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 White/Solid Backgrounds: Use remove_color() method Complex Backgrounds: Use grabcut() method High Contrast Subjects: Edge detection works well Portraits: GrabCut with edge refinement 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