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Use when users want to remove backgrounds,...\n\nTags: latest:1.0.0\n\nVersion history:\n\nv1.0.0 | 2026-03-14T12:50:37.505Z | user\n\n- Initial release of \"sam-segmentation\" skill for background removal and image segmentation.\n- Extracts foreground subjects from images using Meta's Segment Anything Model (SAM) and saves as transparent PNGs.\n- Supports multiple model sizes (`vit_b`, `vit_l`, `vit_h`) for different speed and quality needs.\n- Allows foreground hint points, grid-sweep mode for extracting all distinct elements, and various mask filtering parameters.\n- Automatically installs needed dependencies (`segment_anything`, Pillow, numpy, torch, torchvision) on first use.\n- Model checkpoint is auto-downloaded if not provided.\n\nArchive index:\n\nArchive v1.0.0: 4 files, 5304 bytes\n\nFiles: scripts/segment.py (6396b), skill-card.md (2108b), SKILL.md (2993b), _meta.json (122b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: sam-segmentation\ndescription: 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.\nmetadata:\n  openclaw:\n    requires:\n      bins:\n        - python3\n    install:\n      - kind: uv\n        package: pillow\n      - kind: uv\n        package: numpy\n      - kind: uv\n        package: torch\n      - kind: uv\n        package: torchvision\n---\n\n# SAM Background Removal\n\nExtract foreground subjects from images using Meta's Segment Anything Model, outputting transparent PNGs.\n\n## Quick Start\n\n```bash\npython3 scripts/segment.py <input_image> <output.png>\n```\n\nDefaults to the image center as the foreground hint — works well for portraits and product shots where the subject is centered.\n\n## Parameters\n\n| Param | Description | Default |\n|---|---|---|\n| `input` | Input image path | required |\n| `output` | Output PNG path (single mode) or directory (`--all` mode) | required |\n| `--model` | Model size: `vit_b` (fast) · `vit_l` (medium) · `vit_h` (best quality) | `vit_h` |\n| `--checkpoint` | Local checkpoint path; auto-downloaded if omitted | auto |\n| `--points` | Foreground hint points as `x,y`, multiple allowed | center |\n| `--all` | Grid-sweep mode: extract all distinct elements | off |\n| `--grid` | Grid density for `--all`; 16 means 16×16=256 probe points | `16` |\n| `--iou-thresh` | Minimum predicted IoU to accept a mask (`--all`) | `0.88` |\n| `--min-area` | Minimum mask area as fraction of image (`--all`) | `0.001` |\n\n## Examples\n\n```bash\n# Basic background removal (auto-downloads vit_h ~2.5GB)\npython3 scripts/segment.py photo.jpg output.png\n\n# Specify hint point when subject is off-center\npython3 scripts/segment.py photo.jpg output.png --points 320,240\n\n# Multiple hints with lightweight model\npython3 scripts/segment.py photo.jpg output.png --model vit_b --points 320,240 400,300\n\n# Extract all elements (one PNG per element)\npython3 scripts/segment.py photo.jpg ./elements/ --all\n\n# Denser grid to capture small objects\npython3 scripts/segment.py photo.jpg ./elements/ --all --grid 32\n\n# Use a local checkpoint\npython3 scripts/segment.py photo.jpg output.png --checkpoint /path/to/sam_vit_h_4b8939.pth\n```\n\n## Dependencies\n\n`segment_anything` is auto-installed on first run, or install manually:\n\n```bash\npip install git+https://github.com/facebookresearch/segment-anything.git\npip install pillow numpy torch torchvision\n```\n\n## Workflow\n\n1. User provides image path\n2. Ask if hint points are needed (when subject is off-center)\n3. Run script; checkpoint auto-downloads on first use to `~/.cache/sam/`\n4. Output transparent-background PNG\n\n## Model Selection\n\n| Model | Size | Speed | Quality |\n|---|---|---|---|\n| `vit_b` | ~375 MB | fastest | good |\n| `vit_l` | ~1.25 GB | medium | better |\n| `vit_h` | ~2.5 GB | slower | best |\n\nCUDA is used automatically when a GPU is available.\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn78vxgbf838q8g7h25g6d84md82v700\",\n  \"slug\": \"sam\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1773492637505\n}\n\nFile v1.0.0:skill-card.md\n\n## Description:\n\nUses SAM (Segment Anything Model) to remove image backgrounds and extract foreground subjects as transparent PNGs.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[scikkk](https://clawhub.ai/user/scikkk)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers, designers, and content operations teams use this skill to run SAM on local image files for background removal, subject cutouts, foreground extraction, and multi-element segmentation.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: First-run execution can install mutable remote code.\n\nMitigation: Use an isolated Python environment and preinstall a pinned, reviewed segment_anything dependency before running the skill.\n\nRisk: Model checkpoints can be downloaded as large unverified files into a local cache.\n\nMitigation: Use locally verified SAM checkpoints and pass them with the --checkpoint option.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/scikkk/skills/sam)\n- [Segment Anything dependency](https://github.com/facebookresearch/segment-anything.git)\n- [SAM ViT-B checkpoint](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth)\n- [SAM ViT-L checkpoint](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth)\n- [SAM ViT-H checkpoint](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, shell commands, configuration, files, guidance]\n\n**Output Format:** [Markdown guidance with bash commands; runtime output is transparent PNG image files.]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Single-subject mode writes one PNG file; all-elements mode writes one PNG per detected element.]\n\n## Skill Version(s):\n\n1.0.0 (source: server release metadata)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.","readmeExcerpt":"Skill: Segment Anything Owner: scikkk Summary: Use SAM (Segment Anything Model) to remove image backgrounds and extract foreground subjects as transparent PNGs. Use when users want to remove backgrounds,... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-03-14T12:50:37.505Z | user - Initial release of \"sam-segmentation\" skill for background removal and image segmentation. - Extracts foreground subjects from images ","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"python3 scripts/segment.py <input_image> <output.png>"},{"language":"bash","snippet":"# Basic background removal (auto-downloads vit_h ~2.5GB)\npython3 scripts/segment.py photo.jpg output.png\n\n# Specify hint point when subject is off-center\npython3 scripts/segment.py photo.jpg output.png --points 320,240\n\n# Multiple hints with lightweight model\npython3 scripts/segment.py photo.jpg output.png --model vit_b --points 320,240 400,300\n\n# Extract all elements (one PNG per element)\npython3 scripts/segment.py photo.jpg ./elements/ --all\n\n# Denser grid to capture small objects\npython3 scripts/segment.py photo.jpg ./elements/ --all --grid 32\n\n# Use a local checkpoint\npython3 scripts/segment.py photo.jpg output.png --checkpoint /path/to/sam_vit_h_4b8939.pth"},{"language":"bash","snippet":"pip install git+https://github.com/facebookresearch/segment-anything.git\npip install pillow numpy torch torchvision"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: sam-segmentation\ndescription: 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.\nmetadata:\n  openclaw:\n    requires:\n      bins:\n        - python3\n    install:\n      - kind: uv\n        package: pillow\n      - kind: uv\n        package: numpy\n      - kind: uv\n        package: torch\n      - kind: uv\n        package: torchvision\n---\n\n# SAM Background Removal\n\nExtract foreground subjects from images using Meta's Segment Anything Model, outputting transparent PNGs.\n\n## Quick Start\n\n```bash\npython3 scripts/segment.py <input_image> <output.png>\n```\n\nDefaults to the image center as the foreground hint — works well for portraits and product shots where the subject is centered.\n\n## Parameters\n\n| Param | Description | Default |\n|---|---|---|\n| `input` | Input image path | required |\n| `output` | Output PNG path (single mode) or directory (`--all` mode) | required |\n| `--model` | Model size: `vit_b` (fast) · `vit_l` (medium) · `vit_h` (best quality) | `vit_h` |\n| `--checkpoint` | Local checkpoint path; auto-downloaded if omitted | auto |\n| `--points` | Foreground hint points as `x,y`, multiple allowed | center |\n| `--all` | Grid-sweep mode: extract all distinct elements | off |\n| `--grid` | Grid density for `--all`; 16 means 16×16=256 probe points | `16` |\n| `--iou-thresh` | Minimum predicted IoU to accept a mask (`--all`) | `0.88` |\n| `--min-area` | Minimum mask area as fraction of image (`--all`) | `0.001` |\n\n## Examples\n\n```bash\n# Basic background removal (auto-downloads vit_h ~2.5GB)\npython3 scripts/segment.py photo.jpg output.png\n\n# Specify hint point when subject is off-center\npython3 scripts/segment.py photo.jpg output.png --points 320,240\n\n# Multiple hints with lightweight model\npython3 scripts/segment.py photo.jpg output.png --model vit_b --points 320,240 400,300\n\n# Extract all elements (one PNG per element)\npython3 scripts/segment.py photo.jpg ./elements/ --all\n\n# Denser grid to capture small objects\npython3 scripts/segment.py photo.jpg ./elements/ --all --grid 32\n\n# Use a local checkpoint\npython3 scripts/segment.py photo.jpg output.png --checkpoint /path/to/sam_vit_h_4b8939.pth\n```\n\n## Dependencies\n\n`segment_anything` is auto-installed on first run, or install manually:\n\n```bash\npip install git+https://github.com/facebookresearch/segment-anything.git\npip install pillow numpy torch torchvision\n```\n\n## Workflow\n\n1. User provides image path\n2. Ask if hint points are needed (when subject is off-center)\n3. Run script; checkpoint auto-downloads on first use to `~/.cache/sam/`\n4. Output transparent-background PNG\n\n## Model Selection\n\n| Model | Size | Speed | Quality |\n|---|---|---|---|\n| `vit_b` | ~375 MB | fastest | good |\n| `vit_l` | ~1.25 GB | medium | better |\n| `vit_h` | ~2.5 GB | slower | best |\n\nCUDA is used automatically when a GPU is available."},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn78vxgbf838q8g7h25g6d84md82v700\",\n  \"slug\": \"sam\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1773492637505\n}"},{"path":"skill-card.md","content":"## Description:\n\nUses SAM (Segment Anything Model) to remove image backgrounds and extract foreground subjects as transparent PNGs.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[scikkk](https://clawhub.ai/user/scikkk)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers, designers, and content operations teams use this skill to run SAM on local image files for background removal, subject cutouts, foreground extraction, and multi-element segmentation.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: First-run execution can install mutable remote code.\n\nMitigation: Use an isolated Python environment and preinstall a pinned, reviewed segment_anything dependency before running the skill.\n\nRisk: Model checkpoints can be downloaded as large unverified files into a local cache.\n\nMitigation: Use locally verified SAM checkpoints and pass them with the --checkpoint option.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/scikkk/skills/sam)\n- [Segment Anything dependency](https://github.com/facebookresearch/segment-anything.git)\n- [SAM ViT-B checkpoint](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth)\n- [SAM ViT-L checkpoint](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth)\n- [SAM ViT-H checkpoint](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, shell commands, configuration, files, guidance]\n\n**Output Format:** [Markdown guidance with bash commands; runtime output is transparent PNG image files.]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Single-subject mode writes one PNG file; all-elements mode writes one PNG per detected element.]\n\n## Skill Version(s):\n\n1.0.0 (source: server release metadata)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Use SAM (Segment Anything Model) to remove image backgrounds and extract foreground subjects as transparent PNGs. Use when users want to remove backgrounds,... Skill: Segment Anything Owner: scikkk Summary: Use SAM (Segment Anything Model) to remove image backgrounds and extract foreground subjects as transparent PNGs. Use when users want to remove backgrounds,... 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