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Spaghetti Lab
FGC replay analysis powered by computer vision
This project is in active development and currently in preview mode. Features may be incomplete, change significantly, or break between updates. Expect rough edges — your feedback helps shape the tool.
Extract per-frame game state from Guilty Gear Strive footage. Track health bars, detect rounds, find highlights, split VODs, and export clips — all from one desktop app.
How It Works
Features
Health Bar Tracking
Per-frame health extraction using computer vision. Dual-band activation handles different overlay styles with rolling median smoothing to filter screen flashes and dimming.
Round Detection
Automatic round boundary detection via timer jumps and HP resets. Organizes rounds into first-to-2 matches with KO and timeout detection.
Highlight Finder
Automatically detects comebacks, close rounds, big damage, and perfect rounds. Each highlight is scored by severity and exportable as a clip.
Match Statistics
Detailed per-match and per-round stats: damage dealt/taken, biggest hits, average round duration, comeback frequency, and win rates.
VOD Splitter
Split long tournament recordings into individual sets. Interactive overlay detection with automated gameplay scanning and fast ffmpeg cutting — no re-encoding.
Clip Export
Export any round, highlight, or custom time range as standalone MP4. Adjustable padding and instant output — perfect for social media clips.
Interactive Dashboard
Everything synced together in one app. Click on a round to jump the video there. Hover the health timeline to see exact values. Switch between tabs for matches, rounds, highlights, damage log, and notes.
Who Is It For?
Tournament Organizers
- •Split VODs into individual match files
- •Generate stats and highlights for recap content
- •No re-encoding — fast processing
Content Creators
- •Find the best moments automatically
- •Export clips directly from the app
- •Damage breakdowns for commentary overlays
Players & Analysts
- •Frame-by-frame health timeline
- •Per-round damage analysis
- •Identify comeback patterns and tendencies
Built With
Available for Windows (NSIS installer), macOS (Intel & Apple Silicon), and Linux (AppImage & .deb)
Want to try it out?
Free, open-source, and under active development. Things may break — but your feedback directly shapes where this goes next.
Download from GitHub