phenotypic.prefab#

Prefab pipelines for fungal colony plate processing.

Ready-to-run chains of enhancement, detection, refinement, and measurement steps tuned for common agar plate scenarios. Includes watershed-heavy pipelines for clustered colonies, Otsu-based pipelines for clean backgrounds, grid-section pipelines for tiled inputs, and grid-aware Gitter-style processing for dense arrays.

Classes

`HeavyWatershedPipeline`	Detect and measure colonies using watershed segmentation for touching colonies.
`HeavyOtsuPipeline`	Detect and measure colonies using multi-stage Otsu thresholding with refinement.
`GridSectionPipeline`	Detect and measure colonies using per-section processing on grid plates.
`HeavyRoundPeaksPipeline`	Detect and measure round colonies using peak detection with full refinement.
`RoundPeaksPipeline`	Detect and measure round colonies using lightweight peak-based detection.
`FilamentousFungiPipeline`	Ready-to-use pipeline for filamentous fungi detection with StableDenoise denoising and spatial measurements.
`SpImagerPipeline`	A prefabricated pipeline for light image processing task for images from the S&P Robotics Imager

class phenotypic.prefab.FilamentousFungiPipeline(bm3d_block_size: int = 4, bm3d_stage_arg: Literal['all_stages', 'hard_thresholding'] = 'all_stages', homo_sigma: float = 300.0, homo_gamma_low: float = 0.5, homo_gamma_high: float = 1.5, inoculum_min_diameter: float = 30.0, inoculum_max_diameter: float = 100.0, inoculum_detector: ObjectDetector | ImagePipeline | None = None, max_colony_radius_px: float = 250.0, min_branch_width_px: int = 3, ignore_borders: bool = True, edge_noise_threshold: float = 6.0, reconnection_tolerance: float = 2.5, max_gap_length: int = 30, border_margin_px: int = 50, frag_reach_px: int = 10, gap_crossing_penalty: float = 4.0, gauss_sigma: float | None = None, pct_min_wavelength: float | None = None, coherence_window_radius: int | None = None, mad_window: int | None = None, snr_margin: int | None = None, path_dilation_radius: int | None = None, tile_size: int | None = None, tile_overlap: int | None = None, texture_scale: int = 5, texture_warn: bool = False, benchmark: bool = False, verbose: bool = False)[source]

Bases: PrefabPipeline

Ready-to-use pipeline for filamentous fungi detection with StableDenoise denoising and spatial measurements.

Pipeline Steps:

StableDenoise – Variance-stabilized BM3D denoising for Poisson-Gaussian noise removal on gray and detect_mat channels.
HomomorphicFilter – Illumination normalization via frequency-domain filtering on detect_mat.
FilamentousFungiDetector – Two-stage detection (inoculum + dual-mask reconnection) with Euclidean Voronoi partition and Dijkstra branch reconnection.

Measurements:

MeasureGridSpatial – Grid-level spatial statistics.
MeasureShape – Per-colony shape descriptors.
MeasureIntensity – Per-colony intensity statistics.
MeasureTexture – Haralick texture features.

Parameters:

bm3d_block_size (int) – BM3D patch size for denoising. Default 8.
bm3d_stage_arg (Literal['all_stages', 'hard_thresholding']) – BM3D processing mode. 'all_stages' gives best quality; 'hard_thresholding' is faster.
homo_sigma (float) – Gaussian cutoff sigma for the homomorphic filter.
homo_gamma_low (float) – Gain for low-frequency (illumination) components.
homo_gamma_high (float) – Gain for high-frequency (reflectance) components.
inoculum_min_diameter (float) – Smallest expected inoculum diameter in pixels for the default InoculumDetector. Ignored when inoculum_detector is provided. Default 30.0.
inoculum_max_diameter (float) – Largest expected inoculum diameter in pixels for the default InoculumDetector. Ignored when inoculum_detector is provided. Default 100.0.
inoculum_detector (Union[ObjectDetector, ImagePipeline, None]) – Custom ObjectDetector or ImagePipeline that identifies fungal centers/nuclei. When None, builds a default pipeline of InoculumDetector + GridSectionLargest.
max_colony_radius_px (float) – Largest colony radius (in pixels) the detector should handle. Sizes scene-derived spatial parameters for this worst case. Default 250.
min_branch_width_px (int) – Narrowest hyphal branch width (in pixels) to detect. Sizes signal-scale parameters. Default 3.
ignore_borders (bool) – If True, drops objects touching the image border.
edge_noise_threshold (float) – Noise threshold scaling factor for phase congruency edge detection.
reconnection_tolerance (float) – IQR multiplier for path quality threshold calibration (higher = more permissive).
max_gap_length (int) – Maximum acceptable length (pixels) of a suspicious cost stretch along a reconnection path.
border_margin_px (int) – Border penalty buffer width in pixels.
frag_reach_px (int) – Maximum 2D distance (pixels) from a fragment’s boundary to the nearest routable pixel; fragments more isolated than this are dropped before Dijkstra routing.
gap_crossing_penalty (float) – Distance-gap penalty strength during Dijkstra routing.
gauss_sigma (Optional[float]) – Override for SubtractGaussian sigma; None auto-derives.
pct_min_wavelength (Optional[float]) – Override for log-Gabor minimum wavelength; None auto-derives.
coherence_window_radius (Optional[int]) – Override for orientation coherence radius; None auto-derives.
mad_window (Optional[int]) – Override for local MAD window (odd); None auto-derives.
snr_margin (Optional[int]) – Override for SNR ring radius; None auto-derives.
path_dilation_radius (Optional[int]) – Override for path dilation radius; None auto-derives.
tile_size (Optional[int]) – Override for tile side length; None auto-derives.
tile_overlap (Optional[int]) – Override for tile overlap; None auto-derives.
texture_scale (int) – Scale parameter for Haralick texture features.
texture_warn (bool) – Whether to warn on texture computation errors.
benchmark (bool) – Enable per-step timing and memory benchmarks.
verbose (bool) – Enable verbose logging during pipeline execution.

See also

Tutorial 10: Detecting Filamentous Fungi for a visual walkthrough of filamentous fungi detection. The Filamentous Fungi Detection Algorithm for the theory behind the reconnection algorithm. Prefab Pipelines: Which One for Which Organism for guidance on choosing a prefab pipeline.

__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.

__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

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

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

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.

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

class phenotypic.prefab.GridSectionPipeline(gaussian_sigma: int = 10, gaussian_mode: str = 'reflect', gaussian_truncate: float = 4.0, clahe_kernel_size: int | None = None, clahe_clip_limit: float = 0.01, median_mode: str = 'nearest', median_cval: float = 0.0, otsu_ignore_zeros: bool = True, otsu_ignore_borders: bool = True, border_remover_size: int | float | None = 50, circularity_cutoff: float = 0.6, small_object_min_size: int = 100, outlier_axis: int | None = None, outlier_stddev_multiplier: float = 1.5, outlier_max_coeff_variance: int = 1, aligner_axis: int = 0, aligner_mode: str = 'edge', section_blur_sigma: int = 5, section_blur_mode: str = 'reflect', section_blur_truncate: float = 4.0, section_median_mode: str = 'nearest', section_median_cval: float = 0.0, section_contrast_lower_percentile: int = 2, section_contrast_upper_percentile: int = 98, section_otsu_ignore_zeros: bool = True, section_otsu_ignore_borders: bool = True, grid_apply_reset_enh_matrix: bool = True, small_object_min_size_2: int = 100, color_white_chroma_max: float = 4.0, color_chroma_min: float = 8.0, color_include_XYZ: bool = False, texture_scale: int | list[int] = 5, texture_quant_lvl: Literal[8, 16, 32, 64] = 32, texture_enhance: bool = False, texture_warn: bool = False, benchmark: bool = False, **kwargs)[source]

Bases: PrefabPipeline

Detect and measure colonies using per-section processing on grid plates.

Applies a sub-pipeline independently to each grid section, enabling section-specific thresholds and parameters. Useful when colony properties vary across the plate (e.g., different strains in different wells).

Steps:

GaussianBlur + CLAHE — global preprocessing
OtsuDetector — initial global detection
BorderObjectRemover, SmallObjectRemover — cleanup
GridAligner — straighten the grid
GridApply — apply per-section sub-pipeline
ReduceMultipleGridObjects — final grid refinement

Measurements: MeasureShape, MeasureColor, MeasureIntensity, MeasureTexture.

Best For:

Plates where colony properties vary significantly across wells.
Pre-tiled grid sections that need independent processing.
Experiments with different strains or conditions in each well.

Consider Also:

HeavyOtsuPipeline when uniform processing across the plate is sufficient.
RoundPeaksPipeline for a faster approach on consistent round colonies.

See also

Prefab Pipelines: Which One for Which Organism for guidance on choosing a prefab pipeline.

Parameters:

gaussian_sigma (int)
gaussian_mode (str)
gaussian_truncate (float)
clahe_kernel_size (int | None)
clahe_clip_limit (float)
median_mode (str)
median_cval (float)
otsu_ignore_zeros (bool)
otsu_ignore_borders (bool)
border_remover_size (int | float | None)
circularity_cutoff (float)
small_object_min_size (int)
outlier_axis (int | None)
outlier_stddev_multiplier (float)
outlier_max_coeff_variance (int)
aligner_axis (int)
aligner_mode (str)
section_blur_sigma (int)
section_blur_mode (str)
section_blur_truncate (float)
section_median_mode (str)
section_median_cval (float)
section_contrast_lower_percentile (int)
section_contrast_upper_percentile (int)
section_otsu_ignore_zeros (bool)
section_otsu_ignore_borders (bool)
grid_apply_reset_enh_matrix (bool)
small_object_min_size_2 (int)
color_white_chroma_max (float)
color_chroma_min (float)
color_include_XYZ (bool)
texture_scale (int | list[int])
texture_quant_lvl (Literal[8, 16, 32, 64])
texture_enhance (bool)
texture_warn (bool)
benchmark (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.

__init__(gaussian_sigma: int = 10, gaussian_mode: str = 'reflect', gaussian_truncate: float = 4.0, clahe_kernel_size: int | None = None, clahe_clip_limit: float = 0.01, median_mode: str = 'nearest', median_cval: float = 0.0, otsu_ignore_zeros: bool = True, otsu_ignore_borders: bool = True, border_remover_size: int | float | None = 50, circularity_cutoff: float = 0.6, small_object_min_size: int = 100, outlier_axis: int | None = None, outlier_stddev_multiplier: float = 1.5, outlier_max_coeff_variance: int = 1, aligner_axis: int = 0, aligner_mode: str = 'edge', section_blur_sigma: int = 5, section_blur_mode: str = 'reflect', section_blur_truncate: float = 4.0, section_median_mode: str = 'nearest', section_median_cval: float = 0.0, section_contrast_lower_percentile: int = 2, section_contrast_upper_percentile: int = 98, section_otsu_ignore_zeros: bool = True, section_otsu_ignore_borders: bool = True, grid_apply_reset_enh_matrix: bool = True, small_object_min_size_2: int = 100, color_white_chroma_max: float = 4.0, color_chroma_min: float = 8.0, color_include_XYZ: bool = False, texture_scale: int | list[int] = 5, texture_quant_lvl: Literal[8, 16, 32, 64] = 32, texture_enhance: bool = False, texture_warn: bool = False, benchmark: bool = False, **kwargs)[source]

Initializes the GridSectionPipeline with customizable operations and measurements.

Parameters:

gaussian_sigma (int) – Standard deviation for Gaussian kernel in initial smoothing.
gaussian_mode (str) – Mode for handling image boundaries during Gaussian smoothing.
gaussian_truncate (float) – Truncate the Gaussian kernel at this many standard deviations.
clahe_kernel_size (int | None) – Size of kernel for CLAHE. If None, automatically calculated.
clahe_clip_limit (float) – Contrast limit for CLAHE.
median_mode (str) – Boundary mode for median filter.
median_cval (float) – Constant value for median filter when mode is ‘constant’.
otsu_ignore_zeros (bool) – Whether to ignore zero pixels in Otsu thresholding.
otsu_ignore_borders (bool) – Whether to ignore border objects in Otsu detection.
border_remover_size (int | float | None) – Size of border region where objects are removed.
circularity_cutoff (float) – Minimum circularity threshold for objects to be retained.
small_object_min_size (int) – Minimum size of objects to retain in first removal step.
outlier_axis (Optional[int]) – Axis for outlier analysis. None for both, 0 for rows, 1 for columns.
outlier_stddev_multiplier (float) – Multiplier for standard deviation in outlier detection.
outlier_max_coeff_variance (int) – Maximum coefficient of variance for outlier analysis.
aligner_axis (int) – Axis for grid alignment (0 for rows, 1 for columns).
aligner_mode (str) – Mode for grid alignment rotation.
section_blur_sigma (int) – Standard deviation for Gaussian kernel in section-level detection.
section_blur_mode (str) – Mode for Gaussian smoothing in section-level detection.
section_blur_truncate (float) – Truncate for Gaussian kernel in section-level detection.
section_median_mode (str) – Boundary mode for median filter in section-level detection.
section_median_cval (float) – Constant value for median filter in section-level detection.
section_contrast_lower_percentile (int) – Lower percentile for contrast stretching in sections.
section_contrast_upper_percentile (int) – Upper percentile for contrast stretching in sections.
section_otsu_ignore_zeros (bool) – Whether to ignore zeros in section-level Otsu detection.
section_otsu_ignore_borders (bool) – Whether to ignore borders in section-level Otsu detection.
grid_apply_reset_enh_matrix (bool) – Whether to reset detect_mat before applying section-level pipeline.
small_object_min_size_2 (int) – Minimum size of objects to retain in second removal step.
color_white_chroma_max (float) – Maximum white chroma value for color measurement.
color_chroma_min (float) – Minimum chroma value for color measurement.
color_include_XYZ (bool) – Whether to include XYZ color space measurements.
texture_scale (int | list[int]) – Scale parameter(s) for Haralick texture features.
texture_quant_lvl (Literal[8, 16, 32, 64]) – Quantization level for texture computation.
texture_enhance (bool) – Whether to enhance image before texture measurement.
texture_warn (bool) – Whether to warn on texture computation errors.
benchmark (bool) – Indicates whether benchmarking is enabled across the pipeline.

__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

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

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

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.

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

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

Detect and measure colonies using multi-stage Otsu thresholding with refinement.

A robust general-purpose pipeline that chains preprocessing, Otsu detection, morphological cleanup, grid alignment, and re-detection for reliable colony segmentation on standard grid plates.

Steps:

GaussianBlur — smooth noise
CLAHE — boost local contrast
MedianFilter — remove residual speckle
SobelFilter — enhance colony edges
OtsuDetector — threshold-based detection
MaskOpener — smooth mask boundaries
BorderObjectRemover — remove partial edge colonies
SmallObjectRemover — remove noise fragments
MaskFill — fill interior holes
GridOversizedObjectRemover — remove merged multi-well objects
ReduceMultipleGridObjects — keep one colony per well
GridAligner — straighten the grid

Measurements: MeasureShape, MeasureColor, MeasureTexture, MeasureIntensity.

Best For:

Standard 96-well or 384-well yeast plates with clean backgrounds.
General-purpose colony detection when you are unsure which detector to use.
Plates with uniform illumination and bimodal intensity histograms.

Consider Also:

HeavyWatershedPipeline when colonies touch or overlap.
RoundPeaksPipeline for a faster, lighter approach on well-separated round colonies.
FilamentousFungiPipeline for filamentous organisms.

See also

Tutorial 8: Using Prefab Pipelines for a visual comparison of prefab pipelines. Prefab Pipelines: Which One for Which Organism for guidance on choosing a prefab pipeline.

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)

__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.

__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.
shape – Deprecated, use mask_opener_footprint.
min_size – Deprecated, use small_object_min_size.
border_size – Deprecated, use border_remover_size.
benchmark (bool)
verbose (bool)

__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

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

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

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.

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

class phenotypic.prefab.HeavyRoundPeaksPipeline(bm3d_sigma: float = 0.02, bm3d_block_size: int = 8, bm3d_stage_arg: Literal['all_stages', 'hard_thresholding'] = 'all_stages', bm3d_clip: bool = True, clahe_kernel_size: int | None = None, clahe_clip_limit: float = 0.01, median_mode: Literal['nearest', 'reflect', 'constant', 'mirror', 'wrap'] = 'nearest', median_shape: Literal['disk', 'square', 'diamond'] = 'diamond', median_radius: int = 5, median_cval: float = 0.0, detector_thresh_method: Literal['otsu', 'mean', 'local', 'triangle', 'minimum', 'isodata', 'li'] = 'otsu', detector_subtract_background: bool = True, detector_remove_noise: bool = True, detector_footprint_width: int = 6, detector_noise_radius: int = 1, detector_smoothing_sigma: float = 2.0, detector_min_peak_distance: int | None = None, detector_peak_prominence: float | None = None, detector_edge_refinement: bool = True, detector_selection_mode: Literal['dominant', 'centered', 'regularized'] = 'dominant', detector_split_merged: bool = True, grid_aligner_axis: int = 0, grid_aligner_mode: str = 'edge', mask_opener_footprint: Literal['auto', 'disk', 'square', 'diamond'] | ndarray | None = 'auto', mask_opener_width: int = 5, mask_opener_n_iter: int = 1, border_remover_size: int | float = 1, small_object_min_size: int = 50, mask_fill_structure: ndarray | None = None, mask_fill_origin: int = 0, texture_scale: int | list[int] = 5, texture_quant_lvl: Literal[8, 16, 32, 64] = 32, texture_enhance: bool = False, texture_warn: bool = False, color_white_chroma_max: float = 4.0, color_chroma_min: float = 8.0, color_include_XYZ: bool = False, benchmark: bool = False, verbose: bool = False)[source]

Bases: PrefabPipeline

Detect and measure round colonies using peak detection with full refinement.

An extended version of RoundPeaksPipeline that adds BM3D denoising, CLAHE contrast enhancement, morphological refinement, grid alignment, and a second detection pass for improved accuracy on challenging plates.

Steps:

BM3DDenoiser — high-quality denoising
CLAHE — boost local contrast
MedianFilter — remove residual speckle
RoundPeaksDetector — first detection pass
MaskOpener, BorderObjectRemover, SmallObjectRemover, MaskFill — cleanup
GridOversizedObjectRemover, ReduceMultipleGridObjects — grid refinement
GridAligner — straighten the grid
RoundPeaksDetector — second detection pass after alignment
BorderObjectRemover, SmallObjectRemover, MaskFill — final cleanup
ReduceMultipleGridObjects — final grid refinement

Measurements: MeasureShape, MeasureColor, MeasureIntensity, MeasureTexture.

Best For:

Round colonies on grid plates that need thorough refinement.
Noisy or low-contrast plates where RoundPeaksPipeline produces too many artifacts.
Plates with slight rotation that benefits from grid alignment between detection passes.

Consider Also:

RoundPeaksPipeline for a faster, lighter version when plates are clean and well-lit.
HeavyOtsuPipeline for general-purpose Otsu-based detection.

See also

Tutorial 8: Using Prefab Pipelines for a visual comparison of prefab pipelines.

Parameters:

bm3d_sigma (float)
bm3d_block_size (int)
bm3d_stage_arg (Literal['all_stages', 'hard_thresholding'])
bm3d_clip (bool)
clahe_kernel_size (int | None)
clahe_clip_limit (float)
median_mode (Literal['nearest', 'reflect', 'constant', 'mirror', 'wrap'])
median_shape (Literal['disk', 'square', 'diamond'])
median_radius (int)
median_cval (float)
detector_thresh_method (Literal['otsu', 'mean', 'local', 'triangle', 'minimum', 'isodata', 'li'])
detector_subtract_background (bool)
detector_remove_noise (bool)
detector_footprint_width (int)
detector_noise_radius (int)
detector_smoothing_sigma (float)
detector_min_peak_distance (int | None)
detector_peak_prominence (float | None)
detector_edge_refinement (bool)
detector_selection_mode (Literal['dominant', 'centered', 'regularized'])
detector_split_merged (bool)
grid_aligner_axis (int)
grid_aligner_mode (str)
mask_opener_footprint (Literal['auto', 'disk', 'square', 'diamond'] | ~numpy.ndarray | None)
mask_opener_width (int)
mask_opener_n_iter (int)
border_remover_size (int | float)
small_object_min_size (int)
mask_fill_structure (ndarray | None)
mask_fill_origin (int)
texture_scale (int | list[int])
texture_quant_lvl (Literal[8, 16, 32, 64])
texture_enhance (bool)
texture_warn (bool)
color_white_chroma_max (float)
color_chroma_min (float)
color_include_XYZ (bool)
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.

__init__(bm3d_sigma: float = 0.02, bm3d_block_size: int = 8, bm3d_stage_arg: Literal['all_stages', 'hard_thresholding'] = 'all_stages', bm3d_clip: bool = True, clahe_kernel_size: int | None = None, clahe_clip_limit: float = 0.01, median_mode: Literal['nearest', 'reflect', 'constant', 'mirror', 'wrap'] = 'nearest', median_shape: Literal['disk', 'square', 'diamond'] = 'diamond', median_radius: int = 5, median_cval: float = 0.0, detector_thresh_method: Literal['otsu', 'mean', 'local', 'triangle', 'minimum', 'isodata', 'li'] = 'otsu', detector_subtract_background: bool = True, detector_remove_noise: bool = True, detector_footprint_width: int = 6, detector_noise_radius: int = 1, detector_smoothing_sigma: float = 2.0, detector_min_peak_distance: int | None = None, detector_peak_prominence: float | None = None, detector_edge_refinement: bool = True, detector_selection_mode: Literal['dominant', 'centered', 'regularized'] = 'dominant', detector_split_merged: bool = True, grid_aligner_axis: int = 0, grid_aligner_mode: str = 'edge', mask_opener_footprint: Literal['auto', 'disk', 'square', 'diamond'] | ndarray | None = 'auto', mask_opener_width: int = 5, mask_opener_n_iter: int = 1, border_remover_size: int | float = 1, small_object_min_size: int = 50, mask_fill_structure: ndarray | None = None, mask_fill_origin: int = 0, texture_scale: int | list[int] = 5, texture_quant_lvl: Literal[8, 16, 32, 64] = 32, texture_enhance: bool = False, texture_warn: bool = False, color_white_chroma_max: float = 4.0, color_chroma_min: float = 8.0, color_include_XYZ: bool = False, benchmark: bool = False, verbose: bool = False) → None[source]

Represents an image processing pipeline for analyzing microbe colonies on solid media agar. The pipeline includes preprocessing, detection, morphological refinement, and measurement steps.

Parameters:

bm3d_sigma (float)
bm3d_block_size (int)
bm3d_stage_arg (Literal['all_stages', 'hard_thresholding'])
bm3d_clip (bool)
clahe_kernel_size (int | None)
clahe_clip_limit (float)
median_mode (Literal['nearest', 'reflect', 'constant', 'mirror', 'wrap'])
median_shape (Literal['disk', 'square', 'diamond'])
median_radius (int)
median_cval (float)
detector_thresh_method (Literal['otsu', 'mean', 'local', 'triangle', 'minimum', 'isodata', 'li'])
detector_subtract_background (bool)
detector_remove_noise (bool)
detector_footprint_width (int)
detector_noise_radius (int)
detector_smoothing_sigma (float)
detector_min_peak_distance (int | None)
detector_peak_prominence (float | None)
detector_edge_refinement (bool)
detector_selection_mode (Literal['dominant', 'centered', 'regularized'])
detector_split_merged (bool)
grid_aligner_axis (int)
grid_aligner_mode (str)
mask_opener_footprint (Literal['auto', 'disk', 'square', 'diamond'] | ~numpy.ndarray | None)
mask_opener_width (int)
mask_opener_n_iter (int)
border_remover_size (int | float)
small_object_min_size (int)
mask_fill_structure (ndarray | None)
mask_fill_origin (int)
texture_scale (int | list[int])
texture_quant_lvl (Literal[8, 16, 32, 64])
texture_enhance (bool)
texture_warn (bool)
color_white_chroma_max (float)
color_chroma_min (float)
color_include_XYZ (bool)
benchmark (bool)
verbose (bool)

Return type:

None

bm3d_sigma: Controls the degree of noise reduction during BM3D denoising. Lower values retain more fine details, which might preserve subtle colony textures. Higher values remove more noise but may blur colony edges, affecting detection accuracy.

bm3d_block_size: Block size for BM3D denoising. Larger blocks capture more spatial context for noise estimation but increase computation time.

bm3d_stage_arg: Specifies the stage of BM3D denoising. “all_stages” applies more comprehensive denoising, potentially enhancing signal uniformity but may result in detail loss. “hard_thresholding” retains more high-frequency details but may leave more background noise intact.

bm3d_clip: Whether to clip denoised values to the [0, 1] range. Disabling may preserve subtle intensity variations but can produce out-of-range values.

clahe_kernel_size: Determines the size of the kernel used for local contrast enhancement via CLAHE. Larger sizes improve contrast over broader areas, but may over-amplify large background variations. Smaller sizes enhance localized details but may introduce noise.

clahe_clip_limit: Contrast clipping limit for CLAHE. Lower values produce more subtle enhancement; higher values amplify local contrast more aggressively, which can over-enhance noise.

median_mode: Boundary handling mode for median filtering. Controls how pixel values are extrapolated at image edges.

median_shape: Defines the morphological shape (“disk”, “square”, “diamond”) used for median filtering. The choice impacts how texture and artifacts are smoothed. For instance, “disk” may preserve radial features, whereas “square” provides edge-focused filtering.

median_radius: Dictates the width for median filtering. Smaller values enhance fine textural differences, whereas larger radii smooth broader regions, potentially affecting the precise detection of small colonies.

median_cval: Constant value used to pad image borders when median_mode is “constant”.

detector_thresh_method: Specifies the thresholding method for binary segmentation. “otsu” (default) applies global thresholding, “mean” uses mean-based threshold, “local” adapts to background variations, “li” uses Li’s iterative minimum cross-entropy method, and “triangle”, “minimum”, “isodata” offer alternative thresholding strategies.

detector_subtract_background: Toggles white tophat background subtraction before thresholding. Enabling this helps standardize varying lighting or agar density but may also obscure genuine gradients or subtle ring colonies.

detector_remove_noise: Sets whether morphological opening is applied to remove small noise artifacts. True ensures a cleaner output but may falsely discard tiny colonies. False retains all details, which can increase false-positive noise levels.

detector_footprint_width: Width in pixels for the morphological footprint used in background subtraction. Larger values remove larger-scale background variations but may erode colony edges.

detector_noise_radius: Radius in pixels for the morphological opening used to remove small noise artifacts. Larger values remove larger noise but may erode fine colony features.

detector_smoothing_sigma: Standard deviation for Gaussian smoothing of intensity profiles before peak detection. Higher values increase robustness to noise but may merge nearby peaks. Set to 0 to disable smoothing.

detector_min_peak_distance: Minimum distance between detected peaks in pixels. If None, automatically estimated from grid dimensions. Prevents detection of spurious peaks too close together.

detector_peak_prominence: Minimum prominence of peaks for detection. If None, automatically estimated from signal statistics. Higher values are more selective.

detector_edge_refinement: Whether to refine grid edges using local intensity profiles. Improves accuracy but adds computational cost.

detector_selection_mode: Strategy for selecting the primary grid when multiple candidate grids are found. “dominant” selects the grid with the most colonies, “centered” prefers grids near the image center, “regularized” balances colony count with spatial regularity.

detector_split_merged: Whether to attempt splitting merged colonies that appear as single large objects. Improves accuracy for dense plates where colonies grow together but may over-segment irregular colony morphologies.

grid_aligner_axis: Axis along which to align the grid (0 for rows, 1 for columns).

grid_aligner_mode: Padding mode used when rotating the image for alignment.

mask_opener_footprint: Describes the morphological shape for noise removal or mask refinement. “auto” lets the system adapt, named shapes (“disk”, “square”, “diamond”) use a standard footprint, an ndarray specifies a custom structuring element, and None disables opening.

mask_opener_width: Width of the structuring element when using a named shape for mask opening. Larger values remove more noise but may erode small colonies.

mask_opener_n_iter: Number of iterations for the morphological opening. More iterations apply stronger smoothing to the mask.

border_remover_size: Specifies the width of the border region to remove. Larger sizes eliminate edge artifacts and colonies cropped by image edges but may discard valid colonies near borders.

small_object_min_size: Specifies the size threshold for considering objects as colonies. Increasing this parameter reduces false detection of small artifacts but risks ignoring small colonies.

mask_fill_structure: Binary structuring element for hole filling in masks. Larger or more connected structures fill bigger holes. None uses the default cross-shaped element.

mask_fill_origin: Origin offset for the structuring element used in hole filling.

texture_scale: Defines the spatial scale(s) at which texture features are measured. Larger scales focus on macro-textures; smaller scales enhance granular detail assessment. Can be a list to compute features at multiple scales simultaneously.

texture_quant_lvl: Number of gray levels for quantizing intensity values in texture analysis. Higher values capture finer intensity distinctions but increase computation time and may be sensitive to noise.

texture_enhance: Whether to apply contrast enhancement to the texture input before computing GLCM features. May improve texture discrimination for low-contrast colonies.

texture_warn: Boolean that enables warnings when texture measurements may not be reliable. Use this to flag potential inconsistencies in the captured texture data or image quality issues.

color_white_chroma_max: Maximum chroma value for classifying a colony as white. Colonies with chroma below this threshold are labeled as white/achromatic.

color_chroma_min: Minimum chroma value for assigning a chromatic hue. Colonies with chroma below this value receive a neutral color classification.

color_include_XYZ: Whether to include CIE XYZ color space measurements in addition to the standard Lab and descriptive color features.

benchmark: Enables time benchmarking for each pipeline step. Useful for performance debugging but adds overhead to the computation.

verbose: Specifies whether to output detailed process information during execution. True provides step-by-step logs, which are useful for debugging, while False ensures silent execution suitable for batch processing.

__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

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

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

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.

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

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)[source]

Bases: PrefabPipeline

Detect and measure colonies using watershed segmentation for touching colonies.

A robust pipeline that uses watershed region-growing to separate touching or overlapping colonies that single-threshold methods merge into one object. Includes multi-stage preprocessing, morphological refinement, and grid alignment.

Steps:

GaussianBlur — smooth noise
CLAHE — boost local contrast
MedianFilter — remove residual speckle
WatershedDetector — region-growing segmentation
BorderObjectRemover — remove partial edge colonies
LowCircularityRemover — remove non-circular artifacts
GridOversizedObjectRemover — remove merged multi-well objects
ReduceMultipleGridObjects — keep one colony per well
GridAligner — straighten the grid
MaskFill — fill interior holes

Measurements: MeasureShape, MeasureColor, MeasureTexture, MeasureIntensity.

Best For:

Dense plates where colonies touch or overlap.
Late time-point plates with large, merging colonies.
Plates where Otsu detection merges adjacent colonies into single objects.

Consider Also:

HeavyOtsuPipeline when colonies are well-separated.
RoundPeaksPipeline for a faster approach on round, non-touching colonies.

See also

Tutorial 8: Using Prefab Pipelines for a visual comparison of prefab pipelines. Prefab Pipelines: Which One for Which Organism for guidance on choosing a prefab pipeline.

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)

__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.

__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)[source]

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.

__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

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

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

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.

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

class phenotypic.prefab.RoundPeaksPipeline(*, blur_sigma: int = 5, blur_mode: str = 'reflect', blur_cval: float = 0.0, blur_truncate: float = 4.0, detector_thresh_method: Literal['otsu', 'mean', 'local', 'triangle', 'minimum', 'isodata'] = 'otsu', detector_subtract_background: bool = True, detector_remove_noise: bool = True, detector_footprint_radius: int = 5, detector_smoothing_sigma: float = 2.0, detector_min_peak_distance: int | None = None, detector_peak_prominence: float | None = None, detector_edge_refinement: bool = True, texture_scale: int | List[int] = 5, texture_quant_lvl: Literal[8, 16, 32, 64] = 32, texture_enhance: bool = False, texture_warn: bool = False, benchmark: bool = False, verbose: bool = False)[source]

Bases: PrefabPipeline

Detect and measure round colonies using lightweight peak-based detection.

A fast, minimal pipeline that applies Gaussian smoothing and grid-aware peak detection for circular colonies. Fewer stages and parameters than the Heavy variants, making it the fastest prefab option.

Steps:

GaussianBlur — smooth noise
RoundPeaksDetector — grid-aware circular colony detection

Measurements: MeasureShape, MeasureIntensity, MeasureTexture, MeasureColor.

Parameters:

blur_sigma (int) – Gaussian blur sigma. Typical range: 1–5. Default: 5.
blur_mode (str) – Boundary handling ('reflect', 'constant', 'nearest'). Default: 'reflect'.
blur_cval (float) – Fill value when blur_mode='constant'. Default: 0.0.
blur_truncate (float) – Kernel extent in standard deviations. Default: 4.0.
detector_thresh_method (Literal['otsu', 'mean', 'local', 'triangle', 'minimum', 'isodata']) – Thresholding method ('otsu', 'mean', 'local', 'triangle', 'minimum', 'isodata'). Default: 'otsu'.
detector_subtract_background (bool) – Normalize background before thresholding. Default: True.
detector_remove_noise (bool) – Morphological opening to remove specks. Default: True.
detector_footprint_radius (int) – Radius for morphological operations. Default: 5.
detector_smoothing_sigma (float) – Sigma for grid profile smoothing. Default: 2.0.
detector_min_peak_distance (int | None) – Minimum grid line spacing. None auto-estimates. Default: None.
detector_peak_prominence (float | None) – Minimum peak prominence. None auto-estimates. Default: None.
detector_edge_refinement (bool) – Refine grid edges using local profiles. Default: True.
texture_scale (int | List[int]) – Scale(s) for Haralick texture features. Default: 5.
texture_quant_lvl (Literal[8, 16, 32, 64]) – Quantization level (8, 16, 32, 64). Default: 32.
texture_enhance (bool) – Enhance contrast before texture measurement. Default: False.
texture_warn (bool) – Warn on unreliable texture measurements. Default: False.
benchmark (bool) – Enable per-step timing. Default: False.
verbose (bool) – Enable verbose logging. Default: False.

Best For:

Well-separated round colonies on grid plates.
High-throughput screening where speed matters.
Plates with consistent colony sizes and regular spacing.
Quick prototyping before switching to a heavier pipeline.

Consider Also:

HeavyRoundPeaksPipeline when additional refinement stages are needed for cleaner results.
HeavyOtsuPipeline for general-purpose detection with more robust preprocessing.
FilamentousFungiPipeline for filamentous organisms.

See also

Tutorial 8: Using Prefab Pipelines for a visual comparison of prefab pipelines. Prefab Pipelines: Which One for Which Organism for guidance on choosing a prefab pipeline.

__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.

__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

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

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

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.

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

class phenotypic.prefab.SpImagerPipeline[source]

Bases: PrefabPipeline

A prefabricated pipeline for light image processing task for images from the S&P Robotics Imager

__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.

__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

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

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

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.

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

phenotypic.prefab#

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