Scripting Napari using Python

Napari is an interactive program for working with image data. It can be programmed from Python.

In this notebook, we will open a napari viewer, add images and perform some interactions with them.

from skimage.io import imread
from skimage.filters import threshold_otsu, gaussian

Opening the napari Viewer

In order to open the viewer, we first have to import napari

import napari

Now, we can open the viewer with the following command:

viewer = napari.Viewer()

Napari should open in a separated window. Some warning messages in the cell above are normal.

Let’s show a screenshot of the viewer here. We pass the variable viewer to the function.

napari.utils.nbscreenshot(viewer)

Opening images in napari from a notebook

Now, let’s load some example images here and visualize them in napari. This loads a 3D image:

mri = imread("https://github.com/clEsperanto/clesperanto_example_data/raw/main/Haase_MRT_tfl3d1.tif")
mri.shape

(120, 160, 160)

To open an image in napari from a notebook, we use the command add_image() from the viewer.

viewer.add_image(mri)

<Image layer 'mri' at 0x2171b7e7700>

This adds the image as a layer in napari. The layers list can be seen at the bottom left of the screen. For now, we have a single image layer there. Let’s take a new screenshot so that it’s late reproducible what we’re doing.

napari.utils.nbscreenshot(viewer)

Visualization configuration

We can configure how to view the data when adding it to the viewer. We can for example tune the contrast_limits.

viewer.add_image(mri, contrast_limits=(12000, 40000))

napari.utils.nbscreenshot(viewer)

We can also keep the layer that was just added to napari in a variable and modify visualization afterwwards.

image_layer = viewer.add_image(mri)

napari.utils.nbscreenshot(viewer)

image_layer.contrast_limits = (2000, 50000)
image_layer.colormap = "viridis"

napari.utils.nbscreenshot(viewer)

Removing layers

We can also remove layers, for example the second one (that has index 1).

viewer.layers.remove(viewer.layers[1])

Or we remove the last one (on top, which has index -1).

viewer.layers.remove(viewer.layers[-1])

napari.utils.nbscreenshot(viewer)

Segmentation visualization

You can also add a segmentation result to the viewer, which will get overlayed with the original image.

blurred = gaussian(mri, sigma=5)

binary_image = blurred > threshold_otsu(blurred)

viewer.add_labels(binary_image)

napari.utils.nbscreenshot(viewer)

Segmentation results can also be visualized with their outlines.

labels_layer = viewer.layers[-1]

labels_layer.contour = 3

napari.utils.nbscreenshot(viewer)

Exercise

Create another viewer window and add the image https://github.com/clEsperanto/clesperanto_example_data/raw/main/Lund_000500_resampled-cropped.tif. Binarize the image, label connected components and add the result to a new viewer.