Skip to main content

Front end tools for composite images for EM connectomics

Project description

ImageryClient

Connectomics data often involves a combination of microscopy imagery and segmentation, labels of distinct objects applied to this imagery. While exploring the data in tools like Neuroglancer is great, a common task is often to make figures overlaying 2d images and segmentation sliced from the larger data. ImageryClient is designed to make it easy to generate aligned cutouts from imagery and segmentation, and make it efficient to produce attractive, publication-ready overlay images.

Because of the size of these volumes, cloud-based serverless n-d array file storage systems are often used to host this data. CloudVolume has become an excellent general purpose tool for accessing such data. However, imagery and segmentation for the same data are hosted at distinct cloud locations and can differ in basic properties like base resolution. Moreover, imagery and segmentation have data that means intrensically different things. Values in imagery indicate pixel intensity in order to produce a picture, while values in segmentation indicate the object id at a given location. ImageryClient acts as a front end for making aligned cutouts from multiple cloudvolume sources, splitting segmentations into masks for each object, and more.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

imageryclient-1.0.3.tar.gz (19.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

imageryclient-1.0.3-py3-none-any.whl (14.8 kB view details)

Uploaded Python 3

File details

Details for the file imageryclient-1.0.3.tar.gz.

File metadata

  • Download URL: imageryclient-1.0.3.tar.gz
  • Upload date:
  • Size: 19.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.9

File hashes

Hashes for imageryclient-1.0.3.tar.gz
Algorithm Hash digest
SHA256 7ff3e990ba0699f0aca605bf97ccc7ce7331b602f2d4cc2f04b45568382706e6
MD5 49fa0303ea5ad86d5cad5d90f55d60e7
BLAKE2b-256 82a36c98dfaf3d5a670ecc2b52d654090b3867191d8e580dd3e2dd89fe3e5c1c

See more details on using hashes here.

File details

Details for the file imageryclient-1.0.3-py3-none-any.whl.

File metadata

File hashes

Hashes for imageryclient-1.0.3-py3-none-any.whl
Algorithm Hash digest
SHA256 9dc8780a7805e6773c8e688a9568b0c69c6536ef4add5aba1dd3273db75c713a
MD5 1c9f67c063ff999c7fd64e8b4ef8d381
BLAKE2b-256 597502136cfc082652d0bf50eda0a327a5d3f58e19a2e79b2b01e622d902679c

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page