kwcoco.demo.perterb

Module Contents

Functions

perterb_coco(coco_dset, **kwargs)	Perterbs a coco dataset
_demo_construct_probs(pred_cxs, pred_scores, classes, rng, hacked=1)	Constructs random probabilities for demo data

kwcoco.demo.perterb.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)

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)

kwcoco.demo.perterb._demo_construct_probs(pred_cxs, pred_scores, classes, rng, hacked=1)

Constructs random probabilities for demo data

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)