napari — Multi-dimensional Image Viewer

napari — Multi-dimensional Image Viewer

Overview

napari is a fast, interactive multi-dimensional viewer for scientific data built on PyQt5 and VisPy. It displays NumPy arrays and zarr arrays as layered visualizations — Image layers for raw data, Labels layers for segmentation masks, Points layers for cell centroids, and Shapes layers for ROI annotations. napari integrates with scikit-image, Cellpose, and StarDist via plugins, making it the standard visualization and annotation tool in Python bioimage analysis pipelines. For headless environments (HPC, CI), napari supports offscreen rendering and viewer.screenshot() for automated figure generation.

When to Use

• Visually inspecting and quality-checking microscopy images and segmentation masks before quantitative analysis
• Annotating training data for deep learning segmentation models (Cellpose, StarDist)
• Overlaying multiple image channels (DAPI, GFP, mCherry) with independent contrast and colormap control
• Reviewing 3D z-stacks and 4D time-lapse experiments with slider-based navigation
• Exporting annotated screenshots or label masks from GUI for publication figures
• Running plugin-based analysis (Cellpose napari plugin, StarDist plugin, n2v denoising) interactively
• Use ImageJ/FIJI for macro/batch scripting with minimal Python dependency
• Use ITK-SNAP as an alternative for medical imaging (DICOM, NIfTI) segmentation

Prerequisites

• Python packages: napari, numpy, scikit-image
• Qt backend: requires display server; for headless use QT_QPA_PLATFORM=offscreen
• Optional plugins: napari-cellpose, napari-stardist, napari-animation

# Install with all backends
pip install "napari[all]"

# Or minimal install
pip install napari pyqt5

# Verify
python -c "import napari; print(napari.__version__)"
# 0.5.5

# Install useful plugins
pip install napari-cellpose napari-animation

Quick Start