Simplify to single-segmenter pipelines; add screen_color prior; drop rescue + recolor

Strategic pivot: the dropout-fill rescue, the anime-seg fill backend, the
BiRefNet+anime-seg intersection, and the recolor pass all existed to repair
source images whose hair was already green-contaminated or broken at generation
time. Fixing the source instead (clean pastel-background generation) makes the
matte high-contrast, so that whole compensation layer is unnecessary. Remove it.

- Two pipelines via segmentation.enabled:
    false -> chroma-only (Chroma + ViTMatte)
    true  -> single segmenter (default anime-seg, switchable birefnet) -> trimap
  then ViTMatte refines, pymatting estimates foreground, despill cleans spill.

- Generalise the green-hardcoded colour logic to a screen_color prior (hex, e.g.
  "#CFEFFF"; default null = green auto-detect, byte-identical). chroma keys off
  Lab/RGB distance to the colour; despill removes chroma along its Lab direction.
  Added --screen-color CLI flag.

- Removed dead code: fill_seg_dropouts + seg_fill*/intersection params,
  SegmentationSettings.fill_backend/fill_model_name/sharpen, unsharp_mask,
  recolor.py + RecolorSettings.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

bgfilter/segmentation.py

@@ -111,19 +111,6 @@ class AnimeSegSegmenter:
         return np.asarray(mask_img, dtype=np.float32) / 255.0
 
 
-def unsharp_mask(mask: np.ndarray, amount: float, radius: int) -> np.ndarray:
-    """Sharpen a soft mask: raise thin-strand peaks and crisp the blur so strands
-    reach the sure-foreground threshold instead of dissolving into the unknown band."""
-    if amount <= 0:
-        return mask
-    from .deps import require_cv2
-
-    cv2 = require_cv2()
-    k = max(1, radius) * 2 + 1
-    blur = cv2.GaussianBlur(mask, (k, k), 0)
-    return np.clip(mask + amount * (mask - blur), 0.0, 1.0).astype(np.float32)
-
-
 def make_segmenter(settings: SegmentationSettings):
     if settings.backend == "birefnet":
         return BiRefNetSegmenter(settings)