https://github.com/aplbrain/vsvi2precomputed
Script to convert VSVI (used in VAST) image datasets to precomputed volumes
https://github.com/aplbrain/vsvi2precomputed
Last synced: 12 months ago
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Script to convert VSVI (used in VAST) image datasets to precomputed volumes
- Host: GitHub
- URL: https://github.com/aplbrain/vsvi2precomputed
- Owner: aplbrain
- License: apache-2.0
- Created: 2024-04-02T17:43:58.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2025-03-31T16:49:49.000Z (about 1 year ago)
- Last Synced: 2025-04-14T20:38:03.587Z (about 1 year ago)
- Language: Python
- Size: 4.72 MB
- Stars: 0
- Watchers: 5
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# vsvi2precomputed
Package for converting VSVI (used in VAST) image datasets to precomputed volumes. Supports conversion of local and AWS S3 datasets.
Requirements:
* Python
* AWS CLI (if using S3)
## Usage
Convert a cloud dataset and store in new cloud path:
```
pip install -r requirements.txt
python vsvi2precomputed.py -i s3://path/to/config.vsvi -o s3://path/to/output/dir/
```
Don't forget the trailing slash on the output dir.
Convert a local dataset and upload to the cloud:
```
python vsvi2precomputed.py --i path/to/config.vsvi --o s3://path/to/output/dir/
```
Convert a cloud dataset and upload to the cloud:
```
python vsvi2precomputed.py --i s3://path/to/config.vsvi --o path/to/output/dir/
```
Convert a dataset locally:
```
python vsvi2precomputed.py --i path/to/config.vsvi --o path/to/output/dir/
```
Optional Arguments
| Argument | Description | Default |
|-----------|----------------------|---------|
| --profile | AWS CLI profile name | default |
## Tests
```
pip install pytest
pytest
```
To use an non-default AWS CLI profile:
```
pytest --profile
```
## About VSVI and precomputed formats
VSVI format is native to the [VAST](https://lichtman.rc.fas.harvard.edu/vast/) ecosystem. Precomputed format is native to the [Neuroglancer](https://github.com/google/neuroglancer)/[CloudVolume](https://github.com/seung-lab/cloud-volume) ecosystem.
To view converted data in Neuroglancer:
* Navigate to neuroglancer.bossdb.io.
* Add a new layer using the Data Source URL input box on the top right.
* S3: The Data Source URL will be the S3 URI of the directory containing the info file, prepended with `precomputed://`. Example: `precomputed://s3://mambo-datalake/connects49a/vsvi2precomputed/local_aligned/`.
* Local: You will need to serve the data first. Navigate to the directory containing the info file, then open a terminal and run the following code. The Data Source URL will then follow the format `precomputed://localhost:/`.
```
from cloudvolume import CloudVolume
cv = CloudVolume("file://.")
cv.viewer()
```
* Click the yellow "Create as image layer" button at the bottom right.
## Acknowledgements
We thank the Visual Computing Group at Harvard for building the VAST software. https://www.frontiersin.org/journals/neural-circuits/articles/10.3389/fncir.2018.00088/full
---
Copyright (c) 2024 The Johns Hopkins University Applied Physics Laboratory LLC.