farzaa/gemini-bball
None observed · 2026-08-28
Health v2 · maintenance only
31/100
- Activity 29
- Release rhythm 35
- Longevity 30
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 427
- days_rel: n/a
- days_push: 427
- n_releases_24m: 0
Adoption not part of the score
1166 stars · 306 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A demo project from a viral tweet that uses Google's Gemini API to analyze basketball video frames, with an OpenCV-based visualizer. The core logic lives in a JSON prompt/config file, with guidance on extending it to a real-time product.
Use cases
- analyze basketball video with gemini
- detect basketball plays from video frames
- build a sports video analysis demo
- overlay ai annotations on sports footage
- learn how to send video frames to gemini api
When to choose
- you want a minimal reference for combining Gemini video understanding with OpenCV rendering
- you're prototyping an AI sports video analysis app
When to avoid
- you need a production-ready or real-time system
- you need a maintained project with a license or active support
Facets
application · maturity experimental
computer-vision video-processing llm-inference image-processing computer-vision artificial-intelligence large-language-models sports python cross-platform gemini-api basketball opencv demo video-analysis
1 source
- readme: https://github.com/farzaa/gemini-bball · fetched 2026-08-28 · cecf237d01cc
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| farzaa/gemini-bball | main | 31 |
For agents
markdown · JSON · MCP: product_card(name="farzaa/gemini-bball")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem