adbbb9d620
Colour-drifted background trapped between hair strands defeats every single-signal defence: the chroma key reads it as foreground (bgc ~0.07), the segmenter backs it, ViTMatte rates it opaque, and post-hoc removal is a proven dead end (it shreds the hair volume the same pixels belong to). A second, trimap-free matting model (BiRefNet_HR-matting) is the only tested model that separates this residue from the subject, so its opinion is fused in as a veto: min-fusion that may only LOWER alpha, restricted to the background-hued bright suspect zone (proj >= 3, L >= 45, feathered) and gated by primary-alpha confidence (0.70 -> 0.95 ramp) so soft wisps and dark hair are exempt by construction. - settings/config/CLI: cross_check block, --cross-check/--no-cross-check - alpha_post.cross_check_alpha after clean_alpha; second opinion reuses BiRefNetSegmenter; saved to debug as cross_check_alpha.png - chroma.bg_hue_projection extracted and shared with the trimap - docs: methodology.md (new), hair_gap_artifacts.md (investigation log) Verified: cross-check ON reproduces the visually-reviewed B1gate prototype byte-for-byte on TestImage3; --no-cross-check reproduces the previous baseline byte-for-byte; pink-bg FixImage1 face untouched. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
178 lines
7.1 KiB
Python
178 lines
7.1 KiB
Python
from __future__ import annotations
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import numpy as np
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from .chroma import bg_hue_projection
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from .deps import require_cv2
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from .settings import TrimapSettings
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def radius_from_ratio(shape: tuple[int, int], ratio: float, minimum: int) -> int:
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return max(minimum, int(round(max(shape) * ratio)))
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def elliptical_kernel(radius: int) -> np.ndarray:
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cv2 = require_cv2()
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size = radius * 2 + 1
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return cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (size, size))
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def generate_trimap(
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bg_confidence: np.ndarray, settings: TrimapSettings
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) -> tuple[np.ndarray, dict[str, int]]:
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cv2 = require_cv2()
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shape = bg_confidence.shape
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unknown_radius = radius_from_ratio(
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shape, settings.unknown_radius_ratio, settings.min_unknown_radius
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)
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fg_safe_radius = radius_from_ratio(
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shape, settings.fg_safe_radius_ratio, settings.min_fg_safe_radius
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)
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sure_bg = bg_confidence >= settings.sure_bg_threshold
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low_bg = bg_confidence <= settings.sure_fg_threshold
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bg_u8 = sure_bg.astype(np.uint8)
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unknown_band = cv2.dilate(bg_u8, elliptical_kernel(unknown_radius)).astype(bool)
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fg_safe = ~cv2.dilate(bg_u8, elliptical_kernel(fg_safe_radius)).astype(bool)
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sure_fg = low_bg & fg_safe
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trimap = np.full(shape, 128, dtype=np.uint8)
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trimap[sure_bg] = 0
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trimap[sure_fg] = 255
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# Keep a protective unknown band around all sure background, including holes.
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trimap[unknown_band & ~sure_bg & ~sure_fg] = 128
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stats = {
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"sure_bg_pixels": int((trimap == 0).sum()),
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"unknown_pixels": int((trimap == 128).sum()),
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"sure_fg_pixels": int((trimap == 255).sum()),
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"unknown_radius": int(unknown_radius),
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"fg_safe_radius": int(fg_safe_radius),
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}
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return trimap, stats
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def fuse_trimap(
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seg_mask: np.ndarray, bg_confidence: np.ndarray, settings: TrimapSettings
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) -> tuple[np.ndarray, dict[str, int]]:
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"""Build a trimap from a semantic subject mask, refined by the chroma key.
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Authority split: the segmentation mask decides subject *topology* (it keeps
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colour-contaminated hair as foreground and drops see-through holes), while the
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chroma key sharpens the flat-background boundary. ViTMatte then refines the
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unknown band.
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"""
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cv2 = require_cv2()
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shape = seg_mask.shape
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fg_safe_radius = radius_from_ratio(
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shape, settings.fg_safe_radius_ratio, settings.min_fg_safe_radius
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)
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band_radius = max(settings.min_fg_safe_radius, fg_safe_radius // 2)
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screen_bg = bg_confidence >= settings.sure_bg_threshold # confident flat background
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core = (seg_mask >= settings.seg_core_threshold).astype(np.uint8)
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loose = (seg_mask >= settings.seg_loose_threshold).astype(np.uint8)
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# A small protective band around the mask boundary lets ViTMatte anti-alias
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# crisp edges; hair gets extra width from BiRefNet's own soft region. The band
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# is deliberately small so it does not swallow small holes (finger gaps).
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kernel = elliptical_kernel(band_radius)
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band = cv2.dilate(loose, kernel).astype(bool) & ~cv2.erode(loose, kernel).astype(bool)
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# Background follows the mask directly so interior holes stay background, minus
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# the boundary band; foreground is the confident subject, never the background.
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sure_fg = core.astype(bool) & ~band & ~screen_bg
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sure_bg = ((loose == 0) & ~band) | (screen_bg & (loose == 0))
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trimap = np.full(shape, 128, dtype=np.uint8)
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trimap[sure_bg] = 0
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trimap[sure_fg] = 255
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stats = {
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"sure_bg_pixels": int((trimap == 0).sum()),
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"unknown_pixels": int((trimap == 128).sum()),
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"sure_fg_pixels": int((trimap == 255).sum()),
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"band_radius": int(band_radius),
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"fg_safe_radius": int(fg_safe_radius),
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"seg_subject_pixels": int((core > 0).sum()),
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}
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return trimap, stats
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def fuse_trimap_directional(
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seg_mask: np.ndarray,
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bg_confidence: np.ndarray,
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lab: np.ndarray,
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lab_center: tuple[float, float, float],
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settings: TrimapSettings,
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) -> tuple[np.ndarray, dict[str, int]]:
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"""Three-signal trimap: chroma magnitude + seg + a directional chroma test.
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Per pixel (``bgc`` = chroma bg-confidence, ``seg`` = subject confidence)::
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BG if bgc >= sure_bg_threshold (clean screen colour)
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OR (seg < seg_low AND bgc > sure_fg_threshold) (seg says not-subject,
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but never override a pixel chroma is sure is foreground -> keeps
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fine wisps the segmenter underestimates)
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FG if bgc <= sure_fg_threshold AND seg >= seg_low (clearly non-screen colour)
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else (chroma-unknown): if seg >= seg_core_threshold, split by hue --
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a pixel *not* displaced toward the background hue (projection <
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bg_hue_proj_min: e.g. a neutral white shirt) becomes FG, while one
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strongly displaced toward it (e.g. blue between hair strands) is left
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unknown for ViTMatte / chroma-suppress. Lower-seg unknowns stay unknown.
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A thin unknown band at the segmentation silhouette is preserved so ViTMatte can
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anti-alias the boundary (it is deliberately small so interior holes survive).
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"""
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cv2 = require_cv2()
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shape = seg_mask.shape
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fg_safe_radius = radius_from_ratio(
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shape, settings.fg_safe_radius_ratio, settings.min_fg_safe_radius
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)
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band_radius = max(settings.min_fg_safe_radius, fg_safe_radius // 2)
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chroma_bg = bg_confidence >= settings.sure_bg_threshold
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chroma_fg = bg_confidence <= settings.sure_fg_threshold
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chroma_unknown = ~chroma_bg & ~chroma_fg
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core = seg_mask >= settings.seg_core_threshold
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# Directional chroma: positive = colour displaced toward the background hue.
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proj = bg_hue_projection(lab, lab_center)
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bg_hued = proj >= settings.bg_hue_proj_min
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bg = chroma_bg | ((seg_mask < settings.seg_low) & ~chroma_fg)
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fg = chroma_fg & (seg_mask >= settings.seg_low)
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rule3_fg = chroma_unknown & core & ~bg_hued
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if settings.mode == "directional-hard-bg":
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# Aggressive variant: hard-remove background-hued pixels instead of leaving
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# them unknown. Can eat cool/shadowed white cloth, so it is not the default.
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bg = bg | (chroma_unknown & core & bg_hued)
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trimap = np.full(shape, 128, dtype=np.uint8)
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trimap[bg] = 0
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trimap[fg | rule3_fg] = 255
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loose = (seg_mask >= settings.seg_loose_threshold).astype(np.uint8)
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kernel = elliptical_kernel(band_radius)
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band = cv2.dilate(loose, kernel).astype(bool) & ~cv2.erode(loose, kernel).astype(bool)
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trimap[band & ~chroma_bg] = 128
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stats = {
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"sure_bg_pixels": int((trimap == 0).sum()),
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"unknown_pixels": int((trimap == 128).sum()),
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"sure_fg_pixels": int((trimap == 255).sum()),
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"band_radius": int(band_radius),
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"rule3_fg_pixels": int(rule3_fg.sum()),
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"bg_hued_pixels": int((bg_hued & chroma_unknown & core).sum()),
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}
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return trimap, stats
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def trimap_to_alpha_seed(trimap: np.ndarray, bg_confidence: np.ndarray) -> np.ndarray:
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alpha = np.clip(1.0 - bg_confidence, 0.0, 1.0).astype(np.float32)
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alpha[trimap == 0] = 0.0
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alpha[trimap == 255] = 1.0
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return alpha
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