The easiest way to install this package is via conda (right now only available withour gurobi or cplex ):

conda install -c abailoni nifty

• git clone https://github.com/abailoni/nifty.git
• git submodule init
• git submodule update
• conda create -n nifty -c conda-forge xtensor-python boost-cpp scikit-image h5py vigra
• cmake . -DCMAKE_PREFIX_PATH=/path/to/nifty/env -DBUILD_NIFTY_PYTHON=ON (add links to gurobi or cplex if needed)
• make

Master:

Master: Dev:

A nifty library for 2D and 3D image segmentation, graph based segmentation an opt. This library provided building blocks for segmentation algorithms and complex segmentation pipelines. The core is implemented in C++ but the suggested language to use this library from is python.

A very tentative documentation of the nifty python module.

• Multicut:

  • Multicut-Ilp (Kappes et al. 2011)
    • Multicut-Ilp-Cplex
    • Multicut-Ilp-Gurobi
    • Multicut-Ilp-Glpk
  • Decomposing Solver (Alush and Goldberger 2012)
  • Cut Glue & Cut (Beier et al 2014)
    • Cut Glue & Cut - QPBO
  • Greedy Additive Clustering / Energy based Hierarchical Clustering (Beier et al. 2015)
  • Fusion Moves for Correlation clustering (Beier et al. 2015)
    • Perturbed Random Seed Watershed Proposal Generator
    • Perturbed Greedy Additive Clustering Proposal Generator
  • Kernighan-Lin Algorithm with Joins (Keuper et al 2015)
  • Message Passing for the Minimum Cost Multicut Problem (Swoboda 2016)

• Lifted Multicut: (Andres et al. 2015, Keuper et al 2015)

  • Kernighan-Lin Algorithm with Joins (Keuper et al 2015)
  • Greedy Additive Clustering (Keuper et al 2015)
  • Lifted-Multicut-Ilp (does not scale to meaningful problems, just for verification)
    • Lifted-Multicut-Ilp-Cplex
    • Lifted-Multicut-Ilp-Gurobi
    • Lifted-Multicut-Ilp-Glpk
  • Fusion Moves for Lifted Multicuts (Beier et al. 2016)
    • Perturbed Random Seed Watershed Proposal Generator
    • Perturbed Greedy Additive Clustering Proposal Generator
  • Message Passing for the Minimum Cost Multicut Problem (Swoboda 2016)

• Mincut/Maxcut:

  • QPBO

• Agglomerative Clustering

  • Easy to extend / Custom cluster policies
  • UCM Transform

• CGP 2D (Cartesian Grid Partitioning)

• Many Data Structures:

  • Union Find Data Structure
  • Histogram

• Coming Eventually:

  • MultiwayCut:
  • ModifiedMultiwayCut:
  • LiftedModifiedMultiwayCut:

The Python API is at present the easiest to use. The C++ API is mostly for power users. We recommend to use library from Python. Almost any class / function in the Python API is calling into C++ via pybind11.