def test_calc_detection_voc_prec_rec(self):
prec, rec = calc_detection_voc_prec_rec(
self.pred_bboxes, self.pred_labels, self.pred_scores,
self.gt_bboxes, self.gt_labels, self.gt_difficults,
iou_thresh=self.iou_thresh)
self.assertEqual(len(prec), len(self.prec))
for prec_l, expected_prec_l in zip(prec, self.prec):
if prec_l is None and expected_prec_l is None:
continue
np.testing.assert_equal(prec_l, expected_prec_l)
self.assertEqual(len(rec), len(self.rec))
for rec_l, expected_rec_l in zip(rec, self.rec):
if rec_l is None and expected_rec_l is None:
continue
np.testing.assert_equal(rec_l, expected_rec_l)
def test_calc_detection_voc_prec_rec(self):
prec, rec = calc_detection_voc_prec_rec(self.pred_bboxes,
self.pred_labels,
self.pred_scores,
self.gt_bboxes,
self.gt_labels,
self.gt_difficults,
iou_thresh=self.iou_thresh)
self.assertEqual(len(prec), len(self.prec))
for prec_l, expected_prec_l in zip(prec, self.prec):
if prec_l is None and expected_prec_l is None:
continue
np.testing.assert_equal(prec_l, expected_prec_l)
self.assertEqual(len(rec), len(self.rec))
for rec_l, expected_rec_l in zip(rec, self.rec):
if rec_l is None and expected_rec_l is None:
continue
np.testing.assert_equal(rec_l, expected_rec_l)
def calc(self):
pred_bboxes, pred_labels, pred_scores, gt_bboxes, gt_labels = self.data
if self.metric == 'all' or self.metric == 'voc_detection':
print('Calculating voc_detection ...')
result = eval_detection_voc(pred_bboxes,
pred_labels,
pred_scores,
gt_bboxes,
gt_labels,
use_07_metric=True)
print('mAP: {:f}'.format(result['map']))
print('person mAP: {:f}'.format(
result['ap'][voc_utils.voc_bbox_label_names.index('person')]))
if self.metric == 'all' or self.metric == 'pr_voc_detection':
print('Calculating pr_voc_detection ...')
prec, rec = calc_detection_voc_prec_rec(pred_bboxes,
pred_labels,
pred_scores,
gt_bboxes,
gt_labels,
gt_difficults=None,
iou_thresh=0.5)
person_prec = prec[voc_utils.voc_bbox_label_names.index('person')]
person_rec = rec[voc_utils.voc_bbox_label_names.index('person')]
print('Avg person precision: {:f}'.format(np.average(person_prec)))
print('Avg person recall: {:f}'.format(np.average(person_rec)))
if self.plottings:
self.plot(recall=person_rec,
precision=person_prec,
metric='pr_voc_detection')
if self.metric == 'all' or self.metric == 'mot':
print('Calculating mot_metrics ...')
self.motmetrics()
yl_images.append(tmp_yl_images[curr_index])
print('Gerenrating plots...')
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
fig_dir = 'plottings/metrics/all_models/{}_{}_{}/'.format(
'PTI01', len(fr_images), now)
os.makedirs(fig_dir, exist_ok=True)
print('Saving metric in {}.'.format(fig_dir))
print('YOLOv2')
yl_prec, yl_rec = calc_detection_voc_prec_rec(yl_pred_bboxes,
yl_pred_labels,
yl_pred_scores,
yl_gt_bboxes,
yl_gt_labels,
gt_difficults=None,
iou_thresh=0.5)
yl_person_prec = yl_prec[voc_utils.voc_bbox_label_names.index('person')]
yl_person_rec = yl_rec[voc_utils.voc_bbox_label_names.index('person')]
print("Avg prec {}, Avg rec {}".format(np.average(yl_person_prec),
np.average(yl_person_rec)))
plt.step(yl_person_rec, yl_person_prec, label='YOLOv2')
print('SSD512')
s5_prec, s5_rec = calc_detection_voc_prec_rec(s5_pred_bboxes,
s5_pred_labels,
s5_pred_scores,
s5_gt_bboxes,
s5_gt_labels,
