Although there may be things beyond what was mentioned, the guiding principle gives enough context for us to easily categorize almost everything.
I totally agree that we should appreciate the ability to remove minor distractions or flaws that are anyways removable (and a lot of people do it on a daily basis) quickly in contrast to the boring and time consuming methods. Also the inability to easily change the narrative of the image is emphasized enough.
I might add a small list of things that may require some thinking in terms of “do we want to see it implemented if someone wants to work on it” or “no we are good without it” -
I saw Darktable AI Rating & Tagging - Lua-Script and embed-openclip-vitb32.dtmodel
is already there which I appreciate a lot.
I am curious about your thoughts on these-
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Facial recognition for sorting or tagging even (with names?)
I know a few people who do a ton of sports photography or work full time as a photographer for a team and similar cases. Although they will benefit greatly from this apparently, this is not limited to just them and I do see others finding it a very useful tool. -
Another similar idea for culling is finding the sharpest photo in the burst or a selection of images.
I know someone would need to either make a model or find one that checks all the boxes for these but I would like to know how the devs and community receive this idea.
Things like GitHub - sharif-apu/BJDD_CVPR21: This is the official implementation of Beyond Joint Demosaicking and Denoising from CVPRW21. · GitHub and GitHub - zhaoyuzhi/QRNet: Modeling Dual-Exposure Quad-Bayer Patterns for Joint Denoising and Deblurring. IEEE TIP, 2024 · GitHub have existed for a while but we would need open source licensed models for any hope for integration in darktable. Quad Bayer demosaicing has been something I have hoped for a while but I don’t think there’s enough demand for it or interest so I will leave it at that .
I hope I can still add some cats to my photos, guess I’ll just have to wait till a cat choses me.