Repository of tools to interface with, visualize, and edit the metadata information in a collection or archive of visual media (i.e. photos and videos).
For more information about how to use this library and what it can do, please refer to the examples.
Codes and tools in this repository are all built in Python -- however, to access file metadata, we require a local installation of the free command-line tool exiftool. All interfacing between this library and exiftool is done through the bash shell, so this is not a sophisticated Python wrapper for any of the exiftool functionality. Rather, MetaViz is a library of surface-layer, user-friendly codes and tools to interface with the metadata in a collection of visual media. We provide functions to search/filter through the metadata in your library to find information you're interested in, update that metadata as needed, and we provide tons of good looking, ready-to-use plotting routines for visualizing that information.
We try to keep these functions quick by minimizing our calls to exiftool, which can be computationally expensive for large media collections. To do this, we use exiftool to save all the metadata information for your library into a 'metadatabase' of .csv files, and then use Pandas to directly access and process that information in Python. To create figures and visualizations, we require at minumum an installation of matplotlib, with many optional plotting routines that make use of other popular libraries like seaborn and Pillow. Our goal is that the aesthetic adjustments made behind the scenes in the plotting routines will provide nice, good-looking visualizations right out of the box -- or, at minimum, example functions that can be used as starting points to make your own.
We also provide a number of tools which can help to generally manage collections or their metadata. These aren't necessarily related to visualization, they're just helper functions. Example applications include shifting a list of dates by a constant time-delta, stitching together panoramic photos, and geocoding photos based on their location name.
Examples of these visualizations can be seen below and in the gallery folder.
MetaViz has been tested in Python 3.7+ (mostly on MacOS), and should work in any version of Python in which the underlying packages work. To install MetaViz, clone this repository using git clone and run python setup.py install from the cloned directory. In order to use MetaViz, the local environment must include at minimum numpy, Pandas, and matplotlib, along with a functioning installation of exiftool. We also strongly recommend the use of Jupyter Lab. In order to use the full suite of tools and functions available, the optional packages we use for select functions are listed below. All links go to the relevant install page.
seabornPillowchord(requiresJupyter Lab)OpenCV
This package is just a personal project developed at home in our spare time.
- Kyle Wright - Environmental Engineer, Ph.D. Student
- Mickey Lanning - Archivist, M.S. Information Science
We welcome contributions to the MetaViz project. Please open an issue or a pull request if there is functionality you would like to see or propose, and I will gladly try to help!