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https://github.com/ruvnet/RuView.git
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5c50fa8588
Trains a real fal.ai LoRA fine-tune (fal-ai/flux-lora-fast-training) on frames extracted from the real fal.ai-generated room-visualization MP4s already captured this session, so display-layer generation has a specific trained style target instead of prompt engineering against a stock model. Architecture boundary: this model only ever produces supplementary synthetic visual/pixel output. It has no path into measured/derived RF, pose, vitals, identity, or confidence values, which remain local and authoritative. Real run: request 01a060b2-c940-7972-abf9-ea48847eb2ae, 1000 steps, 27 training images, ~$2.04 spend, validated with a real inference call against the trained LoRA (assets/fal-visual-teacher-sample.png).
99 lines
3.9 KiB
Python
99 lines
3.9 KiB
Python
#!/usr/bin/env python3
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"""Extract training stills from the real fal.ai room-visualization MP4s.
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Reads the source videos (real fal.ai-generated room visualizations, one per
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style preset) and writes JPEG frames + a caption .txt per frame into a local
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(gitignored) training-data directory, ready to be zipped for fal.ai LoRA
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training. Frame interval is time-based (not frame-count-based) so each
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source contributes visual diversity proportional to its length.
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"""
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import argparse
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import shutil
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import subprocess
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import sys
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from pathlib import Path
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REPO_ROOT = Path(__file__).resolve().parents[2]
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# (source mp4 path, style tag, caption) -- sources are real fal.ai output,
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# captured earlier this session by the fal-room-viz / room-viz-h3-upgrade
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# agents. Caption describes the RuView visual style so the LoRA learns the
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# recurring motifs (glowing pose overlay, WiFi signal arcs, vitals readout,
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# dark tech aesthetic) rather than any single room layout.
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SOURCES = [
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("/tmp/room-viz-architectural/room-viz.mp4", "architectural",
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"ruview style room visualization, architectural wireframe overlay, "
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"glowing cyan pose skeleton, wifi signal arcs, dark tech aesthetic"),
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("/tmp/room-viz-abstract/room-viz.mp4", "abstract",
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"ruview style room visualization, abstract particle field, "
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"glowing pose overlay, wifi signal arcs, dark tech aesthetic"),
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("/tmp/room-viz-branded/room-viz.mp4", "branded",
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"ruview style room visualization, branded hud overlay, "
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"glowing cyan and emerald pose skeleton, vitals readout, dark tech aesthetic"),
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("/tmp/room-viz-h3-architectural/room-viz.mp4", "h3-architectural",
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"ruview style room visualization, architectural wireframe overlay, "
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"glowing pose skeleton, wifi signal arcs, vitals readout, dark tech aesthetic"),
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("/tmp/room-viz-h3-cinematic/room-viz.mp4", "h3-cinematic",
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"ruview style room visualization, cinematic lighting, "
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"glowing pose overlay, wifi signal arcs, vitals readout, dark tech aesthetic"),
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("/tmp/room-viz-h3-minimal/room-viz.mp4", "h3-minimal",
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"ruview style room visualization, minimal clean overlay, "
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"glowing pose skeleton, subtle wifi signal arcs, dark tech aesthetic"),
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("/tmp/room-viz-h3-abstract/room-viz.mp4", "h3-abstract",
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"ruview style room visualization, abstract particle field, "
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"glowing pose overlay, wifi signal arcs, dark tech aesthetic"),
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]
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FRAME_INTERVAL_SEC = 1.5
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def extract(src: Path, style: str, caption: str, out_dir: Path) -> int:
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pattern = out_dir / f"{style}_%03d.jpg"
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cmd = [
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"ffmpeg", "-y", "-loglevel", "error",
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"-i", str(src),
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"-vf", f"fps=1/{FRAME_INTERVAL_SEC}",
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"-q:v", "2",
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str(pattern),
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]
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subprocess.run(cmd, check=True)
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frames = sorted(out_dir.glob(f"{style}_*.jpg"))
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for frame in frames:
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frame.with_suffix(".txt").write_text(caption, encoding="utf-8")
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return len(frames)
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def main() -> int:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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"--out", default=str(REPO_ROOT / "scripts" / "fal-visual-teacher" / "training-data"),
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help="Output directory for extracted frames + captions (gitignored)",
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)
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args = parser.parse_args()
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out_dir = Path(args.out)
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if out_dir.exists():
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shutil.rmtree(out_dir)
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out_dir.mkdir(parents=True)
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total = 0
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for src_str, style, caption in SOURCES:
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src = Path(src_str)
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if not src.exists():
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print(f"SKIP (missing): {src}", file=sys.stderr)
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continue
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n = extract(src, style, caption, out_dir)
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print(f"{style}: {n} frames from {src}")
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total += n
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print(f"\nTotal frames extracted: {total}")
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print(f"Training data dir: {out_dir}")
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if total < 4:
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print("ERROR: fal.ai training needs at least 4 images", file=sys.stderr)
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return 1
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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