phenotypic.abc_.ImageCorrector#

class phenotypic.abc_.ImageCorrector[source]#

Bases: ImageOperation, ABC

Abstract base class for whole-image transformation operations affecting all components.

ImageCorrector is a specialized subclass of ImageOperation for global image transformations that modify every image component together (rgb, gray, detect_mat, objmask, objmap). Unlike ImageEnhancer (modifies only detect_mat) or ObjectDetector/ObjectRefiner (modify only detection results), an ImageCorrector transforms the entire image geometry or structure, ensuring all components remain synchronized.

Quick Decision Guide: Which Operation Type?

ImageEnhancer: Modify only image.detect_mat for preprocessing. Use for: noise reduction, contrast enhancement, background subtraction.
ObjectDetector: Analyze image, produce only objmask and objmap. Use for: colony/object detection and labeling.
ObjectRefiner: Edit mask and map (filtering, merging, removing objects). Use for: post-detection cleanup and refinement.
ImageCorrector (this class): Transform entire image (rotation, resampling, perspective). Use for: geometric corrections, coordinate system changes, alignment. Example: [GridAligner](src/phenotypic/correction/_grid_aligner.py).

What is ImageCorrector?

ImageCorrector handles operations where it is impossible or meaningless to modify only a single component. When you rotate, warp, or apply perspective transforms to an image, the rgb and gray representations must change together, and any existing detection masks and maps must be rotated identically. ImageCorrector guarantees this synchronization without requiring manual alignment of separate components.

Key Design Principle: No Integrity Checks

Unlike ImageEnhancer and ObjectDetector, ImageCorrector uses no @validate_operation_integrity decorator. This is by design: since all components must change together in a coordinated way, there is nothing to “protect” or “validate”. The entire image is intentionally modified as a unit. The absence of integrity checks reflects this design, not a security weakness.

Typical Use Cases

ImageCorrector is designed for operations that physically transform the image:

Rotation: Align plate image with detected grid structure to make colony rows parallel to axes.
Perspective transformation: Correct camera angle or lens distortion effects.
Image resampling: Change resolution or interpolation method for downstream processing.
Global color correction: Apply white balance or color space mapping to entire image.
Alignment: Register image to reference coordinate system for grid-based analysis.

When NOT to use ImageCorrector

Do NOT use ImageCorrector for operations that affect only specific image aspects:

Don’t use for: Enhancing only detect_mat (preprocessing). Use ImageEnhancer instead.
Don’t use for: Detecting colonies or objects. Use ObjectDetector instead.
Don’t use for: Filtering or editing detection results. Use ObjectRefiner instead.
Don’t use for: Local edits (e.g., removing a specific region). These typically require custom masking or ObjectRefiner.

The key question: Are you transforming the entire image geometry, or only a specific aspect? If only an aspect, use the specialized operation type (Enhancer, Detector, Refiner).

Why ImageCorrector is Rare in Practice

Most image processing operations are targeted to specific aspects of the image:

Colony detection focuses on finding objects in image data.
Post-detection cleanup focuses on refining the mask/map.
Preprocessing focuses on making detection more robust.

Operations transforming the entire image structure are comparatively rare because:

Plate images are typically already well-oriented from the scanner/camera.
Most analysis works directly with image data as acquired (no rotation needed).
Grid-based alignment is a specialized step, not routine preprocessing.

However, when needed, ImageCorrector provides the correct abstraction.

Subclass References

ImageCorrector implementations are rare. The canonical example is:

[GridAligner](src/phenotypic/correction/_grid_aligner.py): Rotates entire GridImage to align detected colonies with expected grid structure. Demonstrates synchronizing all components during transformation.

How to Implement a Custom ImageCorrector

Inherit from ImageCorrector and implement the _operate() instance method. Access parameters via self attributes within the method body.

from phenotypic.abc_ import ImageCorrector
from phenotypic import Image

class MyRotator(ImageCorrector):
    angle: float  # Annotated class-level field

    def _operate(self, image: Image) -> Image:
        # Rotate ALL image components together
        image.rotate(angle_of_rotation=self.angle, mode='edge')
        return image

# Usage
rotator = MyRotator(angle=5.0)
rotated_image = rotator.apply(image)

Key Implementation Rules

_operate() must be an instance method (access parameters via self).
All parameters must be declared as annotated class-level fields.
The method must transform all components equally (rgb, gray, detect_mat, objmask, objmap).
Never modify only one component—this breaks the “whole-image transformation” contract.
Use the Image class’s helper methods (image.rotate()) whenever possible for consistency.
Always return the modified Image object after transformation.

Parameter Declaration Example

Operation parameters are declared as annotated class-level fields; pydantic generates the constructor and handles serialization automatically:

class RotatorCorrector(ImageCorrector):
    angle: float  # Annotated class-level field

    def _operate(self, image: Image) -> Image:
        image.rotate(angle_of_rotation=self.angle, mode='edge')
        return image

Critical Implementation Detail: Updating All Components

Your _operate() method must ensure all image components are updated together. When any geometric transformation is applied, it must affect every component identically:

def _operate(self, image: Image) -> Image:
    # Rotate rgb and gray (color representation)
    image.rotate(angle_of_rotation=self.angle, mode='edge')

    # The following are automatically handled by image.rotate():
    # - Rotate detect_mat (enhanced version for detection)
    # - Rotate objmask and objmap (detection results)
    # - Synchronize all caches and metadata

    return image

What happens if components get out of sync?

If you accidentally rotate only image.rgb without rotating image.objmap, downstream analysis breaks because pixel coordinates no longer match object labels. The Image class’s helper methods protect against this by guaranteeing synchronized updates.

Access image data through accessors (never direct attributes):

When implementing custom transformations, always use the accessor interface:

Reading: image.rgb[:], image.gray[:], image.detect_mat[:], image.objmask[:], image.objmap[:]
Modifying: image.rgb[:] = new_data, image.objmap[:] = new_map

The accessor interface ensures that:

Caches are invalidated appropriately after modifications.
Color space conversions remain synchronized with RGB data.
Object detection results stay consistent with image geometry.

The Image class provides helper methods for common transformations:

image.rotate(angle_of_rotation, mode='edge') - Rotates all components identically
For custom transformations, apply the same operation to all components
Always verify that helper methods exist before implementing custom transform code

Pipeline Integration and Serialization

ImageCorrector operations are fully serializable and can be included in ImagePipeline for batch processing. The static method design enables distributed execution:

Automatic parameter passing: Instance attributes are extracted when apply() is called.
Serialization: Operations can be saved to JSON/YAML and reconstructed on worker processes.
Batch processing: Use ImagePipeline.apply_and_measure() for automatic benchmarking.
Reproducibility: Serialized pipelines document the exact transformations applied.

Performance and Interpolation Considerations

When rotating or resampling, use appropriate interpolation for each component:

Color data (rgb, gray): Use smooth interpolation (order=1 bilinear or higher) to preserve color gradients and colony boundaries.
Detection data (objmask, objmap): Use nearest-neighbor interpolation (order=0) to preserve discrete object identities and integer labels.
Detection matrix (detect_mat): Use same interpolation as color data for consistency.

Example with explicit interpolation control:

>>> from scipy.ndimage import rotate as ndimage_rotate
>>> from skimage.transform import rotate as skimage_rotate
>>> # For rgb/gray: use bilinear interpolation
>>> rotated_rgb = skimage_rotate(image.rgb[:], angle=5.0, order=1, preserve_range=True)
>>> # For objmap: use nearest-neighbor to preserve integer labels
>>> rotated_objmap = ndimage_rotate(image.objmap[:], angle=5.0, order=0, reshape=False)

Edge Handling During Transformation

Transformations introduce edge artifacts; choose mode based on downstream analysis:

‘edge’ mode: Replicas image border pixels (minimal artifacts, safest for colony detection).
‘constant’ mode: Fills with constant value (usually 0 for dark edge, may create false boundaries).
‘reflect’ mode: Reflects image at boundary (avoids abrupt discontinuities but changes image content).

Common Pitfalls and Best Practices

Pitfall: Modifying only one component (e.g., rotating RGB but not objmap). Result: pixel coordinates become misaligned.
Best practice: Use Image class helper methods (image.rotate()) which synchronize all components automatically.
Pitfall: Using smooth interpolation for object maps. Result: object labels become non-integer, breaking downstream analysis.
Best practice: Use order=0 (nearest-neighbor) for masks and maps to preserve discrete identities.
Pitfall: Forgetting to handle edge artifacts. Result: false objects detected at image boundaries.
Best practice: Choose ‘edge’ mode for colony detection (minimal artifacts) or use image padding before transformation.

Attributes

ImageCorrector has no public attributes; subclasses define operation-specific parameters as instance attributes.
All subclass attributes must match _operate() method signature for parallelization support.

Methods

apply(image, inplace=False) - Execute the correction (default: returns new image).
_operate(image, **kwargs) - Abstract method you implement with transformation logic.

Notes

Instance method: _operate() is an instance method; access parameters via self.
Field declaration: Operation parameters are declared as annotated class-level fields.
No copy by default: Operations return modified copies by default (inplace=False).
Coordinate system changes: Downstream operations may need re-detection after transformation.
Grid alignment workflow: [GridAligner](src/phenotypic/correction/_grid_aligner.py) is the canonical example.

Examples

Basic rotation operation:

>>> from phenotypic.abc_ import ImageCorrector
>>> from phenotypic.data import load_synth_yeast_plate
>>>
>>> class SimpleRotator(ImageCorrector):
...     angle: float = 5.0
...     def _operate(self, image):
...         image.rotate(angle_of_rotation=self.angle, mode='edge')
...         return image
>>>
>>> image = load_synth_yeast_plate()
>>> rotator = SimpleRotator(angle=5.0)
>>> rotated = rotator.apply(image)
>>> # All components rotated together: rgb, gray, detect_mat, objmask, objmap
>>> rotated.shape == image.shape
False

Custom perspective correction (preserving component synchronization):

>>> from phenotypic.abc_ import ImageCorrector
>>> from phenotypic.data import load_synth_yeast_plate
>>> import numpy as np
>>>
>>> class PerspectiveCorrector(ImageCorrector):
...     tilt_angle: float = 10.0
...     def _operate(self, image):
...         # Apply perspective transform to all components
...         image.rotate(angle_of_rotation=self.tilt_angle, mode='edge')
...         return image
>>>
>>> image = load_synth_yeast_plate()
>>> corrector = PerspectiveCorrector(tilt_angle=10.0)
>>> corrected = corrector.apply(image)
>>> # All components are transformed together, maintaining synchronization

Methods

`__init__`	Create a new model by parsing and validating input data from keyword arguments.
`apply`	Applies the operation to an image, either in-place or on a copy.
`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

Applies the operation to an image, either in-place or on a copy.

Parameters:

image (Image) – The arr image to apply the operation on.
inplace (bool) – If True, modifies the image in place; otherwise, operates on a copy of the image.

Returns:

The modified image after applying the operation.

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_.ImageCorrector#

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