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>
101 lines
3.0 KiB
Python
101 lines
3.0 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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@dataclass(frozen=True)
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class ChromaSettings:
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green_margin: float = 0.08
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min_green_value: float = 0.22
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min_saturation: float = 0.20
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hue_low: float = 35.0
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hue_high: float = 95.0
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border_ratio: float = 0.04
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min_samples: int = 2048
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hue_sigma_min: float = 7.0
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lab_sigma_min: float = 10.0
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rgb_sigma_min: float = 0.08
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@dataclass(frozen=True)
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class TrimapSettings:
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sure_bg_threshold: float = 0.92
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sure_fg_threshold: float = 0.12
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unknown_radius_ratio: float = 0.012
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fg_safe_radius_ratio: float = 0.006
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min_unknown_radius: int = 4
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min_fg_safe_radius: int = 2
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# Semantic fusion thresholds (used by fuse_trimap when segmentation is on).
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seg_core_threshold: float = 0.60
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seg_loose_threshold: float = 0.15
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@dataclass(frozen=True)
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class AlphaPostSettings:
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min_component_area_ratio: float = 0.00001
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fill_hole_area_ratio: float = 0.00002
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alpha_floor: float = 0.002
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alpha_ceil: float = 0.998
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# Chroma-guided alpha suppression: in the unknown band, pull alpha toward 0
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# where background (green) confidence is high, clearing spill in gaps/holes.
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chroma_suppress: bool = True
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chroma_suppress_bg_low: float = 0.35
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chroma_suppress_bg_high: float = 0.80
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chroma_suppress_strength: float = 1.0
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@dataclass(frozen=True)
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class ForegroundSettings:
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enabled: bool = True
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method: str = "ml" # "ml" (pymatting) or "unmix" (legacy heuristic)
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fallback_to_unmix: bool = True
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ml_regularization: float = 1e-5
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# Legacy "unmix" method parameters (used only when method == "unmix").
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edge_low: float = 0.005
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edge_high: float = 0.995
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min_unmix_alpha: float = 0.08
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unmix_strength: float = 0.70
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local_strength: float = 0.45
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local_blur_radius: int = 11
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local_alpha_threshold: float = 0.92
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local_bg_confidence_max: float = 0.20
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green_excess_margin: float = 0.015
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bg_confidence_weight: float = 0.70
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@dataclass(frozen=True)
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class DespillSettings:
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enabled: bool = True
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edge_low: float = 0.005
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strength: float = 0.92
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green_excess_margin: float = 0.015
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edge_expand_radius: int = 2
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alpha_weight_floor: float = 0.35
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@dataclass(frozen=True)
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class ModelSettings:
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model_name: str = "hustvl/vitmatte-small-composition-1k"
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device: str = "cuda"
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matting_method: str = "vitmatte"
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fallback_to_chroma_alpha: bool = False
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@dataclass(frozen=True)
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class SegmentationSettings:
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enabled: bool = True
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model_name: str = "ZhengPeng7/BiRefNet"
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device: str = "cuda"
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input_size: int = 1024
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@dataclass(frozen=True)
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class PipelineSettings:
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chroma: ChromaSettings = ChromaSettings()
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trimap: TrimapSettings = TrimapSettings()
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alpha_post: AlphaPostSettings = AlphaPostSettings()
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foreground: ForegroundSettings = ForegroundSettings()
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despill: DespillSettings = DespillSettings()
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model: ModelSettings = ModelSettings()
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segmentation: SegmentationSettings = SegmentationSettings()
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