:py:mod:`kwcoco.data.grab_spacenet`
===================================

.. py:module:: kwcoco.data.grab_spacenet

.. autoapi-nested-parse::

   .. rubric:: References

   https://medium.com/the-downlinq/the-spacenet-7-multi-temporal-urban-development-challenge-algorithmic-baseline-4515ec9bd9fe
   https://arxiv.org/pdf/2102.11958.pdf
   https://spacenet.ai/sn7-challenge/



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   kwcoco.data.grab_spacenet.Archive



Functions
~~~~~~~~~

.. autoapisummary::

   kwcoco.data.grab_spacenet.unarchive_file
   kwcoco.data.grab_spacenet.grab_spacenet7
   kwcoco.data.grab_spacenet.convert_spacenet_to_kwcoco
   kwcoco.data.grab_spacenet.main



.. py:class:: Archive(fpath, mode='r')

   Bases: :py:obj:`object`

   Abstraction over zipfile and tarfile

   SeeAlso:
       https://github.com/RKrahl/archive-tools
       https://pypi.org/project/arlib/

   .. rubric:: Example

   >>> from kwcoco.data.grab_spacenet import *  # NOQA
   >>> from os.path import join
   >>> dpath = ub.ensure_app_cache_dir('ubelt', 'tests', 'archive')
   >>> ub.delete(dpath)
   >>> dpath = ub.ensure_app_cache_dir(dpath)
   >>> import pathlib
   >>> dpath = pathlib.Path(dpath)
   >>> #
   >>> #
   >>> mode = 'w'
   >>> self1 = Archive(str(dpath / 'demo.zip'), mode=mode)
   >>> self2 = Archive(str(dpath / 'demo.tar.gz'), mode=mode)
   >>> #
   >>> open(dpath / 'data_1only.txt', 'w').write('bazbzzz')
   >>> open(dpath / 'data_2only.txt', 'w').write('buzzz')
   >>> open(dpath / 'data_both.txt', 'w').write('foobar')
   >>> #
   >>> self1.add(dpath / 'data_both.txt')
   >>> self1.add(dpath / 'data_1only.txt')
   >>> #
   >>> self2.add(dpath / 'data_both.txt')
   >>> self2.add(dpath / 'data_2only.txt')
   >>> #
   >>> self1.close()
   >>> self2.close()
   >>> #
   >>> self1 = Archive(str(dpath / 'demo.zip'), mode='r')
   >>> self2 = Archive(str(dpath / 'demo.tar.gz'), mode='r')
   >>> #
   >>> extract_dpath = ub.ensuredir(str(dpath / 'extracted'))
   >>> extracted1 = self1.extractall(extract_dpath)
   >>> extracted2 = self2.extractall(extract_dpath)
   >>> for fpath in extracted2:
   >>>     print(open(fpath, 'r').read())
   >>> for fpath in extracted1:
   >>>     print(open(fpath, 'r').read())

   .. py:method:: __iter__(self)


   .. py:method:: add(self, fpath, arcname=None)


   .. py:method:: close(self)


   .. py:method:: __enter__(self)


   .. py:method:: __exit__(self, *args)


   .. py:method:: extractall(self, output_dpath='.', verbose=1, overwrite=True)



.. py:function:: unarchive_file(archive_fpath, output_dpath='.', verbose=1, overwrite=True)


.. py:function:: grab_spacenet7(data_dpath)

   .. rubric:: References

   https://spacenet.ai/sn7-challenge/

   Requires:
       awscli


.. py:function:: convert_spacenet_to_kwcoco(extract_dpath, coco_fpath)

   Converts the raw SpaceNet7 dataset to kwcoco

   .. note::

      * The "train" directory contains 60 "videos" representing a region over time.
      
      * Each "video" directory contains :
          * images           - unmasked images
          * images_masked    - images with masks applied
          * labels           - geojson polys in wgs84?
          * labels_match     - geojson polys in wgs84 with track ids?
          * labels_match_pix - geojson polys in pixels with track ids?
          * UDM_masks - unusable data masks (binary data corresponding with an image, may not exist)
      
      File names appear like:
          "global_monthly_2018_01_mosaic_L15-1538E-1163N_6154_3539_13"


.. py:function:: main()