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napari: a Qt- and VisPy-based ndarray visualization tool
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napari

multi-dimensional image viewer for python

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napari is a fast, interactive, multi-dimensional image viewer for Python. It's designed for browsing, annotating, and analyzing large multi-dimensional images. It's built on top of Qt (for the GUI), vispy (for performant GPU-based rendering), and the scientific Python stack (numpy, scipy).

We're developing napari in the open! But the project is in an alpha stage, and there will still likely be breaking changes with each release. You can follow progress on this repository, test out new versions as we release them, and contribute ideas and code.

We're working on in-depth tutorials, but you can also quickly get started by looking below.

installation

napari can be installed on most macOS and Linux systems with Python 3.6 or 3.7 by calling

$ pip install napari

We're working on improving Windows support. For mac0S we also require at least version 10.12.

To install from the master branch on Github use

$ pip install git+https://github.com/napari/napari

To clone the repository locally and install in editable mode use

$ git clone https://github.com/napari/napari.git
$ cd napari
$ pip install -e .

For more information see our installation tutorial

simple example

From inside an IPython shell (started with ipython --gui=qt) or Jupyter notebook you can open up an interactive viewer by calling

%gui qt5
from skimage import data
import napari
viewer = napari.view(data.astronaut(), rgb=True)

image

To do the same thing inside a script call

from skimage import data
import napari

with napari.gui_qt():
    viewer = napari.view(data.astronaut(), rgb=True)

features

Check out the scripts in the examples folder to see some of the functionality we're developing!

For example, you can add multiple images in different layers and tweak their opacity on GUI to see blended images

from skimage import data
from skimage.color import rgb2gray
import napari

with napari.gui_qt():
    # create the viewer with four layers
    viewer = napari.view(astronaut=rgb2gray(data.astronaut()),
                         photographer=data.camera(),
                         coins=data.coins(),
                         moon=data.moon())
    # remove a layer
    viewer.layers.remove('coins')
    # swap layer order
    viewer.layers['astronaut', 'moon'] = viewer.layers['moon', 'astronaut']

image

You can add points on top of an image

import numpy as np
from skimage import data
from skimage.color import rgb2gray
import napari

with napari.gui_qt():
    # set up viewer
    viewer = napari.Viewer()
    viewer.add_image(rgb2gray(data.astronaut()))
    # create three xy coordinates
    points = np.array([[100, 100], [200, 200], [333, 111]])
    # specify three sizes
    size = np.array([10, 20, 20])
    # add them to the viewer
    points = viewer.add_points(points, size=size)

image

napari supports bidirectional communication between the viewer and the Python kernel, which is especially useful in Jupyter notebooks -- in the example above you can retrieve the locations of the points, including any additional ones you have drawn, by calling

>>> points.data
[[100, 100],
 [200, 200],
 [333, 111]]

You can render and quickly browse slices of multi-dimensional arrays

import numpy as np
from skimage import data
import napari

with napari.gui_qt():
    # create fake 3d data
    blobs = np.stack([data.binary_blobs(length=128, blob_size_fraction=0.05,
                                        n_dim=3, volume_fraction=f)
                     for f in np.linspace(0.05, 0.5, 10)], axis=0)
    # add image data to the viewer
    viewer = napari.view(blobs.astype(float))

    # add points to the viewer
    points = np.array(
        [
            [0, 0, 100, 100],
            [0, 0, 50, 120],
            [1, 0, 100, 40],
            [2, 10, 110, 100],
            [9, 8, 80, 100],
        ]
    )
    viewer.add_points(
        points, size=[0, 6, 10, 10], face_color='blue', n_dimensional=True
    )

image

You can draw lines and polygons on an image, including selection and adjustment of shapes and vertices, and control over fill and stroke color. Run examples/add_shapes.py to generate and interact with the following example.

image

You can also paint pixel-wise labels, useful for creating masks for segmentation, and fill in closed regions using the paint bucket. Run examples/add_labels.py to generate and interact with the following example.

image

For details checkout our in-depth tutorials

plans

We're working on several features, including

  • support for 3D volumetric rendering
  • support for multiple canvases
  • a plugin ecosystem for integrating image processing and machine learning tools

See this issue for some of the features on the roadmap for our 0.2 release. Feel free to add comments or ideas!

contributing

Contributions are encouraged! Please read our contributing guide to get started. Given that we're in an early stage, you may want to reach out on Github Issues before jumping in.

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