BoofCV VS OpenCV

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.

Referenceshttps https://www.kaggle.com/datasets/riyaelizashaju/skin-disease-image-dataset-balanced?fbclid=IwAR3wbTp8l5yo_5fx6HAX8Vd2-9cca3khAc8EiBGFObaALfdVid29IuB_rYE https://keras.io/api/applications/vgg/ https://www.tensorflow.org/tutorials/images/cnn?hl=th https://opencv.org/

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.

You'll need to plot the predictions. There are a few open source tools to do that, supervision is one you can use (https://github.com/roboflow/supervision) and opencv is another common option (https://github.com/opencv/opencv)

You might be able to achieve this with scripting tools like AutoHotkey or Python with libraries for GUI automation and image recognition (e.g., PyAutoGUI https://pyautogui.readthedocs.io/en/latest/, OpenCV https://opencv.org/).

The OpenCV4NodeJs A.I. library provides an interface for calling OpenCV routines in NodeJS.