        "description": "# Thin wrapper for Maxflow\nThin 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.\n\n## Maxflow vs. QPBO\nA 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.\n\n## Installation\nInstall package using `pip install thinmaxflow` or clone this repository (including [submodule](https://github.com/Skielex/maxflow)). Building the package requires Cython.\n\n## Graph types\nCurrently, 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.\n\n## Tiny example\n```python\nimport thinmaxflow as tf\n\n# Create graph object.\ngraph = tf.GraphInt()\n\n# Number of nodes to add.\nnodes_to_add = 2\n\n# Add two nodes.\nfirst_node_id = graph.add_node(nodes_to_add)\n\n# Add edges.\ngraph.add_tweights(0, 5, 0) # s     --5->   n(0)\ngraph.add_tweights(0, 0, 1) # n(0)  --1->   t\ngraph.add_tweights(1, 0, 3) # n(1)  --3->   t\ngraph.add_edge(0, 1, 2, 1)  # n(0)  --2->   n(1)\n                            # n(1)  --1->   n(0)\n\n# Find maxflow/cut graph.\nflow = graph.maxflow()\n\nfor n in range(nodes_to_add):\n    segment = graph.what_segment(n)\n    print('Node %d belongs to segment %d.' % (n, segment))\n# Node 0 belongs to segment 0.\n# Node 1 belongs to segment 1.\n\nprint('Maximum flow: %s' % flow)\n# Maximum flow: 3\n```\n\n## License\nAs the Maxflow implementation is distributed under the GPLv3 license, so is this package.\n\n",
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