Note: This repository is no longer maintained, find the maintained version: here https://github.com/ilastik/dpct/

by Carsten Haubold, 2016

This is a stand-alone tool for running tracking of divisible objects using a modified successive shortest paths solver.

On OSX and Linux you can install the python module of this package within a conda environment using:

conda install dpct -c chaubold -c ilastik

Requirements:

• a compiler capable of C++11 (clang or GCC >= 4.8)
• cmake >= 2.8 for configuration (on OSX e.g. brew install cmake)
• boost (e.g. brew install boost)
• the lemon graph library

If you want to parse the JSON files with comments, use e.g. commentjson for python, or Jackson for Java.

The bin folder contains the tracking tool that can be run from the command line. It uses a JSON file formats as input and output (see below). Invok it once to see usage instructions.

• track: given a graph and weights, return the best tracking result

Example:

$ ls
>>> weights.json	track	Makefile	model.json	train

$ ./track -m model.json -w weights.json -o trackingresult.json
>>> lots of output...

Or if you want to use it from python, you can create the model and weight as dictionaries (exactly same structure as the JSON format) and then in python run the following:

import dpct

# run tracking
mymodel = {...}
myweights = {"weights": [10,10,500,500]}
result = dpct.trackFlowBased(mymodel, myweights)

See test/test.py for a complete example.

See the Readme of the accompanying ILP solver for details of the JSON file format. The formats are compatible (but only size_t ids are allowed here), the only difference is that here we use also the start and end-timestep of each detection to order the nodes by time. See test/test.py.

The algorithm implemented here is described in:

• C. Haubold, J. Ales, S. Wolf, F. A. Hamprecht. A Generalized Successive Shortest Paths Solver for Tracking Dividing Targets. ECCV 2016 Proceedings. Bibtex