Christoph Gohlke

napari-oiffile

A napari plugin for reading files via oiffile

Daniel Krentzel

napari-clemreg





A plugin for registering multimodal image volumes based on common segmented structures of interest with point-clouds.

This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.

Installation
You can install napari-clemreg via pip:
pip install napari-clemreg

When installing clemreg on a Windows machine you may encounter the following error:
error Microsoft Visual C++ 14.0 is required

Ensure that Visual Studios C++ 14.00 is installed
Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the MIT license,
"napari-clemreg" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-clemreg

A plugin for registering multimodal image volumes based on common segmented structures of interest with point-clouds.

Jacopo Abramo

napari-live-recording






A napari plugin for live video recording with a generic camera device and generate a stack of TIFF images from said device.

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

Installation
You can install napari-live-recording via pip:
pip install napari-live-recording

Documentation
You can view the full documentation at napari-live-recording.readthedocs.io
Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the MIT license,
"napari-live-recording" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-live-recording

A napari plugin for live video recording with a generic camera device.

Marc Boucsein, Marc Buckmakowski

napari-medical-image-formats



A Plugin in order to read medical image formats such as DICOM and NIfTI without any meta informations. This will be updated soon.

Installation
You can install napari-medical-image-formats via pip:
pip install napari-medical-image-formats

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-medical-image-formats" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-medical-image-formats

A Plugin in order to read medical image formats such as DICOM and NIfTI

Allen Institute for Cell Science

napari-allencell-segmenter





A plugin that enables 3D image segmentation provided by Allen Institute for Cell Science
The Allen Cell & Structure Segmenter plugin for napari provides an intuitive graphical user interface to access the powerful segmentation capabilities of an open source 3D segmentation software package developed and maintained by the Allen Institute for Cell Science (classic workflows only with v1.0). ​The Allen Cell & Structure Segmenter is a Python-based open source toolkit developed at the Allen Institute for Cell Science for 3D segmentation of intracellular structures in fluorescence microscope images. This toolkit brings together classic image segmentation and iterative deep learning workflows first to generate initial high-quality 3D intracellular structure segmentations and then to easily curate these results to generate the ground truths for building robust and accurate deep learning models. The toolkit takes advantage of the high replicate 3D live cell image data collected at the Allen Institute for Cell Science of over 30 endogenous fluorescently tagged human induced pluripotent stem cell (hiPSC) lines. Each cell line represents a different intracellular structure with one or more distinct localization patterns within undifferentiated hiPS cells and hiPSC-derived cardiomyocytes.
More details about Segmenter can be found at https://allencell.org/segmenter

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

Installation
Option 1 (recommended):
After you installed the lastest version of napari, you can go to "Plugins" --> "Install/Uninstall Package(s)". Then, you will be able to see all available napari plugins and you can find us by name napari-allencell-segmenter. Just click the "install" button to install the Segmenter plugin.
Option 2:
You can also install napari-allencell-segmenter via pip:
pip install napari-allencell-segmenter

Quick Start
In the current version, there are two parts in the plugin: workflow editor and batch processing. The workflow editor allows users adjusting parameters in all the existing workflows in the lookup table, so that the workflow can be optimized on users' data. The adjusted workflow can be saved and then applied to a large batch of files using the batch processing part of the plugin. 

Open a file in napari (the plugin is able to support multi-dimensional data in .tiff, .tif. ome.tif, .ome.tiff, .czi)
Start the plugin (open napari, go to "Plugins" --> "napari-allencell-segmenter" --> "workflow editor")
Select the image and channel to work on
Select a workflow based on the example image and target segmentation based on user's data. Ideally, it is recommend to start with the example with very similar morphology as user's data.
Click "Run All" to execute the whole workflow on the sample data.
Adjust the parameters of steps, based on the intermediate results. For instruction on the details on each function and the effect of each parameter, click the tooltip button. A complete list of all functions can be found here
Click "Run All" again after adjusting the parameters and repeat step 6 and 7 until the result is satisfactory.
Save the workflow
Close the plugin and open the batch processing part by (go to "Plugins" --> "napari-allencell-segmenter" --> "batch processing")
Load the customized workflow (or an off-the-shelf workflow) json file
Load the folder with all the images to process
Click "Run"

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-allencell-segmenter" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-allencell-segmenter

A plugin that enables 3D image segmentation provided by Allen Institute for Cell Science

Robert Haase

napari-script-editor






A python script editor for napari based on PyQode.

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

Usage
Start the script editor from the menu Tools > Scripts > Script Editor. Use the auto-completion while typing, 
check out the napari tutorials and the
example scripts. 
Use the Run button to execute a script.

If you save the script to the folder ".napari-scripts" in your home directory, you will find the script in the 
Tools > Scripts menu in napari. You can then also start it from there.

Note: If you have scripts, that might be useful to others, please send them as 
pull-request to the examples in 
repository or share them in any other way that suits you.
Installation

Get a python environment, e.g. via mini-conda. 
  If you never used python/conda environments before, please follow the instructions 
  here first.
Install napari using conda. 

conda install -c conda-forge napari
Afterwards, install napari-script-editor using pip:
pip install napari-script-editor
Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-script-editor" is free and open source software
Known issues

Sometimes, the script editor thinks, the file has been changed on disk and asks to reload it.

Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-script-editor

Philipp Schoennenbeck


The developer has not yet provided a napari-hub specific description.

napari-mrcfile-handler

A simple plugin to read, write and adjust mrcfiles in napari.

napari-imagecodecs

A napari plugin for reading files via imagecodecs

natari



Napari gaming
Cell counting arcade
Commander! Cells are intruding our dish! Control your tiny space ship using 1 and 2 keys to move it left/right.
Use the 9 key to shoot a anti-cell bullet.

The image originates from BBBC022v1 (Gustafsdottir et al., PLOS ONE, 2013), available from the Broad Bioimage Benchmark Collection (Ljosa et al., Nature Methods, 2012).
Snake
Two mitochondria navigating in a cell searching for stress granules. 
The two players can control their mito using the W, A, S, D and I, J, K, L  keys, respectively.

Ping pong
Don't drop the organoid! Use your racket and hit it back to your colleague!
The two players can use W, S and I, K to control their racket, respectively.

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.
Installation
You can install natari via pip:
pip install natari

Known issues

To make the keyboard buttons work, you sometimes have to click within the image after starting the game.

Contributing
Contributions are very welcome. 
License
"natari" is free and open source software. The code is in the public domain.
See also: unlicense.org
Issues
If you encounter any problems, please file an issue along with a detailed description.

natari

Talley Lambert

napari-ndtiffs





napari plugin for nd tiff folders with optional OpenCl-based deskewing.
Built-in support for folders of (skewed) lattice light sheet tiffs.


This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.
Features

Drag and drop a folder of tiffs onto napari window to view easily 
(currently designed to detect  lattice light sheet tiffs, but easily
    adjustable)
If lattice Settings.txt file is found, will deskew automatically (only if
  necessary)
Lazily loads dataset on demand.  quickly load preview your data.
Handles .zip archives as well!  Just directly compress your tiff folder,
  then drop it into napari.
All-openCL deskewing, works on GPU as well as CPU, falls back to scipy if
  pyopencl is unavailable.

It would not be hard to support arbitrary filenaming patterns!  If you have a
folder of tiffs with a consistent naming scheme and would like to take advantage
of this plugin, feel free to open an issue!
Installation
You can install napari-ndtiffs via pip:
shell
pip install napari-ndtiffs
To also install PyOpenCL (for faster deskewing):
shell
pip install napari-ndtiffs[opencl]
Usage
In most cases, just drop your folder onto napari, or use viewer.open("path")
Overriding parameters
You can control things like voxel size and deskewing angle as follows:
```python
from napari_ndtiffs import parameter_override
import napari
viewer = napari.Viewer()
with parameter_override(angle=45, name="my image"):
    viewer.open("path/to/folder", plugin="ndtiffs")
```
Valid keys for parameter_override include:

dx: (float) the pixel size, in microns
dz: (float)the z step size, in microns
deskew: (bool) whether or not to deskew, (by default, will deskew if angle > 0, or if a lattice metadata file is detected that requires deskewing) 
angle: (float) the angle of the light sheet relative to the coverslip
padval: (float) the value with which to pad the image edges when deskewing (default is 0)
contrast_limits: (2-tuple of int) (min, max) contrast_limits to use when viewing the image
name: (str) an optional name for the image

Sample data
Try it out with test data: download sample data
You can unzip if you like, or just drag the zip file onto the napari window.
Or, from command line, use:
bash
napari path/to/lls_mitosis.zip
Debugging
To monitor file io and deskew activity, enter the following in the napari console:
python
import logging
logging.getLogger('napari_llsfolder').setLevel('DEBUG')
Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-ndtiffs" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-ndtiffs

napari plugin for nd tiff folders with OpenCl deskew

Matt McCormick

napari-itk-io





File IO with itk for napari.
Supported image file formats:

BioRad
BMP
DICOM
DICOM Series
ITK HDF5
JPEG
GE4,GE5,GEAdw
Gipl (Guys Image Processing Lab)
LSM
MetaImage
MINC 2.0
MGH
MRC
NifTi
NRRD
Portable Network Graphics (PNG)
Tagged Image File Format (TIFF)
VTK legacy file format for images

For DICOM Series, select the folder containing the series with File -> Open
Folder.... The first series will be selected and sorted spatially.
Installation
You can install napari-itk-io via pip:
pip install napari-itk-io

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
Follow the itk contributing
guidelines
and the itk code of
conduct.
License
Distributed under the terms of the Apache Software License 2.0 license,
"napari-itk-io" is free and open source software.
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-itk-io

napari-czifile

A napari plugin for reading files via czifile

Jonas Windhager

Supported file formats:
 - Fluidigm® MCD™
 - Fluidigm® TXT
Supported image types:
  - Panoramas (single-channel, color)
  - Acquisitions (multi-channel, grayscale)
All images are loaded co-registered within the machine's coordinate system.

napari-imc

Imaging Mass Cytometry (IMC) file type support for napari

napari-accelerated-pixel-and-object-classification (APOC)





clEsperanto meets scikit-learn
A yet experimental OpenCL-based Random Forest Classifier for pixel and labeled object classification in napari.

The processed example image maize_clsm.tif
is licensed by David Legland under 
CC-BY 4.0 license
For using the accelerated pixel and object classifiers in python, check out apoc.

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.
Installation
You can install napari-accelerated-pixel-and-object-classification via pip. Note: you also need pyopencl.
conda install pyopencl
pip install napari-accelerated-pixel-and-object-classification

In case of issues in napari, make sure these dependencies are installed properly:
pip install pyclesperanto_prototype
pip install apoc

Usage
Usage: Object and Semantic Segmentation
Starting point is napari with at least one image layer and one labels layer (your annotation).

You find Object and Semantic Segmentation in the main plugins menu:

When clicking one of the first two, the following graphical user interface will show up.


Choose one or multiple images to train on. These images will be considered as multiple channels. Thus, they need to be spatially correlated. 
   Training from multiple images showing different scenes is not (yet) supported from the graphical user interface. Check out this notebook if you want to train from multiple image-annotation pairs.
Select a file where the classifier should be saved. If the file exists already, it will be overwritten.
Select the ground-truth annotation labels layer. 
Select which label corresponds to foreground (not available in Semantic Segmentation)
Select the feature images that should be considered for segmentation. If segmentation appears pixelated, try increasing the selected sigma values and untick Consider original image.
Tree depth and number of trees allow you to fine-tune how to deal with manifold regions of different characteristics. The higher these numbers, the longer segmentation will take. In case you use many images and many features, high depth and number of trees might be necessary. (See also max_depth and n_estimators in the scikit-learn documentation of the Random Forest Classifier.
The estimation of memory consumption allows you to tune the configuration to your GPU-hardware. Also consider the GPU-hardware of others who want to use your classifier.
Click on Run when you're done with configuring. If the segmentation doesn't fit after the first execution, consider fine-tuning the ground-truth annotation and try again.

A successful segmentation can for example look like this:

After your classifier has been trained successfully, click on the "Application / Prediction" tab. If you apply the classifier again, python code will be generated. 
You can use this code for example to apply the same classifier to a folder of images. If you're new to this, check out this notebook.

Usage: Object classification
Click the menu Plugins > Segmentation (Accelerated Pixel and Object Classification) > Object classifier. 

This user interface will be shown:


The image layer will be used for intensity based feature extraction (see below).
The labels layer should be contain the segmentation of objects that should be classified. 
   You can use the Object Segmenter explained above to create this layer.
The annotation layer should contain manual annotations of object classes. 
   You can draw lines crossing single and multiple objects of the same kind. 
   For example draw a line through some elongated objects with label "1" and another line through some rather roundish objects with label "2".
   If these lines touch the background, that will be ignored.
Tree depth and number of trees allow you to fine-tune how to deal with manifold objects of different characteristics. The higher these numbers, the longer classification will take. In case you use many features, high depth and number of trees might be necessary. (See also max_depth and n_estimators in the scikit-learn documentation of the Random Forest Classifier.
Select the right features for training. For example, for differentiating objects according to their shape as suggested above, select "shape".
   The features are extracted using clEsperanto and are shown by example in this notebook.
Click on the Run button. If classification doesn't perform well in the first attempt, try changing selected features.  

If classification worked well, it may for example look like this. Note the two thick lines which were drawn to annotate elongated and roundish objects with brown and cyan:

Usage: Under-the-hood functions
Open an image in napari and add a labels layer. Annotate foreground and background with two different label identifiers. You can also add a third, e.g. a membrane-like region in between to improve segmentation quality.

Click the menu Plugins > Segmentation (Accelerated Pixel and Object Classification) > Train pixel classifier. 
Consider changing the featureset. There are three options for selecting 
small (about 1 pixel sized) objects, 
medium (about 5 pixel sized) object and 
large (about 25 pixel sized) objects.
Make sure the right image and annotation layers are selected and click on Run.

The classifier was saved as temp.cl to disc. You can later re-use it by clicking the menu Plugins > OpenCL Random Forest Classifiers > Predict pixel classifier
Optional: Hide the annotation layer.
Click the menu Plugins > Segmentation (Accelerated Pixel and Object Classification) > Connected Component Labeling.
Make sure the right labels layer is selected. It is supposed to be the result layer from the pixel classification.
Select the object class identifier you used for annotating objects, that's the intensity you drew on objects in the annotation layer.
Hint: If you want to analyse touching neigbors afterwards, activate the fill gaps between labels checkbox.
Click on the Run button.

Optional: Hide the pixel classification result layer. Change the opacity of the connected component labels layer.
Add a new labels layer and annotate different object classes by drawing lines through them. 
In the following example objects with different size and shape were annotated in three classes:
* round, small
* round, large
* elongated

Click the menu Plugins > Segmentation (Accelerated Pixel and Object Classification) > Train object classifier. Select the right layers for training.
The labels layer should be the result from connected components labeling.
The annotation layer should be the just annotated object classes layer.
Select the right features for training. Click on the Run button. 
After training, the classifier will be stored to disc in the file you specified.
You can later re-use it by clicking the menu Plugins > Segmentation (Accelerated Pixel and Object Classification) > Predict label classifier

This is an experimental napari plugin. Feedback is very welcome!
Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-accelerated-pixel-and-object-classification" is free and open source software
Issues
If you encounter any problems, please open a thread on image.sc along with a detailed description and tag @haesleinhuepf.

napari-accelerated-pixel-and-object-classification

Pixel and label classification using OpenCL-based Random Forest Classifiers

Carsen Stringer

cellpose-napari 











a napari plugin for anatomical segmentation of general cellular images

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.
The plugin code was written by Carsen Stringer, and the cellpose code was written by Carsen Stringer and Marius Pachitariu. To learn about Cellpose, read the paper or watch this talk. 
For support with the plugin, please open an issue. For support with cellpose, please open an issue on the cellpose repo. 
If you use this plugin please cite the paper:
::
  @article{stringer2021cellpose,
  title={Cellpose: a generalist algorithm for cellular segmentation},
  author={Stringer, Carsen and Wang, Tim and Michaelos, Michalis and Pachitariu, Marius},
  journal={Nature Methods},
  volume={18},
  number={1},
  pages={100--106},
  year={2021},
  publisher={Nature Publishing Group}
  }


Installation
Install an Anaconda distribution of Python -- Choose Python 3 and your operating system. Note you might need to use an anaconda prompt if you did not add anaconda to the path.
Install napari with pip: pip install napari[all]. Then install cellpose-napari via [pip]:
pip install cellpose-napari

Or install the plugin inside napari in the plugin window.
If install fails in your base environment, create a new environment:
1. Download the environment.yml file from the repository. You can do this by cloning the repository, or copy-pasting the text from the file into a text document on your local computer.
2. Open an anaconda prompt / command prompt with conda for python 3 in the path
3. Change directories to where the environment.yml is and run conda env create -f environment.yml
4. To activate this new environment, run conda activate cellpose-napari
5. You should see (cellpose-napari) on the left side of the terminal line. 
If you have issues with cellpose installation, see the cellpose docs for more details, and then if the suggestions fail, open an issue.
Upgrading software
You can upgrade the plugin with
~~~
pip install cellpose-napari --upgrade
~~~
and you can upgrade cellpose with
~~~
pip install cellpose --upgrade
~~~
GPU version (CUDA) on Windows or Linux
If you plan on running many images, you may want to install a GPU version of torch (if it isn't already installed).
Before installing the GPU version, remove the CPU version:
~~~
pip uninstall torch
~~~
Follow the instructions here to determine what version to install. The Anaconda install is recommended along with CUDA version 10.2. For instance this command will install the 10.2 version on Linux and Windows (note the torchvision and torchaudio commands are removed because cellpose doesn't require them):
~~~
conda install pytorch cudatoolkit=10.2 -c pytorch
~~~~
When upgrading GPU Cellpose in the future, you will want to ignore dependencies (to ensure that the pip version of torch does not install):
~~~
pip install --no-deps cellpose --upgrade
~~~
Installation of github version
Follow steps from above to install the dependencies. In the github repository, run pip install -e . and the github version will be installed. If you want to go back to the pip version of cellpose-napari, then say pip install cellpose-napari.
Running the software
Open napari with the cellpose-napari dock widget open
napari -w cellpose-napari
There is sample data in the File menu, or get started with your own images!
Detailed usage documentation.
Contributing
Contributions are very welcome. Tests are run with pytest.
License
Distributed under the terms of the BSD-3 license,
"cellpose-napari" is free and open source software.
Dependencies
cellpose-napari relies on the following excellent packages (which are automatically installed with conda/pip if missing):
- napari
- magicgui
cellpose relies on the following excellent packages (which are automatically installed with conda/pip if missing):
- torch
- numpy (>=1.16.0)
- numba
- scipy
- natsort
- tifffile
- opencv

cellpose-napari

a generalist algorithm for anatomical segmentation

napari-dv





Deltavision/MRC file reader for napari
Installation
You can install napari-dv via pip:
pip install napari-dv

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the MIT license,
"napari-dv" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-dv

OME Team

Description
This plugin provides a reader for zarr backed OME-NGFF images in napari. The reader
will inspect the .zattrs metadata provided and pass any relevant metadata, including channel, scale and colormap metadata.

The example above uses the image at https://idr.openmicroscopy.org/webclient/?show=image-6001240
Supported Data
This plugin is designed to allow bioimaging researchers and analysts to explore their
multi-resolution images stored in Zarr filesets (according to the OME zarr spec)
without needing an intricate understanding of zarr, or the spec itself.
This plugin supports reading all images recognised as ome-zarr, namely, containing
well-formed .zattrs and .zgroup files, as well as the appropriate directory
hierarchy as described in the spec.
The image metadata from OMERO will be used to set channel names, colormaps and rendering settings in napari.
Quickstart
You can open local or remote images using napari at the terminal and the path to your file:
```
$ napari 'https://s3.embassy.ebi.ac.uk/idr/zarr/v0.1/6001240.zarr/'
also works with local files
$ napari 6001240.zarr
```
OR in python:
```python
import napari
viewer = napari.Viewer()
viewer.open('https://s3.embassy.ebi.ac.uk/idr/zarr/v0.1/6001240.zarr/')
napari.run()
```
If a single zarray is passed to the plugin, it will be opened without the use of
the metadata:
$ napari '/tmp/6001240.zarr/0'
If an image group contains labels, they will also be opened, and added as a
separate layer in napari.
When the labels group metadata additionally contains "rgba" and "properties" keys,
the labels will be given appropriate colors and the properties will be displayed
in the status bar.
Working with ome-zarr images can be more convenient using the command-line interface
and utility functions of our associated library ome-zarr. For more information
please see the package documentation for ome-zarr.
Getting Help
If you discover a bug with the plugin, or would like to request a new feature, please
raise an issue on our repository at https://github.com/ome/napari-ome-zarr.
If you would like assistance with using the plugin, or converting images to
ome-zarr format, please reach out on image.sc.
How to Cite OME-NGFF:
Next-generation file format (NGFF) specifications for storing bioimaging data in the cloud. J. Moore, et al. Editors. Open Microscopy Environment Consortium, 20 November 2020. This edition of the specification is https://ngff.openmicroscopy.org/0.1/. The latest edition is available at https://ngff.openmicroscopy.org/latest/. (doi:10.5281/zenodo.4282107)

napari-ome-zarr

A reader for zarr backed OME-NGFF images.

Ervin Tasnadi

napari-nucleaizer





GUI for the nucleaAIzer method in Napari.

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

Installation

Create & enable a virtual env.
Instal napari.
Install nucleaizer_backend.
Install this project into the same virtual environment.

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-nucleaizer" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-nucleaizer

Napari integration of the nucleaizer algorithm. (https://nucleaizer.org)

napari-tifffile

A napari plugin for reading files via tifffile

Lorenzo Gaifas

napari-em-reader





A napari plugin to read .em files

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

Installation
You can install napari-em-reader via pip:
pip install napari-em-reader

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-em-reader" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-em-reader

Luis Perdigao

Red lionfish (RL) deconvolution
Richardson-Lucy deconvolution for fishes, scientists and engineers.
Richardson-Lucy is an iterative deconvolution algorithm that is useful for removing
point spread function (PSF) or optical transfer function artifacts in experimental images.
The method was originally developed for astronomy, to remove optical effects and simultaneously reduce poisson noise in 2D images.
Lucy, L. B. An iterative technique for the rectification of observed distributions. The Astronomical Journal 79, 745 (1974). DOI: 10.1086/111605
It has a version that runs on CPU and another that runs on GPU using OpenCL through Reikna library.
By default it uses the GPU OpenCL version.
If data is too big for doing GPU deconvolution, it will do the calculation in blocks, therefore preventing memory issues.
Installation
It is strongly recommended to install this package under a python anaconda or miniconda environment.
This is because some calculations use PyOpenCL, and this is best installed in a conda environment.
conda install reikna pyopencl -c conda-forge
pip install redlionfish
In Linux , the package ocl-icd-system is also useful.
conda install reikna pyopencl ocl-icd-system -c conda-forge
pip install redlionfish
Napari plugin
If you follow the installation instructions above, and install the napari in the same conda environment
then the plugin should be immediately available in the Menu -> Plugins -> RedLionfish.
Alternatively, you can use the Napari's plugin installation in Menu -> Plugins -> Install/Uninstall Plugins....
If you chose to use this method, GPU acceleration will not be available and it will use the CPU backend.
Anaconda/miniconda installation
At the moment of this writting this package is NOT AVAILABLE available yet in conda-forge.
This work is under progress.
Manual installation using the conda package file.
Download the appropriate conda package .bz2 at https://github.com/rosalindfranklininstitute/RedLionfish/releases
In the command line, successively run:
conda install <filename.bz2>
conda update --all -c conda-forge
The second line is needed because you are installing from a local file, conda installer will not install dependencies. Right after this you should run the update command given.
Manual installation (advanced and for developers)
Please note that in order to use OpenCL GPU accelerations, PyopenCL must be installed.
The best way to get it working is to install it under a conda environment.
conda install reikna pyopencl
or
conda install reikna pyopencl ocl-icd-system -c conda-forge (Linux)
Clone/donload from source https://github.com/rosalindfranklininstitute/RedLionfish/
and run
python setup.py install
Debug
If you want to test and modify the code then you should probably install instead using:
python setup.py develop
Coding
Please feel free to browse /test folder for examples.
In your code, add the import.
import RedLionfishDeconv
in order to use the functions.
The most useful function is perhaps the following.
def doRLDeconvolutionFromNpArrays(data_np , psf_np ,*, niter=10, method='gpu', useBlockAlgorithm=False, callbkTickFunc=None, resAsUint8 = False)
This will do the Richardson-Lucy deconvolution on the data_np (numpy, 3 dimensional data volume) using the provided PSF data volume, for 10 iterations. GPU method is generally faster but it may fail. If it does fail, the program will automatically use the CPU version from scipy.imaging.
Manually building the conda package
For this installation, ensure that the conda-build package is installed
conda install conda-build
In windows, simply execute
conda-create-package.bat
Or, execute the command-line to create the installation package for RedLionfish
conda-build --output-folder ./conda-built-packages -c conda-forge conda-recipe
and the conda package will be created in folder conda-built-packages.
Otherwise, navigate to conda-recipe, and execute on the command-line conda build .
It will take a while to complete.

RedLionfish

Fast Richardson-Lucy deconvolution of 3D volume data using GPU or CPU with napari plugin.

Viktor van der Valk

napari-checkerboard





Compare two images with the itk checkerboard filter


This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

Installation
You can install napari-checkerboard via pip:
pip install napari-checkerboard

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the Apache Software License 2.0 license,
"napari-checkerboard" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-checkerboard

Compare two images with the itk checkerboard filter

Simon Biberger

napari-ccp4map

Enables napari to read .map files in the ccp4 format. Drag&Drop or press Ctrl+O to read files.

napari-tabu





A plugin for handling multiple napari windows


Usage
To open a new window, first click the menu Plugins > napari-tabu: open new window

Afterwards, select the layer which should be opened in the new window and click on Run:

When you're done with working with the new window, you can send back the result of your work using the Send current layer back to main napari butoon:

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.
Installation
You can install napari-tabu via pip:
pip install napari-tabu

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-tabu" is free and open source software
Issues
If you encounter any problems, please open a thread on image.sc along with a detailed description and tag @haesleinhuepf.

napari-tabu

A plugin for handling multiple napari windows

Sylvain Prigent

Description
napari-sdeconv is a suite of Napari plugins for the sdeconv library
<https://sylvainprigent.github.io/sdeconv/>_ dedicated to 2D and 3D images deconvolution. It contains multiple
deconvolution algorithms and PSF Generators.

Example of deconvolution with Spitfire



Example of how to generate a 3D PSF


Installation
You can install napari-sdeconv via pip:
pip install napari-sdeconv

Note that the current version of the package on support python 3.9    
The deconvolution depends on FFTW c++ library. FFTW must be installed for the 
deconvolution plugin to work. The easiest method to install FFTW is to use 
conda:
conda install -c conda-forge fftw

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the GNU GPL v3.0 license,
"napari-tracks-reader" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-sdeconv

napari-curtain






View one image over another as curtain

This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.
Usage
You find the Curtain plugin in the menu Tools > Visualization > Curtain. Move the position of the slider left/right 
as shown in the video above. In case one image is much bright than the other, you can modify the factors above the 
slider until visualization pleases.
Installation
You can install napari-curtain via pip:
pip install napari-curtain

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-curtain" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-curtain

napari-sdtfile

A napari plugin for reading files via sdtfile

napari-plot-profile





Plot intensities along a line in napari.

Usage


Open some images in napari.


Add a shapes layer.




Activate the line drawing tool or the path tool and draw a line.



After drawing a line, click on the menu Plugins > Measurements (Plot Profile)
If you modify the line, you may want to click the "Refresh" button to redraw the profile.


To see how these steps can be done programmatically from python, check out the demo notebook

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.
Installation
You can install napari-plot-profile via pip:
pip install napari-plot-profile

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-plot-profile" is free and open source software
Issues
If you encounter any problems, please create a thread on image.sc along with a detailed description and tag @haesleinhuepf.

napari-plot-profile

devbio-napari



A collection of napari plugins useful for studying developmental biology.

File input/output plugins
aicsimageio
napari-itk-io 
ome-zarr
Image exploration and visualization tools
animation
folder-browser
Image segmentation plugins
accelerated-pixel-and-object-classification
pyclesperanto-assistant
Manual split & merge labels
segment-blobs-and-things-with-membranes
Image registration
PlatyMatch
Quantitative measurements
scikit-image regionprops
plot-profile
Utilities
brightness-contrast
plugin-search


This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.
Installation
You can install devbio-napari via pip:
conda install -c conda-forge pyopencl jupyter notebook
pip install devbio-napari

Windows users should install pyopencl via conda in advance:
conda install -c conda-forge pyopencl

See also.
Contributing
Contributions are very welcome. 
If you want to suggest a new napari plugin to become part of this distribution, please make sure it interoperates nicely with the other plugins. 
For example, if the plugin you suggest provided cell segmentation algorithms, please check if the resulting segmented cells can be analysed using napari-skimage-regionprops.
Furthermore, please make sure the README of the plugin you are proposing comes with user documentation, e.g. a step-by-step guide with screenshots explaining what users can do with the plugin and how to use it. 
It is recommended to provide example data as well so that end-users can try out the plugin under conditions it was developed for.
License
Distributed under the terms of the BSD-3 license,
"devbio-napari" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

devbio-napari

A collection of napari plugins useful for studying developmental biology

napari-mouse-controls






Control zoom, slicing and contrast windowing with mouse and touch screen

Usage
You find the mouse control panel in the menu Tools > Utilities > Mouse controls
Zoom
After clicking the Zoom button , you can click in the napari canvas and move the mouse up and down to zoom in and out.

Slicing
After clicking the Slicing button , you can control the currently displayed slice by moving the mouse.
By moving the mouse up and down, you control the currently selected Z-plane.
By moving the mouse left and right, you control the currently seleted time point.

Windowing
After clicking the Windowing button , you can modify the brightness and contrast by moving the mouse. 
By moving the mouse up and down, you control window width of the range of displayed grey values (max - min).
By moving the mouse left and right, you control the center of the grey value window. 

Normal / default mode
Click the Default button 
to return to napari's normal mode.
This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.
Installation
You can install napari-mouse-controls via pip:
pip install napari-mouse-controls

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-mouse-controls" is free and open source software
Issues
If you encounter any problems, please create a thread on image.sc along with a detailed description and tag @haesleinhuepf.

napari-mouse-controls

napari-mahotas-image-processing (n-mahotas)






Image processing based using the Mahotas library in napari
Usage
Gaussian blur
Applies a Gaussian blur to an
image. This might be useful for denoising, e.g. before applying the Threshold-Otsu method.

Otsu's threshold
Binarizes an image using scikit-image's threshold Otsu algorithm, also known as 
Otsu's method.

Split connected objects
In case objects stick together after thresholding, this tool might help.
It aims to deliver similar results as ImageJ's watershed implementation.

Connected component labeling
Takes a binary image and produces a label image with all separated objects labeled differently. Under the hood, it uses
mahotas' label function.

Seeded watershed
Starting from an image showing high-intensity membranes and a seed-image where objects have been labeled,
objects are labeled that are constrained by the membranes. Hint: you may want to blur the membrane channel a bit in advance.


This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.
Installation
Before installing this napari plugin, please install mahotas, e.g. using conda:
conda config --add channels conda-forge
conda install mahotas
Afterwards, you can install napari-mahotas-image-processing via pip:
pip install napari-mahotas-image-processing

To install latest development version :
pip install git+https://github.com/haesleinhuepf/napari-mahotas-image-processing.git

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-mahotas-image-processing" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-mahotas-image-processing

Image processing based using the Mahotas library in napari

napari-cupy-image-processing





GPU-accelerated image processing using cupy and CUDA


This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.
Installation
Follow the instructions for installing cupy on your computer first.
Afterwards, you can install napari-cupy-image-processing via pip:
pip install napari-cupy-image-processing

A more detailed example for installation (change 10.2 to your CUDA version):
conda create --name cupy_p38 python=3.8
conda activate cupy_p38
conda install -c conda-forge cupy cudatoolkit=10.2
conda install napari
pip install napari-cupy-image-processing
Known issues
When executing the first operation right after CUDA installation, napari may freeze and crash. Wait a minute and try again.
Contributing
Contributions are very welcome. Adding cupy ndimage functions is quite easy as you can see in the 
implementation of the current operations. 
If you need another function in napari, just send a PR.
Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the MIT license,
"napari-cupy-image-processing" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-cupy-image-processing

GPU-accelerated image processing using CUDA

Emma Zhou

napari-dvid





DVID loader for napari, from a url

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

Installation
You can install napari-dvid via pip:
pip install napari-dvid

Examples
Once installed, run napari --with napari-dvid to get the plugin sidebar:

Paste in a URL to a DVID volume and hit "Load" to load the volume! As an example, try:
https://emdata.janelia.org/api/node/ab6e610d4/grayscale/raw/0_1_2/256_256_256/7500_7000_4400
Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the MIT license,
"napari-dvid" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-dvid

napari-properties-plotter





A napari plugin that automatically generates interactive plots based on layer properties.

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

Installation
You can install napari-properties-plotter via pip:
pip install napari-properties-plotter

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-properties-plotter" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-properties-plotter

A napari plugin that automatically generates interactive plots based on layer properties.

napari-roi






Select regions of interest (ROIs) using napari
Installation
You can install napari-roi via pip:
pip install napari-roi

To install latest development version :
pip install git+https://github.com/BodenmillerGroup/napari-roi.git

Authors
Created and maintained by Jonas Windhager jonas.windhager@uzh.ch
Contributing
Contributing
Changelog
Changelog
License
MIT

napari-roi

Select regions of interest (ROIs) using napari

Duway Nicolas Lesmes Leon, Pranjal Dhole

napari-yapic-prediction





A napari widget plugin to perform YAPiC model segmentation prediction in the napari window. 

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

Description
This napari plugin provides a widget to upload a YAPiC trained model and perform segmentation over all the present images in the napari window. The segmentation results are uploaded as napari layers into the viewer automatically with the name structure of imgename_prediction.
Installation


Please install either GPU or CPU version of tensorflow that is compatible with your cuda and cudnn libraries before installing the plugin depending on your system.
One of the plugin dependency is yapic that currently has sensitivity to tensorflow versions.


You can install napari-yapic-prediction via pip:
pip install napari-yapic-prediction


Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the GNU GPL v3.0 license,
"napari-yapic-prediction" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-yapic-prediction

napari widget that performs image segmentation with yapic model in the napari window. Install TENSORFLOW to use this plugin.

Alister Burt

napari-subboxer





A napari plugin for visualising and interacting with electron cryotomograms.
Installation
You can install napari-subboxer via pip:
pip install napari-subboxer

Usage
This plugin provides a user interface for opening electron cryotomograms in 
napari as both volumes and slices through volumes.

The plugin can be opened from the plugins menu in napari, or with 
napari-subboxer at the command line.

```yaml
Usage: napari-subboxer [TOMOGRAM_FILE]
An interactive tool for defining and applying relative transforms
  on sets of particles in napari.
Arguments:
  [TOMOGRAM_FILE]
Options:
  --help                          Show this message and exit.
```
Contributing
Contributions are very welcome. 
License
Distributed under the terms of the BSD-3 license,
"napari-subboxer" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-subboxer

A napari plugin for interacting with electron cryotomograms

napari-autolign





A plugin for registering multimodal image volumes based on common segmented structures of interest with point-clouds.

This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.

Installation
You can install napari-autolign via pip:
pip install napari-autolign

When installing autolign on a Windows machine you may encounter the following error:
error Microsoft Visual C++ 14.0 is required

Ensure that Visual Studios C++ 14.00 is installed
Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the MIT license,
"napari-autolign" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-autolign

PDF reader for napari
Reads PDF files into napari

napari-pdf-reader

napari-mrcfile-reader





Read MRC format image files into napari using the mrcfile package from CCP-EM


This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.
Installation
You can install napari-mrcfile-reader via pip:
pip install napari-mrcfile-reader

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-mrcfile-reader" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-mrcfile-reader

Manan Lalit










Registration of Multi-modal Volumetric Images by Establishing Cell Correspondence
Table of Contents

Introduction
Dependencies
Getting Started
Datasets
Registering your data
Contributing
Issues
Citation

Introduction
This repository hosts the version of the code used for the publication Registration of Multi-modal Volumetric Images by Establishing Cell Correspondence. 
We refer to the techniques elaborated in the publication, here as PlatyMatch. PlatyMatch performs a linear registration of volumetric, microscopy images of embryos by establishing correspondences between cells. 
PlatyMatch first detects nuclei in the two images being considered, next calculates unique shape context features for each nucleus detection which encapsulates the neighborhood as seen by that nucleus, and finally identifies pairs of matching nuclei through maximum bipartite matching applied to the pairwise distance matrix generated from these features. 
Dependencies
You can install PlatyMatch via pip:
conda create -y -n PlatyMatchEnv python==3.8
conda activate PlatyMatchEnv
python3 -m pip install PlatyMatch
Getting Started
Type in the following commands in a new terminal window.
conda activate PlatyMatchEnv
napari
Next, select PlatyMatch from Plugins> Add Dock Widget.
Datasets
Datasets are available in bic_eccv_data.zip as release assets here.
These comprise of images, nuclei detections and keypoint locations for confocal images of 12 individual specimens under the 01-insitus directory and static snapshots of a live embryo imaged through Light Sheet Microscopy under the 02-live directory. 
Folders with the same name in these two directories correspond in their developmental age, for example, 01-insitus/02 corresponds to 02-live/02, 01-insitus/03 corresponds to 02-live/03 and so on.   
Registering your data

Detect Nuclei 
Drag and drop your images in the viewer 
Click on Sync with Viewer button to refresh the drop-down menus 
Select the appropriate image in the drop down menu (for which nuclei detections are desired)
Select Detect Nuclei from the drop-down menu
Specify the anisotropy factor (Anisotropy (Z)) (i.e. the ratio of the size of the z pixel with respect to the x or y pixel. This factor is typically more than 1.0 because the z dimension is often undersampled)
Ideally min scales and max scales should be estimated from your data (min_scale should be set as min_radius/sqrt(3) and max_scale should be set as max_radius/sqrt(3). The default values of min_scale=5 and max_scale=9 generally works well).  
Click Run Scale Space Log button. Please note that this step takes a few minutes.
Wait until a confirmation message suggesting that nuclei detection is over shows up on the terminal
Export the nuclei locations (Export detections to csv) to a csv file
Repeat this step for all images which need to be matched



https://user-images.githubusercontent.com/34229641/120660618-cd5d3980-c487-11eb-8996-326264a4df87.mp4

Estimate Transform
In case, nuclei were exported to a csv in the Detect Nuclei panel, tick csv checkbox
If the nuclei detected were specified in the order id, z, y and x in the csv file, then tick IZYXR checkbox
Additionally if there is a header in the csv file, tick Header checkbox
Load the detections for the Moving Image, which is defined as the image which will be transformed to later match another fixed image
Load the detections for the Fixed Image
Click on Run pushbutton. Once the calculation is complete, a confirmation message shows up in the terminal. Export the transform matrix to a csv (Note that this step can take a few minutes)
It is also possible to estimate the transform in a supervised fashion. For this, upload the locations of a few matching keypoints in both images. These locations serve to provide a good starting point for the transform calculation. Once the keypoint files have been uploaded for both the images, then click Run and then export the transform matrix to a csv file 



https://user-images.githubusercontent.com/34229641/120685628-53857a00-c4a0-11eb-8f92-7ffac730e28a.mp4

Evaluate Metrics
Drag images which need to be transformed, in the viewer
Click on Sync with Viewer button to refresh the drop-down menus
Specify the anisotropy factor (Moving Image Anisotropy (Z) and Fixed Image Anisotropy (Z)) (i.e. the ratio of the size of the z pixel with respect to the x or y pixel. This factor is typically more than 1.0 because the z dimension is often undersampled)
Load the transform which was calculated in the previous steps
If you simply wish to export a transformed version of the moving image, click on Export Transformed Image
Additionally, one could quantify metrics such as average registration error evaluated on a few keypoints. To do so, tick the csv checkbox, if keypoints and detections are available as a csv file. Then load the keypoints for the moving image (Moving Kepoints) and the fixed image (Fixed Keypoints)
Also, upload the detections calculated in the previous steps (Detect Nuclei)  by uploading the Moving Detections and the Fixed Detections
Click on the Run push button
The text fields such as Matching Accuracy(0 to 1, with 1 being the best) and Average Registration Error (the lower the better) should become populated once the results are available



https://user-images.githubusercontent.com/34229641/120685654-5b451e80-c4a0-11eb-8d7d-de58b8b8304d.mp4
Contributing
Contributions are very welcome. Tests can be run with tox.
Issues
If you encounter any problems, please file an issue along with a detailed description.
Citation
If you find our work useful in your research, please consider citing:
bibtex
@InProceedings{10.1007/978-3-030-66415-2_30,
author="Lalit, Manan and Handberg-Thorsager, Mette and Hsieh, Yu-Wen and Jug, Florian and Tomancak, Pavel",
editor="Bartoli, Adrien
and Fusiello, Andrea",
title="Registration of Multi-modal Volumetric Images by Establishing Cell Correspondence",
booktitle="Computer Vision -- ECCV 2020 Workshops",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="458--473",
isbn="978-3-030-66415-2"
}
PlatyMatch plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

PlatyMatch

PlatyMatch allows registration of volumetric images of embryos by establishing correspondences between cells

napari-layer-details-display






A display for layer information and properties


This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.
Installation
You can install napari-layer-details-display via pip:
pip install napari-layer-details-display

To install latest development version :
pip install git+https://github.com/haesleinhuepf/napari-layer-details-display.git

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-layer-details-display" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-layer-details-display

A display for layer information and properties

napari-tomoslice





A napari plugin for visualising and interacting with electron cryotomograms.
Installation
You can install napari-tomoslice via pip:
pip install napari-tomoslice

Usage
This plugin provides a user interface for opening electron cryotomograms in 
napari as both volumes and slices through volumes.

The plugin can be opened from the plugins menu in napari, or with 
napari-tomoslice at the command line.

```yaml
Usage: napari-tomoslice [TOMOGRAM_FILE]
An interactive tomogram slice viewer in napari.
Controls: 
  x/y/z - align normal vector along x/y/z axis 
  click and drag - shift plane along its normal vector
  alt-click - add point on plane (if points layer is active)
  o - align plane normal to view direction
  [] - decrease/increase plane thickness
Arguments:
  [TOMOGRAM_FILE]
Options:
  --help                          Show this message and exit.
```
Contributing
Contributions are very welcome. 
License
Distributed under the terms of the BSD-3 license,
"napari-tomoslice" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-tomoslice

Draga Doncila

napari-compressed-labels-io





Description
This napari plugin provides readers and writers for labels and their corresponding image layers into zarr format for compression and portability. Each reader/writer pair supports a round trip of saving and loading image and labels layers.
Writers
Two writers are provided by this plugin, each with its own reader.
labels_to_zarr
This writer is an alternative to napari's default label writer and will write an entire labels layer, regardless of its dimensions, into a single zarr file. This writer provides the best compression option and its associated reader get_zarr_labels will read the layer back into napari.
This writer will be called when the user tries to save a selected labels layer into a path ending with .zarr
label_image_pairs_to_zarr
This writer will save 3-dimensional labels and image layers from the viewer into individual zarrs for portability and convenience. For example, given one labels and one image layer of the shape (10, 200, 200) saved to my_stacks.zarr, 10 subdirectories will be created, each with two zarrs inside of shape (200, 200) corresponding to the labels and image layer.
This writer allows users to load stacks of associated images, label them, and then quickly save these stacks out into individual slices for easy loading, viewing and interaction. Its associated reader supports the loading into napari of the whole stack, all layers at one slice of the stack, and an individual layer of a given slice of the stack.
The writer currently supports only 3D layers, with the exception of RGB images of the form (z, y, x, 3), which are also supported.
Readers
Two readers are provided by this plugin for loading the formats saved by each writer. These are detailed below.
get_zarr_labels
This reader will open any zarr file with a .zarray at the top level in path as a labels layer. This is to be used in conjunction with labels_to_zarr.
get_label_image_stack
This reader will open any zarr containing a .zmeta file as layers into napari. Depending on what is being opened, the reader will either load a full stack of labels and images, one slice of a stack of images and labels or an individual layer within a slice. This is to be used in conjunction with label_image_pairs_to_zarr.
.zmeta
This metadata file contains information about the layer types in the stack and in each individual slice, as well as the number of image/label slices. This allows the reader plugin to load the correct layer types with appropriate names both at a stack level and at the individual slice level.
An example .zmeta specification
```json
{
    "meta": {
        "stack": 7                               # number of slices in the entire stack (1 for an individual slice, 0 for a layer within a slice)
    },
    "data": {
        "image" : [                              # all image layers must be listed here
            {
                "name": "leaves_example_data",
                "shape": [790, 790, 3],
                "dtype": "uint8",
                "rgb": true                      # where rgb is false the image will be loaded as greyscale (colormap support has not yet been implemented)
            }
        ],
        "labels" : [
            {
                "name": "oak",
                "shape": [790, 790],
                "dtype": "int64"
            },
            {
                "name": "bg",
                "shape": [790, 790],
                "dtype": "int64"
            }
        ]
    }
}
```

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

Installation
You can install napari-compressed-labels-io via pip:
pip install napari-compressed-labels-io

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the MIT license,
"napari-compressed-labels-io" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-compressed-labels-io

Plugin exploring different options for reading and writing compressed and portable labels layers in napari.

Description
This plugin makes the elastix toolbox for rigid and nonrigid registration of images available in napari.
elastix is open source software, based on the well-known Insight Segmentation and Registration Toolkit (ITK). The software consists of a collection of algorithms that are commonly used to solve (medical) image registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application.
Who is This For?
With this plugin both 2D and 3D images in all file formats available in ITK can be registered.
The plugin supports various transformations including rigid, affine and bspline.
Registration within the plugin is done based on user defined parameters, but for novice users
defaults for each transformation model are available.
How to Guide
Load the images you want to register into napari and select them in the fixed (or reference) and moving image dropdowns of plugin interface.
For fast and easy registration only the preferred transformation (rigid, affine or bspline) has to be selected (see Transformations section for explanation).
For more advanced registrations the following adjustments can be made in the plugin:

Masks for both the fixed and the moving images can be selected to let elastix only include certain areas in the registration. These masks have to be loaded into napari and selected in the correct mask dropdown menus, which appear when the masks box is ticked.
Point sets for both the fixed and the moving images can de selected to use certain points to aid registration. These point set files have to be .txt files in the following format:

index/point\
  #points\
  point1 x point1 y [point1 z]\
  point2 x point2 y [point2 z]
The first line indicates whether the points are given as “indices” (of the fixed image), or as “points” (in
  physical coordinates). The second line stores the number of points that will be specified. After that the
  point data is given. For example:
point\
  3\
  2.32 5.34 -4.12\
  -1.56 0.12 9.23\
  1.00 7.34 -0.23


An initial transform file that specifies a transform that is applied before the registration is done, can be uploaded as a .txt file. For the latest file and transform formats that are supported, see the elastix manual


For the most common registration parameters adjustments can be made in the plugin GUI


Other, less common registration parameters can be adjusted by uploading custom transform parameter file(s). (Select 'custom' in the preset dropdown).



Transformations
In the plugin 3 common transformations are available as presets, other transformations can be done with the 'custom' option in the preset dropdown. The plugin then has the ability to upload custom parameter files in which other transformations can be specified.
The three common transformations are:


Rigid Transform:
Also known as a Euclidean transformation, this transform preserves the Euclidean
distance between each pair of points on the image. This includes rotation,
translation and reflection but not scaling or shearing.


Affine Transform:
This transfrom preserves
lines and parallelism, but not necessarily distance and angles. Translation,
scaling, similarity, reflection, rotation and shearing are all valid
affine transformations.


BSpline Transform:
This is a deformable transformation that preserves none of the properties mentioned in the transforms describe above.


Getting Help
If you find a bug in the elastix napari plugin, or would like support with using it, please raise an
issue on the GitHub repository.
For question specifically about the elastix toolbox we have a mailing list.
Contributions
Contributions to the elastix_napari plugin, itkelastix (the python wrapper) or elastix (the C++ core) on which the plugin is build, are welcome.

elastix-napari

A toolbox for rigid and nonrigid registration of images.

Nicholas Sofroniew

napari-svg





A plugin for reading and writing svg files with napari

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

Installation
You can install napari-svg via pip:
pip install napari-svg

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-svg" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-svg

A plugin for reading and writing svg files with napari

napari-time-slicer






A meta plugin for processing timelapse data timepoint by timepoint. It 
enables a list of napari plugins to process 2D+t or 3D+t data step by step when the user goes 
through the timelapse. Currently, these plugins are using napari-time-slicer:
* napari-segment-blobs-and-things-with-membranes
* napari-cupy-image-processing
* napari-pyclesperanto-assistant
* napari-accelerated-pixel-and-object-classification
* napari-mahotas-image-processing
napari-time-slicer enables inter-plugin communication, e.g. allowing to combine the plugins listed above in 
one image processing workflow for segmenting a timelapse dataset:

The workflow can then also be exported as a script. The 'Generate Code' button can be found in the Workflow Viewer
that opens when clicking the menu Tools > Visualization > Workflow Viewer if the napari-script-editor is installed.

If you want to convert a 3D dataset into as 2D + time dataset, use the 
menu Tools > Utilities > Convert 3D stack to 2D timelapse (time-slicer). It will turn the 3D dataset to a 4D datset
where the Z-dimension (index 1) has only 1 element, which will in napari be displayed with a time-slider. Note: It is 
recommended to remove the original 3D dataset after this conversion.
Usage for plugin developers
Plugins which implement the napari_experimental_provide_function hook can make use of the @time_slicer. At the moment,
only functions which take napari.types.ImageData, napari.types.LabelsData and basic python types such as int 
and float are supported. If you annotate such a function with @time_slicer it will internally convert any 4D dataset
to a 3D dataset according to the timepoint currently selected in napari. Furthermore, when the napari user changes the
current timepoint or the input data of the function changes, a re-computation is invoked. Thus, it is recommended to 
only use the time_slicer for functions which can provide [almost] real-time performance. Another constraint is that 
these annotated functions have to have a viewer parameter. This is necessary to read the current timepoint from the 
viewer when invoking the re-computions.
Example
```python
import napari
from napari_time_slicer import time_slicer
@time_slicer
def threshold_otsu(image:napari.types.ImageData, viewer: napari.Viewer = None) -> napari.types.LabelsData:
    # ...
```
You can see a full implementations of this concept in the napari plugins listed above.

This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.
Installation
You can install napari-time-slicer via pip:
pip install napari-time-slicer

To install latest development version :
pip install git+https://github.com/haesleinhuepf/napari-time-slicer.git

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-time-slicer" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-time-slicer

A meta plugin for processing timelapse data timepoint by timepoint

napari-console (WIP, under active development)





A plugin that adds a console to napari

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

Installation
You can install napari-console via pip:
pip install napari-console

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-console" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-console

napari-plugin-search





Find napari plugins

Usage
Enter the name of the plugin you are searching for and use the up and down arrow keys to navigate between them. 
Hit Enter to start a plugin.

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.
Installation
You can install napari-plugin-search via pip:
pip install napari-plugin-search

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-plugin-search" is free and open source software
Issues
If you encounter any problems, please create a thread on image.sc along with a detailed description and tag @haesleinhuepf.

napari-plugin-search

napari-bioformats






Bioformats plugin for napari using
pims-bioformats

Use this plugin as a fallback!
Anyone coming to napari from the Fiji/ImageJ world will likely be aware of the
incredible Bio-Formats
library.  A heroic effort, built over years, to read
more than a 100 file formats.  Naturally, we want some of that goodness for napari ... hence this plugin.
However: it's important to note that this plugin still
requires having a java runtime engine installed.  This is easy enough to do
(the plugin will ask to install it for you if you're in a conda environment), but
it definitely makes for a more complicated environment setup, it's not very
"pythonic", and the performance will likely not feel as snappy as a native "pure"
python module.
So, before you reflexively install this plugin to fill that bio-formats
sized hole in your python heart, consider trying some of the other pure-python
plugins designed to read your format of interest:

Zeiss (.czi): napari-aicsimageio, napari-czifile2
Nikon (.nd2): napari-nikon-nd2, nd2-dask
Leica (.lif): napari-aicsimageio
Olympus (.oif): no plugin?  (but see oiffile )
DeltaVision (.dv, .mrc): napari-dv


if you have a pure-python reader for a bio-formats-supported file format that
you'd like to see added to this list, please open an issue

Installation
The easiest way to install napari-bioformats is via conda, from the
conda-forge channel:
conda install -c conda-forge napari-bioformats

It is also possible to install via pip, but you will need to have a working
JVM installed, and may need to set the JAVA_HOME environment variable
pip install napari-bioformats

First Usage
The first time you attempt to open a file with napari-bioformats, you will
likely notice a long delay as pims downloads the loci_tools.jar (speed will
depend on your internet connection). Subsequent files should open more quickly.
License
Distributed under the terms of the GPLv3 license,
"napari-bioformats" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.
This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

napari-bioformats

Johannes Elferich

napari-cryofibsem-monitor

A plugin to monitor the creation of lamella using AutoTEM on a TFS Acquilos instrument

Rocco D'Antuono

Giuseppina Pisignano

napari-zelda





ZELDA: a 3D Image Segmentation and Parent-Child relation plugin for microscopy image analysis in napari
Authors: Rocco D'Antuono, Giuseppina Pisignano

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

What you can do with ZELDA plugin for napari


Segment objects such as cells and organelles in 2D/3D.


Segment two populations in 2D/3D (e.g. cells and organelles, nuclei and nuclear spots, tissue structures and cells) establishing the "Parent-Child" relation: count how many mitochondria are contained in each cell, how many spots localize in every nucleus, how many cells are within a tissue compartment.


Example: cell cytoplasms (parent objects) and mitochondria (child objects)
    Actin |   Mitochondria|   Merge
  ------ | ------| -----
    Parent cell cytoplasms |   Children mitochondria|   Children labelled by Parents


Plot results within napari interface.
  Histogram |   Scatterplot|
------ | ------|


Customize an image analysis workflow in graphical mode (no scripting knowledge required).
|   Custom image analysis workflow |
------ |


Import and Export Protocols (image analysis workflows) in graphical mode (share with the community!).
|   Import and Export of ZELDA Protocols |
------ |


Installation
Option A. You can install napari-zelda via pip. For the best experience, create a conda environment and use napari!=0.4.11, using the following instructions:
conda create -y -n napari-env python==3.8  
conda activate napari-env  
pip install "napari[all]"  
pip install napari-zelda

Option B. Alternatively, clone the repository and install locally via pip:
pip install -e .

Option C. Another option is to use the napari interface to install it (make sure napari!=0.4.11):
1. Plugins / Install/Uninstall Package(s)



Choose ZELDA



ZELDA is installed



Launch ZELDA



Contributing
Contributions are welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-zelda" is free and open source software
Issues
If you encounter any problems, please [file an issue] along with a detailed description.

napari-zelda

ZELDA: a 3D Image Segmentation and Parent-Child relation plugin for microscopy image analysis in napari

Mauro Silberberg

text/markdown; charset=UTF-8; variant=GFM

SMO
SMO is a Python package that implements the Silver Mountain Operator (SMO), which allows to recover an unbiased estimation of the background intensity distribution in a robust way.
We provide an easy to use Python package and plugins for some of the major image processing softwares: napari, CellProfiler, and ImageJ / FIJI. See Plugins section below.
Usage
To obtain a background-corrected image, it is as straightforward as:
```python
import skimage.data
from smo import SMO
image = skimage.data.human_mitosis()
smo = SMO(sigma=0, size=7, shape=(1024, 1024))
background_corrected_image = smo.bg_corrected(image)
```
where we used a sample image from scikit-image.
A notebook explaining in more detail the meaning of the parameters and other possible uses for SMO is available here: smo/examples/usage.ipynb .
Installation
It can be installed with pip from PyPI:
pip install smo
Plugins
Napari
A napari plugin is available.
To install:


Option 1: in napari, go to Plugins > Install/Uninstall Plugins... in the top menu, search for smo and click on the install button.


Option 2: just pip install this package in the napari environment.


It will appear in the Plugins menu.
CellProfiler
A CellProfiler plugin in available in the smo/plugins/cellprofiler folder.

To install, save this file into your CellProfiler plugins folder. You can find (or change) the location of your plugins directory in File > Preferences > CellProfiler plugins directory.
ImageJ / FIJI
An ImageJ / FIJI plugin is available in the smo/plugins/imagej folder.

To install, download this file and:


Option 1: in the ImageJ main window, click on Plugins > Install... (Ctrl+Shift+M), which opens a file chooser dialog. Browse and select the downloaded file. It will prompt to restart ImageJ for changes to take effect.


Option 2: copy into your ImageJ plugins folder (File > Show Folder > Plugins).


To use the plugin, type smo on the bottom right search box:

select smo in the Quick Search window and click on the Run button.

Note: the ImageJ plugin does not check that saturated pixels are properly excluded.
Development
Code style is enforced via pre-commit hooks. To set up a development environment, clone the repository, optionally create a virtual environment, install the [dev] extras and the pre-commit hooks:
git clone https://github.com/maurosilber/SMO
cd SMO
conda create -n smo python pip numpy scipy
pip install -e .[dev]
pre-commit install

Implementation of the Silver Mountain Operator (SMO) for the estimation of background distributions.

napari-lfdfiles

A napari plugin for reading files via lfdfiles

napari-folder-browser





Browse folders of images and open them using double-click or . You can also navigate through the list using arrow up/down keys.


This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.
Installation
You can install napari-folder-browser from within napari by clicking menu Plugins > Install/uninstall Plugins... and entering here:

You can install napari-folder-browser via pip:
pip install napari-folder-browser

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-folder-browser" is free and open source software
Issues
If you encounter any problems, please create a thread on image.sc along with a detailed description and tag @haesleinhuepf.

napari-folder-browser

Browse folders of images and open them using double-click

Nicolas Pielawski

napari-tissuumaps

A plugin to add compatibility with Tissuumaps

napari-czifile2















Carl Zeiss Image (.czi) file type support for napari
Open .czi files and interactively view scenes co-registered in the machine's coordinate system using napari
Installation
You can install napari-czifile2 via pip:
pip install napari-czifile2

Authors
Created and maintained by Jonas Windhager jonas.windhager@uzh.ch
Contributing
Contributing
Changelog
Changelog
License
MIT

napari-czifile2

Carl Zeiss Image (.czi) file support for napari

Robert Haase, Talley Lambert

napari-pyclesperanto-assistant







The py-clEsperanto-assistant is a yet experimental napari plugin for building GPU-accelerated image processing workflows. 
It is part of the clEsperanto project and thus, aims at removing programming language related barriers between image processing ecosystems in the life sciences. 
It uses pyclesperanto and with that pyopencl as backend for processing images.
This plugin was generated with Cookiecutter using with napari's cookiecutter-napari-plugin template.

Installation
It is recommended to install the assistant via conda:
shell
conda create --name bio11 python==3.8.5 
conda activate bio11 
conda install -c conda-forge pyopencl==2021.2.1
pip install napari-pyclesperanto-assistant
pip install "napari[all]"
Alternatively, you can install the assistant using napari's plugin installer in the menu Plugins > Install/uninstall Packages.
Windows users should paste this URL
https://github.com/clEsperanto/napari_pyclesperanto_assistant/blob/master/installation_help/pyopencl-2020.3.1+cl12-cp38-cp38-win_amd64.whl?raw=true
in this field and click on Install before proceeding:

Afterwards, click install clEsperanto like by clicking on Install here:

You can then start napari, e.g. from command line, and find the assistant in the Plugins menu.
shell
napari

Features
pyclesperanto offers various possibilities for processing images. It comes from developers who work in life sciences and thus, it may be focused towards processing two- and three-dimensional microscopy image data showing cells and tissues. A selection of pyclesperanto's functionality is available via the assistant user interface. Typical workflows which can be built with this assistant include
* image filtering
  * denoising / noise reduction (mean, median, Gaussian blur)
  * background subtraction for uneven illumination or out-of-focus light (bottom-hat, top-hat, subtract Gaussian background)
  * grey value morphology (local minimum, maximum. variance)
  * gamma correction
  * Laplace operator
  * Sobel operator
* combining images
  * masking
  * image math (adding, subtracting, multiplying, dividing images) 
  * absolute / squared difference
* image transformations
  * translation
  * rotation
  * scale
  * reduce stack
  * sub-stacks
* image projections
  * minimum / mean / maximum / sum / standard deviation projections
* image segmentation
  * binarization (thresholding, local maxima detection)
  * labeling
  * regionalization
  * instance segmentation
  * semantic segmentation
  * detect label edges
  * label spots
  * connected component labeling
  * Voronoi-Otsu-labeling
* post-processing of binary images
  * dilation
  * erosion
  * binary opening
  * binary closing 
  * binary and / or / xor
* post-processing of label images
  * dilation (expansion) of labels
  * extend labels via Voronoi
  * exclude labels on edges
  * exclude labels within / out of size / value range
  * merge touching labels
* parametric maps
  * proximal / touching neighbor count
  * distance measurements to touching / proximal / n-nearest neighbors
  * pixel count map
  * mean / maximum / extension ratio map
* label measurements / post processing of parametric maps
  * minimum / mean / maximum / standard deviation intensity maps
  * minimum / mean / maximum / standard deviation of touching / n-nearest / neighbors
* neighbor meshes
  * touching neighbors
  * n-nearest neighbors
  * proximal neighbors
  * distance meshes
* measurements based on label images
  * bounding box 2D / 3D
  * minimum / mean / maximum / sum / standard deviation intensity
  * center of mass
  * centroid
  * mean / maximum distance to centroid (and extension ratio shape descriptor)
  * mean / maximum distance to center of mass (and extension ratio shape descriptor)
* code export
  * python / Fiji-compatible jython
  * python jupyter notebooks
* pyclesperanto scripting
  * cell segmentation
  * cell counting
  * cell differentiation
  * tissue classification
Usage
Start up the assistant
Start up napari, e.g. from the command line:
napari
Load example data, e.g. from the menu File > Open Samples > clEsperanto > CalibZAPWfixed and 
start the assistant from the menu Plugins > clEsperanto > Assistant. Select a GPU in case you are asked to.

In case of two dimensional timelapse data, an initial conversion step might be necessary depending on your data source. 
Click the menu Plugins > clEsperanto > Convert to 2d timelapse. In the dialog, select the dataset and click ok. 
You can delete the original dataset afterwards:

Set up a workflow
Choose categories of operations in the top right panel, for example start with denoising using a Gaussian Blur with sigma 1 in x and y.

Continue with background removal using the top-hat filter with radius 5 in x and y.

For labeling the objects, use Voronoi-Otsu-Labeling with both sigma parameters set to 2.

The labeled objects can be extended using a Voronoi diagram to derive a estimations of cell boundaries.

You can then configure napari to show the label boundaries on top of the original image:

When your workflow is set up, click the play button below your dataset:

Code generation
You can also export your workflow as Python/Jython code or as notebook.

After exporting your workflow as Jupyter notebook, you can start the notebook from the command line using
jupyter notebook my_notebook.ipynb
In some cases you need to replace the command cle.imread('None)` with a command loading your image data. 
After that, you can execute the notebook.

You can also export code to the clipboard or as python code to disc. 
This python code can also be executed in Fiji`s Jython, in case the CLIJx-assistant is installed.

Alternatively, you can also generate code and edit it directly in the Script Editor. 
Therefore, the napari-script-editor must be installed.

Also note: The generated python/jython code is not capable of processing timelapse data,
you need to program a for-loop processing timepoints individually yourself. 
See also this notebook for how to do this.
Work in progress, contributions welcome.
For developers
Getting the recent code from github and locally installing it
```
git clone https://github.com/clesperanto/napari_pyclesperanto_assistant.git
pip install -e ./napari_pyclesperanto_assistant
```
Optional: Also install pyclesperantos recent source code from github:
```
git clone https://github.com/clEsperanto/pyclesperanto_prototype.git
pip install -e ./pyclesperanto_prototype
```
Feedback welcome!
clEsperanto is developed in the open because we believe in the open source community. See our community guidelines. Feel free to drop feedback as github issue or via image.sc
Imprint

napari-pyclesperanto-assistant

GPU-accelerated image processing in napari using OpenCL

Grzegorz Bokota

PartSeg












PartSeg is a GUI and a library for segmentation algorithms. PartSeg also provide napari plugins for IO and labels measurement.
This application is designed to help biologist with segmentation based on threshold and connected components.

Tutorials

Tutorial: Chromosome 1 (as gui) link
Data for chromosome 1 tutorial link
Tutorial: Different neuron types (as library) link

Installation


From binaries:

Windows (build on Windows 10)
Linux (build on Ubuntu 18.04)
MacOS (build on MacOS Mojave)



With pip:

From pypi: pip install PartSeg[pyqt]
From repository: pip install git+https://github.com/4DNucleome/PartSeg.git



Installation troubleshooting information could be found in wiki: wiki.
If this information does not solve problem you can open issue.
Running
If you downloaded binaries, run the PartSeg (or PartSeg.exe for Windows) file inside the PartSeg folder
If you installed from repository or from pip, you can run it with PartSeg command or python -m PartSeg.
First option does not work on Windows.
PartSeg export few commandline options:

--no_report - disable error reporting
--no_dialog - disable error reporting and error dialog. Use only when running from terminal.
segmentation_analysis - skip launcher and start analysis gui
segmentation - skip launcher and start segmentation gui

napari plugin
PartSeg provides napari plugins for io to allow reading projects format in napari viewer.
Save Format
Saved projects are tar files compressed with gzip or bz2.
Metadata is saved in data.json file (in json format).
Images/masks are saved as *.npy (numpy array format).
Interface
Launcher. Choose the program that you will launch:

Main window of Segmentation Analysis:

Main window of Segmentation Analysis with view on measurement result:

Window for creating a set of measurements:

Main window of Mask Segmentation:

Laboratory
Laboratory of Functional and Structural Genomics
http://4dnucleome.cent.uw.edu.pl/
Cite as
Bokota, G., Sroka, J., Basu, S. et al. PartSeg: a tool for quantitative feature extraction
from 3D microscopy images for dummies. BMC Bioinformatics 22, 72 (2021).
https://doi.org/10.1186/s12859-021-03984-1
Changelog
0.13.12
Bug Fixes

Importing previous version of settings (#406)
Cutting without masking data (#407)
Save in subdirecory in batch plan (#414)
Loading plugins for batch processing (#423)

Features

Add randomization option for correlation calculation (#421)
Add Imagej TIFF writter for image. (#405)
Mask create widget for napari (#395)
In napari roi extraction method show information from roi extraction method (#408)
Add *[0-9].tif button in batch processing window (#412)
Better lebel representation in 3d view (#418)

Refactor

Use Font Awesome instead of custom symbols (#424)

0.13.11
Bug Fixes

Adding mask in Prepare Plan for batch (#383)
Set proper completion mode in SearchComboBox (#384)
Showing warnings on error with ROI load (#385)

Features

Add CellFromNucleusFlow "Cell from nucleus flow" cell segmentation method  (#367)
When cutting components in PartSeg roi mask allow not masking outer data (#379)
Theme selection in GUI (#381)
Allow return points from ROI extraction algorithm (#382)
Add measurement to get ROI annotation by name. (#386)
PartSeg ROI extraction algoritms as napari plugins (#387)
Add  Pearson, Mander's, Intensity, Spearman colocalizatiom measurements (#392)
Separate standalone napari settings from PartSeg embeded napari settings (#397)

Performance

Use faster calc bound function (#375)

Refactor

Remove CustomApplication (#389)

0.13.10

change tiff save backend to ome-tiff
add DistanceROIROI and ROINeighbourhoodROI measurements

0.13.9

annotation show bugfix

0.13.8

napari deprecation fixes
speedup simple measurement
bundle plugins initial support

0.13.7

add measurements widget for napari
fix bug in pipeline usage

0.13.6

Hotfix release
Prepare for a new napari version

0.13.5

Small fixes for error reporting
Fix mask segmentation

0.13.4

Bugfix for outdated profile/pipeline preview

0.13.3

Fix saving roi_info in multiple files and history

0.13.2

Fix showing label in select label tab

0.13.1

Add Haralick measurements
Add obsep file support

0.13.0

Add possibility of custom input widgets for algorithms
Switch to napari Colormaps instead of custom one
Add points visualization
Synchronization widget for builtin (View menu) napari viewer
Drop Python 3.6

0.12.7

Fixes for napari 0.4.6

0.12.6

Fix prev_mask_get
Fix cache mechanism on mask change
Update PyInstaller build

0.12.5

Fix bug in pipeline execute

0.12.4

Fix ROI Mask windows related build (signal not properly connected)

0.12.3

Fix ROI Mask

0.12.2

Fix windows bundle

0.12.1

History of last opened files
Add ROI annotation and ROI alternatives
Minor bugfix

0.12.0

Toggle multiple files widget in View menu
Toggle Left panel in ROI Analysis in View Menu
Rename Mask Segmentation to ROI Mask
Add documentation for interface
Add Batch processing tutorial
Add information about errors to batch processing output file
Load image from the batch prepare window
Add search option in part of list and combo boxes
Add drag and drop mechanism to load list of files to batch window.

0.11.5

add side view to viewer
fix horizontal view for Measurements result table

0.11.4

bump to napari 0.3.8 in bundle
fix bug with not presented segmentation loaded from project
add frame (1 pix) to image cat from base one based on segmentation
pin to Qt version to 5.14

0.11.3

prepare for napari 0.3.7
split napari io plugin on multiple part
better reporting for numpy array via sentry
fix setting color for mask marking

0.11.2

Speedup image set in viewer using async calls
Fix bug in long name of sheet with parameters

0.11.1

Add screenshot option in View menu
Add Voxels measurements

0.11.0

Make sprawl algorithm name shorter
Unify capitalisation of measurement names
Add simple measurements to mask segmentation
Use napari as viewer
Add possibility to preview additional output of algorithms (In View menu)
Update names of available Algorithm and Measurement to be more descriptive.

0.10.8

fix synchronisation between viewers in Segmentation Analysis
fix batch crash on error during batch run, add information about file on which calculation fails
add changelog preview in Help > About

0.10.7

in measurements, on empty list of components mean will return 0

0.10.6

fix border rim preview
fix problem with size of image preview
zoom with scroll and moving if rectangle zoom is not marked

0.10.5

make PartSeg PEP517 compatible.
fix multiple files widget on Windows (path normalisation)

0.10.4

fix slow zoom

0.10.3

deterministic order of elements in batch processing.

0.10.2

bugfixes

0.10.1

bugfixes

0.10.0

Add creating custom label coloring.
Change execs interpreter to python 3.7.
Add masking operation in Segmentation Mask.
Change license to BSD.
Allow select root type in batch processing.
Add median filter in preview.

0.9.7

fix bug in compare mask

0.9.6

fix bug in loading project with mask
upgrade PyInstaller version (bug  GHSA-7fcj-pq9j-wh2r)

0.9.5

fix bug in loading project in "Segmentation analysis"

0.9.4

read mask segmentation projects
choose source type in batch
add initial support to OIF and CZI file format
extract utils to PartSegCore module
add automated tests of example notebook
reversed mask
load segmentation parameters in mask segmentation
allow use sprawl in segmentation tool
add radial split of mask for measurement
add all measurement results in batch, per component sheet

0.9.3

start automated build documentation
change color map backend and allow for user to create custom color map.
segmentation compare
update test engines
support of PySide2

0.9.2.3

refactor code to make easier create plugin for mask segmentation
create class base updater for update outdated algorithm description
fix save functions
fix different bugs

0.9.2.2

extract static data to separated package
update marker of fix range and add mark of gauss in channel control

0.9.2.1

add VoteSmooth and add choosing of smooth algorithm

0.9.2


add pypi base check for update


remove resetting image state when change state in same image


in stack segmentation add options to picking components from segmentation's


in mask segmentation add:

preview of segmentation parameters per component,
save segmentation parameters in save file
new implementation of batch mode.



0.9.1


Add multiple files widget


Add Calculating distances between segmented object and mask


Batch processing plan fixes:

Fix adding pipelines to plan
Redesign mask widget



modify measurement backend to allow calculate multi channel measurements.


0.9
Begin of changelog

PartSeg

PartSeg is python GUI and set of napari plugins for bio imaging analysis especially nucleus analysis,

napari-segment-blobs-and-things-with-membranes (nsbatwm)





A plugin based on scikit-image for segmenting nuclei and cells based on fluorescent microscopy images with high intensity in nuclei and/or membranes. 
The available functions and their user interface based on magicgui are shown below. You can also call these functions
as shown in the demo notebook.
Voronoi-Otsu-Labeling
This algorithm uses Otsu's thresholding method in combination with 
Gaussian blur and a 
Voronoi-Tesselation 
approach to label bright objects such as nuclei in an intensity image. The alogrithm has two sigma parameters which allow
you to fine-tune where objects should be cut (spot_sigma) and how smooth outlines should be (outline_sigma).
This implementation aims to be similar to Voronoi-Otsu-Labeling in clesperanto.

Seeded Watershed
Starting from an image showing high-intensity membranes and a seed-image where objects have been labeled (e.g. using Voronoi-Otsu-Labeling),
objects are labeled that are constrained by the membranes.

Seeded Watershed using local minima as starting points
Similar to the Seeded Watershed and Voronoi-Otsu-Labeling explained above, you can use this tool to segment an image
showing membranes without an additional image showing nuclei. The two sigma parameters allow to fine tune how close 
objects can be and how precise their boundaries are detected.

Gaussian blur
Applies a Gaussian blur to an
image. This might be useful for denoising, e.g. before applying the Threshold-Otsu method.

Subtract background
Subtracts background using scikit-image's rolling-ball algorithm. 
This might be useful, for example to make intensity of membranes more similar in different regions of an image.

Threshold Otsu
Binarizes an image using scikit-image's threshold Otsu algorithm, also known as 
Otsu's method.

Split touching objects (formerly known as binary watershed).
In case objects stick together after thresholding, this tool might help.
It aims to deliver similar results as ImageJ's watershed implementation.

Connected component labeling
Takes a binary image and produces a label image with all separated objects labeled differently. Under the hood, it uses
scikit-image's label function.


This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.
Installation
Download, unzip and install napari from its github releases page:

Afterwards, go to the menu Plugins > Install/uninstall plugins... and click on the install button next to napari-segment-blobs-and-things-with-membranes:

You can also install napari-segment-blobs-and-things-with-membranes via pip:
pip install napari-segment-blobs-and-things-with-membranes

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-segment-blobs-and-things-with-membranes" is free and open source software
Issues
If you encounter any problems, please create a thread on image.sc along with a detailed description and tag @haesleinhuepf.

napari-segment-blobs-and-things-with-membranes

A plugin based on scikit-image for segmenting nuclei and cells based on fluorescent microscopy images with high intensity in nuclei and/or membranes

Laura Zigutyte, Ryan Savill, Marcelo Zoccoler, Robert Haase

napari-clusters-plotter





A plugin to use with napari for clustering objects according to their properties.

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

Usage
Starting point
For clustering objects according to their properties, the starting point is a grey-value image and a label image
representing a segmentation of objects. For segmenting objects, you can for example use the 
Voronoi-Otsu-labeling approach
in the napari plugin napari-segment-blobs-and-things-with-membranes.

Measurements
The first step is deriving measurements from the labeled image and the corresponding pixels in the grey-value image.
You can use the menu Tools > Measurement > Measure intensity, shape and neighbor counts (ncp) for that. 
Just select the image, the corresponding label image and the measurements to analyse and click on Run.
A table with the measurements will open:

Afterwards, you can save and/or close the measurement table. Also, close the Measure widget. Or if you want you can
interact with labels and see which row of the table corresponds to which labelled object. For this, use the Pick mode
in napari and activate the show selected checkbox. Alternatively, you can also select a specific row of the table and
appropriate label is displayed (make sure that show selected checkbox is selected).
Plotting
Once measurements were made, these measurements were saved in the properties of the labels layer which was analysed.
You can then plot these measurements using the menu Tools > Measurement > Plot measurement (ncp).
In this widget, you can select the labels layer which was analysed and the measurements which should be plotted
on the X- and Y-axis. Click on Run to draw the data points in the plot area.

You can also manually select a region in the plot. To use lasso (freehand) tool use left mouse click, and to use a
rectangle - right click. The resulting manual clustering will also be visualized in the original image. To optimize
visualization in the image, turn off the visibility of the analysed labels layer.

You can also select a labeled object using the Pick mode in napari and see which data point in the plot it 
corresponds to.

Dimensionality reduction: UMAP
For getting more insights into your data, you can reduce the dimensionality of the measurements, e.g.
using the UMAP algorithm.
To apply it to your data use the menu Tools > Measurement > UMAP dimensionality reduction (ncp).
Select the label image that was analyzed and in the list below, select all measurements that should be
dimensionality reduced. Make sure to unselect "label" and other "clustering" results, but
only quantitative measurements such as intensity, size and shape. Click on "Run" and after a moment,
the table of measurements will re-appear with two additional columns representing the reduced dimensions of the
dataset.

Afterwards, you can again save and/or close the table. Also, close the UMAP widget.
k-means clustering
If manual clustering, as shown above, is not an option, you can automatically cluster your data, e.g. using the
k-means clustering algorithm. 
Therefore, click the menu Tools > Measurement > Clustering (ncp),
again, select the analyzed labels layer.
This time select the measurements for clustering, e.g. select only the UMAP measurements.
Select the clustering method KMeans and click on Run. 
The table of measurements will reappear with an additional column KMEANS_CLUSTERING_ID containing the cluster ID of 
each datapoint.

Afterwards, you can again save and/or close the table. Also, close the clustering widget.
Plotting k-means clustering results.
Return to the Plotter widget using the menu Tools > Measurement > Plot measurement (ncp).
Select UMAP_0 and UMAP_1 as X- and Y-axis and the KMEANS_CLUSTERING_ID as Cluster

Installation

Get a python environment, e.g. via mini-conda. 
  If you never used python/conda environments before, please follow the instructions 
  here first.
Install pyopencl, e.g. via conda:

conda install -c conda-forge pyopencl
Afterwards, you can install napari-clusters-plotter via pip:
pip install napari-clusters-plotter

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-clusters-plotter" is free and open source software
Issues
If you encounter any problems, please [file an issue] along with a detailed description.

napari-clusters-plotter

A plugin to use with napari for clustering objects according to their properties

Alan R. Lowe







Arboretum
Overview
A dockable widget for Napari for visualizing cell lineage trees.
Features:
+ Lineage tree plot widget
+ Integration with btrack
This project has changed considerably. The Tracks layer, originally developed for this plugin, is now an official layer type in napari. Read the napari documentation here:
 https://napari.org/api/stable/napari.layers.Tracks.html#napari.layers.Tracks
To view the legacy version of this plugin, visit the legacy branch:
https://github.com/quantumjot/arboretum/tree/v1-legacy

Automated cell tracking and lineage tree reconstruction.

Installation
We recommend that you first install napari. Detailed instructions are here: https://github.com/napari/napari.
sh
pip install 'napari[all]'
You can install arboretum directly from napari (using the Plugins > Install/Uninstall Packages(s)... menu) and searching for napari-arboretum.
Alternatively, you can install directly from source:
sh
git clone https://github.com/quantumjot/arboretum.git
cd arboretum
pip install -e .
Usage
Once installed, Arboretum will be visible in the Plugins > Add Dock Widget > napari-arboretum menu in napari.  To visualize a lineage tree, click on one of the tracks in a napari Tracks layer.
You can use btrack to generate tracks from your image data. See the example notebook here:
https://github.com/quantumjot/BayesianTracker/blob/master/examples/segmentation_to_btrack_to_napari.ipynb

TODO:

[ ] Highlight cells in the viewer from the lineage tree view
[ ] Visualize merges
[ ] Color trees by properties



napari-arboretum

Track graph and lineage tree visualization with napari

Edgar Lefevre

napari-deepmeta





Segment mouse lungs and metastasis on MRI images.
This plugin is a demo for the Deepmeta project.


This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

Installation
You can install napari-deepmeta via pip:
pip install napari-deepmeta


We advise you to create a specific python virtual environment in order to have a clean installation.

Usage
In a terminal, just type napari to open napari.
Open a (x, 128, 128) image, go in the plugin menu, add deepmeta plugin to the dock viewer and click on the button.

By adding the dock widget, a menu will be created on the left of your Napari instance.

In this panel you will find two buttons and one checkbox:

The first button Run lung seg process segmentation and show the result (With the widget segment metas, the button is called Run meta seg).
The second, Reprocess Volume, is useful when you modify contours. It will reprocess all slices to give you a new volume.
The checkbox is here to enhance contrast if your image is dark.

Demo
If you just want to see what we've done, you can try the plugin with the Demo button, this button will load an image and process it
as if you use the plugin in a classic way.
Conf file and custom models
The first time you run the plugin a config file will be created at ~/.config/deepmeta/config.ini.

In this file you can find parameters for postprocessing loop and the path for the models.
Feel free to change values and colors to fit to your needs.

If you want to try another model, you can change the path. Be careful to not having custom objects in you model, otherwise, you'll have to modify the code.

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the MIT license,
"napari-deepmeta" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-deepmeta

Segment mouse lungs and metastasis on MRI images.

Johannes Müller

napari-stl-exporter





This plugin allows to convert 3D label images to 3D-printable .stl files using the marching cubes algorithm implemented in scikit-image. The generated stl-files can then be read by common 3D-printing slicer programs (see below).

Usage
The napari-stl-exporter requires labeled, 3D input data. To segment your 3D image and create 3D label images out of it, see this list of napari's image segmentation plugins.
The 3D label image can then be converted to a 3D-printable .stl file by specifying the ".stl"-file extension uppon image export in napari using the menu File > Save selected layer(s).... 
Preparing label data

Interactively: After loading a binary image (example data), e.g. by drag and drop the file onto the napari viewer, it might be neccessary to convert it to a labels layer using the right-click menu on the layer in the layer list and selecting Convert to Labels: 



Programmatically: A Napari Label layer can be added to the viewer as described in the napari reference with this code snippet:
```python
import napari
from skimage import io

Load and binarize image
data = io.imread('/Path/to/input/data')
data[data != 0] = 1
Add data to viewer
viewer = napari.Viewer()
label_layer = viewer.add_labels(data, name='3D object')
save the layer as 3D printable file to disc
napari.save_layers(r'/some/path/test.stl', [label_layer])
```
3D-printing
To actually send your object to a 3D-printer, it has to be further converted to the .gcode format with a Slicer program. The latter convert the 3D object to machine-relevant parameters (printing detail, motor trajectories, etc). Popular slicers are:

Slic3r: Documentation here
Prusa Slicer: Tutorial here

You can also upload the STL file to github.com and interact with it in the browser:


This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

Installation
You can install napari-stl-exporter via pip:
pip install napari-stl-exporter

Contributing
Contributions are very welcome. Tests can be run with tox, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-stl-exporter" is free and open source software
Issues
If you encounter any problems, please [file an issue] along with a detailed description or post to image.sc and tag El_Pollo_Diablo

napari-stl-exporter

Exports label images to 3D-printable stl files.

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