kwcoco.metrics.detect_metrics module

class kwcoco.metrics.detect_metrics.DetectionMetrics(classes=None)

Bases: ubelt.util_mixins.NiceRepr

Variables:

• gid_to_true_dets (Dict) – maps image ids to truth
• gid_to_pred_dets (Dict) – maps image ids to predictions
• classes (CategoryTree) – category coder

Example

>>> dmet = DetectionMetrics.demo(
>>>     nimgs=100, nboxes=(0, 3), n_fp=(0, 1), nclasses=8, score_noise=0.9, hacked=False)
>>> print(dmet.score_kwcoco(bias=0, compat='mutex', prioritize='iou')['mAP'])
...
>>> # NOTE: IN GENERAL NETHARN AND VOC ARE NOT THE SAME
>>> print(dmet.score_voc(bias=0)['mAP'])
0.8582...
>>> #print(dmet.score_coco()['mAP'])

clear()

classmethod from_coco(true_coco, pred_coco, gids=None, verbose=0)

Create detection metrics from two coco files representing the truth and predictions.

Parameters:

• true_coco (kwcoco.CocoDataset)
• pred_coco (kwcoco.CocoDataset)

Example

>>> import kwcoco
>>> true_coco = kwcoco.CocoDataset.demo('shapes')
>>> pred_coco = true_coco
>>> self = DetectionMetrics.from_coco(true_coco, pred_coco)
>>> self.score_voc()

add_predictions(pred_dets, imgname=None, gid=None)

Register/Add predicted detections for an image

Parameters:

• pred_dets (Detections) – predicted detections
• imgname (str) – a unique string to identify the image
• gid (int, optional) – the integer image id if known

add_truth(true_dets, imgname=None, gid=None)

Register/Add groundtruth detections for an image

Parameters:

• true_dets (Detections) – groundtruth
• imgname (str) – a unique string to identify the image
• gid (int, optional) – the integer image id if known

true_detections(gid)

gets Detections representation for groundtruth in an image

pred_detections(gid)

gets Detections representation for predictions in an image

confusion_vectors(ovthresh=0.5, bias=0, gids=None, compat='all', prioritize='iou', ignore_classes='ignore', background_class=NoParam, verbose='auto', workers=0, track_probs='try')

Assigns predicted boxes to the true boxes so we can transform the detection problem into a classification problem for scoring.

Parameters:

• ovthresh (float, default=0.5) – bounding box overlap iou threshold required for assignment
• bias (float, default=0.0) – for computing bounding box overlap, either 1 or 0
• gids (List[int], default=None) – which subset of images ids to compute confusion metrics on. If not specified all images are used.
• compat (str, default=’all’) – can be (‘ancestors’ | ‘mutex’ | ‘all’). determines which pred boxes are allowed to match which true boxes. If ‘mutex’, then pred boxes can only match true boxes of the same class. If ‘ancestors’, then pred boxes can match true boxes that match or have a coarser label. If ‘all’, then any pred can match any true, regardless of its category label.
• prioritize (str, default=’iou’) – can be (‘iou’ | ‘class’ | ‘correct’) determines which box to assign to if mutiple true boxes overlap a predicted box. if prioritize is iou, then the true box with maximum iou (above ovthresh) will be chosen. If prioritize is class, then it will prefer matching a compatible class above a higher iou. If prioritize is correct, then ancestors of the true class are preferred over descendents of the true class, over unreleated classes.
• ignore_classes (set, default={‘ignore’}) – class names indicating ignore regions
• background_class (str, default=ub.NoParam) – Name of the background class. If unspecified we try to determine it with heuristics. A value of None means there is no background class.
• verbose (int, default=’auto’) – verbosity flag. In auto mode, verbose=1 if len(gids) > 1000.
• workers (int, default=0) – number of parallel assignment processes
• track_probs (str, default=’try’) – can be ‘try’, ‘force’, or False. if truthy, we assume probabilities for multiple classes are available.

Ignore:

globals().update(xdev.get_func_kwargs(dmet.confusion_vectors))

score_kwant(ovthresh=0.5)

Scores the detections using kwant

score_kwcoco(ovthresh=0.5, bias=0, gids=None, compat='all', prioritize='iou')

our scoring method

score_voc(ovthresh=0.5, bias=1, method='voc2012', gids=None, ignore_classes='ignore')

score using voc method

Example

>>> dmet = DetectionMetrics.demo(
>>>     nimgs=100, nboxes=(0, 3), n_fp=(0, 1), nclasses=8,
>>>     score_noise=.5)
>>> print(dmet.score_voc()['mAP'])
0.9399...

score_coco(verbose=0)

score using ms-coco method

Example

>>> # xdoctest: +REQUIRES(--pycocotools)
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=100, nboxes=(0, 3), n_fp=(0, 1), nclasses=8)
>>> print(dmet.score_coco()['mAP'])
0.711016...

classmethod demo(**kwargs)

Creates random true boxes and predicted boxes that have some noisy offset from the truth.

Kwargs:

nclasses (int, default=1): number of foreground classes. nimgs (int, default=1): number of images in the coco datasts. nboxes (int, default=1): boxes per image. n_fp (int, default=0): number of false positives. n_fn (int, default=0): number of false negatives. box_noise (float, default=0): std of a normal distribution used to

perterb both box location and box size.

cls_noise (float, default=0): probability that a class label will

change. Must be within 0 and 1.

anchors (ndarray, default=None): used to create random boxes null_pred (bool, default=0):

if True, predicted classes are returned as null, which means only localization scoring is suitable.

with_probs (bool, default=1):

if True, includes per-class probabilities with predictions

Example

>>> kwargs = {}
>>> # Seed the RNG
>>> kwargs['rng'] = 0
>>> # Size parameters determine how big the data is
>>> kwargs['nimgs'] = 5
>>> kwargs['nboxes'] = 7
>>> kwargs['nclasses'] = 11
>>> # Noise parameters perterb predictions further from the truth
>>> kwargs['n_fp'] = 3
>>> kwargs['box_noise'] = 0.1
>>> kwargs['cls_noise'] = 0.5
>>> dmet = DetectionMetrics.demo(**kwargs)
>>> print('dmet.classes = {}'.format(dmet.classes))
dmet.classes = <CategoryTree(nNodes=12, maxDepth=3, maxBreadth=4...)>
>>> # Can grab kwimage.Detection object for any image
>>> print(dmet.true_detections(gid=0))
<Detections(4)>
>>> print(dmet.pred_detections(gid=0))
<Detections(7)>

Example

>>> # Test case with null predicted categories
>>> dmet = DetectionMetrics.demo(nimgs=30, null_pred=1, nclasses=3,
>>>                              nboxes=10, n_fp=10, box_noise=0.3,
>>>                              with_probs=False)
>>> dmet.gid_to_pred_dets[0].data
>>> dmet.gid_to_true_dets[0].data
>>> cfsn_vecs = dmet.confusion_vectors()
>>> binvecs_ovr = cfsn_vecs.binarize_ovr()
>>> binvecs_per = cfsn_vecs.binarize_peritem()
>>> measures_per = binvecs_per.measures()
>>> measures_ovr = binvecs_ovr.measures()
>>> print('measures_per = {!r}'.format(measures_per))
>>> print('measures_ovr = {!r}'.format(measures_ovr))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> pr_per.draw(fnum=1)
>>> measures_ovr['perclass'].draw(key='pr', fnum=2)

summarize(out_dpath=None, plot=False, title='')

Example

>>> from kwcoco.metrics.confusion_vectors import *  # NOQA
>>> from kwcoco.metrics.detect_metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>>     n_fp=(0, 128), n_fn=(0, 4), nimgs=512, nboxes=(0, 32),
>>>     nclasses=3, rng=0)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> dmet.summarize(plot=True, title='DetectionMetrics summary demo')
>>> kwplot.show_if_requested()

kwcoco.metrics.detect_metrics.eval_detections_cli(**kw)

CommandLine:

xdoctest -m ~/code/kwcoco/kwcoco/metrics/detect_metrics.py eval_detections_cli