ConstantsWarp

class lib.training.data.augmentation.ConstantsWarp(maps: ndarray, pad: tuple[int, int], slices: slice, scale: float, lm_edge_anchors: ndarray, lm_grids: ndarray, lm_scale: float)

Bases: object

Dataclass for holding constants for warping an image

Parameters:

  • maps (numpy.ndarray) – The stacked (x, y) mappings for image warping

  • pad (tuple[int, int]) – The padding to apply for image warping

  • slices (slice) – The slices for extracting a warped image

  • lm_edge_anchors (numpy.ndarray) – The edge anchors for landmark based warping

  • lm_grids (numpy.ndarray) – The grids for landmark based warping

  • scale (float)

  • lm_scale (float)

lm_edge_anchors: ndarray = <dataclasses._MISSING_TYPE object>

The edge anchors for landmark based warping

lm_grids: ndarray = <dataclasses._MISSING_TYPE object>

The grids for landmark based warping

lm_scale: float = <dataclasses._MISSING_TYPE object>

The scaling to apply to landmark based warping

maps: ndarray = <dataclasses._MISSING_TYPE object>

The stacked (x, y) mappings for image warping

pad: tuple[int, int] = <dataclasses._MISSING_TYPE object>

The padding to apply for image warping

scale: float = <dataclasses._MISSING_TYPE object>

The scaling to apply to standard warping

slices: slice = <dataclasses._MISSING_TYPE object>

The slices for extracting a warped image