OpenClaw Backup
110 lines
2.2 KiB
Markdown
110 lines
2.2 KiB
Markdown
# Background Removal
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Extract subjects from images with transparent backgrounds.
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## Tools
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### remove.bg (API)
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```bash
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curl -X POST "https://api.remove.bg/v1.0/removebg" \
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-H "X-Api-Key: YOUR_API_KEY" \
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-F "image_file=@photo.jpg" \
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-F "size=auto" \
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-o "result.png"
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```
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```python
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import requests
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response = requests.post(
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"https://api.remove.bg/v1.0/removebg",
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files={"image_file": open("photo.jpg", "rb")},
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data={"size": "auto"},
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headers={"X-Api-Key": "YOUR_API_KEY"}
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)
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with open("result.png", "wb") as f:
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f.write(response.content)
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```
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**Pricing:** ~$0.20/image (50 free/month)
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### ClipDrop (Stability AI)
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```python
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import requests
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response = requests.post(
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"https://clipdrop-api.co/remove-background/v1",
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files={"image_file": open("photo.jpg", "rb")},
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headers={"x-api-key": "YOUR_API_KEY"}
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)
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```
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**Features:** Background removal, cleanup, relighting
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### Photoroom API
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```python
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response = requests.post(
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"https://sdk.photoroom.com/v1/segment",
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files={"image_file": open("photo.jpg", "rb")},
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headers={"x-api-key": "YOUR_API_KEY"}
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)
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```
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### Local (rembg)
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```bash
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pip install rembg
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# CLI
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rembg i input.jpg output.png
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# Python
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from rembg import remove
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from PIL import Image
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output = remove(Image.open("input.jpg"))
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output.save("output.png")
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```
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**Models:**
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- `u2net` — General purpose (default)
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- `u2net_human_seg` — Optimized for people
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- `silueta` — Faster, smaller
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## Batch Processing
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```python
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from rembg import remove
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from pathlib import Path
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for img_path in Path("input/").glob("*.jpg"):
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result = remove(Image.open(img_path))
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result.save(f"output/{img_path.stem}.png")
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```
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## Edge Refinement
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Raw removal often has rough edges:
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1. **Feather edges** — Gaussian blur on alpha channel
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2. **Matting models** — Use dedicated matting for hair/fur
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3. **Manual cleanup** — Touch up in photo editor
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## Use Cases
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- Product photography
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- Profile pictures
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- Compositing
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- E-commerce listings
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- Marketing materials
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## Quality Tips
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- **Good lighting** — clear subject separation helps
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- **High contrast** — distinct foreground/background
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- **Clean backgrounds** — simpler = better results
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- **Check hair/fur** — often needs manual refinement
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