raygeo.image.preprocess

Functions

compute_adaptive_threshold()

compute_adaptive_threshold(areas: list[int]) -> int

Compute an adaptive area threshold to separate noise from content.

Analyses the distribution of connected component areas and finds the largest gap to determine a threshold that separates noise (small components) from meaningful content.

Parameter	Type	Description
areas	list[int]	Sorted list of component pixel areas.
Returns	int	Adaptive threshold value (minimum area to keep).
Complexity		O(n) where n = number of unique area values

Adaptive threshold from component area distribution

denoise_binary()

denoise_binary(
    binary: numpy.NDArray[numpy.uint8],
) -> numpy.NDArray[numpy.uint8]

Remove small noise components from a binary image using adaptive thresholding.

Computes connected components, finds the largest gap in component area distribution to separate noise from content, and removes small components. Uses the same algorithm as the legacy Python _find_adaptive_area_threshold.

Parameter	Type	Description
binary	numpy.NDArray[numpy.uint8]	2D binary uint8 array (values 0 or 1).
Returns	numpy.NDArray[numpy.uint8]	2D binary uint8 array with noise removed.
Complexity		O(w*h)

Binary image denoised via adaptive thresholding

filter_components()

filter_components(
    binary: numpy.NDArray[numpy.uint8],
    min_area: int,
) -> numpy.NDArray[numpy.uint8]

Remove connected components smaller than min_area.

Uses 8-connectivity for component detection.

Parameter	Type	Description
binary	numpy.NDArray[numpy.uint8]	2D binary uint8 array (values 0 or 1).
min_area	int	Minimum pixel count to keep a component.
Returns	numpy.NDArray[numpy.uint8]	2D binary uint8 array (values 0 or 1).
Complexity		O(w*h)

Component filtering by minimum area

get_component_areas()

get_component_areas(binary: numpy.NDArray[numpy.uint8]) -> list[int]

Compute the pixel area of each connected component.

Uses 8-connectivity. Areas are returned sorted ascending. Background (0-valued pixels) is excluded.

Parameter	Type	Description
binary	numpy.NDArray[numpy.uint8]	2D binary uint8 array (values 0 or 1).
Returns	list[int]	Sorted list of component pixel areas.
Complexity		O(w*h)

Connected component areas sorted ascending

grayscale_to_binary()

grayscale_to_binary(
    gray: numpy.NDArray[numpy.uint8],
    threshold: float = 0.5,
    invert: bool = False,
    auto_threshold: bool = True,
) -> numpy.NDArray[numpy.uint8]

Convert grayscale image to binary using Otsu or fixed threshold.

Pixels at or below the threshold become foreground (1). Uses Otsu's method when auto_threshold is True.

Parameter	Type	Description
gray	numpy.NDArray[numpy.uint8]	2D grayscale uint8 image.
threshold	float = 0.5	Fixed threshold (0.0-1.0), used only if auto_threshold is False.
invert	bool = False	If True, pixels above threshold become foreground.
auto_threshold	bool = True	If True, compute threshold via Otsu's method.
Returns	numpy.NDArray[numpy.uint8]	2D binary uint8 array (values 0 or 1).
Complexity		O(w*h)

Grayscale to binary via Otsu and fixed threshold