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Fix confidence handling for keypoint evaluation #5344

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Jan 7, 2025
Merged

Fix confidence handling for keypoint evaluation #5344

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21 changes: 17 additions & 4 deletions fiftyone/utils/eval/coco.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@

import eta.core.utils as etau

import fiftyone.core.labels as fol
import fiftyone.core.plots as fop
import fiftyone.utils.iou as foui

Expand Down Expand Up @@ -552,14 +553,19 @@ def _coco_evaluation_setup(
label = obj.label if classwise else "all"
cats[label]["preds"].append(obj)

if isinstance(preds, fol.Keypoints):
sort_key = lambda p: np.nanmean(p.confidence) if p.confidence else -1
else:
sort_key = lambda p: p.confidence or -1

# Compute IoUs within each category
pred_ious = {}
for objects in cats.values():
gts = objects["gts"]
preds = objects["preds"]

# Highest confidence predictions first
preds = sorted(preds, key=lambda p: p.confidence or -1, reverse=True)
preds = sorted(preds, key=sort_key, reverse=True)

if max_preds is not None:
preds = preds[:max_preds]
Expand Down Expand Up @@ -587,6 +593,13 @@ def _compute_matches(

# Match each prediction to the highest available IoU ground truth
for pred in objects["preds"]:
if isinstance(pred, fol.Keypoint):
pred_conf = (
np.nanmean(pred.confidence) if pred.confidence else None
)
else:
pred_conf = pred.confidence

if pred.id in pred_ious:
best_match = None
best_match_iou = iou_thresh
Expand Down Expand Up @@ -638,7 +651,7 @@ def _compute_matches(
gt.label,
pred.label,
best_match_iou,
pred.confidence,
pred_conf,
gt.id,
pred.id,
iscrowd(gt),
Expand All @@ -651,7 +664,7 @@ def _compute_matches(
None,
pred.label,
None,
pred.confidence,
pred_conf,
None,
pred.id,
None,
Expand All @@ -665,7 +678,7 @@ def _compute_matches(
None,
pred.label,
None,
pred.confidence,
pred_conf,
None,
pred.id,
None,
Expand Down
21 changes: 17 additions & 4 deletions fiftyone/utils/eval/openimages.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,7 @@

import numpy as np

import fiftyone.core.labels as fol
import fiftyone.core.plots as fop
import fiftyone.utils.iou as foui

Expand Down Expand Up @@ -545,14 +546,19 @@ def _open_images_evaluation_setup(
if config.expand_pred_hierarchy and label != "all":
_expand_detection_hierarchy(cats, obj, config, "preds")

if isinstance(preds, fol.Keypoints):
sort_key = lambda p: np.nanmean(p.confidence) if p.confidence else -1
else:
sort_key = lambda p: p.confidence or -1

# Compute IoUs within each category
pred_ious = {}
for objects in cats.values():
gts = objects["gts"]
preds = objects["preds"]

# Highest confidence predictions first
preds = sorted(preds, key=lambda p: p.confidence or -1, reverse=True)
preds = sorted(preds, key=sort_key, reverse=True)

if max_preds is not None:
preds = preds[:max_preds]
Expand Down Expand Up @@ -583,6 +589,13 @@ def _compute_matches(

# Match each prediction to the highest available IoU ground truth
for pred in objects["preds"]:
if isinstance(pred, fol.Keypoint):
pred_conf = (
np.nanmean(pred.confidence) if pred.confidence else None
)
else:
pred_conf = pred.confidence

if pred.id in pred_ious:
best_match = None
best_match_iou = iou_thresh
Expand Down Expand Up @@ -667,7 +680,7 @@ def _compute_matches(
gt.label,
pred.label,
best_match_iou,
pred.confidence,
pred_conf,
gt.id,
pred.id,
)
Expand All @@ -679,15 +692,15 @@ def _compute_matches(
None,
pred.label,
None,
pred.confidence,
pred_conf,
None,
pred.id,
)
)
elif pred.label == cat:
pred[eval_key] = "fp"
matches.append(
(None, pred.label, None, pred.confidence, None, pred.id)
(None, pred.label, None, pred_conf, None, pred.id)
)

# Leftover GTs are false negatives
Expand Down
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