phenotypic.prefab.HeavyWatershedPipeline

class phenotypic.prefab.HeavyWatershedPipeline(gaussian_sigma: int = 5, gaussian_mode: str = 'reflect', gaussian_truncate: float = 4.0, watershed_footprint: Literal['auto'] | ndarray | int | None = None, watershed_min_size: int = 50, watershed_compactness: float = 0.001, watershed_connectivity: int = 1, watershed_relabel: bool = True, watershed_ignore_zeros: bool = True, border_remover_size: int = 25, circularity_cutoff: float = 0.5, texture_scale: int = 5, texture_warn: bool = False, benchmark: bool = False, **kwargs)

Bases: PrefabPipeline

Provides an image processing pipeline with robust preprocessing/post-processing and watershed segmentation.

This class defines a sequence of operations and measurements designed for image analysis. It includes smoothing, enhancement, segmentation, border object removal, and various analysis steps. The pipeline is highly customizable for tasks such as image segmentation and feature extraction, making it suitable for applications involving image quantification and preprocessing.

Note

This pipeline uses computationally intensive operations aimed at cases where there is heavy background noise

Methods

__init__	Initializes an image processing pipeline for various image analysis tasks such as object detection, segmentation, and measurement.
apply	The class provides an abc_ to process and apply a series of operations on an Image.
apply_and_measure	Applies processing to the given image and measures the results.
benchmark_results	Returns a table of execution times for operations and measurements.
dispose_widgets	Drop references to the UI widgets.
from_json	Deserialize a pipeline from JSON format.
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.
sync_widgets_from_state	Push internal state into widgets.
to_json	Serialize the pipeline configuration to JSON format.
widget	Return (and optionally display) the root widget.

Parameters:

• gaussian_sigma (int)

• gaussian_mode (str)

• gaussian_truncate (float)

• watershed_footprint (Literal['auto'] | ~numpy.ndarray | int | None)

• watershed_min_size (int)

• watershed_compactness (float)

• watershed_connectivity (int)

• watershed_relabel (bool)

• watershed_ignore_zeros (bool)

• border_remover_size (int)

• circularity_cutoff (float)

• texture_scale (int)

• texture_warn (bool)

• benchmark (bool)

__init__(gaussian_sigma: int = 5, gaussian_mode: str = 'reflect', gaussian_truncate: float = 4.0, watershed_footprint: Literal['auto'] | ndarray | int | None = None, watershed_min_size: int = 50, watershed_compactness: float = 0.001, watershed_connectivity: int = 1, watershed_relabel: bool = True, watershed_ignore_zeros: bool = True, border_remover_size: int = 25, circularity_cutoff: float = 0.5, texture_scale: int = 5, texture_warn: bool = False, benchmark: bool = False, **kwargs)

Initializes an image processing pipeline for various image analysis tasks such as object detection, segmentation, and measurement. This pipeline uses a combination of operations, including filtering, segmentation, and morphological processing, followed by shape, intensity, texture, and color measurements.

Parameters:

• gaussian_sigma (int, optional) – Standard deviation for Gaussian blur filter. Defaults to 5.

• gaussian_mode (str, optional) – Mode parameter for Gaussian blur filter (e.g., ‘reflect’). Defaults to ‘reflect’.

• gaussian_truncate (float, optional) – Truncate value for Gaussian kernel to limit its size. Defaults to 4.0.

• watershed_footprint (Literal['auto'] | np.ndarray | int | None, optional) – Footprint size or structure for the watershed algorithm. Defaults to None.

• watershed_min_size (int, optional) – Minimum size of the objects to be retained after watershed segmentation. Defaults to 50.

• watershed_compactness (float, optional) – Compactness parameter for the watershed algorithm to control how tightly regions are formed. Defaults to 0.001.

• watershed_connectivity (int, optional) – Connectivity parameter for region connectivity in watershed segmentation. Defaults to 1.

• watershed_relabel (bool, optional) – Whether to relabel the regions after watershed segmentation. Defaults to True.

• watershed_ignore_zeros (bool, optional) – Whether to ignore zero-valued regions in the watershed algorithm. Defaults to True.

• border_remover_size (int, optional) – Size of the border in pixels to be removed during border object removal. Defaults to 25.

• circularity_cutoff (float, optional) – Threshold for object circularity below which objects will be removed. Defaults to 0.5.

• texture_scale (int, optional) – Scale parameter for texture measurement. Defaults to 5.

• texture_warn (bool, optional) – Whether to issue warnings for invalid texture measurements. Defaults to False.

• benchmark (bool, optional) – Whether to enable benchmarking of pipeline performance. Defaults to False.

• **kwargs – Additional keyword arguments for parent class initialization.

__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: Image, inplace: bool = False, reset: bool = True) → GridImage | Image

The class provides an abc_ 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) – Whether to reset the image before applying the pipeline

Return type:

Union[GridImage, Image]

apply_and_measure(image: Image, inplace: bool = False, reset: bool = True, 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) – Whether to reset any previous processing on the image before applying the current method. Default is True.

• 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

benchmark_results() → pandas.DataFrame

Returns a table of execution times for operations and measurements.

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

Returns:

A DataFrame containing execution times for each operation and measurement.

Return type:

pd.DataFrame

dispose_widgets() → None

Drop references to the UI widgets.

Return type:

None

classmethod from_json(json_data: str | Path) → SerializablePipeline

Deserialize a pipeline from JSON format.

This method reconstructs a pipeline from a JSON string or file, restoring all operations, measurements, and configuration flags. Classes are imported from the phenotypic namespace and instantiated with their saved parameters.

Parameters:

json_data (str | Path) – Either a JSON string or a path to a JSON file.

Returns:

A new pipeline instance with the loaded configuration.

Return type:

SerializablePipeline

Raises:

• ValueError – If the JSON is invalid or cannot be parsed.

• ImportError – If a required operation or measurement class cannot be imported.

• AttributeError – If a class cannot be found in the phenotypic namespace.

Example

Deserialize a pipeline from JSON format

>>> from phenotypic import ImagePipeline
>>>
>>> # Load from file
>>> pipe = ImagePipeline.from_json('my_pipeline.json')
>>>
>>> # Load from string
>>> json_str = '{"ops": {...}, "meas": {...}}'
>>> pipe = ImagePipeline.from_json(json_str)

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] | Dict[str, ImageOperation])

Sets the operations to be performed. The operations can be passed as either a list of ImageOperation instances or a dictionary mapping operation names to ImageOperation 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] | Dict[str, ImageOperation]) – A list of ImageOperation objects or a dictionary where keys are operation names and values are ImageOperation objects.

Raises:

TypeError – If the input is not a list or a dictionary.

sync_widgets_from_state() → None

Push internal state into widgets.

Return type:

None

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

Serialize the pipeline configuration to JSON format.

This method captures the pipeline’s operations, measurements, and configuration flags. 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(ops=[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.