OpenCV

How do you approach image optimization today: libraries, cloud services, or browser-based tools?

Image optimization shows up in almost every workflow I touch — from resizing and compressing assets to format conversion, previews, and performance tuning.

I’ve used and explored multiple approaches over time:

What I’ve noticed is that the “best” approach often depends on context:

Lately, I’m curious how others decide when to use heavy libraries, when to rely on SaaS, and when browser-based tools are “good enough.”

👉 How do you handle image optimization today in real projects?👉 Do you mix multiple approaches, or stick to one stack?

Would love to learn from real-world use cases.

Hi Team,

Could you please suggest which one need to be used in between OpenCV and FFMPEG.

Thank you in Advance.

CV glue. Modified libraries for pattern-detection. Some pattern training tasks. HoG matching. Transform OpenCV

I use openCV to serve as "motion capture" logic for my home security cameras. Which means that instead of capturing in a dumb way based on motion, it captures video when it recognizes human faces or bodies. This saves a lot of disk, but at the expense of CPU. OpenCV

I used both scikit-image and OpenCV for image processing and cell identification on the backend. Trained to identify malaria cells based on image datasets online. When it comes to quick training for image processing, OpenCV and scikit-image are the two best choices in my opinion. The approach I took to cell detection was template-matching and edge detection based. Both are highly tested and very powerful features of the Scikit Image and OpenCV libraries, and also have great Python interfaces. OpenCV