dv-commandline-utils

This repository contains a collection of commandline utilities for simple image and mesh manipulation, in order to aid batch processing. The utilities are built on ITK. The scope of each utility is similar to the various ITK examples that come with the repository, with the following important changes:

• Commandline options are parsed using the boost::program_options library.
• Ideally, the pixel type is determined from the pixel type of the input file, and the appropriate template instantiation is determined dynamically.
• The output is written to disk, as opposed to the many ITK examples which use the QuickView utility.

Building

The preferred mechanism for building this repository is using Docker. Please make sure that Docker is installed and running on your system. At that point, the repository can be built as follows:

$ git clone https://github.com/DVigneault/dv-commandline-utils.git \
    ~/Developer/repositories/dv-commandline-utils/src
$ cd ~/Developer/repositories/dv-commandline-utils/src
$ ./docker-build.sh

dv-commandline-utils depends on quite a few packages, and three large c++ libraries (Boost, ITK, and VTK), which must be built from source--so this command could take a while to finish. Once it finishes, you should be able to see sudomakeinstall/dv-commandline-utils in the list of docker images on your computer:

$ docker image ls
REPOSITORY                             TAG                 IMAGE ID            CREATED             SIZE
sudomakeinstall/dv-commandline-utils   latest              848b802899d4        43 seconds ago      3.08GB

Running

Spinning up a container to play around with the utilities can be as easy as:

$ ./docker-run.sh
(dkr) $ ./dv-add-mesh-noise --help
Allowed options:
  --help                Print usage information.
  --input-mesh arg      Filename of the input mesh.
  --output-mesh arg     Filename of the output image.
  --sigma arg           Amount of noise to be added.

Congrats, you're up and running! However, in order to access data on your host machine, or build the local copy of the code, you'll want to mount some volumes, similar to docker-compose.override.yml.example. Copy this over and edit the paths as necessary to get started:

$ cp ./docker-compose.override.yml.example ./docker-compose.override.yml
$ vim ./docker-compose.override.yml # Edit paths; this file will be ignored by git.

In ./docker-compose.override.yml, change the source to the path on your host machine that you would like to be available inside your container, and change the target to the path on your container where you would like your data to be available:

      # Mount a directory to access data
      - type: bind
        source: ${HOME}/Dropbox/datasets/
        target: /data

And that's it, you're ready to run your container!

$ ./docker-run.sh

Running Tests

Unit tests are not automatically built in the docker image. The steps to build and run tests are as follows:

$ cd ~/Developer/repositories/dv-commandline-utils/src
$ ./docker-run.sh
(dkr) $ ccmake ../src -DBUILD_TESTING=ON
# configure, configure, generate in the curses GUI.
(dkr) $ make -j$(nproc)
(dkr) $ ctest

You can also make and run one specific test:

(dkr) $ make dvCameraStateTest
# V for Verboase, R for Regex
(dkr) $ ctest -V -R dvCameraState