phenotypic.detect.ChanVeseDetector#

class phenotypic.detect.ChanVeseDetector(*, mu: Annotated[float, TuneSpec(low=0.0, high=1.0, step=None, log=False, categories=None, tunable=True)] = 0.25, lambda1: Annotated[float, TuneSpec(low=0.5, high=2.0, step=None, log=False, categories=None, tunable=True)] = 1.0, lambda2: Annotated[float, TuneSpec(low=0.5, high=2.0, step=None, log=False, categories=None, tunable=True)] = 1.0, max_num_iter: Annotated[int, TuneSpec(low=100, high=1000, step=None, log=False, categories=None, tunable=True)] = 500, tol: Annotated[float, TuneSpec(low=1e-05, high=0.01, step=None, log=True, categories=None, tunable=True)] = 0.001, dt: Annotated[float, TuneSpec(low=0.1, high=1.0, step=None, log=False, categories=None, tunable=True)] = 0.5, init_level_set: str = 'checkerboard', min_size: Annotated[int, TuneSpec(low=10, high=500, step=None, log=True, categories=None, tunable=True)] = 50, connectivity: Annotated[int, Ge(ge=1), Le(le=2), TuneSpec(low=None, high=None, step=None, log=False, categories=1, 2, tunable=True)] = 2)[source]#

Bases: ObjectDetector

Detect colonies by evolving a level-set contour that minimises region-intensity variance via the Chan-Vese method.

Partition detect_mat into foreground and background by iterating a level-set that minimises the summed intensity variance inside and outside the detected regions. Because the energy is driven by region statistics rather than edge gradients, colonies with diffuse boundaries, gradual transitions into agar, or heterogeneous surface texture are captured cleanly as smooth, well-regularised outlines. The contour is initialised and evolved until the per-pixel change falls below tol or max_num_iter is reached, after which the binary mask is labelled into individual connected-component colonies.

For a comparison of region-based and edge-based detection strategies, see Detection Strategies Compared.

Best For:

Mucoid or spreading colonies whose boundaries lack sharp intensity gradients.
Plates with uneven colony pigmentation or sector-variant morphology that fragments threshold-based masks.
Low-contrast imaging conditions where colony and agar intensities overlap in the histogram.
Morphology studies requiring smooth, well-regularised colony outlines rather than jagged threshold masks.

Consider Also:

OtsuDetector when colonies and background form two distinct histogram peaks and a fast global threshold suffices.
HysteresisDetector when colony brightness varies but edges remain reasonably sharp.
CannyDetector when colonies are best delineated by edge contrast rather than interior region homogeneity.
WatershedDetector when touching, compact colonies must be separated after an initial binary detection.

Parameters:

mu (Annotated[float, TuneSpec(low=0.0, high=1.0, step=None, log=False, categories=None, tunable=True)]) – Contour-length penalty weight in the Chan-Vese energy functional. Higher values produce shorter, smoother outlines and merge nearby colonies; lower values allow the contour to track fine boundary detail at the cost of noise sensitivity. Typical range: 0–1 (lower for diffuse mucoid edges, higher for noisy plates). Default: 0.25.
lambda1 (Annotated[float, TuneSpec(low=0.5, high=2.0, step=None, log=False, categories=None, tunable=True)]) – Fidelity weight for intensity deviation inside detected regions. Increasing lambda1 forces the contour to enclose pixels whose intensity matches the estimated colony mean more tightly. Equal weights (lambda1 = lambda2 = 1.0) are the standard default recommended by Chan & Vese (2001); use asymmetric weights when colony and agar intensity distributions differ strongly. Default: 1.0.
lambda2 (Annotated[float, TuneSpec(low=0.5, high=2.0, step=None, log=False, categories=None, tunable=True)]) – Fidelity weight for intensity deviation outside detected regions (background). Increasing lambda2 relative to lambda1 penalises background heterogeneity more strongly (follows directly from the energy functional); useful when agar is uniform but colony texture is heterogeneous. Default: 1.0.
max_num_iter (Annotated[int, TuneSpec(low=100, high=1000, step=None, log=False, categories=None, tunable=True)]) – Hard cap on level-set evolution iterations. The algorithm stops early if tol is satisfied. Increase for complex or slowly converging shapes (mucoid, low-contrast); decrease to trade boundary accuracy for speed. Typical range: 100–1000. Default: 500.
tol (Annotated[float, TuneSpec(low=1e-05, high=0.01, step=None, log=True, categories=None, tunable=True)]) – Early-stopping criterion: per-pixel level-set change normalised by image area. Smaller values give more precise convergence but require more iterations; values below the noise floor exhaust max_num_iter without improving results. Typical range: 1e-5–1e-2. Default: 1e-3.
dt (Annotated[float, TuneSpec(low=0.1, high=1.0, step=None, log=False, categories=None, tunable=True)]) – Step-size multiplier for each level-set iteration. Larger values converge faster but may lead to convergence problems on complex or low-contrast shapes; keep within 0.1–1.0 on real plate images. Default: 0.5.
init_level_set (str) – Shape used to initialise the level-set function before evolution. Accepted values: "checkerboard" (sin × sin pattern; fast multi-front convergence, well-suited to arrayed plates with spatially distributed colonies), "disk" (large circle shrinking inward; slower but more likely to detect implicit edges), "small disk" (small expanding circle at the image centre). Default: "checkerboard".
min_size (Annotated[int, TuneSpec(low=10, high=500, step=None, log=True, categories=None, tunable=True)]) – Minimum connected-component area in pixels. Components smaller than this are discarded after labelling. Increase to remove condensation droplets, debris, or noise fragments; decrease for high-density formats where each colony occupies few pixels. Typical range: 10–500. Default: 50.
connectivity (Annotated[int, Ge(ge=1), Le(le=2), TuneSpec(low=None, high=None, step=None, log=False, categories=(1, 2), tunable=True)]) – Pixel connectivity for connected-component labelling. 1 uses 4-connectivity (orthogonal neighbours only; keeps colonies that touch only at corners separate); 2 uses 8-connectivity (all neighbours including diagonals; merges corner-touching regions). Default: 2.

Returns:

Input image with objmask set to the binary segmentation mask and objmap set to consecutively labelled connected components.

Return type:

Image

Raises:

ValueError – If init_level_set is not one of the recognised initialisation strings.

References

[1] T. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Trans. Image Process., vol. 10, no. 2, pp. 266–277, Feb. 2001.

[2] P. Getreuer, “Chan-Vese segmentation,” Image Process. On Line, vol. 2, pp. 214–224, 2012.

See also

Tutorial 2: Detecting Colonies: Step-by-step tutorial for basic colony detection on plate images.
How To: Choose a Detection Algorithm: Guide for selecting the right detector for your imaging conditions.
Detection Strategies Compared: In-depth comparison of all detection strategies and their failure modes.

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.
`mu`
`lambda1`
`lambda2`
`max_num_iter`
`tol`
`dt`
`init_level_set`
`min_size`
`connectivity`

mu: Annotated[float, TuneSpec(0.0, 1.0)]#

lambda1: Annotated[float, TuneSpec(0.5, 2.0)]#

lambda2: Annotated[float, TuneSpec(0.5, 2.0)]#

max_num_iter: Annotated[int, TuneSpec(100, 1000)]#

tol: Annotated[float, TuneSpec(1e-05, 0.01, log=True)]#

dt: Annotated[float, TuneSpec(0.1, 1.0)]#

init_level_set: str#

min_size: Annotated[int, TuneSpec(10, 500, log=True)]#

connectivity: Annotated[int, TuneSpec(categories=[1, 2])]#

__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, inplace=False)#

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 = {'connectivity': FieldInfo(annotation=int, required=False, default=2, description='Pixel connectivity for connected-component labelling. ``1`` uses 4-connectivity (orthogonal neighbours only; keeps colonies that touch only at corners separate); ``2`` uses 8-connectivity (all neighbours including diagonals; merges corner-touching regions). Default: 2.', metadata=[Ge(ge=1), Le(le=2), TuneSpec(low=None, high=None, step=None, log=False, categories=(1, 2), tunable=True)]), 'dt': FieldInfo(annotation=float, required=False, default=0.5, description='Step-size multiplier for each level-set iteration. Larger values converge faster but may lead to convergence problems on complex or low-contrast shapes; keep within 0.1--1.0 on real plate images.', metadata=[TuneSpec(low=0.1, high=1.0, step=None, log=False, categories=None, tunable=True)]), 'init_level_set': FieldInfo(annotation=str, required=False, default='checkerboard', description='Shape used to initialise the level-set function before evolution. Accepted values: ``"checkerboard"`` (sin × sin pattern; fast multi-front convergence, well-suited to arrayed plates with spatially distributed colonies), ``"disk"`` (large circle shrinking inward; slower but more likely to detect implicit edges), ``"small disk"`` (small expanding circle at the image centre). Default: ``"checkerboard"``.'), 'lambda1': FieldInfo(annotation=float, required=False, default=1.0, description='Fidelity weight for intensity deviation inside detected regions. Increasing ``lambda1`` forces the contour to enclose pixels whose intensity matches the estimated colony mean more tightly. Equal weights (``lambda1`` = ``lambda2`` = 1.0) are the standard default recommended by Chan & Vese (2001); use asymmetric weights when colony and agar intensity distributions differ strongly. Default: 1.0.', metadata=[TuneSpec(low=0.5, high=2.0, step=None, log=False, categories=None, tunable=True)]), 'lambda2': FieldInfo(annotation=float, required=False, default=1.0, description='Fidelity weight for intensity deviation outside detected regions (background). Increasing ``lambda2`` relative to ``lambda1`` penalises background heterogeneity more strongly (follows directly from the energy functional); useful when agar is uniform but colony texture is heterogeneous. Default: 1.0.', metadata=[TuneSpec(low=0.5, high=2.0, step=None, log=False, categories=None, tunable=True)]), 'max_num_iter': FieldInfo(annotation=int, required=False, default=500, description='Hard cap on level-set evolution iterations. The algorithm stops early if ``tol`` is satisfied. Increase for complex or slowly converging shapes (mucoid, low-contrast); decrease to trade boundary accuracy for speed. Typical range: 100--1000. Default: 500.', metadata=[TuneSpec(low=100, high=1000, step=None, log=False, categories=None, tunable=True)]), 'min_size': FieldInfo(annotation=int, required=False, default=50, description='Minimum connected-component area in pixels. Components smaller than this are discarded after labelling. Increase to remove condensation droplets, debris, or noise fragments; decrease for high-density formats where each colony occupies few pixels. Typical range: 10--500. Default: 50.', metadata=[TuneSpec(low=10, high=500, step=None, log=True, categories=None, tunable=True)]), 'mu': FieldInfo(annotation=float, required=False, default=0.25, description='Contour-length penalty weight in the Chan-Vese energy functional. Higher values produce shorter, smoother outlines and merge nearby colonies; lower values allow the contour to track fine boundary detail at the cost of noise sensitivity. Typical range: 0--1 (lower for diffuse mucoid edges, higher for noisy plates).', metadata=[TuneSpec(low=0.0, high=1.0, step=None, log=False, categories=None, tunable=True)]), 'tol': FieldInfo(annotation=float, required=False, default=0.001, description='Early-stopping criterion: per-pixel level-set change normalised by image area. Smaller values give more precise convergence but require more iterations; values below the noise floor exhaust ``max_num_iter`` without improving results. Typical range: 1e-5--1e-2. Default: 1e-3.', metadata=[TuneSpec(low=1e-05, high=0.01, step=None, log=True, categories=None, tunable=True)])}#

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.detect.ChanVeseDetector#

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