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config.py
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config.py
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from __future__ import annotations
import json
import logging
import os
from enum import Enum
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union
import matplotlib.pyplot as plt
import numpy as np
from pydantic import (
BaseModel,
ConfigDict,
Field,
TypeAdapter,
ValidationInfo,
field_serializer,
field_validator,
)
from pydantic.fields import PrivateAttr
from frigate.const import (
ALL_ATTRIBUTE_LABELS,
AUDIO_MIN_CONFIDENCE,
CACHE_DIR,
CACHE_SEGMENT_FORMAT,
DEFAULT_DB_PATH,
MAX_PRE_CAPTURE,
REGEX_CAMERA_NAME,
YAML_EXT,
)
from frigate.detectors import DetectorConfig, ModelConfig
from frigate.detectors.detector_config import BaseDetectorConfig
from frigate.ffmpeg_presets import (
parse_preset_hardware_acceleration_decode,
parse_preset_hardware_acceleration_scale,
parse_preset_input,
parse_preset_output_record,
)
from frigate.plus import PlusApi
from frigate.util.builtin import (
deep_merge,
escape_special_characters,
get_ffmpeg_arg_list,
load_config_with_no_duplicates,
)
from frigate.util.config import StreamInfoRetriever, get_relative_coordinates
from frigate.util.image import create_mask
from frigate.util.services import auto_detect_hwaccel
logger = logging.getLogger(__name__)
# TODO: Identify what the default format to display timestamps is
DEFAULT_TIME_FORMAT = "%m/%d/%Y %H:%M:%S"
# German Style:
# DEFAULT_TIME_FORMAT = "%d.%m.%Y %H:%M:%S"
FRIGATE_ENV_VARS = {k: v for k, v in os.environ.items() if k.startswith("FRIGATE_")}
# read docker secret files as env vars too
if os.path.isdir("/run/secrets") and os.access("/run/secrets", os.R_OK):
for secret_file in os.listdir("/run/secrets"):
if secret_file.startswith("FRIGATE_"):
FRIGATE_ENV_VARS[secret_file] = Path(
os.path.join("/run/secrets", secret_file)
).read_text()
DEFAULT_TRACKED_OBJECTS = ["person"]
DEFAULT_ALERT_OBJECTS = ["person", "car"]
DEFAULT_LISTEN_AUDIO = ["bark", "fire_alarm", "scream", "speech", "yell"]
DEFAULT_DETECTORS = {"cpu": {"type": "cpu"}}
DEFAULT_DETECT_DIMENSIONS = {"width": 1280, "height": 720}
DEFAULT_TIME_LAPSE_FFMPEG_ARGS = "-vf setpts=0.04*PTS -r 30"
# stream info handler
stream_info_retriever = StreamInfoRetriever()
class FrigateBaseModel(BaseModel):
model_config = ConfigDict(extra="forbid", protected_namespaces=())
class LiveModeEnum(str, Enum):
jsmpeg = "jsmpeg"
mse = "mse"
webrtc = "webrtc"
class TimeFormatEnum(str, Enum):
browser = "browser"
hours12 = "12hour"
hours24 = "24hour"
class DateTimeStyleEnum(str, Enum):
full = "full"
long = "long"
medium = "medium"
short = "short"
class UIConfig(FrigateBaseModel):
timezone: Optional[str] = Field(default=None, title="Override UI timezone.")
time_format: TimeFormatEnum = Field(
default=TimeFormatEnum.browser, title="Override UI time format."
)
date_style: DateTimeStyleEnum = Field(
default=DateTimeStyleEnum.short, title="Override UI dateStyle."
)
time_style: DateTimeStyleEnum = Field(
default=DateTimeStyleEnum.medium, title="Override UI timeStyle."
)
strftime_fmt: Optional[str] = Field(
default=None, title="Override date and time format using strftime syntax."
)
class TlsConfig(FrigateBaseModel):
enabled: bool = Field(default=True, title="Enable TLS for port 8080")
class AuthModeEnum(str, Enum):
native = "native"
proxy = "proxy"
class HeaderMappingConfig(FrigateBaseModel):
user: str = Field(
default=None, title="Header name from upstream proxy to identify user."
)
class AuthConfig(FrigateBaseModel):
mode: AuthModeEnum = Field(default=AuthModeEnum.native, title="Authentication mode")
reset_admin_password: bool = Field(
default=False, title="Reset the admin password on startup"
)
cookie_name: str = Field(
default="frigate_token", title="Name for jwt token cookie", pattern=r"^[a-z]_*$"
)
cookie_secure: bool = Field(default=False, title="Set secure flag on cookie")
session_length: int = Field(
default=86400, title="Session length for jwt session tokens", ge=60
)
refresh_time: int = Field(
default=43200,
title="Refresh the session if it is going to expire in this many seconds",
ge=30,
)
header_map: HeaderMappingConfig = Field(
default_factory=HeaderMappingConfig,
title="Header mapping definitions for proxy auth mode.",
)
failed_login_rate_limit: Optional[str] = Field(
default=None,
title="Rate limits for failed login attempts.",
)
trusted_proxies: List[str] = Field(
default=[],
title="Trusted proxies for determining IP address to rate limit",
)
logout_url: Optional[str] = Field(
default=None, title="Redirect url for logging out in proxy mode."
)
# As of Feb 2023, OWASP recommends 600000 iterations for PBKDF2-SHA256
hash_iterations: int = Field(default=600000, title="Password hash iterations")
class StatsConfig(FrigateBaseModel):
amd_gpu_stats: bool = Field(default=True, title="Enable AMD GPU stats.")
intel_gpu_stats: bool = Field(default=True, title="Enable Intel GPU stats.")
network_bandwidth: bool = Field(
default=False, title="Enable network bandwidth for ffmpeg processes."
)
class TelemetryConfig(FrigateBaseModel):
network_interfaces: List[str] = Field(
default=[],
title="Enabled network interfaces for bandwidth calculation.",
)
stats: StatsConfig = Field(
default_factory=StatsConfig, title="System Stats Configuration"
)
version_check: bool = Field(default=True, title="Enable latest version check.")
class MqttConfig(FrigateBaseModel):
enabled: bool = Field(title="Enable MQTT Communication.", default=True)
host: str = Field(default="", title="MQTT Host")
port: int = Field(default=1883, title="MQTT Port")
topic_prefix: str = Field(default="frigate", title="MQTT Topic Prefix")
client_id: str = Field(default="frigate", title="MQTT Client ID")
stats_interval: int = Field(default=60, title="MQTT Camera Stats Interval")
user: Optional[str] = Field(None, title="MQTT Username")
password: Optional[str] = Field(None, title="MQTT Password", validate_default=True)
tls_ca_certs: Optional[str] = Field(None, title="MQTT TLS CA Certificates")
tls_client_cert: Optional[str] = Field(None, title="MQTT TLS Client Certificate")
tls_client_key: Optional[str] = Field(None, title="MQTT TLS Client Key")
tls_insecure: Optional[bool] = Field(None, title="MQTT TLS Insecure")
@field_validator("password")
def user_requires_pass(cls, v, info: ValidationInfo):
if (v is None) != (info.data["user"] is None):
raise ValueError("Password must be provided with username.")
return v
class ZoomingModeEnum(str, Enum):
disabled = "disabled"
absolute = "absolute"
relative = "relative"
class PtzAutotrackConfig(FrigateBaseModel):
enabled: bool = Field(default=False, title="Enable PTZ object autotracking.")
calibrate_on_startup: bool = Field(
default=False, title="Perform a camera calibration when Frigate starts."
)
zooming: ZoomingModeEnum = Field(
default=ZoomingModeEnum.disabled, title="Autotracker zooming mode."
)
zoom_factor: float = Field(
default=0.3,
title="Zooming factor (0.1-0.75).",
ge=0.1,
le=0.75,
)
track: List[str] = Field(default=DEFAULT_TRACKED_OBJECTS, title="Objects to track.")
required_zones: List[str] = Field(
default_factory=list,
title="List of required zones to be entered in order to begin autotracking.",
)
return_preset: str = Field(
default="home",
title="Name of camera preset to return to when object tracking is over.",
)
timeout: int = Field(
default=10, title="Seconds to delay before returning to preset."
)
movement_weights: Optional[Union[str, List[str]]] = Field(
default=[],
title="Internal value used for PTZ movements based on the speed of your camera's motor.",
)
enabled_in_config: Optional[bool] = Field(
None, title="Keep track of original state of autotracking."
)
@field_validator("movement_weights", mode="before")
@classmethod
def validate_weights(cls, v):
if v is None:
return None
if isinstance(v, str):
weights = list(map(str, map(float, v.split(","))))
elif isinstance(v, list):
weights = [str(float(val)) for val in v]
else:
raise ValueError("Invalid type for movement_weights")
if len(weights) != 5:
raise ValueError("movement_weights must have exactly 5 floats")
return weights
class OnvifConfig(FrigateBaseModel):
host: str = Field(default="", title="Onvif Host")
port: int = Field(default=8000, title="Onvif Port")
user: Optional[str] = Field(None, title="Onvif Username")
password: Optional[str] = Field(None, title="Onvif Password")
autotracking: PtzAutotrackConfig = Field(
default_factory=PtzAutotrackConfig,
title="PTZ auto tracking config.",
)
class RetainModeEnum(str, Enum):
all = "all"
motion = "motion"
active_objects = "active_objects"
class RetainConfig(FrigateBaseModel):
default: float = Field(default=10, title="Default retention period.")
mode: RetainModeEnum = Field(default=RetainModeEnum.motion, title="Retain mode.")
objects: Dict[str, float] = Field(
default_factory=dict, title="Object retention period."
)
class EventsConfig(FrigateBaseModel):
pre_capture: int = Field(
default=5, title="Seconds to retain before event starts.", le=MAX_PRE_CAPTURE
)
post_capture: int = Field(default=5, title="Seconds to retain after event ends.")
objects: Optional[List[str]] = Field(
None,
title="List of objects to be detected in order to save the event.",
)
retain: RetainConfig = Field(
default_factory=RetainConfig, title="Event retention settings."
)
class RecordRetainConfig(FrigateBaseModel):
days: float = Field(default=0, title="Default retention period.")
mode: RetainModeEnum = Field(default=RetainModeEnum.all, title="Retain mode.")
class RecordExportConfig(FrigateBaseModel):
timelapse_args: str = Field(
default=DEFAULT_TIME_LAPSE_FFMPEG_ARGS, title="Timelapse Args"
)
class RecordQualityEnum(str, Enum):
very_low = "very_low"
low = "low"
medium = "medium"
high = "high"
very_high = "very_high"
class RecordPreviewConfig(FrigateBaseModel):
quality: RecordQualityEnum = Field(
default=RecordQualityEnum.medium, title="Quality of recording preview."
)
class RecordConfig(FrigateBaseModel):
enabled: bool = Field(default=False, title="Enable record on all cameras.")
sync_recordings: bool = Field(
default=False, title="Sync recordings with disk on startup and once a day."
)
expire_interval: int = Field(
default=60,
title="Number of minutes to wait between cleanup runs.",
)
retain: RecordRetainConfig = Field(
default_factory=RecordRetainConfig, title="Record retention settings."
)
events: EventsConfig = Field(
default_factory=EventsConfig, title="Event specific settings."
)
export: RecordExportConfig = Field(
default_factory=RecordExportConfig, title="Recording Export Config"
)
preview: RecordPreviewConfig = Field(
default_factory=RecordPreviewConfig, title="Recording Preview Config"
)
enabled_in_config: Optional[bool] = Field(
None, title="Keep track of original state of recording."
)
class MotionConfig(FrigateBaseModel):
enabled: bool = Field(default=True, title="Enable motion on all cameras.")
threshold: int = Field(
default=30,
title="Motion detection threshold (1-255).",
ge=1,
le=255,
)
lightning_threshold: float = Field(
default=0.8, title="Lightning detection threshold (0.3-1.0).", ge=0.3, le=1.0
)
improve_contrast: bool = Field(default=True, title="Improve Contrast")
contour_area: Optional[int] = Field(default=10, title="Contour Area")
delta_alpha: float = Field(default=0.2, title="Delta Alpha")
frame_alpha: float = Field(default=0.01, title="Frame Alpha")
frame_height: Optional[int] = Field(default=100, title="Frame Height")
mask: Union[str, List[str]] = Field(
default="", title="Coordinates polygon for the motion mask."
)
mqtt_off_delay: int = Field(
default=30,
title="Delay for updating MQTT with no motion detected.",
)
enabled_in_config: Optional[bool] = Field(
None, title="Keep track of original state of motion detection."
)
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 RuntimeMotionConfig(MotionConfig):
raw_mask: Union[str, List[str]] = ""
mask: np.ndarray = 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:
config["mask"] = create_mask(frame_shape, mask)
else:
empty_mask = np.zeros(frame_shape, np.uint8)
empty_mask[:] = 255
config["mask"] = empty_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
@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
model_config = ConfigDict(arbitrary_types_allowed=True, extra="ignore")
class StationaryMaxFramesConfig(FrigateBaseModel):
default: Optional[int] = Field(None, title="Default max frames.", ge=1)
objects: Dict[str, int] = Field(
default_factory=dict, title="Object specific max frames."
)
class StationaryConfig(FrigateBaseModel):
interval: Optional[int] = Field(
None,
title="Frame interval for checking stationary objects.",
gt=0,
)
threshold: Optional[int] = Field(
None,
title="Number of frames without a position change for an object to be considered stationary",
ge=1,
)
max_frames: StationaryMaxFramesConfig = Field(
default_factory=StationaryMaxFramesConfig,
title="Max frames for stationary objects.",
)
class DetectConfig(FrigateBaseModel):
height: Optional[int] = Field(
None, title="Height of the stream for the detect role."
)
width: Optional[int] = Field(None, title="Width of the stream for the detect role.")
fps: int = Field(
default=5, title="Number of frames per second to process through detection."
)
enabled: bool = Field(default=True, title="Detection Enabled.")
min_initialized: Optional[int] = Field(
None,
title="Minimum number of consecutive hits for an object to be initialized by the tracker.",
)
max_disappeared: Optional[int] = Field(
None,
title="Maximum number of frames the object can disappear before detection ends.",
)
stationary: StationaryConfig = Field(
default_factory=StationaryConfig,
title="Stationary objects config.",
)
annotation_offset: int = Field(
default=0, title="Milliseconds to offset detect annotations by."
)
class FilterConfig(FrigateBaseModel):
min_area: int = Field(
default=0, title="Minimum area of bounding box for object to be counted."
)
max_area: int = Field(
default=24000000, title="Maximum area of bounding box for object to be counted."
)
min_ratio: float = Field(
default=0,
title="Minimum ratio of bounding box's width/height for object to be counted.",
)
max_ratio: float = Field(
default=24000000,
title="Maximum ratio of bounding box's width/height for object to be counted.",
)
threshold: float = Field(
default=0.7,
title="Average detection confidence threshold for object to be counted.",
)
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."