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

Zach Marin

Robert Haase

napari-pymeshlab






Interfaces between napari and the pymeshlab library to allow import, export, construction and processing of surfaces. 
This is a WIP and feature requests are welcome. Please check PyMeshLab
for possible features.

Feature list

Read/write .3ds, .apts, .asc, .bre, .ctm, .dae, .e57, .es, .fbx, .glb, .gltf, .obj, .off, .pdb, .ply,
                  .ptx, .qobj, .stl, .vmi, .wrl, .x3d, .x3dv
Screened Poisson Surface Reconstruction
Convex hull of a surface
Laplacian smoothing of surfaces
Smoothing surfaces using Taubin's method
Surface simplification using clustering decimation
colorize_curvature_apss

Some functions are shown in the demo notebook.


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

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

napari-pymeshlab

Interfaces between napari and pymeshlab library to allow import, export and construction of surfaces.

Sylvain Prigent

Description
This plugin allows to open particle tracking results from multiple formats into the Napari 
Tracks Layer.
Supported formats
The formats currently supported by this plugin are:
CSV
The most basic format to store particle tracking tracks is a CSV file containing the tracks table.
In this format each line is a particle and each column a property of the particle. The table
headers must be TrackID, t, x, y, z. Note that the header order does not matter:
| TrackID       | t | x | y | z |
| :------------ | :----------: | :----------: | :----------: | -----------: |
| 0 | 16   | 41.5828343348868  | 47.505930020081664| 0 |
| 0 | 17   | 41.48425270538317 | 51.6023835597057 | 0 |
The raw CSV file is a classical comma-separated values format: 
csv
TrackID,t,x,y,z
0,16, 41.5828343348868, 47.505930020081664, 0
0,17, 41.48425270538317, 51.6023835597057, 0
...
[!NOTE]
This CSV format does not support split and merge events
TrackMate
The TrackMate format is the XML model file générated by the TrackMate Fiji 
plugin. 
The XML file from TrackMate should not be manually modified and and contains a Model element:
```xml


...





...
```
All the particles features from the TrackMate model file are loaded in the napari tracks properties. 
[!NOTE]
This format supports split and merge events
Icy
The Icy format is a XML file generated by the Icy software.
The XML file from ICY should not be manually modified and starts with the root element:
```xml







      ...
```
[!TIP]
This format supports split and merge events
ISBI
The ISBI format is a XML format used for the ISBI tracking challenge. This format must contain 
a root element and a list of particles in a TrackContestISBI2012 element:
```xml







``` 
[!NOTE]
This format does not support split and merge events
Quickstart
You can open local tracks using napari at the terminal and the path to your file:
$ napari /path/to/your/tracks.xml
OR in python:
```python
import napari
viewer = napari.Viewer()
viewer.open('/path/to/your/tracks.xml')
napari.run()
```
Getting Help
If you discover a bug with the plugin, or would like to request a new feature, please
raise an issue on our repository at https://github.com/sylvainprigent/napari-tracks-reader.

napari-tracks-reader

Read tracks from txt (xml, csv) files to napari

Tom Burke

napari-labeling






This is a napari-plugin based on the labeling project.
It allows the generation of overlapping labels in one layer, save and load of this layer in a json-based file format and
it contains a widget to explore the overlapping labels layer and select specific segments with a mouse click .
Please note that currently, the widget part only works by adding it through code with:
from napari_labeling import edit_widget
viewer = napari.Viewer()
viewer.window.add_dock_widget(edit_widget)

An example on how to achieve this can be found in the main.py on GitHub.

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

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

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

napari-labeling

A napari plugin for handling overlapping labeling data

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

napari-plugin-search





Find napari plugins

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

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

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

napari-plugin-search

Volker Baecker

napari-J





A plugin to exchange data with FIJI and to use FIJI image analysis from napari.
Current features are:

get the active image from FIJI
send a screenshot to FIJI
get a set of points from the FIJI results table
filter the points in napari
send the filtered points back to FIJI

Known problems:

Crashes on linux  when the file-dialog is opened. Workaround: Set the option Use JFileChooser to open/save from the Edit>Options>Input/Output menu.


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

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

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

napari-J

A plugin to exchange data with FIJI and to use FIJI image analysis from napari

Jordão Bragantini

Grabber: A Tool to Improve Convergence in Interactive Image Segmentation





A tool for contour-based segmentation correction (2D only).
This repository provides a demo code of the paper:

Grabber: A Tool to Improve Convergence in Interactive Image Segmentation
Jordão Bragantini, Bruno Moura, Alexandre X. Falcão, Fábio A. M. Cappabianco

https://user-images.githubusercontent.com/21022743/145699960-57da06a5-668f-4e81-82b5-7f3d3ddf8ee3.mp4

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

Installation
You can install grabber-ift via pip:
pip install grabber-ift

Known Limitations
This implementation doesn't support the items below, feel free to open a PR to add them.

It only support 2D image, supporting 3D images isn't trivial, but it could be applied per slice with minor changes.

Citation
If this work was useful for your research, please cite our paper:
@article{bragantini2020grabber,
  title={Grabber: A Tool to Improve Convergence in Interactive Image Segmentation,
  author={Bragantini, Jord{\~a}o and Bruno Moura, Falc{\~a}o, Alexandre Xavier and Cappabianco, F{\'a}bio AM,
  journal={Pattern Recognition Letters},
  year={2020}
}
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,
"grabber" is free and open source software
Issues
If you encounter any problems, please [file an issue] along with a detailed description.

grabber-ift

A tool for contour-based segmentation correction (2D only).

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.

Marc Boucsein

Robin Koch

Image-Part-Selecter



A napari plugin in order to select parts of images

Installation
You can install Image-Part-Selecter via pip:
pip install Image-Part-Selecter

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

Image-Part-Selecter

A napari plugin in order to select parts of images

World2Data



A napari plugin in order to convert the world information to the data of a 2D/3D layer.

Installation
You can install World2Data via pip:
pip install World2Data

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,
"World2Data" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

World2Data

A napari plugin in order to convert the world information to the data of a 2D/3D layer

Johannes Müller

napari-stl-exporter





This plugin allows to import and export surface data in Napari to common file formats. The generated file formats can be read by other common applications, and - in particular - allow 3D-printing.

Usage
This section explains which data can be written with the napari-stl-exported and how you can do so.
Supported file formats:
Currently supported file formats for export include and rely on the vedo io API.
* .stl: Standard triangle language
* .ply: Polygon file format
* .obj: Wavefront object
Supported Napari layers:
Currently supported Napari layer types are:
* Surface layers
* Label layers: The label data is converted to surface data under the hood using the marching cubes algorithm implemented in scikit-image and is then exported using Vedo. Warning: This can be slow for large image data!
Import/export
Interactively: To export the data, simply save the selected layer with File > Save Selected Layer(s) and specify the file ending to be some_file.[file_ending], for supported file types, see above. Similarly, supported file types can be imported into Napari with File >
From code: A Napari Label layer can be added to the viewer as described in the napari reference with this code snippet:
```python
import napari
import numpy as np
Load and binarize image
label_image = np.zeros((100, 100, 100), dtype=int)
label_image[25:75, 25:75, 25:75] = 1
Add data to viewer
viewer = napari.Viewer()
label_layer = viewer.add_labels(data, name='3D object')
save the layer as 3D printable file to disc
napari.save_layers(r'/some/path/test.stl', [label_layer])
```
Sample data
You can create sample label/surface data for export using the built-in functions as shown here:

...or from code with
```Python
import napari_stl_exporter
pyramid = napari_stl_exporter.make_pyramid_surface()
```
3D-printing
To actually send your object to a 3D-printer, it has to be further converted to the .gcode format with a Slicer program. The latter convert the 3D object to machine-relevant parameters (printing detail, motor trajectories, etc). Popular slicers are:

Slic3r: Documentation here
Prusa Slicer: Tutorial here

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


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

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

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

napari-stl-exporter

Exports label images to 3D-printable stl files.

Sebastian Gonzalez-Tirado

napari-spatial-omics






A simple plugin to visualize spatial omic data stored in CSV format

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

Installation
You can install napari-spatial-omics via pip:
pip install napari-spatial-omics

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

napari-spatial-omics

A simple plugin to visualize spatial omic data stored in CSV format

Laura Zigutyte

Ryan Savill

Marcelo Zoccoler

napari-clusters-plotter










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

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


Jump to:
- Usage
  - Starting point
  - Measurements
  - Time-Lapse Measurements
  - Plotting
  - Time-Lapse Plotting
  - Dimensionality reduction: UMAP, t-SNE or PCA
  - Clustering
  - Plotting clustering results
- Installation
- Troubleshooting installation
- Contributing
- License
- Acknowledgements
Usage
Starting point
For clustering objects according to their properties, the starting point is a grey-value image and a label image
representing a segmentation of objects. For segmenting objects, you can for example use the
Voronoi-Otsu-labelling approach
in the napari plugin napari-segment-blobs-and-things-with-membranes.

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

Afterwards, you can save and/or close the measurement table. Also, close the Measure widget.
If you are uploading your own measurements make sure that there is a column that specifies the which measurement belongs to which label
by adding a column with the name "label". If you don't specify this column it will be assumed that measurements start at 1 and each 
column describes the next label.
Time-Lapse Measurements
If you have 3D time-lapse data this will automatically be detected. In case you have 2D time-lapse data you need to
convert it into a suitable shape using the function: Tools > Utilities > Convert 3D stack to 2D time-lapse (time-slicer),
which can be found in the napari time slicer.
Note that tables for time-lapse data will include an additional column named "frame", which indicates which slice in
time the given row refers to. If you want to import your own csv files for time-lapse data make sure to include this column!
If you have tracking data where each column specifies measurements for a track instead of a label at a specific time point, 
this column must not be added.
Plotting
Once measurements were made, these measurements were saved in the properties of the labels layer which was analysed.
You can then plot these measurements using the menu Tools > Measurement > Plot measurement (ncp).
In this widget, you can select the labels layer which was analysed and the measurements which should be plotted
on the X- and Y-axis. If you cannot see any options in axes selection boxes, but you have performed measurements, click
on Update Axes/Clustering Selection Boxes to refresh them. Click on Run to draw the data points in the plot area.

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

Hold down the SHIFT key while annotating regions in the plot to manually select multiple clusters.

Time-Lapse Plotting
When you plot your time-lapse datasets you will notice that the plots look slightly different.
Datapoints of the current time frame are highlighted in white and you can see the datapoints move through the plot if you press play:

You can also manually select groups using the lasso tool and plot a measurement per frame and see how the group behaves in time.
Furthermore, you could also select a group in time and see where the datapoints lie in a different feature space:

Dimensionality reduction: UMAP, t-SNE or PCA
For getting more insights into your data, you can reduce the dimensionality of the measurements, e.g.
using the UMAP algorithm, t-SNE
or PCA.
To apply it to your data use the menu Tools > Measurement > Dimensionality reduction (ncp).
Select the label image that was analysed and in the list below, select all measurements that should be
dimensionality reduced. By default, all measurements are selected in the box. If you cannot see any measurements, but
you have performed them, click on Update Measurements to refresh the box. You can read more about parameters of both
algorithms by hovering over question marks or by clicking on them. When you are done with the selection, click on Run
and after a moment, the table of measurements will re-appear with two additional columns representing the reduced
dimensions of the dataset. These columns are automatically saved in the properties of the labels layer so there is no
need to save them for usage in other widgets unless you wish to do so.

Afterwards, you can again save and/or close the table. Also, close the Dimensionality Reduction widget.
Clustering
If manual clustering, as shown above, is not an option, you can automatically cluster your data, using these implemented algorithms:
* k-means clustering (KMEANS)
* Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN)
* Gaussian Mixture Model (GMM)
* Mean Shift (MS)
* Agglomerative clustering (AC)
Therefore, click the menu Tools > Measurement > Clustering (ncp),
again, select the analysed labels layer.
This time select the measurements for clustering, e.g. select only the UMAP measurements.
Select the clustering method KMeans and click on Run.
The table of measurements will reappear with an additional column ALGORITHM_NAME_CLUSTERING_ID containing the cluster
ID of each datapoint.

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

Example of k-means clustering results:

Installation

Get a python environment, e.g. via mini-conda.
  If you never used python/conda environments before, please follow the instructions
  here first. It is recommended to
  install python 3.9 to your new conda environment from the start. The plugin is not yet supported with Python 3.10.
  Create a new environment, for example, like this:

conda create --name ncp-env python=3.9

Activate the new environment via conda:

conda activate ncp-env

Install pyopencl, e.g. via conda:

conda install -c conda-forge pyopencl

Install napari, e.g. via pip:

python -m pip install "napari[all]"
Afterwards, you can install napari-clusters-plotter via pip:
pip install napari-clusters-plotter
Troubleshooting installation

If the plugin does not appear in napari 'Plugins' menu, and in 'Plugin errors...' you can see such an error:

ImportError: DLL load failed while importing _cl
Try downloading and installing a pyopencl with a lower cl version, e.g. cl12 : pyopencl=2020.1. However, in this case,
you will need an environment with a lower python version (python=3.8).

Error: Could not build wheels for hdbscan which use PEP 517 and cannot be installed directly

Install hdbscan via conda before installing the plugin:
conda install -c conda-forge hdbscan

ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject

Similar to the above-described error, this error can occur when importing hdbscan through pip or in the wrong order. This can be fixed by installing packages separately through conda and in the following order:
bash
conda install -c conda-forge napari pyopencl hdbscan
pip install napari-clusters-plotter

WARNING: No ICDs were found or LogicError: clGetPlatformIDs failed: PLATFORM_NOT_FOUND_KHR

Make your system-wide implementation visible by installing either of the following conda packages:
conda install -c conda-forge ocl-icd-system
conda install -c conda-forge ocl_icd_wrapper_apple
Contributing
Contributions are very welcome. Tests can be run with pytest, please ensure
the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license,
"napari-clusters-plotter" is free and open source software
Acknowledgements
This project was supported by the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy – EXC2068 - Cluster of Excellence "Physics of Life" of TU Dresden.
This project has been made possible in part by grant number 2021-240341 (Napari plugin accelerator grant) from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation.
Issues
If you encounter any problems, please file an issue along
with a detailed description.

napari-clusters-plotter

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

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

Hector Munoz

napari-mat-images



Features
This plugin loads image variables stored in MATLAB .mat files into napari.
It loads any variable that looks like an image.
Presently, that includes any array with more than two dimensions with size greater than 20 pixels (determined by shape_is_image()).
If loading a variable with 3 or more dimensions, the plugin assumes that it is a stack of images, and the dimension with greatest size is the axis of the stack.
Loading Large Files
If loading a large .mat file saved in HDF5/v7.3 format, chunks of the images are loaded as needed, resulting in fast initial load, but potentially slower scrolling.
Slices of the image stacks are randomly sampled to determine min/max contrast values.
Requirements
This plugin relies on scipy to load small .mat files and h5py (with dask) to load larger HDF5/v7.3 .mat files.
It implicitly requires napari for use.
Installation
napari-mat-images requires napari to be installed, although it is not listed as a requirement for installation.
This plugin relies on plugin functionality found in napari version > 0.2.12. This can be installed via pip from PyPI:
$ pip install napari>0.2.12

You can install napari-mat-images via pip from PyPI:
$ pip install napari-mat-images

Usage
Once installed, the plugin will be used whenever trying to load a .mat file.
This can be done from the napari GUI or commandline:
$ napari my_file.mat

Contributing
Contributions are very welcome.
Tests can be run with pytest,
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-mat-images 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 along with napari\'s cookiecutter-napari-plugin template.

napari-mat-images

A plugin to load images stored in MATLAB .mat files with napari

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

Chris Havlin


yt-napari provides plugins to help load data from yt into napari. It includes a graphical data loader, json-based file loading and helper-functions for jupyter notebook interaction. See the full documentation for more details.

yt-napari

Viktor van der Valk

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

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

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


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


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


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



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


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


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


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


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

elastix-napari

A toolbox for rigid and nonrigid registration of images.

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

Hanjin Liu

napari-text-layer
Napari text layer for bio-image annotation.


Installation
You can install using pip:
pip install napari-text-layer
Keybindings and mouse callbacks

"→", "←", "↑", "↓" ... Move selected shapes by 1 pixel.
"F2" ... Enter edit mode at the selected shape (or the last one if no shape is selected).
"Enter" ... Finish edit mode or add a new shape at the same interval.
"Ctrl" + "Shift" + "<" or ">" ... Change font size.
double click ... Enter edit mode at the clicked shape.


napari-text-layer

napari-svg





A plugin for reading and writing svg files with napari

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

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

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

napari-svg

A plugin for reading and writing svg files with napari

John Fozard

skimage-sift-registration






Simple plugin for 2D keypoint detection and affine registration with RANSAC.



Artificial data 


Moving and fixed images showing inlier keypoints after RANSAC

This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.
It uses the scikit-image SIFT keypoint detection routines to find distinctive image points and generate local descriptions of the image around them.
Correspondences between the two images are then found by looking for pairs of keypoints, one in each of the two images, with closely matching descriptors.
For typical images, many of these correspondences will be wrong. To reduce these false correspondences, the plugin applies the RANSAC algorithm. This randomly selects a small subset of the matching pairs of keypoints, estimates the affine transformation between this subset of keypoints, and then evaluates how many of the other pairs of keypoints also closely agree with this affine transformation ("inliers"). A large number of random samples are tested, and the transformation with the most inliers retained.
The plugin outputs two points layers, one for each image, containing all the corresponding (inlier) SIFT keypoints. It also uses the estimated affine transformation between the two images to deform the "moving" image layer onto the "fixed" image layer.
This approach is an attempt to provide similar functionality to the Stephan Saalfeld's Fiji "Extract SIFT Correspondences" plugin extract, and more-or-less
just provides a napari interface to the existing routines in scikit-image. There are great examples in the scikit-image documentation (e.g. SIFT-example and RANSAC-example) that can be used if you would like to use these routines in your own analysis scripts.
Installation
You can install napari-sift-registration via pip:
pip install napari-sift-registration

To install the latest development version :
pip install git+https://github.com/jfozard/napari-sift-registration.git

Usage
Basic usage

Load two 2D single channel images in Napari.
Select the menu item Plugins > napari-sift-registration
Select these two images as the "Moving image layer" and the "Fixed image layer". The moving image will be deformed by the transformation to look like the fixed image.
The remaining parameters are the default settings from scikit-image; try these default values first.

Advanced usage
The parameter values for SIFT feature detection, keypoint matching and RANSAC are accessible from the plugin gui. For further information about their use, see the appropriate scikit-image documentation:
Upsampling before feature detection, maximum number of octaves, maximum number of scales in every octave, blur level of seed image, feature descriptor size, feature descriptor orientation bins: see scikit-image-SIFT.
Closest/next closest ratio: see scikit-image-match_descriptors
Minimum number of points sampled for each RANSAC model, distance for points to be inliers in RANSAC model, maximum number of trials in RANSAC model: see scikit-image-RANSAC
Only show inlier keypoints: If checked, only show corresponding keypoints that are inliers after RANSAC. If unchecked, show all corresponding keypoints.
Limitations
Only 2D, single channel images (for now).
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-sift-registration" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-sift-registration

Simple plugin for SIFT keypoint detection, and affine registration with RANSAC, based on scikit-image

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

Jonas Windhager

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

napari-imc

Imaging Mass Cytometry (IMC) file type support for napari

natari



Napari gaming
Sliding puzzle
Restore the image by reordering the superpixels using the W, A, S, D keys! 

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

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

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

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

Known issues

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

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

natari

Adam Tyson

Horst Obenhaus









brainreg-segment
Segmentation of 1/2/3D brain structures in a common anatomical space
brainreg-segment is a companion to brainreg allowing manual segmentation of regions/objects within the brain (e.g. injection sites, probes etc.) allowing for automated analysis of brain region distribution, and visualisation (e.g. in brainrender).
brainreg-segment and brainreg were developed by Adam Tyson and Charly Rousseau in the Margrie Lab, based on aMAP by Christian Niedworok. The work was generously supported by the Sainsbury Wellcome Centre.
Installation
brainreg-segment comes bundled with brainreg, so see the brainreg installation instructions.
brainreg-segment can be installed on it's own (pip install brainreg-segment), but you will need to register your data with brainreg first.
Usage
See user guide.
If you have any questions, head over to the image.sc forum.
Contributing
Contributions are very welcome. Please see the contributing guide.
Citing brainreg-segment
If you find brainreg-segment useful, and use it in your research, please let us know and also cite the paper:

Tyson, A. L., Vélez-Fort, M.,  Rousseau, C. V., Cossell, L., Tsitoura, C., Lenzi, S. C., Obenhaus, H. A., Claudi, F., Branco, T.,  Margrie, T. W. (2022). Accurate determination of marker location within whole-brain microscopy images. Scientific Reports, 12, 867 doi.org/10.1038/s41598-021-04676-9


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.


brainreg-segment

Manual segmentation of 3D brain structures in a common anatomical space

napari-multitask
Multitasking on napari.

Layers and opened dock widgets are stored in the task panels below. Switch your tasks at any time.
Installation
pip install napari-multitask

napari-multitask

Marc Buckmakowski

napari-medical-image-formats



A Plugin in order to read and write medical image formats such as DICOM, DICOM Series and NIfTI. The meta information is supported by the package napari-itk-io. 

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

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

napari-medical-image-formats

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

Alan M Watson





Description
View datasets archived at the Brain Image Library.
**NOTE: This plugin is under early development.  Currently, only a subset of single-channel, fMOST datasets which include projections are available to view.  An example can be found here.

Features

Multiscale Rendering
In datasets that include multiple resolution representations of the data, each resolution can be combined to improve the speed of browsing and user experience.  An example of a dataset with multiple resolution projections can be found here.
All datasets included in the current release of napari-bil-data-viewer use multi-resolution datasets.
3D rendering of whole datasets.  The lowest resolution is used for rendering.  Currently, this is a limitation imposed by napari.
The plugin does NOT require a BIL account as datasets are already accessible via https.

Known Issues / limitations

Currently the only datasets that are available are those which have been manually selected by the developers.  If you would like a specific dataset to be included please consider adding the dataset(s) to the dataset_info.py file and submitting a pull request.
To inquire about this plugin please contact Brain Image Library support:  bil-support@psc.edu
The plugin is still under development.  We appreciate all reports of issues / errors which occur during use.


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

Installation
Option #1: Install plugin via the napari plugin menu

Menu: Plugins >> Install/Uninstall Plugins
Search: napari-bil-data-viewer
Select install

Option #2:  Install a fresh python virtual environment
```bash
Example of venv creation using conda
conda create -y -n bil-viewer python=3.8
conda activate bil-viewer
Install napari-bil-data-viewer
pip install napari-bil-data-viewer
Run Napari
napari
```
Contributing
Please consider contributing to this project!  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-bil-data-viewer" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.
Change Log:
v0.1.0:
Initial release.
v0.1.1 & v0.1.2:
Changes to documentation

napari-bil-data-viewer

Napari plugin for viewing Brain Image Library datasets

Ben J. Gross

Elijah Shelton

Carlos Gomez

Otger Campas










napari-stress
This plugin provides tools for the analysis of surfaces in Napari, such as utilities to determine and refine the surface-representation of objects using a ray-casting approach and calculate the curvature of surfaces. 
It re-implements code in Napari that was written for Gross et al. (2021): STRESS, an automated geometrical characterization of deformable particles for in vivo measurements of cell and tissue mechanical stresses 
and has been made open source in this repository.

Usage
Functionality in this repository is divided in two groups: Recipes and plugins.
Recipes
Receipes are workflows for processing images, points and surface data step-by-step.
| Recipe| Description |
| --- | --- |
|  | Confocal data (.tif), 3D+t: Interactive tutorial on how to extract surfaces from intensity image data |
|| Confocal data (.tif), 3D+t:  Interactive tutorial on how to extract curvature from surfaces|
|| Confocal data (.tif), 3D+t:  Jupyter notebook for processing single channel data and extracting gaussian curvature.|
Plugins
All functions in napari-stress are documented separately for interactive usage from the napari viewer as well as Jupyter notebooks. 
|Function| Links |
| --- | --- |
| |Fit spherical harmonics: Interactive Code |
| |Surface tracing: Code |
||Reconstruct surface: Code|
Utilities
Data to be used for this plugin is typically of the form [TZYX] (e.g., 3D + time). 
Napari-stress offers convenient ways to use functions from other repositories (which are often made for 3D data) on timelapse data with the frame_by_frame function and the TimelapseConverter class. 
Both are described in more detail in this notebook.
Installation
Create a new conda environment with the following command. 
If you have never used conda before, please read this guide first.
conda create -n napari-stress Python=3.9 napari jupyterlab -c conda-forge
conda activate napari-stress
You can then install napari-stress using pip:
pip install napari-stress
Issues
To report bugs, request new features or get in touch, please open an issue or tag @EL_Pollo_Diablo on image.sc.
See also
There are other napari plugins with similar / overlapping functionality

morphometrics
napari-pymeshlab
napari-process-points-and-surfaces

Contributing
Contributions are very welcome. Tests can be run with pytest, 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-stress" is free and open source software
Acknowledgements
This project was supported by the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy – EXC2068 - Cluster of Excellence "Physics of Life" of TU Dresden.

napari-stress

Interactive surface analysis in napari for measuring mechanical stresses in biological tissues

napari-cupy-image-processing







GPU-accelerated image processing using cupy and CUDA
Usage
This napari plugin adds some menu entries to the Tools menu. You can recognize them with their suffix (n-cupy) in brackets.
Furthermore, it can be used from the napari-assistant graphical user interface. 
Therefore, just click the menu Tools > Utilities > Assistant (na) or run naparia from the command line.

You can also call operations from python, e.g. as shown in this demo notebook.
Installation
Follow the instructions for installing cupy on your computer first.
conda install -c conda-forge cupy

Afterwards, you can install napari-cupy-image-processing via pip:
pip install napari-cupy-image-processing

A more detailed example for installation (change 11.2 to your desired CUDA version):
conda create --name cupy_p39 python=3.9
conda activate cupy_p39
conda install -c conda-forge cupy cudatoolkit=11.2 napari
pip install napari-cupy-image-processing
Contributing
Contributions are very welcome. Adding cupy ndimage functions is quite easy as you can see in the 
implementation of the current operations. 
If you need another function in napari, just send a PR. Please make sure the tests pass locally before submitting a PR.
pip install pytest-cov pytest-qt
pytest --cov=napari_cupy_image_processing
This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.
License
Distributed under the terms of the MIT license,
"napari-cupy-image-processing" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-cupy-image-processing

GPU-accelerated image processing using CUDA

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

Hieu Hoang

napari-pram






Plugin for PRAM data annotation and processing.


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

Usage
Open napari-pram toolbox:
On the toolbar, select [Plugins] > napari-pram: Open PRAM's toolbox
Load PRAM image and annotations:
Press Command/Control + O: 
- Select *.json files for annotations (from either VGG Annotator or napari-pram)
- Select *.png files for PRAM image
Annotate

Press Annotate
Click the plus-in-circle icon on the top-left panel and start editing

Run PRAM particles detector

Select a proper threshold between 1 (ultra sensitive) - 10 (less sensitive)
Press Run Detector

Evaluate

Press Evaluate
Hide/Unhide true positive/ false postive/false negative layers

Load new image

Press Clear All to remove all layers

Export to JSON

Press Save to File to export all annotations, predictions from the algorithm to a JSON file

Installation
Following this tutorial to install napari. 
Alternatively, you can follow my instructions as follows:
You will need a python environment. I recommend Conda. Create a new environment, for example:
conda create --name napari-env python=3.7 pip

Activate the new environment:
conda activate napari-env

Install napari via pip:
pip install napari[all]

Then you can finally install our plugin napari-pram via pip:
pip install napari-pram

Alternatively, the plugin can be installed using napari-GUI
[Plugins] > Install/Uninstall Plugins and search for napari-pram
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-pram" is free and open source software
Issues
If you encounter any problems, please [file an issue] along with a detailed description.

napari-pram

plugin for PRAM data annotation and processing

Nathan Heath Patterson

Lukasz Migas

napari-imsmicrolink





napari plugin to perform MALDI IMS - microscopy registration using laser ablation marks as described in Anal. Chem. 2018, 90, 21, 12395–12403. This plugin is a work-in-progress but is mostly functional.
N.B. This tool is NOT a general purpose registration framework to find transforms between IMS (MALDI or otherwise)
and microscopy. It is built to align MALDI IMS pixels to their corresponding laser ablation marks as captured by microscopy AFTER the IMS experiment. 
This approach has the advantage of providing direct evidence of registration performance as IMS pixels are aligned 
to their explicit spatial origin in microscopy space, improving overall accuracy and confidence of microscopy-driven IMS 
data analysis.
Installation
You can install napari-imsmicrolink via pip:
pip install napari-imsmicrolink

Typical experiment workflow

Acquire pre-IMS microscopy (autofluorescence, brightfield) - optional
Perform normal IMS sample preparation.

Acquire post-IMS microscopy (autofluorescence, brightfield) with matrix still on sample
that reveals laser ablation marks.


Gather IMS data that contains XY integer coordinates for the IMS experiment
   (.imzML, Bruker spotlist (.txt, .csv), Bruker peaks.sqlite (FTICR),
   Bruker .tsf (TIMS qTOF only))


Run napari-imsmicrolink with data 3 and 4


Once registered, use wsireg to align other microscopy modalities to IMS-registered post-IMS
microscopy



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

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

napari-imsmicrolink

Plugin to perform IMS to microscopy registration using laser ablation marks.

Graham Dellaire

BBii-Decon






Projected Barzilai-Borwein Image Deconvolution with Infeasible Iterates (BBii-Decon)
The projected Barzilai-Borwein method of image deconvolution utilizing infeasible iterates (BBii-Decon), utilizes Barzilai-Borwein (BB) or projected BB (PBB) method and enforces a nonnegativity constraint, but allows for infeasible iterates between projections. This algorithm (BBii) results in faster convergence than the basic PBB method, while achieving better quality images, with reduced background than the unconstrained BB method (1). 
The code represented is based on the original BBii algorithm written in MatLab by Kathleen Fraser and Dirk Arnold, which was ported to python 3.8 by Graham Dellaire, Dirk Arnold and Kathleen Fraser for non-commercial use.
The first implementation shown here is for 2D deconvolution using a known 2D PSF of 256 X 256 pixels, and images of at least 256 pixels in one dimension. One file implements just the deconvolution of a blurred image, while the second file contains a modification of the BBii-Decon algorithm that has a built in heuristic for measuring image reconstruction error relative to a ground truth image. For general 2D deconvolution, either a theoretical 2D PSF (if you know the optical properties of your system) or the central in focus image of a fluorescent bead taken with the same imaging setup (lens, magnification, camera) can produce a suitable PSF.
GPU-acceleration
For most 2D deconvolution, optimal results are obtained with 10 iterations of the algorithm. However, if processing takes too long, acceleration using graphics processing units (GPUs) may make sense, especially for processing larger images with >10 iterations or 3D images. (Note: At this time BBii-Decon is optimized for 2D deconvolution, with a 3D implementation planned in future). 
This plugin supports accelerated processing using the cupy library. To make use of it, please follow 
the instructions to install cupy. 
Installation may look like this:
conda create --name cupy_p38 python=3.8
conda activate cupy_p38
conda install -c conda-forge cupy cudatoolkit=10.2
If cupy installation worked out, you will find another checkbox in the user interface. By activating it, processing 
should become faster by factor 5-10, depending on processed image data and use GPU hardware.

Usage - napari
You can use the BBii deconvolution from within napari by clicking the menu Plugins > bbii-decon > bbii deconvolution. 
In the dialog, select the PSF, the image to process (a) and click on Run. After a moment, the deconvolved image (b) 
will show up.

Usage from python
You can also call the function from python. There is a full working example in this notebook.
```
from bbii_decon import bbii
bbii(PSF, image, number_of_iterations = 15, tau = 1.0e-08, rho = 0.98)
```
Citation
1) Kathleen Fraser, Dirk V. Arnold, and Graham Dellaire (2014). Projected Barzilai-Borwein
method with infeasible iterates for nonnegative least-squares image deblurring. In Proceedings
of the Eleventh Conference on Computer and Robot Vision (CRV 2014), Montreal, Canada, pp.
189--194.

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

Installation
You can install bbii-decon via pip:
pip install bbii-decon

Installation for developers
Clone the github repository:
```
conda install git
git clone https://github.com/gdellaire/BBii-Decon.git
cd BBii-Decon
pip install -e .
```
Deployment to pypi
For deploying the plugin to the python package index (pypi), one needs a pypi user account 
first. For deploying the plugin to pypi, one needs to install some tools:
python -m pip install --user --upgrade setuptools wheel
python -m pip install --user --upgrade twine
The following command allows us to package the souce code as a python wheel. Make sure that the 'dist' and 'build' folders are deleted before doing this:
python setup.py sdist bdist_wheel
This command ships the just generated to pypi:
python -m twine upload --repository pypi dist/*
Read more about distributing your python package via pypi.
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,
"bbii-decon" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

bbii-decon

Projected Barzilai-Borwein Image Deconvolution with Infeasible Iterates (BBii-Decon)

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

Uwe Schmidt

Martin Weigert

StarDist Napari Plugin




This project provides the napari plugin for StarDist, a deep learning based 2D and 3D object detection method with star-convex shapes. StarDist has originally been developed (see papers) for the segmentation of densely packed cell nuclei in challenging images with low signal-to-noise ratios. The plugin allows to apply pretrained and custom trained models from within napari.
If you use this plugin for your research, please cite us.

Installation
Install the plugin with pip install stardist-napari or from within napari via Plugins > Install/Uninstall Plugins…. If you want GPU-accelerated prediction, please read the more detailed installation instructions for StarDist.

You can activate the plugin in napari via Plugins > stardist-napari: StarDist.
Example images for testing are provided via File > Open Sample > stardist-napari.

Documentation
The two main buttons at the bottom of the plugin are (see right side of screenshot above):
Restore Defaults: Restore default values for inputs (exceptions: Input Image, Image Axes, Custom Model).
Run: Start the prediction with the selected inputs and create the outputs when done.
All plugin activity is shown in the napari activity dock, which can be shown/hidden by clicking on the word activity next to the little arrow at the bottom right of the napari window.
Inputs
The plugin does perform input validation, i.e. it will disable the Run button if it detects a problem with the selected inputs. Problematic input fields are highlighted with a "lightcoral" background color , and their tooltips typically explain what the problem is. Some error messages are shown at the bottom in napari's status bar, such as for incompatibilities between multiple input fields. Input fields with warnings (also explained via tooltips) are highlighted with an orange background color .
Input Image: Select a napari layer of type Image as the input.
Tooltip: Shows the shape of the image.
Image Axes: String that describes the semantic image axes and their order, e.g. YX for a 2D image. This parameter is automatically chosen (i.e. guessed) when a new input image is selected and should work in most cases. Permissible axis values are: X (width/columns), Y (height/rows), Z (depth/planes), C (channels), T (frames/time).
Tooltip: Shows the mapping of semantic axes to the shape of the selected input image.
Predict on field of view (only for 2D models in 2D view): If enabled, the StarDist prediction is only applied to the current field of view of the napari viewer. As the name of this checkbox indicates, this only works for 2D StarDist models and when the napari viewer is in 2D viewing mode. The checkbox is not even shown if those conditions are not met.
Neural Network Prediction
Model Type: Choice whether to use registered pre-trained models (2D, 3D) or provide a path to a model folder (Custom 2D/3D). Based on this choice, either the input for Pre-trained Model or Custom Model is shown below.
(Further information regarding pre-trained models: how to register your own model, model registration in StarDist.)
Pre-trained Model: Select a registered pre-trained model from a list. The first time a model is selected, it is downloaded and cached locally.
Custom Model: Provide a path to a StarDist model folder, containing at least config.json and a compatible neural network weights file (with suffix .h5 or .hdf5). If present, thresholds.json is also loaded and its values can be used via the button Set optimized postprocessing thresholds (for selected model).
Model Axes: A read-only text field that shows the semantic axes that the currently selected model expects as input. Additionally, we show the number of expected input channels, e.g. YXC[2] to indicate that the model expects a 2D input image with 2 channels. Seeing the model axes is helpful to understand whether the axes of the input image are compatible or not.
Normalize Image: A checkbox to indicate whether to perform percentile-based input image normalization or not. This should be checked if the input image wasn't manually normalized such that most pixel values are in the range 0 to 1. If unchecked, inputs Percentile low and Percentile high are hidden.
Percentile low: Percentile value of input pixel distribution that is mapped to 0 (~min value). If there aren't any outlier pixels in your image, you may use percentile 0 to do a standard min-max image normalization.
Percentile high: Percentile value of input pixel distribution that is mapped to 1 (~max value). If there aren't any outlier pixels in your image, you may use percentile 100 to do a standard min-max image normalization.
Input image scaling: Number or list of numbers (one per input axis) to scale the input image before prediction and rescale the output accordingly. For example, a value of 0.5 indicates that all spatial axes are downscaled to half their size before prediction, and that the outputs are scaled to double their size. This is useful to adapt to different object sizes in the input image.
Tooltip: Shows the mapping of scale values to the semantic axes of the selected input image.
NMS Postprocessing
Probability/Score Threshold: Determine the number of object candidates to enter non-maximum suppression. Higher values lead to fewer segmented objects, but will likely avoid false positives. The selected model may have an associated threshold value, which can be loaded via the Set optimized postprocessing thresholds (for selected model) button.
Overlap Threshold: Determine when two objects are considered the same during non-maximum suppression. Higher values allow segmented objects to overlap substantially. The selected model may have an associated threshold value, which can be loaded via the Set optimized postprocessing thresholds (for selected model) button.
Output Type: Choose format of outputs (see below for details). Selecting Label Image will create the outputs StarDist labels and StarDist class labels (for multi-class models only) as napari Labels layers. Selecting Polygons / Polyhedra will instead return the output StarDist polygons as a napari Shapes layer for a 2D model, or StarDist polyhedra as a napari Surface layer for a 3D model. Selecting Both will return both types of outputs.
Advanced Options
Number of Tiles: String None (to disable tiling) or list of integer numbers (one per axis of input image) to determine how the input image is tiled before the CNN prediction is computed on each tile individually. This is needed to avoid (GPU) memory issues that can occur for large input images. Note that the NMS postprocessing is still run only once with candidates from the predictions of all image tiles.
Tooltip: Shows the mapping of tile values to the semantic axes of the selected input image.
Normalization Axes: String of semantic axes which are jointly normalized (if they are present in the input image). For example, the default value ZYX indicates that all spatial axes are always normalized together; if an image has multiple channels, the pixels will be normalized separately per channel (e.g. this is what typically makes sense for fluorescence microscopy where channels are independent). On the other hand, the channels in RGB color images typically need to be normalized jointly, hence using ZYXC makes sense in this case. Note: if an image is explicitly opened with rgb=True in napari, the channels are automatically normalized together.
Tooltip: Shows a brief explanation.
Time-lapse Labels: If the input is a time-lapse/movie, each frame is first independently processed by StarDist. If Separate per frame (no processing) is chosen, the object ids in the label images of each frame are not modified, i.e. they are consecutive integers that always start at 1. Selecting Unique through time will cause object ids to be unique over time, i.e. the smallest object id in a given frame is larger than the largest object id of the previous frame. Finally, choosing Match to previous frame (via overlap) will perform a simple form of greedy matching/tracking, where object ids are propagated from one frame to the next based on object overlap.
Show CNN Output: Create additional outputs (see below for details) StarDist probability and StarDist distances that show the direct results of the CNN prediction which are the inputs to the NMS postprocessing. Additionally, StarDist class probabilities is created for multi-class models.
Set optimized postprocessing thresholds (for selected model): Button to set Probability/Score Threshold and Overlap Threshold to the values provided by the selected model. Nothing is changed if the model does not provide threshold values.
Outputs
StarDist polygons: The detected/segmented 2D objects as polygons (napari Shapes layer).
StarDist polyhedra: The detected/segmented 3D objects as surfaces (napari Surface layer).
StarDist labels: The detected/segmented 2D/3D objects as a label image (napari Labels layer). In an integer-valued label image, the value of a given pixel denotes the id of the object that it belongs to. For example, all pixels with value 5 belong to the object with id 5. All background pixels (that don't belong to any object) have value 0.
StarDist class labels (multi-class models only): The classes of detected/segmented 2D/3D objects as a semantic segmentation labeling (napari Labels layer). The integer value of a given pixel denotes the class id of the object that it belongs to. For example, all pixels with value 3 belong to the object class 3. Note that all pixels that belong to a specific object instance (as returned by StarDist labels) do have the same object class here. All background pixels (that don't belong to an object class) have value 0.
StarDist probability: The object probabilities predicted by the neural network as a single-channel image (napari Image layer).
StarDist distances: The radial distances predicted by the neural network as a multi-channel image (napari Image layer).
StarDist class probabilities (multi-class models only): The object class probabilities predicted by the neural network as a multi-channel image (napari Image layer).
Troubleshooting & Support

The image.sc forum is the best place to start getting help and support. Make sure to use the tag stardist, since we are monitoring all questions with this tag.
For general questions about StarDist, it's worth taking a look at the frequently asked questions (FAQ).
If you have technical questions or found a bug, feel free to open an issue.

Other resources
A demonstration of an earlier version of the plugin is shown in this video.
Many of the parameters are identical to those of our StarDist ImageJ/Fiji plugin, which are documented here.

stardist-napari

Riley M Shea

avidaq





controls for napari and micromanger

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

Installation
Standard installation
You can install avidaq via pip:
shell
pip install napari[all] avidaq
Install from plugin menu
Alternatively you can install avidaq via the napari plugin menu:

Running
First start micromanager.  Make sure the server port checkbox is activated.
Then to start napari with the avidaq plugin active run:
napari -w avidaq

Updating presets
MDA presets are stored in a json file in the user's home directory.
```shell
C:\\Users\YourName\.avidaq\mda_presets.json
```
This file should exist after plugin installation with some defaults. You do not need to create the file yourself.
Add or modify the values and reload napari to see the changes.
All parameter entries are optional, if not provided the default value will be used.
The parameter names and their descriptions can be found [here] (https://github.com/micro-manager/pycro-manager/blob/main/pycromanager/acq_util.py#L102-L115)
The format is as follows:
json
{
    "gui_display_name": {
        "parameter_name": value,
        "parameter_name": value,
        ...
    },
    "gui_display_name": {
        "parameter_name": value,
        "parameter_name": value,
        ...
    },
    ...
}
defaults:
json
{
  "Basic": {
    "num_time_points": 5,
    "z_start": 0,
    "z_end": 6,
    "z_step": 0.4
  },
  "Simple": {
    "num_time_points": 2,
    "z_start": 0,
    "z_end": 2,
    "z_step": 0.1
  },
  "Detailed": {
    "num_time_points": 10,
    "z_start": 0,
    "z_end": 12,
    "z_step": 0.2
  }
}
Issues
If you encounter any problems, please [file an issue] along with a detailed description.
Development
You should have python3.8 or higher installed.

clone this repo
create a virtual environment python -m venv .venv && source .venv/bin/activate
run pip install -e '.[testing,build]'
run pre-commit install

To run unit tests
pytest
typical workflow

edit code in /src
run napari -w avidaq
repeat

Releasing to pypi
Project is automically built and deployed to pypi upon


avidaq

napari-wsireg
Plugin to perform whole slide image registration based on wsireg.
Please see wsireg for more info image formats, features and how registration works.
Usage
Add images from napari layers or from file and set up "registration paths" between them. OME-TIFF is best supported format.
Installation
You can install napari-wsireg via pip:
pip install napari-wsireg

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

napari-wsireg

plugin to perform whole slide image registration with wsireg

Ervin Tasnadi

napari_nucleaizer






GUI for the nucleaAIzer method in Napari.

Overview
This is a napari plugin to execute the nucleaizer nuclei segmentation algorithm.
Main functionalities
Using this plugin will be able to

Load your image into Napar, then outline the nuclei.
Specify an image folder containing lots of images and an output folder, and automatically segment all of the images in the input folder.
If you are not satisfied with the results, you can train your own model:
You can use our pretrained models and fine tune them on your data.
You can skip the nucleaizer pipeline and train only on your data.



Supported image types
We have several pretrained models for the following image modelities:
* fluorescent microscopy images
* IHC stained images
* brightfield microscopy images,
among others. For the detailed descriptions of our models, see: https://zenodo.org/record/6800341.
How it works?
For the description of the algorithm, see our paper: "Hollandi et al.: nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer, Cell Systems, 2020. https://doi.org/10.1016/j.cels.2020.04.003"
The original code (https://github.com/spreka/biomagdsb) is partially transformed into a python package (nucleaizer_backend) to actually perform the operations. See the project page of the backend at: https://github.com/etasnadi/nucleaizer_backend.
If you wish to use the web interface, check: http://nucleaizer.org.


Install


Create an environment (recommended).


Install napari: pip install "napari[pyqt5]". Other methods: https://napari.org/tutorials/fundamentals/installation.html


Install the plugin into napari:


User mode from PyPI: start Napari (command line: napari) and select he Install/Uninstall Plugins... under the Plugins menu. In the popup, filter for napari-nucleaizer.


Developer mode: clone this project and use pythhon3 -m pip install -e <path> to install the project locally into the same evnrionment as napari. It has the advantage that you will have the latest version.


Run


Start Napari by calling napari from the command line.

Then, activate the plugin in the Plugins menu. If you successfully installed the plugin, you have to see something like this:


Further help
See the documentation (work in progress).
Issues
Use the github issue tracker if you experinece unexpected behaviour.
Contact
You can contact me in e-mail where MY-INSTITUTE is brc.hu.

napari-nucleaizer

A GUI interface for training and prediction using the nucleAIzer nuclei detection method.

The segmentation game - for napari







Gamified image segmentation quality estimation


Usage
The Segmentation Game allows to quantitatively compare segmentation results to a given ground truth annotation.
This allows fine-tuning parameters of image processing workflows to get optimal segmentation quality. 
It also allows comparing different segmentation algorithms and identify which algorithm performs best objectively.
The game can be found in napari's Tools > Games > The Segmentation Game menu.
Ground Truth Annotation
Before you can start playing the game, some annotated cells/nuclei are necessary to later compute segmentation quality from.
Depending on the used metric, it might be sufficient to annotate a hand full of objects. 
Use napari's annotation tools as shown below. 
Use the + and - keys on your keyboard to increase and decrease the label number that is currently drawn.

Parameter tuning
If you work with one of napari's segmentation plugins that produce labels layers,
you can tune their parameters and see how this influences segmentation quality.

Segmentation algorithm comparison
If you aim at comparing different segmentation algorithms, you can collect their results in label layers in the napari viewer.
You can then select the segmentation result from the corresponding pulldown and save quantitative comparison results in the Highscore table.

Metrics
Currently, these metrics are implemented:
* Jaccard Index (sparse): The Jaccard Index is a measure of overlap. 
  It lies between 0 (no overlap) and 1 (perfect overlap). 
  For each annotated ground truth label, the maximum overlap of any segmented label is determined. 
  The mean overlap of all annotated labels serves as metric result.
* Jaccard Index (binary): The annotated ground truth labels and the segmentation result are first binarized considering all annotated pixels as positive and all other labels as negative.
  Afterwards, the overlap between the two binary images is computed. This allows comparing binary segmentation algorithms, such as thresholding techniques.
* Jaccard Index (binary, sparse): The annotated ground truth image can contain values 1 (negative, false) and 2 (positive, true) and
  the segmentation result image will be binarized (0: False, otherwise: True). This allows comparing object/no-object annotations with label images.
Related plugins
If you aim at automatically optimizing segmentation quality, there are also napari plugins available with this capability:

napari-accelerated-pixel-and-object-classification
napari-workflow-optimizer

Installation
You can install the-segmentation-game via pip:
pip install the-segmentation-game

Contributing
Contributions - especially new image segmentation quality metrics - 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,
"the-segmentation-game" is free and open source software
Issues
If you encounter any problems, please open a thread on image.sc along with a detailed description.

the-segmentation-game

Gamified image segmentation quality estimation

Manan Lalit










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

Introduction
Dependencies
Getting Started
Datasets
Registering your data
Contributing
Issues
Citation

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

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



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

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



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

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



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

PlatyMatch

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

Lennart Kowitz

vessel-express-napari






A simple plugin for 3D vessel segmentation of LSFM images
This napari plugin can be used to optimize the segmentation parameters for the main VesselExpress software platform.

Installation
The easiest way to install the plugin is to open napari, go to Plugins, then Install/Uninstall plugins. You will be able to find the plugin by name "vessel-express-napari". 
Or, you can install vessel-express-napari via pip:
pip install vessel-express-napari

To install latest development version :
pip install git+https://github.com/MMV-Lab/vessel-express-napari.git

Documentation
We provide a quick start guide to explain the important pieces of this plugin. Suggestions and feature quests are very welcomed. 
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,
"vessel-express-napari" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

vessel-express-napari

A simple plugin for 3D vessel segmentation

Philipp Schoennenbeck

napari-mrcfile_handler





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

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

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

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

napari-mrcfile-handler

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

napari-tabu





A plugin for handling multiple napari windows


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

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

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

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

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

napari-tabu

A plugin for handling multiple napari windows

Alexandr Dibrov

napari-kics







A plugin to estimate chromosome sizes from karyotype images.

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

Install
Usage
Example
Citation
Maintainer
Contributing
License

Install
You can install napari-kics via pip:
sh
pip install napari-kics
This will install all required dependencies as well. We recommend installing it in a virtual environment, e.g. using conda:
sh
conda create -n kics python
conda activate kics
pip install napari-kics
We recommend using mamba as a faster alternative to conda.
Usage

Launch Napari via command line (napari).
Activate the plugin via menu Plugins -> napari-kics: Karyotype Widget.
Select file via File -> Open File.
Follow instructions in the panel on the right.

You may use the interactive analysis plots directly via command line:
sh
karyotype-analysis-plots
Example

Launch Napari via command line (napari).
Activate the plugin via menu Plugins -> napari-kics: Karyotype Widget.
Select file via File -> Open Sample -> napari-kics: sample.
Follow instructions in the panel on the right.

Try out the interactive analysis plots directly via command line:
sh
karyotype-analysis-plots --example
Citation

Arne Ludwig, Alexandr Dibrov, Gene Myers, Martin Pippel.
Estimating chromosome sizes from karyotype images enables validation of
de novo assemblies. To be published.

License
Distributed under the terms of the BSD-3 license,
"napari-kics" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.
Contributing
Contributions are very welcome. Please file a pull request with your
contribution.
You can setup a local development environment for napari-kics via pip:
sh
git clone https://github.com/mpicbg-csbd/napari-kics.git
cd napari-kics
pip install -e .
Overview
https://user-images.githubusercontent.com/17703905/139654249-685703b5-2196-4a73-a036-d40d578ebcdf.mp4

napari-kics

A plugin to estimate chromosome sizes from karyotype images.

napari-tomoslice





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

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

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

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

napari-tomoslice

A napari plugin for interacting with electron cryotomograms

Christopher Nauroth-Kress

napari-time_series_plotter







Description
Napari-time_series_plotter (TSP) is a plugin for the napari image viewer. TSP adds widgets to the viewer to select and visulise pixel / voxel values from one or multiple image layers as line plots. A plot represents the intensity of the selected pixel / voxel over the first dimension (time). TSP supports 3D and 4D images (4D: t+3D, 3D: t+2D).

Installation
You can either install the latest version via pip or conda.
pip:
pip install napari-time-series-plotter

conda:
conda install -c conda-forge napari-time-series-plotter

Alternatively you can install the plugin directly in the napari viewer plugin manager.

To install the latest development version use:
pip install git+https://github.com/ch-n/napari-time_series_plotter.git

Usage




Select the two TSP widgets LayerSelectror and VoxelPlotter in the Plugins tab in the viewer
Use the LayerSelector to chose the image layers you want to select for plotting
Move the mouse over the image while holding Shift
The plotter will display graphs for all selected layers and a legend

ToDo

[ ] Add Sphinx documentation

Version 0.1.0 Milstones

[ ] Update to napari-plugin-engine2 #5
[ ] Update widget GUI #6
[ ] Add widget to save pixel / voxel time series to file #7

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

References
This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.
Images used in the demo gif were taken from: The Cancer Imaging Archive 
DOI: https://doi.org/10.7937/K9/TCIA.2015.VOSN3HN1
Images: 1.3.6.1.4.1.9328.50.16.281868838636204210586871132130856898223
        1.3.6.1.4.1.9328.50.16.254461916058189583774506642993503110733


napari-time-series-plotter

A Plugin for napari to visualize pixel values over the first dimension (time -> t+3D, t+2D) as graphs.

Thorsten Beier

napari-splineit






A napari plugin for spline manipulation

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

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

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

napari-splineit

Jay Unruh

napari-IP-workflow






A plugin to do image preprocessing, segmentation, and measurements on other images.  The typical workflow is background subtraction followed by smoothing, thresholding, and size filtering.  This is typically done on nuclear stained images.  Segmentation can optionally be followed by circular label expansion to find cytoplasmic signals. The labeled signals are then measured on background subtracted images.
General organization
The code is separated into non-interactive processing functions (ipfunctions module) and an interactive widget (segwidget module).  Please look at the code on github for examples: Github. The expected workflow is from jupyter notebooks with an interactive workflow shown in src/napari-ip-workflow/_tests/standard_segementation_widget.ipynb and a non-interactive workflow shown in src/napari-ip-workflow/_tests/standard_segmentation.ipynb.  The expectation is to find the best parameters in an interactive way (ideally testing on several images) and then use the non-interactive workflow to batch through more data sets.  All image processing algorithms are in the ipfunctions module and the segwidget module has the Napari widget code.  Below I describe the strategies that are utilized in the workflow.
Background subtraction strategy
Automated background subtraction (e.g. as in Fiji) is often accomplished with a low pass filter-style approach like rolling ball background subtraction.  This approach fails as feature sizes grow larger or measurements approach the background.  Manual selection of the background is more robust but introduces human variability and isn't compatible with high throughput analyses.  Our approach is to attempt to automate regional selection of background as follows.  First the image is smoothed with a Gaussian filter to eliminate background noise.  Next, minimum values are subtracted from each channel and the resulting images are summed.   Next, a uniform 2D boxcar smoothing is applied to the image--background level regions in the resulting image are at least the boxcar size distance away from foreground objects.  The minimum pixel in that resulting image is a good approximation for the background region of the image.  A thick border is specified to avoid lower intensity regions at the border of the image.  This algorithm is implemented in the ipfunctions module as findBackground.  Once the background region is found, it can be measured with measureCirc.
Segmentation and thresholding strategy
There are many automated thresholding algorithms available via python and, by extension, Napari.  This program uses a very simplistic but powerful method.  Most segmentable images consist of foreground and background components.  In imaging, the foreground is more noisy than the background.  Ideally a smoothed background subtracted image will have a maximum intensity that represents the foreground well and a background intensity of 0.  In that case, the threshold level can be easily defined as a fraction of that smoothed maximum intensity.  A threshold fraction of 0.25 tends to work well but lower values may be more robust if background is fairly smooth and the foreground is noisier.  In some cases the foreground has anomalous high values that will skew the estimation.  In that case it may be better to estimate the foreground as e.g. the 99th percentile of the intensity.  In some cases it may be useful to use the average intensity as a reference point instead or use the raw intensity value (statistic is Identity).  Those last options tend to be less robust and it may be desired in those cases to use some of the more complex autothresholding methods.  After thresholding, objects on the image edge are eliminated and objects are filtered according to size.  The minimum area can easy remove small debris that can contaminate a measurement.  The maximum area can be used for large contaminants or poorly segmented clusters of cells that might not be desired in the analysis.

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

Installation
You can install napari-IP-workflow via pip:
pip install napari-IP-workflow

To install latest development version :
pip install git+https://github.com/jayunruh/napari-IP-workflow.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 GNU GPL v3.0 license,
"napari-IP-workflow" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.

napari-IP-workflow

A plugin to do image preprocessing, segmentation, and measurements on other images.

napari-folder-browser





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


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

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

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

napari-folder-browser

Browse folders of images and open them using double-click

Image-Composer



A napari plugin in order to compose a background image with a foreground image.

Installation
You can install Image-Composer via pip:
pip install Image-Composer

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

Image-Composer

A napari plugin in order to compose a background image with a foreground image

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

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