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These are the (Julia) codes for the optical flow experiments of the manuscript “Predictive online optimisation with applications to optical flow” by Tuomo Valkonen (arXiv:2002.03053).
\n\nPrerequisites
\n\nThese codes were written for Julia 1.3. The Julia package prequisites are from November 2019 when our experiments were run, and have not been updated to maintain the same environment we used to do the experiments in the manuscript. You may get Julia from julialang.org.
\n\nUsing
\n\nNavigate your unix shell to the directory containing this README.md
and then run:
$ julia --project=PredictPDPS\n
\n\nThe first time doing this, to ensure all the dependencies are installed, run
\n\n$ ]instantiate\n
\n\nAfterwards in the Julia shell, type:
\n\n> using PredictPDPS\n
\n\nThis may take a while as Julia precompiles the code. Then, to generate all the experiments in the manuscript, run:
\n\n> batchrun_article()\n
\n\nThis will save the results under img/
. To see the experiments running visually, and not save the results, run
> demo_known1()\n
\n\nor any of demo_XY()
, where X
=1,2,3 and Y
=known
,unknown
. Further parameters and experiments are available via run_experiments
. See the source code for details.
To run the data generation multi-threadeadly parallel to the algorithm, set the JULIA_NUM_THREADS
environment variable to a number larger than one.