Skip to main content

Simple linear iterative clustering (SLIC) in a region of interest (ROI)

Project description

maskSLIC

Simple linear iterative clustering (SLIC) in a region of interest

Outline

This code demonstrates the adaption of SLIC for a defined region of interest. The main contribution is in the placement of seed points within the ROI.

The code is a modification of the SLIC implementation provided by Scikit-image (http://scikit-image.org/)

An online demo is available at: http://maskslic.birving.com/index

An outline of the method is available at: http://arxiv.org/abs/1606.09518

Figure 1: An example region and the automatically placed seed points using this method.

seed points

Figure 2: The final superpixel regions within the ROI

superpixels

Getting started

Install dependencies
pip install -r requirements.txt

build cython code
python setup.py build_ext --inplace

run example
python run_example.py

Using a python virtualenv is reccommended on linux to get the latest versions of the dependencies.

Development

This code is still a work in progress.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

maskslic-0.3.2.post1.tar.gz (14.8 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page