napari-manual-labels-editor 0.1.1

Manual fine-tuning toolkit for napari Labels: compute stats, assign new IDs, delete/merge labels, relabel to 1..N, and save LZW BigTIFF safely.

napari-manual-labels-editor

Manual fine-tuning toolkit for napari Labels (integer label masks). Designed for “after segmentation” workflows where you need to inspect, fix, and export large label images safely (e.g., TIFF/LZW/BigTIFF). Tutorial video: https://www.youtube.com/watch?v=cQf_7ExGQHk&t=15s

What it does

This plugin operates on a napari Labels layer (background must be 0) and provides a small, fast panel to:

• Pick Active Layer: choose the current active Image/Labels layer; if it’s an Image, it can be converted to Labels (integer).
• Compute Stats (safe): compute maxID and cells without heavy np.unique (safer for large arrays).
• New ID (max+1): set selected label to maxID + 1 so you can paint a new object.
• Delete selected ID: delete the currently selected label ID (set its pixels to 0).
• Compact IDs Order (1..N): relabel present IDs to consecutive 1..N (useful before export).
• Merge (Shift-click): when merge is enabled, Shift-click A, then Shift-click B to merge B → A (into A).

Export:

• Save labels to TIFF (LZW) (BigTIFF enabled). If you provide a directory as “Save path”, the plugin auto-appends a filename.

Compatibility

• Python: 3.10–3.13 (tested on 3.11)
• napari: 0.6.x
• Layer type: Labels (integer), background 0

Installation

From PyPI (recommended)

pip install napari-manual-labels-editor

If you don’t have napari yet:

pip install "napari[all]" napari-manual-labels-editor

From GitHub (latest main)

pip install -U "git+https://github.com/yaohualee1215-bit/napari-manual-labels-editor.git"

Usage (inside napari)

1. Launch napari and load your Labels layer (and optional background image).

2. Open the plugin panel:

   Plugins → Manual Labels Editor → Manual Labels Editor

3. Click Pick Active Layer (Image or Labels) Make sure the layer you want is the active layer in the layer list first.

4. Click Compute Stats (safe) The status area will show selected, cells, maxID, and merge state.

Editing actions

• New ID (max+1) → then paint to add a new label
• Delete selected ID → remove a label
• Compact IDs Order (1..N) → relabel to 1..N
• Merge (Shift-click) → enable merge, then Shift-click label A then label B to merge B → A

New in v0.1.1

• Added local closed-shape fill (draw a closed outline, then fill inside).
• Added small-label area filtering to a new layer (keeps original layer unchanged).
• Added undo/redo for merge and delete (recent edits only).
• Added automatic refresh after merge/delete so edits display immediately.
• Added blink controls for quick visual checking after edits.

Export

1. Set Save path (file path or directory)
2. Click Save labels to TIFF (LZW)

Notes:

• TIFF writer uses compression="lzw" and bigtiff=True.
• If “Save path” is a directory, output becomes: <dir>/<layer_name>_edited_LZW.tif (or your configured suffix).

Notes

Large LZW TIFF support

If LZW read/write fails or is slow, install imagecodecs:

conda install -c conda-forge imagecodecs

Publish to PyPI (maintainers)

Maintainer-only. Regular users should install from PyPI above.

Release workflow (one block)

# from repo root
# 1) bump version in pyproject.toml (e.g., 0.1.0 -> 0.1.1)

# 2) run checks
pre-commit run --all-files

# 3) build + upload
python -m pip install -U build twine
rm -rf dist build *.egg-info
python -m build
twine check dist/*
twine upload dist/*

# 4) tag on GitHub
git tag v0.1.1
git push --tags

Clean env smoke test (optional)

conda create -n napari-mlabels-test -y python=3.11
conda activate napari-mlabels-test
python -m pip install -U pip
python -m pip install napari-manual-labels-editor

python - <<'PY'
import importlib.metadata as im
print("pkg version:", im.version("napari-manual-labels-editor"))
eps = [e for e in im.entry_points(group="napari.manifest") if "manual-labels-editor" in e.name]
print("entrypoints:", eps)
PY

napari

License

BSD-3-Clause