phenotypic.measure.MeasureIntensity#

class phenotypic.measure.MeasureIntensity(*args, **kwargs)[source]

Bases: MeasureFeatures

Measure grayscale intensity statistics of detected colonies.

Compute per-colony intensity metrics from the grayscale channel: integrated intensity, percentiles (min, Q1, median, Q3, max), standard deviation, coefficient of variation, and area-normalized density. These statistics reflect colony optical density, biomass accumulation, and internal heterogeneity.

Returns:

pd.DataFrame: Object-level intensity statistics with columns:

  • Label, IntegratedIntensity, MinimumIntensity, MaximumIntensity, MeanIntensity, MedianIntensity.

  • LowerQuartileIntensity, UpperQuartileIntensity, InterquartileRangeIntensity.

  • StandardDeviationIntensity, CoefficientVarianceIntensity.

  • Density (integrated intensity / area), ConvexDensity (integrated intensity / convex area).

Best For:

  • Tracking colony growth over time via integrated intensity as an optical-density proxy.

  • Detecting metabolically stressed or slow-growing colonies through low mean intensity.

  • Identifying sectored or chimeric colonies by high within-colony intensity variance.

  • Automated colony picking based on biomass thresholds.

Consider Also:

  • MeasureSize for lightweight area and integrated intensity without full statistics.

  • MeasureColor for multi-channel color statistics when pigmentation is relevant.

  • MeasureTexture for surface-roughness features that complement intensity metrics.

See Also:

Tutorial 7: Measuring and Exporting for a walkthrough of measuring and exporting colony data. Measurement Metrics and Their Biological Meaning for interpreting intensity metrics in a biological context.

Category: INTENSITY#

Name

Description

IntegratedIntensity

The sum of the object’s pixels

Density

The ratio of the object’s intensity to the max possible intensity of the object

ConvexDensity

The ratio of the objects intensity to the max possible intensity of the object’s convex hull

MinimumIntensity

The minimum intensity of the object

MaximumIntensity

The maximum intensity of the object

MeanIntensity

The mean intensity of the object

MedianIntensity

The median intensity of the object

StandardDeviationIntensity

The standard deviation of the object

CoefficientVarianceIntensity

The coefficient of variation of the object

LowerQuartileIntensity

The lower quartile intensity of the object

UpperQuartileIntensity

The upper quartile intensity of the object

InterquartileRangeIntensity

The interquartile range of the object

Methods

__init__

measure

Execute the measurement operation on a detected-object image.
__del__()

Automatically stop tracemalloc when the object is deleted.

measure(image, include_meta=False)

Execute the measurement operation on a detected-object image.

This is the main public API method for extracting measurements. It handles: input validation, parameter extraction via introspection, calling the subclass-specific _operate() method, optional metadata merging, and exception handling.

How it works (for users):

  1. Pass your processed Image (with detected objects) to measure()

  2. The method calls your subclass’s _operate() implementation

  3. Results are validated and returned as a pandas DataFrame

  4. If include_meta=True, image metadata (filename, grid info) is merged in

How it works (for developers):

When you subclass MeasureFeatures, you only implement _operate(). This measure() method automatically:

  • Extracts __init__ parameters from your instance (introspection)

  • Passes matched parameters to _operate() as keyword arguments

  • Validates the Image has detected objects (objmap)

  • Wraps exceptions in OperationFailedError with context

  • Merges grid/object metadata if requested

Parameters:

  • image (Image) – A PhenoTypic Image object with detected objects (must have non-empty objmap from a prior detection operation).

  • include_meta (bool, optional) – If True, merge image metadata columns (filename, grid position, etc.) into the results DataFrame. Defaults to False.

Returns:

Measurement results with structure:

  • First column: OBJECT.LABEL (integer IDs from image.objmap[:])

  • Remaining columns: Measurement values (float, int, or string)

  • One row per detected object

If include_meta=True, additional metadata columns are prepended before OBJECT.LABEL (e.g., Filename, GridRow, GridCol).

Return type:

pd.DataFrame

Raises:

OperationFailedError – If _operate() raises any exception, it is caught and re-raised as OperationFailedError with details including the original exception type, message, image name, and operation class. This provides consistent error handling across all measurers.

Notes

  • This method is the main entry point; do not override in subclasses

  • Subclasses implement _operate() only, not this method

  • Automatic memory profiling is available via logging configuration

  • Image must have detected objects (image.objmap should be non-empty)

Examples

Basic measurement extraction:

>>> from phenotypic import Image
>>> from phenotypic.measure import MeasureSize
>>> from phenotypic.detect import OtsuDetector
>>> # Load and detect
>>> image = Image('plate.jpg')
>>> image = OtsuDetector().operate(image)
>>> # Extract measurements
>>> measurer = MeasureSize()
>>> df = measurer.measure(image)
>>> print(df.head())

Include metadata in measurements:

>>> # With image metadata (filename, grid info)
>>> df_with_meta = measurer.measure(image, include_meta=True)
>>> print(df_with_meta.columns)
# Output: ['Filename', 'GridRow', 'GridCol', 'OBJECT.LABEL',
#          'Area', 'IntegratedIntensity', ...]

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