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Victor Kuznetsov
a2c33af284
Add scripts/fidelity_metrics.py: an objective eval harness comparing watermark-removal outputs against the original (reference) across four groups -- OCR character error rate (EasyOCR), ArcFace identity cosine (insightface), face texture (LPIPS + Laplacian-variance ratio), and whole-image LPIPS/SSIM/ PSNR. PEP 723 inline deps so it stays out of the package / uv.lock; metrics self-gate (faces only where faces, text only where text). The metrics overturned an eyeball conclusion: at EQUAL strength Qwen beats controlnet on TEXT (OpenAI typography 0.10: OCR CER 0.25 vs 0.37) but controlnet beats Qwen on FACES (gemini_3, 18 faces, 0.15 each: Laplacian-variance retention 0.62 vs 0.41, face LPIPS 0.09 vs 0.13 -- Qwen smooths faces MORE; ArcFace identity ~tied). So Qwen is the better TEXT-preserving remover, not a universal fidelity win. Correct the earlier "qwen keeps faces faithful where controlnet plasticizes" claim in CLAUDE.md, module-internals.md, known-limitations.md, README. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>