by 详细分析 ▾
运行时依赖
版本
- Initial release of "sam-segmentation" skill for background removal and image segmentation. - Extracts foreground subjects from images using Meta's Segment Anything Model (SAM) and saves as transparent PNGs. - Supports multiple model sizes (`vit_b`, `vit_l`, `vit_h`) for different speed and quality needs. - Allows foreground hint points, grid-sweep mode for extracting all distinct elements, and various mask filtering parameters. - Automatically installs needed dependencies (`segment_anything`, Pillow, numpy, torch, torchvision) on first use. - Model checkpoint is auto-downloaded if not provided.
安装命令 点击复制
技能文档
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
- User provides image path
- Ask if hint points are needed (when subject is off-center)
- Run script; checkpoint auto-downloads on first use to
~/.cache/sam/ - 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 |
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