Remove assume_ai, add tophat front-end and rival margin, fix two CLI defects

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

scripts/render_vendor_silhouettes.py

@@ -0,0 +1,104 @@
+"""Render SYNTHETIC detection silhouettes for the CJK vendor text marks (data-safe).
+
+Adding a mark needs only a DETECTION silhouette, and it must be font-rendered rather
+than derived from user uploads: the corpus is real user content and may never reach a
+tracked asset (see the repo CLAUDE.md data-safety rule). Seeing real samples to learn
+the glyphs, weight and layout is fine; the committed template stays synthetic.
+
+Covered here:
+  qwen    "千问AI生成"  -- Alibaba Tongyi Qianwen, bottom-right, 3-lobed logo + text
+  xinghui "星绘AI生成"  -- ByteDance 星绘, bottom-right, 4-point sparkle + text
+
+The leading LOGO is deliberately NOT rendered. It is the part that varies most between
+releases and is hardest to reproduce synthetically, while the CJK run is stable and is
+what actually discriminates one vendor from another (the shared `AI生成` tail is exactly
+what does NOT discriminate -- see the rival-margin mechanism in _text_mark_engine).
+
+Regenerate with:  uv run python scripts/render_vendor_silhouettes.py
+
+STATUS 2026-07-18: these two marks are NOT registered, and this script is kept as the
+method + the record of why. Measured on 14 hand-verified 千问 positives from the corpus,
+the current detect architecture (top-hat glyph blob -> binary TM_CCOEFF_NORMED) cannot
+see this mark AT ALL:
+
+    same pipeline, each mark scored with its OWN template, on real positives
+      doubao   n=40   mean NCC 0.723   median 0.835   >= 0.40 gate: 82%
+      qwen     n=14   mean NCC 0.170   median 0.179   >= 0.40 gate:  0%
+
+Three checks ruled out the obvious explanations, in order:
+  1. NOT the synthetic render. A template cut from an ACTUAL Qwen mark scores the same
+     as the font-rendered one (real-vs-real 0.307 vs synthetic 0.308) -- and real masks
+     do not even match EACH OTHER.
+  2. NOT the morphology kernel size. Scaling MORPH_OPEN/CLOSE with the box height (they
+     are fixed 5px, ~9% of a 57px-tall box) gained only +0.014 mean and moved nothing
+     across the gate.
+  3. NOT the appearance thresholds. Sweeping tophat_delta / logo_min_luma / kernel
+     reached at best mean 0.35 with 4/14 over the gate.
+
+The blocker is SEGMENTATION on a faint mark: Doubao is stamped bold and opaque, so the
+white top-hat returns a clean glyph blob; the Qwen mark is a thin translucent overlay
+that shatters into specks, and no template can match a blob that is not there. Adding
+it therefore needs a detection front-end that does not depend on binarizing the glyph
+(grayscale/edge correlation on the raw top-hat, or a learned patch classifier) -- not a
+new silhouette. Shipping it on the current front-end would mean a detector that finds
+almost nothing and, at any threshold low enough to fire, fires on arbitrary corner text.
+
+星绘 additionally has only ONE confirmed example in the corpus, so even a working
+front-end could not have its threshold calibrated yet.
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+
+_ASSETS = Path(__file__).resolve().parents[1] / "src" / "remove_ai_watermarks" / "assets"
+# STHeiti Medium approximates the semibold CJK sans these marks are set in; the exact
+# family is unpublished for every vendor (GB 45438-2025 only requires a legible face).
+_FONT = "/System/Library/Fonts/STHeiti Medium.ttc"
+
+MARKS = {
+    "qwen_alpha.png": "千问AI生成",
+    "xinghui_alpha.png": "星绘AI生成",
+}
+
+
+def render(text: str, width: int = 335) -> np.ndarray:
+    """Binary glyph silhouette (255 = glyph), sized to the doubao asset's convention.
+
+    Matching doubao's 335px asset width keeps the `alpha_*_frac` numbers transferable,
+    since these marks are the same house style and scale.
+    """
+    probe = Image.new("L", (10, 10))
+    d0 = ImageDraw.Draw(probe)
+    size = 8
+    while size < 200:  # grow until the run fills the target width
+        f = ImageFont.truetype(_FONT, size)
+        if d0.textbbox((0, 0), text, font=f)[2] >= width * 0.98:
+            break
+        size += 1
+    font = ImageFont.truetype(_FONT, size)
+    bb = d0.textbbox((0, 0), text, font=font)
+    w, h = bb[2] - bb[0], bb[3] - bb[1]
+    pad = max(2, int(h * 0.12))
+    im = Image.new("L", (w + 2 * pad, h + 2 * pad), 0)
+    ImageDraw.Draw(im).text((pad - bb[0], pad - bb[1]), text, font=font, fill=255)
+    return np.array(im)
+
+
+def main() -> None:
+    try:
+        for name, text in MARKS.items():
+            sil = render(text)
+            Image.fromarray(sil).save(_ASSETS / name)
+            print(f"wrote {_ASSETS / name}  ({sil.shape[1]}x{sil.shape[0]})  text={text!r}")
+    except OSError as e:
+        print(f"Font not found ({e}); install a CJK font or edit _FONT.", file=sys.stderr)
+        raise SystemExit(1) from e
+
+
+if __name__ == "__main__":
+    main()