Kavli Affiliate: Lindsey Bleem
| First 5 Authors: Ryan Walker, Ryan Walker, , ,
| Summary:
A wide range of scientific imaging datasets benefit from human inspection for
purposes ranging from prosaic-such as fault identification and quality
inspection-to profound, enabling the discovery of new phenomena. As such, these
datasets come in a wide variety of forms, with diverse inspection needs. In
this paper we present a software package, Image Marker, designed to help
facilitate human categorization of images. The software allows for quick
seeking through images and enables flexible marking and logging of up to 9
different classes of features and their locations in files of FITS, TIFF, PNG,
and JPEG format. Additional tools are provided to add text-based comments to
the marking logs and for displaying external mark datasets on images during the
classification process. As our primary use case will be the identification of
features in astronomical survey data, Image Marker will also utilize standard
world coordinate system (WCS) headers embedded in FITS headers and TIFF
metadata when available. The lightweight software, based on the Qt Framework to
build the GUI application, enables efficient marking of thousands of images on
personal-scale computers. We provide Image Marker as a Python package, and as
Mac and Windows 11 executables. It is available on GitHub or via pip
installation.
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