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MLStructFP/__init__.py

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__version__ = '0.7.4'
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# URL
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__url__ = 'https://github.com/MLSTRUCT/MLSTRUCT-FP'
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__url_bug_tracker__ = 'https://github.com/MLSTRUCT/MLSTRUCT-FP/issues'
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__url_documentation__ = 'https://github.com/MLSTRUCT/MLSTRUCT-FP'
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__url_source_code__ = 'https://github.com/MLSTRUCT/MLSTRUCT-FP'
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__url__ = 'https://github.com/MLSTRUCT/MLStructFP'
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__url_bug_tracker__ = 'https://github.com/MLSTRUCT/MLStructFP/issues'
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__url_documentation__ = 'https://github.com/MLSTRUCT/MLStructFP'
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__url_source_code__ = 'https://github.com/MLSTRUCT/MLStructFP'

README.rst

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===========
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MLSTRUCT-FP
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===========
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==========
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MLStructFP
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==========
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.. image:: https://img.shields.io/github/actions/workflow/status/MLSTRUCT/MLSTRUCT-FP/ci.yml?branch=master
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:target: https://github.com/MLSTRUCT/MLSTRUCT-FP/actions/workflows/ci.yml
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.. image:: https://img.shields.io/github/actions/workflow/status/MLSTRUCT/MLStructFP/ci.yml?branch=master
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:target: https://github.com/MLSTRUCT/MLStructFP/actions/workflows/ci.yml
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:alt: Build status
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.. image:: https://img.shields.io/github/issues/MLSTRUCT/MLSTRUCT-FP
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:target: https://github.com/MLSTRUCT/MLSTRUCT-FP/issues
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.. image:: https://img.shields.io/github/issues/MLSTRUCT/MLStructFP
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:target: https://github.com/MLSTRUCT/MLStructFP/issues
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:alt: Open issues
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.. image:: https://badge.fury.io/py/MLStructFP.svg
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:target: https://pypi.org/project/MLStructFP
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:alt: PyPi package
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.. image:: https://codecov.io/gh/MLSTRUCT/MLSTRUCT-FP/branch/master/graph/badge.svg?token=EJ8S2AAGUO
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:target: https://codecov.io/gh/MLSTRUCT/MLSTRUCT-FP
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.. image:: https://codecov.io/gh/MLSTRUCT/MLStructFP/branch/master/graph/badge.svg?token=EJ8S2AAGUO
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:target: https://codecov.io/gh/MLSTRUCT/MLStructFP
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:alt: Codecov
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.. image:: https://img.shields.io/badge/license-MIT-blue.svg
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Description
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-----------
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This repo contains the base library to load and parse floor plans from the MLSTRUCT-FP dataset, which
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This repo contains the base library to load and parse floor plans from the MLStructFP dataset, which
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contains over 954 large-scale floor plan images, alongside annotations for their walls in JSON
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format. The database loader loads in memory the Floor, Walls, and Slab objects, and also
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offers methods to create custom images from floor plans by applying a crop, a rotation, and a custom
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JSON file. Both image and wall polygons are consistent in their placement.
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See more information in our `published article <https://doi.org/10.1016/j.autcon.2023.105132>`_; also,
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check out the `AI segmentation model <https://github.com/MLSTRUCT/MLSTRUCT-FP_benchmarks>`_ that tests this dataset.
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check out the `AI segmentation model <https://github.com/MLSTRUCT/MLStructFP_benchmarks>`_ that tests this dataset.
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First steps
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----------
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The primary usage of the API is illustrated on the
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`jupyter notebook <https://github.com/MLSTRUCT/MLSTRUCT-FP/blob/master/example.ipynb>`_. The most fundamental
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object is `DbLoader <https://github.com/MLSTRUCT/MLSTRUCT-FP/blob/master/MLStructFP/db/_db_loader.py>`_,
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`jupyter notebook <https://github.com/MLSTRUCT/MLStructFP/blob/master/example.ipynb>`_. The most fundamental
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object is `DbLoader <https://github.com/MLSTRUCT/MLStructFP/blob/master/MLStructFP/db/_db_loader.py>`_,
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which receives the path of the ``fp.json`` file.
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.. code-block:: python
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:width: 640
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:alt: Example tabulate
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DbLoader creates a dict of `Floor <https://github.com/MLSTRUCT/MLSTRUCT-FP/blob/master/MLStructFP/db/_floor.py>`_ object,
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which each contains a dict of `Rect <https://github.com/MLSTRUCT/MLSTRUCT-FP/blob/master/MLStructFP/db/_c_rect.py>`_ and
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`Slab <https://github.com/MLSTRUCT/MLSTRUCT-FP/blob/master/MLStructFP/db/_c_slab.py>`_ objects. Each item is associated
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DbLoader creates a dict of `Floor <https://github.com/MLSTRUCT/MLStructFP/blob/master/MLStructFP/db/_floor.py>`_ object,
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which each contains a dict of `Rect <https://github.com/MLSTRUCT/MLStructFP/blob/master/MLStructFP/db/_c_rect.py>`_ and
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`Slab <https://github.com/MLSTRUCT/MLStructFP/blob/master/MLStructFP/db/_c_slab.py>`_ objects. Each item is associated
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with their respective IDs. Floor objects also have many methods to retrieve their elements, plot, and apply
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transformations (aka mutations) such as scaling or rotation using the ``mutate()`` method:
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:alt: Example plot
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Finally, the most important classes are
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`RectBinaryImage <https://github.com/MLSTRUCT/MLSTRUCT-FP/blob/master/MLStructFP/db/image/_rect_binary.py>`_ and
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`RectFloorPhoto <https://github.com/MLSTRUCT/MLSTRUCT-FP/blob/master/MLStructFP/db/image/_rect_photo.py>`_, whose
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`RectBinaryImage <https://github.com/MLSTRUCT/MLStructFP/blob/master/MLStructFP/db/image/_rect_binary.py>`_ and
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`RectFloorPhoto <https://github.com/MLSTRUCT/MLStructFP/blob/master/MLStructFP/db/image/_rect_photo.py>`_, whose
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main responsibilities are creating plan crops for machine learning model training. These classes perform crops
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and downsampling on any image size and scale factor. For both objects, the main methods are:
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