Add calibrated SynthID pixel detector

scripts/synthid_periodic_tile.py

@@ -1,50 +1,12 @@
-"""Shared periodic-residual helpers for SynthID research probes."""
+"""Compatibility imports for shared periodic-residual helpers."""
 
 from __future__ import annotations
 
-import cv2
 import numpy as np
 
+from remove_ai_watermarks.synthid_detector import fold_residual_template, unit_tile
 
-def fold_residual_template(
-    pixels: np.ndarray,
-    *,
-    tile_height: int,
-    tile_width: int,
-    denoise_sigma: float,
-) -> np.ndarray:
-    """Estimate a zero-mean periodic residual template by modulo folding."""
-    if pixels.ndim != 3 or pixels.shape[2] != 3:
-        raise ValueError("pixels must have shape (height, width, 3)")
-    if tile_height < 1 or tile_width < 1 or denoise_sigma <= 0.0:
-        raise ValueError("tile dimensions and denoise sigma must be positive")
-    height, width = pixels.shape[:2]
-    if height % tile_height != 0 or width % tile_width != 0:
-        raise ValueError("image geometry must be divisible by the tile geometry")
-    source = pixels.astype(np.float32)
-    denoised = cv2.GaussianBlur(
-        source,
-        (0, 0),
-        sigmaX=denoise_sigma,
-        sigmaY=denoise_sigma,
-        borderType=cv2.BORDER_REFLECT_101,
-    )
-    residual = source - denoised
-    repeats_y = height // tile_height
-    repeats_x = width // tile_width
-    folded = residual.reshape(repeats_y, tile_height, repeats_x, tile_width, 3).mean(
-        axis=(0, 2),
-        dtype=np.float64,
-    )
-    return folded - np.mean(folded, axis=(0, 1), keepdims=True)
-
-
-def unit_tile(tile: np.ndarray) -> tuple[np.ndarray, float]:
-    """Return TILE normalized by its L2 norm and the original norm."""
-    norm = float(np.linalg.norm(tile))
-    if norm == 0.0:
-        return np.zeros_like(tile, dtype=np.float64), 0.0
-    return np.asarray(tile, dtype=np.float64) / norm, norm
+__all__ = ["cyclic_tile_correlations", "fold_residual_template", "unit_tile"]
 
 
 def cyclic_tile_correlations(template: np.ndarray, tile: np.ndarray) -> np.ndarray: