phenotypic.abc_.ImageOperation#

class phenotypic.abc_.ImageOperation[source]#

Bases: BaseOperation, LazyWidgetMixin, ABC

Core abstract base class for all single-image transformation operations in PhenoTypic.

ImageOperation is the foundation of PhenoTypic’s algorithm system. It defines the interface for algorithms that transform an Image object by modifying specific components. Unlike GridOperation (which handles grid-aligned operations on plate images), ImageOperation acts on a single image independently.

What is ImageOperation?

ImageOperation manages the distinction between:

apply() method: The user-facing interface that handles memory management (copy vs. in-place) and integrity validation
_operate() method: The abstract algorithm-specific method that subclasses implement with the actual processing logic

This separation ensures consistent behavior, automatic memory tracking, and validation across all image operations.

The Operation Hierarchy

ImageOperation has four main subclass categories, each modifying different image components with different integrity guarantees:

ImageOperation (this class)
├── ImageEnhancer
│   └── Modifies ONLY image.detect_mat
│       ├── GaussianBlur, EnhanceLocalContrast, RankMedianEnhancer, ...
│       └── Use for: noise reduction, contrast, edge sharpening
│
├── ObjectDetector
│   └── Modifies ONLY image.objmask and image.objmap
│       ├── OtsuDetector, CannyDetector, RoundPeaksDetector, ...
│       └── Use for: discovering and labeling colonies/particles
│
├── ObjectRefiner
│   └── Modifies ONLY image.objmask and image.objmap
│       ├── Size filtering, merging, removing objects
│       └── Use for: cleaning up detection results
│
└── ImageCorrector
    └── Modifies ALL image components
        ├── GridAligner, rotation, color correction
        └── Use for: general-purpose transformations

When to inherit from each subclass:

ImageEnhancer: You only modify image.detect_mat (detection matrix). Original image.rgb and image.gray are protected by integrity checks. Typical use: preprocessing before detection.
ObjectDetector: You analyze image data and produce only image.objmask (binary mask) and image.objmap (labeled object map). Input image data is protected. Typical use: colony detection, particle finding.
ObjectRefiner: You edit the mask and map (filtering, merging, removing). Input image data is protected. Typical use: post-detection cleanup.
ImageCorrector: You transform the entire Image (every component may change). No integrity checks are performed. Typical use: rotation, alignment, global color correction.

Never inherit directly from ImageOperation. Always choose one of the four subclasses above, as each provides appropriate integrity validation and shared utilities (e.g., _make_footprint() for morphology operations).

How apply() and _operate() work together

The user-facing method apply(image, inplace=False) is the entry point:

Calls ``_apply_to_single_image()`` with the operation logic
Handles copy/inplace semantics:
- If inplace=False (default): Image is copied before modification, original unchanged
- If inplace=True: Image is modified in-place for memory efficiency
Calls your _operate() instance method with the image
Validates integrity (subclass-specific via @validate_operation_integrity) - Detects unexpected modifications to protected image components - Only enabled if VALIDATE_OPS=True in environment

Your subclass only needs to implement _operate(self, image) -> Image.

The _operate() method contract

_operate() is an instance method (no @staticmethod decorator):

Signature: def _operate(self, image: Image) -> Image:
Parameters: Access operation parameters directly via self.param_name
Behavior: Modify only the allowed image components (determined by subclass)
Returns: The modified Image object

Example implementation:

class MyEnhancer(ImageEnhancer):
    sigma: float  # Annotated class-level field

    def _operate(self, image: Image) -> Image:
        # Access parameters via self
        image.detect_mat[:] = gaussian_filter(image.detect_mat[:], sigma=self.sigma)
        return image

The instance method pattern is simpler and more Pythonic than the old static method approach.

Data access through accessors

Within _operate(), always access image data through accessors (never direct attribute modification). This ensures lazy evaluation, caching, and consistency:

Reading data:

image.detect_mat[:] - Detection matrix (for enhancers)
image.rgb[:] - Original RGB data
image.gray[:] - Luminance grayscale
image.objmask[:] - Binary object mask
image.objmap[:] - Labeled object map
image.color.Lab[:], image.color.HSV[:] - Color spaces

Modifying data:

image.detect_mat[:] = new_array - Set detection matrix
image.objmask[:] = binary_array - Set object mask
image.objmap[:] = labeled_array - Set object map

Never do this:

# ✗ WRONG - direct attribute modification
image.rgb = new_data
image._detect_mat = new_data
image.objects_handler.detect_mat = new_data

Do this instead:

# ✓ CORRECT - use accessors
image.detect_mat[:] = new_data
image.objmask[:] = new_mask

Integrity validation with @validate_operation_integrity

Intermediate subclasses use the @validate_operation_integrity decorator to enforce that modifications are limited to specific components. For example:

class ImageEnhancer(ImageOperation, ABC):
    @validate_operation_integrity('image.rgb', 'image.gray')
    def apply(self, image: Image, inplace=False) -> Image:
        return super().apply(image=image, inplace=inplace)

This decorator:

Calculates MurmurHash3 signatures of protected arrays before apply()
Calls the parent apply() method
Recalculates signatures after operation completes
Raises OperationIntegrityError if any protected component changed

Only enabled if VALIDATE_OPS=True in environment (for performance).

Operation chaining and pipelines

Operations are designed for method chaining:

result = (GaussianBlur(sigma=2).apply(image)
         .apply_operation(OtsuDetector()))

Or use ImagePipeline for multi-step workflows with automatic benchmarking:

pipeline = ImagePipeline()
pipeline.add(GaussianBlur(sigma=2))
pipeline.add(OtsuDetector())
pipeline.add(GridFinder())

results = pipeline.operate([image1, image2, image3])

Parallel execution support

ImageOperation supports parallel execution through operation serialization. When ImagePipeline runs with multiple images, it:

Serializes the operation instance with all fields (pydantic model_dump())
Sends the operation to worker processes
Workers reconstruct the operation (restoring all field values)
Workers call operation.apply(image) which invokes _operate(self, image)

Instance methods work perfectly with parallel execution because the entire operation object (with all parameters) is serialized together.

None#

Type:: all operation state is stored in subclass instances as fields

apply(image, inplace=False)[source]#: User-facing method that applies the operation. Handles copy/inplace logic, calls _operate(), and validates integrity.

_operate(self, image)[source]#: Abstract instance method implemented by subclasses with algorithm logic. Access parameters via self.param_name.

_apply_to_single_image(cls_name, image, operation, inplace)[source]#: Static helper method that performs the actual apply operation. Handles copy/inplace logic and error handling. Called internally by apply(). Also used by ImagePipeline for parallel execution.

Notes

No direct Image attribute modification: Never write to image.rgb, image.gray, or other attributes directly. Use the accessor pattern (image.component[:] = new_data).
Immutability by default: Operations return modified copies by default. Original image is unchanged unless inplace=True is explicitly passed.
Instance method pattern: The _operate() method should be an instance method (no @staticmethod decorator). Access operation parameters directly via self.param_name. This is simpler and more Pythonic than the old static method approach.
Parallel execution compatibility: Instance methods work seamlessly with parallel execution. Operations are serialized with all fields via pydantic and reconstructed in worker processes with full state restored.
Automatic memory/performance tracking: BaseOperation (parent class) automatically tracks memory usage and execution time when the logger is configured for INFO level or higher. Disable by setting logger to WARNING.
Cross-platform compatibility: Some dependencies (rawpy, pympler) are platform-specific. Code must gracefully handle missing optional dependencies.
Integrity validation is optional: The @validate_operation_integrity decorator only runs if VALIDATE_OPS=True in environment. This provides development-time safety without production overhead.

Examples

Implementing a custom ImageEnhancer:

>>> from phenotypic.abc_ import ImageEnhancer
>>> from phenotypic import Image
>>> from scipy.ndimage import gaussian_filter
>>> class GaussianEnhancer(ImageEnhancer):
...     '''Custom enhancer applying Gaussian blur to detect_mat.'''
...
...     sigma: float = 1.0  # Annotated class-level field
...
...     def _operate(self, image: Image) -> Image:
...         '''Apply Gaussian blur to detect_mat.'''
...         # Read detection matrix
...         enh = image.detect_mat[:]
...         # Apply Gaussian filter (access parameter via self)
...         blurred = gaussian_filter(enh.astype(float), sigma=self.sigma)
...         # Modify detect_mat through accessor
...         image.detect_mat[:] = blurred.astype(enh.dtype)
...         return image
>>> # Usage
>>> enhancer = GaussianEnhancer(sigma=2.5)
>>> enhanced = enhancer.apply(image)  # Original unchanged
>>> enhanced_inplace = enhancer.apply(image, inplace=True)  # Original modified

Implementing a custom ObjectDetector:

>>> from phenotypic.abc_ import ObjectDetector
>>> from phenotypic import Image
>>> from skimage.feature import peak_local_max
>>> from skimage.measure import label as measure_label
>>> import numpy as np
>>> class PeakDetector(ObjectDetector):
...     '''Detector using local peak finding to locate colonies.'''
...
...     min_distance: int = 10  # Annotated class-level fields
...     threshold_abs: int = 100
...
...     def _operate(self, image: Image) -> Image:
...         '''Find peaks in detect_mat and create object mask/map.'''
...         # Find local maxima (colony peaks) - access parameters via self
...         coords = peak_local_max(
...             image.detect_mat[:],
...             min_distance=self.min_distance,
...             threshold_abs=self.threshold_abs
...         )
...         # Create binary mask from peaks
...         mask = np.zeros(image.detect_mat.shape, dtype=bool)
...         for y, x in coords:
...             mask[y, x] = True
...         # Create labeled map from mask
...         labeled_map = measure_label(mask)
...         # Set detection results
...         image.objmask[:] = mask
...         image.objmap[:] = labeled_map
...         return image
>>> # Usage - automatic integrity validation in ImageDetector
>>> detector = PeakDetector(min_distance=15, threshold_abs=120)
>>> detected = detector.apply(image)
>>> colonies = detected.objects
>>> print(f"Detected {len(colonies)} colonies")

Understanding inplace parameter and memory efficiency:

>>> from phenotypic.enhance import GaussianBlur
>>> from phenotypic import Image
>>> image = Image.imread('colony_plate.jpg')
>>> enhancer = GaussianBlur(sigma=2.0)
>>> # Default: inplace=False (safe, creates copy)
>>> enhanced = enhancer.apply(image)
>>> print(f"Same object? {id(image) == id(enhanced)}")  # False
>>> # For memory efficiency with large images
>>> result = enhancer.apply(image, inplace=True)
>>> print(f"Same object? {id(image) == id(result)}")  # True
# inplace=True is useful in pipelines with many large images
# to minimize memory overhead, but modifies the original

Using operations in a processing pipeline:

>>> from phenotypic import Image, ImagePipeline
>>> from phenotypic.enhance import GaussianBlur
>>> from phenotypic.detect import OtsuDetector
>>> from phenotypic.grid import GridFinder
>>> # Load image
>>> image = Image.imread('colony_plate.jpg')
>>> # Sequential chaining
>>> enhanced = GaussianBlur(sigma=2).apply(image)
>>> detected = OtsuDetector().apply(enhanced)
>>> grid = GridFinder().apply(detected)
>>> # Or use ImagePipeline for batch processing
>>> pipeline = ImagePipeline()
>>> pipeline.add(GaussianBlur(sigma=2))
>>> pipeline.add(OtsuDetector())
>>> pipeline.add(GridFinder())
>>> # Process multiple images with automatic parallelization
>>> images = [Image.imread(f) for f in image_files]
>>> results = pipeline.operate(images)
# Results are fully processed images

How instance methods work with parallel execution:

>>> from phenotypic.abc_ import ImageOperation
>>> from phenotypic import Image
>>> class CustomThreshold(ImageOperation):
...     threshold: int  # Annotated class-level fields
...     min_size: int = 5
...
...     def _operate(self, image: Image) -> Image:
...         # Access parameters via self
...         binary = image.detect_mat[:] > self.threshold
...         image.objmask[:] = binary
...         return image
>>> # When apply() is called:
>>> op = CustomThreshold(threshold=100, min_size=10)
# apply() internally:
# 1. Calls _apply_to_single_image() with self._operate (bound method)
# 2. _apply_to_single_image calls operation(image)
# 3. The bound method includes self, so all parameters are available
>>> result = op.apply(image)
# For parallel execution, the entire operation object (with all
# fields) is serialized via pydantic and sent to worker processes

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.

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

__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

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_.ImageOperation#

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