blakeblackshear.frigate/frigate/config.py

from __future__ import annotations

import json
import logging
import os
from enum import Enum
from typing import Dict, List, Optional, Tuple, Union

import matplotlib.pyplot as plt
import numpy as np
import yaml
from pydantic import BaseModel, Extra, Field, validator, parse_obj_as
from pydantic.fields import PrivateAttr

from frigate.const import (
    BASE_DIR,
    CACHE_DIR,
    REGEX_CAMERA_NAME,
    YAML_EXT,
)
from frigate.util import (
    create_mask,
    deep_merge,
    get_ffmpeg_arg_list,
    escape_special_characters,
    load_config_with_no_duplicates,
    load_labels,
)
from frigate.ffmpeg_presets import (
    parse_preset_hardware_acceleration_decode,
    parse_preset_hardware_acceleration_scale,
    parse_preset_input,
    parse_preset_output_record,
    parse_preset_output_rtmp,
)
from frigate.detectors import (
    PixelFormatEnum,
    InputTensorEnum,
    ModelConfig,
    DetectorConfig,
)
from frigate.version import VERSION


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_")}

DEFAULT_TRACKED_OBJECTS = ["person"]
DEFAULT_DETECTORS = {"cpu": {"type": "cpu"}}


class FrigateBaseModel(BaseModel):
    class Config:
        extra = Extra.forbid


class UIConfig(FrigateBaseModel):
    use_experimental: bool = Field(default=False, title="Experimental UI")


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(title="MQTT Username")
    password: Optional[str] = Field(title="MQTT Password")
    tls_ca_certs: Optional[str] = Field(title="MQTT TLS CA Certificates")
    tls_client_cert: Optional[str] = Field(title="MQTT TLS Client Certificate")
    tls_client_key: Optional[str] = Field(title="MQTT TLS Client Key")
    tls_insecure: Optional[bool] = Field(title="MQTT TLS Insecure")

    @validator("password", pre=True, always=True)
    def validate_password(cls, v, values):
        if (v is None) != (values["user"] is None):
            raise ValueError("Password must be provided with username.")
        return v


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.")
    post_capture: int = Field(default=5, title="Seconds to retain after event ends.")
    required_zones: List[str] = Field(
        default_factory=list,
        title="List of required zones to be entered in order to save the event.",
    )
    objects: Optional[List[str]] = Field(
        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 RecordConfig(FrigateBaseModel):
    enabled: bool = Field(default=False, title="Enable record on all cameras.")
    expire_interval: int = Field(
        default=60,
        title="Number of minutes to wait between cleanup runs.",
    )
    # deprecated - to be removed in a future version
    retain_days: Optional[float] = Field(title="Recording retention period in days.")
    retain: RecordRetainConfig = Field(
        default_factory=RecordRetainConfig, title="Record retention settings."
    )
    events: EventsConfig = Field(
        default_factory=EventsConfig, title="Event specific settings."
    )


class MotionConfig(FrigateBaseModel):
    threshold: int = Field(
        default=25,
        title="Motion detection threshold (1-255).",
        ge=1,
        le=255,
    )
    improve_contrast: bool = Field(default=False, title="Improve Contrast")
    contour_area: Optional[int] = Field(default=30, title="Contour Area")
    delta_alpha: float = Field(default=0.2, title="Delta Alpha")
    frame_alpha: float = Field(default=0.2, title="Frame Alpha")
    frame_height: Optional[int] = Field(default=50, 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.",
    )


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 = config.get("mask", "")
        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().dict(**kwargs)
        if "mask" in ret:
            ret["mask"] = ret["raw_mask"]
            ret.pop("raw_mask")
        return ret

    class Config:
        arbitrary_types_allowed = True
        extra = Extra.ignore


class StationaryMaxFramesConfig(FrigateBaseModel):
    default: Optional[int] = Field(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(
        default=0,
        title="Frame interval for checking stationary objects.",
        ge=0,
    )
    threshold: Optional[int] = Field(
        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: int = Field(default=720, title="Height of the stream for the detect role.")
    width: int = Field(default=1280, 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.")
    max_disappeared: Optional[int] = Field(
        title="Maximum number of frames the object can dissapear before detection ends."
    )
    stationary: StationaryConfig = Field(
        default_factory=StationaryConfig,
        title="Stationary objects config.",
    )


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(
        title="Detection area polygon mask for this filter configuration.",
    )


class RuntimeFilterConfig(FilterConfig):
    mask: Optional[np.ndarray]
    raw_mask: Optional[Union[str, List[str]]]

    def __init__(self, **config):
        mask = config.get("mask")
        config["raw_mask"] = mask

        if mask is not None:
            config["mask"] = create_mask(config.get("frame_shape", (1, 1)), mask)

        super().__init__(**config)

    def dict(self, **kwargs):
        ret = super().dict(**kwargs)
        if "mask" in ret:
            ret["mask"] = ret["raw_mask"]
            ret.pop("raw_mask")
        return ret

    class Config:
        arbitrary_types_allowed = True
        extra = 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."
    )
    objects: 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

    def __init__(self, **config):
        super().__init__(**config)

        self._color = config.get("color", (0, 0, 0))
        coordinates = config["coordinates"]

        if isinstance(coordinates, list):
            self._contour = np.array(
                [[int(p.split(",")[0]), int(p.split(",")[1])] for p in coordinates]
            )
        elif isinstance(coordinates, str):
            points = coordinates.split(",")
            self._contour = np.array(
                [[int(points[i]), int(points[i + 1])] 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: Optional[Dict[str, FilterConfig]] = Field(title="Object filters.")
    mask: Union[str, List[str]] = Field(default="", title="Object mask.")


class BirdseyeModeEnum(str, Enum):
    objects = "objects"
    motion = "motion"
    continuous = "continuous"


class BirdseyeConfig(FrigateBaseModel):
    enabled: bool = Field(default=True, title="Enable birdseye view.")
    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,
    )
    mode: BirdseyeModeEnum = Field(
        default=BirdseyeModeEnum.objects, title="Tracking mode."
    )


# 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.")
    mode: BirdseyeModeEnum = Field(
        default=BirdseyeModeEnum.objects, title="Tracking mode for camera."
    )


FFMPEG_GLOBAL_ARGS_DEFAULT = ["-hide_banner", "-loglevel", "warning"]
FFMPEG_INPUT_ARGS_DEFAULT = "preset-rtsp-generic"
DETECT_FFMPEG_OUTPUT_ARGS_DEFAULT = ["-f", "rawvideo", "-pix_fmt", "yuv420p"]
RTMP_FFMPEG_OUTPUT_ARGS_DEFAULT = "preset-rtmp-generic"
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.",
    )
    rtmp: Union[str, List[str]] = Field(
        default=RTMP_FFMPEG_OUTPUT_ARGS_DEFAULT,
        title="RTMP role FFmpeg output arguments.",
    )


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_factory=list, 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.",
    )


class CameraRoleEnum(str, Enum):
    record = "record"
    restream = "restream"
    rtmp = "rtmp"
    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.")

    @validator("inputs")
    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 not "detect" 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(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(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 RtmpConfig(FrigateBaseModel):
    enabled: bool = Field(default=False, title="RTMP restreaming enabled.")


class JsmpegStreamConfig(FrigateBaseModel):
    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 RestreamCodecEnum(str, Enum):
    copy = "copy"
    h264 = "h264"
    h265 = "h265"


class RestreamConfig(FrigateBaseModel):
    enabled: bool = Field(default=True, title="Restreaming enabled.")
    video_encoding: RestreamCodecEnum = Field(
        default=RestreamCodecEnum.copy, title="Method for encoding the restream."
    )
    force_audio: bool = Field(
        default=True, title="Force audio compatibility with the browser."
    )
    birdseye: bool = Field(default=False, title="Restream the birdseye feed via RTSP.")
    jsmpeg: JsmpegStreamConfig = Field(
        default_factory=JsmpegStreamConfig, title="Jsmpeg Stream Configuration."
    )


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(title="Camera name.", regex=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.",
    )
    zones: Dict[str, ZoneConfig] = Field(
        default_factory=dict, title="Zone configuration."
    )
    record: RecordConfig = Field(
        default_factory=RecordConfig, title="Record configuration."
    )
    rtmp: RtmpConfig = Field(
        default_factory=RtmpConfig, title="RTMP restreaming configuration."
    )
    restream: RestreamConfig = Field(
        default_factory=RestreamConfig, title="Restreaming configuration."
    )
    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."
    )
    motion: Optional[MotionConfig] = Field(title="Motion detection configuration.")
    detect: DetectConfig = Field(
        default_factory=DetectConfig, title="Object detection 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 = plt.cm.get_cmap("tab10", len(config["zones"]))
            config["zones"] = {
                name: {**z, "color": tuple(round(255 * c) for c in colors(idx)[:3])}
                for idx, (name, z) in enumerate(config["zones"].items())
            }

        # add roles to the input if there is only one
        if len(config["ffmpeg"]["inputs"]) == 1:
            has_rtmp = "rtmp" in config["ffmpeg"]["inputs"][0].get("roles", [])

            config["ffmpeg"]["inputs"][0]["roles"] = [
                "record",
                "detect",
                "restream",
            ]

            if has_rtmp:
                config["ffmpeg"]["inputs"][0]["roles"].append("rtmp")

        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 "rtmp" in ffmpeg_input.roles and self.rtmp.enabled:
            rtmp_args = get_ffmpeg_arg_list(
                parse_preset_output_rtmp(self.ffmpeg.output_args.rtmp)
                or self.ffmpeg.output_args.rtmp
            )

            ffmpeg_output_args = (
                rtmp_args + [f"rtmp://127.0.0.1/live/{self.name}"] + ffmpeg_output_args
            )
        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)
                or self.ffmpeg.output_args.record
            )

            ffmpeg_output_args = (
                record_args
                + [f"{os.path.join(CACHE_DIR, self.name)}-%Y%m%d%H%M%S.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
        )
        hwaccel_args = get_ffmpeg_arg_list(
            parse_preset_hardware_acceleration_decode(ffmpeg_input.hwaccel_args)
            or ffmpeg_input.hwaccel_args
            or parse_preset_hardware_acceleration_decode(self.ffmpeg.hwaccel_args)
            or self.ffmpeg.hwaccel_args
        )
        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=os.path.join(BASE_DIR, "frigate.db"), 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."
    )


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 not "record" in assigned_roles:
        raise ValueError(
            f"Camera {camera_config.name} has record enabled, but record is not assigned to an input."
        )

    if camera_config.rtmp.enabled and not "rtmp" in assigned_roles:
        raise ValueError(
            f"Camera {camera_config.name} has rtmp enabled, but rtmp is not assigned to an input."
        )

    if camera_config.restream.enabled and not "restream" in assigned_roles:
        raise ValueError(
            f"Camera {camera_config.name} has restream enabled, but restream is not assigned to an input."
        )


def verify_old_retain_config(camera_config: CameraConfig) -> None:
    """Leave log if old retain_days is used."""
    if not camera_config.record.retain_days is None:
        logger.warning(
            "The 'retain_days' config option has been DEPRECATED and will be removed in a future version. Please use the 'days' setting under 'retain'"
        )
        if camera_config.record.retain.days == 0:
            camera_config.record.retain.days = camera_config.record.retain_days


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."
                )


class FrigateConfig(FrigateBaseModel):
    mqtt: MqttConfig = Field(title="MQTT Configuration.")
    database: DatabaseConfig = Field(
        default_factory=DatabaseConfig, title="Database configuration."
    )
    environment_vars: Dict[str, str] = Field(
        default_factory=dict, title="Frigate environment variables."
    )
    ui: UIConfig = Field(default_factory=UIConfig, title="UI configuration.")
    model: ModelConfig = Field(
        default_factory=ModelConfig, title="Detection model configuration."
    )
    detectors: Dict[str, DetectorConfig] = 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."
    )
    rtmp: RtmpConfig = Field(
        default_factory=RtmpConfig, title="Global RTMP restreaming configuration."
    )
    restream: 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."
    )
    motion: Optional[MotionConfig] = Field(
        title="Global motion detection configuration."
    )
    detect: DetectConfig = Field(
        default_factory=DetectConfig, title="Global object tracking configuration."
    )
    cameras: Dict[str, CameraConfig] = Field(title="Camera configuration.")
    timestamp_style: TimestampStyleConfig = Field(
        default_factory=TimestampStyleConfig,
        title="Global timestamp style configuration.",
    )

    @property
    def runtime_config(self) -> FrigateConfig:
        """Merge camera config with globals."""
        config = self.copy(deep=True)

        # 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)

        # Global config to propagate down to camera level
        global_config = config.dict(
            include={
                "birdseye": ...,
                "record": ...,
                "snapshots": ...,
                "rtmp": ...,
                "restream": ...,
                "objects": ...,
                "motion": ...,
                "detect": ...,
                "ffmpeg": ...,
                "timestamp_style": ...,
            },
            exclude_unset=True,
        )

        for name, camera in config.cameras.items():
            merged_config = deep_merge(camera.dict(exclude_unset=True), global_config)
            camera_config: CameraConfig = CameraConfig.parse_obj(
                {"name": name, **merged_config}
            )

            # 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

            # FFMPEG input substitution
            for input in camera_config.ffmpeg.inputs:
                input.path = input.path.format(**FRIGATE_ENV_VARS)

            # 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 = (
                        camera_config.objects.mask
                        if isinstance(camera_config.objects.mask, list)
                        else [camera_config.objects.mask]
                    )
                    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.dict(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.dict(exclude_unset=True),
                )

            verify_config_roles(camera_config)
            verify_old_retain_config(camera_config)
            verify_recording_retention(camera_config)
            verify_recording_segments_setup_with_reasonable_time(camera_config)
            verify_zone_objects_are_tracked(camera_config)

            if camera_config.rtmp.enabled:
                logger.warning(
                    "RTMP restream is deprecated in favor of the restream role, recommend disabling RTMP."
                )

            # 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))

        for key, detector in config.detectors.items():
            detector_config: DetectorConfig = parse_obj_as(DetectorConfig, detector)
            if detector_config.model is None:
                detector_config.model = config.model
            else:
                model = detector_config.model
                schema = ModelConfig.schema()["properties"]
                if (
                    model.width != schema["width"]["default"]
                    or model.height != schema["height"]["default"]
                    or model.labelmap_path is not None
                    or model.labelmap is not {}
                    or model.input_tensor != schema["input_tensor"]["default"]
                    or model.input_pixel_format
                    != schema["input_pixel_format"]["default"]
                ):
                    logger.warning(
                        "Customizing more than a detector model path is unsupported."
                    )
            merged_model = deep_merge(
                detector_config.model.dict(exclude_unset=True),
                config.model.dict(exclude_unset=True),
            )
            detector_config.model = ModelConfig.parse_obj(merged_model)
            config.detectors[key] = detector_config

        return config

    @validator("cameras")
    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.parse_obj(config)

    @classmethod
    def parse_raw(cls, raw_config):
        config = load_config_with_no_duplicates(raw_config)
        return cls.parse_obj(config)