Add project files, tests, and documentation for GitHub release

- CLI with visible, invisible, all, metadata, and batch commands
- Gemini watermark removal via reverse alpha blending
- Invisible watermark removal via diffusion regeneration (SynthID, TreeRing)
- AI metadata stripping (EXIF, PNG text, C2PA)
- Face protection (YOLO/Haar) and analog humanizer
- 137 tests covering all CLI modes and core engines
- Ruff and Pyright clean

tests/test_face_protector.py

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+import numpy as np
+
+from remove_ai_watermarks.face_protector import FaceProtector
+
+
+def test_face_protector_initialization():
+    # Will fallback to Haar cascade if ultralytics is missing
+    fp = FaceProtector(use_yolo=False)
+    assert fp.use_yolo is False
+    assert fp.haar_cascade is not None
+
+
+def test_face_protector_lifecycle():
+    fp = FaceProtector(use_yolo=False)
+
+    # Create dummy black image
+    img = np.zeros((200, 200, 3), dtype=np.uint8)
+
+    # Since it's a black image, haar cascade should find 0 faces
+    faces = fp.extract_faces(img)
+    assert isinstance(faces, list)
+    assert len(faces) == 0
+
+    # Restoring 0 faces should result in strictly equal image
+    restored = fp.restore_faces(img, faces)
+    assert np.array_equal(img, restored)
+
+
+def test_face_protector_restore_bypass_on_size_mismatch():
+    fp = FaceProtector(use_yolo=False)
+    img_small = np.zeros((100, 100, 3), dtype=np.uint8)
+
+    # Manually mock a face that is OUT OF BOUNDS for img_small
+    mock_bbox = (80, 80, 130, 130)
+    mock_crop = np.ones((50, 50, 3), dtype=np.uint8) * 255
+    mock_faces = [(mock_bbox, mock_crop)]
+
+    # Attempt to restore onto an image too small for this box
+    restored = fp.restore_faces(img_small, mock_faces)
+
+    # Should safely skip restoring and not crash
+    assert np.array_equal(restored, img_small)
+
+
+def test_face_protector_restore_blending():
+    fp = FaceProtector(use_yolo=False)
+    # Background is black
+    img_target = np.zeros((100, 100, 3), dtype=np.uint8)
+
+    # Face crop is white
+    mock_bbox = (25, 25, 75, 75)
+    mock_crop = np.ones((50, 50, 3), dtype=np.uint8) * 255
+    mock_faces = [(mock_bbox, mock_crop)]
+
+    restored = fp.restore_faces(img_target, mock_faces)
+
+    # The center of the face should be perfectly white (255)
+    assert restored[50, 50, 0] >= 254
+    # The corner of the target should remain perfectly black (0)
+    assert restored[0, 0, 0] == 0
+    # We should have a blending gradient between them due to the gaussian blur mask
+    # For example, around (30, 30) or similar
+    assert 0 <= restored[28, 28, 0] <= 255