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Nifty

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.

Important:

To use nifty one needs to checkout some submodules via:

git submodule init
git submodule update

If WITH_MP_LP is active, one needs:

git submodule update --init --recursive

Documentation:

A very tentative documentation of the nifty python module.

Features (Highlights):

• 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:

C++ vs Python:

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.

Install:

Conda:

The easiest way to install this version is via conda (right now it is not available with gurobi or cplex, you will need to build from source for this):

conda install -c conda-forge nifty

From Source:

To build nifty from source, you will need to set up an environment with all necessary dependencies. We recommend to use conda for this as well and provide the file environment.yaml` for this. You can set up this environment and build nifty with the following commands:

$ conda env create -f environment.yaml
$ conda activate nifty-dev
$ mkdir bld
$ cd bld
$ cmake -DCMAKE_PREFIX_PATH=/path/to/conda/envs/nifty-dev -DWITH_Z5=ON -DWITH_HDF5=ON -DWITH_ZLIB=ON -DBUILD_NIFTY_PYTHON=ON ..
$ make

This would build nifty with additional hdf5 and z5 dependencies; and add zlib support for z5. You can find all build options in CMakeLists.txt; note that you may need to install additional dependencies.

Troubleshooting:

TODO

Changelog:

TODO