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nabu.stitching.overlap

source module nabu.stitching.overlap

Classes

Functions

  • compute_image_minimum_divergence — Algorithm to improve treatment of high frequency.

  • compute_image_higher_signal — the higher signal will pick pixel on the image having 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

  • check_overlaps — check over frames if there is a single overlap other juxtaposed frames (at most and at least)

source enum OverlapStitchingStrategy(*args, **kwds)

Bases : Enum

Attributes

  • MEAN

  • COSINUS_WEIGHTS

  • LINEAR_WEIGHTS

  • CLOSEST

  • IMAGE_MINIMUM_DIVERGENCE

  • HIGHER_SIGNAL

source class OverlapKernelBase()

source class ImageStichOverlapKernel(stitching_axis: int, frame_unstitched_axis_size: tuple, stitching_strategy: OverlapStitchingStrategy = DEFAULT_OVERLAP_STRATEGY, overlap_size: int = DEFAULT_OVERLAP_SIZE, extra_params: Optional[dict] = None)

Bases : OverlapKernelBase

Stitch two images along Y (axis 0 in image space)

:param stitching_axis: axis along which stitching is operate. Must be in '0', '1' :param frame_unstitched_axis_size: according to the stitching axis the stitched framed will always have a constant size: * If stitching_axis == 0 then it will be the frame width * If stitching_axis == 1 then it will be the frame height :param stitching_strategy: stategy / algorithm to use in order to generate the stitching :param overlap_size: size (int) of the overlap (stitching) between the two images :param extra_params: possibly extra parameters to operate the stitching

Attributes

  • unstitched_axis : int — util function. The kernel is operating stitching on images along a single axis (stitching_axis). This property is returning the other axis.

Methods

source property ImageStichOverlapKernel.stitched_axis: int

source property ImageStichOverlapKernel.unstitched_axis: int

util function. The kernel is operating stitching on images along a single axis (stitching_axis). This property is returning the other axis.

source property ImageStichOverlapKernel.overlap_size: int

source property ImageStichOverlapKernel.img_2: numpy.ndarray

source property ImageStichOverlapKernel.weights_img_1: Optional[numpy.ndarray]

source property ImageStichOverlapKernel.weights_img_2: Optional[numpy.ndarray]

source property ImageStichOverlapKernel.stitching_strategy: OverlapStitchingStrategy

source method ImageStichOverlapKernel.compute_weights()

Raises

  • ValueError

  • NotImplementedError

source method ImageStichOverlapKernel.stitch(img_1, img_2, check_input=True) → tuple

Compute overlap region from the defined strategy

Raises

  • ValueError

source compute_image_minimum_divergence(img_1: numpy.ndarray, img_2: numpy.ndarray, high_frequency_threshold, stitching_axis: int)

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 parts will be stitched by taking the lower divergent pixels

Raises

  • ValueError

source compute_image_higher_signal(img_1: numpy.ndarray, img_2: numpy.ndarray)

the higher signal will pick pixel on the image having 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

source check_overlaps(frames: Union[tuple, numpy.ndarray], positions: tuple, axis: int, raise_error: bool)

check over frames if there is a single overlap other juxtaposed frames (at most and at least)

:param frames: liste of ordered / sorted frames along axis to test (from higher to lower) :param positions: positions of frames in 3D space as (position axis 0, position axis 1, position axis 2) :param axis: axis to check :param raise_error: if True then raise an error if two frames don't have at least and at most one overlap. Else log an error

Raises

  • TypeError

  • ValueError

  • NotImplementedError