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pl-dice_coeff

https://travis-ci.org/FNNDSC/dice_coeff.svg?branch=master

An app to calculate model testing accuracy by dice coefficient

python dice_coeff.py                                           \
    [-v <level>] [--verbosity <level>]                          \
    [--version]                                                 \
    [--man]                                                     \
    [--meta]                                                    \
    [--prediction <prediction dir>]                             \
    [--ground_truth <ground truth dir>]                         \
    <inputDir>
    <outputDir>

dice_coeff.py is a ChRIS-based application that...

[-v <level>] [--verbosity <level>]
Verbosity level for app. Not used currently.

[--version]
If specified, print version number.

[--man]
If specified, print (this) man page.

[--meta]
If specified, print plugin meta data.

[--prediction <prediction dir>]
The directory where the predicted images are stored.

[--ground_truth <ground truth dir>]
The directory where the ground truth images are stored.

This plugin can be run in two modes: natively as a python package or as a containerized docker image.

To run from PyPI, simply do a

pip install dice_coeff

and run with

dice_coeff.py --man --predition pred --ground_truth gt /tmp /tmp

to get inline help. The app should also understand being called with only two positional arguments

dice_coeff.py --prediction pred_dir_name --ground_truth grnd_trh_dir_name /some/input/directory /destination/directory

To run using docker, be sure to assign an "input" directory to /incoming and an output directory to /outgoing. Make sure that the $(pwd)/out directory is world writable!

Now, prefix all calls with

docker run --rm -v $(pwd)/out:/outgoing                             \
        fnndsc/pl-dice_coeff dice_coeff.py                        \

Thus, getting inline help is:

mkdir in out && chmod 777 out
docker run --rm -v $(pwd)/in:/incoming -v $(pwd)/out:/outgoing      \
        fnndsc/pl-dice_coeff dice_coeff.py                        \
        --man                                                       \
        --prediction pred                                           \
        --ground_truth gt                                           \
        /incoming /outgoing