phenotypic.abc_.PrefabPipeline#

Bases: ImagePipeline

Marker class for pre-built, validated image processing pipelines from the PhenoTypic team.

PrefabPipeline is a specialized subclass of ImagePipeline that distinguishes “official” pre-built pipelines maintained by the PhenoTypic development team from user-created custom pipelines. It serves as a marker class (no additional functionality) that signals “this pipeline is validated, documented, and recommended for specific use cases in microbe colony phenotyping.”

What is PrefabPipeline?

PrefabPipeline is NOT an operation ABC and does NOT inherit from BaseOperation. Instead, it’s a subclass of ImagePipeline that:

Is a marker class: Inherits all ImagePipeline functionality unchanged; no new methods.
Indicates official status: Subclasses of PrefabPipeline are pre-built, validated pipelines with documented performance, parameter settings, and recommended use cases.
Enables classification: Code can distinguish official pipelines (isinstance(obj, PrefabPipeline)) from user-defined pipelines for documentation, discovery, or defaulting.
Provides templates: Each PrefabPipeline subclass is a complete processing workflow (enhancement, detection, refinement, measurement) ready to use out-of-the-box.

Quick Decision Guide: PrefabPipeline vs Custom ImagePipeline

Use PrefabPipeline for standard colony phenotyping on agar plates with validated workflows
Use custom ImagePipeline for novel imaging scenarios, optimization experiments, or specialized workflows
Start with PrefabPipeline to understand the pipeline structure, then customize via parameter tuning
Clone and modify a PrefabPipeline subclass when you need significant algorithm changes
Combine multiple PrefabPipelines sequentially for complex multi-stage workflows

Available PrefabPipeline Subclasses

The PhenoTypic team maintains several pre-built pipelines optimized for different imaging scenarios:

[HeavyOtsuPipeline](src/phenotypic/prefab/_heavy_otsu_pipeline.py): Multi-layer Otsu detection with aggressive refinement. Robust detection on challenging images (uneven lighting, varied sizes). Computationally expensive.
[HeavyWatershedPipeline](src/phenotypic/prefab/_heavy_watershed_pipeline.py): Watershed segmentation for separated colonies. Handles closely-spaced or merged colonies. Very expensive; for small batches or deep analysis.
[RoundPeaksPipeline](src/phenotypic/prefab/_round_peaks_pipeline.py): Peak detection for circular, well-separated colonies. Fast and suitable for high-throughput screening of early-time-point growth.
[GridSectionPipeline](src/phenotypic/prefab/_grid_section_pipeline.py): Per-well section extraction and fine-grained analysis. Moderate cost; enables per-well quality control and segmentation.
[FilamentousFungiPipeline](src/phenotypic/prefab/_filamentous_fungi_pipeline.py): Two-stage filamentous fungi detection with optional Dijkstra branch reconnection. For irregular spreading colonies.

When to use PrefabPipeline vs Custom ImagePipeline

Use PrefabPipeline if: - You’re analyzing colony growth on agar plates (the intended use case). - You want an immediately usable, tested workflow without configuration. - You want to reproduce results matching published benchmarks or team documentation. - You need a baseline for custom extensions (subclass or copy and modify).
Create a custom ImagePipeline if: - Your imaging scenario is novel (unusual plate format, different organisms, special preparation). - You want to experiment with different detector/refiner/measurement combinations. - You have labeled ground truth and want to optimize parameters for your specific images. - You need pipeline extensions (custom operations not in standard library).

Using a PrefabPipeline

PrefabPipeline subclasses are used exactly like ImagePipeline:

from phenotypic import Image, GridImage
from phenotypic.prefab import HeavyOtsuPipeline

# Load image(s)
image = GridImage.imread('plate.jpg', nrows=8, ncols=12)

# Instantiate and apply pipeline
pipeline = HeavyOtsuPipeline()
result = pipeline.apply(image)  # or .operate([image])

# Access results
colonies = result.objects
measurements = result.measurements
print(f"Detected: {len(colonies)} colonies")
print(f"Measurements shape: {measurements.shape}")

Customizing a PrefabPipeline

PrefabPipelines accept tunable parameters in __init__() to adapt to your images without rebuilding the pipeline structure:

from phenotypic.prefab import HeavyOtsuPipeline

# Use defaults (recommended for most cases)
pipeline1 = HeavyOtsuPipeline()

# Tune for noisier images
pipeline2 = HeavyOtsuPipeline(
    gaussian_sigma=7,                    # Stronger blur
    small_object_min_size=150,           # More aggressive noise removal
    border_remover_size=2                # Remove more edge objects
)

# Parameters are typically named after the algorithm or parameter they control.
# See pipeline docstring for available parameters and typical values.

When Parameters Fail: Creating a Custom Pipeline

If PrefabPipeline parameter tuning doesn’t solve your problem:

Analyze failures: Which step fails (detection, refinement, measurement)?

Use pipeline.benchmark=True, verbose=True to trace execution.

Visually inspect intermediate results (detection masks, refined masks).

Create a custom pipeline:

from phenotypic import ImagePipeline
from phenotypic.enhance import GaussianBlur, CLAHE
from phenotypic.detect import CannyDetector  # Different detector
from phenotypic.refine import SmallObjectRemover, MaskFill
from phenotypic.measure import MeasureShape, MeasureColor

# Custom pipeline for your specific use case
custom = ImagePipeline()
custom.add(GaussianBlur(sigma=3))
custom.add(CLAHE())
custom.add(CannyDetector(sigma=1.5, low_threshold=0.1, high_threshold=0.4))
custom.add(SmallObjectRemover(min_size=100))
custom.add(MaskFill())
custom.add(MeasureShape())
custom.add(MeasureColor())

# Test and iterate
result = custom.operate([image])

Share successful custom pipelines: If you develop a successful custom pipeline for a new imaging scenario, consider contributing it as a PrefabPipeline subclass to the project.

Pipeline Composition Pattern

Combine multiple PrefabPipelinesor mix PrefabPipeline with custom operations:

from phenotypic import ImagePipeline
from phenotypic.prefab import HeavyOtsuPipeline, RoundPeaksPipeline
from phenotypic.refine import SmallObjectRemover
from phenotypic.measure import MeasureIntensity

# Combine different detection strategies with shared refinement
pipeline = ImagePipeline()
pipeline.add(HeavyOtsuPipeline())  # First detection attempt
pipeline.add(SmallObjectRemover(min_size=100))  # Noise removal
pipeline.add(MeasureIntensity())  # Measurement

# Apply to image
result = pipeline.apply(image)

Pipeline Serialization Pattern

Save and load pipelines for reproducible batch processing:

from phenotypic.prefab import HeavyOtsuPipeline

# Create, configure, and save
pipeline = HeavyOtsuPipeline(gaussian_sigma=2.0, small_object_min_size=150)
pipeline.to_json('my_colony_pipeline.json')  # Save configuration
# pipeline.to_yaml('my_colony_pipeline.yaml')  # Alternative format

# Load for batch processing (reproducible results)
loaded = HeavyOtsuPipeline.from_json('my_colony_pipeline.json')
results = loaded.operate([image1, image2, image3])

Extending PrefabPipeline

To create a new official PrefabPipeline subclass:

from phenotypic.abc_ import PrefabPipeline
from phenotypic.enhance import GaussianBlur, CLAHE
from phenotypic.detect import OtsuDetector
from phenotypic.refine import SmallObjectRemover
from phenotypic.measure import MeasureShape

class MyCustomPrefabPipeline(PrefabPipeline):
    '''Brief description of when to use this pipeline.'''

    def __init__(self, param1: int = 100, param2: float = 1.5,
                 benchmark: bool = False, verbose: bool = False):
        '''Initialize with tunable parameters.'''
        pipe_cfgs = [
            GaussianBlur(sigma=param2),
            CLAHE(),
            OtsuDetector(),
            SmallObjectRemover(min_size=param1),
        ]
        meas = [MeasureShape()]
        super().__init__(pipe_cfgs=pipe_cfgs, meas=meas, benchmark=benchmark,
                       verbose=verbose)

Notes

Is a marker, not an operation: PrefabPipeline does not inherit from BaseOperation. It’s a convenient subclass of ImagePipeline for classification and discovery.
Inheritance of ImagePipeline features: PrefabPipeline inherits all ImagePipeline functionality: sequential operation chaining, benchmarking, verbose logging, batch processing via .operate(), and serialization via .to_yaml() / .from_yaml().
Parameter tuning via __init__(): Most PrefabPipeline subclasses expose key algorithm parameters in __init__() (e.g., detection threshold, smoothing sigma, refinement shape). Adjust these for your specific images before scaling to large batches.
Benchmarking for profiling: Set benchmark=True when instantiating to track execution time and memory usage per operation. Useful for identifying bottlenecks in large batch runs.
Documentation and examples: Each PrefabPipeline subclass is documented with use cases, typical parameters, performance characteristics, and example code. Check the subclass docstring for guidance.
Not for operations: Use PrefabPipeline only for complete pipelines. For individual operations (detection, enhancement, measurement), use operation ABCs directly.

Examples

Quick start: Detect colonies with HeavyOtsuPipeline:

>>> from phenotypic import GridImage
>>> from phenotypic.prefab import HeavyOtsuPipeline
>>> # Load a 96-well plate image
>>> image = GridImage.imread('agar_plate.jpg', nrows=8, ncols=12)
>>> # Use the pre-built, validated pipeline
>>> pipeline = HeavyOtsuPipeline()
>>> result = pipeline.apply(image)
>>> # Access results
>>> print(f"Detected {len(result.objects)} colonies")
>>> print(f"Measurements: {result.measurements.columns.tolist()}")

Batch processing multiple plates with a PrefabPipeline:

>>> from phenotypic import GridImage
>>> from phenotypic.prefab import HeavyOtsuPipeline
>>> import glob
>>> # Load multiple plate images
>>> image_paths = glob.glob('batch_*.jpg')
>>> images = [GridImage.imread(p, nrows=8, ncols=12)
...           for p in image_paths]
>>> # Create pipeline (reusable for all images)
>>> pipeline = HeavyOtsuPipeline(benchmark=True)
>>> # Batch process
>>> results = pipeline.operate(images)
>>> # Collect results
>>> for i, result in enumerate(results):
...     print(f"Image {i}: {len(result.objects)} colonies")
...     print(f"Measurements shape: {result.measurements.shape}")

Customizing pipeline parameters for difficult images:

>>> from phenotypic import GridImage
>>> from phenotypic.prefab import HeavyOtsuPipeline
>>> image = GridImage.imread('noisy_plate.jpg', nrows=8, ncols=12)
>>> # Increase smoothing and noise removal for difficult images
>>> pipeline = HeavyOtsuPipeline(
...     gaussian_sigma=8,                      # Stronger blur
...     small_object_min_size=200,             # Aggressive noise removal
...     border_remover_size=2                  # More border filtering
... )
>>> result = pipeline.apply(image)
>>> print(f"Robust detection: {len(result.objects)} colonies")

Comparing PrefabPipeline vs custom pipeline:

>>> from phenotypic import GridImage, ImagePipeline
>>> from phenotypic.prefab import HeavyOtsuPipeline
>>> from phenotypic.detect import CannyDetector
>>> from phenotypic.refine import SmallObjectRemover
>>> image = GridImage.imread('plate.jpg', nrows=8, ncols=12)
>>> # Option 1: Use pre-built validated pipeline
>>> prefab = HeavyOtsuPipeline()
>>> result1 = prefab.apply(image)
>>> # Option 2: Create custom pipeline for comparison
>>> custom = ImagePipeline()
>>> from phenotypic.enhance import GaussianBlur
>>> custom.add(GaussianBlur(sigma=2))
>>> custom.add(CannyDetector(sigma=1.5, low_threshold=0.1, high_threshold=0.4))
>>> custom.add(SmallObjectRemover(min_size=100))
>>> result2 = custom.apply(image)
>>> # Compare results
>>> print(f"Prefab: {len(result1.objects)}, Custom: {len(result2.objects)}")

Methods

`__init__`	This class represents a processing and measurement interface for Image operations and feature extraction.
`apply`	The class provides an interface to process and apply a series of operations on an Image.
`apply_and_measure`	Applies processing to the given image and measures the results.
`apply_napari`	Apply the pipeline and progressively add layers to a napari viewer.
`apply_with_intermediates`	Apply the pipeline and capture a snapshot of the image after each operation.
`benchmark_results`	Return execution times and memory usage for operations and measurements.
`from_json`	Deserialize a PrefabPipeline from JSON.
`get_meas`	Get a copy of the measurements dictionary.
`get_ops`	Get a copy of the operations dictionary.
`measure`	Measures properties of a given image and optionally includes metadata.
`set_meas`	Sets the measurements to be used for further computation.
`set_ops`	Sets the operations to be performed.
`set_post`	Set the post-measurement transforms.
`to_json`	Serialize the pipeline configuration to JSON format.
`widget`	Return (and optionally display) the root widget.

Attributes

desc

Get pipeline description.

Parameters:

ops (List[ImageOperation | ImagePipeline] | Dict[str, ImageOperation | ImagePipeline] | None)
meas (List[MeasureFeatures] | Dict[str, MeasureFeatures] | None)
post (List[PostMeasurement] | Dict[str, PostMeasurement] | None)
benchmark (bool)
verbose (bool)
name (Optional[str])
desc (Optional[str])
reset (bool)

classmethod from_json(json_data: str | Path | dict, benchmark: bool = False, verbose: bool = False) → PrefabPipeline[source]

Deserialize a PrefabPipeline from JSON.

PrefabPipeline subclasses build their ops/meas inside __init__, so the base from_json (which passes ops= directly) would conflict. This override deserializes via ImagePipeline and re-tags the instance as the correct PrefabPipeline subclass.

Parameters:

json_data (str | Path | dict) – A JSON string, path to a JSON file, or a pre-parsed dict.
benchmark (bool) – Whether to enable benchmarking for the pipeline.
verbose (bool) – Whether to enable verbose output.

Returns:

A PrefabPipeline (or subclass) instance with the loaded configuration.

Return type:

PrefabPipeline

__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.

This class represents a processing and measurement interface for Image operations and feature extraction. It initializes operational and measurement queues based on the provided dictionaries.

Parameters:

ops (List[ImageOperation | ImagePipeline] | Dict[str, ImageOperation | ImagePipeline] | None) – A list or dictionary of ImageOperation or ImagePipeline objects. If a list, class names are used as keys. If a dictionary, keys are operation names (strings) and values are ImageOperation or ImagePipeline objects responsible for performing specific Image processing tasks.
meas (List[MeasureFeatures] | Dict[str, MeasureFeatures] | None) – An optional dictionary where the keys are feature names (strings) and the values are FeatureExtractor objects responsible for extracting specific features.
benchmark (bool) – A flag indicating whether to track execution times for operations and measurements. Defaults to False.
verbose (bool) – A flag indicating whether to print progress information when benchmark mode is on. Defaults to False.
name (Optional[str]) – An optional string identifier for this pipeline. If not provided, a randomly generated UUID4 string will be assigned automatically.
desc (Optional[str]) – An optional description for this pipeline. If not provided, the class docstring will be used when accessing the desc property.
reset (bool) – Default reset behavior for the apply() method. When True, the image will be reset before applying operations. Can be overridden per-call in apply() and apply_and_measure(). Defaults to False.
post (List[PostMeasurement] | Dict[str, PostMeasurement] | None)

__str__() → str

Return a JSON-formatted string representation of the pipeline.

The generated JSON string provides a structured representation of the object’s current state, including its operations and measurements. This output can be used for logging, debugging, or to recreate the object’s configuration in another context.

Returns:: A JSON-formatted string that encodes the object’s current configuration in a human-readable manner. This includes the phenotypic version, pipeline name, description, and the lists of operations and measurements.
Return type:: str

apply(image: Image, inplace: bool = False, reset: bool | None = None) → GridImage | Image

The class provides an interface to process and apply a series of operations on an Image. The operations are maintained in a queue and executed sequentially when applied to the given Image.

Parameters:

image (Image) – The arr Image to be processed. The type Image refers to an instance of the Image object to which transformations are applied.
inplace (bool, optional) – A flag indicating whether to apply the transformations directly on the provided Image (True) or create a copy of the Image before performing transformations (False). Defaults to False.
reset (bool, optional) – Whether to reset the image before applying the pipeline. If None (default), uses the pipeline’s reset setting from __init__. If explicitly set to True or False, overrides the pipeline setting.

Return type:

Union[GridImage, Image]

apply_and_measure(image: Image, inplace: bool = False, reset: bool | None = None, include_metadata: bool = True) → pd.DataFrame

Applies processing to the given image and measures the results.

This function first applies a processing method to the supplied image, adjusting it based on the given parameters. After processing, the resulting image is measured, and a DataFrame containing the measurement data is returned.

Parameters:

image (Image) – The image to process and measure.
inplace (bool) – Whether to modify the original image directly or work on a copy. Default is False.
reset (bool, optional) – Whether to reset any previous processing on the image before applying the current method. If None (default), uses the pipeline’s reset setting. If explicitly set, overrides the pipeline setting.
include_metadata (bool) – Whether to include metadata in the measurement results. Default is True.

Returns:

A DataFrame containing measurement data for the processed image.

Return type:

pd.DataFrame

apply_napari(image: Image, inplace: bool = False, reset: bool | None = None, viewer: napari.Viewer | None = None) → NapariPipelineResult

Apply the pipeline and progressively add layers to a napari viewer.

Creates (or reuses) a napari viewer and adds the original image layers as a baseline, then adds the modified layer after each operation completes. Layer names follow the pattern {step:02d}_{OperationName}_{accessor}.

Parameters:

image (Image) – The input image to process.
inplace (bool) – If True the image is modified in place; otherwise a copy is made first. Defaults to False.
reset (bool | None) – Whether to reset the image before applying operations. None (default) uses the pipeline-level setting.
viewer (napari.Viewer | None) – An existing napari viewer to add layers to. If None (default), a new viewer is created.

Returns:

Named tuple with the final image and the napari viewer reference.

Return type:

NapariPipelineResult

Raises:

ImportError – If napari is not installed.

apply_with_intermediates(image: Image, inplace: bool = False, reset: bool | None = None, output_dir: str | Path | None = None) → IntermediateResult

Apply the pipeline and capture a snapshot of the image after each operation.

Behaves identically to apply() (respecting inplace, reset, benchmark timing, and verbose/tqdm progress) but additionally records the image state after every operation completes.

Parameters:

image (Image) – The input image to process.
inplace (bool) – If True the image is modified in place; otherwise a copy is made first. Defaults to False.
reset (Optional[bool]) – Whether to reset the image before applying operations. None (default) uses the pipeline-level setting.
output_dir (Optional[Union[str, Path]]) – Optional directory path. When provided, each intermediate image is persisted to an HDF5 file inside this directory (created automatically) and the corresponding dict value is set to None to conserve memory. When None, intermediates are kept in memory as Image copies.

Returns:

A named tuple containing the final image and a dictionary mapping operation names to intermediate snapshots (or None when output_dir is used).

Return type:

IntermediateResult

benchmark_results() → pandas.DataFrame

Return execution times and memory usage for operations and measurements.

This method should be called after applying the pipeline on an image to get the execution times and memory consumption of the different processes.

When an operation is itself an ImagePipelineCore (nested pipeline), its sub-operations are expanded as indented sub-rows beneath the parent entry with names like "ParentOp > ChildOp".

Returns:

A DataFrame with columns Process Type,: Process Name, Execution Time (s), Memory Delta (MB), and RSS After (MB).

Return type:

pd.DataFrame

property desc: str: Get pipeline description. Returns class docstring if no description set.

get_meas() → Dict[str, MeasureFeatures]

Get a copy of the measurements dictionary.

Returns a shallow copy to prevent accidental mutation of internal state.

Returns:

Dictionary mapping measurement names to: MeasureFeatures instances.

Return type:

Dict[str, MeasureFeatures]

get_ops() → Dict[str, ImageOperation]

Get a copy of the operations dictionary.

Returns a shallow copy to prevent accidental mutation of internal state.

Returns:

Dictionary mapping operation names to: ImageOperation instances.

Return type:

Dict[str, ImageOperation]

measure(image: Image, include_metadata=True) → pd.DataFrame

Measures properties of a given image and optionally includes metadata. The method performs measurements using a set of predefined measurement operations. If benchmarking is enabled, the execution time of each measurement is recorded. When verbose mode is active, detailed logging of the measurement process is displayed. A progress bar is used to track progress if the tqdm library is available.

Parameters:

image (Image) – The image object for which measurements are performed. It must support the info method and optionally a grid or objects attribute.
include_metadata (bool, optional) – Indicates whether metadata should be included in the measurements. Defaults to True.

Returns:

A DataFrame containing the results of all performed measurements combined: on the same index.

Return type:

pd.DataFrame

Raises:

Exception – An exception is raised if a measurement operation fails while being applied to the image.

set_meas(measurements: List[MeasureFeatures] | Dict[str, MeasureFeatures])

Sets the measurements to be used for further computation. The input can be either a list of MeasureFeatures objects or a dictionary with string keys and MeasureFeatures objects as values.

The method processes the given input to construct a dictionary mapping measurement names to MeasureFeatures instances. If a list is passed, unique class names of the MeasureFeatures instances in the list are used as keys.

Parameters:: measurements (List[MeasureFeatures] | Dict[str, MeasureFeatures]) – A collection of measurement features either as a list of MeasureFeatures objects, where class names are used as keys for dictionary creation, or as a dictionary where keys are predefined strings and values are MeasureFeatures objects.
Raises:: TypeError – If the measurements argument is neither a list nor a dictionary.

set_ops(ops: List[ImageOperation | ImagePipeline] | Dict[str, ImageOperation | ImagePipeline])

Sets the operations to be performed. The operations can be passed as either a list of ImageOperation or ImagePipeline instances or a dictionary mapping operation names to ImageOperation or ImagePipeline instances. This method ensures that each operation in the list has a unique name. Raises a TypeError if the input is neither a list nor a dictionary.

Parameters:: ops (List[ImageOperation | ImagePipeline] | Dict[str, ImageOperation | ImagePipeline]) – A list of ImageOperation or ImagePipeline objects, or a dictionary where keys are operation names and values are ImageOperation or ImagePipeline objects.
Raises:: TypeError – If the input is not a list or a dictionary.

set_post(post: List[PostMeasurement] | Dict[str, PostMeasurement])

Set the post-measurement transforms.

Parameters:: post (List[PostMeasurement] | Dict[str, PostMeasurement]) – A list or dictionary of PostMeasurement objects. If a list, class names are used as keys.
Raises:: TypeError – If post is neither a list nor a dictionary.

to_json(filepath: str | Path | None = None) → str

Serialize the pipeline configuration to JSON format.

This method captures the pipeline’s operations and measurements. It excludes internal state (attributes starting with ‘_’) and pandas DataFrames to keep the serialization clean and focused on reproducible configuration.

Parameters:: filepath (str | Path | None) – Optional path to save the JSON. If None, returns JSON string. Can be a string or Path object.
Returns:: JSON string representation of the pipeline configuration.
Return type:: str

Example

Serialize a pipeline to JSON format:

>>> from phenotypic import ImagePipeline
>>> from phenotypic.detect import OtsuDetector
>>> from phenotypic.measure import MeasureShape
>>> pipe = ImagePipeline(pipe_cfgs=[OtsuDetector()], meas=[MeasureShape()])
>>> json_str = pipe.to_json()
>>> pipe.to_json('my_pipeline.json')  # Save to file

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.

phenotypic.abc_.PrefabPipeline#

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