phenotypic.detect.FilamentousFungiDetector#

class phenotypic.detect.FilamentousFungiDetector(*, inoculum_detector: ~typing.Annotated[~typing.Any, ~pydantic.functional_validators.BeforeValidator(func=~phenotypic.sdk_.typing_._deserialize_operation_value, json_schema_input_type=PydanticUndefined), ~pydantic.functional_validators.AfterValidator(func=<phenotypic.sdk_.typing_._RequireValue object at 0x7f95bce62b00>), ~pydantic.functional_serializers.PlainSerializer(func=~phenotypic.sdk_.typing_._serialize_operation_value, return_type=PydanticUndefined, when_used=always), _OperationFieldMarker()] | None = None, max_colony_radius_px: ~typing.Annotated[float, TuneSpec(low=50.0, high=500.0, step=None, log=True, categories=None, tunable=True)] = 250.0, min_branch_width_px: ~typing.Annotated[int, TuneSpec(low=2, high=10, step=None, log=False, categories=None, tunable=True)] = 3, ignore_borders: bool = True, edge_noise_threshold: ~typing.Annotated[float, TuneSpec(low=2.0, high=12.0, step=None, log=False, categories=None, tunable=True)] = 6.0, reconnection_tolerance: ~typing.Annotated[float, TuneSpec(low=1.0, high=5.0, step=None, log=False, categories=None, tunable=True)] = 2.5, max_gap_length: ~typing.Annotated[int, TuneSpec(low=10, high=60, step=None, log=False, categories=None, tunable=True)] = 30, border_margin_px: ~typing.Annotated[int, TuneSpec(low=20, high=100, step=None, log=False, categories=None, tunable=True)] = 50, frag_reach_px: ~typing.Annotated[int, TuneSpec(low=5, high=30, step=None, log=False, categories=None, tunable=True)] = 10, gap_crossing_penalty: ~typing.Annotated[float, TuneSpec(low=1.0, high=10.0, step=None, log=False, categories=None, tunable=True)] = 4.0, gauss_sigma: ~typing.Annotated[float | None, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)] = None, tile_size: ~typing.Annotated[int | None, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)] = None, tile_overlap: ~typing.Annotated[int | None, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)] = None, pct_min_wavelength: ~typing.Annotated[float | None, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)] = None, mad_window: ~typing.Annotated[int | None, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)] = None, path_dilation_radius: ~typing.Annotated[int | None, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)] = None, snr_margin: ~typing.Annotated[int | None, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)] = None, coherence_window_radius: ~typing.Annotated[int | None, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)] = None)[source]#

Bases: GridObjectDetector

Detect and individually label filamentous fungal colonies by two-stage inoculum-plus-hyphae detection with Euclidean Voronoi partition.

First detect compact inoculation centres with inoculum_detector, then capture the full hyphal network using phase-congruency edge detection combined with Gaussian background subtraction. Disconnected branch fragments are reconnected to their parent colonies via quality-filtered Dijkstra pathfinding on a composite cost surface derived from phase congruency energy, local texture, and orientation coherence. Inoculum centroids seed a Euclidean Voronoi partition that assigns every fungal pixel to its nearest colony, with connectivity-based correction enforcing uniform labelling within each connected component.

For an algorithm overview and a comparison with other detection strategies, see Detection Strategies Compared and The Filamentous Fungi Detection Algorithm.

Best For:

Filamentous fungal colonies (Aspergillus, Neurospora, Trichoderma) with irregular, spreading hyphal morphologies.
Dense plates where neighbouring fungal colonies touch or overlap and must be individually labelled.
Time-course experiments tracking hyphal extension radially outward from compact inoculation sites.
Grid-based fungal culture plates where one colony per well must be quantified separately.
High-throughput fungal phenotyping screens requiring consistent separation quality across hundreds of plates.

Consider Also:

WatershedDetector when colonies are compact and roughly circular (yeast or bacterial morphology).
OtsuDetector when fungi are well-separated and a single binary mask suffices without individual colony labelling.
CompositeDetector when combining multiple detection strategies is preferred over the two-stage centre-plus-hyphae approach.
InoculumDetector when only the compact inoculation centres are needed and full hyphal reconstruction is not required.

Parameters:

inoculum_detector (Annotated[Any, BeforeValidator(func=~phenotypic.sdk_.typing_._deserialize_operation_value, json_schema_input_type=PydanticUndefined), AfterValidator(func=<phenotypic.sdk_.typing_._RequireValue object at 0x7f95bce62b00>), PlainSerializer(func=~phenotypic.sdk_.typing_._serialize_operation_value, return_type=PydanticUndefined, when_used=always), _OperationFieldMarker()] | None) – ObjectDetector or ImagePipeline used to locate compact fungal centres. Should produce small, tight regions at inoculation points; centroids from this detector seed the final Voronoi partition. When None (default), an internal InoculumDetector + KeepSectionLargest pipeline is used. Default: None.
follow (# Scene-scale parameters — set these first; derived params)
max_colony_radius_px (Annotated[float, TuneSpec(low=50.0, high=500.0, step=None, log=True, categories=None, tunable=True)]) – Expected maximum colony radius in pixels. Acts as the master scene knob: proportionally scales gauss_sigma, tile_size, and tile_overlap when those are left at None. A reasonable starting point is the radius of the largest colony in pixels at your imaging resolution (e.g. measure colony extent in your image viewer before setting this). Reduce for short-incubation plates or high-well-count formats; increase for slow-growing species with extensive radial growth. Typical range: 50–400. Default: 250.0.
min_branch_width_px (Annotated[int, TuneSpec(low=2, high=10, step=None, log=False, categories=None, tunable=True)]) – Expected narrowest hyphal branch width in pixels. Scales signal-detection parameters (pct_min_wavelength, mad_window, path_dilation_radius, snr_margin, coherence_window_radius) when those are left at None. Set to the thinnest hyphae visible at your imaging resolution; the derived pct_min_wavelength (2 × min_branch_width_px) is clamped at the Nyquist floor of 2 px. Typical range: 2–8. Default: 3.
control (# Detection)
ignore_borders (bool) – Drop objects touching the image border during hysteresis-threshold branch detection. Enable (default) to avoid partial colonies at plate edges; disable when genuine peripheral hyphal growth must be retained. Default: True.
edge_noise_threshold (Annotated[float, TuneSpec(low=2.0, high=12.0, step=None, log=False, categories=None, tunable=True)]) – Noise-suppression multiplier k for the phase congruency detector. Only features whose phase energy exceeds the estimated noise mean plus k standard deviations of the noise energy are accepted as real edges. Higher values suppress agar texture artefacts at the cost of rejecting weak peripheral hyphae; lower values recover fine structure but may pass background noise on textured media. Typical range: 2.0–10.0. Default: 6.0.
quality (# Reconnection)
reconnection_tolerance (Annotated[float, TuneSpec(low=1.0, high=5.0, step=None, log=False, categories=None, tunable=True)]) – IQR multiplier for calibrating reconnection path quality thresholds from confirmed calibration branches. Thresholds are set at median ± reconnection_tolerance × IQR across five quality metrics. Higher values accept more candidate paths (permissive); lower values require paths to closely resemble calibration branches (conservative). Typical range: 1.5–4.0. Default: 2.5.
max_gap_length (Annotated[int, TuneSpec(low=10, high=60, step=None, log=False, categories=None, tunable=True)]) – Maximum contiguous stretch of high-cost pixels tolerated along a reconnection path, in pixels. Paths containing a window worse than the calibrated threshold are rejected as routing through bare agar. Increase to bridge longer hyphal gaps; decrease to reject longer detours through background. Typical range: 10–100. Default: 30.
border_margin_px (Annotated[int, TuneSpec(low=20, high=100, step=None, log=False, categories=None, tunable=True)]) – Width of the border penalty ramp applied to image-edge pixels in the Dijkstra cost surface. Prevents reconnection paths from routing along plate borders instead of through genuine hyphal corridors. Set to 0 to disable. Typical range: 0–150. Default: 50.
frag_reach_px (Annotated[int, TuneSpec(low=5, high=30, step=None, log=False, categories=None, tunable=True)]) – Pre-screening radius in pixels. Fragments whose nearest routable pixel exceeds this distance from the colony boundary are discarded before Dijkstra, saving computation. Fragments within this radius are forwarded for full quality-filtered reconnection. Typical range: 5–40. Default: 10.
gap_crossing_penalty (Annotated[float, TuneSpec(low=1.0, high=10.0, step=None, log=False, categories=None, tunable=True)]) – Scaling factor for the distance-weighted gap penalty applied to Dijkstra path costs. Higher values strongly penalise traversal of bare agar far from the colony, keeping paths near established structure; lower values allow longer background detours. Typical range: 1.0–10.0. Default: 4.0.
overrides (# Scene-derivation)
gauss_sigma (Annotated[float | None, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)]) – Gaussian sigma for background subtraction, in pixels. When None, set to 1.2 × max_colony_radius_px (300 px at the default radius). Must exceed the largest colony radius so the Gaussian estimates only the illumination gradient, not colony signal. Typical range: 50–600. Default: None.
tile_size (Annotated[int | None, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)]) – Side length of square processing tiles in pixels. When None, set to int(round(4.8 × max_colony_radius_px)) (1200 px at the default radius). Must be large enough to contain an entire colony and its satellite fragments within one tile. Typical range: 200–3000. Default: None.
tile_overlap (Annotated[int | None, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)]) – Overlap between adjacent tiles in pixels. When None, set to int(round(2.4 × max_colony_radius_px)) (600 px at the default radius). Larger overlap ensures fragments near tile boundaries are co-located with their parent colony in at least one tile. Typical range: 50–1500. Default: None.
pct_min_wavelength (Annotated[float | None, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)]) – Minimum log-Gabor filter wavelength in pixels for phase congruency detection. When None, set to 2.0 × min_branch_width_px (6 px at the default width). Must be ≥ 2 (Nyquist). Match to the thinnest hyphae width at your imaging resolution. Typical range: 2–20. Default: None.
mad_window (Annotated[int | None, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)]) – Side length of the square median-filter kernel for local MAD texture computation (must be odd). When None, set to 2 × min_branch_width_px + 1 forced odd (7 at the default width). Should span approximately one branch diameter plus background buffer on each side. Typical range: 3–21. Default: None.
path_dilation_radius (Annotated[int | None, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)]) – Disk radius for dilating accepted reconnection paths before painting colony labels. When None, set to max(1, round(0.5 × min_branch_width_px)) (2 at the default width). Also sets the inner band radius for path quality metrics. Match to half the expected hyphal width. Typical range: 1–10. Default: None.
snr_margin (Annotated[int | None, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)]) – Extra pixel margin beyond path_dilation_radius that forms the background annular ring for local SNR estimation. When None, set to max(2, round(0.5 × min_branch_width_px)) (2 at the default width). Keep narrow on dense hyphal networks to avoid sampling adjacent hyphae as background. Typical range: 1–8. Default: None.
coherence_window_radius (Annotated[int | None, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)]) – Radius of the square averaging kernel for orientation coherence computation. When None, set to round(5.0 × min_branch_width_px) (15 at the default width). Larger radius captures long-range directional consistency; reduce for highly curved or heavily branching networks. Typical range: 5–50. Default: None.

Returns:

Input image with objmask set to a binary mask of all detected fungal pixels and objmap set to a labelled colony map where each fungal colony receives a unique consecutive integer label via Voronoi assignment.

Return type:

Image

Raises:

TypeError – If inoculum_detector is not an ObjectDetector or ImagePipeline instance.
ValueError – If no inoculum centres are detected, or no detected centres overlap with the branch structure after filtering.

References

[1] P. Kovesi, “Phase congruency: A low-level image invariant,” Psychol. Res., vol. 64, no. 2, pp. 136–148, 2000.

[2] E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numer. Math., vol. 1, no. 1, pp. 269–271, 1959.

See also

Tutorial 10: Detecting Filamentous Fungi: Dedicated tutorial for filamentous fungi detection workflows.
How To: Choose a Detection Algorithm: Guide for selecting the right detector for your plate images.
The Filamentous Fungi Detection Algorithm: Algorithm details for the two-stage detection and Voronoi partition approach.
Detection Strategies Compared: 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

`beta`
`delta`
`gamma`
`gauss_n_iter`
`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.
`pct_n_orient`
`inoculum_detector`
`max_colony_radius_px`
`min_branch_width_px`
`ignore_borders`
`edge_noise_threshold`
`reconnection_tolerance`
`max_gap_length`
`border_margin_px`
`frag_reach_px`
`gap_crossing_penalty`
`gauss_sigma`
`tile_size`
`tile_overlap`
`pct_min_wavelength`
`mad_window`
`path_dilation_radius`
`snr_margin`
`coherence_window_radius`

beta: ClassVar[float] = 2.0#

gamma: ClassVar[float] = 1.2#

gauss_n_iter: ClassVar[int] = 2#

delta: ClassVar[float] = 1.0#

pct_n_orient: ClassVar[int] = 8#

inoculum_detector: OperationField | None#

max_colony_radius_px: Annotated[float, TuneSpec(50.0, 500.0, log=True)]#

min_branch_width_px: Annotated[int, TuneSpec(2, 10)]#

ignore_borders: bool#

edge_noise_threshold: Annotated[float, TuneSpec(2.0, 12.0)]#

reconnection_tolerance: Annotated[float, TuneSpec(1.0, 5.0)]#

max_gap_length: Annotated[int, TuneSpec(10, 60)]#

border_margin_px: Annotated[int, TuneSpec(20, 100)]#

frag_reach_px: Annotated[int, TuneSpec(5, 30)]#

gap_crossing_penalty: Annotated[float, TuneSpec(1.0, 10.0)]#

gauss_sigma: Annotated[float | None, TuneSpec(tunable=False)]#

tile_size: Annotated[int | None, TuneSpec(tunable=False)]#

tile_overlap: Annotated[int | None, TuneSpec(tunable=False)]#

pct_min_wavelength: Annotated[float | None, TuneSpec(tunable=False)]#

mad_window: Annotated[int | None, TuneSpec(tunable=False)]#

path_dilation_radius: Annotated[int | None, TuneSpec(tunable=False)]#

snr_margin: Annotated[int | None, TuneSpec(tunable=False)]#

coherence_window_radius: Annotated[int | None, TuneSpec(tunable=False)]#

__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 = {'border_margin_px': FieldInfo(annotation=int, required=False, default=50, description='Width of the border penalty ramp applied to image-edge pixels in the Dijkstra cost surface. Prevents reconnection paths from routing along plate borders instead of through genuine hyphal corridors. Set to 0 to disable. Typical', metadata=[TuneSpec(low=20, high=100, step=None, log=False, categories=None, tunable=True)]), 'coherence_window_radius': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, description='Radius of the square averaging kernel for orientation coherence computation. When ``None``, set to ``round(5.0 × min_branch_width_px)`` (15 at the default width). Larger radius captures long-range directional consistency; reduce for highly curved or heavily branching networks. Typical', metadata=[TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)]), 'edge_noise_threshold': FieldInfo(annotation=float, required=False, default=6.0, description='Noise-suppression multiplier ``k`` for the phase congruency detector. Only features whose phase energy exceeds the estimated noise mean plus ``k`` standard deviations of the noise energy are accepted as real edges. Higher values suppress agar texture artefacts at the cost of rejecting weak peripheral hyphae; lower values recover fine structure but may pass background noise on textured media. Typical range: 2.0--10.0. Default: 6.0. # Reconnection quality', metadata=[TuneSpec(low=2.0, high=12.0, step=None, log=False, categories=None, tunable=True)]), 'frag_reach_px': FieldInfo(annotation=int, required=False, default=10, description='Pre-screening radius in pixels. Fragments whose nearest routable pixel exceeds this distance from the colony boundary are discarded before Dijkstra, saving computation. Fragments within this radius are forwarded for full quality-filtered reconnection. Typical range: 5--40. Default: 10.', metadata=[TuneSpec(low=5, high=30, step=None, log=False, categories=None, tunable=True)]), 'gap_crossing_penalty': FieldInfo(annotation=float, required=False, default=4.0, description='Scaling factor for the distance-weighted gap penalty applied to Dijkstra path costs. Higher values strongly penalise traversal of bare agar far from the colony, keeping paths near established structure; lower values allow longer background detours. Typical range: 1.0--10.0. Default: 4.0. # Scene-derivation overrides (leave at None to auto-derive)', metadata=[TuneSpec(low=1.0, high=10.0, step=None, log=False, categories=None, tunable=True)]), 'gauss_sigma': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Gaussian sigma for background subtraction, in pixels. When ``None``, set to ``1.2 × max_colony_radius_px`` (300 px at the default radius). Must exceed the largest colony radius so the Gaussian estimates only the illumination gradient, not colony signal. Typical range: 50--600. Default: None.', metadata=[TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)]), 'ignore_borders': FieldInfo(annotation=bool, required=False, default=True, description='Drop objects touching the image border during hysteresis-threshold branch detection. Enable (default) to avoid partial colonies at plate edges; disable when genuine peripheral hyphal growth must be retained. Default: True.'), 'inoculum_detector': FieldInfo(annotation=Union[Annotated[Any, BeforeValidator, AfterValidator, PlainSerializer, _OperationFieldMarker], NoneType], required=False, default=None, description='ObjectDetector or ImagePipeline used to locate compact fungal centres. Should produce small, tight regions at inoculation points; centroids from this detector seed the final Voronoi partition. When ``None`` (default), an internal ``InoculumDetector`` + ``KeepSectionLargest`` pipeline is used.'), 'mad_window': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, description='Side length of the square median-filter kernel for local MAD texture computation (must be odd). When ``None``, set to ``2 × min_branch_width_px + 1`` forced odd (7 at the default width). Should span approximately one branch diameter plus background buffer on each side. Typical range: 3--21.', metadata=[TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)]), 'max_colony_radius_px': FieldInfo(annotation=float, required=False, default=250.0, description='Expected maximum colony radius in pixels. Acts as the master scene knob: proportionally scales ``gauss_sigma``, ``tile_size``, and ``tile_overlap`` when those are left at ``None``. A reasonable starting point is the radius of the largest colony in pixels at your imaging resolution (e.g. measure colony extent in your image viewer before setting this). Reduce for short-incubation plates or high-well-count formats; increase for slow-growing species with extensive radial growth. Typical range: 50--400. Default: 250.0.', metadata=[TuneSpec(low=50.0, high=500.0, step=None, log=True, categories=None, tunable=True)]), 'max_gap_length': FieldInfo(annotation=int, required=False, default=30, description='Maximum contiguous stretch of high-cost pixels tolerated along a reconnection path, in pixels. Paths containing a window worse than the calibrated threshold are rejected as routing through bare agar. Increase to bridge longer hyphal gaps; decrease to reject longer detours through background. Typical', metadata=[TuneSpec(low=10, high=60, step=None, log=False, categories=None, tunable=True)]), 'min_branch_width_px': FieldInfo(annotation=int, required=False, default=3, description='Expected narrowest hyphal branch width in pixels. Scales signal-detection parameters (``pct_min_wavelength``, ``mad_window``, ``path_dilation_radius``, ``snr_margin``, ``coherence_window_radius``) when those are left at ``None``. Set to the thinnest hyphae visible at your imaging resolution; the derived ``pct_min_wavelength`` (``2 × min_branch_width_px``) is clamped at the Nyquist floor of 2 px. Typical range: 2--8. Default: 3. # Detection control', metadata=[TuneSpec(low=2, high=10, step=None, log=False, categories=None, tunable=True)]), 'path_dilation_radius': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, description='Disk radius for dilating accepted reconnection paths before painting colony labels. When ``None``, set to ``max(1, round(0.5 × min_branch_width_px))`` (2 at the default width). Also sets the inner band radius for path quality metrics. Match to half the expected hyphal width. Typical range: 1--10.', metadata=[TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)]), 'pct_min_wavelength': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Minimum log-Gabor filter wavelength in pixels for phase congruency detection. When ``None``, set to ``2.0 × min_branch_width_px`` (6 px at the default width). Must be ≥ 2 (Nyquist). Match to the thinnest hyphae width at your imaging resolution. Typical range: 2--20. Default: None.', metadata=[TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)]), 'reconnection_tolerance': FieldInfo(annotation=float, required=False, default=2.5, description='IQR multiplier for calibrating reconnection path quality thresholds from confirmed calibration branches. Thresholds are set at median ± ``reconnection_tolerance`` × IQR across five quality metrics. Higher values accept more candidate paths (permissive); lower values require paths to closely resemble calibration branches (conservative). Typical range: 1.5--4.0.', metadata=[TuneSpec(low=1.0, high=5.0, step=None, log=False, categories=None, tunable=True)]), 'snr_margin': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, description='Extra pixel margin beyond ``path_dilation_radius`` that forms the background annular ring for local SNR estimation. When ``None``, set to ``max(2, round(0.5 × min_branch_width_px))`` (2 at the default width). Keep narrow on dense hyphal networks to avoid sampling adjacent hyphae as background. Typical range: 1--8. Default: None.', metadata=[TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)]), 'tile_overlap': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, description='Overlap between adjacent tiles in pixels. When ``None``, set to ``int(round(2.4 × max_colony_radius_px))`` (600 px at the default radius). Larger overlap ensures fragments near tile boundaries are co-located with their parent colony in at least one tile. Typical range: 50--1500. Default: None.', metadata=[TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)]), 'tile_size': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, description='Side length of square processing tiles in pixels. When ``None``, set to ``int(round(4.8 × max_colony_radius_px))`` (1200 px at the default radius). Must be large enough to contain an entire colony and its satellite fragments within one tile. Typical range: 200--3000. Default: None.', metadata=[TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)])}#

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.FilamentousFungiDetector#

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