Saravanan666/pypi-org huggingface dataset:
Package: napari-umap
napari-umapA simple plugin to use with napariThisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-umapviapip:pip install napari-umapTo install latest development version :pip install git+https://github.com/royerlab/napari-umap.gitContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"napari-umap" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-unicell
napari-unicelluniversal cell segmentation modelsThisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-unicellviapip:pip install napari-unicellContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theApache Software License 2.0license,
"napari-unicell" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-validate-random-label-predictions
napari-validate-random-label-predictionsValidate separate instances of label predictions manuallyThisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-validate-random-label-predictionsviapip:pip install napari-validate-random-label-predictionsTo install latest development version :pip install git+https://github.com/gatoniel/napari-validate-random-label-predictions.gitContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"napari-validate-random-label-predictions" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-vedo-bridge
napari-vedo-bridgeTo be able to use interactive processing of meshes in napari, this plugin provides a bridge to the vedo library. It allows to transfer meshes between napari and vedo and to use the interactive processing capabilities of vedo in napari.Interactive mesh cuttingTo interactively cut meshes in the napari-vedo MeshCutter, install the plugin (see below) and open the plugin it from the napari plugins menu (Plugins > Mesh Cutter (napari-vedo-bridge)).To cut meshes you can use the following cutters:PlaneCutter: cuts a mesh with a planeSphereCutter: cuts a mesh with a sphereBoxCutter: cuts a mesh with a boxTo send and get data into and from the plugin, you can:Retrieve the current mesh from napari (clickRetrieve mesh from napari) - this imports thecurrently selected mesh layerfrom napariLoad a mesh from file (clickLoad mesh)Send a mesh to napari (clickSend back to napari) - this creates a new mesh layer in napariThisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-vedo-bridgeviapip:pip install napari-vedo-bridgeContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"napari-vedo-bridge" is free and open source softwareIssuesIf you encounter any problems, please [file an issue] along with a detailed description.
Package: napari-vemseg
napari-vemsegA simple plugin to do semi-automated segmentation within napari using vemseg built on
XGBoost.InstallationcondaInstallation and Environment CreationTo start with a conda environment must be created.conda create -n vemseg-env python=3.8
conda activate vemseg-envnapariInstillationWe must then installnapari:pip install "napari[all]"napari-vemsegInstillationYou can finally installnapari-vemsegviapip:conda install pyopencl
pip install napari-vemsegUsageTrain VEMClassifierPredict Using Pretrained VEMClassifierContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"napari-vemseg" is free and open source softwareIssuesIf you encounter any problems, please [file an issue] along with a detailed description.
Package: napari-vesicles-segmentation
napari-vesicles-segmentationA simple plugin to detect vesicles in cells images.Thisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-vesicles-segmentationviapip:pip install napari-vesicles-segmentationTo install latest development version :pip install git+https://github.com/alexisjapas/napari-vesicles-segmentation.gitUsageOpen napariOpen your dataLaunch the vesicles-segmentation pluginSelect the data you want to segment and set the parameters of the segmentationimage: The image to segment vesicles in. The image can be a 2D or 3D temporal stack of images.minimum vesicles size: The minimum size of the vesicles to detect. Smaller detected vesicles are removed.membrane erosion: The size of the disk radius used for eroding the cell. This is used to remove the external membrane. This parameter scales when downsizing the image, for more information see 'downsizing ratio' parameter.closing size: The size of the disk radius used for closing the cell. This is used to fill holes in the cell. This parameter scales when downsizing the image, for more information see 'downsizing ratio' parameter.clip: If set to zero, no standardization is performed. Otherwise, the standard deviation of the image is set to n_sigma * the standard deviation of the image, the image is standardized and its values are clipped to the range [-1, 1] in order to remove outliers. The higher the value of n_sigma, the less outliers are removed. This operation can lead to a better detection of the cell.downsampling ratio: The downsampling ratio used for the downsampled image. This is used to speed up the computation. Downsampling the image have impact in reducing the resolution of erosion and closing e.g. for a downsize ratio of 2, setting the erosion size to 3 will result in an erosion size of 6.display cell detection: If set to True, the cell detection is displayed in the viewer instead of the vesicle detection.Click on the "Segment" button to start the segmentation. This can take few seconds or minutes depending on the size of the data. The result is added to the viewer as below.ContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"napari-vesicles-segmentation" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari_video
napari-videoNapari plugin for working with videos.Relies onpyvideoreaderas a backend which itself usesopencvfor reading videos.Installationpipinstallnapari[all]napari_videoUsageFrom a terminal:naparivideo.aviOr from within python:importnapari
fromnapari_video.napari_videoimportVideoReaderNPpath='video.mp4'vr=VideoReaderNP(path)withnapari.gui_qt():viewer=napari.view_image(vr,name=path)Internalsnapari_video.napari_video.VideoReaderNPexposes a video with a numpy-like interface, using opencv as a backend.For instance, open a video:vr=VideoReaderNP('video.avi')print(vr)video.avi with 60932 frames of size (920, 912, 3) at 100.00 fpsThenvr[100]will return the 100th frame as a numpy array with shape(902, 912, 3).vr[100:200:10]will return 10 frames evenly spaced between frame number 100 and 200 (shape(10, 902, 912, 3)).Note that by default, single-frame and slice indexing return 3D and 4D arrays, respectively. To consistently return 4D arrays, open the video withremove_leading_singleton=False.vr[100]will then return a(1, 902, 912, 3)array.We can also request specific ROIs and channels. For instance,vr[100:200:10,100:400,800:850,1]will return an array with shape(10, 300, 50, 1).
Package: napari-video-cvdask
napari-video-cvdaskA Video File Reader that used to use OpenCV2 and Dask Arrays, and now uses dask-image, which does the same thing but better.  (Pro-tip, never name a package after its dependencies!)This [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.InstallationYou can installnapari-video-cvdaskvia [pip]:pip install napari-video-cvdaskTo install latest development version :pip install git+https://github.com/nickdelgrosso/napari-video-cvdask.gitContributingContributions are very welcome. Tests can be run with [tox], please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of the [MIT] license,
"napari-video-cvdask" is free and open source softwareIssuesIf you encounter any problems, please [file an issue] along with a detailed description.
Package: napari-vodex
napari-vodexA plugin to load volumetric data based on experimental conditions.Thisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-vodexviapip:pip install napari-vodexTo install latest development version :pip install git+https://github.com/LemonJust/napari-vodex.gitHow-To GuideTo get started with napari_vodex, please see details and examples inHow-To Guide.ContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"napari-vodex" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-webcam
This napari plugin has been decommissioned; it is no longer maintained. Please trynapari-live-recordinginstead.
It has a more user-friendly interface and advanced features.napari-webcamThisnapariplugin was generated withCookiecutterusing with@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-webcamviapip:pip install napari-webcam==0.1.18This plugin worked with napari 0.4.12.ContributingDevelopment of this plugin was halted.LicenseDistributed under the terms of theBSD-3license,
"napari-webcam" is free and open source softwareIssuesThis plugin is no longer supported.
Package: napari-woggle-opacity
napari-woggle-opacityDynamicallywogglea layer's opacity in Napari. Select a wogglingspeedand atransition function(SmoothorSharp).Why usenapari-woggle-opacity?For example:To visually inspect the results of animage registrationoperationTo inspectmulti-modalityormulti-channelimagesInstallationYou can installnapari-woggle-opacityviapip:pip install napari-woggle-opacityUsageSelect the plugin from thePluginsmenu of Napari.ContributingContributions are very welcome. Please get in touch if you'd like to be involved in improving or extending the package.LicenseDistributed under the terms of theBSD-3license,
"napari-woggle-opacity" is free and open source softwareIssuesIf you encounter any problems, please file an issue along with a detailed description.Thisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.
Package: napari-workflow-inspector
napari-workflow-inspectorInspect relationships between image processing operations in active workflows in napari. Open the inspector by clicking the menuTools > Visualization > Workflow Inspector.Also install thenapari-script-editorto generate code from active workflows.For recording workflows, all napari image processing plugins that use the@time_slicerinterface are supported. Seenapari-time-slicerfor a list. More to come, stay tuned.InstallationYou can installnapari-workflow-inspectorviapip:pip install napari-workflow-inspectorContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"napari-workflow-inspector" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-workflows
napari-workflowsThis repository contains data structures for managing image processing workflows in napari.
There are no user interface in this repository as it serves as backend.
Multiple front-ends for managing workflows are available in these napari plugins:napari-assistantnapari-latticenapari-script-editornapari-workflow-optimizernapari-workflow-inspectorUsing workflows, you can combine functions from these napari plugins in workflows:devbio-naparinapari-accelerated-pixel-and-object-classificationnapari-cupy-image-processingnapari-segment-blobs-and-things-with-membranesnapari-simpleitk-image-processingnapari_pyclesperanto_assistantIf you want to learn how these workflows function under the hood, thedemo notebookgives some insights.This repository was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-workflowsviapip:pip install napari-workflowsContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"napari-workflows" is free and open source softwareAcknowledgementsThis 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 number2021-240341 (Napari plugin accelerator grant)from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation.IssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-workshop-browser
napari-workshop-browserA plugin to browse and follow napari workshopsThisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.How to use thisDownload napari as an app (e.g. the latest napari releases are
available here:https://github.com/napari/napari/releases)Open napariSelectPlugins \ Plugin ManagerInstall this plugin, and restart napari.Run this plugin and enter the URL of your workshop's zip fileClickLaunch workshopInstallationYou can installnapari-workshop-browserviapip:pip install napari-workshop-browserTo install latest development version :pip install git+https://github.com/kephale/napari-workshop-browser.gitContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"napari-workshop-browser" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-workshop-plugin
napari-workshop-pluginThe purpose of this repository is to provide a template for a napari plugin that can be used as a starting point for the napari software development workshop 2023.
It should help you to see the basics of building documentation, testing, and continuous integration for a napari plugin.Thisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.
The cookiecutter plugin is also a great jumping off point for your own plugin development.Check out a template you can use for your own READMEtemplate.mdInstallationYou can installnapari-workshop-pluginviapip:pip install napari-workshop-pluginLicenseDistributed under the terms of theMITlicense,
"napari-workshop-plugin" is free and open source softwareIssuesIf you encounter any problems, please file an issue along with a detailed description.
Package: napari-wsi
napari-wsiA plugin to read whole slide images withinnapari.InstallationYou can installnapari-wsiviapip:pipinstallnapari-wsiTo install the latest development version, run:pipinstallgit+https://github.com/AstraZeneca/napari-wsi.gitDescriptionThisnapariplugin provides a reader for various whole slide image formats.By default, any of the following formats is read using thetifffilelibrary.
If the image file contains a tagGDAL_METADATA, therasteriolibrary is used
instead..bif.ndpi.qptiff.scn.svs.tif.tiffQuickstartFrom the terminal:napariCMU-1.svsFrom python:importnapariviewer=napari.Viewer()viewer.open("CMU-1.svs")
Package: napari-wsireg
napari-wsiregPlugin to perform whole slide image registration based on wsireg.Please seewsiregfor more info image formats, features and how registration works.UsageAdd images from napari layers or from file and set up "registration paths" between them. OME-TIFF is best supported format.Constructed registration graph in actionSolid arrows: direct registration between two images.Dashed arrows: indriect registration paths.InstallationYou can installnapari-wsiregviapip:pip install napari-wsiregTo install latest development version :pip install git+https://github.com/nhpatterson/napari-wsireg.gitContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"napari-wsireg" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-yapic-prediction
napari-yapic-predictionA napari widget plugin to perform YAPiC model segmentation prediction in the napari window.Thisnapariplugin was generated withCookiecutterusing with@napari'scookiecutter-napari-plugintemplate.DescriptionThis napari plugin provides a widget to upload aYAPiCtrained model and perform segmentation over all the present images in the napari window. The segmentation results are uploaded as napari layers into the viewer automatically with the name structure ofimgename_prediction.InstallationPlease install either GPU or CPU version of tensorflow that is compatible with yourcudaandcudnnlibraries before installing the plugin depending on your system.
One of the plugin dependency isyapicthat currently has sensitivity to tensorflow versions.You can installnapari-yapic-predictionviapip:pip install napari-yapic-predictionContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theGNU GPL v3.0license,
"napari-yapic-prediction" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-yolo5-mitosis-detector
napari-yolo5-mitosis-detectorA simple plugin to use yolo5 for mitosis detection with naparitpoquillon@gmail.comThisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installnapari-yolo5-mitosis-detectorviapip:pip install napari-yolo5-mitosis-detectorTo install latest development version :pip install git+https://github.com/TPoquillon/napari-yolo5-mitosis-detector.gitContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"napari-yolo5-mitosis-detector" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: napari-yolov5
napari-yolov5Plugin adapted from Ultralytics to bring YOLOv5 into Napari.Training and detection can be done using the GUI. Training dataset must be prepared prior to using this plugin. Further development will allow users to use Napari to prepare the dataset. Follow instructions stated onUltralytics Githubto prepare the dataset.The plugin includes 3 pre-trained networks that are able to identify mitosis stages or apoptosis on soSPIM images. More details can be found on thepre-print.Thisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationFirst install conda and create an environment for the pluginconda create --prefix env-napari-yolov5 python=3.9
conda activate env-napari-yolov5You can installnapari-yolov5andnapariviapip:pip install napari-yolov5
pip install napari[all]For GPU support :pip uninstall torch
pip install torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.htmlUsageFirst select if you would like to train a new network or detect objects.ForTraining:Data preparation should be done followingUltralytics'instructions.Select the size of the network, the number of epochs, the number of images per batch to load on the GPU, the size of the images (must be a stride of 32), and the name of the network.An example of the YAML config file is provided insrc/napari_yolov5/resourcesfolder.Progress can be seen on the Terminal or on the right-hand side of the viewer.ForDetection:It is possible to perform the detection on a single layer chosen in the list, all the layers opened, or by giving a folder path. For folder detection, all the images will be loaded as a single stack.Nucleus size of the prediction layer has te be filled to resize the image to the training dataset. Nucleus size of the training dataset will be asked in case of a custom network.Confidence threshold defines the minimum value for a detected object to be considered positive.
iou nms threshold (intersection-over-union non-max-suppression) defines the overlapping area of two boxes as a single object. Only the box with the maximum confidence is kept.
Progress can be seen on the Terminal.Few options allow for modification on how the boxes are being displayed (default : box + class + confidence score ; box + class ; box only) and if the box coordinates and the image overlay will be exported.
Post-processing option will perform a simple 3D assignment based on 3D connected component analysis. A median filter (1x1x3 XYZ) is applied prior to the assignment.
The centroid of each object is then saved into a new point layer as a 3D point with a random color for each class.The localisation of each centroid is saved and the path is shown in the Terminal at the end of the detection. It is also possible now to define the export folder.ContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theGNU GPL v3.0license,
"napari-yolov5" is free and open source softwareIssuesIf you encounter any problems, please [file an issue] along with a detailed description.
Package: napari-z-plotter
napari-z-plotterIn 2D view, click on an pixel to plot the intensity along the depth (Z) axis of a 3D image at that location.InstallationYou can installnapari-z-plotterviapip:pip install napari-z-plotterUsageSelect the plugin from thePluginsmenu of Napari.ContributingContributions are very welcome. Please get in touch if you'd like to be involved in improving or extending the package.LicenseDistributed under the terms of theBSD-3license,
"napari-z-plotter" is free and open source softwareIssuesIf you encounter any problems, please file an issue along with a detailed description.Thisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.