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

.. py:module:: kwcoco.demo.perterb


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


Functions
~~~~~~~~~

.. autoapisummary::

   kwcoco.demo.perterb.perterb_coco
   kwcoco.demo.perterb._demo_construct_probs



.. py:function:: perterb_coco(coco_dset, **kwargs)

   Perterbs a coco dataset

   :Parameters: * **rng** (*int, default=0*)
                * **box_noise** (*int, default=0*)
                * **cls_noise** (*int, default=0*)
                * **null_pred** (*bool, default=False*)
                * **with_probs** (*bool, default=False*)
                * **score_noise** (*float, default=0.2*)
                * **hacked** (*int, default=1*)

   .. rubric:: Example

   >>> from kwcoco.demo.perterb import *  # NOQA
   >>> from kwcoco.demo.perterb import _demo_construct_probs
   >>> import kwcoco
   >>> coco_dset = true_dset = kwcoco.CocoDataset.demo('shapes8')
   >>> kwargs = {
   >>>     'box_noise': 0.5,
   >>>     'n_fp': 3,
   >>>     'with_probs': 1,
   >>>     'with_heatmaps': 1,
   >>> }
   >>> pred_dset = perterb_coco(true_dset, **kwargs)
   >>> pred_dset._check_json_serializable()
   >>> # xdoctest: +REQUIRES(--show)
   >>> import kwplot
   >>> kwplot.autompl()
   >>> gid = 1
   >>> canvas = true_dset.delayed_load(gid).finalize()
   >>> canvas = true_dset.annots(gid=gid).detections.draw_on(canvas, color='green')
   >>> canvas = pred_dset.annots(gid=gid).detections.draw_on(canvas, color='blue')
   >>> kwplot.imshow(canvas)


.. py:function:: _demo_construct_probs(pred_cxs, pred_scores, classes, rng, hacked=1)

   Constructs random probabilities for demo data

   .. rubric:: Example

   >>> import kwcoco
   >>> import kwarray
   >>> rng = kwarray.ensure_rng(0)
   >>> classes = kwcoco.CategoryTree.coerce(10)
   >>> hacked = 1
   >>> pred_cxs = rng.randint(0, 10, 10)
   >>> pred_scores = rng.rand(10)
   >>> probs = _demo_construct_probs(pred_cxs, pred_scores, classes, rng, hacked)
   >>> probs.sum(axis=1)