phenotypic.detect.WatershedDetector

class phenotypic.detect.WatershedDetector(footprint: Literal['auto'] | numpy.ndarray | int | None = None, min_size: int = 50, compactness: float = 0.001, connectivity: int = 1, relabel: bool = True, ignore_zeros: bool = True)

Bases: ThresholdDetector

Class for detecting objects in an image using the Watershed algorithm.

The WatershedDetector class processes images to detect and segment objects by applying the watershed algorithm. This class extends the capabilities of ThresholdDetector and includes customization for parameters such as footprint size, minimum object size, compactness, and connectivity. This is useful for image segmentation tasks, where proximity-based object identification is needed.

Note

Its recommended to use GaussianBlur beforehand

Parameters:

• footprint (Literal['auto'] | np.ndarray | int | None)

• min_size (int)

• compactness (float)

• connectivity (int)

• relabel (bool)

• ignore_zeros (bool)

footprint

Structure element to define the neighborhood for dilation and erosion operations. Can be specified directly as ‘auto’, an ndarray, an integer for diamond size, or None for implementation-based determination.

Type:

Literal[‘auto’] | np.ndarray | int | None

min_size

Minimum size of objects to retain during segmentation. Objects smaller than this other_image are removed.

Type:

int

compactness

Compactness parameter controlling segment shapes. Higher values enforce more regularly shaped objects.

Type:

float

connectivity

The connectivity level used for determining connected components. Represents the number of dimensions neighbors need to share (1 for fully connected, higher values for less connectivity).

Type:

int

relabel

Whether to relabel segmented objects during processing to ensure consistent labeling.

Type:

bool

ignore_zeros

Whether to exclude zero-valued pixels from threshold calculation. When True, Otsu threshold is calculated using only non-zero pixels, and zero pixels are automatically treated as background. When False, all pixels (including zeros) are used for threshold calculation. Default is True, which is useful for microscopy images where zero pixels represent true background or imaging artifacts.

Type:

bool

Methods

__init__
apply	Binarizes the given image gray using the Yen threshold method.
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.

__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)

Binarizes the given image gray using the Yen threshold method.

This function modifies the arr image by applying a binary mask to its enhanced gray (enh_gray). The binarization threshold is automatically determined using Yen’s method. The resulting binary mask is stored in the image’s objmask attribute.

Parameters:

image (Image) – The arr image object. It must have an enh_gray attribute, which is used as the basis for creating the binary mask.

Returns:

The arr image object with its objmask attribute updated

to the computed binary mask other_image.

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.