mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-12-19 19:06:16 +01:00
1666 lines
59 KiB
Python
1666 lines
59 KiB
Python
from __future__ import annotations
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import json
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import logging
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import os
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from enum import Enum
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Tuple, Union
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import numpy as np
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from pydantic import (
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BaseModel,
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ConfigDict,
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Field,
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TypeAdapter,
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ValidationInfo,
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field_serializer,
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field_validator,
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)
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from pydantic.fields import PrivateAttr
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from frigate.const import (
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ALL_ATTRIBUTE_LABELS,
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AUDIO_MIN_CONFIDENCE,
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CACHE_DIR,
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CACHE_SEGMENT_FORMAT,
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DEFAULT_DB_PATH,
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FREQUENCY_STATS_POINTS,
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MAX_PRE_CAPTURE,
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REGEX_CAMERA_NAME,
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YAML_EXT,
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)
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from frigate.detectors import DetectorConfig, ModelConfig
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from frigate.detectors.detector_config import BaseDetectorConfig
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from frigate.ffmpeg_presets import (
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parse_preset_hardware_acceleration_decode,
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parse_preset_hardware_acceleration_scale,
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parse_preset_input,
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parse_preset_output_record,
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)
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from frigate.plus import PlusApi
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from frigate.util.builtin import (
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deep_merge,
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escape_special_characters,
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generate_color_palette,
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get_ffmpeg_arg_list,
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load_config_with_no_duplicates,
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)
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from frigate.util.config import StreamInfoRetriever, get_relative_coordinates
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from frigate.util.image import create_mask
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from frigate.util.services import auto_detect_hwaccel
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logger = logging.getLogger(__name__)
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# TODO: Identify what the default format to display timestamps is
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DEFAULT_TIME_FORMAT = "%m/%d/%Y %H:%M:%S"
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# German Style:
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# DEFAULT_TIME_FORMAT = "%d.%m.%Y %H:%M:%S"
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FRIGATE_ENV_VARS = {k: v for k, v in os.environ.items() if k.startswith("FRIGATE_")}
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# read docker secret files as env vars too
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if os.path.isdir("/run/secrets") and os.access("/run/secrets", os.R_OK):
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for secret_file in os.listdir("/run/secrets"):
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if secret_file.startswith("FRIGATE_"):
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FRIGATE_ENV_VARS[secret_file] = (
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Path(os.path.join("/run/secrets", secret_file)).read_text().strip()
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)
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DEFAULT_TRACKED_OBJECTS = ["person"]
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DEFAULT_ALERT_OBJECTS = ["person", "car"]
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DEFAULT_LISTEN_AUDIO = ["bark", "fire_alarm", "scream", "speech", "yell"]
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DEFAULT_DETECTORS = {"cpu": {"type": "cpu"}}
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DEFAULT_DETECT_DIMENSIONS = {"width": 1280, "height": 720}
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DEFAULT_TIME_LAPSE_FFMPEG_ARGS = "-vf setpts=0.04*PTS -r 30"
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# stream info handler
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stream_info_retriever = StreamInfoRetriever()
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class FrigateBaseModel(BaseModel):
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model_config = ConfigDict(extra="forbid", protected_namespaces=())
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class LiveModeEnum(str, Enum):
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jsmpeg = "jsmpeg"
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mse = "mse"
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webrtc = "webrtc"
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class TimeFormatEnum(str, Enum):
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browser = "browser"
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hours12 = "12hour"
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hours24 = "24hour"
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class DateTimeStyleEnum(str, Enum):
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full = "full"
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long = "long"
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medium = "medium"
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short = "short"
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class UIConfig(FrigateBaseModel):
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timezone: Optional[str] = Field(default=None, title="Override UI timezone.")
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time_format: TimeFormatEnum = Field(
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default=TimeFormatEnum.browser, title="Override UI time format."
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)
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date_style: DateTimeStyleEnum = Field(
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default=DateTimeStyleEnum.short, title="Override UI dateStyle."
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)
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time_style: DateTimeStyleEnum = Field(
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default=DateTimeStyleEnum.medium, title="Override UI timeStyle."
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)
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strftime_fmt: Optional[str] = Field(
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default=None, title="Override date and time format using strftime syntax."
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)
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class TlsConfig(FrigateBaseModel):
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enabled: bool = Field(default=True, title="Enable TLS for port 8971")
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class HeaderMappingConfig(FrigateBaseModel):
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user: str = Field(
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default=None, title="Header name from upstream proxy to identify user."
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)
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class ProxyConfig(FrigateBaseModel):
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header_map: HeaderMappingConfig = Field(
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default_factory=HeaderMappingConfig,
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title="Header mapping definitions for proxy user passing.",
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)
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logout_url: Optional[str] = Field(
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default=None, title="Redirect url for logging out with proxy."
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)
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auth_secret: Optional[str] = Field(
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default=None,
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title="Secret value for proxy authentication.",
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)
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class AuthConfig(FrigateBaseModel):
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enabled: bool = Field(default=True, title="Enable authentication")
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reset_admin_password: bool = Field(
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default=False, title="Reset the admin password on startup"
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)
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cookie_name: str = Field(
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default="frigate_token", title="Name for jwt token cookie", pattern=r"^[a-z]_*$"
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)
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cookie_secure: bool = Field(default=False, title="Set secure flag on cookie")
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session_length: int = Field(
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default=86400, title="Session length for jwt session tokens", ge=60
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)
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refresh_time: int = Field(
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default=43200,
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title="Refresh the session if it is going to expire in this many seconds",
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ge=30,
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)
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failed_login_rate_limit: Optional[str] = Field(
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default=None,
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title="Rate limits for failed login attempts.",
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)
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trusted_proxies: List[str] = Field(
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default=[],
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title="Trusted proxies for determining IP address to rate limit",
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)
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# As of Feb 2023, OWASP recommends 600000 iterations for PBKDF2-SHA256
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hash_iterations: int = Field(default=600000, title="Password hash iterations")
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class StatsConfig(FrigateBaseModel):
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amd_gpu_stats: bool = Field(default=True, title="Enable AMD GPU stats.")
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intel_gpu_stats: bool = Field(default=True, title="Enable Intel GPU stats.")
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network_bandwidth: bool = Field(
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default=False, title="Enable network bandwidth for ffmpeg processes."
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)
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class TelemetryConfig(FrigateBaseModel):
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network_interfaces: List[str] = Field(
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default=[],
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title="Enabled network interfaces for bandwidth calculation.",
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)
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stats: StatsConfig = Field(
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default_factory=StatsConfig, title="System Stats Configuration"
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)
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version_check: bool = Field(default=True, title="Enable latest version check.")
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class MqttConfig(FrigateBaseModel):
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enabled: bool = Field(title="Enable MQTT Communication.", default=True)
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host: str = Field(default="", title="MQTT Host")
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port: int = Field(default=1883, title="MQTT Port")
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topic_prefix: str = Field(default="frigate", title="MQTT Topic Prefix")
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client_id: str = Field(default="frigate", title="MQTT Client ID")
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stats_interval: int = Field(
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default=60, ge=FREQUENCY_STATS_POINTS, title="MQTT Camera Stats Interval"
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)
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user: Optional[str] = Field(None, title="MQTT Username")
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password: Optional[str] = Field(None, title="MQTT Password", validate_default=True)
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tls_ca_certs: Optional[str] = Field(None, title="MQTT TLS CA Certificates")
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tls_client_cert: Optional[str] = Field(None, title="MQTT TLS Client Certificate")
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tls_client_key: Optional[str] = Field(None, title="MQTT TLS Client Key")
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tls_insecure: Optional[bool] = Field(None, title="MQTT TLS Insecure")
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@field_validator("password")
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def user_requires_pass(cls, v, info: ValidationInfo):
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if (v is None) != (info.data["user"] is None):
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raise ValueError("Password must be provided with username.")
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return v
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class ZoomingModeEnum(str, Enum):
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disabled = "disabled"
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absolute = "absolute"
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relative = "relative"
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class PtzAutotrackConfig(FrigateBaseModel):
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enabled: bool = Field(default=False, title="Enable PTZ object autotracking.")
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calibrate_on_startup: bool = Field(
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default=False, title="Perform a camera calibration when Frigate starts."
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)
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zooming: ZoomingModeEnum = Field(
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default=ZoomingModeEnum.disabled, title="Autotracker zooming mode."
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)
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zoom_factor: float = Field(
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default=0.3,
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title="Zooming factor (0.1-0.75).",
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ge=0.1,
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le=0.75,
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)
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track: List[str] = Field(default=DEFAULT_TRACKED_OBJECTS, title="Objects to track.")
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required_zones: 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 begin autotracking.",
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)
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return_preset: str = Field(
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default="home",
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title="Name of camera preset to return to when object tracking is over.",
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)
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timeout: int = Field(
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default=10, title="Seconds to delay before returning to preset."
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)
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movement_weights: Optional[Union[str, List[str]]] = Field(
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default=[],
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title="Internal value used for PTZ movements based on the speed of your camera's motor.",
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)
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enabled_in_config: Optional[bool] = Field(
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None, title="Keep track of original state of autotracking."
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)
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@field_validator("movement_weights", mode="before")
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@classmethod
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def validate_weights(cls, v):
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if v is None:
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return None
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if isinstance(v, str):
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weights = list(map(str, map(float, v.split(","))))
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elif isinstance(v, list):
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weights = [str(float(val)) for val in v]
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else:
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raise ValueError("Invalid type for movement_weights")
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if len(weights) != 5:
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raise ValueError("movement_weights must have exactly 5 floats")
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return weights
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class OnvifConfig(FrigateBaseModel):
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host: str = Field(default="", title="Onvif Host")
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port: int = Field(default=8000, title="Onvif Port")
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user: Optional[str] = Field(None, title="Onvif Username")
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password: Optional[str] = Field(None, title="Onvif Password")
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autotracking: PtzAutotrackConfig = Field(
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default_factory=PtzAutotrackConfig,
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title="PTZ auto tracking config.",
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)
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ignore_time_mismatch: bool = Field(
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default=False,
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title="Onvif Ignore Time Synchronization Mismatch Between Camera and Server",
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)
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class RetainModeEnum(str, Enum):
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all = "all"
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motion = "motion"
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active_objects = "active_objects"
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class RetainConfig(FrigateBaseModel):
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default: float = Field(default=10, title="Default retention period.")
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mode: RetainModeEnum = Field(default=RetainModeEnum.motion, title="Retain mode.")
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objects: Dict[str, float] = Field(
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default_factory=dict, title="Object retention period."
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)
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class EventsConfig(FrigateBaseModel):
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pre_capture: int = Field(
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default=5, title="Seconds to retain before event starts.", le=MAX_PRE_CAPTURE
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)
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post_capture: int = Field(default=5, title="Seconds to retain after event ends.")
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objects: Optional[List[str]] = Field(
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None,
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title="List of objects to be detected in order to save the event.",
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)
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retain: RetainConfig = Field(
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default_factory=RetainConfig, title="Event retention settings."
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)
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class RecordRetainConfig(FrigateBaseModel):
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days: float = Field(default=0, title="Default retention period.")
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mode: RetainModeEnum = Field(default=RetainModeEnum.all, title="Retain mode.")
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class RecordExportConfig(FrigateBaseModel):
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timelapse_args: str = Field(
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default=DEFAULT_TIME_LAPSE_FFMPEG_ARGS, title="Timelapse Args"
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)
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class RecordQualityEnum(str, Enum):
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very_low = "very_low"
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low = "low"
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medium = "medium"
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high = "high"
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very_high = "very_high"
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class RecordPreviewConfig(FrigateBaseModel):
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quality: RecordQualityEnum = Field(
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default=RecordQualityEnum.medium, title="Quality of recording preview."
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)
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class RecordConfig(FrigateBaseModel):
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enabled: bool = Field(default=False, title="Enable record on all cameras.")
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sync_recordings: bool = Field(
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default=False, title="Sync recordings with disk on startup and once a day."
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)
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expire_interval: int = Field(
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default=60,
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title="Number of minutes to wait between cleanup runs.",
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)
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retain: RecordRetainConfig = Field(
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default_factory=RecordRetainConfig, title="Record retention settings."
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)
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events: EventsConfig = Field(
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default_factory=EventsConfig, title="Event specific settings."
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)
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export: RecordExportConfig = Field(
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default_factory=RecordExportConfig, title="Recording Export Config"
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)
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preview: RecordPreviewConfig = Field(
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default_factory=RecordPreviewConfig, title="Recording Preview Config"
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)
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enabled_in_config: Optional[bool] = Field(
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None, title="Keep track of original state of recording."
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)
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class MotionConfig(FrigateBaseModel):
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enabled: bool = Field(default=True, title="Enable motion on all cameras.")
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threshold: int = Field(
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default=30,
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title="Motion detection threshold (1-255).",
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ge=1,
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le=255,
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)
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lightning_threshold: float = Field(
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default=0.8, title="Lightning detection threshold (0.3-1.0).", ge=0.3, le=1.0
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)
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improve_contrast: bool = Field(default=True, title="Improve Contrast")
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contour_area: Optional[int] = Field(default=10, title="Contour Area")
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delta_alpha: float = Field(default=0.2, title="Delta Alpha")
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frame_alpha: float = Field(default=0.01, title="Frame Alpha")
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frame_height: Optional[int] = Field(default=100, title="Frame Height")
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mask: Union[str, List[str]] = Field(
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default="", title="Coordinates polygon for the motion mask."
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)
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mqtt_off_delay: int = Field(
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default=30,
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title="Delay for updating MQTT with no motion detected.",
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)
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enabled_in_config: Optional[bool] = Field(
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None, title="Keep track of original state of motion detection."
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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 RuntimeMotionConfig(MotionConfig):
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raw_mask: Union[str, List[str]] = ""
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mask: np.ndarray = None
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def __init__(self, **config):
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frame_shape = config.get("frame_shape", (1, 1))
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mask = get_relative_coordinates(config.get("mask", ""), frame_shape)
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config["raw_mask"] = mask
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if mask:
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config["mask"] = create_mask(frame_shape, mask)
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else:
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empty_mask = np.zeros(frame_shape, np.uint8)
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empty_mask[:] = 255
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config["mask"] = empty_mask
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super().__init__(**config)
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def dict(self, **kwargs):
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ret = super().model_dump(**kwargs)
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if "mask" in ret:
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ret["mask"] = ret["raw_mask"]
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ret.pop("raw_mask")
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return ret
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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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model_config = ConfigDict(arbitrary_types_allowed=True, extra="ignore")
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class StationaryMaxFramesConfig(FrigateBaseModel):
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default: Optional[int] = Field(None, title="Default max frames.", ge=1)
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objects: Dict[str, int] = Field(
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default_factory=dict, title="Object specific max frames."
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)
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class StationaryConfig(FrigateBaseModel):
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interval: Optional[int] = Field(
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None,
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title="Frame interval for checking stationary objects.",
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gt=0,
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)
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threshold: Optional[int] = Field(
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None,
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title="Number of frames without a position change for an object to be considered 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 for stationary objects.",
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)
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class DetectConfig(FrigateBaseModel):
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height: Optional[int] = Field(
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None, title="Height of the stream for the detect role."
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)
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width: Optional[int] = Field(None, title="Width of the stream for the detect role.")
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fps: int = Field(
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default=5, title="Number of frames per second to process through detection."
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)
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enabled: bool = Field(default=True, title="Detection Enabled.")
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|
min_initialized: Optional[int] = Field(
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None,
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|
title="Minimum number of consecutive hits for an object to be initialized by the tracker.",
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)
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max_disappeared: Optional[int] = Field(
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None,
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title="Maximum number of frames the object can disappear before detection ends.",
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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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)
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annotation_offset: int = Field(
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default=0, title="Milliseconds to offset detect annotations by."
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)
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class FilterConfig(FrigateBaseModel):
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min_area: int = Field(
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default=0, title="Minimum area of bounding box for object to be counted."
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)
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max_area: int = Field(
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default=24000000, title="Maximum area of bounding box for object to be counted."
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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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|
)
|
|
min_score: float = Field(
|
|
default=0.5, title="Minimum detection confidence for object to be counted."
|
|
)
|
|
mask: Optional[Union[str, List[str]]] = Field(
|
|
None,
|
|
title="Detection area polygon mask for this filter configuration.",
|
|
)
|
|
raw_mask: Union[str, List[str]] = ""
|
|
|
|
@field_serializer("mask", when_used="json")
|
|
def serialize_mask(self, value: Any, info):
|
|
return self.raw_mask
|
|
|
|
@field_serializer("raw_mask", when_used="json")
|
|
def serialize_raw_mask(self, value: Any, info):
|
|
return None
|
|
|
|
|
|
class AudioFilterConfig(FrigateBaseModel):
|
|
threshold: float = Field(
|
|
default=0.8,
|
|
ge=AUDIO_MIN_CONFIDENCE,
|
|
lt=1.0,
|
|
title="Minimum detection confidence threshold for audio to be counted.",
|
|
)
|
|
|
|
|
|
class RuntimeFilterConfig(FilterConfig):
|
|
mask: Optional[np.ndarray] = None
|
|
raw_mask: Optional[Union[str, List[str]]] = None
|
|
|
|
def __init__(self, **config):
|
|
frame_shape = config.get("frame_shape", (1, 1))
|
|
mask = get_relative_coordinates(config.get("mask"), frame_shape)
|
|
|
|
config["raw_mask"] = mask
|
|
|
|
if mask is not None:
|
|
config["mask"] = create_mask(frame_shape, mask)
|
|
|
|
super().__init__(**config)
|
|
|
|
def dict(self, **kwargs):
|
|
ret = super().model_dump(**kwargs)
|
|
if "mask" in ret:
|
|
ret["mask"] = ret["raw_mask"]
|
|
ret.pop("raw_mask")
|
|
return ret
|
|
|
|
model_config = ConfigDict(arbitrary_types_allowed=True, extra="ignore")
|
|
|
|
|
|
# this uses the base model because the color is an extra attribute
|
|
class ZoneConfig(BaseModel):
|
|
filters: Dict[str, FilterConfig] = Field(
|
|
default_factory=dict, title="Zone filters."
|
|
)
|
|
coordinates: Union[str, List[str]] = Field(
|
|
title="Coordinates polygon for the defined zone."
|
|
)
|
|
inertia: int = Field(
|
|
default=3,
|
|
title="Number of consecutive frames required for object to be considered present in the zone.",
|
|
gt=0,
|
|
)
|
|
loitering_time: int = Field(
|
|
default=0,
|
|
ge=0,
|
|
title="Number of seconds that an object must loiter to be considered in the zone.",
|
|
)
|
|
objects: Union[str, List[str]] = Field(
|
|
default_factory=list,
|
|
title="List of objects that can trigger the zone.",
|
|
)
|
|
_color: Optional[Tuple[int, int, int]] = PrivateAttr()
|
|
_contour: np.ndarray = PrivateAttr()
|
|
|
|
@property
|
|
def color(self) -> Tuple[int, int, int]:
|
|
return self._color
|
|
|
|
@property
|
|
def contour(self) -> np.ndarray:
|
|
return self._contour
|
|
|
|
@field_validator("objects", mode="before")
|
|
@classmethod
|
|
def validate_objects(cls, v):
|
|
if isinstance(v, str) and "," not in v:
|
|
return [v]
|
|
|
|
return v
|
|
|
|
def __init__(self, **config):
|
|
super().__init__(**config)
|
|
|
|
self._color = config.get("color", (0, 0, 0))
|
|
self._contour = config.get("contour", np.array([]))
|
|
|
|
def generate_contour(self, frame_shape: tuple[int, int]):
|
|
coordinates = self.coordinates
|
|
|
|
# masks and zones are saved as relative coordinates
|
|
# we know if any points are > 1 then it is using the
|
|
# old native resolution coordinates
|
|
if isinstance(coordinates, list):
|
|
explicit = any(p.split(",")[0] > "1.0" for p in coordinates)
|
|
try:
|
|
self._contour = np.array(
|
|
[
|
|
(
|
|
[int(p.split(",")[0]), int(p.split(",")[1])]
|
|
if explicit
|
|
else [
|
|
int(float(p.split(",")[0]) * frame_shape[1]),
|
|
int(float(p.split(",")[1]) * frame_shape[0]),
|
|
]
|
|
)
|
|
for p in coordinates
|
|
]
|
|
)
|
|
except ValueError:
|
|
raise ValueError(
|
|
f"Invalid coordinates found in configuration file. Coordinates must be relative (between 0-1): {coordinates}"
|
|
)
|
|
|
|
if explicit:
|
|
self.coordinates = ",".join(
|
|
[
|
|
f'{round(int(p.split(",")[0]) / frame_shape[1], 3)},{round(int(p.split(",")[1]) / frame_shape[0], 3)}'
|
|
for p in coordinates
|
|
]
|
|
)
|
|
elif isinstance(coordinates, str):
|
|
points = coordinates.split(",")
|
|
explicit = any(p > "1.0" for p in points)
|
|
try:
|
|
self._contour = np.array(
|
|
[
|
|
(
|
|
[int(points[i]), int(points[i + 1])]
|
|
if explicit
|
|
else [
|
|
int(float(points[i]) * frame_shape[1]),
|
|
int(float(points[i + 1]) * frame_shape[0]),
|
|
]
|
|
)
|
|
for i in range(0, len(points), 2)
|
|
]
|
|
)
|
|
except ValueError:
|
|
raise ValueError(
|
|
f"Invalid coordinates found in configuration file. Coordinates must be relative (between 0-1): {coordinates}"
|
|
)
|
|
|
|
if explicit:
|
|
self.coordinates = ",".join(
|
|
[
|
|
f"{round(int(points[i]) / frame_shape[1], 3)},{round(int(points[i + 1]) / frame_shape[0], 3)}"
|
|
for i in range(0, len(points), 2)
|
|
]
|
|
)
|
|
else:
|
|
self._contour = np.array([])
|
|
|
|
|
|
class ObjectConfig(FrigateBaseModel):
|
|
track: List[str] = Field(default=DEFAULT_TRACKED_OBJECTS, title="Objects to track.")
|
|
filters: Dict[str, FilterConfig] = Field(default={}, title="Object filters.")
|
|
mask: Union[str, List[str]] = Field(default="", title="Object mask.")
|
|
|
|
|
|
class AlertsConfig(FrigateBaseModel):
|
|
"""Configure alerts"""
|
|
|
|
labels: List[str] = Field(
|
|
default=DEFAULT_ALERT_OBJECTS, title="Labels to create alerts for."
|
|
)
|
|
required_zones: Union[str, List[str]] = Field(
|
|
default_factory=list,
|
|
title="List of required zones to be entered in order to save the event as an alert.",
|
|
)
|
|
|
|
@field_validator("required_zones", mode="before")
|
|
@classmethod
|
|
def validate_required_zones(cls, v):
|
|
if isinstance(v, str) and "," not in v:
|
|
return [v]
|
|
|
|
return v
|
|
|
|
|
|
class DetectionsConfig(FrigateBaseModel):
|
|
"""Configure detections"""
|
|
|
|
labels: Optional[List[str]] = Field(
|
|
default=None, title="Labels to create detections for."
|
|
)
|
|
required_zones: Union[str, List[str]] = Field(
|
|
default_factory=list,
|
|
title="List of required zones to be entered in order to save the event as a detection.",
|
|
)
|
|
|
|
@field_validator("required_zones", mode="before")
|
|
@classmethod
|
|
def validate_required_zones(cls, v):
|
|
if isinstance(v, str) and "," not in v:
|
|
return [v]
|
|
|
|
return v
|
|
|
|
|
|
class ReviewConfig(FrigateBaseModel):
|
|
"""Configure reviews"""
|
|
|
|
alerts: AlertsConfig = Field(
|
|
default_factory=AlertsConfig, title="Review alerts config."
|
|
)
|
|
detections: DetectionsConfig = Field(
|
|
default_factory=DetectionsConfig, title="Review detections config."
|
|
)
|
|
|
|
|
|
class AudioConfig(FrigateBaseModel):
|
|
enabled: bool = Field(default=False, title="Enable audio events.")
|
|
max_not_heard: int = Field(
|
|
default=30, title="Seconds of not hearing the type of audio to end the event."
|
|
)
|
|
min_volume: int = Field(
|
|
default=500, title="Min volume required to run audio detection."
|
|
)
|
|
listen: List[str] = Field(
|
|
default=DEFAULT_LISTEN_AUDIO, title="Audio to listen for."
|
|
)
|
|
filters: Optional[Dict[str, AudioFilterConfig]] = Field(
|
|
None, title="Audio filters."
|
|
)
|
|
enabled_in_config: Optional[bool] = Field(
|
|
None, title="Keep track of original state of audio detection."
|
|
)
|
|
num_threads: int = Field(default=2, title="Number of detection threads", ge=1)
|
|
|
|
|
|
class BirdseyeModeEnum(str, Enum):
|
|
objects = "objects"
|
|
motion = "motion"
|
|
continuous = "continuous"
|
|
|
|
@classmethod
|
|
def get_index(cls, type):
|
|
return list(cls).index(type)
|
|
|
|
@classmethod
|
|
def get(cls, index):
|
|
return list(cls)[index]
|
|
|
|
|
|
class BirdseyeLayoutConfig(FrigateBaseModel):
|
|
scaling_factor: float = Field(
|
|
default=2.0, title="Birdseye Scaling Factor", ge=1.0, le=5.0
|
|
)
|
|
max_cameras: Optional[int] = Field(default=None, title="Max cameras")
|
|
|
|
|
|
class BirdseyeConfig(FrigateBaseModel):
|
|
enabled: bool = Field(default=True, title="Enable birdseye view.")
|
|
restream: bool = Field(default=False, title="Restream birdseye via RTSP.")
|
|
width: int = Field(default=1280, title="Birdseye width.")
|
|
height: int = Field(default=720, title="Birdseye height.")
|
|
quality: int = Field(
|
|
default=8,
|
|
title="Encoding quality.",
|
|
ge=1,
|
|
le=31,
|
|
)
|
|
inactivity_threshold: int = Field(
|
|
default=30, title="Birdseye Inactivity Threshold", gt=0
|
|
)
|
|
mode: BirdseyeModeEnum = Field(
|
|
default=BirdseyeModeEnum.objects, title="Tracking mode."
|
|
)
|
|
layout: BirdseyeLayoutConfig = Field(
|
|
default_factory=BirdseyeLayoutConfig, title="Birdseye Layout Config"
|
|
)
|
|
|
|
|
|
# uses BaseModel because some global attributes are not available at the camera level
|
|
class BirdseyeCameraConfig(BaseModel):
|
|
enabled: bool = Field(default=True, title="Enable birdseye view for camera.")
|
|
order: int = Field(default=0, title="Position of the camera in the birdseye view.")
|
|
mode: BirdseyeModeEnum = Field(
|
|
default=BirdseyeModeEnum.objects, title="Tracking mode for camera."
|
|
)
|
|
|
|
|
|
# Note: Setting threads to less than 2 caused several issues with recording segments
|
|
# https://github.com/blakeblackshear/frigate/issues/5659
|
|
FFMPEG_GLOBAL_ARGS_DEFAULT = ["-hide_banner", "-loglevel", "warning", "-threads", "2"]
|
|
FFMPEG_INPUT_ARGS_DEFAULT = "preset-rtsp-generic"
|
|
DETECT_FFMPEG_OUTPUT_ARGS_DEFAULT = [
|
|
"-threads",
|
|
"2",
|
|
"-f",
|
|
"rawvideo",
|
|
"-pix_fmt",
|
|
"yuv420p",
|
|
]
|
|
RECORD_FFMPEG_OUTPUT_ARGS_DEFAULT = "preset-record-generic"
|
|
|
|
|
|
class FfmpegOutputArgsConfig(FrigateBaseModel):
|
|
detect: Union[str, List[str]] = Field(
|
|
default=DETECT_FFMPEG_OUTPUT_ARGS_DEFAULT,
|
|
title="Detect role FFmpeg output arguments.",
|
|
)
|
|
record: Union[str, List[str]] = Field(
|
|
default=RECORD_FFMPEG_OUTPUT_ARGS_DEFAULT,
|
|
title="Record role FFmpeg output arguments.",
|
|
)
|
|
_force_record_hvc1: bool = PrivateAttr(default=False)
|
|
|
|
|
|
class FfmpegConfig(FrigateBaseModel):
|
|
global_args: Union[str, List[str]] = Field(
|
|
default=FFMPEG_GLOBAL_ARGS_DEFAULT, title="Global FFmpeg arguments."
|
|
)
|
|
hwaccel_args: Union[str, List[str]] = Field(
|
|
default="auto", title="FFmpeg hardware acceleration arguments."
|
|
)
|
|
input_args: Union[str, List[str]] = Field(
|
|
default=FFMPEG_INPUT_ARGS_DEFAULT, title="FFmpeg input arguments."
|
|
)
|
|
output_args: FfmpegOutputArgsConfig = Field(
|
|
default_factory=FfmpegOutputArgsConfig,
|
|
title="FFmpeg output arguments per role.",
|
|
)
|
|
retry_interval: float = Field(
|
|
default=10.0,
|
|
title="Time in seconds to wait before FFmpeg retries connecting to the camera.",
|
|
)
|
|
|
|
|
|
class CameraRoleEnum(str, Enum):
|
|
audio = "audio"
|
|
record = "record"
|
|
detect = "detect"
|
|
|
|
|
|
class CameraInput(FrigateBaseModel):
|
|
path: str = Field(title="Camera input path.")
|
|
roles: List[CameraRoleEnum] = Field(title="Roles assigned to this input.")
|
|
global_args: Union[str, List[str]] = Field(
|
|
default_factory=list, title="FFmpeg global arguments."
|
|
)
|
|
hwaccel_args: Union[str, List[str]] = Field(
|
|
default_factory=list, title="FFmpeg hardware acceleration arguments."
|
|
)
|
|
input_args: Union[str, List[str]] = Field(
|
|
default_factory=list, title="FFmpeg input arguments."
|
|
)
|
|
|
|
|
|
class CameraFfmpegConfig(FfmpegConfig):
|
|
inputs: List[CameraInput] = Field(title="Camera inputs.")
|
|
|
|
@field_validator("inputs")
|
|
@classmethod
|
|
def validate_roles(cls, v):
|
|
roles = [role for i in v for role in i.roles]
|
|
roles_set = set(roles)
|
|
|
|
if len(roles) > len(roles_set):
|
|
raise ValueError("Each input role may only be used once.")
|
|
|
|
if "detect" not in roles:
|
|
raise ValueError("The detect role is required.")
|
|
|
|
return v
|
|
|
|
|
|
class SnapshotsConfig(FrigateBaseModel):
|
|
enabled: bool = Field(default=False, title="Snapshots enabled.")
|
|
clean_copy: bool = Field(
|
|
default=True, title="Create a clean copy of the snapshot image."
|
|
)
|
|
timestamp: bool = Field(
|
|
default=False, title="Add a timestamp overlay on the snapshot."
|
|
)
|
|
bounding_box: bool = Field(
|
|
default=True, title="Add a bounding box overlay on the snapshot."
|
|
)
|
|
crop: bool = Field(default=False, title="Crop the snapshot to the detected object.")
|
|
required_zones: List[str] = Field(
|
|
default_factory=list,
|
|
title="List of required zones to be entered in order to save a snapshot.",
|
|
)
|
|
height: Optional[int] = Field(None, title="Snapshot image height.")
|
|
retain: RetainConfig = Field(
|
|
default_factory=RetainConfig, title="Snapshot retention."
|
|
)
|
|
quality: int = Field(
|
|
default=70,
|
|
title="Quality of the encoded jpeg (0-100).",
|
|
ge=0,
|
|
le=100,
|
|
)
|
|
|
|
|
|
class ColorConfig(FrigateBaseModel):
|
|
red: int = Field(default=255, ge=0, le=255, title="Red")
|
|
green: int = Field(default=255, ge=0, le=255, title="Green")
|
|
blue: int = Field(default=255, ge=0, le=255, title="Blue")
|
|
|
|
|
|
class TimestampPositionEnum(str, Enum):
|
|
tl = "tl"
|
|
tr = "tr"
|
|
bl = "bl"
|
|
br = "br"
|
|
|
|
|
|
class TimestampEffectEnum(str, Enum):
|
|
solid = "solid"
|
|
shadow = "shadow"
|
|
|
|
|
|
class TimestampStyleConfig(FrigateBaseModel):
|
|
position: TimestampPositionEnum = Field(
|
|
default=TimestampPositionEnum.tl, title="Timestamp position."
|
|
)
|
|
format: str = Field(default=DEFAULT_TIME_FORMAT, title="Timestamp format.")
|
|
color: ColorConfig = Field(default_factory=ColorConfig, title="Timestamp color.")
|
|
thickness: int = Field(default=2, title="Timestamp thickness.")
|
|
effect: Optional[TimestampEffectEnum] = Field(None, title="Timestamp effect.")
|
|
|
|
|
|
class CameraMqttConfig(FrigateBaseModel):
|
|
enabled: bool = Field(default=True, title="Send image over MQTT.")
|
|
timestamp: bool = Field(default=True, title="Add timestamp to MQTT image.")
|
|
bounding_box: bool = Field(default=True, title="Add bounding box to MQTT image.")
|
|
crop: bool = Field(default=True, title="Crop MQTT image to detected object.")
|
|
height: int = Field(default=270, title="MQTT image height.")
|
|
required_zones: List[str] = Field(
|
|
default_factory=list,
|
|
title="List of required zones to be entered in order to send the image.",
|
|
)
|
|
quality: int = Field(
|
|
default=70,
|
|
title="Quality of the encoded jpeg (0-100).",
|
|
ge=0,
|
|
le=100,
|
|
)
|
|
|
|
|
|
class CameraLiveConfig(FrigateBaseModel):
|
|
stream_name: str = Field(default="", title="Name of restream to use as live view.")
|
|
height: int = Field(default=720, title="Live camera view height")
|
|
quality: int = Field(default=8, ge=1, le=31, title="Live camera view quality")
|
|
|
|
|
|
class RestreamConfig(BaseModel):
|
|
model_config = ConfigDict(extra="allow")
|
|
|
|
|
|
class CameraUiConfig(FrigateBaseModel):
|
|
order: int = Field(default=0, title="Order of camera in UI.")
|
|
dashboard: bool = Field(
|
|
default=True, title="Show this camera in Frigate dashboard UI."
|
|
)
|
|
|
|
|
|
class CameraConfig(FrigateBaseModel):
|
|
name: Optional[str] = Field(None, title="Camera name.", pattern=REGEX_CAMERA_NAME)
|
|
enabled: bool = Field(default=True, title="Enable camera.")
|
|
ffmpeg: CameraFfmpegConfig = Field(title="FFmpeg configuration for the camera.")
|
|
best_image_timeout: int = Field(
|
|
default=60,
|
|
title="How long to wait for the image with the highest confidence score.",
|
|
)
|
|
webui_url: Optional[str] = Field(
|
|
None,
|
|
title="URL to visit the camera directly from system page",
|
|
)
|
|
zones: Dict[str, ZoneConfig] = Field(
|
|
default_factory=dict, title="Zone configuration."
|
|
)
|
|
record: RecordConfig = Field(
|
|
default_factory=RecordConfig, title="Record configuration."
|
|
)
|
|
live: CameraLiveConfig = Field(
|
|
default_factory=CameraLiveConfig, title="Live playback settings."
|
|
)
|
|
snapshots: SnapshotsConfig = Field(
|
|
default_factory=SnapshotsConfig, title="Snapshot configuration."
|
|
)
|
|
mqtt: CameraMqttConfig = Field(
|
|
default_factory=CameraMqttConfig, title="MQTT configuration."
|
|
)
|
|
objects: ObjectConfig = Field(
|
|
default_factory=ObjectConfig, title="Object configuration."
|
|
)
|
|
review: ReviewConfig = Field(
|
|
default_factory=ReviewConfig, title="Review configuration."
|
|
)
|
|
audio: AudioConfig = Field(
|
|
default_factory=AudioConfig, title="Audio events configuration."
|
|
)
|
|
motion: Optional[MotionConfig] = Field(
|
|
None, title="Motion detection configuration."
|
|
)
|
|
detect: DetectConfig = Field(
|
|
default_factory=DetectConfig, title="Object detection configuration."
|
|
)
|
|
onvif: OnvifConfig = Field(
|
|
default_factory=OnvifConfig, title="Camera Onvif Configuration."
|
|
)
|
|
ui: CameraUiConfig = Field(
|
|
default_factory=CameraUiConfig, title="Camera UI Modifications."
|
|
)
|
|
birdseye: BirdseyeCameraConfig = Field(
|
|
default_factory=BirdseyeCameraConfig, title="Birdseye camera configuration."
|
|
)
|
|
timestamp_style: TimestampStyleConfig = Field(
|
|
default_factory=TimestampStyleConfig, title="Timestamp style configuration."
|
|
)
|
|
_ffmpeg_cmds: List[Dict[str, List[str]]] = PrivateAttr()
|
|
|
|
def __init__(self, **config):
|
|
# Set zone colors
|
|
if "zones" in config:
|
|
colors = generate_color_palette(len(config["zones"]))
|
|
|
|
config["zones"] = {
|
|
name: {**z, "color": color}
|
|
for (name, z), color in zip(config["zones"].items(), colors)
|
|
}
|
|
|
|
# add roles to the input if there is only one
|
|
if len(config["ffmpeg"]["inputs"]) == 1:
|
|
has_audio = "audio" in config["ffmpeg"]["inputs"][0].get("roles", [])
|
|
|
|
config["ffmpeg"]["inputs"][0]["roles"] = [
|
|
"record",
|
|
"detect",
|
|
]
|
|
|
|
if has_audio:
|
|
config["ffmpeg"]["inputs"][0]["roles"].append("audio")
|
|
|
|
super().__init__(**config)
|
|
|
|
@property
|
|
def frame_shape(self) -> Tuple[int, int]:
|
|
return self.detect.height, self.detect.width
|
|
|
|
@property
|
|
def frame_shape_yuv(self) -> Tuple[int, int]:
|
|
return self.detect.height * 3 // 2, self.detect.width
|
|
|
|
@property
|
|
def ffmpeg_cmds(self) -> List[Dict[str, List[str]]]:
|
|
return self._ffmpeg_cmds
|
|
|
|
def create_ffmpeg_cmds(self):
|
|
if "_ffmpeg_cmds" in self:
|
|
return
|
|
ffmpeg_cmds = []
|
|
for ffmpeg_input in self.ffmpeg.inputs:
|
|
ffmpeg_cmd = self._get_ffmpeg_cmd(ffmpeg_input)
|
|
if ffmpeg_cmd is None:
|
|
continue
|
|
|
|
ffmpeg_cmds.append({"roles": ffmpeg_input.roles, "cmd": ffmpeg_cmd})
|
|
self._ffmpeg_cmds = ffmpeg_cmds
|
|
|
|
def _get_ffmpeg_cmd(self, ffmpeg_input: CameraInput):
|
|
ffmpeg_output_args = []
|
|
if "detect" in ffmpeg_input.roles:
|
|
detect_args = get_ffmpeg_arg_list(self.ffmpeg.output_args.detect)
|
|
scale_detect_args = parse_preset_hardware_acceleration_scale(
|
|
ffmpeg_input.hwaccel_args or self.ffmpeg.hwaccel_args,
|
|
detect_args,
|
|
self.detect.fps,
|
|
self.detect.width,
|
|
self.detect.height,
|
|
)
|
|
|
|
ffmpeg_output_args = scale_detect_args + ffmpeg_output_args + ["pipe:"]
|
|
|
|
if "record" in ffmpeg_input.roles and self.record.enabled:
|
|
record_args = get_ffmpeg_arg_list(
|
|
parse_preset_output_record(
|
|
self.ffmpeg.output_args.record,
|
|
self.ffmpeg.output_args._force_record_hvc1,
|
|
)
|
|
or self.ffmpeg.output_args.record
|
|
)
|
|
|
|
ffmpeg_output_args = (
|
|
record_args
|
|
+ [f"{os.path.join(CACHE_DIR, self.name)}@{CACHE_SEGMENT_FORMAT}.mp4"]
|
|
+ ffmpeg_output_args
|
|
)
|
|
|
|
# if there arent any outputs enabled for this input
|
|
if len(ffmpeg_output_args) == 0:
|
|
return None
|
|
|
|
global_args = get_ffmpeg_arg_list(
|
|
ffmpeg_input.global_args or self.ffmpeg.global_args
|
|
)
|
|
|
|
camera_arg = (
|
|
self.ffmpeg.hwaccel_args if self.ffmpeg.hwaccel_args != "auto" else None
|
|
)
|
|
hwaccel_args = get_ffmpeg_arg_list(
|
|
parse_preset_hardware_acceleration_decode(
|
|
ffmpeg_input.hwaccel_args,
|
|
self.detect.fps,
|
|
self.detect.width,
|
|
self.detect.height,
|
|
)
|
|
or ffmpeg_input.hwaccel_args
|
|
or parse_preset_hardware_acceleration_decode(
|
|
camera_arg,
|
|
self.detect.fps,
|
|
self.detect.width,
|
|
self.detect.height,
|
|
)
|
|
or camera_arg
|
|
or []
|
|
)
|
|
input_args = get_ffmpeg_arg_list(
|
|
parse_preset_input(ffmpeg_input.input_args, self.detect.fps)
|
|
or ffmpeg_input.input_args
|
|
or parse_preset_input(self.ffmpeg.input_args, self.detect.fps)
|
|
or self.ffmpeg.input_args
|
|
)
|
|
|
|
cmd = (
|
|
["ffmpeg"]
|
|
+ global_args
|
|
+ hwaccel_args
|
|
+ input_args
|
|
+ ["-i", escape_special_characters(ffmpeg_input.path)]
|
|
+ ffmpeg_output_args
|
|
)
|
|
|
|
return [part for part in cmd if part != ""]
|
|
|
|
|
|
class DatabaseConfig(FrigateBaseModel):
|
|
path: str = Field(default=DEFAULT_DB_PATH, title="Database path.")
|
|
|
|
|
|
class LogLevelEnum(str, Enum):
|
|
debug = "debug"
|
|
info = "info"
|
|
warning = "warning"
|
|
error = "error"
|
|
critical = "critical"
|
|
|
|
|
|
class LoggerConfig(FrigateBaseModel):
|
|
default: LogLevelEnum = Field(
|
|
default=LogLevelEnum.info, title="Default logging level."
|
|
)
|
|
logs: Dict[str, LogLevelEnum] = Field(
|
|
default_factory=dict, title="Log level for specified processes."
|
|
)
|
|
|
|
|
|
class CameraGroupConfig(FrigateBaseModel):
|
|
"""Represents a group of cameras."""
|
|
|
|
cameras: Union[str, List[str]] = Field(
|
|
default_factory=list, title="List of cameras in this group."
|
|
)
|
|
icon: str = Field(default="generic", title="Icon that represents camera group.")
|
|
order: int = Field(default=0, title="Sort order for group.")
|
|
|
|
@field_validator("cameras", mode="before")
|
|
@classmethod
|
|
def validate_cameras(cls, v):
|
|
if isinstance(v, str) and "," not in v:
|
|
return [v]
|
|
|
|
return v
|
|
|
|
|
|
def verify_config_roles(camera_config: CameraConfig) -> None:
|
|
"""Verify that roles are setup in the config correctly."""
|
|
assigned_roles = list(
|
|
set([r for i in camera_config.ffmpeg.inputs for r in i.roles])
|
|
)
|
|
|
|
if camera_config.record.enabled and "record" not in assigned_roles:
|
|
raise ValueError(
|
|
f"Camera {camera_config.name} has record enabled, but record is not assigned to an input."
|
|
)
|
|
|
|
if camera_config.audio.enabled and "audio" not in assigned_roles:
|
|
raise ValueError(
|
|
f"Camera {camera_config.name} has audio events enabled, but audio is not assigned to an input."
|
|
)
|
|
|
|
|
|
def verify_valid_live_stream_name(
|
|
frigate_config: FrigateConfig, camera_config: CameraConfig
|
|
) -> ValueError | None:
|
|
"""Verify that a restream exists to use for live view."""
|
|
if (
|
|
camera_config.live.stream_name
|
|
not in frigate_config.go2rtc.model_dump().get("streams", {}).keys()
|
|
):
|
|
return ValueError(
|
|
f"No restream with name {camera_config.live.stream_name} exists for camera {camera_config.name}."
|
|
)
|
|
|
|
|
|
def verify_recording_retention(camera_config: CameraConfig) -> None:
|
|
"""Verify that recording retention modes are ranked correctly."""
|
|
rank_map = {
|
|
RetainModeEnum.all: 0,
|
|
RetainModeEnum.motion: 1,
|
|
RetainModeEnum.active_objects: 2,
|
|
}
|
|
|
|
if (
|
|
camera_config.record.retain.days != 0
|
|
and rank_map[camera_config.record.retain.mode]
|
|
> rank_map[camera_config.record.events.retain.mode]
|
|
):
|
|
logger.warning(
|
|
f"{camera_config.name}: Recording retention is configured for {camera_config.record.retain.mode} and event retention is configured for {camera_config.record.events.retain.mode}. The more restrictive retention policy will be applied."
|
|
)
|
|
|
|
|
|
def verify_recording_segments_setup_with_reasonable_time(
|
|
camera_config: CameraConfig,
|
|
) -> None:
|
|
"""Verify that recording segments are setup and segment time is not greater than 60."""
|
|
record_args: list[str] = get_ffmpeg_arg_list(
|
|
camera_config.ffmpeg.output_args.record
|
|
)
|
|
|
|
if record_args[0].startswith("preset"):
|
|
return
|
|
|
|
seg_arg_index = record_args.index("-segment_time")
|
|
|
|
if seg_arg_index < 0:
|
|
raise ValueError(
|
|
f"Camera {camera_config.name} has no segment_time in recording output args, segment args are required for record."
|
|
)
|
|
|
|
if int(record_args[seg_arg_index + 1]) > 60:
|
|
raise ValueError(
|
|
f"Camera {camera_config.name} has invalid segment_time output arg, segment_time must be 60 or less."
|
|
)
|
|
|
|
|
|
def verify_zone_objects_are_tracked(camera_config: CameraConfig) -> None:
|
|
"""Verify that user has not entered zone objects that are not in the tracking config."""
|
|
for zone_name, zone in camera_config.zones.items():
|
|
for obj in zone.objects:
|
|
if obj not in camera_config.objects.track:
|
|
raise ValueError(
|
|
f"Zone {zone_name} is configured to track {obj} but that object type is not added to objects -> track."
|
|
)
|
|
|
|
|
|
def verify_required_zones_exist(camera_config: CameraConfig) -> None:
|
|
for det_zone in camera_config.review.detections.required_zones:
|
|
if det_zone not in camera_config.zones.keys():
|
|
raise ValueError(
|
|
f"Camera {camera_config.name} has a required zone for detections {det_zone} that is not defined."
|
|
)
|
|
|
|
for det_zone in camera_config.review.alerts.required_zones:
|
|
if det_zone not in camera_config.zones.keys():
|
|
raise ValueError(
|
|
f"Camera {camera_config.name} has a required zone for alerts {det_zone} that is not defined."
|
|
)
|
|
|
|
|
|
def verify_autotrack_zones(camera_config: CameraConfig) -> ValueError | None:
|
|
"""Verify that required_zones are specified when autotracking is enabled."""
|
|
if (
|
|
camera_config.onvif.autotracking.enabled
|
|
and not camera_config.onvif.autotracking.required_zones
|
|
):
|
|
raise ValueError(
|
|
f"Camera {camera_config.name} has autotracking enabled, required_zones must be set to at least one of the camera's zones."
|
|
)
|
|
|
|
|
|
def verify_motion_and_detect(camera_config: CameraConfig) -> ValueError | None:
|
|
"""Verify that required_zones are specified when autotracking is enabled."""
|
|
if camera_config.detect.enabled and not camera_config.motion.enabled:
|
|
raise ValueError(
|
|
f"Camera {camera_config.name} has motion detection disabled and object detection enabled but object detection requires motion detection."
|
|
)
|
|
|
|
|
|
class FrigateConfig(FrigateBaseModel):
|
|
mqtt: MqttConfig = Field(title="MQTT configuration.")
|
|
database: DatabaseConfig = Field(
|
|
default_factory=DatabaseConfig, title="Database configuration."
|
|
)
|
|
tls: TlsConfig = Field(default_factory=TlsConfig, title="TLS configuration.")
|
|
proxy: ProxyConfig = Field(
|
|
default_factory=ProxyConfig, title="Proxy configuration."
|
|
)
|
|
auth: AuthConfig = Field(default_factory=AuthConfig, title="Auth configuration.")
|
|
environment_vars: Dict[str, str] = Field(
|
|
default_factory=dict, title="Frigate environment variables."
|
|
)
|
|
ui: UIConfig = Field(default_factory=UIConfig, title="UI configuration.")
|
|
telemetry: TelemetryConfig = Field(
|
|
default_factory=TelemetryConfig, title="Telemetry configuration."
|
|
)
|
|
model: ModelConfig = Field(
|
|
default_factory=ModelConfig, title="Detection model configuration."
|
|
)
|
|
detectors: Dict[str, BaseDetectorConfig] = Field(
|
|
default=DEFAULT_DETECTORS,
|
|
title="Detector hardware configuration.",
|
|
)
|
|
logger: LoggerConfig = Field(
|
|
default_factory=LoggerConfig, title="Logging configuration."
|
|
)
|
|
record: RecordConfig = Field(
|
|
default_factory=RecordConfig, title="Global record configuration."
|
|
)
|
|
snapshots: SnapshotsConfig = Field(
|
|
default_factory=SnapshotsConfig, title="Global snapshots configuration."
|
|
)
|
|
live: CameraLiveConfig = Field(
|
|
default_factory=CameraLiveConfig, title="Live playback settings."
|
|
)
|
|
go2rtc: RestreamConfig = Field(
|
|
default_factory=RestreamConfig, title="Global restream configuration."
|
|
)
|
|
birdseye: BirdseyeConfig = Field(
|
|
default_factory=BirdseyeConfig, title="Birdseye configuration."
|
|
)
|
|
ffmpeg: FfmpegConfig = Field(
|
|
default_factory=FfmpegConfig, title="Global FFmpeg configuration."
|
|
)
|
|
objects: ObjectConfig = Field(
|
|
default_factory=ObjectConfig, title="Global object configuration."
|
|
)
|
|
review: ReviewConfig = Field(
|
|
default_factory=ReviewConfig, title="Review configuration."
|
|
)
|
|
audio: AudioConfig = Field(
|
|
default_factory=AudioConfig, title="Global Audio events configuration."
|
|
)
|
|
motion: Optional[MotionConfig] = Field(
|
|
None, title="Global motion detection configuration."
|
|
)
|
|
detect: DetectConfig = Field(
|
|
default_factory=DetectConfig, title="Global object tracking configuration."
|
|
)
|
|
cameras: Dict[str, CameraConfig] = Field(title="Camera configuration.")
|
|
camera_groups: Dict[str, CameraGroupConfig] = Field(
|
|
default_factory=dict, title="Camera group configuration"
|
|
)
|
|
timestamp_style: TimestampStyleConfig = Field(
|
|
default_factory=TimestampStyleConfig,
|
|
title="Global timestamp style configuration.",
|
|
)
|
|
version: Optional[float] = Field(default=None, title="Current config version.")
|
|
|
|
def runtime_config(self, plus_api: PlusApi = None) -> FrigateConfig:
|
|
"""Merge camera config with globals."""
|
|
config = self.model_copy(deep=True)
|
|
|
|
# Proxy secret substitution
|
|
if config.proxy.auth_secret:
|
|
config.proxy.auth_secret = config.proxy.auth_secret.format(
|
|
**FRIGATE_ENV_VARS
|
|
)
|
|
|
|
# MQTT user/password substitutions
|
|
if config.mqtt.user or config.mqtt.password:
|
|
config.mqtt.user = config.mqtt.user.format(**FRIGATE_ENV_VARS)
|
|
config.mqtt.password = config.mqtt.password.format(**FRIGATE_ENV_VARS)
|
|
|
|
# set default min_score for object attributes
|
|
for attribute in ALL_ATTRIBUTE_LABELS:
|
|
if not config.objects.filters.get(attribute):
|
|
config.objects.filters[attribute] = FilterConfig(min_score=0.7)
|
|
elif config.objects.filters[attribute].min_score == 0.5:
|
|
config.objects.filters[attribute].min_score = 0.7
|
|
|
|
# auto detect hwaccel args
|
|
if config.ffmpeg.hwaccel_args == "auto":
|
|
config.ffmpeg.hwaccel_args = auto_detect_hwaccel()
|
|
|
|
# Global config to propagate down to camera level
|
|
global_config = config.model_dump(
|
|
include={
|
|
"audio": ...,
|
|
"birdseye": ...,
|
|
"record": ...,
|
|
"snapshots": ...,
|
|
"live": ...,
|
|
"objects": ...,
|
|
"review": ...,
|
|
"motion": ...,
|
|
"detect": ...,
|
|
"ffmpeg": ...,
|
|
"timestamp_style": ...,
|
|
},
|
|
exclude_unset=True,
|
|
)
|
|
|
|
for name, camera in config.cameras.items():
|
|
merged_config = deep_merge(
|
|
camera.model_dump(exclude_unset=True), global_config
|
|
)
|
|
camera_config: CameraConfig = CameraConfig.model_validate(
|
|
{"name": name, **merged_config}
|
|
)
|
|
|
|
if camera_config.ffmpeg.hwaccel_args == "auto":
|
|
camera_config.ffmpeg.hwaccel_args = config.ffmpeg.hwaccel_args
|
|
|
|
for input in camera_config.ffmpeg.inputs:
|
|
need_record_fourcc = False and "record" in input.roles
|
|
need_detect_dimensions = "detect" in input.roles and (
|
|
camera_config.detect.height is None
|
|
or camera_config.detect.width is None
|
|
)
|
|
|
|
if need_detect_dimensions or need_record_fourcc:
|
|
stream_info = {"width": 0, "height": 0, "fourcc": None}
|
|
try:
|
|
stream_info = stream_info_retriever.get_stream_info(input.path)
|
|
except Exception:
|
|
logger.warn(
|
|
f"Error detecting stream parameters automatically for {input.path} Applying default values."
|
|
)
|
|
stream_info = {"width": 0, "height": 0, "fourcc": None}
|
|
|
|
if need_detect_dimensions:
|
|
camera_config.detect.width = (
|
|
stream_info["width"]
|
|
if stream_info.get("width")
|
|
else DEFAULT_DETECT_DIMENSIONS["width"]
|
|
)
|
|
camera_config.detect.height = (
|
|
stream_info["height"]
|
|
if stream_info.get("height")
|
|
else DEFAULT_DETECT_DIMENSIONS["height"]
|
|
)
|
|
|
|
if need_record_fourcc:
|
|
# Apple only supports HEVC if it is hvc1 (vs. hev1)
|
|
camera_config.ffmpeg.output_args._force_record_hvc1 = (
|
|
stream_info["fourcc"] == "hevc"
|
|
if stream_info.get("hevc")
|
|
else False
|
|
)
|
|
|
|
# Warn if detect fps > 10
|
|
if camera_config.detect.fps > 10:
|
|
logger.warning(
|
|
f"{camera_config.name} detect fps is set to {camera_config.detect.fps}. This does NOT need to match your camera's frame rate. High values could lead to reduced performance. Recommended value is 5."
|
|
)
|
|
|
|
# Default min_initialized configuration
|
|
min_initialized = int(camera_config.detect.fps / 2)
|
|
if camera_config.detect.min_initialized is None:
|
|
camera_config.detect.min_initialized = min_initialized
|
|
|
|
# Default max_disappeared configuration
|
|
max_disappeared = camera_config.detect.fps * 5
|
|
if camera_config.detect.max_disappeared is None:
|
|
camera_config.detect.max_disappeared = max_disappeared
|
|
|
|
# Default stationary_threshold configuration
|
|
stationary_threshold = camera_config.detect.fps * 10
|
|
if camera_config.detect.stationary.threshold is None:
|
|
camera_config.detect.stationary.threshold = stationary_threshold
|
|
# default to the stationary_threshold if not defined
|
|
if camera_config.detect.stationary.interval is None:
|
|
camera_config.detect.stationary.interval = stationary_threshold
|
|
|
|
# FFMPEG input substitution
|
|
for input in camera_config.ffmpeg.inputs:
|
|
input.path = input.path.format(**FRIGATE_ENV_VARS)
|
|
|
|
# ONVIF substitution
|
|
if camera_config.onvif.user or camera_config.onvif.password:
|
|
camera_config.onvif.user = camera_config.onvif.user.format(
|
|
**FRIGATE_ENV_VARS
|
|
)
|
|
camera_config.onvif.password = camera_config.onvif.password.format(
|
|
**FRIGATE_ENV_VARS
|
|
)
|
|
# set config pre-value
|
|
camera_config.audio.enabled_in_config = camera_config.audio.enabled
|
|
camera_config.record.enabled_in_config = camera_config.record.enabled
|
|
camera_config.onvif.autotracking.enabled_in_config = (
|
|
camera_config.onvif.autotracking.enabled
|
|
)
|
|
|
|
# Add default filters
|
|
object_keys = camera_config.objects.track
|
|
if camera_config.objects.filters is None:
|
|
camera_config.objects.filters = {}
|
|
object_keys = object_keys - camera_config.objects.filters.keys()
|
|
for key in object_keys:
|
|
camera_config.objects.filters[key] = FilterConfig()
|
|
|
|
# Apply global object masks and convert masks to numpy array
|
|
for object, filter in camera_config.objects.filters.items():
|
|
if camera_config.objects.mask:
|
|
filter_mask = []
|
|
if filter.mask is not None:
|
|
filter_mask = (
|
|
filter.mask
|
|
if isinstance(filter.mask, list)
|
|
else [filter.mask]
|
|
)
|
|
object_mask = (
|
|
get_relative_coordinates(
|
|
(
|
|
camera_config.objects.mask
|
|
if isinstance(camera_config.objects.mask, list)
|
|
else [camera_config.objects.mask]
|
|
),
|
|
camera_config.frame_shape,
|
|
)
|
|
or []
|
|
)
|
|
filter.mask = filter_mask + object_mask
|
|
|
|
# Set runtime filter to create masks
|
|
camera_config.objects.filters[object] = RuntimeFilterConfig(
|
|
frame_shape=camera_config.frame_shape,
|
|
**filter.model_dump(exclude_unset=True),
|
|
)
|
|
|
|
# Convert motion configuration
|
|
if camera_config.motion is None:
|
|
camera_config.motion = RuntimeMotionConfig(
|
|
frame_shape=camera_config.frame_shape
|
|
)
|
|
else:
|
|
camera_config.motion = RuntimeMotionConfig(
|
|
frame_shape=camera_config.frame_shape,
|
|
raw_mask=camera_config.motion.mask,
|
|
**camera_config.motion.model_dump(exclude_unset=True),
|
|
)
|
|
camera_config.motion.enabled_in_config = camera_config.motion.enabled
|
|
|
|
# generate zone contours
|
|
if len(camera_config.zones) > 0:
|
|
for zone in camera_config.zones.values():
|
|
zone.generate_contour(camera_config.frame_shape)
|
|
|
|
# Set live view stream if none is set
|
|
if not camera_config.live.stream_name:
|
|
camera_config.live.stream_name = name
|
|
|
|
verify_config_roles(camera_config)
|
|
verify_valid_live_stream_name(config, camera_config)
|
|
verify_recording_retention(camera_config)
|
|
verify_recording_segments_setup_with_reasonable_time(camera_config)
|
|
verify_zone_objects_are_tracked(camera_config)
|
|
verify_required_zones_exist(camera_config)
|
|
verify_autotrack_zones(camera_config)
|
|
verify_motion_and_detect(camera_config)
|
|
|
|
# generate the ffmpeg commands
|
|
camera_config.create_ffmpeg_cmds()
|
|
config.cameras[name] = camera_config
|
|
|
|
# get list of unique enabled labels for tracking
|
|
enabled_labels = set(config.objects.track)
|
|
|
|
for _, camera in config.cameras.items():
|
|
enabled_labels.update(camera.objects.track)
|
|
|
|
config.model.create_colormap(sorted(enabled_labels))
|
|
config.model.check_and_load_plus_model(plus_api)
|
|
|
|
for key, detector in config.detectors.items():
|
|
adapter = TypeAdapter(DetectorConfig)
|
|
model_dict = (
|
|
detector
|
|
if isinstance(detector, dict)
|
|
else detector.model_dump(warnings="none")
|
|
)
|
|
detector_config: DetectorConfig = adapter.validate_python(model_dict)
|
|
if detector_config.model is None:
|
|
detector_config.model = config.model.model_copy()
|
|
else:
|
|
path = detector_config.model.path
|
|
detector_config.model = config.model.model_copy()
|
|
detector_config.model.path = path
|
|
|
|
if "path" not in model_dict or len(model_dict.keys()) > 1:
|
|
logger.warning(
|
|
"Customizing more than a detector model path is unsupported."
|
|
)
|
|
|
|
merged_model = deep_merge(
|
|
detector_config.model.model_dump(exclude_unset=True, warnings="none"),
|
|
config.model.model_dump(exclude_unset=True, warnings="none"),
|
|
)
|
|
|
|
if "path" not in merged_model:
|
|
if detector_config.type == "cpu":
|
|
merged_model["path"] = "/cpu_model.tflite"
|
|
elif detector_config.type == "edgetpu":
|
|
merged_model["path"] = "/edgetpu_model.tflite"
|
|
|
|
detector_config.model = ModelConfig.model_validate(merged_model)
|
|
detector_config.model.check_and_load_plus_model(
|
|
plus_api, detector_config.type
|
|
)
|
|
detector_config.model.compute_model_hash()
|
|
config.detectors[key] = detector_config
|
|
|
|
return config
|
|
|
|
@field_validator("cameras")
|
|
@classmethod
|
|
def ensure_zones_and_cameras_have_different_names(cls, v: Dict[str, CameraConfig]):
|
|
zones = [zone for camera in v.values() for zone in camera.zones.keys()]
|
|
for zone in zones:
|
|
if zone in v.keys():
|
|
raise ValueError("Zones cannot share names with cameras")
|
|
return v
|
|
|
|
@classmethod
|
|
def parse_file(cls, config_file):
|
|
with open(config_file) as f:
|
|
raw_config = f.read()
|
|
|
|
if config_file.endswith(YAML_EXT):
|
|
config = load_config_with_no_duplicates(raw_config)
|
|
elif config_file.endswith(".json"):
|
|
config = json.loads(raw_config)
|
|
|
|
return cls.model_validate(config)
|
|
|
|
@classmethod
|
|
def parse_raw(cls, raw_config):
|
|
config = load_config_with_no_duplicates(raw_config)
|
|
return cls.model_validate(config)
|