mirror of
https://github.com/blakeblackshear/frigate.git
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729f5c0833
* use a small yolov9 model for detection * use yolov9 for users without frigate+ and update retention algorithm * new lpr config fields * levenshtein distance package * tweaks * docs
83 lines
2.6 KiB
Python
83 lines
2.6 KiB
Python
from typing import Dict, List, Optional
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from pydantic import Field
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from .base import FrigateBaseModel
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__all__ = [
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"FaceRecognitionConfig",
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"SemanticSearchConfig",
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"LicensePlateRecognitionConfig",
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]
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class BirdClassificationConfig(FrigateBaseModel):
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enabled: bool = Field(default=False, title="Enable bird classification.")
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threshold: float = Field(
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default=0.9,
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title="Minimum classification score required to be considered a match.",
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gt=0.0,
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le=1.0,
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)
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class ClassificationConfig(FrigateBaseModel):
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bird: BirdClassificationConfig = Field(
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default_factory=BirdClassificationConfig, title="Bird classification config."
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)
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class SemanticSearchConfig(FrigateBaseModel):
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enabled: bool = Field(default=False, title="Enable semantic search.")
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reindex: Optional[bool] = Field(
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default=False, title="Reindex all detections on startup."
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)
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model_size: str = Field(
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default="small", title="The size of the embeddings model used."
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)
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class FaceRecognitionConfig(FrigateBaseModel):
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enabled: bool = Field(default=False, title="Enable face recognition.")
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min_score: float = Field(
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title="Minimum face distance score required to save the attempt.",
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default=0.8,
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gt=0.0,
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le=1.0,
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)
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threshold: float = Field(
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default=0.9,
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title="Minimum face distance score required to be considered a match.",
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gt=0.0,
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le=1.0,
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)
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min_area: int = Field(
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default=500, title="Min area of face box to consider running face recognition."
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)
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save_attempts: bool = Field(
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default=True, title="Save images of face detections for training."
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)
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class LicensePlateRecognitionConfig(FrigateBaseModel):
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enabled: bool = Field(default=False, title="Enable license plate recognition.")
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threshold: float = Field(
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default=0.9,
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title="License plate confidence score required to be added to the object as a sub label.",
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)
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min_area: int = Field(
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default=1000,
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title="Minimum area of license plate to consider running license plate recognition.",
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)
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min_plate_length: int = Field(
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default=4,
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title="Minimum number of characters a license plate must have to be added to the object as a sub label.",
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)
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match_distance: int = Field(
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default=1,
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title="Allow this number of missing/incorrect characters to still cause a detected plate to match a known plate.",
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)
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known_plates: Optional[Dict[str, List[str]]] = Field(
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default={}, title="Known plates to track."
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)
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