Configuration

All segmentation and morphology knobs live in a single dataclass, SegConfig. Construct it directly or via the signal-aware factory:

from SegSpy import SegConfig
config = SegConfig.from_signal(signal, min_area=200, threshold_method="otsu")

Auto-detecting TEM/SEM

from_signal inspects HyperSpy metadata: if an Acquisition_instrument.SEM block is present the microscope type is set to SEM, otherwise TEM (the default). This selects sensible preprocessing defaults; override microscope_type explicitly if your metadata is absent or non-standard.

Key tuning fields

Field	Default	Purpose
min_area	100	Drop particles whose contour area (px) is below this
threshold_method	"adaptive"	"adaptive" or "otsu" binarisation
clahe_clip / clahe_tile	3.0 / (8, 8)	CLAHE contrast strength / tile grid
background_kernel	25	Black-hat morphological kernel size (px)
filter_type	"bilateral"	Pre-segmentation noise filter
use_grabcut_refinement	True	Refine masks with GrabCut (traditional backend)

SAM-only fields

When backend = "sam", these control the Auto-Mask Generator and post-filter:

Field	Default	Purpose
sam_model_type	"vit_b"	SAM model variant
sam_checkpoint_path	""	Path to the SAM checkpoint
sam_device	"auto"	Device (auto/cuda/cpu)
sam_points_per_side	32	Grid density of prompt points
sam_pred_iou_thresh	0.88	Minimum predicted mask quality
sam_stability_score_thresh	0.95	Mask stability threshold
sam_min_area_ratio / sam_max_area_ratio	0.0005 / 0.60	Drop masks outside this fraction of the image
sam_intensity_ratio	0.85	Foreground-intensity consistency gate
sam_edge_margin	5	Drop masks touching the image border (px)

For the full field list, see the API reference.