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https://github.com/wiltodelta/remove-ai-watermarks.git
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Victor KuznetsovandClaude Opus 5.5
1dc70df289
- Lock OpenCV 5 and cap NumPy below 2.4 through uv constraint-dependencies so PaddleX 3.7 keeps resolving; Dependabot ignores mirror the cap. - OpenCV 5 draws Hershey putText glyphs differently, so the Dola and Kling video templates ship as committed OpenCV 4 renders with a recipe script, and the OpenArt alpha recipe now refuses any OpenCV but 4.x. - Break Sora/Veo cross-matches in favor of Veo under valid Google AI-video provenance, and require the full Sora mascot shape for mascot-only matches. - Let the Doubao engine detect marks tinted by saturated backgrounds. - Share C2PA claim-generator, credential-level and vendor-platform helpers, JPEG suffixes and the DCT basis instead of per-module copies. - Log model download failures, document the optional GITHUB_TOKEN, mark the photo and source classifier model cards archived, and refresh the lock. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
79 lines
3.0 KiB
Python
79 lines
3.0 KiB
Python
"""Render the frozen OpenCV Hershey-font video templates for Dola and Kling.
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The video detectors were calibrated against these ``cv2.putText`` silhouettes as
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OpenCV 4.x draws them (byte-identical from 4.8 through 4.14). OpenCV 5 reworked
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text rendering, so the same call yields smaller, heavier glyphs, and the Dola
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gallery clip was then selected as Kling. The package therefore loads committed
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PNGs instead of rendering at import time; this script is the recipe that produced
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them. It refuses to run on any OpenCV other than 4.x.
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It also renders the independent Dola test fixture: the same text in the other
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Hershey face at a smaller scale, which tests/test_video.py stamps onto a frame to
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check that the detector is not keyed to its own template's exact pixels.
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Regenerate with:
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uv run --no-project --with 'opencv-python-headless>=4.8,<5' --with numpy \
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python scripts/render_video_hershey_templates.py
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"""
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from __future__ import annotations
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import logging
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from pathlib import Path
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import cv2
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import numpy as np
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log = logging.getLogger(__name__)
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_ROOT = Path(__file__).resolve().parents[1]
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_ASSETS = _ROOT / "src" / "remove_ai_watermarks" / "assets"
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_FIXTURES = _ROOT / "data" / "fixtures" / "synthetic"
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# Asset name -> (text, font, canvas width). Every template is drawn at scale 2.2,
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# thickness 3, anti-aliased, with the baseline origin at (2, 72) on a 100 px canvas.
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TEMPLATES = {
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"video_dola_hershey_duplex.png": ("Dola AI", cv2.FONT_HERSHEY_DUPLEX, 400),
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"video_kling_ai_hershey_simplex.png": ("KLING AI", cv2.FONT_HERSHEY_SIMPLEX, 500),
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"video_kling_ai_hershey_duplex.png": ("KLING AI", cv2.FONT_HERSHEY_DUPLEX, 500),
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"video_klingai_hershey_simplex.png": ("KlingAI", cv2.FONT_HERSHEY_SIMPLEX, 500),
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"video_klingai_hershey_duplex.png": ("KlingAI", cv2.FONT_HERSHEY_DUPLEX, 500),
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}
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def render(
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text: str,
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font: int,
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width: int,
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*,
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height: int = 100,
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origin: tuple[int, int] = (2, 72),
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scale: float = 2.2,
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thickness: int = 3,
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) -> np.ndarray:
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canvas = np.zeros((height, width), dtype=np.uint8)
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cv2.putText(canvas, text, origin, font, scale, 255, thickness, cv2.LINE_AA)
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ys, xs = np.nonzero(canvas)
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return canvas[ys.min() : ys.max() + 1, xs.min() : xs.max() + 1]
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def _write(path: Path, image: np.ndarray) -> None:
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if not cv2.imwrite(str(path), image):
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raise SystemExit(f"failed to write {path}")
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log.info("%s: %dx%d", path.relative_to(_ROOT), image.shape[1], image.shape[0])
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def main() -> None:
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logging.basicConfig(level=logging.INFO, format="%(message)s")
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if not cv2.__version__.startswith("4."):
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raise SystemExit(f"OpenCV {cv2.__version__} draws different glyphs; run with OpenCV 4.x")
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for name, (text, font, width) in TEMPLATES.items():
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_write(_ASSETS / name, render(text, font, width))
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fixture = render("Dola AI", cv2.FONT_HERSHEY_SIMPLEX, 150, height=40, origin=(2, 28), scale=0.9, thickness=2)
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_FIXTURES.mkdir(parents=True, exist_ok=True)
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_write(_FIXTURES / "dola_hershey_simplex.png", fixture)
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if __name__ == "__main__":
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main()
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