phenotypic.detect.TriangleDetector#

class phenotypic.detect.TriangleDetector(*args, **kwargs)[source]

Bases: ThresholdDetector

Detect colonies by triangle thresholding on skewed, background-dominant plate histograms.

Compute a threshold at the base of the triangle formed by the histogram peak, minimum, and maximum. This method excels when colonies occupy a small fraction of the plate so that the intensity histogram is strongly skewed toward background. The resulting binary mask captures sparse or faint colonies that Otsu may miss. For a full comparison see Detection Strategies Compared.

Returns:

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

Return type:

Image

Raises:

ValueError – If threshold computation fails (e.g., degenerate histogram with insufficient intensity variation).

Best For:

  • Plates where colonies are sparse and background dominates the intensity histogram.

  • Faintly pigmented or translucent colonies that produce a small foreground peak relative to the background tail.

  • Early time-point images where colony growth is minimal and most pixels belong to the agar background.

  • Drop-out screens with many empty grid positions and few visible colonies.

Consider Also:

  • OtsuDetector when colonies and background occupy roughly equal histogram areas (balanced bimodal distribution).

  • HysteresisDetector when colony brightness varies across the plate and a single threshold under-segments faint regions.

  • ManualDetector when an empirically determined threshold is known to outperform automatic methods for your plate type.

References

[1] G. W. Zack, W. E. Rogers, and S. A. Latt, “Automatic measurement of sister chromatid exchange frequency,” J. Histochem. Cytochem., vol. 25, no. 7, pp. 741–753, 1977.

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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