BoofCV VS mediapipe

BoofCV https://github.com/lessthanoptimal/BoofCV

Is there any fast way to get pixel values and pixel coordinate? I had to jump through a lot of hoops to get convert BufferedImages into a format that's useful for image processing it to be a reasonable speed in BoofCV. getRGB() is glacial. At one point I was trying to convince the JDK team to make private data structures public again. Right now it's inconsistent what you have access too.

Apologizes if this post runs afoul of the rules. This is a product we are making that started as an open source project (here).

I will take the opportunity to call out one of my favourite libraries, BoofCV (http://boofcv.org)

It comes with a wonderful demonstration tool that allows you to apply the various included algorithms to images and tweak the parameters in real-time – including the Hough transform. A great tool for helping to understand how these kinds of algorithms work!

Speaking of using the vector api, I see there's a class in boofcv that converts rgb to hsv. I've previously written a simd accelerated version of rgb to hsv using the java vector api. For anybody looking to do a bit of code janitor work, converting the one-off library into something that could be contributed to /u/lessthanoptimal's project might be a worthwhile contribution.

For instance I looked at BoofCV and all I found was : https://github.com/lessthanoptimal/BoofCV/issues/265 " the question now seems to be can you compile the library as native. The answer is probably but someone needs to try it."

It's an all-Java computer vision library, see https://boofcv.org/ and https://github.com/lessthanoptimal/BoofCV

project website: https://boofcv.org

There really is a community for everything on Reddit... Anyways, so I've updated the tutorial on QR Codes in BoofCV. If you're not familiar with it, BoofCV is a computer vision library that also includes a high quality QR Code scanner. Plenty of benchmarks and examples can be found on the website to back that up. Here's a link to the tutorial page and please let me know if it all makes sense. The tutorial focuses on applications which are either command line or have a GUI.

mediapipe/docs/solutions/pose.md at master · google/mediapipe · GitHub

Today, we announce MediaPipe diffusion plugins, which enable controllable text-to-image generation to be run on-device. Expanding upon our prior work on GPU inference for on-device large generative models, we introduce new low-cost solutions for controllable text-to-image generation that can be plugged into existing diffusion models and their Low-Rank Adaptation (LoRA) variants.

You can just grab the TFLite version! https://github.com/google/mediapipe/blob/master/docs/solutions/models.md

I believe Google already released transformers under an apache 2 license with a patent grant:

https://github.com/google/mediapipe/blob/master/mediapipe/mo...

I was going to say that I like the MediaPipe Selfie Segmentation model for doing this sort of thing in a web page, but I've just noticed (when getting the GitHub link[1]) that Google have marked the code as legacy[2] ... no idea if the new solution is better/easier to use[3].

For what it's worth, my CodePen using the old model is here: https://codepen.io/kaliedarik/pen/PopBxBM

[1] - https://github.com/google/mediapipe/blob/master/docs/solutio...

[2] - "Attention: Thank you for your interest in MediaPipe Solutions. As of April 4, 2023, this solution was upgraded to a new MediaPipe Solution."

[3] - https://developers.google.com/mediapipe/solutions/vision/ima...

I have used mediapipe very successfully in multiple projects and it's very easy to get running. You can choose from many different vision tasks including hand landmarks ( https://github.com/google/mediapipe/blob/master/docs/solutions/hands.md )

I found MediaPipe's Face Mesh and was impressed with how simple it was to get going, but it just gives you the landmark points and I've not gone any further yet.

We've trained ControlNet on a subset of the LAION-Face dataset using modified output from MediaPipe's face mesh annotator to provide a new level of control when generating images of faces.

Medipaipe library link: https://mediapipe.dev/