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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: