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Comment by ulrikhansen54 | Hacker News Reader

How do you plan to expand the functionality? I've been looking for a more modern alternative to OpenCV for a while - especially replacements some of the aged object tracking functions, there's been a wad of new stuff coming out that OpenCV has been slow to implement.

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Great question! Development has been guided by my asking myself -- and discussing with friends -- what problems I/we would like to solve (or what would be fun!), then building for those use cases. This is evident in the WIP DetectDistinctScenes[] method.

Example script:

    In["file.mov"]
        Classify["building", "garden"]
    Endin

    DetectDistinctFrames[]

This method is more advanced than most. It uses CLIP behind the scenes to detect when classifications change for more than N frames in a video, and generates a list of timestamps you can use to determine scene changes.

I'm keen to build more specific functions for common CV tasks. My code most certainly needs cleaned (the main lang.py is > 2k LOCs right now) but I do have a contributing guide that you can follow to add features should you be interested: https://github.com/capjamesg/visionscript/blob/main/CONTRIBU...

Shameless plug: take a look to our embedded computer vision library SOD: https://sod.pixlab.io. It's a lightweight OpenCV alternative targeting embedded devices with most of the modern image processing algorithms already implemented including an experimental Stable Diffusion implementation.

a new, popular library for basic functionality (converting between annotation formats, evaluating models, doing object tracking) has been supervision: https://github.com/roboflow/supervision

To the extent that VisionScript uses it behind the scenes for various things (i.e. counting objects in a zone https://github.com/capjamesg/visionscript/blob/main/visionsc...)!

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