kwcoco.metrics.voc_metrics module

class kwcoco.metrics.voc_metrics.VOC_Metrics(classes=None)

Bases: ubelt.util_mixins.NiceRepr

API to compute object detection scores using Pascal VOC evaluation method.

To use, add true and predicted detections for each image and then run the score function.

Variables:

• recs (Dict[int, List[dict]) – true boxes for each image. maps image ids to a list of records within that image. Each record is a tlbr bbox, a difficult flag, and a class name.
• cx_to_lines (Dict[int, List]) –

  VOC formatted prediction preditions. mapping from class index to all predictions for that category. Each “line” is a list of [

  [<imgid>, <score>, <tl_x>, <tl_y>, <br_x>, <br_y>]].

add_truth(true_dets, gid)

add_predictions(pred_dets, gid)

score(ovthresh=0.5, bias=1, method='voc2012')

Compute VOC scores for every category

Example

>>> from kwcoco.metrics.detect_metrics import DetectionMetrics
>>> from kwcoco.metrics.voc_metrics import *  # NOQA
>>> dmet = DetectionMetrics.demo(
>>>     nimgs=1, nboxes=(0, 100), n_fp=(0, 30), n_fn=(0, 30), nclasses=2, score_noise=0.9)
>>> self = VOC_Metrics(classes=dmet.classes)
>>> self.add_truth(dmet.true_detections(0), 0)
>>> self.add_predictions(dmet.pred_detections(0), 0)
>>> voc_scores = self.score()
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.figure(fnum=1, doclf=True)
>>> voc_scores['perclass'].draw()

kwplot.figure(fnum=2) dmet.true_detections(0).draw(color=’green’, labels=None) dmet.pred_detections(0).draw(color=’blue’, labels=None) kwplot.autoplt().gca().set_xlim(0, 100) kwplot.autoplt().gca().set_ylim(0, 100)