mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
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2fcd00ced0
Nine findings from a high-effort project-wide review, fixed and verified (571 passed, ruff/pyright clean): Correctness: - all/batch now remove Doubao/Jimeng/Samsung visible text marks: the visible step routes through the registry (new cli._remove_visible_auto) instead of a hardcoded GeminiEngine, so they no longer leave the wordmark intact. - batch always reads the original source (dropped the out_path-reuse that re-processed already-cleaned outputs on a re-run). - img2img_runner only retries the diffusion call on the deprecated-callback TypeError; any other TypeError now propagates instead of double-running. - gemini detect/remove and the reverse-alpha engines normalize channels via a new image_io.to_bgr, fixing a grayscale/BGRA crash in the FP-gate path. - _png_late_metadata advances its cursor by the clamped length, so a malformed chunk length no longer aborts the late AI-label scan. Cleanup / efficiency: - Consolidate the ~90%-identical Doubao/Jimeng/Samsung engines into a shared config-driven _text_mark_engine.TextMarkEngine base; each engine is now a thin subclass (TextMarkConfig + test shims). Behavior is byte-exact (the three engine test suites pass unchanged). Registry adapters collapse to one _text_mark(...) row each. Gemini stays a separate engine. - scan_head is memoized per (path, size, mtime), so identify() reads the file head once instead of ~8 times. - invisible_engine post-processing decodes/encodes the output once (chained in memory) instead of 2-4 times across stages. - Remove the orphaned get_model_id_for_profile (+ CONTROLNET_PROFILE); derive the --strength help from the strength constants (strength_default_help) so it cannot drift; share the --pipeline/--strength click options; simplify the retired --auto resolver. Net -835 lines. Tests added for the registry-routed visible pass, to_bgr, the polish/model/guidance wiring, and strength_default_help. CLAUDE.md updated for the new base module, the engine/registry changes, image_io.to_bgr, and the scan_head cache. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
126 lines
5.0 KiB
Python
126 lines
5.0 KiB
Python
"""Doubao visible watermark removal engine.
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Doubao (ByteDance) stamps every generated image with a visible "豆包AI生成"
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(Doubao AI generated) text strip in the bottom-right corner -- the explicit AIGC
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label mandated by China's TC260 standard, a near-white semi-transparent overlay.
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Removal is **reverse-alpha blending** against a captured alpha map
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(``original = (wm - a*logo)/(1-a)``), always NCC-aligned to the actual mark plus a
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thin residual inpaint over the glyph footprint. This is one of the three text-mark
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engines that share :class:`remove_ai_watermarks._text_mark_engine.TextMarkEngine`;
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this module supplies only Doubao's tuned :class:`TextMarkConfig` (bottom-right corner,
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``assets/doubao_alpha.png`` rebuilt by ``scripts/visible_alpha_solve.py``). Arbitrary-
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region inpainting still lives in ``region_eraser`` / the ``erase`` command.
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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 pathlib import Path
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from numpy.typing import NDArray
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# Locate geometry as a fraction of image WIDTH (the mark scales with width, anchored
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# bottom-right). The box is GENEROUSLY wider than the mark and reaches close to the
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# corner so a per-image re-rasterization shift stays inside the NCC alignment search.
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WM_WIDTH_FRAC = 0.22
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WM_HEIGHT_FRAC = 0.075
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MARGIN_RIGHT_FRAC = 0.004
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MARGIN_BOTTOM_FRAC = 0.004
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# Glyph appearance: a light, low-saturation gray rendered brighter than the local
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# background (white top-hat), so a white-paper document is left untouched.
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MAX_SATURATION = 55 # max channel spread to count a pixel as "grayish"
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LOGO_MIN_LUMA = 150 # glyphs are at least this bright in absolute terms
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TOPHAT_DELTA = 12 # glyph must exceed the local background by this many levels
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# Shape-consistent detection: match the bundled alpha glyph silhouette against the
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# corner candidate via TM_CCOEFF_NORMED (keys on glyph SHAPE, not coverage; #23).
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DETECT_MIN_COVERAGE = 0.04
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DETECT_NCC_THRESHOLD = 0.4
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# Reverse-alpha geometry, emitted by scripts/visible_alpha_solve.py at the captured
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# width. Removal always tries fixed AND NCC-aligned placement and keeps the lower
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# residual, then a thin footprint inpaint clears the leftover edges.
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_ALPHA_NATIVE_WIDTH = 2048
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_ALPHA_LOGO_BGR: tuple[float, float, float] = (255.0, 255.0, 255.0)
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_ALPHA_WIDTH_FRAC = 0.1636 # asset width / image width -- the alignment scale seed
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_ALPHA_HEIGHT_FRAC = 0.0405
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_ALPHA_MARGIN_RIGHT_FRAC = 0.0132
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_ALPHA_MARGIN_BOTTOM_FRAC = 0.0166
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_ALPHA_ALIGN_SEARCH = (0.88, 1.12, 25)
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_RESIDUAL_ALPHA_FLOOR = 0.05
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_RESIDUAL_DILATE = 5
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_RESIDUAL_INPAINT_RADIUS = 2
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_CONFIG = TextMarkConfig(
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name="Doubao",
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asset_name="doubao_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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alpha_margin_x_frac=_ALPHA_MARGIN_RIGHT_FRAC,
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alpha_margin_bottom_frac=_ALPHA_MARGIN_BOTTOM_FRAC,
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alpha_align_search=_ALPHA_ALIGN_SEARCH,
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min_gw=8,
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alpha_logo_bgr=_ALPHA_LOGO_BGR,
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residual_alpha_floor=_RESIDUAL_ALPHA_FLOOR,
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residual_dilate=_RESIDUAL_DILATE,
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residual_inpaint_radius=_RESIDUAL_INPAINT_RADIUS,
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)
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# Doubao-specific aliases for the shared detection result/engine.
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DoubaoDetection = TextMarkDetection
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def _alpha_template() -> NDArray[Any] | None:
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"""The bundled Doubao 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 "豆包AI生成" 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 Doubao 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 DoubaoEngine(TextMarkEngine):
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"""Remove the visible Doubao "豆包AI生成" watermark (locate -> mask -> reverse-alpha)."""
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def __init__(self) -> None:
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super().__init__(_CONFIG)
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def load_image_bgr(path: str | Path) -> NDArray[Any]:
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"""Read an image as BGR ndarray (helper for scripts/tests)."""
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from remove_ai_watermarks import image_io
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img = image_io.imread(path)
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if img is None:
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raise FileNotFoundError(f"Failed to read image: {path}")
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return img
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