Distant Viewing Toolkit for the Analysis of Visual Culture

The Distant Viewing Toolkit is a Python package that facilitates the
computational analysis of still and moving images. The most recent
version of the package focuses on providing a minimal set of functions
that require only a small set of dependencies. Examples of how to make
use of the toolkit are given in the following section.

For more information about setting up the toolkit on your own machine, please
see INSTALL.md. More information about the toolkit and project is
available on the following pages:

Example analysis using aggregated metadata: “Visual Style in Two Network Era Sitcoms”
Theory of the project: “Distant Viewing: Analyzing Large Visual Corpora.”
Software Whitepaper: A Python Package for the Analysis of Visual Culture

If you have any trouble using the toolkit, please open a
GitHub issue. If you
have additional questions or are interested in collaborating, please contact
us at tarnold2@richmond.edu and ltilton@richmond.edu.

Notebooks

If you are interested in learning more about application of computer vision and
the distant viewing toolkit to humanities applications, we offer a self-guided
tutorial through the following Google Colab notebooks. These can be run for free
by anyone with a Google account:

Distant Viewing Tutorial 1: Movie Posters and Color Analysis: [colab], [slides], [notebook], [chapter]
Distant Viewing Tutorial 2: Network-Era Sitcoms and Visual Style: [colab], [slides], [notebook], [chapter]

A shorted demo of the toolkit is also available in the following Google Colab notebook:

Distant Viewing Toolkit Demo: [colab]

Unlike the tutorials, the short demo assumes some prior knowledge of Python.
While a background in machine learning or computer vision is not needed, the
methods are presented with minimal motivation.

The Distant Viewing Toolkit is supported by the National Endowment for the Humanities through a Digital Humanities Advancement Grant.

Citation

If you make use of the toolkit in your work, please cite the relevant papers
describing the tool and its application to the study of visual culture:

@article{,
  title   = "Distant Viewing: Analyzing Large Visual Corpora",
  author  = "Arnold, Taylor B and Tilton, Lauren",
  journal = "Digital Scholarship in the Humanities",
  year    = "2019",
  doi     = "10.1093/digitalsh/fqz013",
  url     = "http://dx.doi.org/10.1093/digitalsh/fqz013"
}

@article{,
  title   = "Visual Style in Two Network Era Sitcoms",
  author  = "Arnold, Taylor B and Tilton, Lauren and Berke, Annie",
  journal = "Cultural Analytics",
  year    = "2019",
  doi     = "10.22148/16.043",
  url     = "http://dx.doi.org/10.22148/16.043"
}

Contributing

Contributions, including bug fixes and new features, to the toolkit are
welcome. When contributing to this repository, please first discuss the change
you wish to make via a GitHub issue or email with the maintainers of this
repository before making a change. Small bug fixes can be given directly
as pull requests.