napari viewer tutorial¶
Welcome to the tutorial on the napari viewer!
This tutorial assumes you have already installed napari and know how to launch the viewer. For help with installation see our installation tutorial. For help getting started with the viewer see our getting started tutorial.
This tutorial will teach you about the napari viewer, including how to use its graphical user interface (GUI) and how the data within it is organized. At the end of the tutorial you should understand the both the layout of the viewer on the screen and the data inside of it.
launching the viewer¶
As discussed in getting started tutorial the napari viewer can be launched from the command-line, a python script, an IPython console, or a jupyter notebook. All four methods launch the same viewer, and anything related to the interacting with the viewer on the screen applies equally to all of them. We will use the syntax inside python scripts so you can copy and paste these examples into scripts and run them.
Let’s get stated by launching a viewer with a simple 2D image.
The fastest way to get the viewer open and throw an image up on the screen is
import napari from skimage import data viewer = napari.view_image(data.astronaut(), rgb=True)
WARNING: QStandardPaths: XDG_RUNTIME_DIR not set, defaulting to '/tmp/runtime-runner'
napari.view_image will return a
Viewer object that is the main
object inside napari. All the data you add to napari will be stored
Viewer object and will be accessible from it. This command will
also open the viewer to create a GUI that you can interact with.
You can also create an empty
Viewer directly and then start adding images to
it. For example:
viewer = napari.Viewer() new_layer = viewer.add_image(data.astronaut(), rgb=True)
add_image accepts the same arguments as
view_image but returns a layer
rather than a
Viewer, (as you must already have a viewer to use it).
After running either of those two commands you should now be able to see the photograph of the astronaut in the napari viewer as shown below
from napari.utils import nbscreenshot nbscreenshot(viewer)
view_image and the
add_image methods accept any numpy-array like
object as an input, including n-dimensional arrays. For more information on
adding images to the viewer see the image layer tutorial. Now we will
continue exploring the rest of the viewer.
layout of the viewer¶
The viewer is organized into a few key areas:
We’ll go through each of these in the next sections.
The main canvas is in the center of the viewer and contains the visual display
of the data passed to napari, including images, point, shapes, and our other
supported data types. Under the hood the canvas is a
object which has built-in support for features such as zooming and panning. As
OpenGL and your graphics card, panning and zooming are highly
performant. You can also return to the original zoom level by clicking the
home button in the viewer buttons panel.
One of the basic napari objects are layers. There are different layer types
Shapes, and other basic data types. They can be added
to the viewer either programmatically or through the GUI. Once added they start
to populate the layer list located on the bottom lefthand side of the main
The layer list contains one widget for each of the layers that have been added
to the viewer and includes a
thumbnail which shows a miniaturized version of
the currently viewed data, a
name that is an editable text box,
button that can be toggled on or off to show or hide the layer, and an
for the layer type.
Adding the following three image layers using the code below adds three-layer widgets to the layer list as follows:
viewer = napari.Viewer() viewer.add_image(data.astronaut(), name='astronaut') viewer.add_image(data.moon(), name='moon') viewer.add_image(data.camera(), name='camera')
Note that we’ve also also named each of the layers using the
add_image, and that name has appeared as a string in the layer
widget. The layer name is coerced into being unique so that it can be used to
index into the
You can select layers, causing them to become outlined, by clicking on their
layer widget. Multiple layers can be simultaneously selected using either
command click to select either all the layers in between clicked-on
layers or just the clicked-on layers respectively.
You can rearrange the order of the layers by dragging them, including dragging multiple layers at the same time.
Viewer object also contains our
LayerList object that allows you to
access the data of all the layers by
[<Image layer 'astronaut' at 0x7f1679cb87c0>, <Image layer 'moon' at 0x7f1679b8ddf0>, <Image layer 'camera' at 0x7f1679ae6a00>]
This object can be indexed like a normal list using an
int or using the
name of the layer as follows
<Image layer 'astronaut' at 0x7f1679cb87c0>
<Image layer 'astronaut' at 0x7f1679cb87c0>
You can rearrange layers by clicking and dragging them.
Above the layers list in the top left corner of the viewer there is a box that contains the layer controls. The controls that you have available to you depend on the layer type that you have selected.
For example, if you add a
Points layer after adding an
Image layer you will
now see different controls present.
import numpy as np viewer = napari.view_image(data.astronaut(), rgb=True) points = np.array([[100, 100], [200, 200], [300, 100]]) viewer.add_points(points, size=30)
Adjusting these properties in the GUI will cause corresponding changes to
properties on the individual layers that are accessible in the console through
For example, the name and opacity of a layer can be changed within the console as follows:
viewer.layers.name = ‘astronaut’ viewer.layers.opacity = 0.7
and these changes will instantly propagate to the GUI. For more information about the different properties for different layer types please see our layer specific tutorials listed at the bottom of this tutorial.
One of the main strengths of napari is that it has been designed from the
beginning to handle n-dimensional data. While much consumer photography is 2D
RGB, scientific image data can often be volumetric (i.e. 3D), volumetric
timeseries (i.e. 4D), or even higher dimensional. napari places no limits on
the dimensionality of its input data for all its layer types.
Adding data with a dimensionality greater than 2D will cause dimension sliders to appear directly underneath the main canvas and above the status bar. As many sliders as needed will appear to ensure the data can be fully browsed. For example, a 3D dataset needs one slider, a 4D dataset needs two sliders, and so on. The widths of the scroll bars of the dimension sliders are directly related to how many slices are in each dimension.
It is also possible to mix data of different shapes and dimensionality in different layers. If a 2D and 4D dataset are both added to the viewer then the sliders will only affect the 4D dataset and the 2D dataset will be remain the same. Effectively, the two datasets are broadcast together using NumPy broadcasting rules.
For example, the following commands from the console will add a both 2D and 3D datasets to the same viewer:
viewer = napari.Viewer() viewer.add_image(data.moon(), name='moon') blobs = np.stack( [ data.binary_blobs( length=512, blob_size_fraction=0.05, n_dim=2, volume_fraction=f ) for f in np.linspace(0.05, 0.5, 10) ], axis=0, ).astype(float) viewer.add_image(blobs, name='blobs', opacity=0.5, colormap='red')
At the very bottom of the GUI there is a status bar that contains useful updates and tips.
On the lefthand side of the status bar there is a message that contains information about the position of the mouse and the values of any images or the indices of any points that are currently hovered over, depending on which layer is selected. The status bar will also display information about what button you are clicking in the layer control panel too.
The righthand side of the status bar contains some helpful tips depending on which layer and tools are currently selected.
changing viewer theme¶
Currently, napari comes with two different themes
is the default. To change the theme, update
theme property of the viewer:
viewer = napari.Viewer() viewer.add_image(data.astronaut(), name='astronaut') # change the viewer theme viewer.theme = 'light'
Adding your own custom theme isn’t too hard either but does require creating
your own color
palette and rebuilding the icons. If people want more themes,
we’re happy to add them or if you look at our contributing
more information about building the icons and add one yourself!
One of the promises of napari is to provide a beginner friendly environment
for interactive analysis. For example, we want to enable workflows where people
can interact with the GUI, say click on the centers of some objects, or paint
over some regions and then perform custom analysis. As a first step towards
enabling custom interactivity we’ve provided support to add your own custom
keybindings to the
Viewer or individual
Layer objects such that when the
corresponding key gets clicked your custom function gets executed. Depending on
which object you bind your key to, your function will either get access to the
state of the entire
For example, to bind function that loops through all layers in the viewer and
prints their names to your console when you press the
p key you can do the
viewer = napari.Viewer() viewer.add_image(data.astronaut(), name='astronaut') @viewer.bind_key('p') def print_names(viewer): print([layer.name for layer in viewer.layers])
By default, your key will bind to the key press event, but it is also possible
to bind to the key release event by including a
yield inside your function.
All code before the
yield will get executed on key press and all code after
yield will get executed on key release. The following example will print
hello when you start to press the
m key and print
goodbye when you release
viewer = napari.Viewer() viewer.add_image(data.astronaut(), name='astronaut') @viewer.bind_key('m') def print_message(viewer): print('hello') yield print('goodbye')
Keys can be bound both to the object class or a particular instance depending on if you want the keybinding to apply to all instances of the class or only one particular instance.
Currently the keybindings only work when the main canvas is in focus, we are working to ensure they always work.
The ability to add custom keybindings dramatically increases what is possible within napari and we hope you take full advantage of them.
Hopefully, this tutorial has given you an overview of the functionality
available on the napari viewer, including the
LayerList and some of the
different layer types. To learn more about the different layer types that
napari supports checkout some more of our tutorials listed below. The image
layer tutorial is a great one to try next as viewing images is a
fundamental part of what napari is about.