pybpmn-parser

Starter code for using the hdBPMN dataset for diagram recognition research.

The dump_coco.py script can be used to convert the images and BPMN XMLs into a COCO dataset. COCO is a common format used in computer vision research to annotate the objects and keypoints in images.

python scripts/dump_coco.py path/to/hdBPMN path/to/target/coco/directory/hdbpmn

Moreover, the demo.ipynb Jupyter notebook can be used to visualize (1) the extracted bounding boxes, keypoints, and relations, and (2) the annotated BPMN diagram overlayed over the hand-drawn image. Note that the latter requires the bpmn-to-image tool, which in turn requires a nodejs installation.

Installation

pip install pybpmn-parser

Development

In order to set up the necessary environment:

1. create an environment pybpmn-parser with the help of conda:

   conda env create -f environment.yml
   

2. activate the new environment with:

   conda activate pybpmn-parser
   

NOTE: The conda environment will have pybpmn-parser installed in editable mode. Some changes, e.g. in setup.cfg, might require you to run pip install -e . again.

Optional and needed only once after git clone:

3. install JupyterLab kernel

   python -m ipykernel install --user --name "${CONDA_DEFAULT_ENV}" --display-name "$(python -V) (${CONDA_DEFAULT_ENV})"
   

4. install several pre-commit git hooks with:

   pre-commit install
   # You might also want to run `pre-commit autoupdate`
   

   and checkout the configuration under .pre-commit-config.yaml. The -n, --no-verify flag of git commit can be used to deactivate pre-commit hooks temporarily.

Project Organization

├── LICENSE.txt             <- License as chosen on the command-line.
├── README.md               <- The top-level README for developers.
├── data
│   ├── external            <- Data from third party sources.
│   ├── interim             <- Intermediate data that has been transformed.
│   ├── processed           <- The final, canonical data sets for modeling.
│   └── raw                 <- The original, immutable data dump.
├── docs                    <- Directory for Sphinx documentation in rst or md.
├── environment.yml         <- The conda environment file for reproducibility.
├── notebooks               <- Jupyter notebooks. Naming convention is a number (for
│                              ordering), the creator's initials and a description,
│                              e.g. `1.0-fw-initial-data-exploration`.
├── pyproject.toml          <- Build system configuration. Do not change!
├── scripts                 <- Analysis and production scripts which import the
│                              actual Python package, e.g. train_model.py.
├── setup.cfg               <- Declarative configuration of your project.
├── setup.py                <- Use `pip install -e .` to install for development or
│                              or create a distribution with `tox -e build`.
├── src
│   └── pybpmn              <- Actual Python package where the main functionality goes.
├── tests                   <- Unit tests which can be run with `py.test`.
├── .coveragerc             <- Configuration for coverage reports of unit tests.
├── .isort.cfg              <- Configuration for git hook that sorts imports.
└── .pre-commit-config.yaml <- Configuration of pre-commit git hooks.

Note

This project has been set up using PyScaffold 4.0.1 and the dsproject extension 0.6.1.