phenotypic.prefab.HeavyOtsuPipeline#

class phenotypic.prefab.HeavyOtsuPipeline(gaussian_sigma: int = 5, gaussian_mode: str = 'reflect', gaussian_truncate: float = 4.0, otsu_ignore_zeros: bool = True, otsu_ignore_borders: bool = True, mask_opener_footprint: Literal['auto'] | ndarray | int | None = 'auto', border_remover_size: int = 1, small_object_min_size: int = 50, texture_scale: int = 5, texture_warn: bool = False, benchmark: bool = False, verbose: bool = False)[source]#

Bases: PrefabPipeline

The HeavyWatershedPipeline class is a composite image processing pipeline that combines multiple layers of preprocessing, detection, and filtering steps that can will select the right colonies in most cases. This comes at the cost of being a more computationally expensive pipeline.

Pipeline Steps:

  1. Gaussian Smoothing

  2. CLAHE

  3. Median Enhancement

  4. Watershed Segmentation

  5. Border Object Removal

  6. Grid Oversized Object Removal

  7. Minimum Residual Error Reduction

  8. Grid Alignment

  9. Repeat Watershed Segmentation

  10. Repeat Border Object Removal

  11. Repeat Minimum Residual Error Reduction

  12. Mask Fill

Measurements:

  • Shape

  • Color

  • Texture

  • Intensity

Methods

__init__

Initializes the object with a sequence of operations and measurements for image processing.

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)

  • otsu_ignore_zeros (bool)

  • otsu_ignore_borders (bool)

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

  • border_remover_size (int)

  • small_object_min_size (int)

  • texture_scale (int)

  • texture_warn (bool)

  • benchmark (bool)

  • verbose (bool)

__init__(gaussian_sigma: int = 5, gaussian_mode: str = 'reflect', gaussian_truncate: float = 4.0, otsu_ignore_zeros: bool = True, otsu_ignore_borders: bool = True, mask_opener_footprint: Literal['auto'] | ndarray | int | None = 'auto', border_remover_size: int = 1, small_object_min_size: int = 50, texture_scale: int = 5, texture_warn: bool = False, benchmark: bool = False, verbose: bool = False)[source]#

Initializes the object with a sequence of operations and measurements for image processing. The sequence includes smoothing, enhance, segmentation, border object removal, and various measurement steps for analyzing images. Customizable parameters allow for adjusting the processing pipeline for specific use cases such as image segmentation and feature extraction.

Parameters:

  • gaussian_sigma (int) – Standard deviation for Gaussian kernel in smoothing.

  • gaussian_mode (str) – Mode for handling image boundaries in Gaussian smoothing.

  • gaussian_truncate (float) – Truncate filter at this many standard deviations.

  • otsu_ignore_zeros (bool) – Whether to ignore zero pixels in Otsu thresholding.

  • otsu_ignore_borders (bool) – Whether to ignore border objects in Otsu detection.

  • mask_opener_footprint (Literal['auto'] | ~numpy.ndarray | int | None) – Structuring element for morphological opening.

  • border_remover_size (int) – Size of border to remove objects from.

  • small_object_min_size (int) – Minimum size of objects to retain.

  • texture_scale (int) – Scale parameter for Haralick texture features.

  • texture_warn (bool) – Whether to warn on texture computation errors.

  • footprint – Deprecated, use mask_opener_footprint.

  • min_size – Deprecated, use small_object_min_size.

  • border_size – Deprecated, use border_remover_size.

  • benchmark (bool)

  • verbose (bool)

__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.