mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-07-12 11:06:33 +02:00
Victor Kuznetsov
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>
105 lines
4.4 KiB
Python
105 lines
4.4 KiB
Python
"""Samsung Galaxy AI visible watermark detector/localizer.
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Samsung's on-device Generative AI photo edits burn a visible "✦ Contenuti generati
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dall'AI" wordmark into the bottom-LEFT corner (the Italian locale variant calibrated
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here; the string is locale-specific -- DETECTION only matches this locale's silhouette,
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so other locales are not yet detected, though the fill mask itself is locale-agnostic).
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It is a faint, near-white semi-transparent overlay, the same overlay class as the
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Doubao/Jimeng marks but bottom-left.
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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 Samsung's tuned :class:`TextMarkConfig` (bottom-LEFT corner, a lower glyph
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luma since the mark is faint, ``assets/samsung_alpha.png`` -- the detection silhouette,
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solved from the flat captures by ``scripts/visible_alpha_solve.py``). Samsung Galaxy AI
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edits are also caught by C2PA + the ``genAIType`` marker, so this is the visible-mark
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*removal* path; it also feeds ``identify`` as the medium-confidence ``visible_samsung``
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signal via the registry.
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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-LEFT).
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WM_WIDTH_FRAC = 0.40
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WM_HEIGHT_FRAC = 0.060
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MARGIN_LEFT_FRAC = 0.004
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MARGIN_BOTTOM_FRAC = 0.002
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# Glyph appearance: a light, low-saturation gray. LOGO_MIN_LUMA is lower than Jimeng's
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# because the mark is faint (peak alpha ~0.38), so on a mid/dark background its glyph
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# luma is lower; a white-paper document is still left untouched.
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MAX_SATURATION = 55
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LOGO_MIN_LUMA = 110
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TOPHAT_DELTA = 8
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# Shape-consistent detection. Threshold 0.40; real marks ~0.79, and Doubao/Jimeng score
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# 0.0 here (and Samsung 0.0 on theirs) -- no cross-fire (the corner also differs).
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DETECT_MIN_COVERAGE = 0.01
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DETECT_NCC_THRESHOLD = 0.40
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# Detection-silhouette geometry, solved by scripts/visible_alpha_solve.py from the flat
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# gray capture (native width 1086). Real photos are ~2958 wide, so the captured glyph is
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# upscaled; width-scale + NCC-align sizes the silhouette for the detection match (removal
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# is the template-free glyph-bbox footprint mask).
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_ALPHA_NATIVE_WIDTH = 1086
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_ALPHA_WIDTH_FRAC = 0.3195 # asset width / image width -- sizes the detection silhouette
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_ALPHA_HEIGHT_FRAC = 0.0378
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_CONFIG = TextMarkConfig(
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name="Samsung Galaxy AI",
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asset_name="samsung_alpha.png",
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corner="bl",
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margin_floor=2,
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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_LEFT_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=3,
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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=16,
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)
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SamsungDetection = TextMarkDetection
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def _alpha_template() -> NDArray[Any] | None:
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"""The bundled Samsung 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 "Contenuti generati dall'AI" silhouette (255 = glyph), 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 Samsung 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 SamsungEngine(TextMarkEngine):
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"""Detect/localize the visible Samsung Galaxy AI text mark (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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