mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-08-06 22:18:36 +02:00
Victor KuznetsovandClaude Opus 4.8
1a955b096a
- Replace reverse-alpha removal with localize -> fill (template-free mask + one shared cv2/MI-GAN/big-LaMa fill) for every mark; drops the colour-shift / dark-pit failure modes, version-robust to a moved or re-rendered mark - Separate perception/decision/action: engines report Candidates, a pure decide(candidates, Context) arbiter owns all policy (sensitivity + provenance + pill gate), remove_auto_marks orchestrates -- behavior-preserving (corpus 46/46/92) - Three orthogonal knobs replace --method: --backend cv2|migan|lama, --sensitivity auto|strict|assume-ai, provenance (auto from metadata) - Add high-level api.remove_visible / visible_provenance (lazy top-level re-export); visible --mark auto delegates to it so CLI and library share ONE path - Read+write HEIC/AVIF on the pixel path via pillow-heif; imwrite preserves the input format at max quality (JPEG q100/4:4:4); a no-op copies the original bytes verbatim - Lossless byte-level JPEG metadata strip (no DCT re-encode); consolidate the two remove_ai_metadata into one, delete legacy noai/cleaner + best_auto_mark - Bump 0.13.0 -> 0.14.0 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
98 lines
3.8 KiB
Python
98 lines
3.8 KiB
Python
"""Jimeng / Dreamina visible watermark detector/localizer.
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Jimeng (即梦AI, ByteDance) stamps generated images with a visible "★ 即梦AI" wordmark
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in the bottom-right corner -- a near-white semi-transparent overlay, the same overlay
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class as the Doubao text strip.
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Detection matches the bundled glyph silhouette against the corner; removal is the
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shared **localize -> fill** (the glyph-bbox :meth:`footprint_mask` feeds
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``region_eraser``), NOT reverse-alpha. This is one of the three text-mark engines that
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share :class:`remove_ai_watermarks._text_mark_engine.TextMarkEngine`; this module
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supplies only Jimeng's tuned :class:`TextMarkConfig` (bottom-right corner,
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``assets/jimeng_alpha.png`` -- the detection silhouette, rebuilt by
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``scripts/visible_alpha_solve.py`` from the gray capture). Jimeng images are also caught
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by the China TC260 AIGC metadata label, so this is the visible-mark *removal* path, not
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a new ``identify`` signal.
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"""
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# The module-level _alpha_template / _glyph_silhouette / _template_match_score below
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# are thin test-facing shims (imported by tests/), so pyright's src-only pass sees them
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# as unused; the use is cross-module.
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# pyright: reportUnusedFunction=false
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from __future__ import annotations
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from typing import TYPE_CHECKING, Any
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from remove_ai_watermarks import _text_mark_engine
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from remove_ai_watermarks._text_mark_engine import TextMarkConfig, TextMarkDetection, TextMarkEngine
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if TYPE_CHECKING:
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from numpy.typing import NDArray
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# Locate geometry as a fraction of image WIDTH (mark scales with width, bottom-right).
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WM_WIDTH_FRAC = 0.27
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WM_HEIGHT_FRAC = 0.092
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MARGIN_RIGHT_FRAC = 0.008
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MARGIN_BOTTOM_FRAC = 0.010
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# Glyph appearance: a light, low-saturation gray brighter than the local background.
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MAX_SATURATION = 55
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LOGO_MIN_LUMA = 150
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TOPHAT_DELTA = 12
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# Shape-consistent detection. Threshold 0.45 cleanly separates real Jimeng marks
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# (>=0.81) from the Doubao strip (0.21), so the two ByteDance marks do not cross-fire.
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DETECT_MIN_COVERAGE = 0.02
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DETECT_NCC_THRESHOLD = 0.45
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# Detection-silhouette geometry, emitted by scripts/visible_alpha_solve.py from the
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# gray capture at the captured width (sizes the silhouette for the detection match;
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# removal is the template-free glyph-bbox footprint mask).
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_ALPHA_NATIVE_WIDTH = 2048
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_ALPHA_WIDTH_FRAC = 0.2021 # asset width / image width -- sizes the detection silhouette
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_ALPHA_HEIGHT_FRAC = 0.0576
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_CONFIG = TextMarkConfig(
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name="Jimeng",
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asset_name="jimeng_alpha.png",
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corner="br",
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margin_floor=4,
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width_frac=WM_WIDTH_FRAC,
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height_frac=WM_HEIGHT_FRAC,
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margin_x_frac=MARGIN_RIGHT_FRAC,
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margin_bottom_frac=MARGIN_BOTTOM_FRAC,
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max_saturation=MAX_SATURATION,
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logo_min_luma=LOGO_MIN_LUMA,
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tophat_delta=TOPHAT_DELTA,
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morph_open_size=5,
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detect_min_coverage=DETECT_MIN_COVERAGE,
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detect_ncc_threshold=DETECT_NCC_THRESHOLD,
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alpha_width_frac=_ALPHA_WIDTH_FRAC,
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alpha_height_frac=_ALPHA_HEIGHT_FRAC,
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min_gw=8,
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)
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JimengDetection = TextMarkDetection
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def _alpha_template() -> NDArray[Any] | None:
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"""The bundled Jimeng alpha template (float [0,1]), or None."""
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return _text_mark_engine.load_alpha_template(_CONFIG.asset_name)
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def _glyph_silhouette() -> NDArray[Any] | None:
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"""Binary "即梦AI" silhouette (255 = glyph) from the alpha map, or None."""
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return _text_mark_engine.glyph_silhouette(_CONFIG.asset_name)
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def _template_match_score(box_mask: NDArray[Any], image_width: int) -> float:
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"""TM_CCOEFF_NORMED of the Jimeng glyph silhouette against ``box_mask``."""
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return _text_mark_engine.template_match_score(box_mask, image_width, _CONFIG)
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class JimengEngine(TextMarkEngine):
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"""Detect/localize the visible Jimeng "★ 即梦AI" watermark (locate -> mask; mask feeds the fill)."""
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def __init__(self) -> None:
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super().__init__(_CONFIG)
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