Add bgfilter/weights.py: resolve_model_source() maps a HuggingFace repo id
to a local folder under the weights dir (models/ by default, override with
BGFILTER_WEIGHTS_DIR) when one named after the repo basename exists; otherwise
the repo id is returned unchanged. Opt-in and backward compatible.
- vitmatte_infer.py / segmentation.py resolve model_name through it; anime-seg
reads <dir>/isnetis.onnx directly instead of hf_hub_download when local.
- .gitignore: models/, model-cache/
- README: document bundling weights as plain project folders.
Verified: anime-seg loads from a local models/anime-seg/ folder offline.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
BiRefNet is the default backend and --seg-backend switches segmenters, so the
birefnet.yaml config (a copy of default.yaml with backend: birefnet) no longer had
a purpose. configs/ now holds just default.yaml, the editable parameter template.
Also dropped the now-redundant --config from the chroma-debug README example.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The ModelSettings.model_name dataclass default was still vitmatte-small (the
phase-1 "start small" choice) while configs/default.yaml used vitmatte-base, so a
bare run (no --config) silently differed from every documented example. Set the
dataclass default to base too: the built-in defaults now match default.yaml
field-for-field, so --config is only needed after editing that file to tune
parameters. README examples drop the now-redundant --config.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- Segmentation backend now defaults to birefnet (ZhengPeng7/BiRefNet); anime-seg is
selected at runtime with --seg-backend anime-seg, which also swaps in the matching
weights (unknown backends raise). Config files stay for detailed tuning, not
backend selection.
- Default device is now cpu everywhere (dataclass defaults, default.yaml,
birefnet.yaml); pass --device cuda for GPU. Avoids a hard CUDA-required failure on
machines without a CUDA-enabled PyTorch build.
- Docs: README + workflow status note updated (backend via --seg-backend, CPU
default, examples no longer force --device cuda).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Replace the hardcoded green-screen auto-detection with a general flat-colour
detector. When screen_color is null the background colour is now detected from the
image border (dominant colour of the border strip) and used directly as the chroma
model, with a failure gate that raises when the border is not one clean flat colour
(gradient / texture / subject filling the frame).
- chroma: add detect_background_color + _background_border_cluster; the null case of
estimate_background_model samples the detected cluster directly (no seed search);
compute_bg_confidence is now always perceptual (Lab/RGB distance). Removed the green
heuristic (initial_green_candidates), the green multiplicative gating, the now-unused
_smoothstep, and five green-only ChromaSettings fields.
- pipeline: reuse the detected colour for de-spill in the auto case.
- settings / default.yaml: add detect_* tuning fields; refresh screen_color docs.
- README: document auto-detection + the failure gate, trimap modes, birefnet config.
A supplied --screen-color hex still uses the seed-search refinement. Validated:
detects green / pastel / text samples correctly, raises on gradient / two-colour, CLI
exits 1 on failure, green and pastel mattes unchanged in quality.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
README rewritten for the current design: two pipelines (chroma-only vs single
segmenter, default anime-seg), the screen_color prior (--screen-color / config,
green default), correct debug-file list, and HF/hf-mirror notes for the seg models.
No rescue/recolour layer is documented (it was removed).
green_screen_matting_workflow.md keeps the phase-1 green-screen spec but gains a
status note pointing to the README for the evolved architecture, and its debug-file
list is corrected (foreground_rgb / color_mask / seg_mask, not the old
foreground_background / foreground_correction / despill_mask).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
After the full-foreground despill (5ddf0cd) the residual green was confined to
semi-transparent hair (alpha < 0.5): 85% of the worst pixels had alpha < 0.3,
where a pixel-wise unmix (divide by small alpha) is too noisy to trust.
Replace the hand-rolled unmix/local-blur estimator with pymatting's
estimate_foreground_ml (Germer et al. multi-level closed form), which propagates
reliable foreground colour from high-alpha neighbours into the fringe and
estimates the background, so green spill is unmixed rather than clamped. The
despill pass stays as a light cleanup on top.
- foreground.method selects "ml" (default) or "unmix" (legacy heuristic kept as
a fallback when pymatting is unavailable, matching the chroma fallback idiom).
- ForegroundEstimate now exposes rgb (F), background (B) and a correction map;
debug outputs become foreground_rgb / foreground_background / foreground_correction.
Controlled comparison (same ViTMatte alpha, only foreground method changed),
edge_green_excess:
TestImage p95 0.176->0.031, mean 0.061->0.005
TestImage2 p95 0.165->0.004, mean 0.052->0.002
Adds pymatting (pulls in numba) to requirements.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
lhk229
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