Napari 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

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.random.randint(0, 65535, (10, 256, 256), dtype=np.uint16)
viewer.add_image(timelapse, name="timelapse", colormap="gray")

# napari shows axis sliders automatically for ndim > 2
print(f"z-stack shape: {zstack.shape} → slider for Z axis")
print(f"timelapse shape: {timelapse.shape} → sliders for T axis")

Module 6: Headless Screenshot Export

Export screenshots without a display (for HPC and CI environments).

import os
os.environ["QT_QPA_PLATFORM"] = "offscreen"  # must be set BEFORE importing napari

import napari
import numpy as np
from skimage import io, data
import matplotlib
matplotlib.use("Agg")  # also set matplotlib backend

viewer = napari.Viewer(show=False)

img = data.cells3d()[30, 1, :, :]  # single z-slice
masks = (img > img.mean()).astype(int)  # simple threshold mask

viewer.add_image(img, name="DAPI", colormap="blue", blending="additive")
viewer.add_labels(masks.astype(np.int32), name="masks", opacity=0.5)

# Export screenshot
screenshot = viewer.screenshot(path="napari_export.png", canvas_only=True)
print(f"Screenshot saved: napari_export.png ({screenshot.shape})")
viewer.close()

Key Parameters

Common Workflows

Workflow 1: Review Cellpose Segmentation Quality

import os
os.environ["QT_QPA_PLATFORM"] = "offscreen"

import napari
import numpy as np
from cellpose import models
from skimage import io
from skimage.measure import regionprops_table

# Segment with Cellpose
img = io.imread("cells.tif")
model = models.Cellpose(model_type="cyto3", gpu=False)
masks, _, _, diams = model.eval(img, diameter=0, channels=[0, 0])

# Visualize in napari (headless for export)
viewer = napari.Viewer(show=False)
viewer.add_image(img, name="raw", colormap="gray")
viewer.add_labels(masks, name=f"masks ({masks.max()} cells)", opacity=0.6)

# Add centroids
props = regionprops_table(masks, properties=["centroid"])
centroids = np.column_stack([props["centroid-0"], props["centroid-1"]])
viewer.add_points(centroids, name="centroids", size=6, face_color="yellow")

viewer.screenshot(path="segmentation_review.png", canvas_only=True)
viewer.close()
print(f"QC export: segmentation_review.png — {masks.max()} cells detected")

Workflow 2: Multi-channel FISH Image Analysis

import napari
import numpy as np
from skimage import io

# Load 4-channel FISH image: DAPI + 3 RNA probes
img = io.imread("fish_4channel.tif")  # shape: (H, W, 4)

viewer = napari.Viewer()
channels = [
    ("DAPI", "blue", img[..., 0]),
    ("probe_A_cy3", "yellow", img[..., 1]),
    ("probe_B_cy5", "red", img[..., 2]),
    ("probe_C_gfp", "green", img[..., 3]),
]

for name, colormap, channel in channels:
    viewer.add_image(channel, name=name, colormap=colormap,
                     blending="additive",
                     contrast_limits=[channel.min(), np.percentile(channel, 99.5)])

napari.run()

Common Recipes

Recipe 1: Export All Layers as Annotated Figure

import os
os.environ["QT_QPA_PLATFORM"] = "offscreen"

import napari
import numpy as np
from skimage import io
import matplotlib.pyplot as plt

viewer = napari.Viewer(show=False)

img = io.imread("cells.tif")
masks = np.load("masks.npy")

viewer.add_image(img, name="raw", colormap="gray")
viewer.add_labels(masks, name="segmentation", opacity=0.5)

# Set camera zoom and position
viewer.camera.zoom = 1.5
viewer.camera.center = (img.shape[0] // 2, img.shape[1] // 2)

screenshot = viewer.screenshot(path="figure_panel.png", canvas_only=True)
viewer.close()

# Add scalebar with matplotlib
fig, ax = plt.subplots(figsize=(6, 6))
ax.imshow(screenshot)
ax.axis("off")
plt.tight_layout()
plt.savefig("figure_final.pdf", dpi=300, bbox_inches="tight")
print("Exported: figure_final.pdf")

Recipe 2: Batch Export Z-stack Projections

import os
os.environ["QT_QPA_PLATFORM"] = "offscreen"

import napari
import numpy as np
from skimage import io
from pathlib import Path

output_dir = Path("projections")
output_dir.mkdir(exist_ok=True)

for img_path in sorted(Path("zstacks").glob("*.tif")):
    zstack = io.imread(img_path)  # shape: (Z, H, W)
    max_proj = zstack.max(axis=0)
    
    viewer = napari.Viewer(show=False)
    viewer.add_image(max_proj, name="max_projection", colormap="gray")
    viewer.screenshot(path=str(output_dir / f"{img_path.stem}_maxproj.png"), canvas_only=True)
    viewer.close()
    print(f"Exported: {img_path.stem}_maxproj.png")

print("All z-stack projections exported.")

Troubleshooting

References

• napari documentation — official API reference and tutorials
• napari GitHub: napari/napari — source code and plugin development guide
• Sofroniew N et al. (2022) "napari: a multi-dimensional image viewer for Python" — Zenodo. DOI:10.5281/zenodo.3555620
• napari-hub — plugin registry with 300+ community analysis plugins