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https://github.com/blakeblackshear/frigate.git
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a5e3dfd107
* add validator for detect width and height require both or neither * coerce semantic search model string to enum Built-in model names (jinav1, jinav2) get converted to the enum, genai provider names that don't match stay as plain strings and follow the existing validation path * formatting
99 lines
3.8 KiB
Python
99 lines
3.8 KiB
Python
from typing import Optional
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from pydantic import Field, model_validator
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from ..base import FrigateBaseModel
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__all__ = ["DetectConfig", "StationaryConfig", "StationaryMaxFramesConfig"]
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class StationaryMaxFramesConfig(FrigateBaseModel):
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default: Optional[int] = Field(
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default=None,
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title="Default max frames",
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description="Default maximum frames to track a stationary object before stopping.",
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ge=1,
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)
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objects: dict[str, int] = Field(
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default_factory=dict,
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title="Object max frames",
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description="Per-object overrides for maximum frames to track stationary objects.",
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)
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class StationaryConfig(FrigateBaseModel):
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interval: Optional[int] = Field(
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default=None,
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title="Stationary interval",
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description="How often (in frames) to run a detection check to confirm a stationary object.",
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gt=0,
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)
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threshold: Optional[int] = Field(
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default=None,
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title="Stationary threshold",
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description="Number of frames with no position change required to mark an object as stationary.",
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ge=1,
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)
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max_frames: StationaryMaxFramesConfig = Field(
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default_factory=StationaryMaxFramesConfig,
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title="Max frames",
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description="Limits how long stationary objects are tracked before being discarded.",
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)
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classifier: bool = Field(
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default=True,
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title="Enable visual classifier",
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description="Use a visual classifier to detect truly stationary objects even when bounding boxes jitter.",
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)
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class DetectConfig(FrigateBaseModel):
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enabled: bool = Field(
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default=False,
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title="Enable object detection",
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description="Enable or disable object detection for all cameras; can be overridden per-camera.",
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)
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height: Optional[int] = Field(
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default=None,
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title="Detect height",
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description="Height (pixels) of frames used for the detect stream; leave empty to use the native stream resolution.",
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)
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width: Optional[int] = Field(
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default=None,
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title="Detect width",
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description="Width (pixels) of frames used for the detect stream; leave empty to use the native stream resolution.",
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)
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fps: int = Field(
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default=5,
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title="Detect FPS",
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description="Desired frames per second to run detection on; lower values reduce CPU usage (recommended value is 5, only set higher - at most 10 - if tracking extremely fast moving objects).",
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)
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min_initialized: Optional[int] = Field(
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default=None,
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title="Minimum initialization frames",
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description="Number of consecutive detection hits required before creating a tracked object. Increase to reduce false initializations. Default value is fps divided by 2.",
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ge=2,
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)
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max_disappeared: Optional[int] = Field(
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default=None,
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title="Maximum disappeared frames",
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description="Number of frames without a detection before a tracked object is considered gone.",
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)
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stationary: StationaryConfig = Field(
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default_factory=StationaryConfig,
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title="Stationary objects config",
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description="Settings to detect and manage objects that remain stationary for a period of time.",
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)
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annotation_offset: int = Field(
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default=0,
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title="Annotation offset",
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description="Milliseconds to shift detect annotations to better align timeline bounding boxes with recordings; can be positive or negative.",
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)
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@model_validator(mode="after")
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def validate_dimensions(self) -> "DetectConfig":
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if (self.width is None) != (self.height is None):
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raise ValueError(
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"detect -> both width and height must be specified together, or both omitted"
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)
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return self
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