mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
e5fe323aca
* Initial work for adding OpenVino detector. Not functional * Load model and submit for inference. Sucessfully load model and initialize OpenVino engine with either CPU or GPU as device. Does not parse results for objects. * Detection working with ssdlite_mobilenetv2 FP16 model * Add OpenVIno support and model to docker image * Add documentation for OpenVino detector configuration * Adds support for ARM32/ARM64 and the Myriad X hardware - Use custom-built openvino wheel for all platforms - Add libusb build without udev for NCS2 support * Add documentation around Intel CPU requirements and NCS2 setup * Print all available output tensors * Update documentation for config parameters
1037 lines
35 KiB
Python
1037 lines
35 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 typing import Dict, List, Optional, Tuple, Union
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import matplotlib.pyplot as plt
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import numpy as np
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import yaml
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from pydantic import BaseModel, Extra, Field, validator
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from pydantic.fields import PrivateAttr
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from frigate.const import (
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BASE_DIR,
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CACHE_DIR,
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REGEX_CAMERA_NAME,
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YAML_EXT,
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)
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from frigate.util import (
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create_mask,
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deep_merge,
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get_ffmpeg_arg_list,
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escape_special_characters,
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load_config_with_no_duplicates,
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load_labels,
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)
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from frigate.ffmpeg_presets import (
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parse_preset_hardware_acceleration,
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parse_preset_input,
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parse_preset_output_record,
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parse_preset_output_rtmp,
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)
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from frigate.version import VERSION
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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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DEFAULT_TRACKED_OBJECTS = ["person"]
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DEFAULT_DETECTORS = {"cpu": {"type": "cpu"}}
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class FrigateBaseModel(BaseModel):
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class Config:
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extra = Extra.forbid
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class DetectorTypeEnum(str, Enum):
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edgetpu = "edgetpu"
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openvino = "openvino"
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cpu = "cpu"
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class DetectorConfig(FrigateBaseModel):
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type: DetectorTypeEnum = Field(default=DetectorTypeEnum.cpu, title="Detector Type")
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device: str = Field(default="usb", title="Device Type")
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num_threads: int = Field(default=3, title="Number of detection threads")
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class UIConfig(FrigateBaseModel):
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use_experimental: bool = Field(default=False, title="Experimental UI")
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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(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(default=60, title="MQTT Camera Stats Interval")
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user: Optional[str] = Field(title="MQTT Username")
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password: Optional[str] = Field(title="MQTT Password")
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tls_ca_certs: Optional[str] = Field(title="MQTT TLS CA Certificates")
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tls_client_cert: Optional[str] = Field(title="MQTT TLS Client Certificate")
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tls_client_key: Optional[str] = Field(title="MQTT TLS Client Key")
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tls_insecure: Optional[bool] = Field(title="MQTT TLS Insecure")
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@validator("password", pre=True, always=True)
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def validate_password(cls, v, values):
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if (v is None) != (values["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 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(default=5, title="Seconds to retain before event starts.")
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post_capture: int = Field(default=5, title="Seconds to retain after event ends.")
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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 save the event.",
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)
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objects: Optional[List[str]] = Field(
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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 RecordConfig(FrigateBaseModel):
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enabled: bool = Field(default=False, title="Enable record on all cameras.")
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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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# deprecated - to be removed in a future version
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retain_days: Optional[float] = Field(title="Recording retention period in days.")
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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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class MotionConfig(FrigateBaseModel):
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threshold: int = Field(
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default=25,
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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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improve_contrast: bool = Field(default=False, title="Improve Contrast")
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contour_area: Optional[int] = Field(default=30, 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=0.2, title="Frame Alpha")
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frame_height: Optional[int] = Field(default=50, 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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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 = config.get("mask", "")
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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().dict(**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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class Config:
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arbitrary_types_allowed = True
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extra = Extra.ignore
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class StationaryMaxFramesConfig(FrigateBaseModel):
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default: Optional[int] = Field(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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default=0,
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title="Frame interval for checking stationary objects.",
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ge=0,
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)
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threshold: Optional[int] = Field(
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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: int = Field(default=720, title="Height of the stream for the detect role.")
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width: int = Field(default=1280, 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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max_disappeared: Optional[int] = Field(
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title="Maximum number of frames the object can dissapear 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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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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)
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min_score: float = Field(
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default=0.5, title="Minimum detection confidence for object to be counted."
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)
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mask: Optional[Union[str, List[str]]] = Field(
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title="Detection area polygon mask for this filter configuration.",
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)
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class RuntimeFilterConfig(FilterConfig):
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mask: Optional[np.ndarray]
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raw_mask: Optional[Union[str, List[str]]]
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def __init__(self, **config):
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mask = config.get("mask")
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config["raw_mask"] = mask
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if mask is not None:
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config["mask"] = create_mask(config.get("frame_shape", (1, 1)), mask)
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super().__init__(**config)
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def dict(self, **kwargs):
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ret = super().dict(**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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class Config:
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arbitrary_types_allowed = True
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extra = Extra.ignore
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# this uses the base model because the color is an extra attribute
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class ZoneConfig(BaseModel):
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filters: Dict[str, FilterConfig] = Field(
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default_factory=dict, title="Zone filters."
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)
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coordinates: Union[str, List[str]] = Field(
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title="Coordinates polygon for the defined zone."
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)
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objects: List[str] = Field(
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default_factory=list,
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title="List of objects that can trigger the zone.",
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)
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_color: Optional[Tuple[int, int, int]] = PrivateAttr()
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_contour: np.ndarray = PrivateAttr()
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@property
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def color(self) -> Tuple[int, int, int]:
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return self._color
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@property
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def contour(self) -> np.ndarray:
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return self._contour
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def __init__(self, **config):
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super().__init__(**config)
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self._color = config.get("color", (0, 0, 0))
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coordinates = config["coordinates"]
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if isinstance(coordinates, list):
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self._contour = np.array(
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[[int(p.split(",")[0]), int(p.split(",")[1])] for p in coordinates]
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)
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elif isinstance(coordinates, str):
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points = coordinates.split(",")
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self._contour = np.array(
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[[int(points[i]), int(points[i + 1])] for i in range(0, len(points), 2)]
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)
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else:
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self._contour = np.array([])
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class ObjectConfig(FrigateBaseModel):
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track: List[str] = Field(default=DEFAULT_TRACKED_OBJECTS, title="Objects to track.")
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filters: Optional[Dict[str, FilterConfig]] = Field(title="Object filters.")
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mask: Union[str, List[str]] = Field(default="", title="Object mask.")
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class BirdseyeModeEnum(str, Enum):
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objects = "objects"
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motion = "motion"
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continuous = "continuous"
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class BirdseyeConfig(FrigateBaseModel):
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enabled: bool = Field(default=True, title="Enable birdseye view.")
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width: int = Field(default=1280, title="Birdseye width.")
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height: int = Field(default=720, title="Birdseye height.")
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quality: int = Field(
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default=8,
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title="Encoding quality.",
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ge=1,
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le=31,
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)
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mode: BirdseyeModeEnum = Field(
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default=BirdseyeModeEnum.objects, title="Tracking mode."
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)
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# uses BaseModel because some global attributes are not available at the camera level
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class BirdseyeCameraConfig(BaseModel):
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enabled: bool = Field(default=True, title="Enable birdseye view for camera.")
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mode: BirdseyeModeEnum = Field(
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default=BirdseyeModeEnum.objects, title="Tracking mode for camera."
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)
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FFMPEG_GLOBAL_ARGS_DEFAULT = [
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"-hide_banner",
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"-loglevel",
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"warning",
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"-user_agent",
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f"FFmpeg Frigate/{VERSION}",
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]
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FFMPEG_INPUT_ARGS_DEFAULT = [
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"-avoid_negative_ts",
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"make_zero",
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"-fflags",
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"+genpts+discardcorrupt",
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"-rtsp_transport",
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"tcp",
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"-timeout",
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"5000000",
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"-use_wallclock_as_timestamps",
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"1",
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]
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DETECT_FFMPEG_OUTPUT_ARGS_DEFAULT = ["-f", "rawvideo", "-pix_fmt", "yuv420p"]
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RTMP_FFMPEG_OUTPUT_ARGS_DEFAULT = ["-c", "copy", "-f", "flv"]
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RECORD_FFMPEG_OUTPUT_ARGS_DEFAULT = [
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"-f",
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"segment",
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"-segment_time",
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"10",
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"-segment_format",
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"mp4",
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"-reset_timestamps",
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"1",
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"-strftime",
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"1",
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"-c",
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"copy",
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"-an",
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]
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class FfmpegOutputArgsConfig(FrigateBaseModel):
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detect: Union[str, List[str]] = Field(
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default=DETECT_FFMPEG_OUTPUT_ARGS_DEFAULT,
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title="Detect role FFmpeg output arguments.",
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)
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record: Union[str, List[str]] = Field(
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default=RECORD_FFMPEG_OUTPUT_ARGS_DEFAULT,
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title="Record role FFmpeg output arguments.",
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)
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rtmp: Union[str, List[str]] = Field(
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default=RTMP_FFMPEG_OUTPUT_ARGS_DEFAULT,
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title="RTMP role FFmpeg output arguments.",
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)
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class FfmpegConfig(FrigateBaseModel):
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global_args: Union[str, List[str]] = Field(
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default=FFMPEG_GLOBAL_ARGS_DEFAULT, title="Global FFmpeg arguments."
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)
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hwaccel_args: Union[str, List[str]] = Field(
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default_factory=list, title="FFmpeg hardware acceleration arguments."
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)
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input_args: Union[str, List[str]] = Field(
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default=FFMPEG_INPUT_ARGS_DEFAULT, title="FFmpeg input arguments."
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)
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output_args: FfmpegOutputArgsConfig = Field(
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default_factory=FfmpegOutputArgsConfig,
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title="FFmpeg output arguments per role.",
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)
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class CameraRoleEnum(str, Enum):
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record = "record"
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restream = "restream"
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rtmp = "rtmp"
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detect = "detect"
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class CameraInput(FrigateBaseModel):
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path: str = Field(title="Camera input path.")
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roles: List[CameraRoleEnum] = Field(title="Roles assigned to this input.")
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global_args: Union[str, List[str]] = Field(
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default_factory=list, title="FFmpeg global arguments."
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)
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hwaccel_args: Union[str, List[str]] = Field(
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default_factory=list, title="FFmpeg hardware acceleration arguments."
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)
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input_args: Union[str, List[str]] = Field(
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default_factory=list, title="FFmpeg input arguments."
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)
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class CameraFfmpegConfig(FfmpegConfig):
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inputs: List[CameraInput] = Field(title="Camera inputs.")
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@validator("inputs")
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def validate_roles(cls, v):
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roles = [role for i in v for role in i.roles]
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roles_set = set(roles)
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if len(roles) > len(roles_set):
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raise ValueError("Each input role may only be used once.")
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if not "detect" in roles:
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raise ValueError("The detect role is required.")
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return v
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class SnapshotsConfig(FrigateBaseModel):
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enabled: bool = Field(default=False, title="Snapshots enabled.")
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clean_copy: bool = Field(
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default=True, title="Create a clean copy of the snapshot image."
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)
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timestamp: bool = Field(
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default=False, title="Add a timestamp overlay on the snapshot."
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)
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bounding_box: bool = Field(
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default=True, title="Add a bounding box overlay on the snapshot."
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)
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crop: bool = Field(default=False, title="Crop the snapshot to the detected object.")
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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 save a snapshot.",
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)
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height: Optional[int] = Field(title="Snapshot image height.")
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retain: RetainConfig = Field(
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default_factory=RetainConfig, title="Snapshot retention."
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)
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quality: int = Field(
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default=70,
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title="Quality of the encoded jpeg (0-100).",
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ge=0,
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le=100,
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)
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class ColorConfig(FrigateBaseModel):
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red: int = Field(default=255, ge=0, le=255, title="Red")
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green: int = Field(default=255, ge=0, le=255, title="Green")
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blue: int = Field(default=255, ge=0, le=255, title="Blue")
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class TimestampPositionEnum(str, Enum):
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tl = "tl"
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tr = "tr"
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bl = "bl"
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br = "br"
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class TimestampEffectEnum(str, Enum):
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solid = "solid"
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shadow = "shadow"
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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 RestreamConfig(FrigateBaseModel):
|
|
enabled: bool = Field(default=True, title="Restreaming enabled.")
|
|
force_audio: bool = Field(
|
|
default=True, title="Force audio compatibility with the browser."
|
|
)
|
|
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)
|
|
|
|
ffmpeg_output_args = (
|
|
[
|
|
"-r",
|
|
str(self.detect.fps),
|
|
"-s",
|
|
f"{self.detect.width}x{self.detect.height}",
|
|
]
|
|
+ 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(
|
|
ffmpeg_input.hwaccel_args
|
|
or parse_preset_hardware_acceleration(self.ffmpeg.hwaccel_args)
|
|
or self.ffmpeg.hwaccel_args
|
|
)
|
|
input_args = get_ffmpeg_arg_list(
|
|
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 PixelFormatEnum(str, Enum):
|
|
rgb = "rgb"
|
|
bgr = "bgr"
|
|
yuv = "yuv"
|
|
|
|
|
|
class InputTensorEnum(str, Enum):
|
|
nchw = "nchw"
|
|
nhwc = "nhwc"
|
|
|
|
|
|
class ModelConfig(FrigateBaseModel):
|
|
path: Optional[str] = Field(title="Custom Object detection model path.")
|
|
labelmap_path: Optional[str] = Field(title="Label map for custom object detector.")
|
|
width: int = Field(default=320, title="Object detection model input width.")
|
|
height: int = Field(default=320, title="Object detection model input height.")
|
|
labelmap: Dict[int, str] = Field(
|
|
default_factory=dict, title="Labelmap customization."
|
|
)
|
|
input_tensor: InputTensorEnum = Field(
|
|
default=InputTensorEnum.nhwc, title="Model Input Tensor Shape"
|
|
)
|
|
input_pixel_format: PixelFormatEnum = Field(
|
|
default=PixelFormatEnum.rgb, title="Model Input Pixel Color Format"
|
|
)
|
|
_merged_labelmap: Optional[Dict[int, str]] = PrivateAttr()
|
|
_colormap: Dict[int, Tuple[int, int, int]] = PrivateAttr()
|
|
|
|
@property
|
|
def merged_labelmap(self) -> Dict[int, str]:
|
|
return self._merged_labelmap
|
|
|
|
@property
|
|
def colormap(self) -> Dict[int, Tuple[int, int, int]]:
|
|
return self._colormap
|
|
|
|
def __init__(self, **config):
|
|
super().__init__(**config)
|
|
|
|
self._merged_labelmap = {
|
|
**load_labels(config.get("labelmap_path", "/labelmap.txt")),
|
|
**config.get("labelmap", {}),
|
|
}
|
|
|
|
cmap = plt.cm.get_cmap("tab10", len(self._merged_labelmap.keys()))
|
|
|
|
self._colormap = {}
|
|
for key, val in self._merged_labelmap.items():
|
|
self._colormap[val] = tuple(int(round(255 * c)) for c in cmap(key)[:3])
|
|
|
|
|
|
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_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={name: DetectorConfig(**d) for name, d in DEFAULT_DETECTORS.items()},
|
|
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 password substitution
|
|
if config.mqtt.password:
|
|
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_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
|
|
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)
|