xthotcoco
xtcocotools-compatible keypoint evaluation (COCO-WholeBody, CrowdPose) on hotcoco's Rust engine. Pure Python.
pip install xthotcoco
Usage
from xthotcoco import COCO, COCOeval, sigmas
gt = COCO("coco_wholebody_val_v1.0.json")
dt = gt.loadRes("results.json")
ev = COCOeval(gt, dt, "keypoints_wholebody", sigmas.WHOLEBODY)
ev.evaluate(); ev.accumulate(); ev.summarize()
print(ev.stats)
# CrowdPose
ev = COCOeval(COCO("crowdpose_test.json"), dt, "keypoints_crowd",
sigmas.CROWD, use_area=False)
ev.run()
iouTypes: keypoints, keypoints_wholebody, keypoints_foot,
keypoints_face, keypoints_lefthand, keypoints_righthand,
keypoints_crowd. COCO(..., test_index=[...]) evaluates a keypoint subset.
Parity and speed
rtmlib RTMPose-WholeBody on 500 COCO val2017 images, COCO-WholeBody v1.0 val:
| iouType | AP | xtcocotools | xthotcoco | max diff |
|---|---|---|---|---|
| keypoints | 0.721 | 0.468 s | 0.005 s | 7e-16 |
| keypoints_wholebody | 0.649 | 0.774 s | 0.008 s | 6e-16 |
| keypoints_foot | 0.734 | 0.511 s | 0.003 s | 2e-15 |
| keypoints_face | 0.822 | 0.638 s | 0.008 s | 3e-15 |
| keypoints_lefthand | 0.614 | 0.585 s | 0.004 s | 1e-15 |
| keypoints_righthand | 0.601 | 0.561 s | 0.004 s | 1e-15 |
All metrics match xtcocotools to double precision; tests/ checks this
against xtcocotools when it is installed.
How it works
The WholeBody/CrowdPose dataset is rewritten into standard COCO keypoints
form (part concatenation, part score, area handling) and passed to
hotcoco.COCOeval with per-part kpt_oks_sigmas. OKS and AP are computed
in hotcoco.
License
MIT. Evaluation semantics follow xtcocotools (MIT).
1 maintainer
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