lhk229
711669ea51
Wire BiRefNet into the pipeline (lazy-loaded) and build the trimap from the semantic subject mask refined by the green key, used when segmentation is enabled. generate_trimap (chroma-only) stays as the no-segmentation fallback. Authority split: the mask decides subject topology (keeps green-tinted hair as foreground, drops see-through holes), the bright-green key sharpens the screen boundary, ViTMatte refines a small protective band. Background follows the mask directly so interior holes survive; the band is small so it does not swallow finger gaps. End-to-end on the samples: green hair preserved, finger-gap green 99% transparent (BiRefNet marks it background; was 100% foreground before), leg edges smooth -- topology/colour/edge problems local cues could not solve, with no hard alpha surgery. - SegmentationSettings wired into PipelineSettings/config; --device propagates. - TrimapSettings: seg_core_threshold, seg_loose_threshold. - New debug output seg_mask.png. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
131 lines
4.9 KiB
Python
131 lines
4.9 KiB
Python
from __future__ import annotations
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import json
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from dataclasses import asdict
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from pathlib import Path
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import numpy as np
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from .alpha_post import clean_alpha, enforce_trimap, suppress_alpha_by_chroma
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from .chroma import compute_bg_confidence
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from .despill import despill_green
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from .foreground import estimate_foreground_rgb
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from .io import load_rgb, save_gray, save_rgb, save_rgba, write_text
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from .qa import BACKGROUND_COLORS, composite, make_qa_grid, save_previews
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from .settings import PipelineSettings
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from .trimap import fuse_trimap, generate_trimap, trimap_to_alpha_seed
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from .vitmatte_infer import ViTMatteRunner
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class MattingPipeline:
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def __init__(self, settings: PipelineSettings):
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self.settings = settings
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self._runner: ViTMatteRunner | None = None
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self._segmenter = None
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def _segment(self, rgb: np.ndarray) -> np.ndarray:
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if self._segmenter is None:
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from .segmentation import BiRefNetSegmenter
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self._segmenter = BiRefNetSegmenter(self.settings.segmentation)
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return self._segmenter.mask(rgb)
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def _predict_alpha(self, rgb: np.ndarray, trimap: np.ndarray, bg_confidence: np.ndarray) -> tuple[np.ndarray, str]:
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if self.settings.model.matting_method == "chroma":
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return trimap_to_alpha_seed(trimap, bg_confidence), "chroma"
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if self.settings.model.matting_method != "vitmatte":
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raise RuntimeError(
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f"Unsupported matting method '{self.settings.model.matting_method}'. "
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"Use 'vitmatte' or 'chroma'."
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)
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try:
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if self._runner is None:
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self._runner = ViTMatteRunner(self.settings.model)
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return self._runner.predict(rgb, trimap), "vitmatte"
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except RuntimeError:
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if not self.settings.model.fallback_to_chroma_alpha:
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raise
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return trimap_to_alpha_seed(trimap, bg_confidence), "chroma_fallback"
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def run_image(
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self,
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input_path: str | Path,
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output_path: str | Path,
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debug_dir: str | Path | None,
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) -> dict:
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return _run_image(input_path, output_path, debug_dir, self)
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def run_image(
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input_path: str | Path,
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output_path: str | Path,
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debug_dir: str | Path | None,
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settings: PipelineSettings,
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) -> dict:
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return MattingPipeline(settings).run_image(input_path, output_path, debug_dir)
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def _run_image(
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input_path: str | Path,
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output_path: str | Path,
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debug_dir: str | Path | None,
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pipeline: MattingPipeline,
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) -> dict:
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settings = pipeline.settings
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rgb = load_rgb(input_path)
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bg_confidence, model = compute_bg_confidence(rgb, settings=settings.chroma)
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if settings.segmentation.enabled:
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seg_mask = pipeline._segment(rgb)
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trimap, trimap_stats = fuse_trimap(seg_mask, bg_confidence, settings.trimap)
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else:
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seg_mask = None
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trimap, trimap_stats = generate_trimap(bg_confidence, settings.trimap)
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alpha, alpha_source = pipeline._predict_alpha(rgb, trimap, bg_confidence)
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alpha = enforce_trimap(alpha, trimap)
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alpha = suppress_alpha_by_chroma(alpha, bg_confidence, trimap, settings.alpha_post)
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alpha = clean_alpha(alpha, trimap, settings.alpha_post)
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foreground = estimate_foreground_rgb(
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rgb, alpha, bg_confidence, model, settings.foreground
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)
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corrected_rgb, despill_mask = despill_green(
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foreground.rgb, alpha, model, settings.despill
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)
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save_rgba(output_path, corrected_rgb, alpha)
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result = {
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"input": str(input_path),
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"output": str(output_path),
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"alpha_source": alpha_source,
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"background_model": model.to_dict(),
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"trimap": trimap_stats,
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"settings": asdict(settings),
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}
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if debug_dir is not None:
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debug = Path(debug_dir)
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debug.mkdir(parents=True, exist_ok=True)
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save_gray(debug / "bg_confidence.png", bg_confidence)
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save_gray(debug / "trimap.png", trimap)
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if seg_mask is not None:
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save_gray(debug / "seg_mask.png", seg_mask)
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save_gray(debug / "alpha.png", alpha)
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save_rgb(debug / "foreground_rgb.png", foreground.rgb)
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save_rgb(debug / "foreground_background.png", foreground.background)
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save_gray(debug / "foreground_correction.png", foreground.correction)
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save_gray(debug / "despill_mask.png", despill_mask)
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save_previews(debug, corrected_rgb, alpha)
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tiles = {
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"input": rgb,
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"bg confidence": np.repeat((bg_confidence[..., None] * 255).astype(np.uint8), 3, axis=2),
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"trimap": np.repeat(trimap[..., None], 3, axis=2),
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"alpha": np.repeat((alpha[..., None] * 255).astype(np.uint8), 3, axis=2),
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}
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for name, color in BACKGROUND_COLORS.items():
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tiles[f"preview {name}"] = composite(corrected_rgb, alpha, color)
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make_qa_grid(debug, tiles)
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write_text(debug / "metadata.json", json.dumps(result, indent=2, ensure_ascii=False))
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return result
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