lhk229 a6a313d2d0 Add BiRefNet semantic segmentation module
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>
2026-06-30 21:07:24 +08:00

BgFilter

Offline green screen character matting for AI-generated character images.

The first implementation follows the workflow in docs/green_screen_matting_workflow.md:

RGB input -> chroma confidence -> trimap -> ViTMatte -> alpha cleanup -> despill -> RGBA PNG -> QA previews

Environment

Use the conda environment lightML.

conda activate lightML
pip install -r requirements.txt

If the shell is not activated, call the environment Python directly:

D:\MiniConda\envs\lightML\python.exe -m bgfilter.cli --help

Single Image

D:\MiniConda\envs\lightML\python.exe -m bgfilter.cli `
  --input Samples\TestImage.png `
  --output Outputs\TestImage_rgba.png `
  --debug-dir Outputs\TestImage_debug `
  --config configs\default.yaml `
  --device cuda

The CLI reads configs/default.yaml when --config is provided. Command-line options override config values, so tuning can usually happen in YAML while runtime choices such as --device cpu stay on the command line.

Use CPU for validation when CUDA is unavailable:

D:\MiniConda\envs\lightML\python.exe -m bgfilter.cli `
  --input Samples\TestImage.png `
  --output Outputs\TestImage_rgba.png `
  --debug-dir Outputs\TestImage_debug `
  --config configs\default.yaml `
  --device cpu

Batch

D:\MiniConda\envs\lightML\python.exe -m bgfilter.cli `
  --input-dir Samples `
  --output-dir Outputs `
  --debug-dir Outputs\debug `
  --config configs\default.yaml `
  --device cuda

Chroma-Only Debug Mode

This mode skips ViTMatte and uses chroma confidence as an alpha seed. It is useful for fast debugging of chroma confidence, trimap, despill, and QA outputs.

D:\MiniConda\envs\lightML\python.exe -m bgfilter.cli `
  --input-dir Samples `
  --output-dir Outputs\chroma `
  --debug-dir Outputs\chroma_debug `
  --config configs\default.yaml `
  --matting-method chroma `
  --device cpu

Outputs

For each processed image, the CLI writes an RGBA PNG and optional debug files:

bg_confidence.png
trimap.png
alpha.png
foreground_rgb.png
foreground_background.png
foreground_correction.png
despill_mask.png
preview_black.png
preview_white.png
preview_gray.png
preview_red.png
preview_blue.png
qa_grid.png
metadata.json

Quality Check

The quality checker measures alpha validity and green spill on semi-transparent edge pixels.

D:\MiniConda\envs\lightML\python.exe -m bgfilter.quality_cli `
  Outputs\TestImage_rgba.png `
  --max-edge-green-excess-p95 0.30

Run the bundled sample smoke check:

D:\MiniConda\envs\lightML\python.exe scripts\smoke_samples.py `
  --samples-dir Samples `
  --output-dir Outputs\smoke_samples `
  --config configs\default.yaml `
  --device cpu `
  --fallback-to-chroma-alpha `
  --max-edge-green-excess-p95 0.30

Current Notes

  • Samples/ and Outputs/ are ignored by Git.
  • ViTMatte model weights are loaded from Hugging Face on first use.
  • Foreground color estimation uses pymatting's estimate_foreground_ml to propagate clean foreground colour into semi-transparent edges before final de-spill, writing the estimated foreground, background, and a correction map. Set foreground.method: unmix to fall back to the legacy heuristic.
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