It aims to 'knock out' some areas of the image: make them transparent in the digital image so those areas are not part of the final film. This means less of the surface of the t shirt has film on it, allowing it to be breathable and flexible.
I got some DTF prints made before I knew you were meant to do this. The tutorials online required a lot of steps so I made the above for next time. Not tested for real yet.
Very nice results, I really like the way it looks like a printed page. Image processing is addictive - once you start playing around it is hard to stop.
Why is this not a typical data augmentation method for training deep networks? It would allow the networks to learn invariants that's align with humans. Currently, I suppose none of these dithered images would be correctly classified.
If you're doing halftoning with the intent to get custom DTF (direct to film) prints for t shirts, you might find this tool helpful:
https://tools.encona.com/dtf
It aims to 'knock out' some areas of the image: make them transparent in the digital image so those areas are not part of the final film. This means less of the surface of the t shirt has film on it, allowing it to be breathable and flexible.
I got some DTF prints made before I knew you were meant to do this. The tutorials online required a lot of steps so I made the above for next time. Not tested for real yet.
Title here is missing "How" as in "How My Images Are Dithered"
That’s a hn filter to avoid clickbait titles I think
I suppose it is a type of dithering, but really this is Halftoning
Yes, which of course is still cool. Probably the main point.
Very nice results, I really like the way it looks like a printed page. Image processing is addictive - once you start playing around it is hard to stop.
??? This is halftoning, not dithering
See also,
Surface-stable fractal dithering:
https://www.youtube.com/watch?v=EzjWBmhO_1E
And the technical deep dive:
https://www.youtube.com/watch?v=HPqGaIMVuLs
Thanks, that looks cool. This is dithering in principle, but using dynamic spot functions.
To learn more about halftones, I'd recommend PDF specification, pages 303-307.
https://opensource.adobe.com/dc-acrobat-sdk-docs/standards/p...
>Is this even dithering?
>But if you care about the method this is not dithering.
> I looked it up, the translation in this case is, in fact, simply "half tones" :^)
Every image should be dithered below 10 bits/component to prevent banding artifacts.
Why is this not a typical data augmentation method for training deep networks? It would allow the networks to learn invariants that's align with humans. Currently, I suppose none of these dithered images would be correctly classified.