Dear digiKam fans and users,
After five months of active maintenance and long bugs triage, the digiKam team is proud to present version 8.1.0 of its open source digital photo manager.
See below the list of most important features coming with this release.
Print Creator: Add 4 new templates for 6.8 inches photo paper.
General : Improve usability of Image Properties sidebar tab.
Libraw : Update to snapshot 2023-05-14
Bundles : Update Exiv2 to last 0.28 release
Bundles : Update KF5 framework to last 5.106
Bundles : Includes Breeze widgets style in MacOS package to render properly GUI contents.
Tags : Add possibility to remove all face tags from selected items.
Tags : Add possibility to remove all tags from selected items except face tags.
Similarity : Add usability improvements about reference images while searching for duplicates images.
This version arrives with a long review of bugzilla entries. Long time bugs present in older version have been fixed and we spare a lots of time to contact users to validate changes in pre-release to confirm fixes before to deploy the program in production.
The application internationalization has also been updated. digiKam and Showfoto are proposed with 61 different languages for the graphical interface. Go to Settings/Configure Languages dialog and change the localization as you want. Applications need to be restarted to apply changes. If you want to contribute to the internationalization of digiKam, please contact the translator teams, following the translation how-to. The statistics about translation states are available here.
Thanks to the translators who have worked on the online documentation internationalisations. You can read and search over the document here. You are welcome to contribute to application handbook translations following the coordination team instructions.
Future Plans
Next maintenance version will be published in the future with more bug fixes and improvements. This summer, a student works on a new tool based on deep-learning to assign tags automatically, based on content analysis, to detect form, objects, place, animals, plants, monuments, etc. You can read more details about this project in the student blog.
