"""Tests for the Jimeng (即梦AI) visible-watermark engine. No real Jimeng sample is committed (the 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. """ 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.jimeng_engine import ( _ALPHA_HEIGHT_FRAC, _ALPHA_NATIVE_WIDTH, _ALPHA_WIDTH_FRAC, DETECT_NCC_THRESHOLD, JimengEngine, _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 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 = w - margin - gw 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 > 0.2 class TestLocate: def test_box_anchored_bottom_right(self): eng = JimengEngine() 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 = JimengEngine() 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 = JimengEngine() 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 = JimengEngine() 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 = 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 CJK silhouette (2026-06-26 FP: a 48x48 app-icon chevron scored 0.47). 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, _ALPHA_NATIVE_WIDTH) eng = JimengEngine() 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 = JimengEngine() wm, _mark = _compose(_ALPHA_NATIVE_WIDTH, _ALPHA_NATIVE_WIDTH) 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_right(self): wm, _mark = _compose(_ALPHA_NATIVE_WIDTH, _ALPHA_NATIVE_WIDTH) mask = JimengEngine().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 assert xs.mean() > wm.shape[1] / 2 def test_removes_synthetic_mark(self): """localize -> cv2 fill clears the composed mark (re-detect no longer fires).""" wm, _mark = _compose(_ALPHA_NATIVE_WIDTH, _ALPHA_NATIVE_WIDTH) assert JimengEngine().detect(wm).detected out, region = registry.get_mark("jimeng").remove(wm, backend="cv2") assert region is not None assert not JimengEngine().detect(out).detected @pytest.mark.parametrize( ("w", "h"), [ (_ALPHA_NATIVE_WIDTH, _ALPHA_NATIVE_WIDTH), # captured width (1440, 2560), # off-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 = JimengEngine().detect(wm) out, _ = registry.get_mark("jimeng").remove(wm, backend="cv2") assert JimengEngine().detect(out).confidence < before.confidence 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 = JimengEngine() 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 = JimengEngine() gray = np.zeros((2048, 2048), np.uint8) mask = eng.footprint_mask(gray, force=True) assert mask is None or mask.shape == (2048, 2048) def test_bgra_4channel_does_not_raise(self): eng = JimengEngine() bgra = np.zeros((2048, 2048, 4), np.uint8) mask = eng.footprint_mask(bgra, force=True) assert mask is None or mask.shape == (2048, 2048)