Add anime-seg segmentation backend (SkyTNT ISNet)

Add AnimeSegSegmenter (skytnt/anime-seg ISNet ONNX via onnxruntime, no remote
code) and a make_segmenter factory selected by SegmentationSettings.backend
("birefnet" | "anime-seg"). The ONNX output is already 0..1, so it slots into the
same soft-mask interface BiRefNetSegmenter uses.

On the anime samples anime-seg recovers more and more-coherent hair wisps than
BiRefNet (TestImage2 shoulder rescue: added px 1886 -> 3278, largest connected
component 147 -> 454), as expected from an anime-trained model. Default backend
stays birefnet.

Adds onnxruntime to requirements.

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

bgfilter/pipeline.py

@@ -31,9 +31,9 @@ class MattingPipeline:
 
     def _segment(self, rgb: np.ndarray) -> np.ndarray:
         if self._segmenter is None:
-            from .segmentation import BiRefNetSegmenter
+            from .segmentation import make_segmenter
 
-            self._segmenter = BiRefNetSegmenter(self.settings.segmentation)
+            self._segmenter = make_segmenter(self.settings.segmentation)
         return self._segmenter.mask(rgb)
 
     def _predict_alpha(self, rgb: np.ndarray, trimap: np.ndarray, bg_confidence: np.ndarray) -> tuple[np.ndarray, str]:

bgfilter/segmentation.py

@@ -71,3 +71,51 @@ class BiRefNetSegmenter:
             ).resize((rgb.shape[1], rgb.shape[0]), Image.Resampling.BILINEAR)
             pred = np.asarray(pred_img, dtype=np.float32) / 255.0
         return np.clip(pred, 0.0, 1.0).astype(np.float32)
+
+
+class AnimeSegSegmenter:
+    """Anime character segmentation via SkyTNT anime-seg (ISNet, ONNX).
+
+    Anime-trained, run through onnxruntime (no remote code). Same soft-mask
+    interface as :class:`BiRefNetSegmenter`. The ONNX output is already 0..1.
+    Weights load from HuggingFace; behind a firewall set ``HF_ENDPOINT``.
+    """
+
+    def __init__(self, settings: SegmentationSettings):
+        try:
+            import onnxruntime as ort
+            from huggingface_hub import hf_hub_download
+        except ModuleNotFoundError as exc:
+            raise RuntimeError(
+                "Missing anime-seg dependencies. Install them with: "
+                "pip install onnxruntime huggingface_hub"
+            ) from exc
+
+        model_file = hf_hub_download(settings.model_name, "isnetis.onnx")
+        providers = ["CPUExecutionProvider"]
+        if settings.device == "cuda" and "CUDAExecutionProvider" in ort.get_available_providers():
+            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
+        self.session = ort.InferenceSession(model_file, providers=providers)
+        self.input_name = self.session.get_inputs()[0].name
+        self.size = settings.input_size
+
+    def mask(self, rgb: np.ndarray) -> np.ndarray:
+        image = Image.fromarray(rgb.astype(np.uint8), mode="RGB").resize(
+            (self.size, self.size), Image.Resampling.BILINEAR
+        )
+        x = (np.asarray(image, dtype=np.float32) / 255.0).transpose(2, 0, 1)[None]
+        pred = self.session.run(None, {self.input_name: x})[0][0, 0]
+        mask_img = Image.fromarray(
+            np.clip(pred * 255.0, 0, 255).astype(np.uint8), mode="L"
+        ).resize((rgb.shape[1], rgb.shape[0]), Image.Resampling.BILINEAR)
+        return np.asarray(mask_img, dtype=np.float32) / 255.0
+
+
+def make_segmenter(settings: SegmentationSettings):
+    if settings.backend == "birefnet":
+        return BiRefNetSegmenter(settings)
+    if settings.backend == "anime-seg":
+        return AnimeSegSegmenter(settings)
+    raise RuntimeError(
+        f"Unknown segmentation backend '{settings.backend}'. Use 'birefnet' or 'anime-seg'."
+    )

bgfilter/settings.py

@@ -104,6 +104,7 @@ class ModelSettings:
 @dataclass(frozen=True)
 class SegmentationSettings:
     enabled: bool = True
+    backend: str = "birefnet"  # "birefnet" or "anime-seg"
     model_name: str = "ZhengPeng7/BiRefNet"
     device: str = "cuda"
     input_size: int = 1024

configs/default.yaml

@@ -6,6 +6,7 @@ model:
 
 segmentation:
   enabled: true
+  backend: birefnet  # birefnet | anime-seg (anime-seg needs model_name: skytnt/anime-seg)
   model_name: ZhengPeng7/BiRefNet
   device: cuda
   input_size: 1024

requirements.txt

@@ -13,6 +13,7 @@ huggingface_hub
 timm
 einops
 kornia
+onnxruntime
 tqdm
 typer
 rich