A bundle of napari plugins useful for 3D+t image processing and analysis for studying developmental biology.
- accelerated-pixel-and-object-classification
- Instance segmentation
- Semantic segmentation
- Object classification
- Random Forest Classifier training
- animation
- Visualization
- blob-detection
- Detection
- brightness-contrast
- Visualization
- clusters-plotter
- Visualization
- Plotting
- Semantic object segmentation
- Dimensionality reduction
- Unsupervised machine learning
- crop
- Transformation
- curtain
- Visualization
- czifile2
- File input/output
- folder-browser
- File input/output
- layer-details-display
- Visualization
- mouse-controls
- Interaction
- PlatyMatch
- Image registration
- plot-profile
- Visualization
- Quantification
- plugin-search
- Interaction
- pyclesperanto-assistant
- Filtering
- Instance segmentation
- Semantic segmentation
- Quantification
- pystackreg
- Image registration
- Motion correction
- RedLionfish
- Deconvolution
- Processing
- roi
- Manual segmentation
- segment-blobs-and-things-with-membranes
- Filtering
- Instance segmentation
- Semantic segmentation
- simpleitk-image-processing
- Filtering
- Instance segmentation
- Semantic segmentation
- Quantification
- skimage-regionprops
- Quantification
- stl-exporter
- File input/output
- tabu
- Interaction
- the-segmentation-game
- Quantification
- Segmentation quality assurance
- workflow-inspector
- Visualization
- workflow-optimizer
- Interaction
- Optimization
You can install devbio-napari via conda/mamba. If you have never used conda before, please read this guide first.
Start by creating an environment using mamba.
mamba create --name devbio-napari-env napari=0.4.19 python=3.9 devbio-napari pyqt -c conda-forge -c pytorch
Afterwards, activate the environment like this:
mamba activate devbio-napari-env
Afterwards, run this command from the command line
naparia
This window should open. It shows the Assistant graphical user interface. Read more about how to use it in its documentation.
In case error messages contains "ImportError: DLL load failed while importing cl: The specified procedure could not be found" see also or ""clGetPlatformIDs failed: PLATFORM_NOT_FOUND_KHR", please install recent drivers for your graphics card and/or OpenCL device. Select the right driver source depending on your hardware from this list:
- AMD drivers
- NVidia drivers
- Intel GPU drivers
- Intel CPU OpenCL drivers
- Microsoft Windows OpenCL support
Sometimes, mac-users need to install this:
conda install -c conda-forge ocl_icd_wrapper_apple
Sometimes, linux users need to install this:
conda install -c conda-forge ocl-icd-system
In case installation didn't work in the first attempt, you may have to call this command line to reset the napari configuration:
napari --reset
In case pytorch-related plugins fail, install pytorch as explained on its website. Consider replacing conda with mamba in given instructions.
For example if you have an NVidia GPU at hand, install pytorch like this:
mamba install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
Or if not:
mamba install pytorch torchvision torchaudio cpuonly -c pytorch
Contributions are very welcome. If you want to suggest a new napari plugin to become part of this distribution, please make sure it interoperates nicely with the other plugins. For example, if the plugin you suggest provided cell segmentation algorithms, please check if the resulting segmented cells can be analysed using napari-skimage-regionprops. Furthermore, please make sure the README of the plugin you are proposing comes with user documentation, e.g. a step-by-step guide with screenshots explaining what users can do with the plugin and how to use it. It is recommended to provide example data as well so that end-users can try out the plugin under conditions it was developed for.
Distributed under the terms of the BSD-3 license, "devbio-napari" is free and open source software
If you encounter any problems, please file an issue along with a detailed description.