Postprocessing Module

Image refinement and enhancement tools

Overview

The postprocessing module provides tools for refining and enhancing the converted archaeological drawings, focusing on output quality and archaeological detail preservation.

Binarize Image

def binarize_image(
    image: Union[PIL.Image, np.ndarray],
    threshold: int = 127
) -> PIL.Image

Converts grayscale drawings to binary format, ideal for final publication preparation.

Parameters

image: Input image (PIL Image or numpy array)
threshold: Intensity threshold (0-255)

Examples

binary = binarize_image("processed_vessel.png", threshold=150)

Remove White Background

def remove_white_background(
    image: PIL.Image,
    threshold: int = 250
) -> PIL.Image

Creates transparent backgrounds for archaeological drawings, useful for figure composition.

Parameters

image: Input drawing
threshold: Value above which pixels are considered white

Examples

transparent = remove_white_background("vessel.png", threshold=245)

Process Image Binarize

def process_image_binarize(
    image_path: str,
    binarize_threshold: int = 127,
    white_threshold: int = 250,
    save_path: Optional[str] = None
) -> PIL.Image

Combined function for binarization and background removal.

Parameters

image_path: Path to input image
binarize_threshold: Threshold for black/white conversion
white_threshold: Threshold for transparency
save_path: Optional output path

Binarize Folder Images

def binarize_folder_images(
    input_folder: str,
    binarize_threshold: int = 127,
    white_threshold: int = 250
) -> None

Batch processes a folder of drawings, applying binarization and background removal.

Parameters

input_folder: Directory containing drawings
binarize_threshold: Threshold for binarization
white_threshold: Threshold for transparency

Enhance Stippling

def enhance_stippling(
    img: PIL.Image,
    min_size: int = 80,
    connectivity: int = 2
) -> Tuple[PIL.Image, PIL.Image]

Isolates and enhances stippling patterns in archaeological drawings.

Parameters

img: Input drawing
min_size: Minimum object size to preserve
connectivity: Connection parameter for pattern detection

Returns

Returns tuple containing: - processed_image: Drawing with enhanced stippling - stippling_pattern: Isolated stippling mask

Modify Stippling

def modify_stippling(
    processed_img: PIL.Image,
    stippling_pattern: PIL.Image,
    operation: str = 'dilate',
    intensity: float = 0.5,
    opacity: float = 1.0
) -> PIL.Image

Adjusts stippling patterns through morphological operations and intensity modulation.

Parameters

processed_img: Base image without stippling
stippling_pattern: Isolated stippling pattern
operation: Type of modification (‘dilate’, ‘fade’, or ‘both’)
intensity: Morphological modification strength (0.0-1.0)
opacity: Stippling opacity factor (0.0-1.0)

Examples

enhanced = modify_stippling(
    base_img,
    dots_pattern,
    operation='both',
    intensity=0.7,
    opacity=0.8
)

Control Stippling

def control_stippling(
    input_folder: str,
    min_size: int = 50,
    connectivity: int = 2,
    operation: str = 'fade',
    intensity: float = 0.5,
    opacity: float = 0.5
) -> None

Batch processes stippling patterns in a folder of archaeological drawings.

Parameters

input_folder: Directory containing drawings
min_size: Minimum object size to preserve
connectivity: Pattern detection parameter
operation: Modification type (‘dilate’, ‘fade’, ‘both’)
intensity: Modification strength
opacity: Pattern opacity

Examples

control_stippling(
    "vessel_drawings/",
    min_size=60,
    operation='both',
    intensity=0.6
)