ROS VS OpenCV

Robotics is a broad field and is a confluence of many specialties: mechanical engineering, hardware engineering, software engineering, control, machine learning, computer vision, anything in between is a good entrance.

Coming from software, if you are interested, I would suggest either:

- Backend platform development (Python, C++ as main programming languages with a strong focus on ROS[1]).

- Frontend development (nothing too different from what's out there).

As small projects I would suggest playing with ROS to learn it and getting a running simulation with a simple robot that you can teleoperate, most of the stack already exists, it's just connecting everything together [2].

Another venue is open source contribution [1] to get known within the community and potentially attract interest from companies. ROS has multiple packages, from cloud infrastructure to drivers and simulation, if you see anything there you could contribute to, they will gladly take contributions.

In general robotics greatly benefits of good technologies from other areas, if there is a tool we use you believe could be better or a lack of good tooling in a specific area, it will get noticed.

So this would be my suggested path: learn C++/Python if you're not familiar with, learn ROS and watch which specialties appear more often in robot related jos posts [3]. If you are really invested, maybe go to a robotics conference as ROSCon to meet other enthusiasts, which companies are engaged with the community, etc.

Good luck!

Note: not everything robot related is done in ROS, but it's almost a standard within the field save for a few exceptions.

[1]: https://www.ros.org/

ROS is an operating system designed for robotics (it can be run many different ways) it includes simulations for many robots (including sensors etc) and you can even design your own fully inside the software. https://www.ros.org/

Robotics with ROS https://www.ros.org/ (You can do a lot with simulators and don't require actual HW)

In Robotics, the Open Motion Planning Library (OMPL) is a popular library for multi-dimensional motion planning, and is used by ROS and other robotics-related software. There are no Rust bindings to OMPL (though there is Rust support for software like ROS), and the library is written almost exclusively in C++. There are Python bindings, but those are generated using Py++. The header files throughout OMPL are C++ header files, not C, as they contain namespaces, classes, etc.

[3] https://www.ros.org/

+1 to the other comments recommending Google summer of code. If you’re interested in robotics, you might want to check out the ROS project: https://www.ros.org/

You're looking for something more unique? Have you looked into robotics? Eg: https://www.ros.org/

[3] https://www.ros.org/

I am taking a course on Robotics this semester. I don't know anything about it and I am asked to download and demonstrate ROS in the next lecture. I saw some videos and have a basic idea about what it is. A video directed me to this page but what do I do now? I have Windows 11 on my laptop and most sites I saw, talked about ROS on Linux. How should I start now?

I don't remember exactly but with this PR from May 2013 I was trying to get support for the fish shell into ROS. Didn't succeed because fish wasn't an official Ubuntu package yet but I did get my work in later.

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)

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

- [ 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