hidet VS JPEGDEC

Check out Hidet [1]. Not as well funded, but delivers Python based ML acceleration with GPU support (unlike Mojo).

[1] https://github.com/hidet-org/hidet

Thanks for your comment ! Current focus of optimizations is for Nvidia GPUs but others are in the works. Hidet comes with Hidet.Script which abstracts some of the CUDA struggles and may make the ML optimizations efforts easier to implement. It is still evolving so documentation is limited but here are some examples: https://github.com/hidet-org/hidet/tree/main/python/hidet/gr...

Hey @bructhemoose2 can you file an issue, we will try to fix it ASAP: https://github.com/hidet-org/hidet/issues

It takes some time to decode, but I can grab- for example- this 259k JPEG: http://placekitten.com/3840/2160

And display it from the microSD card with this code: https://gist.github.com/Gadgetoid/0b8e352e377135d743338c9483...

As more or less demonstrated by this example: https://github.com/pimoroni/pimoroni-pico/blob/main/micropyt...

Decoding is done via Larry Bank's JPEGDEC: https://github.com/bitbank2/JPEGDEC

It uses roughly 20k RAM to provide the necessary buffers both for decoding JPEGs into blocks which are passed to a drawing routine that handles copying the data into the display RAM buffer (on our larger Inky display this is backed by PSRAM). I don't believe there's a hard limit to the size of the JPEGs you can display. The main bottlenecks are decoding time and the relatively limited scaling options: FULL, HALF, QUARTER, EIGHTH.

This aside, the authors solution is actually quite elegant. As long as you have control over the image pipeline there's no real reason to encumber the device with handling large (both in bytes and pixel dimensions) files. You'll also get much better dithering results writing your own routine to convert files from JPG to the raw 4-bits per pixel format for the display. Our built-in dithering is just a plain ordered dither matrix and, while quaint and retro, it leaves much to be desired visually.

If you're trying to use a public API you can also make GitHub actions automate the whole image conversion process and publish the results to GitHub pages. This works great for, for example, the daily XKCD, serving both to reformat the strip for the display, credit the author, extract the "alt" text and avoid excess requests to the origin. Eg: https://pimoroni.github.io/feed2image/xkcd-800x480-daily.jpg. Though the astute will notice I still opted for jpeg in this case.

I'm doing the same thing with MJPEG and the JPEGDEC library https://github.com/bitbank2/JPEGDEC. I preprocess the video using ffmpeg to scale the video down to 240x240 and remove the compression.