概述

SAM Background Removal

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

Quick Start

python3 scripts/segment.py <input_image> <output.png>

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

CUDA is used automatically when a GPU is available.

版本历史

共 1 个版本

v1.0.0 当前

2026-03-19 20:31 安全安全

安全检测

腾讯云安全 (Keen)

安全，无风险

查看报告

腾讯云安全 (Sanbu)