https://github.com/epfl-center-for-imaging/napari-orientationpy
Analyze orientations in 2D, 3D, and RGB images in Napari.
https://github.com/epfl-center-for-imaging/napari-orientationpy
image-processing napari-plugin
Last synced: 3 months ago
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Analyze orientations in 2D, 3D, and RGB images in Napari.
- Host: GitHub
- URL: https://github.com/epfl-center-for-imaging/napari-orientationpy
- Owner: EPFL-Center-for-Imaging
- License: bsd-3-clause
- Created: 2023-11-13T09:37:25.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-01-17T08:24:04.000Z (over 1 year ago)
- Last Synced: 2025-02-04T14:08:09.592Z (3 months ago)
- Topics: image-processing, napari-plugin
- Language: Python
- Homepage:
- Size: 9.54 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# napari-orientationpy
Analyze orientations in 2D, 3D, and RGB images in Napari. This plugin is based on the [Orientationpy](https://gitlab.com/epfl-center-for-imaging/orientationpy/) project.
## Installation
You can install `napari-orientationpy` via [pip]:
pip install napari-orientationpy
## Usage
To get started, open an image in the Napari viewer and start `napari-orientationpy` from the `Plugins` menu:
```
Plugins > Napari Orientationpy > Orientation measurement
```
1. **Select the structural scale parameter `sigma`**. This value control represents the scale at which the image gradients are computed. Try different values of `sigma` to understand what works best for your images. A reasonable guess would be the order in size, in pixels, of the structures that you are interested in. For example, if you are imaging fibers that appear to be about 4 pixels wide, try to set a value of *sigma=4*.
2. **If you are analyzing a 3D image, select `fiber` or `membrane` mode**. In `fiber` mode, the orientation normals follow fibrous structures. In `membrane` mode, the orientations are normal to the surface of membranous structures.
3. **Decide which outputs you'd like to visualize.**
- The `color-coded orientation` is a pixel-wise representation of 3D orientations as colors (similar colors = similar orientations).
- The `orientation vectors` get rendered in a `Vectors` layer in Napari. They are sampled on a regular grid defined by the `Spacing (X)`, `Spacing (Y)` and `Spacing (Z)` values (for 2D images, the `Z` value is ignored). The length of the vectors can be rescaled based on the `energy` value of the orientation computation.
- You can also output the local `orientation gradient` (misorientation).
4. **Compute orientation**. This button will trigger the orientation computation **only when necessary** (i.e. when the value of `sigma`, the `mode` or the `image` have changed). If you only adjust the `orientation vectors` parameters, clicking the compute button will update the results very fast.
5. **Save orientation (CSV)**. This will save the orientation measurements as a CSV table with columns `X`, `Y`, `Z`, `theta`, `phi`, `energy`, and `coherency` for all the pixels in the image.
### Plotting the 3D orientation distribution
If you have computed **orientation vectors** for a 3D image, you can plot their spatial distribution as a `stereographic projection` along the `X`, `Y` or `Z` direction directly in Napari. Select the widget from:
```
Plugins > Napari Orientationpy > Orientation distribution (3D)
```
## Sample images
We provide a few sample images to test our plugin. Open them from:
```
File > Open Sample > Napari Orientationpy
```
## Contributing
Contributions are very welcome. Please get in touch if you'd like to be involved in improving or extending the package.
## License
Distributed under the terms of the [BSD-3] license,
"napari-orientationpy" is free and open source software
## Issues
If you encounter any problems, please file an issue along with a detailed description.
----------------------------------
[napari]: https://github.com/napari/napari
[BSD-3]: http://opensource.org/licenses/BSD-3-Clause
[pip]: https://pypi.org/project/pip/