mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-06-10 12:53:56 +02:00
Victor Kuznetsov
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>
90 lines
3.2 KiB
Python
90 lines
3.2 KiB
Python
"""Unicode-safe cv2 image IO (issue #17).
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``cv2.imread`` / ``cv2.imwrite`` pass the path to the platform C runtime, which
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on Windows uses the narrow (ANSI) code-page API and therefore fails on paths
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containing non-ASCII characters (Chinese, Cyrillic, ...). The symptom is a
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``can't open/read file`` warning and a ``None`` decode even though the file
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exists.
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These wrappers route through numpy buffers instead: ``np.fromfile`` /
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``ndarray.tofile`` open the path in Python (full Unicode), and
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``cv2.imdecode`` / ``cv2.imencode`` do the codec work. The decoded/encoded
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bytes are byte-for-byte identical to ``imread`` / ``imwrite``. On macOS/Linux
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cv2 already accepts UTF-8 paths, so the wrappers are behavior-neutral there.
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cv2/numpy are imported lazily inside the functions so importing this module
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stays cheap in a bare environment (matching the rest of the package).
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"""
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# cv2 ships no type stubs; mirror the pragma used by the other cv2-using modules.
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# pyright: reportMissingTypeStubs=false, reportUnknownMemberType=false, reportUnknownVariableType=false, reportUnknownArgumentType=false
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from __future__ import annotations
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from pathlib import Path
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from typing import TYPE_CHECKING, Any
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if TYPE_CHECKING:
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from numpy.typing import NDArray
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def imread(path: str | Path, flags: int | None = None) -> NDArray[Any] | None:
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"""Unicode-safe ``cv2.imread``.
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``flags`` defaults to ``cv2.IMREAD_COLOR`` (same as ``cv2.imread``). Returns
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``None`` when the file is missing or cannot be decoded, matching
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``cv2.imread`` semantics so existing ``if img is None`` checks keep working.
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"""
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import cv2
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import numpy as np
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if flags is None:
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flags = cv2.IMREAD_COLOR
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try:
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data = np.fromfile(str(path), dtype=np.uint8)
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except OSError:
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return None
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if data.size == 0:
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return None
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return cv2.imdecode(data, flags)
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def to_bgr(image: NDArray[Any]) -> NDArray[Any]:
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"""Return a 3-channel BGR view of ``image``, promoting grayscale and BGRA.
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The cv2-based engines (sparkle + the reverse-alpha text marks) assume a
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3-channel BGR array for their channel reductions (``mean(axis=2)``, the
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per-pixel logo subtraction). A 2D grayscale or 4-channel BGRA input -- a real
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Gemini-app export is opaque RGBA -- would otherwise crash or mis-broadcast.
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Centralizes the shape coercion that was inlined across the engines. A 3-channel
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input is returned unchanged (no copy).
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"""
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import cv2
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if image.ndim == 2 or image.shape[2] == 1:
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return cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
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if image.shape[2] == 4:
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return cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)
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return image
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def imwrite(path: str | Path, img: NDArray[Any]) -> bool:
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"""Unicode-safe ``cv2.imwrite``.
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The output format is taken from the path extension (e.g. ``.png``), exactly
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like ``cv2.imwrite``. Returns ``True`` on success, ``False`` if the codec
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rejects the image or the path cannot be written (matching ``cv2.imwrite``,
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which returns ``False`` rather than raising on an unwritable path).
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"""
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import cv2
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ext = Path(path).suffix or ".png"
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ok, buf = cv2.imencode(ext, img)
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if not ok:
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return False
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try:
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buf.tofile(str(path))
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except OSError:
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return False
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return True
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