Tutorial: Automation and Interoperability
In this tutorial, we will explore two methods for downloading data: First, we will use the WEBKNOSSOS UI to download data as a Tiff stack. Then, we will learn how to automate this process using Python. Additionally, I will guide you through the process of incorporating a remote dataset into WEBKNOSSOS. Let’s take a look!
Here is full tutorial video, alternatively continue reading below.
Download and Export
WEBKNOSSOS makes it easy to download and export annotations as Tiff images or OME-Tiff. Start by creating a bounding box with the bounding box tool. Adjust it so that it covers the desired area.
Then, access the “Download” option from the dropdown menu and select “Tiff Export”. Choose which layer you want to export and the bounding box covering the region you want to download. Click “Export” and that’s it! The Tiff stack will be downloaded to your computer.
To automate this process with Python, follow the same initial steps. Select “Python client” in the download modal, then copy the provided code snippet to get started. You can also use the Python library documentation for more code examples on interacting with your data.
Run the code with Python on your computer to start the download process. Extend the code as needed for more complex automations. The Python libraries offer both "normal" download of datasets and streaming access for working with larger files.
Interoperability with Other Software Tools
WEBKNOSSOS integrates seamlessly with other analysis software tools, enabling you to work with datasets from tools like Neuroglancer and Fiji. Let’s see an example of importing a Neuroglancer dataset into WEBKNOSSOS.
First, find a released dataset in OME-Zarr, N5 or Neuroglancer-Precomputed format that you would like to import and that is hosted in the cloud (S3, Google Cloud) or on any HTTPS server. Copy the URL pointing to the data.
In WEBKNOSSOS, navigate to the dashboard:
- Click on “Add dataset”.
- Select the “Add remote dataset” tab.
- Paste the URL, then click “Add layer”.
- Similar to adding local data, choose a target folder.
- Provide a name for your dataset, set the voxel size, and click “Import”.
Once the dataset is imported, open it to start exploring!
That’s it, now you know how to export data using the UI or Python as well as how to work with remote datasets.