ncalab.visualization.animation

Attributes

`animator_style_dark`
`animator_styles`

Classes

`AnimatorStyle`
`Animator`	Responsible for rendering NCA predictions as GIFs.

Functions

draw_segmentation_overlay(image, mask, style)

Module Contents

class ncalab.visualization.animation.AnimatorStyle(color_background, color_overlay, color_title, color_progress, underline: bool = True, progress_h: int = 3)

color_background

color_overlay

color_title

color_progress

underline = True

progress_h = 3

apply(fig, ax)

ncalab.visualization.animation.animator_style_dark

ncalab.visualization.animation.animator_styles

ncalab.visualization.animation.draw_segmentation_overlay(image, mask, style)

class ncalab.visualization.animation.Animator(nca: ncalab.models.AbstractNCAModel, seed: torch.Tensor, steps: int | None = None, interval: int = 100, repeat: bool = True, repeat_delay: int = 10000, overlay: bool = False, show_timestep: bool = True, hidden: bool = False, show_input: bool = False, style: str | AnimatorStyle = 'dark')

Responsible for rendering NCA predictions as GIFs.

Parameters:

nca (ncalab.AbstractNCAModel) – NCA model instance
seed (torch.Tensor) – Input image for the NCA model
steps (int, optional) – Number of NCA prediction steps per sample, defaults to 100
interval (int, optional) – Time of each frame (milliseconds), defaults to 100
repeat (bool, optional) – Whether to loop the animation, defaults to True
repeat_delay (int, optional) – Time after which the animation is repeated (milliseconds), defaults to 10000
overlay (bool, optional) – Whether to overlay output channel (segmentation mask), defaults to False
show_timestep (bool, optional) – Whether to display timestep in caption, defaults to True

animation_fig

save(path: str | pathlib.Path)

Save generated figure as GIF

Parameters:: path (str | Path) – Output path