phenotypic.enhance.LaplaceEnhancer#

class phenotypic.enhance.LaplaceEnhancer(kernel_size: int | None = 3, mask: numpy.ndarray | None = None)[source]#

Bases: ImageEnhancer

Laplacian edge enhancement for colony boundaries.

Applies a Laplacian operator that responds to rapid intensity changes and highlights edges. In agar plate images, this can delineate colony margins and ring-like features, improving contour detection or watershed seeds.

Use cases (agar plates): - Emphasize colony edges prior to edge-based segmentation or as a cue for

separating touching colonies.

  • Detect ring patterns around colonies (e.g., swarming fronts) for phenotyping.

Tuning and effects: - kernel_size: Larger kernels produce a smoother, more global edge response

and can suppress small noise; smaller kernels capture fine edges but may amplify noise and agar texture.

  • mask: Restrict processing to the plate region to avoid dish edge glare or labels. A binary mask focusing on the circular plate is often useful.

Caveats: - Laplacian is sensitive to noise; consider a light GaussianBlur first. - May enhance non-biological artifacts (scratches, dust). Combine with masking

or artifact removal if necessary.

Parameters:

  • kernel_size (Optional[int]) – Size of the Laplacian kernel controlling edge scale; smaller captures fine edges, larger smooths noise.

  • mask (Optional[numpy.ndarray]) – Optional boolean/0-1 mask to limit the operation to specific regions (e.g., the plate area).

Methods

__init__

apply

Applies the operation to an image, either in-place or on a copy.

dispose_widgets

Drop references to the UI widgets.

sync_widgets_from_state

Push internal state into widgets.

widget

Return (and optionally display) the root widget.
__init__(kernel_size: int | None = 3, mask: numpy.ndarray | None = None)[source]#
Parameters:

  • kernel_size (Optional[int]) – Controls the edge scale. Smaller values pick up fine edges but increase noise sensitivity; larger values smooth noise and emphasize broader boundaries.

  • mask (Optional[np.ndarray]) – Boolean/0-1 mask to limit processing to regions of interest (e.g., the circular plate), reducing artifacts from dish rims or labels.

__del__()#

Automatically stop tracemalloc when the object is deleted.

__getstate__()#

Prepare the object for pickling by disposing of any widgets.

This ensures that UI components (which may contain unpickleable objects like input functions or thread locks) are cleaned up before serialization.

Note

This method modifies the object state by calling dispose_widgets(). Any active widgets will be detached from the object.

apply(image, inplace=False)#

Applies the operation to an image, either in-place or on a copy.

Parameters:

  • image (Image) – The arr image to apply the operation on.

  • inplace (bool) – If True, modifies the image in place; otherwise, operates on a copy of the image.

Returns:

The modified image after applying the operation.

Return type:

Image

dispose_widgets() → None#

Drop references to the UI widgets.

Return type:

None

sync_widgets_from_state() → None#

Push internal state into widgets.

Return type:

None

widget(image: Image | None = None, show: bool = False) → Widget#

Return (and optionally display) the root widget.

Parameters:

  • image (Image | None) – Optional image to visualize. If provided, visualization controls will be added to the widget.

  • show (bool) – Whether to display the widget immediately. Defaults to False.

Returns:

The root widget.

Return type:

ipywidgets.Widget

Raises:

ImportError – If ipywidgets or IPython are not installed.