OP
waldo-vision/optical.flow.demo
A project that uses optical flow and machine learning to detect aimhacking in video clips.
531 24 +0/wk
GitHub
anti-cheat anticheat deep-learning fps fps-shooter gaming machine-learning opencv opencv-python optical-flow
Trend
0
Star & Fork Trend (17 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
waldo-vision/optical.flow.demo has +0 stars this period . Velocity data will be available after more historical data is collected.
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| Metric | optical.flow.demo | kubedl | DL-Simplified | are-we-learning-yet |
|---|---|---|---|---|
| Stars | 531 | 531 | 530 | 530 |
| Forks | 24 | 78 | 401 | 68 |
| Weekly Growth | +0 | +0 | +1 | +0 |
| Language | Python | Go | Jupyter Notebook | Rust |
| Sources | 1 | 1 | 1 | 1 |
| License | MPL-2.0 | Apache-2.0 | MIT | CC-BY-4.0 |
Capability Radar vs kubedl
optical.flow.demo
kubedl
Maintenance Activity 0
Last code push 1601 days ago.
Community Engagement 69
Fork-to-star ratio: 4.5%. Lower fork ratio may indicate passive usage.
Issue Burden 70
Issue data not yet available.
Growth Momentum 30
No measurable growth in the current period (first-day cold start expected).
License Clarity 70
Licensed under MPL-2.0. Copyleft — check compatibility requirements.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.