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Facial Recognition Config

immichpy.client.generated.models.facial_recognition_config.FacialRecognitionConfig pydantic-model

Bases: BaseModel

FacialRecognitionConfig

Show JSON schema: 
{
  "description": "FacialRecognitionConfig",
  "properties": {
    "enabled": {
      "description": "Whether the task is enabled",
      "title": "Enabled",
      "type": "boolean"
    },
    "maxDistance": {
      "anyOf": [
        {
          "maximum": 2,
          "minimum": 0.1,
          "type": "number"
        },
        {
          "maximum": 2,
          "minimum": 1,
          "type": "integer"
        }
      ],
      "description": "Maximum distance threshold for face recognition",
      "title": "Maxdistance"
    },
    "minFaces": {
      "description": "Minimum number of faces required for recognition",
      "minimum": 1,
      "title": "Minfaces",
      "type": "integer"
    },
    "minScore": {
      "anyOf": [
        {
          "maximum": 1,
          "minimum": 0.1,
          "type": "number"
        },
        {
          "maximum": 1,
          "minimum": 1,
          "type": "integer"
        }
      ],
      "description": "Minimum confidence score for face detection",
      "title": "Minscore"
    },
    "modelName": {
      "description": "Name of the model to use",
      "title": "Modelname",
      "type": "string"
    }
  },
  "required": [
    "enabled",
    "maxDistance",
    "minFaces",
    "minScore",
    "modelName"
  ],
  "title": "FacialRecognitionConfig",
  "type": "object"
}

Config:

  • populate_by_name: True
  • validate_assignment: True
  • protected_namespaces: ()

Fields:

enabled pydantic-field 

enabled: StrictBool

Whether the task is enabled

max_distance pydantic-field 

max_distance: Union[Annotated[float, Field(le=2, strict=True, ge=0.1)], Annotated[int, Field(le=2, strict=True, ge=1)]]

Maximum distance threshold for face recognition

min_faces pydantic-field 

min_faces: Annotated[int, Field(strict=True, ge=1)]

Minimum number of faces required for recognition

min_score pydantic-field 

min_score: Union[Annotated[float, Field(le=1, strict=True, ge=0.1)], Annotated[int, Field(le=1, strict=True, ge=1)]]

Minimum confidence score for face detection

model_name pydantic-field 

model_name: StrictStr

Name of the model to use

from_dict classmethod 

from_dict(obj: Optional[Dict[str, Any]]) -> Optional[Self]

Create an instance of FacialRecognitionConfig from a dict

Source code in immichpy/client/generated/models/facial_recognition_config.py 
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@classmethod
def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
    """Create an instance of FacialRecognitionConfig from a dict"""
    if obj is None:
        return None

    if not isinstance(obj, dict):
        return cls.model_validate(obj)

    _obj = cls.model_validate(
        {
            "enabled": obj.get("enabled"),
            "maxDistance": obj.get("maxDistance"),
            "minFaces": obj.get("minFaces"),
            "minScore": obj.get("minScore"),
            "modelName": obj.get("modelName"),
        }
    )
    return _obj

from_json classmethod 

from_json(json_str: str) -> Optional[Self]

Create an instance of FacialRecognitionConfig from a JSON string

Source code in immichpy/client/generated/models/facial_recognition_config.py 
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@classmethod
def from_json(cls, json_str: str) -> Optional[Self]:
    """Create an instance of FacialRecognitionConfig from a JSON string"""
    return cls.from_dict(json.loads(json_str))

to_dict 

to_dict() -> Dict[str, Any]

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic's self.model_dump(by_alias=True):

  • None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.
Source code in immichpy/client/generated/models/facial_recognition_config.py 
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def to_dict(self) -> Dict[str, Any]:
    """Return the dictionary representation of the model using alias.

    This has the following differences from calling pydantic's
    `self.model_dump(by_alias=True)`:

    * `None` is only added to the output dict for nullable fields that
      were set at model initialization. Other fields with value `None`
      are ignored.
    """
    excluded_fields: Set[str] = set([])

    _dict = self.model_dump(
        by_alias=True,
        exclude=excluded_fields,
        exclude_none=True,
    )
    return _dict

to_json 

to_json() -> str

Returns the JSON representation of the model using alias

Source code in immichpy/client/generated/models/facial_recognition_config.py 
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def to_json(self) -> str:
    """Returns the JSON representation of the model using alias"""
    # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
    return json.dumps(self.to_dict())

to_str 

to_str() -> str

Returns the string representation of the model using alias

Source code in immichpy/client/generated/models/facial_recognition_config.py 
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def to_str(self) -> str:
    """Returns the string representation of the model using alias"""
    return pprint.pformat(self.model_dump(by_alias=True))