/napari-image-viewer

name: "napari-image-viewer"
description: "Interactive viewer for microscopy. Displays 2D/3D/4D arrays as Image, Labels, Points, Shapes, Tracks layers; supports annotation, plugin analysis, headless screenshots. Core visualization for Python bioimage workflows. Use ImageJ/FIJI for macro processing; napari for Python-native interactive visualization and DL segmentation review."
license: "BSD-3-Clause"

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

import napari
import numpy as np
from skimage import data

# Open viewer with a sample image
viewer = napari.Viewer()
viewer.add_image(data.cells3d()[:, 1, :, :], name="DAPI", colormap="blue")
napari.run()   # blocks until viewer closed (use in scripts)

Core API

Module 1: Image Layer — Display Raw Images

Add and configure multi-channel image layers.

import napari
import numpy as np
from skimage import io

viewer = napari.Viewer()

# Add single grayscale image
img = io.imread("cells.tif")  # shape: (H, W)
viewer.add_image(img, name="phase contrast", colormap="gray",
                 contrast_limits=[0, img.max()])

# Add multichannel image (3 channels)
img_mc = io.imread("multichannel.tif")  # shape: (H, W, 3)
viewer.add_image(img_mc[..., 0], name="DAPI", colormap="blue", blending="additive")
viewer.add_image(img_mc[..., 1], name="GFP", colormap="green", blending="additive")
viewer.add_image(img_mc[..., 2], name="mCherry", colormap="red", blending="additive")

print(f"Layers: {[l.name for l in viewer.layers]}")

Module 2: Labels Layer — Visualize Segmentation Masks

Display and edit integer label masks from Cellpose, StarDist, or scikit-image.

import napari
import numpy as np
from skimage import io

viewer = napari.Viewer()

img = io.imread("cells.tif")
masks = np.load("masks.npy")  # integer label array: 0=background, 1..N=cells

# Add raw image
viewer.add_image(img, name="raw", colormap="gray")

# Add label mask (each cell gets a unique random color)
label_layer = viewer.add_labels(masks, name="cell_masks", opacity=0.5)

# Access labels for editing
print(f"Unique cells: {len(np.unique(masks)) - 1}")
print(f"Label layer data shape: {label_layer.data.shape}")

Module 3: Points Layer — Mark Cell Centroids

Add and style point markers for centroids, landmarks, or detected features.

import napari
import numpy as np
import pandas as pd
from skimage.measure import regionprops_table

viewer = napari.Viewer()

# Compute centroids from label mask
masks = np.load("masks.npy")
props = regionprops_table(masks, properties=["centroid", "label"])
centroids = np.column_stack([props["centroid-0"], props["centroid-1"]])

# Add centroids as Points layer
viewer.add_points(
    centroids,
    name=f"centroids ({len(centroids)} cells)",
    size=8,
    face_color="yellow",
    edge_color="black",
    edge_width=0.5,
)
print(f"Cells marked: {len(centroids)}")

Module 4: Shapes Layer — Draw ROIs and Annotations

Add bounding boxes, polygons, and line annotations.

import napari
import numpy as np

viewer = napari.Viewer()

# Add rectangles as ROIs (format: [[y1, x1], [y2, x2]])
rois = [
    np.array([[50, 100], [200, 300]]),   # ROI 1
    np.array([[300, 150], [450, 350]]),  # ROI 2
]

shapes_layer = viewer.add_shapes(
    rois,
    shape_type="rectangle",
    name="ROIs",
    edge_color="cyan",
    face_color="transparent",
    edge_width=2,
)

# Retrieve shapes data for analysis
for i, shape in enumerate(shapes_layer.data):
    y_min, x_min = shape.min(axis=0)
    y_max, x_max = shape.max(axis=0)
    print(f"ROI {i+1}: y={y_min:.0f}-{y_max:.0f}, x={x_min:.0f}-{x_max:.0f}")

Module 5: 3D and Time-lapse Visualization

Display z-stacks and time series with sliders.

import napari
import numpy as np
from skimage import data

viewer = napari.Viewer()

# 3D z-stack: shape (Z, H, W)
zstack = data.cells3d()[:, 1, :, :]   # nuclei channel
viewer.add_image(zstack, name="z-stack nuclei",
                 colormap="cyan", blending="additive")

# 4D time-lapse: shape (T, H, W) or (T, Z, H, W)
timelapse = np.rando