Skip to content

Inference API: add batch-wise image analysis for superanimal prepare_2d #20

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
xiu-cs merged 1 commit into from
Feb 25, 2026
Merged

Inference API: add batch-wise image analysis for superanimal prepare_2d #20

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
21 changes: 11 additions & 10 deletions fmpose3d/inference_api/fmpose3d.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -253,17 +253,18 @@ def predict(
cv2.imwrite(p, frames[idx])
paths.append(p)

# Run DeepLabCut on each frame individually.
# Run DeepLabCut once for all frames.
predictions = superanimal_analyze_images(
superanimal_name=cfg.superanimal_name,
model_name=cfg.sa_model_name,
detector_name=cfg.detector_name,
images=paths,
max_individuals=cfg.max_individuals,
out_folder=tmpdir,
)
# predictions: {image_path: {"bodyparts": (N_ind, K, 3), ...}}
# Iterate in input order to keep frame alignment stable.
for img_path in paths:
predictions = superanimal_analyze_images(
superanimal_name=cfg.superanimal_name,
model_name=cfg.sa_model_name,
detector_name=cfg.detector_name,
images=img_path,
max_individuals=cfg.max_individuals,
out_folder=tmpdir,
)
# predictions: {image_path: {"bodyparts": (N_ind, K, 3), ...}}
payload = predictions.get(img_path) if isinstance(predictions, dict) else None
if payload is None and isinstance(predictions, dict) and len(predictions) == 1:
payload = next(iter(predictions.values()))
Expand Down