from __future__ import annotations from dataclasses import dataclass @dataclass(frozen=True) class ChromaSettings: border_ratio: float = 0.04 min_samples: int = 2048 lab_sigma_min: float = 10.0 rgb_sigma_min: float = 0.08 # Background-colour auto-detection (used when screen_color is None): find the # flat colour that dominates the image border; raise if none is clean enough. detect_cluster_radius: float = 12.0 # Lab radius grouping border pixels into the background cluster detect_min_border_share: float = 0.55 # min fraction of the border that must be this one colour detect_corner_tol: float = 20.0 # max Lab distance a corner may sit from the cluster centre @dataclass(frozen=True) class TrimapSettings: sure_bg_threshold: float = 0.92 sure_fg_threshold: float = 0.12 unknown_radius_ratio: float = 0.012 fg_safe_radius_ratio: float = 0.006 min_unknown_radius: int = 4 min_fg_safe_radius: int = 2 # Semantic fusion thresholds (used by fuse_trimap when segmentation is on). seg_core_threshold: float = 0.60 seg_loose_threshold: float = 0.08 # Trimap mode for the segmentation pipeline (the chroma-only pipeline ignores it): # "directional" (default) -- chroma magnitude + seg + a hue-direction split. In # the chroma-unknown zone a confidently-segmented pixel stays foreground # unless it is displaced toward the background hue: a neutral background- # coloured garment (e.g. a white shirt) is kept, while a background-hued # residual (e.g. blue trapped between hair strands) is left UNKNOWN for # ViTMatte / chroma-suppress to clear. # "seg" -- original fuse_trimap: seg topology + chroma veto, no hue split. # "directional-hard-bg" -- like "directional" but hard-marks background-hued # pixels as background; more aggressive, can eat cool/shadowed white cloth. mode: str = "directional" seg_low: float = 0.15 # seg below this -> background-eligible; at/above -> foreground-eligible bg_hue_proj_min: float = 4.0 # Lab a*/b* projection onto bg direction above which a pixel is background-hued @dataclass(frozen=True) class AlphaPostSettings: min_component_area_ratio: float = 0.00001 fill_hole_area_ratio: float = 0.00002 alpha_floor: float = 0.002 alpha_ceil: float = 0.998 # Chroma-guided alpha suppression: in the unknown band, pull alpha toward 0 # where background-colour confidence is high, clearing spill in gaps/holes. chroma_suppress: bool = True chroma_suppress_bg_low: float = 0.35 chroma_suppress_bg_high: float = 0.80 chroma_suppress_strength: float = 1.0 # Matte-confidence gate: fade the suppression where ViTMatte itself is confident # the pixel is opaque (raw alpha ramps lo -> hi), so colour evidence only vetoes # the matte where the matte is unsure. Protects background-coloured subjects # (white shirt on pastel blue, blush on pink) that chroma alone cannot tell from # background residue. Set suppress_raw_lo >= 1.0 to disable the gate. suppress_raw_lo: float = 0.85 suppress_raw_hi: float = 0.98 @dataclass(frozen=True) class CrossCheckSettings: # Cross-model veto: a trimap-free matting model gives a second opinion that # may only LOWER alpha (min-fusion), only on background-hued, non-dark pixels # (the suspect zone) where the primary result is confident (gate_lo->gate_hi # alpha ramp). Clears colour-drifted background residue between hair strands # that chroma, segmentation and the primary matte all read as foreground; # already-soft wisps and dark hair are exempt by construction. enabled: bool = True model_name: str = "ZhengPeng7/BiRefNet_HR-matting" input_size: int = 2048 proj_min: float = 3.0 # bg-hue projection above which a pixel is suspect l_min: float = 45.0 # Lab lightness below which a pixel is exempt (dark hair) feather_sigma: float = 2.0 # Gaussian feather of the zone boundary, in px gate_lo: float = 0.70 # primary alpha below this -> fully exempt gate_hi: float = 0.95 # primary alpha above this -> fully vetoable @dataclass(frozen=True) class ForegroundSettings: enabled: bool = True method: str = "ml" # "ml" (pymatting) or "unmix" (legacy heuristic) fallback_to_unmix: bool = True ml_regularization: float = 1e-5 # Legacy "unmix" method parameters (used only when method == "unmix"). edge_low: float = 0.005 edge_high: float = 0.995 min_unmix_alpha: float = 0.08 unmix_strength: float = 0.70 local_strength: float = 0.45 local_blur_radius: int = 11 local_alpha_threshold: float = 0.92 local_bg_confidence_max: float = 0.20 green_excess_margin: float = 0.015 bg_confidence_weight: float = 0.70 @dataclass(frozen=True) class DespillSettings: enabled: bool = True edge_low: float = 0.005 strength: float = 0.92 edge_expand_radius: int = 2 alpha_weight_floor: float = 0.35 # Margin, in Lab a*/b* units, of chroma allowed along the background-colour # direction before it counts as spill. Larger keeps more of the subject's own # colour. color_margin_lab: float = 4.0 @dataclass(frozen=True) class ModelSettings: model_name: str = "hustvl/vitmatte-base-composition-1k" device: str = "cpu" matting_method: str = "vitmatte" fallback_to_chroma_alpha: bool = False @dataclass(frozen=True) class SegmentationSettings: enabled: bool = True # False = chroma-only pipeline (no seg model) backend: str = "birefnet" # "birefnet" (general) or "anime-seg" (anime characters) model_name: str = "ZhengPeng7/BiRefNet" device: str = "cpu" input_size: int = 1024 @dataclass(frozen=True) class PipelineSettings: chroma: ChromaSettings = ChromaSettings() trimap: TrimapSettings = TrimapSettings() alpha_post: AlphaPostSettings = AlphaPostSettings() cross_check: CrossCheckSettings = CrossCheckSettings() foreground: ForegroundSettings = ForegroundSettings() despill: DespillSettings = DespillSettings() model: ModelSettings = ModelSettings() segmentation: SegmentationSettings = SegmentationSettings() # Background-colour prior. None = auto-detect the flat background colour from the # image border (raises if there is no clean flat background). A hex string like # "#CFEFFF" sets it explicitly. screen_color: str | None = None