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name: background-matting-v2 description: "Routes BackgroundMattingV2 tasks for background matting inference, export, and training workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

BackgroundMattingV2

BackgroundMattingV2 is a computer-vision repo for high-resolution background matting with a source-image / background-image pair. Use this root skill as a router, not as a full manual.

Start here

• Read references/repo-provenance.md when you need to check whether this skill still matches the repository checkout.
• Run scripts/check_env.py first when you only need a quick import and tiny forward smoke.
• Read references/workflows.md for the high-level route map and the decision points between inference, export, and training.
• Read references/api-reference.md for verified model and dataset signatures.
• Read references/data-formats.md before touching data_path.py or paired foreground/alpha/background directories.
• Read references/backend-compatibility.md before choosing PyTorch, TorchScript, or ONNX paths.
• Read references/troubleshooting.md for cross-cutting install, import, and backend issues.

Install and inspect

This repo is source-tree based rather than a packaged wheel. For inspection, create an isolated Python environment, install the runtime stack used by the repo workflows, and then import the source modules from a checkout of this repo. The verified inspection stack used for this skill was Python 3.11 with:

• torch + matching torchvision
• kornia
• opencv-python
• onnx
• onnxruntime
• tensorboard
• tqdm

A quick smoke is:

python scripts/check_env.py --repo-root <repo-checkout> --device cuda

Use --device cpu when you only need importability and tiny forward coverage. Add onnx when you want the ONNX smoke helper to validate export support.

Route map

Inference and demo

Use sub-skills/inference-and-demo/ when the task is about:

• inference_images.py
• inference_video.py
• inference_webcam.py
• inference_speed_test.py
• choosing model type, backbone, refine mode, device, or output types
• understanding source/background pairing and alignment behavior

Read sub-skills/inference-and-demo/SKILL.md for the trigger terms and linked workflow helpers.

Export and backend compatibility

Use sub-skills/export-and-backends/ when the task is about:

• export_torchscript.py
• export_onnx.py
• TorchScript attribute hoisting
• ONNX patch crop/replace compatibility choices
• validating export/runtime combinations

Read sub-skills/export-and-backends/SKILL.md when you need conversion steps or backend troubleshooting.

Training and data setup

Use sub-skills/training/ when the task is about:

• train_base.py
• train_refine.py
• data_path.py
• paired foreground/alpha/background directory layout
• training checkpoints, logs, and benchmark evaluation
• CUDA/DDP assumptions and dataset-name selection

Read sub-skills/training/SKILL.md before configuring data paths or starting a training run.

Public surface summary

The public source-root modules you are expected to know are model, dataset, inference_utils, and data_path. The primary classes are MattingBase and MattingRefine. The main inference CLIs work with source/background image or video pairs and can optionally apply homographic alignment.

What not to do

• Do not send future agents back to the original repo docs or scripts when a bundled reference or script exists here.
• Do not assume training or webcam workflows are safe to run without the right hardware, data, and display devices.
• Do not treat CPU importability as proof of CUDA readiness.

Local entry points

• scripts/check_env.py
• sub-skills/inference-and-demo/scripts/smoke_forward.py
• sub-skills/export-and-backends/scripts/check_export_support.py
• sub-skills/training/scripts/check_data_layout.py