phenotypic.abc_.ThresholdDetector#

class phenotypic.abc_.ThresholdDetector(*args, **kwargs)[source]

Bases: ObjectDetector, ABC

Marker ABC for threshold-based colony detection strategies.

ThresholdDetector specializes ObjectDetector for algorithms that detect colonies by converting grayscale intensity to a binary mask via thresholding. Unlike edge-based (Canny) or peak-based (RoundPeaks) approaches, thresholding works by partitioning intensity space: pixels above a threshold value become foreground (colonies), pixels below become background.

Quick Decision Guide

Choose your detection strategy based on image characteristics:

  • Threshold-based: Clear intensity separation between colonies and background? Try global threshold (Otsu, Yen) or local adaptive (block-based) thresholding.

  • Edge-based (CannyDetector): Faint or merged colonies? Canny edge detection finds boundaries where gradient is high; invert edges to label regions.

  • Peak-based (RoundPeaksDetector): Well-separated round colonies? Peak detection assumes circular shapes and grows from intensity maxima.

  • Subclass decision: Is your algorithm threshold-based? Subclass ThresholdDetector. Otherwise subclass ObjectDetector directly.

  • Local vs global threshold: Uneven illumination? Local (adaptive) thresholding adjusts per neighborhood; global methods fail on gradient-heavy plates.

  • Advanced strategy: Need dual-threshold with edge tracking? See [HysteresisDetector](src/phenotypic/detect/_hysteresis_detector.py).

Why threshold-based detection?

Thresholding is ideal when:

  • Clear intensity separation: Colonies have distinctly different intensity than background (common on high-contrast agar plates or with good lighting).

  • Simplicity and speed: Single-pass algorithms (no iterative edge tracking or distance computation).

  • Robustness to morphology: Works equally well on round and irregular colonies (unlike peak-based approaches that assume circular shapes).

  • Well-defined boundary: Sharp transitions between foreground and background (less effective on blurry or faded colonies).

Thresholding strategies implemented in PhenoTypic

  • [OtsuDetector](src/phenotypic/detect/_otsu_detector.py): Minimizes within-class variance. Automatic, global, works for balanced histograms.

  • [LiDetector](src/phenotypic/detect/_li_detector.py): Minimizes Kullback-Leibler divergence. Good for dark colonies on bright background.

  • [YenDetector](src/phenotypic/detect/_yen_detector.py): Maximizes object variance. Excellent for sharply defined colonies.

  • [TriangleDetector](src/phenotypic/detect/_triangle_detector.py): Connects histogram extrema. Works well for non-overlapping bimodal distributions.

  • [IsodataDetector](src/phenotypic/detect/_isodata_detector.py): Iteratively refines based on class means. Robust but slower.

  • [MeanDetector](src/phenotypic/detect/_mean_detector.py) / [MinimumDetector](src/phenotypic/detect/_minimum_detector.py): Simple heuristic thresholds. Fast, useful for baseline.

  • [HysteresisDetector](src/phenotypic/detect/_hysteresis_detector.py): Advanced dual-threshold with edge tracking. Handles variable colony intensity.

When to subclass ThresholdDetector vs ObjectDetector directly

Subclass ThresholdDetector if your algorithm uses threshold-based intensity partitioning:

  • Converts intensity to binary mask via threshold comparison (mask = enh > threshold).

  • Uses automatic threshold selection (Otsu, Li, Yen, Triangle, Isodata, etc.).

  • Uses simple heuristic thresholds (mean, minimum, percentile-based).

  • Signals intent to other developers: “this detector groups with thresholding methods.”

  • May share utility methods in future (e.g., post-processing filters).

Subclass ObjectDetector directly if your algorithm uses alternative strategies:

  • Edge detection (find gradients, not intensity levels).

  • Peak finding (assumes round shapes, grows from maxima).

  • Watershed segmentation or other region-based approaches.

  • Hybrid methods that don’t fit threshold → binary mask → label pattern.

Typical workflow: enhance → threshold → label → refine

Most ThresholdDetector implementations follow this pipeline:

  1. Read detection matrix: enh = image.detect_mat[:] (preprocessed for contrast and noise suppression).

  2. Compute threshold: Use chosen strategy (Otsu, Li, Yen, etc.) to find optimal threshold value from histogram.

  3. Create binary mask: mask = enh > threshold or mask = enh >= threshold (test both if edge pixels ambiguous).

  4. Post-process (optional): Remove small noise, clear borders, morphological cleanup to improve mask quality.

  5. Label connected components: Use scipy.ndimage.label() to assign unique integer IDs to each colony (objmap).

  6. Set both outputs: image.objmask = mask, image.objmap = labeled_map.

Parameter tuning guidance

Threshold-based detectors expose parameters affecting detection quality:

  • Threshold value (manual methods only): Higher → fewer, larger colonies; lower → more, noisier. Test range on representative images to find balance.

  • Block size (local/adaptive methods): Larger blocks smooth mask but miss small colonies; smaller blocks add detail but amplify noise. Start with 1/8 to 1/4 of image width.

  • ignore_zeros: Skip pure black pixels in threshold computation. Useful when background has significant black regions (shadows, vignetting).

  • ignore_borders: Automatically remove objects touching image edges. Prevents partial colonies at plate edges from skewing analysis.

  • min_size / max_size: Post-processing filters. Remove objects below min (noise) or above max (artifacts). Measure typical colony size on your plates first.

Comparison with other detection strategies

  • [CannyDetector](src/phenotypic/detect/_canny_detector.py) (edge-based): Finds intensity gradients to locate boundaries. Better for faint or merged colonies; requires tuning gradient thresholds.

  • [RoundPeaksDetector](src/phenotypic/detect/_round_peaks_detector.py) (peak-based): Assumes round shapes, grows from maxima. Excellent for well-separated round colonies; fails on irregular or merged shapes.

  • Threshold-based (this class): Direct intensity partitioning. Robust, fast, works for any shape; requires good intensity separation between colonies and background.

Common pitfalls and remedies

  • Over-segmentation (too many small objects): Use ignore_zeros=True to skip dark pixels, apply morphological opening (remove_small_objects), or refine with ObjectRefiner.

  • Under-segmentation (merged colonies): Try local thresholding (adaptive block-based), morphological closing, or watershed post-processing in ObjectRefiner.

  • False positives at edges: Use ignore_borders=True parameter or clear_border() in ObjectRefiner to remove edge-touching objects.

  • Uneven illumination (vignetting, shadows): Apply enhancement (CLAHE, illumination correction) before detection, or switch to local adaptive thresholding.

  • Threshold too high/low: Visualize objmask on sample images to diagnose. Adjust parameters and re-test on representative plates before batch processing.

Local thresholding pattern (adaptive to uneven illumination)

When images have uneven illumination or vignetting, local (adaptive) thresholding computes a threshold per neighborhood instead of globally. This handles gradual intensity changes:

from skimage import filters
from scipy import ndimage
import numpy as np

enh = image.detect_mat[:]
# Compute local threshold for each pixel
block_size = 31  # Neighborhood size (odd integer)
threshold_map = filters.threshold_local(enh, block_size=block_size)
# Create mask: pixel > its local threshold
mask = enh > threshold_map
# Label connected components
labeled, _ = ndimage.label(mask)
image.objmask[:] = mask
image.objmap[:] = labeled
return image

Implementation pattern: Global automatic threshold

For global automatic thresholding (Otsu, Li, Yen, Triangle, Isodata), follow this pattern:

from skimage import filters
from scipy import ndimage

def _operate(self, image):
    enh = image.detect_mat[:]
    # Compute threshold value via automatic method
    threshold = filters.threshold_otsu(enh)  # or threshold_li, threshold_yen, etc.
    # Create binary mask: pixels above threshold
    mask = enh > threshold
    # Label connected components
    labeled, num_objects = ndimage.label(mask)
    # Set both outputs
    image.objmask[:] = mask
    image.objmap[:] = labeled
    return image

Key points: Read preprocessed detect_mat, compute single threshold, compare all pixels at once, label result. This is fast and deterministic (same image always produces same result).

Interface specification

Subclasses of ThresholdDetector must:

  1. Inherit from ThresholdDetector (which provides ObjectDetector’s interface).

  2. Implement _operate(image: Image) -> Image as an instance method.

  3. Within _operate():

    • Read image.detect_mat[:] (and optionally image.rgb[:], image.gray[:]).

    • Compute threshold (automatically or from parameter).

    • Generate binary mask via comparison: mask = enh > threshold.

    • Label connected components: labeled, _ = ndimage.label(mask).

    • Set both outputs: image.objmask = mask, image.objmap = labeled.

    • Return modified image.

  4. Add to phenotypic.detect.__init__.py exports for public discovery.

Notes

This is a marker ABC with no additional methods. It exists to categorize threshold-based detectors in the class hierarchy and enable flexible discovery and code organization.

Examples

Detect colonies using Otsu’s automatic threshold:

>>> from phenotypic import Image
>>> from phenotypic.detect import OtsuDetector
>>> # Load a plate image
>>> plate = Image.imread("agar_plate.jpg")
>>> # Apply Otsu threshold detection
>>> detector = OtsuDetector(ignore_zeros=True, ignore_borders=True)
>>> detected = detector.apply(plate)
>>> # Access results
>>> mask = detected.objmask[:]  # Binary mask
>>> objmap = detected.objmap[:]  # Labeled map
>>> num_colonies = objmap.max()
>>> print(f"Detected {num_colonies} colonies")
>>> # Iterate over colonies
>>> for colony in detected.objects:
...     print(f"Colony {colony.label}: area={colony.area} px")

Compare different threshold strategies:

>>> from phenotypic import Image
>>> from phenotypic.detect import (
...     OtsuDetector, LiDetector, YenDetector, TriangleDetector
... )
>>> plate = Image.imread("agar_plate.jpg")
>>> # Test multiple threshold strategies
>>> detectors = {
...     "Otsu": OtsuDetector(),
...     "Li": LiDetector(),
...     "Yen": YenDetector(),
...     "Triangle": TriangleDetector(),
... }
>>> for name, detector in detectors.items():
...     result = detector.apply(plate)
...     num = result.objmap[:].max()
...     print(f"{name}: detected {num} colonies")

Build a pipeline with thresholding and refinement:

>>> from phenotypic import Image, ImagePipeline
>>> from phenotypic.enhance import ContrastEnhancer
>>> from phenotypic.detect import OtsuDetector
>>> from phenotypic.refine import RemoveSmallObjectsRefiner
>>> # Create pipeline
>>> pipeline = ImagePipeline()
>>> pipeline.add(ContrastEnhancer(factor=1.5))  # Boost contrast
>>> pipeline.add(OtsuDetector(ignore_zeros=True))  # Threshold
>>> pipeline.add(RemoveSmallObjectsRefiner(min_size=50))  # Cleanup
>>> # Process image
>>> plate = Image.imread("agar_plate.jpg")
>>> result = pipeline.operate([plate])[0]
>>> print(f"Final colonies: {result.objmap[:].max()}")

Tuning threshold detection on your plate images:

>>> from phenotypic import Image
>>> from phenotypic.detect import (
...     OtsuDetector, YenDetector, TriangleDetector
... )
>>> # Load a sample plate image
>>> plate = Image.imread("sample_plate.jpg")
>>> # Test different threshold strategies
>>> strategies = {
...     "Otsu": OtsuDetector(ignore_zeros=True),
...     "Yen": YenDetector(ignore_zeros=True),
...     "Triangle": TriangleDetector(ignore_zeros=True),
... }
>>> best_detector = None
>>> best_count = 0
>>> for name, detector in strategies.items():
...     result = detector.apply(plate)
...     num_colonies = result.objmap[:].max()
...     print(f"{name}: {num_colonies} colonies detected")
...     # Choose detector that finds expected number of colonies
...     if best_detector is None:
...         best_detector = detector
...         best_count = num_colonies
>>> # Use best detector for batch processing
>>> print(f"Selected: {type(best_detector).__name__}")

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.

This Page