nabu.stitching.overlap module

class nabu.stitching.overlap.OverlapStitchingStrategy(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)

Bases: Enum

MEAN = 'mean'

COSINUS_WEIGHTS = 'cosinus weights'

LINEAR_WEIGHTS = 'linear weights'

CLOSEST = 'closest'

IMAGE_MINIMUM_DIVERGENCE = 'image minimum divergence'

HIGHER_SIGNAL = 'higher signal'

class nabu.stitching.overlap.OverlapKernelBase

Bases: object

class nabu.stitching.overlap.ZStichOverlapKernel(frame_width: int, stitching_strategy: OverlapStitchingStrategy = OverlapStitchingStrategy.COSINUS_WEIGHTS, overlap_size: int = None, extra_params: dict | None = None)

Bases: OverlapKernelBase

class used to define overlap between two scans and create stitch between frames (stitch function)

DEFAULT_HIGH_FREQUENCY_THRESHOLD = 2

property overlap_size: int

property img_2: ndarray

property weights_img_1: ndarray | None

property weights_img_2: ndarray | None

property stitching_strategy: OverlapStitchingStrategy

compute_weights()

stitch(img_1, img_2, check_input=True) → tuple

Compute overlap region from the defined strategy

nabu.stitching.overlap.compute_image_minimum_divergence(img_1: ndarray, img_2: ndarray, high_frequency_threshold)

Algorithm to improve treatment of high frequency.

It split the two images into two parts: high frequency and low frequency.

The two low frequency part will be stitched using a ‘sinusoidal’ / cosinus weights approach. When the two high frequency part will be stitched by taking the lower divergent pixels

nabu.stitching.overlap.compute_image_higher_signal(img_1: ndarray, img_2: ndarray)

the higher signal will pick pixel on the image have the higher signal. A use case is that if there is some artefacts on images which creates stripes (from scintillator artefacts for example) it could be removed from this method