OpenCV VS ITK

- [ OpenCV](https://opencv.org/) instead of YoloV8 for computer vision and object detection

I came across a very interesting [project]( (4) Mckay Wrigley on Twitter: "My goal is to (hopefully!) add my house to the dataset over time so that I have an indoor assistant with knowledge of my surroundings. It’s basically just a slow process of building a good enough dataset. I hacked this together for 2 reasons: 1) It was fun, and I wanted to…" / X ) made by Mckay Wrigley and I was wondering what's the easiest way to implement it using free, open-source software. Here's what he used originally, followed by some open source candidates I'm considering but would love feedback and advice before starting: Original Tools: - YoloV8 does the heavy lifting with the object detection - OpenAI Whisper handles voice - GPT-4 handles the “AI” - Google Custom Search Engine handles web browsing - MacOS/iOS handles streaming the video from my iPhone to my Mac - Python for the rest Open Source Alternatives: - [ OpenCV](https://opencv.org/) instead of YoloV8 for computer vision and object detection - Replacing GPT-4 is still a challenge as I know there are some good open-source LLms like Llama 2, but I don't know how to apply this in the code perhaps in the form of api - [DeepSpeech](https://github.com/mozilla/DeepSpeech) rather than Whisper for offline speech-to-text - [Coqui TTS](https://github.com/coqui-ai/TTS) instead of Whisper for text-to-speech - Browser automation with [Selenium](https://www.selenium.dev/) instead of Google Custom Search - Stream video from phone via RTSP instead of iOS integration - Python for rest of code I'm new to working with tools like OpenCV, DeepSpeech, etc so would love any advice on the best way to replicate the original project in an open source way before I dive in. Are there any good guides or better resources out there? What are some pitfalls to avoid? Any help is much appreciated!

Glancing at the code, I think it's detectMultiScaleNoGrouping and then the operator() of CascadeClassifierInvoker gets called. It will probably help you to put a breakpoint and step through that bit of the code.

On GitHub https://github.com/opencv/opencv

OpenCV

You also need C++ if you're going to do things which aren't built in as part of the engine. As an example if you're looking at using compute shaders, inbuilt native APIs such as a mobile phone's location services, or a third-party library such as OpenCV, then you're going to need C++.

wget -O opencv.zip https://github.com/opencv/opencv/archive/4.1.0.zip

The itkImage.h header is ITK's standard n-dimensional image data structure.

In this post, we review how the Insight Toolkit (ITK) leverages the first-interaction GitHub Action to communicate our appreciation of the efforts of first-time contributors, establish norms for behavior, and provide civil pointers on where to find more information.

Our comment starts with a note of welcome to the community and gratitude for the effort made to contribute back to the project. Software development, regardless of the cold and mechanical nature of computers, is a social endeavor; we respect and acknowledge this by beginning with our gratitude.

Jupyter has emerged as a fundamental component in artificial intelligence (AI) solution development and scientific inquiry. Jupyter notebooks are prevelant in modern education, commercial applications, and academic research. The Insight Toolkit (ITK) is an open source, cross-platform toolkit for N-dimensional processing, segmentation, and registration used to obtain quantitative insights from medical, biomicroscopy, material science, and geoscience images. The ITK community highly values scientific reproducibility and software sustainability. As a result, advanced computational methods in the toolkit have a dramatically larger impact because they can be reproducibly applied in derived research or commercial applications.

Yeah, Prolog is pretty cool!

Another technology I found interesting too learn is ITK (https://itk.org/). You need a different mindset using ITK than other image processing libraries.

Lisp is cool as well.

And fully homomorphic encryption.

VTK is a powerful visualization package, but it's more for working with 3D FEM/CFD data. There are lots of things in there though, so it may be useful. The sister project Paraview is an application which can be used to work with data interactively. Both have great Python support. There is also ITK which is focused on with image data, like medical scans - never used it, though.