pyimagej 1.5.0

Description:

pyimagej 1.5.0

PyImageJ: Python wrapper for ImageJ2




PyImageJ provides a set of wrapper functions for integration between ImageJ2
and Python. It also supports the original ImageJ API and data structures.
A major advantage of this approach is the ability to combine ImageJ and ImageJ2
with other tools available from the Python software ecosystem, including NumPy,
SciPy, scikit-image, CellProfiler, OpenCV, ITK and many more.
Quick Start
Jump into the documentation and tutorials to get started!
System Requirements
Hardware Requirements
PyImageJ requires at minimum a standard computer with enough RAM and CPU
performance to support the workflow operations defined by the user. While
PyImageJ will run on a range of hardware, we recommend the following RAM
and CPU specifications:

RAM: >= 2 GB (64 MB minimum)
CPU: >= 1 core

Notably, PyImageJ can be installed and used on server infrastructure for
large scale image processing.
OS Requirements
PyImageJ has been tested on the following operating systems:

Linux (Ubuntu 20.04 LTS)
Windows
macOS

Software Requirements
PyImageJ requires the following packages:

Python >= 3.7
imglyb >= 2.1.0
jgo >= 1.0.3
JPype >= 1.3.0
labeling >= 0.1.14
matplotlib (optional, for ij.py.show function only)
NumPy
scyjava >= 1.8.0
xarray

PyImageJ will not function properly if dependency versions are too old.
In addition, PyImageJ requires OpenJDK and Maven to be installed.
Installation
On Mac and Linux, PyImageJ can be installed using Conda+Mamba. Here is how to create
and activate a new conda environment with PyImageJ available:
conda install mamba -n base -c conda-forge
mamba create -n pyimagej -c conda-forge pyimagej openjdk=8
conda activate pyimagej

You can also pip install pyimagej, but will then need to install OpenJDK and Maven manually.
Installation time takes approximately 20 seconds. Initializing PyImageJ
takes an additional ~30 seconds to ~2-3 minutes (depending on bandwidth)
while it downloads and caches the needed Java libraries.
For detailed installation instructions and requirements, see
Installation.
Usage
The first step when using PyImageJ is to create an ImageJ2 gateway.
This gateway can point to any official release of ImageJ2 or to a local
installation. Using the gateway, you have full access to the ImageJ2 API,
plus utility functions for translating between Python (NumPy, xarray,
pandas, etc.) and Java (ImageJ2, ImgLib2, etc.) structures.
For instructions on how to start up the gateway for various settings, see
How to initialize PyImageJ.
Here is an example of opening an image using ImageJ2 and displaying it:
# Create an ImageJ2 gateway with the newest available version of ImageJ2.
import imagej
ij = imagej.init()

# Load an image.
image_url = 'https://imagej.net/images/clown.jpg'
jimage = ij.io().open(image_url)

# Convert the image from ImageJ2 to xarray, a package that adds
# labeled datasets to numpy (http://xarray.pydata.org/en/stable/).
image = ij.py.from_java(jimage)

# Display the image (backed by matplotlib).
ij.py.show(image, cmap='gray')

For more, see the
tutorial notebooks.
API Reference
For a complete reference of the PyImageJ API, please see the
API Reference.
Getting Help
The Scientific Community Image Forum
is the best place to get general help on usage of PyImageJ, ImageJ2, and any
other image processing tasks. Bugs can be reported to the PyImageJ GitHub
issue tracker.
Contributing
All contributions, reports, and ideas are welcome. Contribution is done
via pull requests onto the pyimagej repository.
Most development discussion takes place on the pyimagej
GitHub repository.
You can also reach the developers at the
Image.sc Zulip chat.
For details on how to develop the PyImageJ codebase, see
Development.md.