mirror of
https://github.com/blakeblackshear/frigate.git
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03c8e5ce8d
* Validate faces using cosine distance and SVC * Formatting * Use opencv instead of face embedding * Update docs for training data * Adjust to score system * Set bounds * remove face embeddings * Update writing images * Add face library page * Add ability to select file * Install opencv deps * Cleanup * Use different deps * Move deps * Cleanup * Only show face library for desktop * Implement deleting * Add ability to upload image * Add support for uploading images
50 lines
1.5 KiB
Python
50 lines
1.5 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 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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threshold: float = Field(
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default=170,
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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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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=500,
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title="Min area of license plate to consider running license plate recognition.",
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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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