Python Packages for Graph Cuts on Images

Python Packages for Graph Cuts on Images

Graph for a small image of 512x512 pixels has 261144 nodes and 523264 edges in the 4-connected pixels case. Graphs in this scale require a fast construction interface. I reviewed a few python packages mainly from this perspective. The comparison is by no means exhaustive and fair!

Based on Kolmogorov min-cut / max-flow C++ library

original Kolmogorov’s code: http://vision.csd.uwo.ca/code/#Max-flow.2Fmin-cut

as of version 3.01 restricts usage only for research purposes

version 2.21 can be commercially licensed

PyMaxflow

https://github.com/pmneila/PyMaxflow

Cython based python interface

well documented at http://pmneila.github.io/PyMaxflow/

α-β-swap and α-expansion algorithms for multi-label optimization

shortcomings:

missing batch addition of edges other than with constant weight

pymaxflow

https://github.com/Rhoana/pymaxflow

Cython based python interface

supports batch addition of edges from numpy arrays

edges are added one by one on the cython part of the interface, but it seems to be fast enough

shortcomings:

not documented (but the package is so simple that it is not a big deal)

no algorithms for multi-label segmentation

Example of batch node and edge addition:

im = ...
g = pymaxflow.PyGraph()
g.add_node(im.size)

# linear indices of pixels arranged in image like matrix
indices = np.arange(im.shape[0]*im.shape[1]).reshape(im.shape[0:2]).astype(np.int32)
# compute differences between neighbouring pixels
diffs = np.abs(im[:, 1:] - im[:, :-1]).ravel() + eps
# indices of the neighbouring pixels
e1 = indices[:, :-1].ravel()
e2 = indices[:, 1:].ravel()
g.add_edge_vectorized(e1, e2, diffs, diffs)

API:

class PyGraph:
__cinit__(self, int node_num_max, int edge_num_max)
add_node(self, int num=1)
add_edge(self, int i, int j, float cap, float rev_cap)
add_tweights(self, int i, float cap_source, float cap_sink)
maxflow(self)
what_segment(self, int i)
add_edge_vectorized(self,
np.ndarray[dtype=np.int32_t, ndim=1, negative_indices=False] i,
np.ndarray[dtype=np.int32_t, ndim=1, negative_indices=False] j,
np.ndarray[dtype=np.float32_t, ndim=1, negative_indices=False] cap,
np.ndarray[dtype=np.float32_t, ndim=1, negative_indices=False] rev_cap)
add_tweights_vectorized(self,
np.ndarray[dtype=np.int32_t, ndim=1, negative_indices=False] i,
np.ndarray[dtype=np.float32_t, ndim=1, negative_indices=False] cap_source,
np.ndarray[dtype=np.float32_t, ndim=1, negative_indices=False] cap_sink)
what_segment_vectorized(self)

pygco

based on Kolmogorov maxflow library and gco library

source: https://github.com/amueller/gco_python

introductory article written by the author: http://peekaboo-vision.blogspot.cz/2012/05/graphcuts-for-python-pygco.html

cython based python interface

well documented

α-β-swap and α-expansion algorithms for multi-label segmentation

supports only multi-label segmentation

shortcomings:

no direct access to a low level functionality as the max-flow algorithm itself

pygraphcut

http://tfinley.net/software/pygraphcut/

Python C API based interface

shortcomings:

- not actively developed

no public repository

no batch addition of nodes and edges

Python C API is hard to maintain

I have not tested this package.

Packages Based on Other Max-flow Implementations

graph-tool library

http://projects.skewed.de/graph-tool/

perfectly documented and feature rich general graph algorithms package

based on Boost Graph library

GPL license

various max-flow algorithms including Kolmogorov’s

shortcomings:

no batch addition of nodes and edges - this shortcoming should be fixed now, see comment (22.10.2014)

One by one addition of nodes and edges implemented in Python is extremely slow for graphs in a scale suitable for images. In my experience ~100x slower than the pymaxflow package due to the graph construction.

MedPy

https://pypi.python.org/pypi/MedPy/0.1.0

3d graphcuts on voxels

I have not tested this package.

Conclusions

I picked the pymaxflow package for my purposes. It’s just simple but quick wrapper for the Kolmogorov min-cut / max-flow C++ library. The wrapper fairly minimalistic and is written in Cython that makes the bindings easy to understand and modify.