phenotypic.detect.MinimumDetector#

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

Bases: ThresholdDetector

Detect colonies by finding the valley between two histogram peaks.

Locate the intensity minimum (valley) between the two dominant peaks of the image histogram and threshold at that point. This works well when colonies and background form two clearly separated intensity populations, as the valley provides a natural separation boundary. For a full comparison see Detection Strategies Compared.

Parameters:

  • ignore_zeros (bool) – 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. Default: True.

  • ignore_borders (bool) – Remove colonies touching image edges via clear_border(). Recommended for grid-based colony counting to eliminate partial colonies at plate boundaries. Default: True.

Returns:

Input image with objmask set to binary mask and objmap set to labeled connected components.

Return type:

Image

Raises:

ValueError – If the histogram has no clear bimodal distribution and no valley can be found.

Best For:

  • High-contrast plates where colony and background intensities form two distinct, well-separated histogram peaks.

  • Standardised imaging setups producing consistently bimodal histograms across plates.

  • Images where the intensity gap between colonies and agar is wide and the valley is unambiguous.

Consider Also:

  • OtsuDetector when the histogram is bimodal but peaks are broad or partially overlapping.

  • LiDetector when the histogram is unimodal or weakly bimodal and a cross-entropy criterion is more appropriate.

  • HysteresisDetector when colony brightness varies and a single valley-based threshold under-segments faint regions.

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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