https://bugs.kde.org/show_bug.cgi?id=518026
--- Comment #2 from Ondrej Zizka <[email protected]> --- Mike, great project to (re-)join. It is extremely useful and very well designed. I would join too if I had enough C++ skills. In the mean time, I have finished my own clustering in Kotlin. I can share the source code. In short, it adds 1 table to the DigiKam DB for the clusters, and then runs an algorithm revolving around centroids with 2 or more passes and average vector recomputation until the clusters "look right". It works spectacularly. But the need to stop DigiKam, copy the DB (just to be safe), run the clustering, then apply it to DigiKam's DB (by creating new artificial People tags under People/clusters/cluster1234) is quite impractical, and also it seems that DigiKam faces rescan somehow changes the tags from People to just normal tags. So, having this directly in DigiKam would be really very helpful, and it would speed up work with faces a lot and improve dealing with false negatives which appear now in DigiKam - one could bump up the Accuracy of the face scan, and handle the "Unknown" people by clusters, so that they would not appear in other People tags as candidates. Thanks a lot to all for the work on this project. -- You are receiving this mail because: You are watching all bug changes.
