SVG++ VS OpenCV

Congratulations, now you replaced a trivial file format that (from a quick glance at the code) needed about ~35 of easily readable and self-contained Lua code to parse with an external dependency that would be much larger and harder to follow and either having (at least) an XML parser as its own dependency or implementing its own XML parsing, as well as being at the mercy of their developers. Also unless you are using some highly popular library, you may end up with some abandoned dependency.

Examples of both are at [0] (C++ based parser, you'd also need to write some bindings for lua) and [1] (Lua based parser for a subset of the format, abandoned for almost a decade).

There are times when using an external dependency might be a good idea, but a text-based file format that describes lines and can be implemented in a few lines of code is not one.

[0] https://github.com/svgpp/svgpp

[1] https://github.com/luapower/svg_parser

- [ 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

Another option is using the OpenCV library with Python to extract features from images and compare them using feature matching algorithms like SIFT, SURF, or ORB. This approach is more computationally intensive but might be more accurate in identifying near-duplicates. You can find more information about OpenCV here: https://opencv.org/