phenotypic.abc_.ThresholdDetector#

class phenotypic.abc_.ThresholdDetector[source]#

Bases: ObjectDetector, ABC

Marker ABC for threshold-based colony detection strategies.

ThresholdDetector specializes ObjectDetector for algorithms that detect colonies by converting grayscale intensity to a binary mask via thresholding. Unlike edge-based (Canny) or peak-based (RoundPeaks) approaches, thresholding works by partitioning intensity space: pixels above a threshold value become foreground (colonies), pixels below become background.

Quick Decision Guide

Choose your detection strategy based on image characteristics:

Threshold-based: Clear intensity separation between colonies and background? Try global threshold (Otsu, Yen) or local adaptive (block-based) thresholding.
Edge-based (CannyDetector): Faint or merged colonies? Canny edge detection finds boundaries where gradient is high; invert edges to label regions.
Peak-based (RoundPeaksDetector): Well-separated round colonies? Peak detection assumes circular shapes and grows from intensity maxima.
Subclass decision: Is your algorithm threshold-based? Subclass ThresholdDetector. Otherwise subclass ObjectDetector directly.
Local vs global threshold: Uneven illumination? Local (adaptive) thresholding adjusts per neighborhood; global methods fail on gradient-heavy plates.
Advanced strategy: Need dual-threshold with edge tracking? See [HysteresisDetector](src/phenotypic/detect/_hysteresis_detector.py).

Why threshold-based detection?

Thresholding is ideal when:

Clear intensity separation: Colonies have distinctly different intensity than background (common on high-contrast agar plates or with good lighting).
Simplicity and speed: Single-pass algorithms (no iterative edge tracking or distance computation).
Robustness to morphology: Works equally well on round and irregular colonies (unlike peak-based approaches that assume circular shapes).
Well-defined boundary: Sharp transitions between foreground and background (less effective on blurry or faded colonies).

Thresholding strategies implemented in PhenoTypic

[OtsuDetector](src/phenotypic/detect/_otsu_detector.py): Minimizes within-class variance. Automatic, global, works for balanced histograms.
[LiDetector](src/phenotypic/detect/_li_detector.py): Minimizes Kullback-Leibler divergence. Good for dark colonies on bright background.
[YenDetector](src/phenotypic/detect/_yen_detector.py): Maximizes object variance. Excellent for sharply defined colonies.
[TriangleDetector](src/phenotypic/detect/_triangle_detector.py): Connects histogram extrema. Works well for non-overlapping bimodal distributions.
[IsodataDetector](src/phenotypic/detect/_isodata_detector.py): Iteratively refines based on class means. Robust but slower.
[MeanDetector](src/phenotypic/detect/_mean_detector.py) / [MinimumDetector](src/phenotypic/detect/_minimum_detector.py): Simple heuristic thresholds. Fast, useful for baseline.
[HysteresisDetector](src/phenotypic/detect/_hysteresis_detector.py): Advanced dual-threshold with edge tracking. Handles variable colony intensity.

When to subclass ThresholdDetector vs ObjectDetector directly

Subclass ThresholdDetector if your algorithm uses threshold-based intensity partitioning:

Converts intensity to binary mask via threshold comparison (mask = enh > threshold).
Uses automatic threshold selection (Otsu, Li, Yen, Triangle, Isodata, etc.).
Uses simple heuristic thresholds (mean, minimum, percentile-based).
Signals intent to other developers: “this detector groups with thresholding methods.”
May share utility methods in future (e.g., post-processing filters).

Subclass ObjectDetector directly if your algorithm uses alternative strategies:

Edge detection (find gradients, not intensity levels).
Peak finding (assumes round shapes, grows from maxima).
Watershed segmentation or other region-based approaches.
Hybrid methods that don’t fit threshold → binary mask → label pattern.

Typical workflow: enhance → threshold → label → refine

Most ThresholdDetector implementations follow this pipeline:

Read detection matrix: enh = image.detect_mat[:] (preprocessed for contrast and noise suppression).
Compute threshold: Use chosen strategy (Otsu, Li, Yen, etc.) to find optimal threshold value from histogram.
Create binary mask: mask = enh > threshold or mask = enh >= threshold (test both if edge pixels ambiguous).
Post-process (optional): Remove small noise, clear borders, morphological cleanup to improve mask quality.
Label connected components: Use scipy.ndimage.label() to assign unique integer IDs to each colony (objmap).
Set both outputs: image.objmask = mask, image.objmap = labeled_map.

Parameter tuning guidance

Threshold-based detectors expose parameters affecting detection quality:

Threshold value (manual methods only): Higher → fewer, larger colonies; lower → more, noisier. Test range on representative images to find balance.
Block size (local/adaptive methods): Larger blocks smooth mask but miss small colonies; smaller blocks add detail but amplify noise. Start with 1/8 to 1/4 of image width.
ignore_zeros: Skip pure black pixels in threshold computation. Useful when background has significant black regions (shadows, vignetting).
ignore_borders: Automatically remove objects touching image edges. Prevents partial colonies at plate edges from skewing analysis.
min_size / max_size: Post-processing filters. Remove objects below min (noise) or above max (artifacts). Measure typical colony size on your plates first.

Comparison with other detection strategies

[CannyDetector](src/phenotypic/detect/_canny_detector.py) (edge-based): Finds intensity gradients to locate boundaries. Better for faint or merged colonies; requires tuning gradient thresholds.
[RoundPeaksDetector](src/phenotypic/detect/_round_peaks_detector.py) (peak-based): Assumes round shapes, grows from maxima. Excellent for well-separated round colonies; fails on irregular or merged shapes.
Threshold-based (this class): Direct intensity partitioning. Robust, fast, works for any shape; requires good intensity separation between colonies and background.

Common pitfalls and remedies

Over-segmentation (too many small objects): Use ignore_zeros=True to skip dark pixels, apply morphological opening (remove_small_objects), or refine with ObjectRefiner.
Under-segmentation (merged colonies): Try local thresholding (adaptive block-based), morphological closing, or watershed post-processing in ObjectRefiner.
False positives at edges: Use ignore_borders=True parameter or clear_border() in ObjectRefiner to remove edge-touching objects.
Uneven illumination (vignetting, shadows): Apply enhancement (EnhanceLocalContrast, illumination correction) before detection, or switch to local adaptive thresholding.
Threshold too high/low: Visualize objmask on sample images to diagnose. Adjust parameters and re-test on representative plates before batch processing.

Local thresholding pattern (adaptive to uneven illumination)

When images have uneven illumination or vignetting, local (adaptive) thresholding computes a threshold per neighborhood instead of globally. This handles gradual intensity changes:

from skimage import filters
from scipy import ndimage
import numpy as np

enh = image.detect_mat[:]
# Compute local threshold for each pixel
block_size = 31  # Neighborhood size (odd integer)
threshold_map = filters.threshold_local(enh, block_size=block_size)
# Create mask: pixel > its local threshold
mask = enh > threshold_map
# Label connected components
labeled, _ = ndimage.label(mask)
image.objmask[:] = mask
image.objmap[:] = labeled
return image

Implementation pattern: Global automatic threshold

For global automatic thresholding (Otsu, Li, Yen, Triangle, Isodata), follow this pattern:

from skimage import filters
from scipy import ndimage

def _operate(self, image):
    enh = image.detect_mat[:]
    # Compute threshold value via automatic method
    threshold = filters.threshold_otsu(enh)  # or threshold_li, threshold_yen, etc.
    # Create binary mask: pixels above threshold
    mask = enh > threshold
    # Label connected components
    labeled, num_objects = ndimage.label(mask)
    # Set both outputs
    image.objmask[:] = mask
    image.objmap[:] = labeled
    return image

Key points: Read preprocessed detect_mat, compute single threshold, compare all pixels at once, label result. This is fast and deterministic (same image always produces same result).

Interface specification

Subclasses of ThresholdDetector must:

Inherit from ThresholdDetector (which provides ObjectDetector’s interface).
Implement _operate(image: Image) -> Image as an instance method.
Within _operate():
- Read image.detect_mat[:] (and optionally image.rgb[:], image.gray[:]).
- Compute threshold (automatically or from parameter).
- Generate binary mask via comparison: mask = enh > threshold.
- Label connected components: labeled, _ = ndimage.label(mask).
- Set both outputs: image.objmask = mask, image.objmap = labeled.
- Return modified image.
Add to phenotypic.detect.__init__.py exports for public discovery.

Notes

This is a marker ABC with no additional methods. It exists to categorize threshold-based detectors in the class hierarchy and enable flexible discovery and code organization.

Examples

Detect colonies using Otsu’s automatic threshold:

>>> from phenotypic import Image
>>> from phenotypic.detect import OtsuDetector
>>> # Load a plate image
>>> plate = Image.imread("agar_plate.jpg")
>>> # Apply Otsu threshold detection
>>> detector = OtsuDetector(ignore_zeros=True, ignore_borders=True)
>>> detected = detector.apply(plate)
>>> # Access results
>>> mask = detected.objmask[:]  # Binary mask
>>> objmap = detected.objmap[:]  # Labeled map
>>> num_colonies = objmap.max()
>>> print(f"Detected {num_colonies} colonies")
>>> # Iterate over colonies
>>> for colony in detected.objects:
...     print(f"Colony {colony.label}: area={colony.area} px")

Compare different threshold strategies:

>>> from phenotypic import Image
>>> from phenotypic.detect import (
...     OtsuDetector, LiDetector, YenDetector, TriangleDetector
... )
>>> plate = Image.imread("agar_plate.jpg")
>>> # Test multiple threshold strategies
>>> detectors = {
...     "Otsu": OtsuDetector(),
...     "Li": LiDetector(),
...     "Yen": YenDetector(),
...     "Triangle": TriangleDetector(),
... }
>>> for name, detector in detectors.items():
...     result = detector.apply(plate)
...     num = result.objmap[:].max()
...     print(f"{name}: detected {num} colonies")

Build a pipeline with thresholding and refinement:

>>> from phenotypic import Image, ImagePipeline
>>> from phenotypic.enhance import ContrastEnhancer
>>> from phenotypic.detect import OtsuDetector
>>> from phenotypic.refine import RemoveSmallObjectsRefiner
>>> # Create pipeline
>>> pipeline = ImagePipeline()
>>> pipeline.add(ContrastEnhancer(factor=1.5))  # Boost contrast
>>> pipeline.add(OtsuDetector(ignore_zeros=True))  # Threshold
>>> pipeline.add(RemoveSmallObjectsRefiner(min_size=50))  # Cleanup
>>> # Process image
>>> plate = Image.imread("agar_plate.jpg")
>>> result = pipeline.operate([plate])[0]
>>> print(f"Final colonies: {result.objmap[:].max()}")

Tuning threshold detection on your plate images:

>>> from phenotypic import Image
>>> from phenotypic.detect import (
...     OtsuDetector, YenDetector, TriangleDetector
... )
>>> # Load a sample plate image
>>> plate = Image.imread("sample_plate.jpg")
>>> # Test different threshold strategies
>>> strategies = {
...     "Otsu": OtsuDetector(ignore_zeros=True),
...     "Yen": YenDetector(ignore_zeros=True),
...     "Triangle": TriangleDetector(ignore_zeros=True),
... }
>>> best_detector = None
>>> best_count = 0
>>> for name, detector in strategies.items():
...     result = detector.apply(plate)
...     num_colonies = result.objmap[:].max()
...     print(f"{name}: {num_colonies} colonies detected")
...     # Choose detector that finds expected number of colonies
...     if best_detector is None:
...         best_detector = detector
...         best_count = num_colonies
>>> # Use best detector for batch processing
>>> print(f"Selected: {type(best_detector).__name__}")

Methods

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

Attributes

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

__copy__() → Self#

Returns a shallow copy of the model.

Return type:: Self

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

Returns a deep copy of the model.

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

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

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

Hook into generating the model’s JSON schema.

Parameters:

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

Returns:

A JSON schema, as a Python object.

Return type:

JsonSchemaValue

__init__(**data: Any) → None#

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

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

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

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

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

So dict(model) works.

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

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

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

Parameters:

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

Return type:

Generator[Any]

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

Populate field descriptions from the subclass docstring.

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

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

classmethod __pydantic_on_complete__() → None#

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

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

Return type:: None

__repr_name__() → str#

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

Return type:: str

__repr_recursion__(object: Any) → str#

Returns the string representation of a recursive object.

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

__rich_repr__() → RichReprResult#

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

Return type:: RichReprResult

apply(image, 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 = {}#

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_.ThresholdDetector#

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