mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-06-05 02:28:00 +02:00
Victor Kuznetsov
411ef16ec3
Add an optional, commercial-safe face-restoration post-pass that recovers face identity the diffusion removal pass drifts (canny holds structure, not likeness) while still scrubbing the pixel watermark in the face regions. - face_restore.py: GFPGANer singleton (CPU unless CUDA), the basicsr torchvision.transforms.functional_tensor shim, and the pure feather _composite_faces helper (unit-tested without the model). GFPGAN re-synthesizes each face from a StyleGAN2 prior, so composited face pixels are GAN-generated (no watermark, no pixel-copy) -- oracle-clean at weight 0.5 with identity preserved. - InvisibleEngine.remove_watermark: restore_faces / restore_faces_weight, best-effort, auto-skips when the extra is absent or no face is detected. - CLI --restore-faces/--no-restore-faces + --restore-faces-weight on invisible/all/batch (on by default). - restore extra (gfpgan/facexlib/basicsr), numpy<2-pinned (scipy<1.18, numba<0.60) and kept out of `all`; basicsr needs Python <3.13 + setuptools<69 to build, so pin .python-version 3.12. Commercial-safe: GFPGAN Apache-2.0, RetinaFace MIT. The CodeFormer alternative is non-commercial and is not shipped. The earlier IP-Adapter FaceID layer was removed (footgun: needs high strength, corrupts faces at the low removal strength). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
86 lines
3.5 KiB
Python
86 lines
3.5 KiB
Python
"""Tests for the GFPGAN face-restoration post-pass.
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The pure feather-composite helper is unit-tested without the model; the
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model-running paths are gated behind ``is_available()`` (a multi-hundred-MB
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download), matching the discipline used for the other ML-adjacent modules.
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"""
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from __future__ import annotations
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import numpy as np
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import pytest
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from remove_ai_watermarks import face_restore
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class TestIsAvailable:
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def test_returns_bool(self):
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assert isinstance(face_restore.is_available(), bool)
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def test_reflects_dependencies(self):
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import importlib.util
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expected = all(importlib.util.find_spec(m) is not None for m in ("gfpgan", "facexlib"))
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assert face_restore.is_available() is expected
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class TestCompositeFaces:
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"""Unit tests for the pure ``_composite_faces`` helper (cv2/numpy only)."""
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def _base_and_restored(self, h: int = 100, w: int = 120):
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base = np.zeros((h, w, 3), dtype=np.uint8) # black
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restored = np.full((h, w, 3), 255, dtype=np.uint8) # white
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return base, restored
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def test_output_shape_and_dtype(self):
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base, restored = self._base_and_restored()
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out = face_restore._composite_faces(base, restored, [(40.0, 30.0, 80.0, 70.0)])
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assert out.shape == base.shape
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assert out.dtype == np.uint8
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def test_box_region_pulls_toward_restored(self):
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base, restored = self._base_and_restored()
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out = face_restore._composite_faces(base, restored, [(40.0, 30.0, 80.0, 70.0)])
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# Center of the box should be near the restored (white) value.
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cy, cx = 50, 60
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assert out[cy, cx].mean() > 200
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def test_far_from_box_stays_base(self):
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base, restored = self._base_and_restored()
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out = face_restore._composite_faces(base, restored, [(40.0, 30.0, 80.0, 70.0)], pad=2)
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# Top-left corner is far from the box and feather, so it stays black.
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assert out[0, 0].mean() < 5
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def test_empty_boxes_returns_base_unchanged(self):
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base, restored = self._base_and_restored()
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out = face_restore._composite_faces(base, restored, [])
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assert np.array_equal(out, base)
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def test_box_fully_outside_is_skipped(self):
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base, restored = self._base_and_restored(h=100, w=120)
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# Box entirely beyond the right/bottom edge -> clipped to empty -> no-op.
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out = face_restore._composite_faces(base, restored, [(200.0, 200.0, 260.0, 260.0)], pad=0)
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assert np.array_equal(out, base)
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def test_near_edge_box_clips_without_error(self):
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base, restored = self._base_and_restored(h=100, w=120)
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# Box reaching past the bottom-right corner must clip, not raise.
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out = face_restore._composite_faces(base, restored, [(100.0, 80.0, 130.0, 110.0)], pad=10)
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assert out.shape == base.shape
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# The clipped in-bounds region still pulls toward white.
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assert out[95, 115].mean() > 100
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@pytest.mark.skipif(not face_restore.is_available(), reason="requires the 'restore' extra (gfpgan/facexlib)")
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class TestRestoreFacesModel:
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"""Model-running smoke test, gated behind the optional extra."""
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def test_no_faces_returns_cleaned_unchanged(self):
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# A flat gray image has no faces; restore_faces must return the cleaned
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# input unchanged (the no-op path).
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cleaned = np.full((128, 128, 3), 127, dtype=np.uint8)
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original = np.full((128, 128, 3), 127, dtype=np.uint8)
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out = face_restore.restore_faces(original, cleaned)
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assert out.shape == cleaned.shape
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assert np.array_equal(out, cleaned)
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