VIPP — Visual Image Processing Platform

Visual workflows for reproducible bioimage analysis.

napari-vipp is the napari-native implementation of VIPP, the Visual Image Processing Platform. Build typed node graphs, inspect intermediate images and tables, tune parameters, save workflows, and repeat the same operations without hiding axis or physical-scale metadata.

Alpha software: expect breaking workflow and parameter changes. Validate outputs on representative data before scientific interpretation or publication.

VIPP's implemented safeguards include stable source revisions, physical-grid checks, exact unsampled diagnostics, detached viewer layers, atomic artifacts, and batch publication only after source reverification. See the scientific integrity boundaries and the contributor scientific behavior requirements.

Install And Open

VIPP requires Python 3.12 or newer. If napari is not already installed, install it with a Qt backend at the same time:

python -m pip install "napari[pyqt6]"
python -m pip install --pre napari-vipp
vipp

The --pre flag is required while VIPP is published as an alpha release. It is kept on the VIPP command so napari itself can continue to resolve to a stable release.

In napari, open:

Plugins > VIPP Workflow (napari-vipp)

Use Open example... for a runnable workflow with synthetic data. A good first choice is Red-Channel Label Cleanup; select nodes from left to right to review their parameters, thumbnails, metadata, and outputs. To explore collection processing, open Deterministic Batch & Provenance; VIPP prepares a small self-contained working copy and opens it already configured and previewed.

What It Supports

Most graph operations are still eager. Large z-stacks and OME-Zarr datasets therefore need deliberate cache, preview, and output choices; see the cache and memory guide.

Optional Microscope Readers

Install only the reader family you need, then restart napari:

These routes are an experimental foundation: axes and common metadata are normalized where the source reader exposes them, but format-specific coverage still needs validation against a broader corpus of real acquisition files.

Workflow Basics

1. Add or select an Image Source for a napari layer, file, or bundled sample.
2. Add nodes from the palette and connect compatible output and input ports.
3. Select a node to tune parameters and inspect its output metadata.
4. Click Calculate for manual/cached nodes such as measurements and deconvolution.
5. Pin important image outputs into napari for full-resolution comparison.
6. Save the graph with Save workflow....
7. Add Batch Output nodes before Batch workspace... when exact saved outputs matter.
8. Click Preview batch to plan the complete collection. Use the persistent representative slider or a preview-table row to inspect any paired item through the graph without running or saving the full batch.
9. Run the collection from the retained workspace, where item-level progress, final statuses, validation, and the vipp_batch_manifest.json path remain available for inspection.
10. To validate the complete batch path without your own files, choose Open example... -> Deterministic Batch & Provenance -> Open batch demo.... Choose where to save its small working copy, review the populated graph, move through all three paired fields with the representative slider, review the three-item/nine-output batch preview, then click Run demo batch. VIPP checks the finished outputs and provenance against exact ground truth automatically.

Workflow JSON stores the graph and optional VIPP UI state, not cached pixels or tables. Export Python... embeds a validated immutable workflow and executes it through the same headless pipeline engine as VIPP, including normalized ImageState propagation. See the user guide for source binding, runtime-version, and command-line details.

Documentation

• Published VIPP documentation
• Categorized 0.12 release notes
• Documentation index
• User guide
• Image import and export
• Example workflow index
• Measurement workflows
• Operator tips
• Developer notes
• Current planning and roadmap

Development

Create a local environment and install the development dependencies:

python -m venv .venv
.venv\Scripts\Activate.ps1
python -m pip install -e ".[dev]"

Run the required checks:

python -m npe2 validate src/napari_vipp/napari.yaml
python -m ruff check .
python -m pytest

Launch a development instance from the repository with ./vipp; it uses the project's .venv-macos environment directly, so shell activation is not required. The installed vipp command and python -m napari_vipp are also supported. To open the synthetic sample with a pipeline run already completed, use python scripts/launch_vipp_sample.py. The architecture reference explains the graph, metadata, execution, persistence, and UI boundaries.

Contributions are welcome. Read CONTRIBUTING.md before opening a pull request, use SUPPORT.md for help and issue-reporting guidance, and report suspected vulnerabilities privately through SECURITY.md. All project interactions follow the Code of Conduct.

0.12 Alpha Highlights

0.12.0a2 is the current alpha. It builds on the 0.12 architecture and reproducibility baseline with:

• isolated node tuning that keeps downstream propagation paused until the latest local result is applied or the session is cancelled;
• bright actionable and dark waiting graph states, an attention-colored Calculate all, and progressive node previews during longer runs;
• exact-pixel napari layer reuse and display-resolution thumbnail rendering that reduce UI stalls without changing scientific arrays;
• configurable port labels and responsive graph layout; and
• clearer PSF preflight, Nyquist, support, centering, and boundary-tail feedback for deconvolution workflows.

The 0.12 foundation also provides:

• workflow schema version 3 records explicit axis, channel, grid, and operation choices instead of restoring ambiguous scientific defaults;
• verified file and live-layer revisions, physical-grid checks, detached viewer layers, and atomic artifacts reject stale or silently repaired inputs;
• generated Python and collection batching now use the same validated headless executor as the interactive graph;
• the retained batch workspace adds reviewed plans, representative navigation, explicit outputs, per-item provenance, collision policies, progress, final statuses, manifests, and deterministic validation;
• exact diagnostics, background workers, and platform-specific memory reporting improve responsiveness without changing the population being measured;
• Richardson-Lucy TV controls now explain parameter effects and provide practical linear or geometric slider windows without limiting exact spinner entry; and
• the former monolithic widget has been decomposed into focused Qt-free core and UI service modules with dependency-direction tests.

Breaking alpha changes are intentional where preserving an older implicit behavior would weaken scientific validity. See the categorized 0.12 release notes, the upgrade and workflow contract, and planning.md for later milestones. Semantic-axis collection iteration, HCS traversal, scalable OME-Zarr previews, and broader scientific validation remain future work.

Citation, Acknowledgement, And License

If VIPP contributes to your work, acknowledge napari-vipp and link to the project repository. Citation metadata is available in CITATION.cff; a DOI or manuscript citation can be added when available.

napari-vipp is distributed under the BSD 3-Clause License. See LICENSE for the full terms.