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https://github.com/wiltodelta/remove-ai-watermarks.git
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107 lines
4.6 KiB
Python
107 lines
4.6 KiB
Python
"""Synthetic regressions for independently gated text-mark layouts."""
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from __future__ import annotations
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import cv2
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import numpy as np
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import pytest
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from remove_ai_watermarks import _text_mark_engine
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from remove_ai_watermarks.doubao_engine import DoubaoEngine, _OutlinedDoubaoEngine
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from remove_ai_watermarks.microsoft_engine import MicrosoftEngine
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from remove_ai_watermarks.samsung_engine import SamsungEngine
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from remove_ai_watermarks.yuanbao_engine import YuanbaoEngine
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def _stamp_variant(engine, variant_index: int, *, width: int = 1280, height: int = 960):
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variant = engine._variants[variant_index]
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config = variant.config
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background = 90 if config.asset_name != "microsoft_text_alpha.png" else 220
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foreground = 235 if background < 128 else 35
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clean = np.full((height, width, 3), background, np.uint8)
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alpha = _text_mark_engine.load_alpha_template(config.asset_name)
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assert alpha is not None
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base = variant.scale_base(clean)
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glyph_width = int(config.alpha_width_frac * base)
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glyph_height = int(config.alpha_height_frac * base)
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alpha = cv2.resize(alpha, (glyph_width, glyph_height), interpolation=cv2.INTER_LINEAR)
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loc = variant.locate(clean)
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x = loc.x if config.corner in {"bl", "tl"} else loc.x + loc.w - glyph_width
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y = loc.y if config.corner in {"tl", "tr"} else loc.y + loc.h - glyph_height
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if config.asset_name == "samsung_ko_alpha.png":
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x += round(1.3 * glyph_height)
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marked = clean.copy()
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if config.asset_name == "microsoft_text_alpha.png":
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pad_x, pad_y = int(width * 0.012), int(width * 0.014)
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cv2.rectangle(
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marked,
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(x - pad_x, y - pad_y),
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(x + glyph_width + pad_x, y + glyph_height + pad_y),
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(242, 238, 232),
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-1,
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)
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region = marked[y : y + glyph_height, x : x + glyph_width].astype(np.float32)
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opacity = alpha[:, :, None]
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marked[y : y + glyph_height, x : x + glyph_width] = np.clip(
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region * (1.0 - opacity) + foreground * opacity, 0, 255
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).astype(np.uint8)
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return clean, marked, (x, y, glyph_width, glyph_height)
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@pytest.mark.parametrize(
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("engine", "asset"),
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[
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(DoubaoEngine(), "doubao_outline_alpha.png"),
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(YuanbaoEngine(), "yuanbao_compact_alpha.png"),
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(SamsungEngine(), "samsung_ko_alpha.png"),
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(MicrosoftEngine(), "microsoft_text_alpha.png"),
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],
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)
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def test_variant_detection_retains_winning_asset(engine, asset: str) -> None:
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_clean, marked, _box = _stamp_variant(engine, 0)
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detection = engine.detect(marked)
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assert detection.detected
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assert detection.template_asset == asset
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def test_microsoft_text_variant_masks_the_enclosing_pill() -> None:
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clean, marked, _box = _stamp_variant(MicrosoftEngine(), 0)
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detection = MicrosoftEngine().detect(marked)
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mask = MicrosoftEngine().footprint_mask(marked, detection=detection)
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assert mask is not None
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changed = np.any(clean != marked, axis=2)
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assert np.count_nonzero(mask[changed]) / np.count_nonzero(changed) >= 0.98
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def test_samsung_korean_variant_masks_the_leading_sparkle_area() -> None:
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_clean, marked, (x, y, _width, height) = _stamp_variant(SamsungEngine(), 0)
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detection = SamsungEngine().detect(marked)
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mask = SamsungEngine().footprint_mask(marked, detection=detection)
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assert mask is not None
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sparkle_x = x - height // 2
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sparkle_y = y + height // 2
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assert mask[sparkle_y, sparkle_x] == 255
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def test_low_confidence_cjk_layouts_require_matching_provenance() -> None:
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doubao = DoubaoEngine()._variants[0].config
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yuanbao_contrast, yuanbao_gray = (variant.config for variant in YuanbaoEngine()._variants)
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assert (doubao.detect_ncc_threshold, doubao.provenance_ncc_factor) == (0.50, 0.25)
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assert (yuanbao_contrast.detect_ncc_threshold, yuanbao_contrast.provenance_ncc_factor) == (0.50, 0.62)
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assert (yuanbao_gray.detect_ncc_threshold, yuanbao_gray.provenance_ncc_factor) == (0.50, 0.50)
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def test_doubao_provenance_does_not_confirm_a_colored_template_shape(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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_clean, marked, _box = _stamp_variant(DoubaoEngine(), 0)
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gray = cv2.cvtColor(marked, cv2.COLOR_BGR2GRAY).astype(np.float32)
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blend = np.clip((gray - 90) / 145, 0, 1)[:, :, None]
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background = np.array([30, 60, 120], np.float32)
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foreground = np.array([60, 180, 250], np.float32)
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colored_shape = (background * (1 - blend) + foreground * blend).astype(np.uint8)
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assert not DoubaoEngine().detect(colored_shape, provenance=True).detected
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monkeypatch.setattr(_OutlinedDoubaoEngine, "_outline_pixels_ok", lambda _cls, _patch: True)
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assert DoubaoEngine().detect(colored_shape, provenance=True).detected
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