"""Tests for the Doubao visible-watermark engine (reverse-alpha only).""" from __future__ import annotations from pathlib import Path import cv2 import numpy as np import pytest from remove_ai_watermarks.doubao_engine import ( _ALPHA_HEIGHT_FRAC, _ALPHA_LOGO_BGR, _ALPHA_MARGIN_BOTTOM_FRAC, _ALPHA_MARGIN_RIGHT_FRAC, _ALPHA_NATIVE_WIDTH, _ALPHA_WIDTH_FRAC, DETECT_NCC_THRESHOLD, DoubaoEngine, _alpha_template, _glyph_silhouette, _template_match_score, load_image_bgr, ) SAMPLE = Path(__file__).resolve().parents[1] / "data" / "samples" / "doubao-1.png" class TestLocate: def test_box_anchored_bottom_right(self): eng = DoubaoEngine() img = np.zeros((2048, 2048, 3), np.uint8) loc = eng.locate(img) assert 2048 - (loc.x + loc.w) < int(2048 * 0.03) assert 2048 - (loc.y + loc.h) < int(2048 * 0.03) def test_box_scales_with_width(self): eng = DoubaoEngine() 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) # ── Detection: alpha-template NCC ─────────────────────────────────── class TestDetect: def test_clean_gradient_not_detected(self): eng = DoubaoEngine() ramp = np.tile(np.linspace(0, 255, 1024, dtype=np.uint8), (1024, 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 = DoubaoEngine() img = np.zeros((1024, 1024, 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 that contains the silhouette -> high score 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 # a uniformly filled box has no glyph structure -> low score solid = np.full_like(box, 255) assert _template_match_score(solid, _ALPHA_NATIVE_WIDTH) < DETECT_NCC_THRESHOLD @pytest.mark.skipif(not SAMPLE.exists(), reason="sample image not present") class TestRealSample: def test_detects_watermark(self): det = DoubaoEngine().detect(load_image_bgr(SAMPLE)) assert det.detected assert det.confidence >= DETECT_NCC_THRESHOLD def test_reverse_alpha_removes_mark(self): eng = DoubaoEngine() img = load_image_bgr(SAMPLE) assert eng.reverse_alpha_available(img) # sample is at the captured width out = eng.remove_watermark_reverse_alpha(img) assert not eng.detect(out).detected # mark gone after recovery def test_far_region_untouched(self): eng = DoubaoEngine() img = load_image_bgr(SAMPLE) out = eng.remove_watermark_reverse_alpha(img) h, w = img.shape[:2] assert np.array_equal(img[: h // 2, : w // 2], out[: h // 2, : w // 2]) # ── Reverse-alpha (exact recovery) ────────────────────────────────── class TestReverseAlpha: 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_available_whenever_asset_present(self): # NCC alignment generalizes to any resolution, so availability is just # "asset loadable" (any non-empty image); the caller gates on detect. eng = DoubaoEngine() assert eng.reverse_alpha_available(np.zeros((1024, 1024, 3), np.uint8)) assert eng.reverse_alpha_available(np.zeros((1773, 1535, 3), np.uint8)) assert not eng.reverse_alpha_available(np.zeros((0, 0, 3), np.uint8)) @staticmethod def _compose(w: int, h: int, bg: float = 100.0): """Composite the real alpha (scaled to width ``w``) onto a flat bg. 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) ax = w - int(_ALPHA_MARGIN_RIGHT_FRAC * w) - gw ay = h - int(_ALPHA_MARGIN_BOTTOM_FRAC * w) - 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 * np.array(_ALPHA_LOGO_BGR, np.float32) + (1 - a3) * img).clip(0, 255).astype(np.uint8) return wm, amap > 0.2 def test_native_returns_exact_reverse_alpha_no_inpaint(self): """At native width the recovery is exact, so it must be returned untouched -- inpainting over exactly-recovered interior pixels degrades quality (regression: native textured error 1.6 reverse-alpha-only vs 2.6 with the old full-footprint inpaint). The output must equal pure reverse-alpha.""" eng = DoubaoEngine() wm, _mark = self._compose(_ALPHA_NATIVE_WIDTH, _ALPHA_NATIVE_WIDTH) out = eng.remove_watermark_reverse_alpha(wm) amap = eng._fixed_alpha_map(wm) assert amap is not None expected = eng._apply_reverse_alpha(wm, amap[0]) assert np.array_equal(out, expected) # no inpaint touched the recovery @pytest.mark.parametrize( ("w", "h", "max_err"), [ (_ALPHA_NATIVE_WIDTH, _ALPHA_NATIVE_WIDTH, 5.0), # native 1:1 -> fixed geometry, ~exact (1773, 2364, 8.0), # 3:4 portrait -> NCC alignment generalizes the single capture ], ) def test_recovers_flat_background(self, w, h, max_err): """Recovers the flat background at native (fixed geometry, exact) AND a non-native resolution (NCC alignment generalizes the single capture).""" eng = DoubaoEngine() wm, mark = self._compose(w, h) assert float(np.abs(wm.astype(np.float32)[mark] - 100.0).mean()) > 15 # mark visible out = eng.remove_watermark_reverse_alpha(wm).astype(np.float32) assert float(np.abs(out[mark] - 100.0).mean()) < max_err