phenotypic.detect.OtsuDetector#

class phenotypic.detect.OtsuDetector(ignore_zeros: bool = False, ignore_borders: bool = True)[source]

Bases: ThresholdDetector

Detect colonies by global Otsu thresholding on bimodal plate histograms.

Automatically compute a single intensity threshold that minimises within-class variance, separating colony foreground from agar background. The resulting binary mask cleanly segments plates whose histograms are bimodal (one peak for background, one for colonies). For a comparison of all available detection strategies see Detection Strategies Compared.

Parameters:

  • ignore_zeros (bool) – If True (default), exclude zero-intensity pixels from threshold computation. Enable for plates with black borders or masked regions; disable only when zero is a meaningful intensity value. Typical range: True for most workflows.

  • ignore_borders (bool) – If True (default), remove colonies touching image edges via clear_border(). Recommended for grid-based colony counting to eliminate partial colonies at plate boundaries.

Returns:

Input image with objmask set to a binary colony mask produced by Otsu thresholding and objmap set to labeled connected components.

Return type:

Image

Raises:

ValueError – If threshold computation fails (e.g., all pixels share the same intensity value, producing a degenerate histogram).

Best For:

  • Plates imaged under standardised lighting where the intensity histogram has two well-separated peaks.

  • Quick baseline detection requiring no parameter tuning.

  • High-throughput screens with uniform agar colour and colony density.

  • Comparing automatic methods – Otsu is the standard reference threshold.

  • Clean plates with minimal dust, scratches, or condensation.

Consider Also:

  • TriangleDetector when colonies occupy a small fraction of the plate and the histogram is skewed.

  • HysteresisDetector when colony intensity varies (e.g., young versus mature growth) and a single threshold under-segments.

  • WatershedDetector when touching colonies must be split into individually labelled objects.

References

[1] N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst., Man, Cybern., vol. 9, no. 1, pp. 62–66, 1979.

See also

Tutorial 2: Detecting Colonies

Step-by-step tutorial for basic colony detection.

How To: Choose a Detection Algorithm

Guide for selecting the right detector for your plate images.

Detection Strategies Compared

In-depth comparison of all detection strategies.

Methods

__init__

apply

Detect colonies using sinusoidal cross-correlation grid estimation.

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)

Detect colonies using sinusoidal cross-correlation grid estimation.

This method performs the core detection workflow: 1. Extract grid dimensions (if GridImage) 2. Threshold the detection matrix with adaptive kernel sizing 3. Remove noise if requested 4. Label connected components 5. Determine or estimate grid edges (via sinusoidal cross-correlation) 6. Assign dominant colonies to grid cells 7. Create final object map

Parameters:

image – Image object to process. Can be a regular Image or GridImage.

Returns:

The processed image with updated objmask and objmap.

Return type:

Image

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.

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