mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-03-04 00:17:22 +01:00
d0e9bcbfdc
* add wheels * move extra index url to bottom * config model option * add postprocess * fix config * jina v2 embedding class * use jina v2 in embeddings * fix ov inference * frontend * update reference config * revert device * fix truncation * return np tensors * use correct embeddings from inference * manual preprocess * clean up * docs * lower batch size for v2 only * docs clarity * wording
106 lines
3.3 KiB
Python
106 lines
3.3 KiB
Python
from enum import Enum
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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 SemanticSearchModelEnum(str, Enum):
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jinav1 = "jinav1"
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jinav2 = "jinav2"
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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 tracked objects on startup."
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)
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model: Optional[SemanticSearchModelEnum] = Field(
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default=SemanticSearchModelEnum.jinav1,
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title="The CLIP model to use for semantic search.",
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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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detection_threshold: float = Field(
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default=0.7,
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title="License plate object confidence score required to begin running recognition.",
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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=1000,
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title="Minimum area of license plate to begin running recognition.",
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)
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recognition_threshold: float = Field(
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default=0.9,
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title="Recognition confidence score required to add the plate to the object as a sub label.",
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gt=0.0,
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le=1.0,
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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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format: Optional[str] = Field(
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default=None,
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title="Regular expression for the expected format of license plate.",
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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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ge=0,
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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 (strings or regular expressions)."
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)
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