:py:mod:`kwcoco.demo.toypatterns`
=================================

.. py:module:: kwcoco.demo.toypatterns


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

Classes
~~~~~~~

.. autoapisummary::

   kwcoco.demo.toypatterns.CategoryPatterns
   kwcoco.demo.toypatterns.Rasters



Functions
~~~~~~~~~

.. autoapisummary::

   kwcoco.demo.toypatterns.star



Attributes
~~~~~~~~~~

.. autoapisummary::

   kwcoco.demo.toypatterns.profile


.. py:data:: profile
   

   

.. py:class:: CategoryPatterns(categories=None, fg_scale=0.5, fg_intensity=0.9, rng=None)

   Bases: :py:obj:`object`

   .. rubric:: Example

   >>> from kwcoco.demo.toypatterns import *  # NOQA
   >>> self = CategoryPatterns.coerce()
   >>> chip = np.zeros((100, 100, 3))
   >>> offset = (20, 10)
   >>> dims = (160, 140)
   >>> info = self.random_category(chip, offset, dims)
   >>> print('info = {}'.format(ub.repr2(info, nl=1)))
   >>> # xdoctest: +REQUIRES(--show)
   >>> import kwplot
   >>> kwplot.autompl()
   >>> kwplot.imshow(info['data'], pnum=(1, 2, 1), fnum=1, title='chip-space')
   >>> kpts = kwimage.Points._from_coco(info['keypoints'])
   >>> kpts.translate(-np.array(offset)).draw(radius=3)
   >>> #####
   >>> mask = kwimage.Mask.coerce(info['segmentation'])
   >>> kwplot.imshow(mask.to_c_mask().data, pnum=(1, 2, 2), fnum=1, title='img-space')
   >>> kpts.draw(radius=3)
   >>> kwplot.show_if_requested()

   .. py:attribute:: _default_categories
      

      

   .. py:attribute:: _default_keypoint_categories
      

      

   .. py:attribute:: _default_catnames
      :annotation: = ['star', 'eff', 'superstar']

      

   .. py:method:: coerce(CategoryPatterns, data=None, **kwargs)
      :classmethod:

      Construct category patterns from either defaults or only with specific
      categories. Can accept either an existig category pattern object, a
      list of known catnames, or mscoco category dictionaries.

      .. rubric:: Example

      >>> data = ['superstar']
      >>> self = CategoryPatterns.coerce(data)


   .. py:method:: __len__(self)


   .. py:method:: __getitem__(self, index)


   .. py:method:: __iter__(self)


   .. py:method:: index(self, name)


   .. py:method:: get(self, index, default=ub.NoParam)


   .. py:method:: random_category(self, chip, xy_offset=None, dims=None, newstyle=True, size=None)

      .. rubric:: Example

      >>> from kwcoco.demo.toypatterns import *  # NOQA
      >>> self = CategoryPatterns.coerce(['superstar'])
      >>> chip = np.random.rand(64, 64)
      >>> info = self.random_category(chip)


   .. py:method:: render_category(self, cname, chip, xy_offset=None, dims=None, newstyle=True, size=None)

      .. rubric:: Example

      >>> from kwcoco.demo.toypatterns import *  # NOQA
      >>> self = CategoryPatterns.coerce(['superstar'])
      >>> chip = np.random.rand(64, 64)
      >>> info = self.render_category('superstar', chip, newstyle=True)
      >>> print('info = {}'.format(ub.repr2(info, nl=-1)))
      >>> info = self.render_category('superstar', chip, newstyle=False)
      >>> print('info = {}'.format(ub.repr2(info, nl=-1)))

      .. rubric:: Example

      >>> from kwcoco.demo.toypatterns import *  # NOQA
      >>> self = CategoryPatterns.coerce(['superstar'])
      >>> chip = None
      >>> dims = (64, 64)
      >>> info = self.render_category('superstar', chip, newstyle=True, dims=dims, size=dims)
      >>> print('info = {}'.format(ub.repr2(info, nl=-1)))


   .. py:method:: _todo_refactor_geometric_info(self, cname, xy_offset, dims)

      This function is used to populate kpts and sseg information in the
      autogenerated coco dataset before rendering. It is redundant with other
      functionality.

      TODO: rectify with _from_elem

      .. rubric:: Example

      >>> self = CategoryPatterns.coerce(['superstar'])
      >>> dims = (64, 64)
      >>> cname = 'superstar'
      >>> xy_offset = None
      >>> self._todo_refactor_geometric_info(cname, xy_offset, dims)


   .. py:method:: _package_info(self, cname, data, mask, kpts, xy_offset, dims, newstyle)

      packages data from _from_elem into coco-like annotation


   .. py:method:: _from_elem(self, cname, chip, size=None)

      .. rubric:: Example

      >>> # hack to allow chip to be None
      >>> chip = None
      >>> size = (32, 32)
      >>> cname = 'superstar'
      >>> self = CategoryPatterns.coerce()
      >>> self._from_elem(cname, chip, size)



.. py:function:: star(a, dtype=np.uint8)

   Generates a star shaped structuring element.

   Much faster than skimage.morphology version


.. py:class:: Rasters

   .. py:method:: superstar()
      :staticmethod:

      test data patch


   .. py:method:: eff()
      :staticmethod:

      test data patch