Semantic Segmentation using Deep Learning ONNX models packaged as *.czann files
Semantic Segmentation of multi-dimensional images using Deep Learning ONNX models packaged as *.czann files.
Before installing, please setup a conda environment. If you have never worked with conda environments, go through this tutorial first.
You can then install
napari-czann-segment via pip:
pip install napari-czann-segment
What does the plugin do
The plugin allows you to:
- Use a *.czann file containing the Deep Neural Network (ONNX) for semantic segmentation and metadata
- Segmentation will be applied per 2D plane for all dimensions
- Processing larger multi-dimensional images it uses the cztile package to chunk the individual 2d arrays using a specific overlap.
- multi-dimensional images will be processed plane-by-plane
What does the plugin NOT do
Before one can actually use a model it needs to be trained, which is NOT done by this plugin.
Therer two main ways hwo such a model can be created:
- Train the segmentation model fully automated on APEER and download the *.czann file
- Train your model in a Jupyter notebook etc. and package it using the czmodel python package as an *.czann
Using this plugin
A test image and a *.czann model file can be downloaded here.
In order to use this plugin the user has to do the following things:
- Open the image using "File - Open Files(s)" (requires napari-aicsimageio plugin).
- Click napari-czann-segment: Segment with CZANN model in the "Plugins" menu.
- *Select a .czann file to use the model for segmentation.
- metadata of the model will be shown (see example below)
|model_type||ModelType.SINGLE_CLASS_SEMANTIC_SEGMENTATION||see: czmodel for details|
|input_shape||[1024, 1024, 1]||tile dimensions of model input|
|output_shape||[1024, 1024, 3]||tile dimensions of model output|
|model_id||ba32bc6d-6bc9-4774-8b47-20646c7cb838||unique GUID for that model|
|min_overlap||[128, 128]||tile overlap used during training (for this model)|
|classes||['background', 'grains', 'inclusions']||availbale classes|
|model_name||APEER-trained model||name of the model|
- Adjust the minimum overlap for the tiling (optional, see cztile for details).
- Select the layer to be segmented.
- Press Segment Selected Image Layer to run the segmentation.
A successful is obviously only the starting point for further image analysis steps to extract the desired numbers from the segmented image. Another example is shown below demonstrating a simple "Grain Size Analysis" using a deep-learning model trained on APEER used in napari
IMPORTANT: Currently the plugin only supports using models trained on a single channel image. Therefore make sure that during the training on APEER or somewhere else the correct inputs images are used. It is quite simple to train an single RGB image, which actually has three channels, load this image in napari and notice only then that the model will not work, because the image will 3 channels inside napari.
- Only the CPU will be used for the inference using the ONNX runtime for the ONNX-CPU runtime
- GPUs are not supported yet and will require ONNX-GPU runtime
Please clone this repository first using your favorite tool.
Feel free to create a new environment using the YAML file at your own risk:
cd the-github-repo-with-YAML-file conda env create --file conda_env_napari_czann_segment.yml conda activate napari_czmodel
- Install the plugin locally
Please run the the following command:
pip install -e .
To install latest development version:
pip install git+https://github.com/sebi06/napari_czann_segment.git
Contributions and Feedback are very welcome.
Distributed under the terms of the BSD-3 license, "napari-czann-segment" is free and open source software
If you encounter any problems, please file an issue along with a detailed description.
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Hashes for napari-czann-segment-0.0.16.tar.gz
Hashes for napari_czann_segment-0.0.16-py3-none-any.whl