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🦀 ClawHub

Segment Anything

by @scikkk

Use SAM (Segment Anything Model) to remove image backgrounds and extract foreground subjects as transparent PNGs. Use when users want to remove backgrounds,...

Versionv1.0.0

Downloads619

#clawhub

View on ClawHub →

TERMINAL

clawhub install sam

📖 About This Skill

name: sam-segmentation description: Use SAM (Segment Anything Model) to remove image backgrounds and extract foreground subjects as transparent PNGs. Use when users want to remove backgrounds, cut out objects, extract foreground subjects, or perform image segmentation. metadata: openclaw: requires: bins: - python3 install: - kind: uv package: pillow - kind: uv package: numpy - kind: uv package: torch - kind: uv package: torchvision

SAM Background Removal

Extract foreground subjects from images using Meta's Segment Anything Model, outputting transparent PNGs.

Quick Start

python3 scripts/segment.py

Defaults to the image center as the foreground hint — works well for portraits and product shots where the subject is centered.

Parameters

| Param | Description | Default | |---|---|---| | input | Input image path | required | | output | Output PNG path (single mode) or directory (--all mode) | required | | --model | Model size: vit_b (fast) · vit_l (medium) · vit_h (best quality) | vit_h | | --checkpoint | Local checkpoint path; auto-downloaded if omitted | auto | | --points | Foreground hint points as x,y, multiple allowed | center | | --all | Grid-sweep mode: extract all distinct elements | off | | --grid | Grid density for --all; 16 means 16×16=256 probe points | 16 | | --iou-thresh | Minimum predicted IoU to accept a mask (--all) | 0.88 | | --min-area | Minimum mask area as fraction of image (--all) | 0.001 |

Examples

# Basic background removal (auto-downloads vit_h ~2.5GB)
python3 scripts/segment.py photo.jpg output.png
Specify hint point when subject is off-center
python3 scripts/segment.py photo.jpg output.png --points 320,240
Multiple hints with lightweight model
python3 scripts/segment.py photo.jpg output.png --model vit_b --points 320,240 400,300
Extract all elements (one PNG per element)
python3 scripts/segment.py photo.jpg ./elements/ --all
Denser grid to capture small objects
python3 scripts/segment.py photo.jpg ./elements/ --all --grid 32
Use a local checkpoint
python3 scripts/segment.py photo.jpg output.png --checkpoint /path/to/sam_vit_h_4b8939.pth

Dependencies

segment_anything is auto-installed on first run, or install manually:

pip install git+https://github.com/facebookresearch/segment-anything.git
pip install pillow numpy torch torchvision

Workflow

1. User provides image path 2. Ask if hint points are needed (when subject is off-center) 3. Run script; checkpoint auto-downloads on first use to ~/.cache/sam/ 4. Output transparent-background PNG

Model Selection

| Model | Size | Speed | Quality | |---|---|---|---| | vit_b | ~375 MB | fastest | good | | vit_l | ~1.25 GB | medium | better | | vit_h | ~2.5 GB | slower | best |

CUDA is used automatically when a GPU is available.

💡 Examples

# Basic background removal (auto-downloads vit_h ~2.5GB)
python3 scripts/segment.py photo.jpg output.png
Specify hint point when subject is off-center
python3 scripts/segment.py photo.jpg output.png --points 320,240
Multiple hints with lightweight model
python3 scripts/segment.py photo.jpg output.png --model vit_b --points 320,240 400,300
Extract all elements (one PNG per element)
python3 scripts/segment.py photo.jpg ./elements/ --all
Denser grid to capture small objects
python3 scripts/segment.py photo.jpg ./elements/ --all --grid 32
Use a local checkpoint
python3 scripts/segment.py photo.jpg output.png --checkpoint /path/to/sam_vit_h_4b8939.pth