background-remove

Background Remove Skill

Remove backgrounds from images using AI (rembg/U2-Net) or built-in methods.

Output: PNG or WebP with transparent background.

Quick Examples

User Says	What Happens
"Remove the background from this photo"	AI removes background, outputs PNG
"Make this image transparent"	Removes background, preserves subject
"Cut out the product from this image"	Isolates subject with clean edges
"Remove backgrounds from all images in /photos"	Batch processes multiple images
"Quick background removal, white background"	Uses fast built-in method

Prerequisites

• rembg - AI-based background removal (recommended)

  pip install rembg
  # Or with GPU acceleration (faster, requires CUDA)
  pip install rembg[gpu]
  

• Pillow - Required for image processing

  pip install Pillow
  

The first run will download the U2-Net model (~170MB) which is cached for future use.

Methods

Method	Description	Best For
rembg	AI-based using U2-Net model	Complex images, photos, products (default)
builtin	White-to-transparent conversion	Icons, graphics with clean white backgrounds

Workflow

Step 1: Gather Requirements (REQUIRED)

Use the AskUserQuestion tool for each question. Ask ONE question at a time.

Q1: Image Source

"Which image(s) should I remove the background from?

Please provide the file path or paste the image."

Wait for response.

Q2: Method (Optional)

"Which removal method?

• AI (rembg) - Best quality, works on any image (default)
• Built-in - Faster, best for white backgrounds"

Wait for response. Default to AI if user doesn't specify.

Q3: Output Location (Optional)

"Where should I save the result?

• Same location with _nobg suffix (default)
• Custom path"

Wait for response.

Step 2: Execute Background Removal

Single image:

python3 ${SKILL_PATH}/skills/background-remove/scripts/background_remove.py \
  -i "/path/to/image.jpg" \
  -o "/path/to/output.png"

Batch processing:

python3 ${SKILL_PATH}/skills/background-remove/scripts/background_remove.py \
  -i "/path/to/img1.jpg" "/path/to/img2.png" "/path/to/img3.webp" \
  -o "/path/to/output_folder"

Using built-in method (faster for white backgrounds):

python3 ${SKILL_PATH}/skills/background-remove/scripts/background_remove.py \
  -i "/path/to/icon.png" \
  -m builtin

Step 3: Deliver Result

1. Show the result to the user
2. Confirm the background was removed successfully
3. Offer to:
   • Process additional images
   • Try a different method if quality isn't satisfactory
   • Adjust output format (PNG vs WebP)

Script Parameters

Parameter	Short	Description	Default
--input	-i	Input image path(s)	Required
--output	-o	Output path or directory	Auto-generated with _nobg suffix
--method	-m	Removal method (rembg, builtin)	rembg

Output Formats

The output format is determined by the file extension:

Extension	Format	Notes
.png	PNG	Best quality, larger file (default)
.webp	WebP	Good compression, modern format

Integration with Other Skills

This skill can be called by other skills that need background removal:

From Python (import)

import sys
sys.path.insert(0, "${SKILL_PATH}/skills/background-remove/scripts")
from background_remove import remove_background

result = remove_background("/path/to/image.png", "/path/to/output.png", method="rembg")
if result.get("success"):
    print(f"Saved to: {result['file']}")
else:
    print(f"Error: {result['error']}")

From Command Line

python3 ${SKILL_PATH}/skills/background-remove/scripts/background_remove.py \
  -i "/path/to/image.png" \
  -o "/path/to/output.png" \
  -m rembg

Error Handling

rembg not installed:

rembg not installed. Install with: pip install rembg[gpu] (or pip install rembg for CPU-only)

The script will automatically fall back to the built-in method.

Image not found:

Image not found: /path/to/image.png

Processing failed:

• Try a different method
• Check if the image file is corrupted
• Ensure sufficient memory for large images

Tips for Best Results

1. Use rembg for photos - AI handles complex edges (hair, fur, transparent objects)
2. Use builtin for graphics - Faster for icons/logos with clean white backgrounds
3. Check edges - If edges are rough, the AI method usually gives better results
4. Batch process - Process multiple images at once for efficiency
5. GPU acceleration - Install rembg[gpu] for faster processing on NVIDIA GPUs

Examples

Remove background from a photo

python3 ${SKILL_PATH}/skills/background-remove/scripts/background_remove.py \
  -i "product_photo.jpg" \
  -o "product_transparent.png"

Batch process a folder

python3 ${SKILL_PATH}/skills/background-remove/scripts/background_remove.py \
  -i photos/*.jpg \
  -o "transparent_photos/"

Fast removal for icons (white background)

python3 ${SKILL_PATH}/skills/background-remove/scripts/background_remove.py \
  -i "icon.png" \
  -m builtin

Output as WebP (smaller file size)

python3 ${SKILL_PATH}/skills/background-remove/scripts/background_remove.py \
  -i "photo.jpg" \
  -o "result.webp"