napari-image-viewerlisted

# 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