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>
108 lines
3.6 KiB
Python
108 lines
3.6 KiB
Python
from __future__ import annotations
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from dataclasses import fields, replace
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from pathlib import Path
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from typing import Any, TypeVar
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from .settings import (
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AlphaPostSettings,
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ChromaSettings,
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DespillSettings,
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ForegroundSettings,
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ModelSettings,
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PipelineSettings,
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SegmentationSettings,
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TrimapSettings,
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)
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T = TypeVar("T")
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def _load_yaml(path: str | Path) -> dict[str, Any]:
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try:
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import yaml
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except ModuleNotFoundError as exc:
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raise RuntimeError("Missing dependency 'PyYAML'. Install it with: pip install PyYAML") from exc
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with Path(path).open("r", encoding="utf-8") as handle:
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data = yaml.safe_load(handle) or {}
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if not isinstance(data, dict):
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raise ValueError(f"Config file must contain a YAML mapping: {path}")
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return data
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def _update_dataclass(instance: T, values: dict[str, Any] | None) -> T:
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if not values:
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return instance
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allowed = {field.name for field in fields(instance)}
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unknown = sorted(set(values) - allowed)
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if unknown:
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cls_name = type(instance).__name__
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raise ValueError(f"Unknown {cls_name} field(s): {', '.join(unknown)}")
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return replace(instance, **values)
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def settings_from_dict(data: dict[str, Any]) -> PipelineSettings:
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allowed_sections = {"chroma", "trimap", "alpha_post", "foreground", "despill", "model", "segmentation"}
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unknown_sections = sorted(set(data) - allowed_sections)
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if unknown_sections:
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raise ValueError(f"Unknown config section(s): {', '.join(unknown_sections)}")
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return PipelineSettings(
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chroma=_update_dataclass(ChromaSettings(), data.get("chroma")),
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trimap=_update_dataclass(TrimapSettings(), data.get("trimap")),
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alpha_post=_update_dataclass(AlphaPostSettings(), data.get("alpha_post")),
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foreground=_update_dataclass(ForegroundSettings(), data.get("foreground")),
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despill=_update_dataclass(DespillSettings(), data.get("despill")),
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model=_update_dataclass(ModelSettings(), data.get("model")),
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segmentation=_update_dataclass(SegmentationSettings(), data.get("segmentation")),
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)
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def load_settings(path: str | Path | None = None) -> PipelineSettings:
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if path is None:
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return PipelineSettings()
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return settings_from_dict(_load_yaml(path))
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def override_settings(settings: PipelineSettings, **overrides: Any) -> PipelineSettings:
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chroma = settings.chroma
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trimap = settings.trimap
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alpha_post = settings.alpha_post
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foreground = settings.foreground
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despill = settings.despill
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model = settings.model
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segmentation = settings.segmentation
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model_updates = {
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key: overrides[key]
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for key in ["model_name", "device", "matting_method", "fallback_to_chroma_alpha"]
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if overrides.get(key) is not None
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}
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trimap_updates = {
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key: overrides[key]
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for key in [
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"sure_bg_threshold",
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"sure_fg_threshold",
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"unknown_radius_ratio",
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"fg_safe_radius_ratio",
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]
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if overrides.get(key) is not None
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}
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despill_updates: dict[str, Any] = {}
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if overrides.get("despill") is not None:
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despill_updates["enabled"] = overrides["despill"]
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seg_updates: dict[str, Any] = {}
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if overrides.get("device") is not None:
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seg_updates["device"] = overrides["device"]
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return PipelineSettings(
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chroma=chroma,
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trimap=_update_dataclass(trimap, trimap_updates),
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alpha_post=alpha_post,
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foreground=foreground,
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despill=_update_dataclass(despill, despill_updates),
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model=_update_dataclass(model, model_updates),
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segmentation=_update_dataclass(segmentation, seg_updates),
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)
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