Background Removal

def remove(
    data: Union[bytes, PILImage, np.ndarray],
    alpha_matting: bool = False,
    alpha_matting_foreground_threshold: int = 240,
    alpha_matting_background_threshold: int = 10,
    alpha_matting_erode_size: int = 10,
    session: Optional[BaseSession] = None,
    only_mask: bool = False,
    post_process_mask: bool = False,
    bgcolor: Optional[Tuple[int, int, int, int]] = None,
    force_return_bytes: bool = False,
    *args,
    **kwargs
) -> Union[bytes, PILImage, np.ndarray]:
    """
    Remove the background from an input image.

    Parameters:
    - data: Input image as bytes, PIL Image, or numpy array
    - alpha_matting: Use advanced alpha matting for smoother edges
    - alpha_matting_foreground_threshold: Threshold for foreground detection (0-255)
    - alpha_matting_background_threshold: Threshold for background detection (0-255)
    - alpha_matting_erode_size: Erosion size for alpha matting refinement
    - session: Specific model session to use (defaults to u2net)
    - only_mask: Return only the binary mask instead of cutout
    - post_process_mask: Apply morphological operations to smooth mask
    - bgcolor: RGBA background color tuple for non-transparent output
    - force_return_bytes: Force output as bytes regardless of input type

    Returns:
    Processed image in same format as input (or bytes if force_return_bytes=True)
    """

from rembg import remove
from PIL import Image

# Basic usage
input_img = Image.open('photo.jpg')
result = remove(input_img)

# With alpha matting for higher quality
result = remove(input_img, alpha_matting=True)

# Get only the mask
mask = remove(input_img, only_mask=True)

# Apply custom background color
result = remove(input_img, bgcolor=(255, 255, 255, 255))  # White background

# Process bytes directly
with open('input.jpg', 'rb') as f:
    input_bytes = f.read()
output_bytes = remove(input_bytes)

def alpha_matting_cutout(
    img: PILImage,
    mask: PILImage,
    foreground_threshold: int,
    background_threshold: int,
    erode_structure_size: int
) -> PILImage:
    """
    Perform alpha matting cutout for high-quality edge refinement.

    Parameters:
    - img: Input PIL image
    - mask: Binary mask PIL image
    - foreground_threshold: Threshold for foreground pixels (0-255)
    - background_threshold: Threshold for background pixels (0-255)
    - erode_structure_size: Size of erosion structure for refinement

    Returns:
    PIL Image with refined alpha channel
    """

def naive_cutout(img: PILImage, mask: PILImage) -> PILImage:
    """
    Perform simple cutout using mask composite operation.

    Parameters:
    - img: Input PIL image
    - mask: Binary mask PIL image

    Returns:
    PIL Image with background removed
    """

def putalpha_cutout(img: PILImage, mask: PILImage) -> PILImage:
    """
    Apply mask directly as alpha channel to image.

    Parameters:
    - img: Input PIL image
    - mask: Binary mask PIL image

    Returns:
    PIL Image with mask applied as alpha channel
    """

def post_process(mask: np.ndarray) -> np.ndarray:
    """
    Post-process mask for smooth boundaries using morphological operations.

    Applies opening operation followed by Gaussian blur and threshold
    to create smoother mask boundaries.

    Parameters:
    - mask: Binary numpy mask array

    Returns:
    Processed numpy mask array with smoother boundaries
    """

def download_models(models: tuple[str, ...]) -> None:
    """
    Download AI models for background removal.

    Parameters:
    - models: Tuple of model names to download. If empty, downloads all models.

    Available models: u2net, u2netp, u2net_human_seg, u2net_cloth_seg, 
    u2net_custom, birefnet_general, birefnet_general_lite, birefnet_portrait,
    birefnet_dis, birefnet_hrsod, birefnet_cod, birefnet_massive, dis_anime,
    dis_custom, dis_general_use, sam, silueta, bria_rmbg, ben_custom
    """

try:
    result = remove(unsupported_data)
except ValueError as e:
    print(f"Unsupported input type: {e}")
    # Use force_return_bytes=True as fallback
    result = remove(unsupported_data, force_return_bytes=True)