def add_pair(self, ann_gt, ann_pred):
labels_gt = filter_labels_by_name(ann_gt.labels, self._gt_to_pred_class_mapping)
all_labels_pred = [label for label in filter_labels_by_name(ann_pred.labels, self._pred_to_gt_class_mapping)]
labels_pred = []
for label in all_labels_pred:
label_confidence = self._get_confidence_value(label)
if label_confidence is None:
logger.warn(f'Found a label with class {label.obj_class.name!r} that does not have a '
f'{self._confidence_tag_name!r} tag attached. Skipping this object for metric computation.')
else:
labels_pred.append(label)
match_result = match_labels_by_iou(labels_1=labels_gt, labels_2=labels_pred, img_size=ann_gt.img_size,
iou_threshold=self._iou_threshold)
for match in match_result.matches:
gt_class = match.label_1.obj_class.name
label_pred = match.label_2
self._counters[gt_class][MATCHES].append(
MatchWithConfidence(is_correct=(label_pred.obj_class.name == self._gt_to_pred_class_mapping[gt_class]),
confidence=self._get_confidence_value(label_pred)))
# Add unmatched predictions to the list as false positive matches.
for umatched_pred in match_result.unmatched_labels_2:
gt_class = self._pred_to_gt_class_mapping[umatched_pred.obj_class.name]
self._counters[gt_class][MATCHES].append(
MatchWithConfidence(is_correct=False, confidence=self._get_confidence_value(umatched_pred)))
for label_1 in labels_gt:
self._counters[label_1.obj_class.name][TOTAL_GROUND_TRUTH] += 1
def add_pair(self, ann_gt: Annotation, ann_pred: Annotation):
labels_gt = filter_labels_by_name(ann_gt.labels, self._unmatched_gt)
labels_pred = filter_labels_by_name(ann_pred.labels, self._unmatched_pred)
match_result = match_labels_by_iou(labels_1=labels_gt, labels_2=labels_pred, img_size=ann_gt.img_size,
iou_threshold=self._iou_threshold)
for match in match_result.matches:
self._confusion_matrix[match.label_1.obj_class.name, match.label_2.obj_class.name] += 1
for unmatched_gt_label in match_result.unmatched_labels_1:
self._unmatched_gt[unmatched_gt_label.obj_class.name] += 1
for unmatched_pred_label in match_result.unmatched_labels_2:
self._unmatched_pred[unmatched_pred_label.obj_class.name] += 1
def add_pair(self, ann_gt, ann_pred):
labels_gt = filter_labels_by_name(ann_gt.labels, self._gt_to_pred_class_mapping)
labels_pred = filter_labels_by_name(ann_pred.labels, self._pred_to_gt_class_mapping)
match_result = match_labels_by_iou(labels_1=labels_gt, labels_2=labels_pred, img_size=ann_gt.img_size,
iou_threshold=self._iou_threshold)
# TODO unify with confusion matrix ?
for match in match_result.matches:
self._counters[match.label_1.obj_class.name][TRUE_POSITIVE] += 1
for label_gt in labels_gt:
self._counters[label_gt.obj_class.name][TOTAL_GROUND_TRUTH] += 1
for label_pred in labels_pred:
self._counters[self._pred_to_gt_class_mapping[label_pred.obj_class.name]][TOTAL_PREDICTIONS] += 1
def add_pair(self, ann_gt, ann_pred):
labels_gt = filter_labels_by_name(ann_gt.labels, self._gt_to_pred_class_mapping)
labels_pred = [label for label in filter_labels_by_name(ann_pred.labels, self._predicted_class_names)
if self._get_confidence_value(label) is not None]
match_result = match_labels_by_iou(labels_1=labels_gt, labels_2=labels_pred, img_size=ann_gt.img_size,
iou_threshold=self._iou_threshold)
for match in match_result.matches:
gt_class = match.label_1.obj_class.name
label_pred = match.label_2
self._counters[gt_class][MATCHES].append(
MatchWithConfidence(is_correct=(label_pred.obj_class.name == self._gt_to_pred_class_mapping[gt_class]),
confidence=self._get_confidence_value(label_pred)))
for label_1 in labels_gt:
self._counters[label_1.obj_class.name][TOTAL_GROUND_TRUTH] += 1
