Files
BGfilter/bgfilter/pipeline.py
T
lhk229 08378e57f8 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>
2026-06-30 23:58:22 +08:00

140 lines
5.1 KiB
Python

from __future__ import annotations
import json
from dataclasses import asdict
from pathlib import Path
import numpy as np
from .alpha_post import (
clean_alpha,
enforce_trimap,
fill_seg_dropouts,
suppress_alpha_by_chroma,
)
from .chroma import compute_bg_confidence
from .despill import despill_green
from .foreground import estimate_foreground_rgb
from .recolor import recolor_foreground
from .io import load_rgb, save_gray, save_rgb, save_rgba, write_text
from .qa import BACKGROUND_COLORS, composite, make_qa_grid, save_previews
from .settings import PipelineSettings
from .trimap import fuse_trimap, generate_trimap, trimap_to_alpha_seed
from .vitmatte_infer import ViTMatteRunner
class MattingPipeline:
def __init__(self, settings: PipelineSettings):
self.settings = settings
self._runner: ViTMatteRunner | None = None
self._segmenter = None
def _segment(self, rgb: np.ndarray) -> np.ndarray:
if self._segmenter is None:
from .segmentation import make_segmenter
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]:
if self.settings.model.matting_method == "chroma":
return trimap_to_alpha_seed(trimap, bg_confidence), "chroma"
if self.settings.model.matting_method != "vitmatte":
raise RuntimeError(
f"Unsupported matting method '{self.settings.model.matting_method}'. "
"Use 'vitmatte' or 'chroma'."
)
try:
if self._runner is None:
self._runner = ViTMatteRunner(self.settings.model)
return self._runner.predict(rgb, trimap), "vitmatte"
except RuntimeError:
if not self.settings.model.fallback_to_chroma_alpha:
raise
return trimap_to_alpha_seed(trimap, bg_confidence), "chroma_fallback"
def run_image(
self,
input_path: str | Path,
output_path: str | Path,
debug_dir: str | Path | None,
) -> dict:
return _run_image(input_path, output_path, debug_dir, self)
def run_image(
input_path: str | Path,
output_path: str | Path,
debug_dir: str | Path | None,
settings: PipelineSettings,
) -> dict:
return MattingPipeline(settings).run_image(input_path, output_path, debug_dir)
def _run_image(
input_path: str | Path,
output_path: str | Path,
debug_dir: str | Path | None,
pipeline: MattingPipeline,
) -> dict:
settings = pipeline.settings
rgb = load_rgb(input_path)
bg_confidence, model = compute_bg_confidence(rgb, settings=settings.chroma)
if settings.segmentation.enabled:
seg_mask = pipeline._segment(rgb)
trimap, trimap_stats = fuse_trimap(seg_mask, bg_confidence, settings.trimap)
else:
seg_mask = None
trimap, trimap_stats = generate_trimap(bg_confidence, settings.trimap)
alpha, alpha_source = pipeline._predict_alpha(rgb, trimap, bg_confidence)
alpha = enforce_trimap(alpha, trimap)
alpha = suppress_alpha_by_chroma(alpha, bg_confidence, trimap, settings.alpha_post)
alpha = clean_alpha(alpha, trimap, settings.alpha_post)
if seg_mask is not None:
alpha = fill_seg_dropouts(alpha, seg_mask, settings.alpha_post)
foreground = estimate_foreground_rgb(
rgb, alpha, bg_confidence, model, settings.foreground
)
corrected_rgb, color_mask = despill_green(
foreground.rgb, alpha, model, settings.despill
)
if settings.recolor.enabled:
recolored = recolor_foreground(corrected_rgb, alpha, seg_mask, settings.recolor)
corrected_rgb, color_mask = recolored.rgb, recolored.mask
save_rgba(output_path, corrected_rgb, alpha)
result = {
"input": str(input_path),
"output": str(output_path),
"alpha_source": alpha_source,
"background_model": model.to_dict(),
"trimap": trimap_stats,
"settings": asdict(settings),
}
if debug_dir is not None:
debug = Path(debug_dir)
debug.mkdir(parents=True, exist_ok=True)
save_gray(debug / "bg_confidence.png", bg_confidence)
save_gray(debug / "trimap.png", trimap)
if seg_mask is not None:
save_gray(debug / "seg_mask.png", seg_mask)
save_gray(debug / "alpha.png", alpha)
save_rgb(debug / "foreground_rgb.png", corrected_rgb)
save_gray(debug / "color_mask.png", color_mask)
save_previews(debug, corrected_rgb, alpha)
tiles = {
"input": rgb,
"bg confidence": np.repeat((bg_confidence[..., None] * 255).astype(np.uint8), 3, axis=2),
"trimap": np.repeat(trimap[..., None], 3, axis=2),
"alpha": np.repeat((alpha[..., None] * 255).astype(np.uint8), 3, axis=2),
}
for name, color in BACKGROUND_COLORS.items():
tiles[f"preview {name}"] = composite(corrected_rgb, alpha, color)
make_qa_grid(debug, tiles)
write_text(debug / "metadata.json", json.dumps(result, indent=2, ensure_ascii=False))
return result