BoofCV VS Pytorch

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.

TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.

> does PyTorch have a similar concept

of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...

fyi should work on most 40xx[1]

[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...

Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...

Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....

> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].

*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.

In PyTorch with @, dot() or matmul():

import torch # we use PyTorch: https://pytorch.org data = torch.tensor(encode(text), dtype=torch.long) print(data.shape, data.dtype) print(data[:1000]) # the 1000 characters we looked at earlier will to the GPT look like this

AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.

Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework.

In PyTorch with * or mul(). ` or mul()` can multiply 0D or more D tensors by element-wise multiplication: