Files
remove-ai-watermarks/tests/test_samsung_engine.py
T
Victor Kuznetsov 1a955b096a feat(visible): localize->fill rewrite, sensitivity/backend + api, HEIC + lossless IO
- 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>
2026-07-09 14:20:52 +03:00

171 lines
7.0 KiB
Python

"""Tests for the Samsung Galaxy AI visible-watermark engine.
No real Samsung sample is committed (the real-photo captures are gitignored, repo
is public), so detection/removal is exercised against a watermark synthesized from
the bundled alpha asset itself -- self-consistent and download-free. The mark is
anchored bottom-LEFT (unlike the bottom-right Doubao/Jimeng marks).
"""
from __future__ import annotations
import cv2
import numpy as np
import pytest
from remove_ai_watermarks import watermark_registry as registry
from remove_ai_watermarks.samsung_engine import (
_ALPHA_HEIGHT_FRAC,
_ALPHA_NATIVE_WIDTH,
_ALPHA_WIDTH_FRAC,
DETECT_NCC_THRESHOLD,
SamsungEngine,
_alpha_template,
_glyph_silhouette,
_template_match_score,
)
def _compose(w: int, h: int, bg: float = 100.0):
"""Composite the real alpha (scaled to width ``w``) onto a flat bg by the
engine's fixed bottom-left geometry. Returns ``(watermarked_uint8, mark_bool_mask)``."""
img = np.full((h, w, 3), bg, np.float32)
at = _alpha_template()
gw, gh = int(_ALPHA_WIDTH_FRAC * w), int(_ALPHA_HEIGHT_FRAC * w)
margin = int(0.015 * w)
ax = margin
ay = h - margin - gh
amap = np.zeros((h, w), np.float32)
amap[ay : ay + gh, ax : ax + gw] = cv2.resize(at, (gw, gh))
a3 = amap[:, :, None]
wm = (a3 * 255.0 + (1 - a3) * img).clip(0, 255).astype(np.uint8)
return wm, amap > 0.15
class TestLocate:
def test_box_anchored_bottom_left(self):
eng = SamsungEngine()
img = np.zeros((1448, 1086, 3), np.uint8)
loc = eng.locate(img)
assert loc.x < int(1086 * 0.03) # hugs the left edge
assert 1448 - (loc.y + loc.h) < int(1086 * 0.03) # hugs the bottom
def test_box_scales_with_width(self):
eng = SamsungEngine()
small = eng.locate(np.zeros((1024, 1024, 3), np.uint8))
large = eng.locate(np.zeros((2048, 2048, 3), np.uint8))
assert large.w == pytest.approx(small.w * 2, rel=0.1)
class TestDetect:
def test_clean_gradient_not_detected(self):
eng = SamsungEngine()
ramp = np.tile(np.linspace(0, 255, 1086, dtype=np.uint8), (1086, 1))
img = cv2.cvtColor(ramp, cv2.COLOR_GRAY2BGR)
assert not eng.detect(img).detected
def test_solid_blob_corner_not_detected(self):
"""A bright blob is not the glyph shape -> low correlation, not detected."""
eng = SamsungEngine()
img = np.zeros((1086, 1086, 3), np.uint8)
x, y, bw, bh = eng.locate(img).bbox
img[y + bh // 4 : y + bh * 3 // 4, x : x + bw // 2] = 200
assert not eng.detect(img).detected
def test_silhouette_loads(self):
sil = _glyph_silhouette()
assert sil is not None
assert set(np.unique(sil)).issubset({0, 255})
def test_match_score_shape_sensitive(self):
"""The glyph silhouette correlates with itself, not with a filled block."""
sil = _glyph_silhouette()
h, w = sil.shape
box = np.zeros((h + 8, int(w / _ALPHA_WIDTH_FRAC * 0.2) + w), np.uint8)
box[4 : 4 + h, 4 : 4 + w] = sil
assert _template_match_score(box, _ALPHA_NATIVE_WIDTH) >= DETECT_NCC_THRESHOLD
solid = np.full_like(box, 255)
assert _template_match_score(solid, _ALPHA_NATIVE_WIDTH) < DETECT_NCC_THRESHOLD
def test_small_image_guarded_from_false_positive(self):
"""Below the minimum short side a tiny geometric shape spuriously NCC-matches
the glyph silhouette (the 2026-06-26 small-icon FP class). The size guard
suppresses detection there. Bracket it: a real mark is detected at native
size, but the same content downscaled below the guard is not."""
wm, _mark = _compose(_ALPHA_NATIVE_WIDTH, int(_ALPHA_NATIVE_WIDTH * 1.33))
eng = SamsungEngine()
assert eng.detect(wm).detected # native: real mark detected
assert not eng.detect(cv2.resize(wm, (150, 150))).detected # below guard: suppressed
def test_synthetic_mark_detected(self):
"""A watermark composed from the real alpha is detected at its threshold."""
eng = SamsungEngine()
wm, _mark = _compose(_ALPHA_NATIVE_WIDTH, int(_ALPHA_NATIVE_WIDTH * 1.33))
det = eng.detect(wm)
assert det.detected
assert det.confidence >= DETECT_NCC_THRESHOLD
class TestAlphaAssetAndRemoval:
def test_alpha_asset_loads(self):
at = _alpha_template()
assert at is not None
assert at.dtype.kind == "f"
assert float(at.min()) >= 0.0
assert float(at.max()) <= 1.0
def test_footprint_mask_in_bottom_left(self):
wm, _mark = _compose(_ALPHA_NATIVE_WIDTH, int(_ALPHA_NATIVE_WIDTH * 1.33))
mask = SamsungEngine().footprint_mask(wm)
assert mask is not None
assert mask.shape == wm.shape[:2]
ys, xs = np.where(mask > 0)
assert ys.mean() > wm.shape[0] / 2 # bottom
assert xs.mean() < wm.shape[1] / 2 # left
def test_removes_synthetic_mark(self):
"""localize -> cv2 fill clears the composed mark (re-detect no longer fires)."""
wm, _mark = _compose(_ALPHA_NATIVE_WIDTH, int(_ALPHA_NATIVE_WIDTH * 1.33))
assert SamsungEngine().detect(wm).detected
out, region = registry.get_mark("samsung").remove(wm, backend="cv2")
assert region is not None
assert not SamsungEngine().detect(out).detected
@pytest.mark.parametrize(
("w", "h"),
[
(_ALPHA_NATIVE_WIDTH, int(_ALPHA_NATIVE_WIDTH * 1.33)), # captured width
(2958, 4054), # real-photo width (~2.7x native) -> template-free footprint generalizes
],
)
def test_fill_removes_and_leaves_far_region(self, w, h):
wm, mark = _compose(w, h)
assert float(np.abs(wm.astype(np.float32)[mark] - 100.0).mean()) > 15 # mark visible
before = SamsungEngine().detect(wm)
out, _ = registry.get_mark("samsung").remove(wm, backend="cv2")
assert SamsungEngine().detect(out).confidence < before.confidence
# The mark is bottom-left; the opposite (top-right) corner stays exact.
assert np.array_equal(out[: h // 2, w // 2 :], wm[: h // 2, w // 2 :])
class TestDegenerateAndChannelInputs:
"""footprint_mask must not crash on degenerate sizes or non-3-channel inputs."""
@pytest.mark.parametrize(("w", "h"), [(2048, 1), (1, 2048), (2048, 8)])
def test_wide_short_does_not_raise(self, w, h):
eng = SamsungEngine()
img = np.zeros((h, w, 3), np.uint8)
mask = eng.footprint_mask(img, force=True)
assert mask is None or mask.shape == (h, w)
def test_grayscale_2d_does_not_raise(self):
eng = SamsungEngine()
gray = np.zeros((1448, 1086), np.uint8)
mask = eng.footprint_mask(gray, force=True)
assert mask is None or mask.shape == (1448, 1086)
def test_bgra_4channel_does_not_raise(self):
eng = SamsungEngine()
bgra = np.zeros((1448, 1086, 4), np.uint8)
mask = eng.footprint_mask(bgra, force=True)
assert mask is None or mask.shape == (1448, 1086)