Skip to main content

Mixture model segmentation

Project description

Python package

Fast Mixture Model Segmentation

Fast mixture model segmentation used in Boone and Andrews labs

Python requirements

  • Numpy
  • Cython
  • scikit-image

Installation

Create a virtual environment (optional)

$ virtualenv -ppython3 segmentation-env
$ source segmentation-env/bin/activate

Install python requirements (needed to build the package)

$ pip install numpy cython

Install our library (pulls in all other dependencies)

$ pip install segmentation

Usage example

import segmentation as seg
from skimage.io import imread

image = imread('./001001000.tiff', plugin='tifffile')[1]  # Read channel 1 of a tiff/flex 
im = seg.blur_frame(image) # gaussian blur
segmented, _ = seg.mixture_model(im, debug=True) # second return argument is currently unused
labels = seg.watershed(im, segmented)

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

segmentation-0.2.2.tar.gz (133.0 kB view hashes)

Uploaded source

Built Distributions

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