Tutorial 7: Measuring and Exporting

Detection tells you where colonies are. Measurement tells you what they are — how big, how round, how bright. In this tutorial you will add measurements to a pipeline, extract a DataFrame of colony features, and export the results for downstream analysis.

What you will learn:

1. Add measurement operations to a pipeline

2. Use pipeline.apply_and_measure() to get a DataFrame

3. Understand the output columns

4. Export to CSV and Parquet

Imports

import phenotypic as pht
from phenotypic.data import load_yeast_plate
from phenotypic.enhance import GaussianBlur, EnhanceLocalContrast
from phenotypic.detect import OtsuDetector
from phenotypic.measure import MeasureSize, MeasureShape, MeasureIntensity

Build a Pipeline with Measurements

The meas parameter accepts a list of measurement operations. Each one extracts a different set of features from the detected colonies.

plate = load_yeast_plate()

pipeline = pht.ImagePipeline(
    ops=[GaussianBlur(sigma=2.0), EnhanceLocalContrast(clip_limit=0.01), OtsuDetector()],
    meas=[MeasureSize(), MeasureShape(), MeasureIntensity()],
)

Apply and Measure

.apply_and_measure() runs the full pipeline (enhance → detect → measure) and returns a pandas DataFrame with one row per detected colony.

df = pipeline.apply_and_measure(plate)
print(f"Measured {len(df)} colonies across {df.shape[1]} features")
df.head()

Measured 7 colonies across 50 features

	Metadata_FileSuffix	Metadata_BitDepth	Metadata_ImageType	Metadata_ImageName	Object_Label	Bbox_CenterRR	Bbox_CenterCC	Bbox_MinRR	Bbox_MinCC	Bbox_MaxRR	...	Intensity_MaximumIntensity	Intensity_MeanIntensity	Intensity_MedianIntensity	Intensity_StandardDeviationIntensity	Intensity_CoefficientVarianceIntensity	Intensity_LowerQuartileIntensity	Intensity_UpperQuartileIntensity	Intensity_InterquartileRangeIntensity	Intensity_Density	Intensity_ConvexDensity
0	.png	8	GridImage	RhodotorulaYeastCenterCrop	1	189.076930	190.595854	102	107	278	...	0.686547	0.584217	0.594960	0.044504	0.076180	0.560265	0.616306	0.056040	0.584217	24.665643
1	.png	8	GridImage	RhodotorulaYeastCenterCrop	2	180.905600	1003.162298	107	930	258	...	0.695336	0.589678	0.600256	0.046125	0.078225	0.576309	0.618405	0.042096	0.589678	21.535227
2	.png	8	GridImage	RhodotorulaYeastCenterCrop	3	179.499034	1409.025799	108	1339	252	...	0.672282	0.574344	0.582071	0.038987	0.067886	0.559134	0.599665	0.040532	0.574344	20.536267
3	.png	8	GridImage	RhodotorulaYeastCenterCrop	4	184.490509	599.195288	118	535	250	...	0.659846	0.564370	0.574488	0.042502	0.075314	0.552342	0.590811	0.038469	0.564370	18.341143
4	.png	8	GridImage	RhodotorulaYeastCenterCrop	5	587.079492	1410.152127	510	1337	665	...	0.704480	0.610154	0.621897	0.045821	0.075102	0.594126	0.639978	0.045853	0.610154	23.005908

5 rows × 50 columns

Explore the Columns

Each measurement operation contributes its own set of columns. Let’s see what we got.

print("All columns:")
for col in df.columns:
    print(f"  {col}")

All columns:
  Metadata_FileSuffix
  Metadata_BitDepth
  Metadata_ImageType
  Metadata_ImageName
  Object_Label
  Bbox_CenterRR
  Bbox_CenterCC
  Bbox_MinRR
  Bbox_MinCC
  Bbox_MaxRR
  Bbox_MaxCC
  Bbox_IntensityWeightedCenterRR
  Bbox_IntensityWeightedCenterCC
  Bbox_DistWeightedCenterRR
  Bbox_DistWeightedCenterCC
  Grid_RowNum
  Grid_ColNum
  Grid_RowMajorIdx
  Grid_ColMajorIdx
  Size_Area
  Size_IntegratedIntensity
  Shape_Area
  Shape_Perimeter
  Shape_Circularity
  Shape_ConvexArea
  Shape_MedianRadius
  Shape_MeanRadius
  Shape_MaxRadius
  Shape_MinFeretDiameter
  Shape_MaxFeretDiameter
  Shape_Eccentricity
  Shape_Solidity
  Shape_Extent
  Shape_BboxArea
  Shape_MajorAxisLength
  Shape_MinorAxisLength
  Shape_Compactness
  Shape_Orientation
  Intensity_IntegratedIntensity
  Intensity_MinimumIntensity
  Intensity_MaximumIntensity
  Intensity_MeanIntensity
  Intensity_MedianIntensity
  Intensity_StandardDeviationIntensity
  Intensity_CoefficientVarianceIntensity
  Intensity_LowerQuartileIntensity
  Intensity_UpperQuartileIntensity
  Intensity_InterquartileRangeIntensity
  Intensity_Density
  Intensity_ConvexDensity

Here are the highlights from each measurement:

MeasureSize:

• Size_Area — colony size in pixels

• Size_IntegratedIntensity — sum of grayscale pixel values

MeasureShape:

• Shape_Circularity — how round the colony is (1.0 = perfect circle)

• Shape_Solidity — ratio of colony area to convex hull area

• Shape_Eccentricity — elongation (0 = circular, approaching 1 = elongated)

• Shape_MajorAxisLength / Shape_MinorAxisLength — fitted ellipse axes

MeasureIntensity:

• Intensity_MeanIntensity / Intensity_MedianIntensity — average colony brightness

• Intensity_StandardDeviationIntensity — variation within the colony

• Intensity_MinimumIntensity / Intensity_MaximumIntensity — intensity extremes

Quick Statistics

Since the result is a standard pandas DataFrame, you can use all the usual pandas methods to explore it.

df[["Size_Area", "Shape_Circularity", "Intensity_MeanIntensity"]].describe()

	Size_Area	Shape_Circularity	Intensity_MeanIntensity
count	7.000000	7.000000	7.000000
mean	16869.857143	0.894006	0.582158
std	3076.517210	0.010850	0.015325
min	13329.000000	0.879643	0.564370
25%	14984.500000	0.885698	0.571459
50%	17055.000000	0.897971	0.583767
75%	17533.000000	0.900684	0.586948
max	22670.000000	0.907665	0.610154

Export to CSV

For sharing with collaborators or importing into spreadsheet software, export to CSV.

df.to_csv("colony_measurements.csv")
print("Saved to colony_measurements.csv")

Saved to colony_measurements.csv

Export to Parquet

For large datasets, Parquet is more efficient — it is compressed, preserves column types, and loads much faster than CSV.

df.to_parquet("colony_measurements.parquet")
print("Saved to colony_measurements.parquet")

Saved to colony_measurements.parquet

Clean Up

import os
os.remove("colony_measurements.csv")
os.remove("colony_measurements.parquet")

Summary

You have extracted colony features and exported them for analysis:

• ``meas=[MeasureSize(), MeasureShape(), MeasureIntensity()]`` — add measurements to a pipeline

• ``pipeline.apply_and_measure(plate)`` — run the full pipeline and get a DataFrame

• ``.to_csv()`` / ``.to_parquet()`` — export for downstream tools

The result is a standard pandas DataFrame, so you can filter, group, plot, and analyze it with any tool in the Python ecosystem.

Next up: Tutorial 8: Using Prefab Pipelines — discover PhenoTypic’s pre-built pipelines for common organisms and plate types.