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

Andy Sweet, Chi-Li Chiu

napari-blob-detection






Detects blobs in images

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

This plugin consists of two widgets:

Detects blobs on images
Convert points layer to labels layer


Detects blobs on images
This widget uses scikit-image's blob detection algorithms to detect bright blobs on dark backgrounds.
Parameters

method: Laplacian of Gaussian (most accurate) or Difference of Gaussian (faster approximation) 
image: Image layer for blob detection. Can be a 2D, 3D, or higher dimensionality image.
dimensionality: users can specify if the image is 2D(+t) or 3D(+t).
min sigma: the smallest blob size to detect
max sigma: the largest blob size to detect
threshold: the lower the threshold, the more low intensity blobs are detected. 

Output
Blobs are represented by the Points layer.
The size of each blob is proportional to Points.feature['sigma'],
which signifies the scale at which the feature point was found.
Convert points layer to labels layer
This widget takes a points layer and converts it into a labels layer, with the image dimension matching the selected image layer.
By converting points to labels, users can leverage feature extraction functions that are available to labels to the detected points.

Installation
You can install napari-blob-detection via pip:
pip install napari-blob-detection

To install latest development version :
pip install git+https://github.com/andy-sweet/napari-blob-detection.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-blob-detection" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-blob-detection

Jonas Windhager

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

Alternatively, you can install napari-czifile2 via conda:
conda install -c conda-forge 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

David Stansby

napari-matplotlib







A plugin to create Matplotlib plots from napari layers

Introduction
napari-matplotlib is a bridge between napari and matplotlib, making it easy to create publication quality Matplotlib plots based on the data loaded in napari layers.
Available widgets
Slice
Plots 1D slices of data along a specified axis.

Histogram
Plots histograms of individual image layers, or RGB histograms of an RGB image

Scatter
Scatters the values of two similarly sized images layers against each other.

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

To install latest development version :
pip install git+https://github.com/dstansby/napari-matplotlib.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-matplotlib is free and open source software.
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-matplotlib

Tim Morello

napari-geojson






Read and write geojson files in napari.


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

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

To install latest development version :
pip install git+https://github.com/tdmorello/napari-geojson.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-geojson" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-geojson

Jordao Bragantini

napari-DEXP





A plugin to interface DEXP with napari.

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

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

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-dexp" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-dexp

Gursharan Ahir

napari-mm3






A plugin for Mother Machine Image Analysis by Jun Lab.

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

Installation
Ensure python and napari are installed in your system. To install the plugin, use:
pip install napari-mm3
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-mm3" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-mm3

a plugin for mother machine image analysis

Simon Biberger

napari-ccp4map





Enables napari to read .map files in the ccp4 format.

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

Installation
You can install napari-ccp4map via pip:
pip install napari-ccp4map

Usage
If the plugin was installed correctly, it will pop up in a napari window under Plugins->Install/Uninstall Plugins.
You can either drag&drop filed into the window to read them, or search for a folder/file using Ctrl+O.
How it works
This plugin simply reads a file and allows gemmi to interact with it. Then, numpy turns the file into an array.
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-ccp4map" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-ccp4map

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

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

Richard De Mets

napari-yolov5






Plugin adapted from Ultralytics to bring YOLOv5 into Napari. 
Training and detection can be done using the GUI. Training dataset must be prepared prior to using this plugin. Further development will allow users to use Napari to prepare the dataset. Follow instructions stated on Ultralytics Github to prepare the dataset.
The plugin includes 3 pre-trained networks that are able to identify mitosis stages or apoptosis on soSPIM images. More details can be found on the pre-print.

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

Installation
First install conda and create an environment for the plugin
conda create --prefix env-napari-yolov5 python=3.9
conda activate env-napari-yolov5
You can install napari-yolov5 and napari via pip:
pip install napari-yolov5 
pip install napari[all]

For GPU support :
pip uninstall torch
pip install torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
Usage
First select if you would like to train a new network or detect objects.

For Training :
Data preparation should be done following Ultralytics' instructions.
Select the size of the network, the number of epochs, the number of images per batch to load on the GPU, the size of the images (must be a stride of 32), and the name of the network.

An example of the YAML config file is provided in src/napari_yolov5/resources folder.

Progress can be seen on the Terminal. The viewer will switch to Detection mode automatically when the network is finished being trained.

For Detection :
It is possible to perform the detection on a single layer chosen in the list, all the layers opened, or by giving a folder path. For folder detection, all the images will be loaded as a single stack.

Nucleus size of the prediction layer has te be filled to resize the image to the training dataset. Nucleus size of the training dataset will be asked in case of a custom network.
Confidence threshold defines the minimum value for a detected object to be considered positive. 
iou nms threshold (intersection-over-union non-max-suppression) defines the overlapping area of two boxes as a single object. Only the box with the maximum confidence is kept.
Progress can be seen on the Terminal.

Few options allow for modification on how the boxes are being displayed (default : box + class + confidence score ; box + class ; box only) and if the box coordinates and the image overlay will be exported.
Post-processing option will perform a simple 3D assignment based on 3D connected component analysis. A median filter (1x1x3 XYZ) is applied prior to the assignment. 
The centroid of each object is then saved into a new point layer as a 3D point with a random color for each class. 

The localisation of each centroid is saved and the path is shown in the Terminal at the end of the detection.

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-yolov5" is free and open source software
Issues
If you encounter any problems, please [file an issue] along with a detailed description.

napari-yolov5

Plugin adapted from Ultralytics to bring YOLOv5 into Napari

Hippolyte Verdier

Palmari





A processing pipeline for PALM movies analysis (pre-processing, localization, drift correction, tracking).
Check out the documentation to get started.


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

Installation
You can install palmari via pip:
pip install palmari

To install latest development version :
pip install git+https://github.com/hippover/palmari.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 CeCILL license.
Issues
If you encounter any problems, please file an issue along with a detailed description.

palmari

A pipeline for PALM movies analysis (pre-processing, localization, drifft correction, tracking)

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

Computational Microscopy Platform, CZ Biohub

recOrder





This package offers a comprehensive pipeline, command line interface, and napari plugin for quantitative label-free microscopy.
In this repo you will find python tools and UI that allow the user to calibrate microscope hardware, acquire multi-modal data, reconstruct density and anisotropy, and visualize the data.
The acquisition, calibration, background correction, reconstruction, and applications of QLIPP are described in the following E-Life Paper:
Syuan-Ming Guo, Li-Hao Yeh, Jenny Folkesson, Ivan E Ivanov, Anitha P Krishnan, Matthew G Keefe, Ezzat Hashemi, David Shin, Bryant B Chhun, Nathan H Cho, Manuel D Leonetti, May H Han, Tomasz J Nowakowski, Shalin B Mehta, "Revealing architectural order with quantitative label-free imaging and deep learning," eLife 2020;9:e55502 DOI: 10.7554/eLife.55502 (2020).
recOrder is to be used alongside the QLIPP module, whose design has been optimized to fit on a conventional widefield microscope (Panel A below).  The QLIPP module allows for the collection of label-free information consisting of the intrinsic anisotropy of the sample and its relative phase (density).  All of these measurements are collected through compensated, polarization diverse illumination and quantitatively recovered through recOrder's computational reconstruction pipeline.  The overall structure of recOrder can be visualized below in Panel B, highlighting the two different usage modes and their features: graphical user interface (GUI) through napari and command line interfact (CLI).

Dataset
Slides and a dataset shared during a workshop on QLIPP and recOrder can be found on Zenodo.


Installation
Easy installation:
(Optional but recommended) install anaconda and create a virtual environment
conda create -n recorder python
conda activate recorder
Install napari:
pip install "napari[all]"
Open napari and use the Plugin > Install/Uninstall Plugins... menu to install recOrder-napari.
Developer installation:
Install git and conda (either anaconda or miniconda).
Create a conda environment dedicated to recOrder:
conda create -n recorder python
conda activate recorder
Clone this repository:
buildoutcfg
git clone https://github.com/mehta-lab/recOrder.git
Install recOrder and its dependencies:
buildoutcfg
cd recOrder
pip install -e ".[dev]"
Optional napari plugin: recOrder includes a napari plugin that can be used to acquire data via MicroManager. To install napari use
pip install "napari[all]"
To run the recOrder plugin use
napari -w recOrder-napari
To acquire data via MicroManager, follow the instructions on the wiki.
Optional GPU: recOrder supports NVIDIA GPU computation with the cupy package. Follow these instructions to install cupy and check its installation with import cupy. To enable gpu processing, set use_gpu: True in the config files.
Command-line usage
Type recOrder.help for instructions on the two command-line modes: recOrder.reconstruct and recOrder.convert.
recOrder.reconstruct
recOrder.reconstruct uses configuration files to select reconstruction parameters. Start with an example configuration file /examples/example_configs/config_example_qlipp.yml and modify the parameters to match your dataset.
Run the reconstruction with
buildoutcfg
recOrder.reconstruct --config <path/to/config.yml>
The following command-line arguments override parameters specified in the configuration file:
--method (str) method of reconstruction: QLIPP,IPS,UPTI'
   --mode (str) mode of reconstruction: 2D, 3D'
   --data_dir (str) path to raw data folder'
   --save_dir (str) path to folder where reconstructed data will be saved'
   --name (str) name under which to save the reconstructed data'
   --config (str) path to configuration file (see /examples/example_configs')
   --overwrite (bool) True/False whether or not to overwrite data that exists under save_dir/name'
For example, this command uses the QLIPP reconstruction method even if the configuration file specifies a different reconstruction method
buildoutcfg
recOrder.reconstruct --config /path/to/config.yml --method QLIPP
recOrder.convert
recOrder.convert converts MicroManager tif files to ome-zarr files. For example
buildoutcfg
recOrder.convert --input <path/to/mm/tifs> --output <path/to/output.zarr>  --data_type ometiff
License
Chan Zuckerberg Biohub Software License
This software license is the 2-clause BSD license plus clause a third clause
that prohibits redistribution and use for commercial purposes without further
permission.
Copyright © 2019. Chan Zuckerberg Biohub.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:


Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.


Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.


Redistributions and use for commercial purposes are not permitted without
the Chan Zuckerberg Biohub's written permission. For purposes of this license,
commercial purposes are the incorporation of the Chan Zuckerberg Biohub's
software into anything for which you will charge fees or other compensation or
use of the software to perform a commercial service for a third party.
Contact ip@czbiohub.org for commercial licensing opportunities.


THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

recOrder-napari

Computational microscopy toolkit for label-free imaging

Trevor Manz

napari-dzi-zarr




An experimental plugin for viewing Deep Zoom Images (DZI) with napari + zarr + dask.

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.
Description
The DZI File Format 
is a pyramidal tile source specification where individual tiles are RGB/RGBA JPEG/PNG images. 
DZI is a very popular tile source for zoomable web-viewers like 
OpenSeadragon, and as a result many tile sources are available over 
HTTP. This plugin wraps a DZI tile source (local or remote) as a multiscale Zarr, where each pyramidal level is a zarr.Array of shape (level_height, level_width, 3/4), allowing the same images to be viewed 
in napari + dask.
Installation
You can install napari-dzi-zarr via pip:
pip install napari-dzi-zarr

Usage
This high-resolution scan of Rembrandt's Night Watch is available thanks to R.G Erdmann. More examples can be found on boschproject.org.
$ napari http://hyper-resolution.org/dzi/Rijksmuseum/SK-C-5/SK-C-5_VIS_20-um_2019-12-21.dzi


Contributing
Contributions are very welcome. Tests can be run with tox.
License
Distributed under the terms of the BSD-3 license,
"napari-dzi-zarr" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-dzi-zarr

An experimental plugin for viewing Deep Zoom Images (DZI) with napari and zarr.

Nicholas A. Del Grosso

Description
This is a simple video reader plugin that creates Dask Arrays using OpenCV' for file reading.
Anything OpenCV's VideoCapture() function can read should work here.
Supported Data
Supports mp4, mov, and avi extensions.s
Quickstart
python
napari.view_image('myvid.avi')

napari-video-cvdask

A Video File Reader that uses OpenCV2 and Dask Arrays

Ben Kantor

napari-bigwarp






BigWarp-like interface for napari

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

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

To install latest development version :
pip install git+https://github.com/bkntr/napari-bigwarp.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-bigwarp" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-bigwarp

Loïc Sauteur

Description
This light-weight plugin provides additional control over label layers.
It is intended to ease your work when annotating data manually.

--> the image link needs to be updated... (to weblink)  <--
It provides you with a widget listing all individual labels.
For each label, you can:
- select it from the list to activate it for further drawing.
- toggle the visibility of individual labels
- locate the drawn label (i.e. move to the centroid location at the current zoom level)
- change the label color with a color picker
- erase the label (sets all the drawn pixels to the label layer background value, not un-doable)
Intended Audience & Supported Data
Everyone that has 2D or 3D data and wants to annotate (or curate annotated data) 
should find a useful extension with this plugin.
The plugin will recognise and work only on label layers.
Note:
The "locate center" button will only work on 2D/3D label layers, i.e.: YX, ZYX, TYX, CYX.
Channels are considered a dimension.
Quickstart

Start napari
Open an image you want to annotate 
Best, an image with the same dimension as you labels layer should have
e.g. File > Open Sample > napari > Binary Blobs (3D)
Add (or load) a labels layer
Start the plugin Plugins > napari-annotator: Annotator
Make sure the labels layer is selected
Start drawing

Known limitations

Lag when drawing (see GitHub README for more info).
Maximum 255 labels supported (see GitHub README for more info).

Getting Help
If you encounter bugs, please [file an issue] along with a detailed description.
Or open a thread on forum.image.sc with a detailed description 
and a @loicsauteur tag.
For general help, reach out via the forum.image.sc with a tag @loicsauteur.
How to Cite
No citation needed. Honorable mention welcome.

napari-annotator

A lightweight plugin extending label layer control

Nicholas Sofroniew, Alister Burt, Guillaume Witz, Faris Abouakil, Talley Lambert

napari-animation (WIP under active development)





napari-animation is a plugin for making animations in napari.

This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.
It is built off of great work from @guiwitz in naparimovie which was initially submitted to napari in PR#851.

Overview
napari-animation provides a framework for the creation of animations in napari and features:
- an easy to use GUI for interactive creation of animations
- Python tools for programmatic creation of animations
This plugin is currently pre-release and under active development. APIs are likely to change before it's first 0.0.1 release,
but feedback and contributions are welcome.
Installation
You can clone this repository with install locally with
pip install -e .

Examples
Examples can be found in our examples folder. Simple examples for both interactive and headless 
use of the plugin follow.
Interactive
napari-animation can be used interactively by creating an AnimationWidget from a napari Viewer and adding it to
the viewer as a dock widget.
```python
from napari_animation import AnimationWidget
animation_widget = AnimationWidget(viewer)
viewer.window.add_dock_widget(animation_widget, area='right')
```

Headless
napari-animation can also be run headless, allowing for reproducible, scripted creation of animations.
```python
from napari_animation import Animation
animation = Animation(viewer)
viewer.dims.ndisplay = 3
viewer.camera.angles = (0.0, 0.0, 90.0)
animation.capture_keyframe()
viewer.camera.zoom = 2.4
animation.capture_keyframe()
viewer.camera.angles = (-7.0, 15.7, 62.4)
animation.capture_keyframe(steps=60)
viewer.camera.angles = (2.0, -24.4, -36.7)
animation.capture_keyframe(steps=60)
viewer.reset_view()
viewer.camera.angles = (0.0, 0.0, 90.0)
animation.capture_keyframe()
animation.animate('demo.mov', canvas_only=False)
```
Is everything animate-able?
Unfortunately, not yet! Currently differences in the following objects are tracked by the Animation class

Viewer.camera
Viewer.dims
Layer.scale
Layer.translate
Layer.rotate
Layer.shear
layer.opacity
Layer.blending
Layer.visible

Support for more layer attributes will be added in future releases.
Contributing
Contributions are very welcome. Tests and additional infrastructure are being setup.
License
Distributed under the terms of the BSD-3 license,
"napari-animation" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-animation

Santiago N. Rodriguez Alvarez

Nfinder
Automatic inference of neighboring cells based on their Delaunay triangulation.
Dependencies
nfinder was tested with:

python = 3.8.5
napari = 0.4.12
numpy = 1.21.2
pandas = 1.3.4
scikit-image = 0.18.3
scipy = 1.7.1
importlib-resources 5.4.0

Installation
It can be installed with pip from PyPI:
pip install nfinder
Usage
For usage examples, please check out the notebook in our GitHub repository.

nfinder

Automatic inference of neighboring cells based on their Delaunay triangulation.

napari-roi






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

Alternatively, you can install napari-roi via conda:
conda install -c conda-forge napari-roi

Usage
The napari-roi plugin can be opened from within napari.
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

Alan R. Lowe







Arboretum

Automated cell tracking and lineage tree reconstruction.
Overview
A dockable widget for Napari for visualizing cell lineage trees.
Features:
+ Lineage tree plot widget
+ Integration with btrack

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.
Examples
You can use the example script to display some sample tracking data in napari and load the arboretum tree viewer:
sh
python ./examples/show_sample_data.py
Alternatively, you can use btrack to generate tracks from your image data. See the example notebook here:
https://github.com/quantumjot/BayesianTracker/blob/master/examples

History
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/napari.layers.Tracks.html
To view the legacy version of this plugin, visit the legacy branch:
https://github.com/quantumjot/arboretum/tree/v1-legacy

napari-arboretum

Track graph and lineage tree visualization with napari

Alan M Watson

Description
This plugin enables viewing of Bitplane Imaris files, including very large datasets.  The GIFs below demonstrate rendering of a ~2TB IMS file containing a 2 color whole mouse brain.  The plugin has been tested on datasets as large as 20TB.
NOTE: For this plugin to work "File/Preferences/Experimental/Render Images Asynchronously" must be selected.
Open IMS file:

Render in 3D:
A plugin is provided to dynamically reload the data after selecting the lowest resolution level to be included in the viewer.  Since napari only renders the lowest resolution, the user can use this plugin to control the quality of 3D rendering.  See features and limitations for tips on suggested usage.

Features

Multiscale Rendering
Image pyramids which are present in the native IMS format are automatically added to napari during file loading.
Chunks are implemented by dask and matched to the chunk sizes stored in each dataset.  (Napari appears to only ask for 2D chunks - unclear how helpful this feature is currently)
Successfully handles multi-terabyte multi-timepoint multi-channel datasets.
Tested with all sample files provided by Bitplane.
Higher 3D rendering quality is enabled by a widget that reloads data after specifying the lowest resolution level (higher number = lower resolution) to be included in the multiscale series.

Known Issues / limitations

Currently, this is only an image loader, and there are no features for loading or viewing objects
Napari sometimes throws errors indicating that it expected a 3D or 5D array but receives the other.
This sometimes but relatively rarely causes napari to crash
Would like to enable Asynchronous Tiling of Images, but this results in more instability and causes crashes.


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

Installation
You can install napari-imaris-loader via pip:
pip install napari-imaris-loader

Change Log:
v0.1.2:
Fixed: Issue where .ims files containing a single color 2D image would not open.
Fixed: Issue where using the widget to change resolutions while in 3D rendering would cause a crash.  Now the viewer is automatically forced into 2D rendering mode when the widget is used.
Dependency change: The loader is now dependent in a separate package for loading IMS files.  https://pypi.org/project/imaris-ims-file-reader/
v0.1.3:
Documentation
v0.1.4:
Add napari to install requirements for plugin compatibility
v0.1.5:
Changes to napari:

now requires napari[all] upon install.
requires >=v0.1.5 of imaris-ims-file-reader

v0.1.6:

Fix issue #7 where contrastLimits possibly unbound in reader

v0.1.7:

For squeeze_output=False when opening .ims file for Napari compatibility

v0.1.8:

Add automatic determination of contrast_limits
Fix bug in squeeze_output

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-imaris-loader" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-imaris-loader

Napari plugin for loading Bitplane imaris files '.ims'

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

Alister Burt

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

Johannes Elferich

napari-cryofibsem-monitor






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

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

Installation
You can install napari-cryofibsem-monitor via pip:
pip install napari-cryofibsem-monitor

To install latest development version :
pip install git+https://github.com/jojoelfe/napari-cryofibsem-monitor.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 MIT license,
"napari-cryofibsem-monitor" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-cryofibsem-monitor

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

Grzegorz Bokota

Description
PartSeg is a specialized GUI for feature extraction from multichannel light microscopy images, but also most of its features are available as napari Widgets.
Information about PartSeg as standalone program or library are here
Who is This For?
This plugin is for 2D and 3D segmentation of distinct objects from images and measuring various parameters of these objects.
This plugin process everything in memory, so it may not work with images stored in dask arrays. Currently, this plugin does not support the processing of Time data.
How-to Guide
The example data is stored here.
https://user-images.githubusercontent.com/3826210/149146100-973498c7-7d2e-4298-a3c8-3051912e3183.mp4
The above video presents simple segmentation and measurement of various parameters of this segmentation (ROI).
As bellow described, algorithms are the result of porting PartSeg utilities to napari
then detailed description could be found in PartSeg documentation/
ROI Extraction (Segmentation, pixel labeling)
The PartSeg is focused on the reproducible ROI Extraction process and offers two groups of algorithms:

ROI Mask Extraction set of algorithms (from PartSeg ROI Analysis) to work on a whole stack and mainly used for extracting nucleus or cell from a stack.
ROI Analysis Extraction set of algorithms (from PartSeg ROI Mask) for detailed segmentation on the level of a single nucleus.
    If possible, they use an inner caching mechanism to improve performance while adjusting parameters.

Algorithms from both groups should support masking.
(perform ROI extraction only on the mask layer's area has non-zero values).
Parameters of ROI Extraction could be saved for later reuse (in the program) or exported to JSON and imported in another instance.
With an accuracy of up to channel selection, they are identical to PartSeg,
so importing should work both ways, but the channel selection step needs to be repeated.
The list of available algorithms could be extensible using the PartSeg plugin mechanism.
Measurement widgets
PartSeg offers two measurement widgets:
Measurement
Interface to PartSeg measurement engine.
In this widget, there are two tabs. *Measurement settings that allow
to define, delete, import, and export set of measurements

and Measurement for performing measures using an already defined set.

The list of available measurements could be extensible using the PartSeg plugin mechanism.
Simple Measurement

Set of measurements that could be calculated per component respecting data voxel size.
In comparison to  Measurement list of available measures is limited to ones that do not need
Mask information and could be calculated per component.
This widget is equivalent to the PartSeg ROI Mask Simple Measurement window.
Search label
Widget to find the layer with the given number By highlighting it or zooming on it. The highlight widget uses white color, so the highlight may not be visible if the label has a bright color.
https://user-images.githubusercontent.com/3826210/154669409-cdac9be8-3dbf-4a0e-a66f-af8a44aed0fb.mp4
Mask create
Transform labels layer into another labels layer with the possibility to dilate, and filling small holes

Reader plugins
In this plugin, there are also all PartSeg readers and writers.
The most important readers are this, which allows loading PartSeg projects to napari.
The one which could impact a user workflow is tiff reader.
In comparison to the napari default one, there are two essential differences.
Napari's built-in plugin loads data as they are in a file.
PartSeg plugin read file metadata and return data in TZYX order.
PartSeg reader returns each channel as a separate layer.
PartSeg reader also tries to parse voxel size metadata and set scale parameters to nanometers' size.

PartSeg

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

Leo Guignard

napari-sc3D-viewer






A plugin to visualise 3D spatial single cell omics

This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.
Installation
You can install napari-sc3D-viewer via pip:
pip install .

(from the correct folder)
or
pip install napari-sc3D-viewer

To install latest development version :
pip install git+https://github.com/GuignardLab/napari-sc3D-viewer.git

To install the surface computation enabled version it is necessary to use Python 3.9 (until VTK is ported to Python 3.10) and you can run one of the following commands:
pip install '.[pyvista]'

from the correct folder or
pip install napari-sc3D-viewer[pyvista]

to install directly from pip or
pip install 'napari-sc3D-viewer[pyvista] @ git+https://github.com/GuignardLab/napari-sc3D-viewer.git'

to install the latest version
Usage
napari-sc3D-viewer allows users to easily visualise and navigate 3D spatial single-cell transcriptomics using napari.
Loading and opening a dataset

The expected dataset is a scanpy/anndata h5ad file together with an optional json file that maps cluster id numbers to actual tissue/cluster name.
The json file should look like that:
json
{
    "1": "Endoderm",
    "2": "Heart",
    "10": "Anterior neuroectoderm"
}
If no json file or a wrong json file is given, the original cluster id numbers are used.
The h5ad file should be informed in (1) and the json file in (2).

Let data be your h5ad data structure. To work properly, the viewer is expecting 4 different columns to be present in the h5ad file:
- the cluster id column (by default named 'predicted.id' that can be accessed as data.obs['predicted.id'])
- the 3D position column (by default named 'X_spatial_registered' that can be accessed as data.obsm['X_spatial_registered'])
- the gene names if not already in the column name (by default named 'feature_name' that can be accessed as data.var['feature_name'])
- umap coordinates (by default named 'X_umap' that can be accessed as data.obsm['X_umap'])
If the default column names are not consistent with your dataset, they can be changed in the tab Parameters (3) next to the tab Loading files
Once all the data paths and fields are correctly informed pressing the Load Atlas button (4) will load the dataset.
Exploring a dataset
Once the dataset is loaded there are few options to explore it.
The viewer should look like to the following:

It is divided in two main parts, the Tissue visualisation (1) part and the Metric visualisation (2) one.
Both of them are themselves split in two and three tabs respectively. All these tabs allow you to visualise and explore the dataset in different fashions.
The Tissues tab (1.1) allows to select the tissues to display, to show the legend and to colour the cells according to their tissue types.
The Surfaces tab (1.2) allows to construct coarse surfaces of tissues and to display them.
The Single metric tab (2.1) allows to display a metric, whether it is a gene intensity or a numerical metric that is embedded in the visualised dataset. This tab also allows to threshold cells according to the viewed metric, to change the contrast and the colour map.
The 2 Genes (2.2) tab allows to display gene coexpression.
The umap tab (2.3) allows to display the umap of the selected cells and to manually select subcategories of cells to be displayed.

Explanatory "videos".
The plugin is meant to be easy to use. That means that you should be able to play with it and figure things out by yourself.
That being said, it is not always that easy. You can find below a series of videos showing how to perform some of the main features.
Loading data

Selecting tissues

Displaying one gene

Displaying two genes co-expression

Playing with the umap

Computing and processing the surface

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-sc3D-viewer" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-sc3D-viewer

A plugin to visualize 3D single cell omics

Zach Marin, Talley Lambert

napari-math






This package provides a GUI interfrace for simple mathematical operations on image, point and surface layers.

addition
subtraction
multiplication
division
logical and, or, xor
z-projection (mean and sum)

Operations can be peformed on a single layer or between Image layers (functionaly pending for Surface and Point layers), 
for example adding one layer to another.
When performing operations on two images of different sizes, the result will be the size of the smallest
of the two images.


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

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-math" is free and open source software
Issues
If you encounter any problems, please file an [issue] along with a detailed description.

napari-math

Simple mathematical operations on image, point and surface layers.

William Patton

napari-affinities






A plugin for creating, visualizing, and processing affinities

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

Installation
Pre-requisites:
1) Install lsds with: `pip install git+https://github.com/pattonw/lsd.git@no-convenience-imports` (This is necessary since we need lsds but the package name `lsd` is already taken on pypi. While we resolve this the specific lsd version needs to be specified)

You can install napari-affinities via pip:
`pip install napari-affinities`

To install latest development version :
`pip install git+https://github.com/pattonw/napari-affinities.git`

Install torch according to your system (follow the instructions here). For example with cuda 10.2 available, run:
conda install pytorch torchvision cudatoolkit=10.2 -c pytorch

Install conda requirements:
conda install -c conda-forge affogato

Download example model:
2D:
epithelial example model
Place the model zip file wherever you want. You can open it in the plugin with the "load from file" button.
3D
lightsheet example model
Unpack the tar file into test data (lightsheet_nuclei_test_data (an hdf5 file)) and model (LightsheetNucleusSegmentation.zip (a bioimageio model)).
Move the data into sample_data which will enable you to load the "Lightsheet Sample" data in napari.
Place the model zip file anywhere you want. You can open it in the plugin with the "load from file" button.
Workarounds to be fixed:

you need to update the rdf.yaml in the LightsheetNucleusSegmentation.zip with the following:
"shape" for "input0" should be updated with a larger minimum input size and "output0" should be updated with a larger halo. If not fixed, there will be significant tiling artifacts.
(Optional) "output0" should be renamed to affinities. The plugin supports multiple outputs and relies on names for figuring out which one is which. If unrecognized names are provided we assume the outputs are ordered (affinities, fgbg, lsds) but this is less reliable than explicit names.
This model also generates foreground in the same array as affinities, i.e. a 10 channel output (fgbg, [-1, 0, 0], [0, -1, 0], [0, 0, -1], [-2, 0, 0], ...). Although predictions will work, post processing such as mutex watershed will break unless you manually separate the first channel.

Use
Requirements for the model:

Bioimageio packaged pytorch model
Outputs with names "affinities", "fgbg"(optional) or "lsds"(optional)
if these names are not used, it will be assumed that the outputs are affinities, fgbg, then lsds in that order

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-affinities" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-affinities

A plugin for creating, visualizing, and processing affinities

Jackson Maxfield Brown

Features

Supports reading metadata and imaging data for:
OME-TIFF
TIFF
CZI (Zeiss)
LIF (Leica)
ND2 (Nikon)
DV (DeltaVision)
Any formats supported by aicsimageio
Any formats supported by bioformats
SLD (Slidebook)
SVS (Aperio)
Full List


Any additional format supported by imageio
PNG
JPG
GIF
AVI
Full List





While upstream aicsimageio is released under BSD-3 license, this plugin is released under GPLv3 license because it installs all format reader dependencies.
Plugin Variants

There are two variants of this plugin that are added during installation:

aicsimageio-in-memory, which reads an image fully into memory
aicsimageio-out-of-memory, which delays reading ZYX chunks until required.
    This allows for incredibly large files to be read and displayed.

Examples of Features
General Image Reading
All image file formats supported by
aicsimageio will be read and all
raw data will be available in the napari viewer.
In addition, when reading an OME-TIFF, you can view all OME metadata directly in the
napari viewer thanks to ome-types.

Multi-Scene Selection
When reading a multi-scene file, a widget will be added to the napari viewer to manage
scene selection (clearing the viewer each time you change scene or adding the
scene content to the viewer) and a list of all scenes in the file.

Access to the AICSImage Object and Metadata

You can access the AICSImage object used to load the image pixel data and
image metadata using the built-in napari console:
python
img = viewer.layers[0].metadata["aicsimage"]
img.dims.order  # TCZYX
img.channel_names  # ["Bright", "Struct", "Nuc", "Memb"]
img.get_image_dask_data("ZYX")  # dask.array.Array
The napari layer metadata dictionary also stores a shorthand
for the raw image metadata:
python
viewer.layers[0].metadata["raw_image_metadata"]
The metadata is returned in whichever format is used by the underlying
file format reader, i.e. for CZI the raw metadata is returned as
an xml.etree.ElementTree.Element, for OME-TIFF the raw metadata is returned
as an OME object from ome-types.
Lastly, if the underlying file format reader has an OME metadata conversion function,
you may additionally see a key in the napari layer metadata dictionary
called "ome_types". For example, because the AICSImageIO
CZIReader and BioformatsReader both support converting raw image metadata
to OME metadata, you will see an "ome_types" key that stores the metadata transformed
into the OME metadata model.
python
viewer.layers[0].metadata["ome_types"]  # OME object from ome-types
Mosaic Reading
When reading CZI or LIF images, if the image is a mosaic tiled image, napari-aicsimageio
will return the reconstructed image:

Citation
If you find aicsimageio (or napari-aicsimageio) useful, please cite as:

AICSImageIO Contributors (2021). AICSImageIO: Image Reading, Metadata Conversion, and Image Writing for Microscopy Images in Pure Python [Computer software]. GitHub. https://github.com/AllenCellModeling/aicsimageio

Free software: GPLv3

napari-aicsimageio

AICSImageIO for napari. Multiple file format reading directly into napari using pure Python.











BayesianTracker (btrack) is a multi object tracking algorithm,
specifically used to reconstruct trajectories in crowded fields.  New
observations are assigned to tracks by evaluating the posterior probability of
each potential linkage from a Bayesian belief matrix for all possible
linkages.
We developed btrack for cell tracking in time-lapse microscopy data.


associated plugins

napari-arboretum - Napari plugin to enable track graph and lineage tree visualization.
napari-btrack - (Experimental) Napari plugin to provide a frontend GUI for btrack.


btrack

A framework for Bayesian multi-object tracking

Adam Tyson

cellfinder-napari














Efficient cell detection in large images (e.g. whole mouse brain images)
cellfinder-napari is a front-end to cellfinder-core to allow ease of use within the napari multidimensional image viewer. For more details on this approach, please see Tyson, Rousseau & Niedworok et al. (2021). This algorithm can also be used within the original
cellfinder software for
whole-brain microscopy analysis.
cellfinder-napari, cellfinder and cellfinder-core were developed by Charly Rousseau and Adam Tyson in the Margrie Lab, based on previous work by Christian Niedworok, generously supported by the Sainsbury Wellcome Centre.


Visualising detected cells in the cellfinder napari plugin

Instructions
Installation
Once you have installed napari.
You can install napari either through the napari plugin installation tool, or
directly from PyPI with:
bash
pip install cellfinder-napari
Usage
Full documentation can be
found here.
This software is at a very early stage, and was written with our data in mind.
Over time we hope to support other data types/formats. If you have any
questions or issues, please get in touch on the forum or by
raising an issue.

Illustration
Introduction
cellfinder takes a stitched, but otherwise raw dataset with at least
two channels:
 * Background channel (i.e. autofluorescence)
 * Signal channel, the one with the cells to be detected:

Raw coronal serial two-photon mouse brain image showing labelled cells
Cell candidate detection
Classical image analysis (e.g. filters, thresholding) is used to find
cell-like objects (with false positives):

Candidate cells (including many artefacts)
Cell candidate classification
A deep-learning network (ResNet) is used to classify cell candidates as true
cells or artefacts:

Cassified cell candidates. Yellow - cells, Blue - artefacts
Citing cellfinder
If you find this plugin useful, and use it in your research, please cite the preprint outlining the cell detection algorithm:

Tyson, A. L., Rousseau, C. V., Niedworok, C. J., Keshavarzi, S., Tsitoura, C., Cossell, L., Strom, M. and Margrie, T. W. (2021) “A deep learning algorithm for 3D cell detection in whole mouse brain image datasets’ PLOS Computational Biology, 17(5), e1009074
https://doi.org/10.1371/journal.pcbi.1009074

If you use this, or any other tools in the brainglobe suite, please
 let us know, and
 we'd be happy to promote your paper/talk etc.

The BrainGlobe project is generously supported by the Sainsbury Wellcome Centre and the Institute of Neuroscience, Technical University of Munich, with funding from Wellcome, the Gatsby Charitable Foundation and the Munich Cluster for Systems Neurology - Synergy.


cellfinder-napari

Robert Haase

napari-brightness-contrast







Advanced layer histogram visualization options, e.g. for brightness / contrast

Note: This will not work for big image data at the moment. 
If the user interface feels slow, consider installing pyclesperanto to speed it up.

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

Installation
You can install napari-brightness-contrast via pip:
pip install napari-brightness-contrast

Contributing
Contributions are very welcome.
After cloning the repo, install using pip install -e .[tests] to enable testing via pytest.
License
Distributed under the terms of the BSD-3 license,
"napari-brightness-contrast" 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-brightness-contrast

Herearii Metuarea

napari-apple






Detection of apple based on YOLOv4 model

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

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

To install latest development version :
pip install git+https://github.com/hereariim/napari-apple.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-apple" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-apple

Meizhu Liang

napari-SIMulator






A simple plugin to use with napari

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

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

To install latest development version :
pip install git+https://github.com/Meizhu-Liang/napari-SIMulator.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-SIMulator" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-SIMulator

Draga Doncila Pop

What is this?
This plugin was created to serve as a semi-meaningful example of a plugin using
the new napari npe2 architecture.
It provides a reader, a writer and two dock widgets to support opening, processing
and writing out cell tracking challenge data.
We've provided comments and example tests that can be used as a reference
when building your own plugin.
Using this plugin
Sample Data
You can download sample data for this plugin from the tracking challenge website. Any 2D+T
sequence should work, but this plugin has been tested only with the 
Human hepatocarcinoma-derived cells expressing the fusion protein YFP-TIA-1 
dataset.
Reading Data
This plugin's reader is designed for tracking challenge segmentation gold standard ground truth
data conforming to the file format described in the data specification.
Ground truth data is only provided for a subset of the frames of the entire sequence. This
reader will attempt to find the number of frames of the associated sequence in a sister
directory of the ground truth data directory and open a labels layer with the same number
of frames, thus ensuring the labelled data is correctly overlaid onto the original sequence.
https://user-images.githubusercontent.com/17995243/146114062-36124c05-f44a-488e-8991-f39a702c917f.mov
Segmenting Data
One of the dock widgets provided by this plugin is "Segment by Threshold". The widget
allows you to select a 2D+T image layer in the viewer (e.g. any of the sequences in the Human 
hepatocarcinoma dataset above) and segment it using a selection of scikit-image thresholding functions.
The segmentation is then returned as a Labels layer into the viewer.
https://user-images.githubusercontent.com/17995243/146114088-f6fb645e-8d78-4880-827b-2f0334dad859.mov
Highlighting Segmentation Differences
The second dock widget provided by this plugin allows you to visually compare your segmentation
against the ground truth data by computing the difference between the two and adding this as a
layer in the napari viewer.
To use this widget, open it from the Plugins menu and select the two layers you wish to compare.
https://user-images.githubusercontent.com/17995243/146114112-c891723f-8640-4708-8014-c78731fb3396.mov
Writing to Zip
Finally, you can save your segmentation to a zip file whose internal directory structure
will closely mimic that of the tracking challenge datasets, so that it may be opened 
again in the viewer.
To save your layer, choose File -> Save selected layer(s) with one labels layer selected,
then select label zipper from the dropdown choices.
https://user-images.githubusercontent.com/17995243/146114163-ee886990-979c-4756-97c5-aaf2c39dccde.mov

workshop-demo

A demo napari plugin incorporating reader, writer and dock widget contributions using the new npe2 plugin architecture.

Marc Boucsein, Robin Koch

napari-elementary-numpy-operations



A napari plugin covers elementary numpy operations like swap axes, flip, sqeeze an array or rotate an arrays by 90° steps.

Installation
You can install napari-elementary-numpy-operations via pip:
napari-elementary-numpy-operations

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-elementary-numpy-operations" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-elementary-numpy-operations

A napari plugin covers elementary numpy operations like swap axes, flip, sqeeze an array or rotate an arrays by 90° steps

Robert Haase, Johannes Müller

napari-process-points-and-surfaces (nppas)







Process and analyze surfaces using open3d and vedo in napari.
Usage
You find a couple of surface generation, smoothing and analysis functions in the menu Tools > Surfaces and Tools > Points. For detailed explanation of the underlying algorithms, please refer to the open3d documentation.
Some code snippets and the knot example data have been taken from the open3d project which is 
MIT licensed 
and from the vedo documentation 
which is MIT licensed.
The Standford Bunny example dataset has been taken from the The Stanford 3D Scanning Repository.
For processing meshes in Python scripts, see the demo notebook. There you also learn how this screenshot is made:

For performing quantitative measurements of meshes in Python scripts, see the demo notebook. 
There you also learn how this screenshot is made:

Installation
You can install napari-process-points-and-surfaces via pip:
pip install napari-process-points-and-surfaces
See also
There are other napari plugins with similar / overlapping functionality
* pymeshlab
* morphometrics
* napari-pyclesperanto-assistant
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-process-points-and-surfaces" 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-process-points-and-surfaces

Process and analyze surfaces using open3d and vedo in napari

Blik Team


blik



it means glance in Dutch

blik is a tool for visualising and interacting with cryo-ET and subtomogram averaging data. It leverages the fast, multi-dimensional napari viewer and the scientific python stack.
DISCLAIMER: this package is in development phase. Expect frequent bugs and crashes. Please, report them on the issue tracker and ask if anything is unclear!
Installation
You can either install blik through the napari plugin system, through pip, or get both napari and blik directly with:
bash
pip install blik[all]
Basic Usage
From the command line:
bash
napari /path/to.star /path/to/mrc/files/* [-w blik]
The optional -w blik addition will open the main blik widget and set up a few things (such as the scale bar) on the napari viewer.
blik is just napari. Particles and images are exposed as simple napari layers, which can be analysed and manipulated with simple python, and most importantly other napari plugins.

blik

Python tool for visualising and interacting with cryo-ET and subtomogram averaging data.

Tristan Cotte

napari-IDS




A simple plugin to use with napari

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

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

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

First utilisation
Suggested environment : 
- Python 3.8
- IDS 1.2.0.5 version installed
To use this package for the first time :
1. Install Napari pip install "napari[all]"
2. Install napari-IDS package
3. Install IDS Python api thanks to the command ids_packages
If your environment is not the suggested environment, you have to install IDS packages manually. 
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-IDS" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-IDS

Plug in which enables to take photo with IDS uEye camera

Juan Nunez-Iglesias

Description
This GUI plugin allows you to quickly find the affine matrix mapping
one image to another using manual correspondence points annotation.
More simply, this plugin allows you to select corresponding points
on an image, and a second image you wish to transform. It computes 
the requisite transformation matrix using Affine Transform, Euclidean Transform, 
or Similarity Transform, and performs this transformation on the
moving image, aligning it to the reference image.
https://user-images.githubusercontent.com/17995243/120086403-f1d0b300-c121-11eb-8000-a44a2ac54339.mp4
Who is This For?
This is a simple plugin which can be used on any 2D images, provided
they can be loaded as layers into napari. The images need not be the same
file format and this plugin also works with labels layers.
No prior understanding of the transformation methods is required, as
they perform in the background based on the reference points selected.
How to Guide
You will need a combination of two or more 2D image and/or labels layers 
loaded into napari. Once you have installed affinder, you can find it in
the dock widgets menu.

The first two dropdown boxes will be populated with the layers currently
loaded into napari. Select a layer to use as reference, and another to
transform.

Next, you can select the transformation model to use (affine is selected by default
and is the least rigid transformation of those available). See below for a
description of the different models.
Finally, you can optionally select a path to a text file for saving out the
resulting transformation matrix.
When you click Start, affinder will add two points layers to napari. 
The plugin will also bring your reference image in focus, and its associated points
layer. You can then start adding reference points by clicking on your image.

Once three points are added, affinder will switch focus to the moving image,
and you should then proceed to select the corresponding three points.

affinder will immediately transform the moving image to align the points you've
selected when you add your third corresponding point to your moving image.

From there, you can continue iteratively adding points until you 
are happy with the alignment. Affinder will switch focus between
reference and moving image with each point.
Click Finish to exit affinder.
Transformation Models
There are three transformation models available for use with affinder.
They are listed here in order of increasing rigidity in the types of
transforms they will allow. The eponymous Affine Transform is the 
least rigid and is the default choice.


Affine Transform: 
the least rigid transformation, it preserves
lines and parallelism, but not necessarily distance and angles. Translation,
scaling, similarity, reflection, rotation and shearing are all valid
affine transformations.


Similarity Transform: 
this is a "shape preserving" transformation, producing objects which are 
geometrically similar. Translation, rotation, reflection and uniform scaling are 
valid similarity transforms. Shearing is not.


Euclidean Transform:
Also known as a rigid 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.


Getting Help
If you find a bug with affinder, or would like support with using it, please raise an
issue on the GitHub repository.
How to Cite
Many plugins may be used in the course of published (or publishable) research, as well as
during conference talks and other public facing events. If you'd like to be cited in
a particular format, or have a DOI you'd like used, you should provide that information here.

affinder

Quickly find the affine matrix mapping one image to another using manual correspondence points annotation

psfmodels




Python bindings for scalar and vectorial models of the point spread function.
Original C++ code and MATLAB MEX bindings Copyright © 2006-2013, Francois
Aguet, distributed under GPL-3.0
license. Python bindings by Talley Lambert
This package contains three models:

The vectorial model is described in Auget et al 20091. For more
information and implementation details, see Francois' Thesis2.
A scalar model, based on Gibson & Lanni3.
A gaussian approximation (both paraxial and non-paraxial), using paramters from Zhang et al (2007)4.


1 F. Aguet et al., (2009) Opt. Express 17(8), pp.
6829-6848
2 F. Aguet. (2009) Super-Resolution Fluorescence Microscopy Based on
Physical Models. Swiss Federal Institute of Technology Lausanne, EPFL Thesis no.
4418
3 F. Gibson and F. Lanni (1992) J. Opt. Soc. Am. A, vol. 9, no. 1, pp. 154-166
4 Zhang et al (2007). Appl Opt
. 2007 Apr 1;46(10):1819-29.

see also:
For a different (faster) scalar-based Gibson–Lanni PSF model, see the
MicroscPSF project, based on Li et al
(2017) which has been implemented in
Python,
MATLAB, and
ImageJ/Java
Install
sh
pip install psfmodels
from source
sh
git clone https://github.com/tlambert03/PSFmodels.git
cd PSFmodels
pip install -e ".[dev]"  # will compile c code via pybind11
Usage
There are two main functions in psfmodels: vectorial_psf and scalar_psf.
Additionally, each version has a helper function called vectorial_psf_centered
and scalar_psf_centered respectively. The main difference is that the _psf
functions accept a vector of Z positions zv (relative to coverslip) at which
PSF is calculated. As such, the point source may or may not actually be in the
center of the rendered volume. The _psf_centered variants, by contrast, do
not accecpt zv, but rather accept nz (the number of z planes) and dz
(the z step size in microns), and always generates an output volume in which the
point source is positioned in the middle of the Z range, with planes equidistant
from each other. All functions accept an argument pz, specifying the position
of the point source relative to the coverslip. See additional keyword arguments
below
Note, all output dimensions (nx and nz) should be odd.
```python
import psfmodels as psfm
import matplotlib.pyplot as plt
from matplotlib.colors import PowerNorm
generate centered psf with a point source at pz microns from coverslip
shape will be (127, 127, 127)
psf = psfm.make_psf(127, 127, dxy=0.05, dz=0.05, pz=0)
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(psf[nz//2], norm=PowerNorm(gamma=0.4))
ax2.imshow(psf[:, nx//2], norm=PowerNorm(gamma=0.4))
plt.show()
```

```python
instead of nz and dz, you can directly specify a vector of z positions
import numpy as np
generate 31 evenly spaced Z positions from -3 to 3 microns
psf = psfm.make_psf(np.linspace(-3, 3, 31), nx=127)
psf.shape  # (31, 127, 127)
```
all PSF functions accept the following parameters. Units should be provided
in microns unless otherwise stated. Python API may change slightly in the
future.  See function docstrings as well.
nx (int):       XY size of output PSF in pixels, must be odd.
dxy (float):    pixel size in sample space (microns) [default: 0.05]
pz (float):     depth of point source relative to coverslip (in microns) [default: 0]
ti0 (float):    working distance of the objective (microns) [default: 150.0]
ni0 (float):    immersion medium refractive index, design value [default: 1.515]
ni (float):     immersion medium refractive index, experimental value [default: 1.515]
tg0 (float):    coverslip thickness, design value (microns) [default: 170.0]
tg (float):     coverslip thickness, experimental value (microns) [default: 170.0]
ng0 (float):    coverslip refractive index, design value [default: 1.515]
ng (float):     coverslip refractive index, experimental value [default: 1.515]
ns (float):     sample refractive index [default: 1.47]
wvl (float):    emission wavelength (microns) [default: 0.6]
NA (float):     numerical aperture [default: 1.4]
Comparison with other models
While these models are definitely slower than the one implemented in Li et al
(2017) and
MicroscPSF, there are some interesting
differences between the scalar and vectorial approximations, particularly with
higher NA lenses, non-ideal sample refractive index, and increasing spherical
aberration with depth from the coverslip.
For an interactive comparison, see the examples.ipynb Jupyter
notebook.
Lightsheet PSF utility function
The psfmodels.tot_psf() function provides a quick way to simulate the total
system PSF (excitation x detection) as might be observed on a light sheet
microscope (currently, only strictly orthogonal illumination and detection are
supported).  See the lightsheet.ipynb Jupyter notebook for
examples.

psfmodels

Scalar and vectorial models of the microscope point spread function (PSF).

Chenqi Zhang

disease-classifier






A napari plugin for disease classification based on iPAC images.
Installation
You can install disease-classifier via pip:
pip install disease-classifier

To install latest development version :
pip install git+https://github.com/zcqwh/disease-classifier.git

Introduction
Load data (.rtdc or .bin)

Drag and drop the data in .rtdc or .bin into the files table.
Click eye button to preview images.


Choose model and classify

Choose the model folder including CNN and RF/PLDA.
Check the data.
Click classify.


Preview classification results

Click the eye button to preview the result.
Click the header to show all.


Save results

Click “Add classification to .rtdc file” button to save results.


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,
"disease-classifier" 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 @napari's cookiecutter-napari-plugin template.


disease-classifier

A disease classifier based on iPAC images.

SUSMITA SAHA

napari-mri





A simple plugin to use with napari for 3D-viewing of Magnetic Resonance Imaging file formats

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

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

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-mri" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-mri

A simple plugin to use with napari for 3D-viewing of                  Magnetic Resonance Imaging file formats

Sylvain Prigent

Description
The STracking suite provides a set of plugins for particles tracking in 2D+t and 3D+t images. 
A classical particles tracking pipeline is made of 5 sequential steps:



A full documentation is available here 
Installation
You can install napari-stracking via pip:
pip install napari-stracking

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-tracks-reader" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-stracking

Tim-Oliver Buchholz

napari-psf-analysis






A plugin to analyze point spread functions (PSFs) of microscopes.

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

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-psf-analysis" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-psf-analysis

A plugin to analyse point spread functions (PSFs).

napari-manual-transforms





Interface to manually edit layer affine transforms.

express rotations as quaternion, euler angle, or axis + angle.
allows rotation around arbitrary origin
currently, focusing on rigid rotations
Alt-Drag to rotate a layer independently of the rest.
image resampling (i.e. "apply" the transformation to create new dataset that can be saved)


caveats:

only works on 3D Image layers for now, open a feature request for other dims/layers.
will likely result in "Non-orthogonal slicing is being requested" warnings in 2D view.

Try it out
```python
import napari
v = napari.Viewer()
v.dims.ndisplay = 3
v.open_sample('napari', 'cells3d')
v.window.add_plugin_dock_widget('napari-manual-transforms')
napari.run()
```

Installation
You can install napari-manual-transforms via pip:
sh
pip install napari-manual-transforms
To install latest development version :
sh
pip install git+https://github.com/tlambert03/napari-manual-transforms.git
License
Distributed under the terms of the BSD-3 license,
"napari-manual-transforms" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-manual-transforms

Interface to manually edit layer affine transforms

napari-flowering-apple-tree-pixel-classification






Get flowering apple tree based on UNET algorithm

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

Installation
You can install napari-flowering-apple-tree-pixel-classification via pip:
pip install napari-flowering-apple-tree-pixel-classification

To install latest development version :
pip install git+https://github.com/hereariim/napari-flowering-apple-tree-pixel-classification.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-flowering-apple-tree-pixel-classification" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-flowering-apple-tree-pixel-classification

Get flowering apple tree based on UNET algorithm

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

Hiroki Kawai

napari-PHILOW






PHILOW 
Python-based platform for human-in-the-loop (HITL)  workflow (PHILOW) 
PHILOW is an interactive deep learning-based platform for 3D datasets implemented on top of napari

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

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

or clone this repository 
then
angular2
cd PHILOW
pip install -e .
Usage
Launch napari 
angular2
napari
load dataset
1) Plugins > napari-PHILOW > Annotation Mode
2) Select original dir : all slices must be in separate PNG and must be sequentially numbered (e.g. 000.png, 001.png ...)
3) Select mask dir : To resume from the middle of the annotation, specify here the name of the directory containing the mask image. The directory must contain the same number of files with the same name as the original image. 
 If you are starting a completely new annotation, you do not need to specify a directory. The directory for mask is automatically created and blank images are generated and stored.
4) Enter a name for the label or model you want to create (e.g. mito, cristae, ...) 
This name will be used as the directory name of the newly created mask dir if no mask dir is specified, 
and as the name of the csv file for training dataset management.
5) Check if you want to create new dataset (new model)
When checked, if there is already a csv file for training dataset management, a new csv file with one sequential number will be generated.
6) Start tracing
create labels
Create a label with the brush function.
more information → https://napari.org/tutorials/fundamentals/labels.html
Orthogonal view
If you want to see orthogonal view, click on the location you want to see while holding down the Shift button.  
The image from xy, yz, and zx will be displayed on the right side of the screen.
Low confident layer
If you are in the second iteration and you are loading the prediction results, you will see a low confidence layer.  
This shows the area where the confidence of the prediction result is low.  
Use this as a reference for correction.   
Small object layer
We provide a small object layer to find small painted areas. 
This is a layer for displaying small objects. 
The slider widget on the left allows you to change the maximum object size to be displayed.   
save labels
If you want to save your label, click the "save" button on the bottom right.
select training dataset
We are providing a way to manage the dataset for use in training. 
If you want to use the currently displayed slice as your training data, click the 'Not Checked' button near the center left to display 'Checked'.
Train and pred with your gpu machine
Train
To train on your GPU machine (or with CPU), 
1) Plugins > napari-PHILOW > Trainer
2) Select original dir : all slices must be in separate PNG and must be sequentially numbered (e.g. 000.png, 001.png ...)   
3) Select labels dir : all label images should be named same as original images and contains data management csv file   
4) Select dir for save trained model   
5) Click on the "start training" button   
6) Dice score and dice loss are displayed. For more detail, check the command line for the progress of training. If you want to stop in the middle, click stop button.   
Predict
To predict labels on your machine,  
1) Plugins > napari-PHILOW > Predicter
2) Select original dir : all slices must be in separate PNG and must be sequentially numbered (e.g. 000.png, 001.png ...)   
3) (Optional) Select labels dir if you want to keep labels witch were used on training, and data management csv file   
4) Select model dir contains hdf5 file   
5) Select output dir for predicted labels   
6) Uncheck the box if you DO NOT want to use TAP (Three-Axis-Prediction)   
7) Click on the "predict" button  
8) Check the command line for the progress of prediction. If you want to stop in the middle, use ctrl+C.   
9) You can start the next round of annotation by selecting the merged_prediction directory as the mask dir in Annotation mode.
Train and predict with Google Colab
If you don't have a GPU machine, you can use Google Colab to perform GPU-based training and prediction for free.    
1) Open train and predict notebook and click "Open in Colab" button
2) You can upload your own dataset to train and predict, or try it on demo data   
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-PHILOW" is free and open source software
Issues
If you encounter any problems, please [file an issue] along with a detailed description.
Authors 
Shogo Suga 
Hiroki Kawai 
Yusuke Hirabayashi 
How to Cite 
Shogo Suga, Koki Nakamura, Bruno M Humbel, Hiroki Kawai, Yusuke Hirabayashi, An interactive deep learning-based approach reveals mitochondrial cristae topologies
https://doi.org/10.1101/2021.06.11.448083
@article {Suga2021.06.11.448083,
    author = {Suga, Shogo and Nakamura, Koki and Humbel, Bruno M and Kawai, Hiroki and Hirabayashi, Yusuke},
    title = {An interactive deep learning-based approach reveals mitochondrial cristae topologies},
    elocation-id = {2021.06.11.448083},
    year = {2021},
    doi = {10.1101/2021.06.11.448083},
    publisher = {Cold Spring Harbor Laboratory},
    URL = {https://www.biorxiv.org/content/early/2021/06/11/2021.06.11.448083},
    eprint = {https://www.biorxiv.org/content/early/2021/06/11/2021.06.11.448083.full.pdf},
    journal = {bioRxiv}
}

napari-PHILOW

PHILOW is an interactive deep learning-based platform for 3D datasets

napari-workflow-inspector







Inspect relationships between image processing operations in active workflows in napari. Open the inspector by clicking the menu Tools > Visualization > Workflow Inspector.

Also install the napari-script-editor 
to generate code from active workflows.

For recording workflows, all napari image processing plugins that use the @time_slicer interface are supported. See
napari-time-slicer for a list. More to come, stay tuned.
Installation
You can install napari-workflow-inspector via pip:
pip install napari-workflow-inspector
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-workflow-inspector" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-workflow-inspector

Inspect relationships between image processing operations in active workflows in napari

Joel Luethi and Max Hess

napari-feature-classifier






An interactive classifier plugin that allows the user to assign objects in a label image to multiple classes and train a classifier to learn those classes based on a feature dataframe.
Usage

To use the napari-feature-classifier, you need to have a label image and a csv file containing measurements that correspond to the object in the label image. The csv file needs to contain a column with integer values corresponding to the label values in the label image.
These interactive classification workflows are well suited to visually define cell types, find mitotic cells in images, do quality control by automatically detecting missegmented cells and other tasks where a user can easily assign objects to groups.
Initialize a classifier:

Start the classifier in napari by going to Plugins -> napari-feature-classifier -> Initialize a Classifier 
Provide a csv file that contains feature measurements and a column with the integer labels corresponding to the label layer you are using.
Choose a name (or relative path from the current working directory) for the classifier. The classifier is initially saved in the current working directory (you can change this later on).
Select the features you want to use for the classifier (you need to do the feature selection before initializing. The feature selection can't be changed after initialization anymore). Hold the command key to select multiple features.


Classify objects:

Make sure you have the label layer selected on which you want to classify
Select the current class with the radio buttons or by pressing 0, 1, 2, 3 or 4
Click on label objects in the viewer to assign them to the currently selected class
While you need to have the label layer active to select, sometimes you want to focus on the intensity images. You can press v (or manually change the opacity of the label layer) to focus on the intensity images.
Once you have trained enough examples, click "Run Classifier" (or press t) to run the classifier and have it make a prediction for all objects. Aim for at least a dozen annotations per class, as the classifier divides your annotations 80/20 in training and test sets. To get good performance readouts, aim for >30 annotations per class.
Once you get predictions, correct mistakes the classifier made and retrain it to improve its performance.
You can save the classifier under a different name (to move it to a new folder or to have a slightly different version of the classifier - but careful, it autosaves whenever you run it). Define the new output location and then click Save Classifier (you need to click the Save Classifier button. Just defining the new output path does not save it yet)


Apply the classifier to additional images:

You can apply a classifier trained on one image to additional label images. Use Plugins -> napari-feature-classifier -> Load Classifier 
Select the classifier (.clf file with the name you gave above) and a csv file containing the same features as the past images.
Click Load Classifier, proceed as above.


Export classifier results

To export the training data and the results of the classifier, define an Export Name (full path to an output file or just a filename ending in .csv) where the results of the classifier shall be saved
Click Export Classifier Result (Just selecting a filename is not enough, you need to click the export button)
The results of the classifier are save in a csv file. The first two columns are index columns: path describes the Feature Path used (and allows you to understand which image / feature dataframe a result is from) and label is an integer of the label object within that image. The predict column contains predictions of the classifier for all objects (except those that contained NaNs in their feature data) and the train column contains the annotations you made (0 for unclassified objects, 1, 2, 3 or 4 for the classes)


There is a simple workflow for the classifier in the examples folder:
- Install jupyter-lab (pip install jupyterlab)
- Open the notebook in jupyter lab (Type jupyter-lab in the terminal when you are in the examples folder)
- Follow the instructions to generate an example dataframe and an example label image
- Use the classifier in napari with this simplified data
Installation
This plugin is written for the new napari npe2 plugin engine. Thus, it requires napari >= 0.4.13.
Activate your environment where you have napari installed (or install napari using pip install "napari[all]"), then install the classifier plugin:
pip install napari-feature-classifier

Contributing
Contributions are very welcome.
License
Distributed under the terms of the BSD-3 license,
"napari-feature-classifier" is free and open source software
Issues
If you encounter any problems, please [file an issue] along with a detailed description.
Contributors
Joel Lüthi & Max Hess

napari-feature-classifier

An interactive classifier plugin to use with label images and feature measurements

Charlotte Godard



# Hesperos plugin for Napari

[![License](https://img.shields.io/pypi/l/hesperos.svg?color=green)](https://github.com/DBC/hesperos/raw/main/LICENSE)
[![PyPI](https://img.shields.io/pypi/v/hesperos.svg?color=green)](https://pypi.org/project/hesperos)
[![Python Version](https://img.shields.io/pypi/pyversions/hesperos.svg?color=green)](https://python.org)
[![tests](https://github.com/DBC/hesperos/workflows/tests/badge.svg)](https://github.com/DBC/hesperos/actions)
[![codecov](https://codecov.io/gh/DBC/hesperos/branch/main/graph/badge.svg)](https://codecov.io/gh/DBC/hesperos)
[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/hesperos)](https://napari-hub.org/plugins/hesperos)

    TODO : DESCRIPTION

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



# Table of Contents
- [Installation and Requirements](#installation-and-requirements)
    * [Automatic installation](#automatic-installation)
    * [Manual installation](#manual-installation)
    * [Upgrade Hesperos version](#upgrade-hesperos-version)
- [Hesperos: Manual Segmentation and Correction](#hesperos-manual-segmentation-and-correction)
    * [Load your image](#load-your-image)



# Installation and Requirements
Hesperos plugin is designed to run on Windows operating system and macOS with Python 3.8, 3.9 or 3.10.

## Automatic installation
1. Install [Anaconda] and deselect *Add to PATH*. Note the path where you install anaconda.

2. Download only the *script_files* folder, for [Windows](/script_files/for_Windows/) or [Macos](/script_files/for_Windows/).

3. Add the Anaconda path in these script files:
    1. For Windows: 
    Right click on the .bat files (for [installation](/script_files/for_Windows/install_hesperos_env.bat) and [running](/script_files/for_Windows/run_hesperos.bat)) and select *Modify*. Change *PATH_TO_ADD* with your Anaconda path. Then save changes.
        > for exemple: $ `anaconda_dir=C:\Users\chgodard\anaconda3` 
    2. For Macos:
        1. Right click on the .command files (for [installation](/script_files/for_Macos/install_hesperos_env.command) and [running](/script_files/for_Macos/run_hesperos.command)) and select *Open with TextEdit*. Change *PATH_TO_ADD* with your Anaconda path. Then save changes.
            > for exemple: $ `source ~/opt/anaconda3/etc/profile.d/conda.sh ` 
        2. On your terminal allow running of your .command files (change *PATH* with the path of your .command files): 
            > $ `chmod u+x PATH/install_hesperos_env.command `

            > $ `chmod u+x PATH/run_hesperos.command `

4. Double click on the **install_hesperos_env file** to create a virtual environment in Anaconda with python 3.9 and Napari 0.4.14. /!\ Hesperos plugin is not yet compatible with Napari version superior to 0.4.14.

5. Double click on the **run_hesperos file** to run napari from your virtual environment.

6. On Napari: 
    1. Go to *Plugins/Install Plugins...*
    2. Search for "hesperos" (it can take a while to load).
    3. Install **hesperos** plugin.
    4. When installation is done, close Napari. A restart of Napari is needed to take in consideration the new installed plugin.

7. Double click on the **run_hesperos file** to run Napari.

8. On Napari, use the hesperos plugin with *Plugins/hesperos*.

## Manual installation
1. Install [Anaconda] and deselect *Add to PATH*.
2. Open your Anaconda prompt command.
3. Create a virtual environment with Python 3.8, 3.9 or 3.10 :
    > $ ` conda create -n hesperos_env python=3.9`  
4. Install Python packages (in your virtual environment):
    > $ ` conda activate hesperos_env` 

    > $ ` conda install -c conda-forge napari=0.4.14` /!\ Hesperos plugin is not yet compatible with napari version superior to 0.4.14.

    > $ ` conda install -c anaconda pyqt` if needed

    > $ ` pip install hesperos`             
5. Launch Napari:
    > $ ` napari`

## Upgrade Hesperos version
1. Double click on the **run_hesperos file** to run Napari. 
2. On Napari: 
    1. Go to *Plugins/Install Plugins...*
    2. Search for "hesperos" (it can take a while to load).
    3. Click on *Update* if a new version of Hesperos have been found. You can check the last version of Hesperos in the [Napari Hub](https://www.napari-hub.org/plugins/hesperos).
    4. When installation is done, close Napari. A restart of Napari is needed to take in consideration the new installed plugin.



# Hesperos: Manual Segmentation and Correction

## Loading panel
Hesperos plugin can be used with Digital imaging and communications in medicine (DICOM), Neuroimaging Informatics Technology Initiative (NIfTI) or Tagged Image File Format (TIFF) images. To improve performances, use images that are located on your disk.

To load a image file (.tiff, .tif, .nii or .nii.gz) use the IMAGE button. To load a unique DICOM serie use the IMAGE button. Folder with multiple DICOM series is not supported.  

After Loading, a slider appears to zoom in/out on the image. Zoom is also possible with the mouse scroller. 
If the image is a DICOM serie, a default contrast  have the possibility to select a default contrast for the image (to highlight bones or soft tissues according to their Hounsfield Units.) 

## 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,
"hesperos" is free and open source software

[napari]: https://github.com/napari/napari
[Cookiecutter]: https://github.com/audreyr/cookiecutter
[@napari]: https://github.com/napari
[BSD-3]: http://opensource.org/licenses/BSD-3-Clause
[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin

[tox]: https://tox.readthedocs.io/en/latest/
[pip]: https://pypi.org/project/pip/
[PyPI]: https://pypi.org/

[Anaconda]: https://www.anaconda.com/products/distribution#Downloads

hesperos

A plugin to manually or semi-automatically segment medical data and correct previous segmentation data.

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

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

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 review the documentation of this plugin here
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.

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