11
2- ===========
3- MLSTRUCT-FP
4- ===========
2+ ==========
3+ MLStructFP
4+ ==========
55
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88 :alt: Build status
99
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11+ :target: https://github.com/MLSTRUCT/MLStructFP /issues
1212 :alt: Open issues
1313
1414.. image :: https://badge.fury.io/py/MLStructFP.svg
1515 :target: https://pypi.org/project/MLStructFP
1616 :alt: PyPi package
1717
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2020 :alt: Codecov
2121
2222.. image :: https://img.shields.io/badge/license-MIT-blue.svg
@@ -29,7 +29,7 @@ MLSTRUCT-FP
2929Description
3030-----------
3131
32- This repo contains the base library to load and parse floor plans from the MLSTRUCT-FP dataset, which
32+ This repo contains the base library to load and parse floor plans from the MLStructFP dataset, which
3333contains over 954 large-scale floor plan images, alongside annotations for their walls in JSON
3434format. The database loader loads in memory the Floor, Walls, and Slab objects, and also
3535offers methods to create custom images from floor plans by applying a crop, a rotation, and a custom
@@ -39,7 +39,7 @@ The images can be generated from the real rasterized plan or by using the polygo
3939JSON file. Both image and wall polygons are consistent in their placement.
4040
4141See more information in our `published article <https://doi.org/10.1016/j.autcon.2023.105132 >`_; also,
42- check out the `AI segmentation model <https://github.com/MLSTRUCT/MLSTRUCT-FP_benchmarks >`_ that tests this dataset.
42+ check out the `AI segmentation model <https://github.com/MLSTRUCT/MLStructFP_benchmarks >`_ that tests this dataset.
4343
4444
4545First steps
@@ -146,8 +146,8 @@ Object API
146146----------
147147
148148The primary usage of the API is illustrated on the
149- `jupyter notebook <https://github.com/MLSTRUCT/MLSTRUCT-FP /blob/master/example.ipynb >`_. The most fundamental
150- object is `DbLoader <https://github.com/MLSTRUCT/MLSTRUCT-FP /blob/master/MLStructFP/db/_db_loader.py >`_,
149+ `jupyter notebook <https://github.com/MLSTRUCT/MLStructFP /blob/master/example.ipynb >`_. The most fundamental
150+ object is `DbLoader <https://github.com/MLSTRUCT/MLStructFP /blob/master/MLStructFP/db/_db_loader.py >`_,
151151which receives the path of the ``fp.json `` file.
152152
153153.. code-block :: python
@@ -162,9 +162,9 @@ which receives the path of the ``fp.json`` file.
162162 :width: 640
163163 :alt: Example tabulate
164164
165- DbLoader creates a dict of `Floor <https://github.com/MLSTRUCT/MLSTRUCT-FP /blob/master/MLStructFP/db/_floor.py >`_ object,
166- which each contains a dict of `Rect <https://github.com/MLSTRUCT/MLSTRUCT-FP /blob/master/MLStructFP/db/_c_rect.py >`_ and
167- `Slab <https://github.com/MLSTRUCT/MLSTRUCT-FP /blob/master/MLStructFP/db/_c_slab.py >`_ objects. Each item is associated
165+ DbLoader creates a dict of `Floor <https://github.com/MLSTRUCT/MLStructFP /blob/master/MLStructFP/db/_floor.py >`_ object,
166+ which each contains a dict of `Rect <https://github.com/MLSTRUCT/MLStructFP /blob/master/MLStructFP/db/_c_rect.py >`_ and
167+ `Slab <https://github.com/MLSTRUCT/MLStructFP /blob/master/MLStructFP/db/_c_slab.py >`_ objects. Each item is associated
168168with their respective IDs. Floor objects also have many methods to retrieve their elements, plot, and apply
169169transformations (aka mutations) such as scaling or rotation using the ``mutate() `` method:
170170
@@ -187,8 +187,8 @@ transformations (aka mutations) such as scaling or rotation using the ``mutate()
187187 :alt: Example plot
188188
189189Finally, the most important classes are
190- `RectBinaryImage <https://github.com/MLSTRUCT/MLSTRUCT-FP /blob/master/MLStructFP/db/image/_rect_binary.py >`_ and
191- `RectFloorPhoto <https://github.com/MLSTRUCT/MLSTRUCT-FP /blob/master/MLStructFP/db/image/_rect_photo.py >`_, whose
190+ `RectBinaryImage <https://github.com/MLSTRUCT/MLStructFP /blob/master/MLStructFP/db/image/_rect_binary.py >`_ and
191+ `RectFloorPhoto <https://github.com/MLSTRUCT/MLStructFP /blob/master/MLStructFP/db/image/_rect_photo.py >`_, whose
192192main responsibilities are creating plan crops for machine learning model training. These classes perform crops
193193and downsampling on any image size and scale factor. For both objects, the main methods are:
194194
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