MCPInstall

background-remover

Remove backgrounds from images using segmentation. Support for color-based, edge detection, and AI-assisted removal methods. Batch processing available.

Installs
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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill background-remover
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads background-remover automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Background Remover

Remove backgrounds from images using multiple detection methods.

Features

  • Color-Based Removal: Remove solid color backgrounds
  • Edge Detection: Detect subject edges for removal
  • GrabCut Algorithm: Interactive foreground extraction
  • Batch Processing: Process multiple images
  • Transparency Output: Export with alpha channel
  • Background Replacement: Replace with color or image

Quick Start

from background_remover import BackgroundRemover

remover = BackgroundRemover()

# Simple removal
remover.load("photo.jpg")
remover.remove_background()
remover.save("photo_transparent.png")

# Remove specific color
remover.load("product.jpg")
remover.remove_color((255, 255, 255), tolerance=30)  # Remove white
remover.save("product_clean.png")

# Replace background
remover.load("portrait.jpg")
remover.remove_background()
remover.replace_background(color=(0, 120, 255))  # Blue background
remover.save("portrait_blue.png")

CLI Usage

# Remove background (auto-detect)
python background_remover.py --input photo.jpg --output result.png

# Remove specific color
python background_remover.py --input image.jpg --color "255,255,255" --tolerance 30 -o clean.png

# Use GrabCut method
python background_remover.py --input photo.jpg --method grabcut -o result.png

# Replace background with color
python background_remover.py --input photo.jpg --replace-color "0,120,255" -o result.png

# Replace background with image
python background_remover.py --input photo.jpg --replace-image bg.jpg -o result.png

# Batch process
python background_remover.py --batch input_folder/ --output-dir output/ --method edge

API Reference

BackgroundRemover Class

class BackgroundRemover:
    def __init__(self)

    # Loading
    def load(self, filepath: str) -> 'BackgroundRemover'
    def load_array(self, array: np.ndarray) -> 'BackgroundRemover'

    # Removal Methods
    def remove_background(self, method: str = "auto") -> 'BackgroundRemover'
    def remove_color(self, color: Tuple, tolerance: int = 20) -> 'BackgroundRemover'
    def remove_edges(self, threshold: int = 50) -> 'BackgroundRemover'
    def grabcut(self, rect: Tuple = None, iterations: int = 5) -> 'BackgroundRemover'

    # Background Operations
    def replace_background(self, color: Tuple = None, image: str = None) -> 'BackgroundRemover'
    def add_shadow(self, offset: Tuple = (5, 5), blur: int = 10) -> 'BackgroundRemover'

    # Refinement
    def refine_edges(self, feather: int = 2) -> 'BackgroundRemover'
    def expand_mask(self, pixels: int = 2) -> 'BackgroundRemover'
    def contract_mask(self, pixels: int = 2) -> 'BackgroundRemover'

    # Output
    def save(self, filepath: str, quality: int = 95) -> str
    def get_image(self) -> Image
    def get_mask(self) -> Image

    # Batch Processing
    def batch_process(self, input_dir: str, output_dir: str,
                     method: str = "auto") -> List[str]

Removal Methods

Auto Detection

# Automatically choose best method
remover.remove_background(method="auto")

Color-Based Removal

# Remove white background
remover.remove_color((255, 255, 255), tolerance=30)

# Remove green screen
remover.remove_color((0, 255, 0), tolerance=50)

# Remove any solid color
remover.remove_color((200, 200, 200), tolerance=40)

Edge Detection

# Use edge detection to find subject
remover.remove_edges(threshold=50)

GrabCut (OpenCV)

# Full image GrabCut
remover.grabcut(iterations=5)

# With bounding rectangle hint
remover.grabcut(rect=(50, 50, 400, 300), iterations=10)

Background Replacement

Solid Color

remover.remove_background()
remover.replace_background(color=(255, 255, 255))  # White
remover.replace_background(color=(0, 0, 0))        # Black
remover.replace_background(color=(135, 206, 235))  # Sky blue

Image Background

remover.remove_background()
remover.replace_background(image="office_bg.jpg")

Transparent (Default)

remover.remove_background()
remover.save("transparent.png")  # PNG preserves alpha

Edge Refinement

# Soften edges with feathering
remover.refine_edges(feather=3)

# Expand mask to include more area
remover.expand_mask(pixels=2)

# Contract mask for tighter crop
remover.contract_mask(pixels=2)

Example Workflows

Product Photography

remover = BackgroundRemover()

# Remove white studio background
remover.load("product_photo.jpg")
remover.remove_color((255, 255, 255), tolerance=25)
remover.refine_edges(feather=2)
remover.save("product_transparent.png")

Portrait Editing

remover = BackgroundRemover()

# Remove background from portrait
remover.load("portrait.jpg")
remover.grabcut(iterations=8)
remover.refine_edges(feather=3)

# Add professional background
remover.replace_background(color=(220, 220, 220))
remover.add_shadow(offset=(5, 5), blur=15)
remover.save("portrait_professional.jpg")

Green Screen Removal

remover = BackgroundRemover()

remover.load("greenscreen_video_frame.jpg")
remover.remove_color((0, 255, 0), tolerance=60)
remover.replace_background(image="virtual_bg.jpg")
remover.save("composited.jpg")

Batch Processing

remover = BackgroundRemover()

processed = remover.batch_process(
    input_dir="product_photos/",
    output_dir="processed/",
    method="color",
    color=(255, 255, 255),
    tolerance=30
)

print(f"Processed {len(processed)} images")

Output Formats

  • PNG: Preserves transparency (recommended)
  • WEBP: Smaller file, supports alpha
  • JPEG: No transparency (use with replace_background)

Tips for Best Results

  1. White/Solid Backgrounds: Use remove_color() method
  2. Complex Backgrounds: Use grabcut() method
  3. High Contrast Subjects: Edge detection works well
  4. Portraits: GrabCut with edge refinement
  5. Product Photos: Color removal with feathering

Limitations

  • Best results with high contrast between subject and background
  • Complex hair/fur edges may need manual touch-up
  • Transparent or semi-transparent subjects are challenging
  • Very busy backgrounds may require manual assistance

Dependencies

  • pillow>=10.0.0
  • opencv-python>=4.8.0
  • numpy>=1.24.0
  • scikit-image>=0.21.0

FAQ

What does the background-remover skill do?

Remove backgrounds from images using segmentation. Support for color-based, edge detection, and AI-assisted removal methods. Batch processing available.

How do I install background-remover?

Run npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill background-remover in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/background-remover/ (personal) or .claude/skills/background-remover/ (project).

Which AI agents can use background-remover?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is background-remover safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains background-remover?

It is published by dkyazzentwatwa in dkyazzentwatwa/chatgpt-skills. It has 122 recorded installs.

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