Image segmentation library and command line tool using clustering methods.
Currently supported algorithms are the SLIC (simple linear iterative clustering) and SNIC (simple non-iterative clustering) superpixel algorithms. The crate also supports drawing basic contours around the image segments.
To use as a library, add the following to your Cargo.toml. Executable builds
can be found at https://github.com/okaneco/simple_clustering/releases.
toml
[dependencies.simple_clustering]
version = "0.1"
default-features = false
Images are from the Berkeley Segmentation Dataset and Benchmark unless noted otherwise.
The algorithm can be selected with the -a option. By default, the algorithm
is snic.
bash
simple_clustering -i 295087.jpg
simple_clustering -i 295087.jpg -a slic
The suggested number of superpixels to find is specified with -k or -n.
Resulting superpixel counts may be slightly greater or less than this value due
to characteristics of seeding or enforcing connectivity of superpixel labels.
The default setting is 1000.
bash
simple_clustering -i 295087.jpg -k 200
The -m option is used to control the "compactness" of each superpixel. The
number ranges from 1 to 20 with a tradeoff between uniform superpixel size
and better boundary adherence. 1 will result in larger and more non-uniform
superpixels while 20 will result in more compact and uniform superpixels. By
default, -m is set to 10.
bash
simple_clustering -i 55067.jpg -m 1
simple_clustering -i 55067.jpg -m 20
Left image: -m 1. Right image: -m 20.
The segment color defaults to black, #000, but can be specified with the
--segment-color option followed by a 3 or 6 digit hexadecimal RGB color. The
following example sets the color to gray, #777. To save the segment contours
over the original image data, add the --no-mean flag to skip generating the
mean color image.
bash
simple_clustering -i 113016.jpg -k 200 --segments --segment-color 777
simple_clustering -i 113016.jpg -k 200 --segments --no-mean
Left image: Segmented image with gray contours. Right image: Superpixel regions
drawn over original image data.
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. SLIC Superpixels. EPFL Technical Report no. 149300, June 2010.
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. SLIC Superpixels Compared to State-of-the-art Superpixel Methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274 – 2282, May 2012.
Achanta, R., & Süsstrunk, S. Superpixels and polygons using simple non-iterative clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017.
Martin, D., Fowlkes, C., Tal, D., & Malik, J. A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. Proc. 8th Int'l Conf. Computer Vision, vol. 2, p. 416 – 423, July 2001.
This crate is licensed under either - the MIT License, or - the Apache License (Version 2.0)
at your option.
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.