phenotypic.abc_.ObjectDetector#

class phenotypic.abc_.ObjectDetector[source]#

Bases: ImageOperation, ABC

Abstract base class for colony detection operations on agar plate images.

ObjectDetector defines the interface for algorithms that identify and label microbial colonies (or other objects) in image data. Detection is a critical step in the PhenoTypic image processing pipeline: it bridges image preprocessing (enhancement) and downstream analysis (measurement, refinement, and statistical analysis).

Quick Decision Guide

Use this guide to choose the right operation for your task:

ObjectDetector: Implementing a novel detection algorithm? Produces both objmask (binary) and objmap (labeled).
ThresholdDetector: Your algorithm converts intensity to binary via thresholding? Subclass ThresholdDetector for specialized threshold strategies.
ImageEnhancer: Need to preprocess image data (blur, contrast, denoise) before detection? Use enhancement to prepare detect_mat for better detection.
ObjectRefiner: Need to clean up existing masks (size filter, morphology, merge)? Refiner operates on objmask/objmap without analyzing image data.
Threshold vs Edge vs Peak: Threshold works when intensity separates colonies from background; edge-based (Canny) finds boundaries; peak-based assumes circular shapes.
Grid-aware analysis: Processing arrayed plates? Use GridObjectRefiner or GridFinder for well-plate-specific logic.

What does ObjectDetector do?

ObjectDetector subclasses analyze image data and produce two outputs that describe detected colonies:

image.objmask (binary mask): A 2D boolean array where True indicates colony pixels and False indicates background. Each True pixel belongs to some colony but the mask does not distinguish which colony each pixel belongs to—that is the role of objmap.
image.objmap (labeled map): A 2D integer array where each pixel value identifies the colony it belongs to. Background is 0, and each unique positive integer (1, 2, 3, …, N) represents a distinct labeled colony. This enables accessing individual colonies via image.objects after detection.

Key principle: ObjectDetector is READ-ONLY for image data

ObjectDetector operations:

Read image.detect_mat[:] (detection matrix), image.rgb[:], and optionally other image data to inform detection.
Write only image.objmask[:] and image.objmap[:].
Protect image.rgb, image.gray, and image.detect_mat via automatic integrity validation (@validate_operation_integrity decorator).

Any attempt to modify protected image components raises OperationIntegrityError when VALIDATE_OPS=True in the environment (enabled during development/testing).

Why is detection central to the pipeline?

Detection enables:

Object identification: Distinguishes individual colonies from background and from each other.
Downstream analysis: Once colonies are labeled, image.objects provides access to properties (area, intensity, centroid, morphology) for each colony.
Refinement: ObjectRefiner operations clean up detection masks/maps post-detection (e.g., removing spurious objects, merging fragments, filtering by size).
Phenotyping: Measurement operations (MeasureFeatures) extract colony features (color, morphology, growth) for statistical analysis.

Differences: objmask vs objmap

objmask (binary): Answers “is this pixel part of any colony?” Simple, useful for visualization or as input to further processing (e.g., morphological operations). Generated by most detectors via thresholding or edge detection.
objmap (labeled): Answers “which colony does this pixel belong to?” Enables per-object analysis. Each colony has a unique integer label, and connected-component labeling (usually scipy.ndimage.label) assigns these labels.

Both are typically set together in _operate() via:

image.objmask[:] = binary_mask
image.objmap[:] = labeled_map

When to use ObjectDetector vs ThresholdDetector vs ObjectRefiner

ObjectDetector (this class): Implement when you have a novel algorithm that produces both objmask and objmap from image data. Examples: [OtsuDetector](src/phenotypic/detect/_otsu_detector.py), [CannyDetector](src/phenotypic/detect/_canny_detector.py), [RoundPeaksDetector](src/phenotypic/detect/_round_peaks_detector.py), [WatershedDetector](src/phenotypic/detect/_watershed_detector.py).
ThresholdDetector (ObjectDetector subclass): Inherit from this if your detection relies on a threshold value. Provides common patterns and signals intent. Examples: [OtsuDetector](src/phenotypic/detect/_otsu_detector.py), [LiDetector](src/phenotypic/detect/_li_detector.py), [YenDetector](src/phenotypic/detect/_yen_detector.py), [TriangleDetector](src/phenotypic/detect/_triangle_detector.py).
ObjectRefiner (different ABC): Use when modifying existing masks/maps without analyzing image data. Examples: size filtering, morphological cleanup, erosion/dilation, merging nearby objects, removing objects near borders.

How to implement a custom ObjectDetector

Create the class:

from phenotypic.abc_ import ObjectDetector
from phenotypic import Image

class MyDetector(ObjectDetector):
    param1: float  # Annotated class-level fields
    param2: int = 10

    def _operate(self, image: Image) -> Image:
        # Detection logic here
        return image

Within _operate(), read image data carefully:
- Access via accessors: image.detect_mat[:], image.gray[:], image.rgb[:]
- Never modify these; integrity validation will catch it
- Consider the data type and range (uint8, uint16, float, etc.)
Perform detection: Use your algorithm to create a binary mask and labeled map. Typical approaches:
- Thresholding-based: Global or local threshold → binary mask → label (see [OtsuDetector](src/phenotypic/detect/_otsu_detector.py))
- Edge-based: Edge detector (Canny) → invert edges → label regions (see [CannyDetector](src/phenotypic/detect/_canny_detector.py))
- Peak-based: Detect intensity peaks → grow regions → label (see [RoundPeaksDetector](src/phenotypic/detect/_round_peaks_detector.py))
- Region-based: Watershed or morphological operations (see [WatershedDetector](src/phenotypic/detect/_watershed_detector.py))

Create and set the binary mask and labeled map:

from scipy import ndimage
import numpy as np

# Example: simple Otsu thresholding
enh = image.detect_mat[:]
threshold = skimage.filters.threshold_otsu(enh)
binary_mask = enh > threshold

# Remove small noise
binary_mask = skimage.morphology.remove_small_objects(binary_mask, min_size=20)

# Label connected components
labeled_map, num_objects = ndimage.label(binary_mask)

# Set both outputs
image.objmask[:] = binary_mask
image.objmap[:] = labeled_map

return image

Post-processing (optional): Some detectors include additional cleanup:
- Morphological operations: Apply erosion, dilation, opening, or closing to refine mask topology (remove noise, bridge fragments, smooth boundaries).
- Clear borders: Use skimage.segmentation.clear_border() to remove objects touching image edges.
- Remove small/large objects: Use skimage.morphology.remove_small_objects() or skimage.morphology.remove_large_objects() to filter by area.
- Relabel: Call image.objmap.relabel(connectivity=...) to ensure consecutive labels.

Helper functions from scipy and scikit-image

Common utilities for ObjectDetector implementations:

scipy.ndimage.label(): Assigns unique integers to connected components in a binary mask. Returns (labeled_array, num_features). Specify structure for connectivity (default 3x3 with all 8 neighbors; use generate_binary_structure(2, 1) for 4-connectivity).
skimage.morphology.remove_small_objects(): Removes binary regions smaller than min_size pixels. Helpful for filtering noise or spurious detections.
skimage.morphology.remove_large_objects(): Removes regions larger than a threshold. Useful for excluding large artefacts or plate boundaries.
skimage.segmentation.clear_border(): Sets pixels on the image border to False, eliminating objects that touch the edge (common in arrayed imaging where wells at plate boundaries may be partially cut off).
skimage.morphology.binary_opening(): Erosion followed by dilation; removes small noise while preserving larger objects. Use with a suitable shape (disk, square, or diamond).
scipy.ndimage.binary_dilation() / binary_erosion(): Expand or shrink objects morphologically. Useful for bridging fragmented colonies or removing small protrusions.
skimage.feature.canny(): Multi-stage edge detection (Gaussian → gradient → non-max suppression → hysteresis). Robust but requires threshold tuning.

Reference implementations in PhenoTypic

Study these implementations to learn detection patterns:

[OtsuDetector](src/phenotypic/detect/_otsu_detector.py): Simple thresholding with global Otsu method
[HysteresisDetector](src/phenotypic/detect/_hysteresis_detector.py): Advanced dual-threshold with edge tracking (excellent reference for complex detection)
[CannyDetector](src/phenotypic/detect/_canny_detector.py): Edge-based detection with connectivity cleanup
[RoundPeaksDetector](src/phenotypic/detect/_round_peaks_detector.py): Peak-based approach for round colonies
[WatershedDetector](src/phenotypic/detect/_watershed_detector.py): Region-based segmentation

When and how to refine detections (post-processing)

Raw detections often need cleanup:

Remove small noise: Spurious single-pixel detections or tiny salt-and-pepper artifacts. Use ObjectRefiner + remove_small_objects.
Clean borders: Colonies at plate edges may be incomplete. Use ObjectRefiner or clear_border() in detector.
Merge fragments: Noise or uneven lighting can fragment a single colony into multiple labels. Use ObjectRefiner with morphological dilation or connected-component merging.
Remove large objects: Plate edges, dust on the scanner, or agar artifacts appear as large regions. Use ObjectRefiner + remove_large_objects.
Grid-aware filtering: In arrayed formats (96-well, 384-well), one object per grid cell is expected. Use GridObjectRefiner to enforce this constraint or GridRefiner to assign dominant objects to grid positions.

Example pipeline with detection + refinement:

from phenotypic import Image, ImagePipeline
from phenotypic.detect import OtsuDetector
from phenotypic.refine import RemoveSmallObjectsRefiner, ClearBorderRefiner

pipeline = ImagePipeline()
pipeline.add(OtsuDetector())  # Initial detection
pipeline.add(ClearBorderRefiner())  # Remove edge-touching objects
pipeline.add(RemoveSmallObjectsRefiner(min_size=100))  # Filter noise

image = Image("plate.jpg")
result = pipeline.operate([image])[0]
# result now has clean, labeled colonies ready for measurement

None#

Type:: all operation parameters are stored in subclass instances

apply(image, inplace=False)[source]#: User-facing method to apply detection to an image. Handles copy/inplace logic and parameter matching.

_operate(image, **kwargs)[source]#: Abstract instance method implemented by subclasses with detection logic. Must set image.objmask and image.objmap.

Notes

Integrity protection: The @validate_operation_integrity decorator on apply() ensures image.rgb, image.gray, and image.detect_mat are not modified. Violations raise OperationIntegrityError during development (VALIDATE_OPS=True).
Binary mask is often intermediate: Many implementations create objmask first, then derive objmap via connected-component labeling. Both must be set for downstream code to work correctly.
Label consistency: Use image.objmap.relabel() after manipulating the labeled map to ensure labels are consecutive (1, 2, 3, …, N) and to update objmask.
Memory efficiency: Large images and detailed segmentations consume memory. Consider inplace=True in pipelines processing many images, or use sparse representations (objmap uses scipy.sparse internally).
Instance _operate() method: Access parameters via self attributes.

Examples

Detect colonies in a plate image and access results:

>>> from phenotypic import Image
>>> from phenotypic.detect import OtsuDetector
>>> # Load a plate image
>>> plate = Image("agar_plate.jpg")
>>> # Apply detection
>>> detector = OtsuDetector()
>>> detected = detector.apply(plate)
>>> # Access binary mask
>>> mask = detected.objmask[:]  # numpy array
>>> print(f"Mask shape: {mask.shape}, True pixels: {mask.sum()}")
>>> # Access labeled map
>>> objmap = detected.objmap[:]
>>> print(f"Detected {objmap.max()} colonies")
>>> # Iterate over colonies and measure properties
>>> for colony in detected.objects:
...     print(f"Colony area: {colony.area} px, "
...           f"centroid: {colony.centroid}")

Detection in a full pipeline with enhancement and refinement:

>>> from phenotypic import Image, ImagePipeline
>>> from phenotypic.enhance import GaussianBlur
>>> from phenotypic.detect import CannyDetector
>>> from phenotypic.refine import RemoveSmallObjectsRefiner
>>> from phenotypic.measure import MeasureColor
>>> # Create a processing pipeline
>>> pipeline = ImagePipeline()
>>> pipeline.add(GaussianBlur(sigma=2.0))  # Preprocessing
>>> pipeline.add(CannyDetector(sigma=1.5))  # Detection
>>> pipeline.add(RemoveSmallObjectsRefiner(min_size=50))  # Cleanup
>>> pipeline.add(MeasureColor())  # Downstream analysis
>>> # Load image and process
>>> image = Image("plate.jpg")
>>> result = pipeline.operate([image])[0]
>>> # Results include enhanced image, detected/refined colonies, and measurements
>>> print(f"Colonies: {result.objmap[:].max()}")
>>> print(f"Measurements: {result.measurements.shape}")

Methods

`__init__`	Create a new model by parsing and validating input data from keyword arguments.
`apply`	Detect colonies using sinusoidal cross-correlation grid estimation.
`construct`
`copy`	Returns a copy of the model.
`dict`
`from_json`	Reconstruct an operation from JSON written by `to_json()`.
`from_orm`
`json`
`model_construct`	Creates a new instance of the Model class with validated data.
`model_copy`	!!! abstract "Usage Documentation"
`model_dump`	!!! abstract "Usage Documentation"
`model_dump_json`	!!! abstract "Usage Documentation"
`model_json_schema`	Generates a JSON schema for a model class.
`model_parametrized_name`	Compute the class name for parametrizations of generic classes.
`model_post_init`	Initialize logging and memory tracking after model construction.
`model_rebuild`	Try to rebuild the pydantic-core schema for the model.
`model_validate`	Validate a pydantic model instance.
`model_validate_json`	!!! abstract "Usage Documentation"
`model_validate_strings`	Validate the given object with string data against the Pydantic model.
`parse_file`
`parse_obj`
`parse_raw`
`schema`
`schema_json`
`to_json`	Serialize this operation to JSON.
`update_forward_refs`
`validate`
`widget`	Return (and optionally display) the root widget.

Attributes

`model_computed_fields`
`model_config`	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
`model_extra`	Get extra fields set during validation.
`model_fields`
`model_fields_set`	Returns the set of fields that have been explicitly set on this model instance.

__copy__() → Self#

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo: dict[int, Any] | None = None) → Self#

Returns a deep copy of the model.

Parameters:: memo (dict[int, Any] | None)
Return type:: Self

__del__()#: Automatically stop tracemalloc when the object is deleted.

classmethod __get_pydantic_json_schema__(core_schema: CoreSchema, handler: GetJsonSchemaHandler, /) → JsonSchemaValue#

Hook into generating the model’s JSON schema.

Parameters:

core_schema (CoreSchema) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Returns:

A JSON schema, as a Python object.

Return type:

JsonSchemaValue

__init__(**data: Any) → None#

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:: data (Any)
Return type:: None

__iter__() → Generator[tuple[str, Any], None, None]#

So dict(model) works.

Return type:: Generator[tuple[str, Any], None, None]

__pretty__(fmt: Callable[[Any], Any], **kwargs: Any) → Generator[Any]#

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Parameters:

fmt (Callable[[Any], Any])
kwargs (Any)

Return type:

Generator[Any]

classmethod __pydantic_init_subclass__(**kwargs: Any) → None#

Populate field descriptions from the subclass docstring.

Runs once per concrete subclass after pydantic has built its model. Copies parameter descriptions parsed from the Google-style Args: docstring block onto each field’s description slot so they surface in model_json_schema() — the machine-readable contract used by downstream tooling (e.g. an MCP server).

Parameters:: **kwargs (Any) – Class-keyword arguments forwarded by pydantic.
Return type:: None

classmethod __pydantic_on_complete__() → None#

This is called once the class and its fields are fully initialized and ready to be used.

This typically happens when the class is created (just before [__pydantic_init_subclass__()][pydantic.main.BaseModel.__pydantic_init_subclass__] is called on the superclass), except when forward annotations are used that could not immediately be resolved. In that case, it will be called later, when the model is rebuilt automatically or explicitly using [model_rebuild()][pydantic.main.BaseModel.model_rebuild].

Return type:: None

__repr_name__() → str#

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_recursion__(object: Any) → str#

Returns the string representation of a recursive object.

Parameters:: object (Any)
Return type:: str

__rich_repr__() → RichReprResult#

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: RichReprResult

apply(image: GridImage, inplace: bool = False) → GridImage[source]#

apply(image: Image, inplace: bool = False) → Image

Detect colonies using sinusoidal cross-correlation grid estimation.

This method performs the core detection workflow: 1. Extract grid dimensions (if GridImage) 2. Threshold the detection matrix with adaptive kernel sizing 3. Remove noise if requested 4. Label connected components 5. Determine or estimate grid edges (via sinusoidal cross-correlation) 6. Assign dominant colonies to grid cells 7. Create final object map

Parameters:: image – Image object to process. Can be a regular Image or GridImage.
Returns:: The processed image with updated objmask and objmap.
Return type:: Image

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Self#

Parameters:

_fields_set (set[str] | None)
values (Any)

Return type:

Self

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include (AbstractSetIntStr | MappingIntStrAny | None) – Optional set or mapping specifying which fields to include in the copied model.
exclude (AbstractSetIntStr | MappingIntStrAny | None) – Optional set or mapping specifying which fields to exclude in the copied model.
update (Dict[str, Any] | None) – Optional dictionary of field-value pairs to override field values in the copied model.
deep (bool) – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

Return type:

Self

Parameters:

include (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)

Return type:

Dict[str, Any]

classmethod from_json(json_data: str | Path | dict) → BaseOperation#

Reconstruct an operation from JSON written by to_json().

Accepts a JSON string, a path to a JSON file, or a pre-parsed envelope dict (same input handling as ImagePipeline.from_json()). Polymorphic: ImageOperation.from_json(path) returns whatever concrete operation the file holds. When called on a narrower subclass, the resolved class must be a subclass of it, else a TypeError is raised.

Parameters:

json_data (str | Path | dict) – A JSON string, path to a JSON file, or envelope dict.

Returns:

The reconstructed operation instance.

Raises:

AttributeError – If the recorded class cannot be resolved in the phenotypic namespace.
TypeError – If called on a concrete subclass and the file holds a class that is not a subclass of it.

Return type:

BaseOperation

Example

>>> import tempfile
>>> from pathlib import Path
>>> from phenotypic.abc_ import ImageOperation
>>> from phenotypic.detect import OtsuDetector
>>> with tempfile.TemporaryDirectory() as d:
...     p = Path(d) / "op.json"
...     OtsuDetector().to_json(p)
...     loaded = ImageOperation.from_json(p)  # polymorphic
>>> type(loaded).__name__
'OtsuDetector'

classmethod from_orm(obj: Any) → Self#

Parameters:: obj (Any)
Return type:: Self

Parameters:

include (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None)
exclude (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
encoder (Callable[[Any], Any] | None)
models_as_dict (bool)
dumps_kwargs (Any)

Return type:

str

model_computed_fields = {}#

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'validate_assignment': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → Self#

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

Return type:

Self

model_copy(*, update: Mapping[str, Any] | None = None, deep: bool = False) → Self#

!!! abstract “Usage Documentation”: [model_copy](../concepts/models.md#model-copy)

Returns a copy of the model.

!!! note: The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Returns:

New model instance.

Return type:

Self

!!! abstract “Usage Documentation”: [model_dump](../concepts/serialization.md#python-mode)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Literal['json', 'python'] | str) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None) – A set of fields to include in the output.
exclude (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None) – A set of fields to exclude from the output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
exclude_computed_fields (bool) – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
fallback (Callable[[Any], Any] | None) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Returns:

A dictionary representation of the model.

Return type:

dict[str, Any]

!!! abstract “Usage Documentation”: [model_dump_json](../concepts/serialization.md#json-mode)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
ensure_ascii (bool) – If True, the output is guaranteed to have all incoming non-ASCII characters escaped. If False (the default), these characters will be output as-is.
include (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None) – Field(s) to include in the JSON output.
exclude (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None) – Field(s) to exclude from the JSON output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool | None) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
exclude_computed_fields (bool) – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
fallback (Callable[[Any], Any] | None) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Returns:

A JSON string representation of the model.

Return type:

str

property model_extra: dict[str, Any] | None#

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {}#

property model_fields_set: set[str]#

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[~pydantic.json_schema.GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: ~typing.Literal['validation', 'serialization'] = 'validation', *, union_format: ~typing.Literal['any_of', 'primitive_type_array'] = 'any_of') → dict[str, Any]#

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
union_format (Literal['any_of', 'primitive_type_array']) –
The format to use when combining schemas from unions together. Can be one of:
- ’any_of’: Use the [anyOf](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)
keyword to combine schemas (the default). - ‘primitive_type_array’: Use the [type](https://json-schema.org/understanding-json-schema/reference/type) keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_of.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

Return type:

dict[str, Any]

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str#

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.
Return type:: str

model_post_init(_BaseOperation__context: Any) → None#

Initialize logging and memory tracking after model construction.

Replaces the legacy __init__ body: creates the per-class logger and, when that logger is enabled for INFO level or higher, starts tracemalloc so per-operation memory usage can be logged.

Parameters:

__context – Pydantic post-init context (unused).
_BaseOperation__context (Any)

Return type:

None

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: MappingNamespace | None = None) → bool | None#

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (MappingNamespace | None) – The types namespace, defaults to None.

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

Return type:

bool | None

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (bool | None) – Whether to enforce types strictly.
extra (Literal['allow', 'ignore', 'forbid'] | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
from_attributes (bool | None) – Whether to extract data from object attributes.
context (Any | None) – Additional context to pass to the validator.
by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.
by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

Return type:

Self

!!! abstract “Usage Documentation”: [JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (bool | None) – Whether to enforce types strictly.
extra (Literal['allow', 'ignore', 'forbid'] | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
context (Any | None) – Extra variables to pass to the validator.
by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.
by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

Return type:

Self

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (bool | None) – Whether to enforce types strictly.
extra (Literal['allow', 'ignore', 'forbid'] | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
context (Any | None) – Extra variables to pass to the validator.
by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.
by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Return type:

Self

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self#

Parameters:

path (str | Path)
content_type (str | None)
encoding (str)
proto (DeprecatedParseProtocol | None)
allow_pickle (bool)

Return type:

Self

classmethod parse_obj(obj: Any) → Self#

Parameters:: obj (Any)
Return type:: Self

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self#

Parameters:

b (str | bytes)
content_type (str | None)
encoding (str)
proto (DeprecatedParseProtocol | None)
allow_pickle (bool)

Return type:

Self

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]#

Parameters:

by_alias (bool)
ref_template (str)

Return type:

Dict[str, Any]

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str#

Parameters:

by_alias (bool)
ref_template (str)
dumps_kwargs (Any)

Return type:

str

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

Serialize this operation to JSON.

Captures the operation as a {"class", "params"} envelope: params is model_dump(mode="json") (every declared field, including nested operations and raw arrays; PrivateAttr state such as loggers and timing is excluded automatically), and class records the concrete class name so from_json() can rebuild the right subclass. This mirrors ImagePipeline.to_json().

Parameters:: filepath (str | Path | None) – Optional path to write the JSON to. When None, the JSON string is returned instead. Accepts a str or Path.
Returns:: The JSON string when filepath is None, otherwise None.
Return type:: str | None

Example

>>> import tempfile
>>> from pathlib import Path
>>> from phenotypic.detect import OtsuDetector
>>> from phenotypic.sdk_ import CONFIG_SUFFIX_OPERATION, ensure_typed_json_suffix
>>> with tempfile.TemporaryDirectory() as d:
...     p = Path(d) / "op.json"
...     saved = ensure_typed_json_suffix(p, CONFIG_SUFFIX_OPERATION)
...     OtsuDetector(ignore_zeros=True).to_json(p)
...     loaded = OtsuDetector.from_json(saved)
>>> loaded.ignore_zeros
True

classmethod update_forward_refs(**localns: Any) → None#

Parameters:: localns (Any)
Return type:: None

classmethod validate(value: Any) → Self#

Parameters:: value (Any)
Return type:: Self

widget(image: Image | None = None, show: bool = False) → Widget#

Return (and optionally display) the root widget.

Parameters:

image (Image | None) – Optional image to visualize. If provided, visualization controls will be added to the widget.
show (bool) – Whether to display the widget immediately. Defaults to False.

Returns:

The root widget.

Return type:

ipywidgets.Widget

Raises:

ImportError – If ipywidgets or IPython are not installed.

phenotypic.abc_.ObjectDetector#

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