Thanks for your kind words and the tip about using canonical names. Will take a look.

Based on my cursory look at `libremarkable`, the SDK looks a lot more capable. You will need to port the code from Python though :/

No, that is what looked okay to me. The font sizing on the Calendar is customizable.

You can also side load custom fonts. I am going to look into this, because the default fonts are a bit meh.

Thanks ! Happy to be able to nudge things along

I started to work on this, because this is something I hoped existed. If there is enough interest, I am open to commercializing it. Its super easy to setup, especially given all my code is already open source :)

I agree. My goal here was to get something that look like bokeh if you squinted at it.

I just wanted to see how far I could take this idea.

FYI, the web page seems entirely unusable on my iPad Pro. It goes blank at the first example, and without a scroll indicator I have no idea what’s going on.

I was able to scroll to the bottom footer, but I can’t scroll back to the top, so there’s literally nothing but a blank page now except for the footer.

Thanks for the feedback. Let me fix that.

Yes. That is correct. Just wanted to see if the idea in my head actually worked. I was surprised by how decent it turned out especially given that I did not do anything “clever”.

The image you used as an example already has some nice shallow depth of field from the beginning, which I think really helps make blend the effect in the end result. Did you try applying the effect to an actual selfie, which usually has more even focus?

One "clever" idea someone had was the use of the depth camera on the new iPhones to isolate the subject.

Rather than using a hardware solution, this is a good software solution that can be accomplished on more devices.

It’s a simple Gaussian kernel multiplied with a triangular mask.

You can do better by playing with intensities of pixel values as suggested in the article I linked.

I meant about where this line comes from:

    mask = masks[0][0]  

Presumably 0 is the class ID? For someone new to ML or object detection, it might not be obvious why you take the first channel here.

Also recent related reading: https://bartwronski.com/2020/03/15/using-jax-numpy-and-optim...

HN Discussion: https://news.ycombinator.com/item?id=22590360&ref=hvper.com&...

This is a quick proof of concept of an idea. This won't work for every use-case but it worked surprisingly well for a use-case I had in mind. Happy to answer any questions.

So, is the ML part just the segmentation, correct?

Yes. That is correct.

Thanks so much !

Reposted in https://news.ycombinator.com/item?id=22708199

Let's keep the discussion there.