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Using SQL to find my best photo of a pelican according to Apple Photos

simonwillison.net

31–38 of 38 posts

Re: Using SQL to find my best photo of a pelican according to Apple Photos

#31
post #22

A feature I've long wanted to see in a photo management tool is ranking based on pairwise comparisons. Say you went on a trip and had 200 "keeper" photos, but that's still too many to publish in an online album, and many of them are near-duplicates anyway. If the photo manager let you compare any set of photos two at a time, you could (with n-choose-2 keypresses) find something close to a total order of the set. Then…

PhotoStructure has image comparison heuristics that is used for deduping, but would enable pairwise similarity grouping. I'd tried some transfer learning in the past to rank photos, but found it to be unusably noisy. I'll be trying again, though, with some new approaches. Fwiw, your PhotoStructure library has a carefully designed schema to let SQLite queries like this be trivial.

It's an interesting idea to let users pick bests out of near duplicates (and maybe prompt the user to workthrough this UI flow on album creation). I've put it on my to-do list!

Re: Using SQL to find my best photo of a pelican according to Apple Photos

#32

I don't have space for all my photos on my laptop, but I would like to use Apple photos. How do you guys go about that?

I've used photos on an external drive, but it must be afs+, and don't expect it to keep working past ~100-200k images. My frustration with photos helped motivate me to write PhotoStructure.

Re: Using SQL to find my best photo of a pelican according to Apple Photos

#33

I don't have space for all my photos on my laptop, but I would like to use Apple photos. How do you guys go about that?

By default it keeps the photos in iCloud and only stores much smaller thumbnails on your local disk.

I've turned that option off because I wanted to be able to upload the images to S3 from the terminal, but if you just want to use it for search etc you wouldn't need to do that.

Re: Using SQL to find my best photo of a pelican according to Apple Photos

#34
post #33

I don't have space for all my photos on my laptop, but I would like to use Apple photos. How do you guys go about that?

By default it keeps the photos in iCloud and only stores much smaller thumbnails on your local disk. I've turned that option off because I wanted to be able to upload the images to S3 from the terminal, but if you just want to use it for search etc you wouldn't need to do that.

I have even less space in iCloud :)

Can I offload images to S3/GCP and still have a organize my images in Apple Photos?

Re: Using SQL to find my best photo of a pelican according to Apple Photos

#35
post #27

Earlier quoted context omitted.

Is the Windows 10 one truly offline or does it eventually phone home in some way? Genuinely asking out of curiosity.

The classification model runs offline. I can say this because i had it classify a lot of pics while I was out on a trip with no internet. But i do think it fetches newer models given the chance.

I'm pretty sure classification happens locally for Apple as well. They call it "on-device machine learning". It doesn't mention it for MacOS photos but I can see `photoanalysisd` running in the background on my Mac.

https://www.apple.com/ios/photos/

Re: Using SQL to find my best photo of a pelican according to Apple Photos

#37

Nice article BUT I really despise that it's so left and so small. Impossible to read on a larger screen.

Thanks, this is useful. Made me realize that I habitually hit Shift-Command-Plus when I'm browsing so I never notice the default text size of anything, even my own sites!

I understand "so small" but what do you mean by "so left"? Is there a usability enhancement in centering designs rather than left-aligning them?

Re: Using SQL to find my best photo of a pelican according to Apple Photos

#38

datasette keeps getting cooler and cooler, huh.

I think it reached peak coolness back in the 70s. https://en.wikipedia.org/wiki/Commodore_Datasette

https://datasette.readthedocs.io/en/stable/ -- i could've been more clear.
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