Semantic Segmentation using Deep Learning ONNX models packaged as *.czann files
Project description
napari-czann-segment
Semantic Segmentation of multidimensional images using Deep Learning ONNX models packaged as *.czann files.
This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.
Installation
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 multidimensional images it uses the cztile package to chunk the individual 2d arrays using a specific overlap.
- multidimensional 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.
There are 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
Sample Data
A test image and a *.czann model file can be downloaded here.
PGC_20X.ome.tiff
--> usePGC_20X_nucleus_detector.czann
to segment
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)
Parameter | Value | Explanation |
---|---|---|
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'] | available 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.
- Toggle Use GPU for inference checkbox to enable / disable using a GPU (Nvidia) for the segmentation (experimental feature).
- 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
Remarks
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 a 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 supported but require the ONNX-GPU runtime and the respective CUDA libraries.
- Please check the YAML for an example environment with GPU support.
- See also pytorch for instruction on how to install pytorch
For developers
-
Please clone this repository first using your favorite tool.
-
Ideally one creates a new conda environment or use an existing environment that already contains Napari.
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 following command:
pip install -e .
To install latest development version:
pip install git+https://github.com/sebi06/napari_czann_segment.git
Contributing
Contributions and Feedback are very welcome.
License
Distributed under the terms of the BSD-3 license, "napari-czann-segment" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file napari-czann-segment-0.0.18.tar.gz
.
File metadata
- Download URL: napari-czann-segment-0.0.18.tar.gz
- Upload date:
- Size: 11.8 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.11.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | aa8bf22f02c914dfbf43fa59785e8fdb77cb55d9a18639ef13b3275ab1e628da |
|
MD5 | b9a6564392028015256129f318a86b36 |
|
BLAKE2b-256 | b8e21636415889b43d18e7e172f9088344b6ad5b146394ec8fd349ee7da54aad |
File details
Details for the file napari_czann_segment-0.0.18-py3-none-any.whl
.
File metadata
- Download URL: napari_czann_segment-0.0.18-py3-none-any.whl
- Upload date:
- Size: 28.1 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.11.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | ac98eba002be4fba4a85ae60e222dd7d86090ac3720ffd4a1e883644ef2afd3e |
|
MD5 | f069bb2188bd95a788654243de1cedf4 |
|
BLAKE2b-256 | 14942c341d37b354c6132be10d5d5189d538055438d8d10afc8c03c8d56454a3 |