/remove-background now accepts either an uploaded `file` or an `image_url`
the service fetches itself (exactly one required). The fetch runs BEFORE
the compute gate so a slow download never holds a pool slot, and is
bounded by its own semaphore (2 x cpu_workers, hard cap 64) plus a shared
httpx client (connection reuse, matching max_connections) -- this restores
the throttling a direct upload has implicitly, so a burst of tiny URL
requests can't exhaust FDs/ephemeral ports or hammer the upstream.
Bounds: 25 MB body cap (413), 5 s connect / 15 s read timeout, <=3
redirects; MAX_IMAGE_PIXELS still applies post-decode. Deliberately NO
SSRF filtering (no private-IP/host allowlist) -- the endpoint trusts
authenticated callers; auth runs before any fetch. Do not expose to
untrusted clients without adding private-address/redirect filtering to
_fetch_image.
Adds httpx. New env knobs BGFILTER_FETCH_MAX_BYTES / BGFILTER_FETCH_TIMEOUT.
Docs (README/README_ZH/DEPLOY/DEPLOY_ZH) updated. _fetch_image unit-tested
locally (happy path, size cap ->413, bad scheme ->400, 404 ->400);
on-box end-to-end still pending (test box went offline).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The GUI opencv-python build needs libGL.so.1 which headless Linux servers lack,
causing "ImportError: libGL.so.1" at first cv2 use. Pin the headless build so a
plain `pip install -r requirements.txt` works on servers out of the box (and
survives a git reset). BgFilter only uses cv2 for computation, so headless is a
drop-in on desktops too. Drop the now-unnecessary sed step from DEPLOY.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add AnimeSegSegmenter (skytnt/anime-seg ISNet ONNX via onnxruntime, no remote
code) and a make_segmenter factory selected by SegmentationSettings.backend
("birefnet" | "anime-seg"). The ONNX output is already 0..1, so it slots into the
same soft-mask interface BiRefNetSegmenter uses.
On the anime samples anime-seg recovers more and more-coherent hair wisps than
BiRefNet (TestImage2 shoulder rescue: added px 1886 -> 3278, largest connected
component 147 -> 454), as expected from an anime-trained model. Default backend
stays birefnet.
Adds onnxruntime to requirements.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds bgfilter/segmentation.py: BiRefNetSegmenter produces a soft subject mask
that defines topology -- it includes colour-contaminated foreground (green-tinted
hair) and excludes see-through holes (finger gaps), the distinction chroma/colour
cues cannot make. Validated on both samples: green hair -> foreground, finger gap
-> background, both correct.
Foundation of the semantic-guided redesign; the mask will drive trimap
construction next, with ViTMatte still refining soft edges.
- SegmentationSettings (ZhengPeng7/BiRefNet, 1024 input, fp32).
- Deps: torchvision, timm, einops, kornia. Weights load from HuggingFace via
trust_remote_code; behind a firewall set HF_ENDPOINT=https://hf-mirror.com and
bypass the proxy.
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
Codex