Expert mode: CLI tools

Expert mode: CLI tools#

Most Annolid users work in the GUI (annolid). The CLI is useful when you want to automate conversions, run batch processing, or integrate Annolid into a pipeline.

The annolid command (GUI entry point)#

annolid launches the GUI, but it also accepts a few helpful CLI flags:

  • Print version: annolid --version

  • Load custom label list: annolid --labels labels.txt (or --labels "mouse_1,mouse_2")

  • Enable event flags: annolid --flags flags.txt (or --flags "rearing,grooming")

  • Use a specific config: annolid --config ~/.labelmerc

  • Auto-save annotations: annolid --autosave

Run annolid --help to see the full list.

Batch utilities (run as Python modules)#

Annolid also ships “utility” modules you can run with python -m ....

Convert LabelMe JSON → CSV#

Export per-frame segmentation/keypoint annotations into a single tracking-style CSV:

python -m annolid.annotation.labelme2csv --json_folder /path/to/video_results_folder

Convert LabelMe JSON → YOLO dataset#

Create a YOLO dataset (images + labels + data.yaml) from LabelMe JSON files:

python -m annolid.main --labelme2yolo /path/to/labelme_json_folder --val_size 0.1 --test_size 0.1

Behavior time budgets from event exports#

If you export behavior events from the GUI, compute a “time budget” summary:

python -m annolid.behavior.time_budget /path/to/exported_events.csv -o time_budget.csv --bin-size 60

If you use a project schema (categories/modifiers), pass it with --schema.

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