kwcoco.demo.perterb module

kwcoco.demo.perterb.perterb_coco(coco_dset, **kwargs)

Perterbs a coco dataset

Parameters:

• rng (int) – defaults to 0

• box_noise (int) – defaults to 0

• cls_noise (int) – defaults to 0

• null_pred (bool) – defaults to False

• with_probs (bool) – defaults to False

• with_heatmaps (bool) – defaults to False

• verbose (int) – defaults to 0

• score_noise (float) – defaults to 0.2

• hacked (int) – defaults to 1

• n_fp (int) – num false positives

• n_fn (int) – num false negatives

Example

>>> from kwcoco.demo.perterb import *  # NOQA
>>> from kwcoco.demo.perterb import _demo_construct_probs
>>> import kwcoco
>>> coco_dset = true_dset = kwcoco.CocoDataset.demo('shapes2')
>>> 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)