mirror of
https://github.com/blakeblackshear/frigate.git
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52295fcac4
* Move genAI object to objects section * Adjust config propogation behavior * Refactor genai config usage * Automatic migration * Always start the embeddings process * Always init embeddings * Config fixes * Adjust reference config * Adjust docs * Formatting * Fix
132 lines
4.6 KiB
Python
132 lines
4.6 KiB
Python
from typing import Any, Optional, Union
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from pydantic import Field, PrivateAttr, field_serializer, field_validator
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from ..base import FrigateBaseModel
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__all__ = ["ObjectConfig", "GenAIObjectConfig", "FilterConfig"]
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DEFAULT_TRACKED_OBJECTS = ["person"]
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class FilterConfig(FrigateBaseModel):
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min_area: Union[int, float] = Field(
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default=0,
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title="Minimum area of bounding box for object to be counted. Can be pixels (int) or percentage (float between 0.000001 and 0.99).",
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)
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max_area: Union[int, float] = Field(
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default=24000000,
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title="Maximum area of bounding box for object to be counted. Can be pixels (int) or percentage (float between 0.000001 and 0.99).",
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)
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min_ratio: float = Field(
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default=0,
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title="Minimum ratio of bounding box's width/height for object to be counted.",
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)
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max_ratio: float = Field(
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default=24000000,
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title="Maximum ratio of bounding box's width/height for object to be counted.",
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)
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threshold: float = Field(
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default=0.7,
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title="Average detection confidence threshold for object to be counted.",
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)
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min_score: float = Field(
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default=0.5, title="Minimum detection confidence for object to be counted."
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)
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mask: Optional[Union[str, list[str]]] = Field(
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default=None,
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title="Detection area polygon mask for this filter configuration.",
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)
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raw_mask: Union[str, list[str]] = ""
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@field_serializer("mask", when_used="json")
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def serialize_mask(self, value: Any, info):
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return self.raw_mask
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@field_serializer("raw_mask", when_used="json")
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def serialize_raw_mask(self, value: Any, info):
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return None
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class GenAIObjectTriggerConfig(FrigateBaseModel):
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tracked_object_end: bool = Field(
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default=True, title="Send once the object is no longer tracked."
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)
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after_significant_updates: Optional[int] = Field(
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default=None,
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title="Send an early request to generative AI when X frames accumulated.",
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ge=1,
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)
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class GenAIObjectConfig(FrigateBaseModel):
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enabled: bool = Field(default=False, title="Enable GenAI for camera.")
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use_snapshot: bool = Field(
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default=False, title="Use snapshots for generating descriptions."
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)
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prompt: str = Field(
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default="Analyze the sequence of images containing the {label}. Focus on the likely intent or behavior of the {label} based on its actions and movement, rather than describing its appearance or the surroundings. Consider what the {label} is doing, why, and what it might do next.",
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title="Default caption prompt.",
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)
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object_prompts: dict[str, str] = Field(
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default_factory=dict, title="Object specific prompts."
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)
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objects: Union[str, list[str]] = Field(
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default_factory=list,
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title="List of objects to run generative AI for.",
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)
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required_zones: Union[str, list[str]] = Field(
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default_factory=list,
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title="List of required zones to be entered in order to run generative AI.",
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)
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debug_save_thumbnails: bool = Field(
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default=False,
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title="Save thumbnails sent to generative AI for debugging purposes.",
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)
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send_triggers: GenAIObjectTriggerConfig = Field(
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default_factory=GenAIObjectTriggerConfig,
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title="What triggers to use to send frames to generative AI for a tracked object.",
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)
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enabled_in_config: Optional[bool] = Field(
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default=None, title="Keep track of original state of generative AI."
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)
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@field_validator("required_zones", mode="before")
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@classmethod
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def validate_required_zones(cls, v):
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if isinstance(v, str) and "," not in v:
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return [v]
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return v
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class ObjectConfig(FrigateBaseModel):
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track: list[str] = Field(default=DEFAULT_TRACKED_OBJECTS, title="Objects to track.")
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filters: dict[str, FilterConfig] = Field(
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default_factory=dict, title="Object filters."
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)
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mask: Union[str, list[str]] = Field(default="", title="Object mask.")
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genai: GenAIObjectConfig = Field(
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default_factory=GenAIObjectConfig,
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title="Config for using genai to analyze objects.",
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)
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_all_objects: list[str] = PrivateAttr()
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@property
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def all_objects(self) -> list[str]:
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return self._all_objects
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def parse_all_objects(self, cameras):
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if "_all_objects" in self:
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return
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# get list of unique enabled labels for tracking
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enabled_labels = set(self.track)
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for camera in cameras.values():
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enabled_labels.update(camera.objects.track)
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self._all_objects = list(enabled_labels)
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