Home› Projects› pydensecrf
pydensecrf
C++

pydensecrf

Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs with gaussian edge potentials.

by lucasb-eyer · GitHub
Stars
Forks
License
Created
Last commit
Language
computer-visioncrfcythonMITC++
View on GitHub
In plain words

Install and use a Python tool for advanced image processing with fully connected conditional random fields.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
pydensecrf — GitHub preview card
📈 Star history
2 0222 021
2026-07-072026-08-31
📈 Track pydensecrf

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs with gaussian edge potentials.

pydensecrf has 2k stars on GitHub. It has been forked 428 times. pydensecrf is written mainly in C++. It has been in active development since 2015. pydensecrf is available under the MIT license. Its main topics are computer-vision, crf, cython, eigen.

❓ Frequently asked questions

What is pydensecrf?

Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs with gaussian edge potentials.

Is pydensecrf open source?

pydensecrf is an open-source project. It is released under the MIT license.

Is pydensecrf free?

Yes. pydensecrf is free and open source — you can use, modify and self-host it.

What license does pydensecrf use?

pydensecrf is available under the MIT license.

What language is pydensecrf written in?

pydensecrf is written mainly in C++.

🏅 Maintainer of this project?
olud.ai badge — pydensecrf

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=lucasb-eyer-pydensecrf)](https://olud.ai/project/lucasb-eyer-pydensecrf.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.