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https://github.com/wiltodelta/remove-ai-watermarks.git
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150 lines
6.0 KiB
Python
150 lines
6.0 KiB
Python
"""Tests for the Kling (可灵AI 3.0) visible-watermark engine (localize -> fill).
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Every tuned constant in ``kling_engine`` was measured on the 30-frame vendor
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cohort (2026-07-21, ``scripts/vendor_mark_calibrate.py``); these tests pin the
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load-bearing ones so a later "cleanup" cannot silently re-inherit Doubao's
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geometry or relax the measured strict-only gate.
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"""
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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 watermark_registry as registry
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from remove_ai_watermarks.kling_engine import (
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_ALPHA_HEIGHT_FRAC,
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_ALPHA_WIDTH_FRAC,
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KlingEngine,
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_alpha_template,
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_glyph_silhouette,
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)
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_MARK_FRAC = 0.12 # measured mark width, fraction of the short side (unimodal)
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_MARGIN = 0.03 # measured right/bottom margin of the real mark
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def _compose(w: int, h: int, mode: float = _MARK_FRAC, bg: float = 100.0):
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"""Composite the Kling silhouette at the measured size onto a flat bg."""
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img = np.full((h, w, 3), bg, np.float32)
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at = _alpha_template()
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short = min(w, h)
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gw = int(mode * short)
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gh = max(4, int(mode * (_ALPHA_HEIGHT_FRAC / _ALPHA_WIDTH_FRAC) * short))
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margin = int(_MARGIN * short)
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ax = w - margin - gw
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ay = h - margin - gh
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amap = np.zeros((h, w), np.float32)
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amap[ay : ay + gh, ax : ax + gw] = cv2.resize(at, (gw, gh))
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a3 = amap[:, :, None]
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wm = (a3 * 255.0 + (1 - a3) * img).clip(0, 255).astype(np.uint8)
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return wm, amap > 0.2
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class TestLocate:
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def test_box_anchored_bottom_right(self):
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eng = KlingEngine()
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img = np.zeros((2048, 2048, 3), np.uint8)
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loc = eng.locate(img)
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assert 2048 - (loc.x + loc.w) == pytest.approx(2048 * 0.03, rel=0.15)
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assert 2048 - (loc.y + loc.h) == pytest.approx(2048 * 0.023, rel=0.15)
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def test_box_scales_with_short_side_not_width(self):
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# scale_basis="short" (measured: mark_w/short 0.118-0.122 across orientations).
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eng = KlingEngine()
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landscape = eng.locate(np.zeros((640, 1280, 3), np.uint8))
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wider = eng.locate(np.zeros((640, 2560, 3), np.uint8))
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assert wider.w == landscape.w # same short side -> same box
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bigger = eng.locate(np.zeros((1280, 1920, 3), np.uint8)) # 2x the short side
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assert bigger.w == pytest.approx(landscape.w * 2, rel=0.05)
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class TestConfig:
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def test_shared_ladder_default(self):
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# The mark is unimodal at 0.12 of the short side, so Kling keeps the shared
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# 3-rung ladder (Qwen's per-mark ladder is the measured exception, not a norm).
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assert KlingEngine().config.ladder == (0.8, 1.0, 1.25)
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def test_strict_only_no_provenance_relaxation(self):
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# The sub-gate band (real Kling variants at 0.17-0.25) overlaps the clean
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# arm's top (p90 0.220), so a relaxed arm cannot separate: factor pinned 1.0.
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assert KlingEngine().config.provenance_ncc_factor == 1.0
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def test_gate_above_clean_arm_max(self):
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# Clean arm scored p99 0.304 / max 0.320 on 286 hand-labeled frames; the
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# gate must sit above that with margin.
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assert KlingEngine().config.detect_ncc_threshold > 0.32
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def test_registry_row(self):
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mark = registry.get_mark("kling")
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assert mark.location == "bottom-right"
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assert "可灵AI" in mark.label
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assert mark.in_auto
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def test_confident_kling_detection_suppresses_the_jimeng_pill(self):
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# A Kling image is TC260 too but is not Jimeng-basic: like Doubao and Qwen,
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# a confident Kling detection must veto the pill (``_keep_pill``).
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from remove_ai_watermarks.watermark_registry import _keep_pill
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assert not _keep_pill({"kling"}, provenance=frozenset({"jimeng"}), footprint_flat=1.0)
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class TestDetect:
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def test_clean_gradient_not_detected(self):
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eng = KlingEngine()
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ramp = np.tile(np.linspace(0, 255, 1024, dtype=np.uint8), (1024, 1))
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img = cv2.cvtColor(ramp, cv2.COLOR_GRAY2BGR)
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assert not eng.detect(img).detected
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def test_solid_blob_corner_not_detected(self):
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eng = KlingEngine()
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img = np.zeros((1024, 1024, 3), np.uint8)
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x, y, bw, bh = eng.locate(img).bbox
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img[y + bh // 4 : y + bh * 3 // 4, x : x + bw // 2] = 200
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assert not eng.detect(img).detected
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def test_silhouette_loads(self):
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sil = _glyph_silhouette()
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assert sil is not None
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assert set(np.unique(sil)).issubset({0, 255})
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def test_composed_mark_detected(self):
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# The registration's core claim: a mark at the measured size scores over the
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# gate. The floor is deliberately far above the gate: the synthetic mark is
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# clean, so it scores high when the geometry is right.
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wm, _ = _compose(853, 640)
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det = KlingEngine().detect(wm)
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assert det.detected
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assert det.confidence >= 0.80
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def test_small_image_guarded(self):
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wm, _ = _compose(853, 640)
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eng = KlingEngine()
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assert eng.detect(wm).detected
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assert not eng.detect(cv2.resize(wm, (150, 112))).detected
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class TestFootprintMaskAndRemoval:
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def test_removes_composed_mark(self):
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wm, mark = _compose(853, 640)
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assert float(np.abs(wm.astype(np.float32)[mark] - 100.0).mean()) > 15 # mark visible
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assert KlingEngine().detect(wm).detected
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out, region = registry.get_mark("kling").remove(wm, backend="cv2")
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assert region is not None
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assert not KlingEngine().detect(out).detected
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h, w = wm.shape[:2]
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assert np.array_equal(out[: h // 2, : w // 2], wm[: h // 2, : w // 2]) # far region exact
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def test_footprint_mask_in_bottom_right(self):
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wm, _ = _compose(853, 640)
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mask = KlingEngine().footprint_mask(wm)
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assert mask is not None
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ys, xs = np.where(mask > 0)
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assert ys.mean() > wm.shape[0] / 2
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assert xs.mean() > wm.shape[1] / 2
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def test_clean_frame_produces_no_mask(self):
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clean = cv2.GaussianBlur(np.full((640, 853, 3), 120, np.uint8), (5, 5), 0)
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assert KlingEngine().footprint_mask(clean, force=False) is None
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