fix: Missing link to go2rtc' logging doc (#16606)

Updated the link to go2rtc docs for logging configuration.

.cspell/frigate-dictionary.txt

@@ -2,6 +2,7 @@ aarch
 absdiff
 airockchip
 Alloc
+alpr
 Amcrest
 amdgpu
 analyzeduration
@@ -61,6 +62,7 @@ dsize
 dtype
 ECONNRESET
 edgetpu
+facenet
 fastapi
 faststart
 fflags
@@ -114,6 +116,8 @@ itemsize
 Jellyfin
 jetson
 jetsons
+jina
+jinaai
 joserfc
 jsmpeg
 jsonify
@@ -187,6 +191,7 @@ openai
 opencv
 openvino
 OWASP
+paddleocr
 paho
 passwordless
 popleft
@@ -308,4 +313,4 @@ yolo
 yolonas
 yolox
 zeep
-zerolatency
+zerolatency

.github/workflows/ci.yml

@@ -77,6 +77,7 @@ jobs:
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64,mode=max
   jetson_jp4_build:
+    if: false
     runs-on: ubuntu-22.04
     name: Jetson Jetpack 4
     steps:
@@ -106,6 +107,7 @@ jobs:
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp4
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp4,mode=max
   jetson_jp5_build:
+    if: false
     runs-on: ubuntu-22.04
     name: Jetson Jetpack 5
     steps:
@@ -162,6 +164,19 @@ jobs:
             tensorrt.tags=${{ steps.setup.outputs.image-name }}-tensorrt
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64,mode=max
+      - name: AMD/ROCm general build
+        env:
+          AMDGPU: gfx
+          HSA_OVERRIDE: 0
+        uses: docker/bake-action@v6
+        with:
+          source: .
+          push: true
+          targets: rocm
+          files: docker/rocm/rocm.hcl
+          set: |
+            rocm.tags=${{ steps.setup.outputs.image-name }}-rocm
+            *.cache-from=type=gha
   arm64_extra_builds:
     runs-on: ubuntu-22.04
     name: ARM Extra Build
@@ -187,46 +202,6 @@ jobs:
           set: |
             rk.tags=${{ steps.setup.outputs.image-name }}-rk
             *.cache-from=type=gha
-  combined_extra_builds:
-    runs-on: ubuntu-22.04
-    name: Combined Extra Builds
-    needs:
-      - amd64_build
-      - arm64_build
-    steps:
-      - name: Check out code
-        uses: actions/checkout@v4
-        with:
-          persist-credentials: false
-      - name: Set up QEMU and Buildx
-        id: setup
-        uses: ./.github/actions/setup
-        with:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      - name: Build and push Hailo-8l build
-        uses: docker/bake-action@v6
-        with:
-          source: .
-          push: true
-          targets: h8l
-          files: docker/hailo8l/h8l.hcl
-          set: |
-            h8l.tags=${{ steps.setup.outputs.image-name }}-h8l
-            *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-h8l
-            *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-h8l,mode=max
-      - name: AMD/ROCm general build
-        env:
-          AMDGPU: gfx
-          HSA_OVERRIDE: 0
-        uses: docker/bake-action@v6
-        with:
-          source: .
-          push: true
-          targets: rocm
-          files: docker/rocm/rocm.hcl
-          set: |
-            rocm.tags=${{ steps.setup.outputs.image-name }}-rocm
-            *.cache-from=type=gha
   # The majority of users running arm64 are rpi users, so the rpi
   # build should be the primary arm64 image
   assemble_default_build:

.github/workflows/pull_request.yml

@@ -6,7 +6,7 @@ on:
       - "docs/**"
 
 env:
-  DEFAULT_PYTHON: 3.9
+  DEFAULT_PYTHON: 3.11
 
 jobs:
   build_devcontainer:

Makefile

@@ -1,7 +1,7 @@
 default_target: local
 
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.15.0
+VERSION = 0.16.0
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
 BOARDS= #Initialized empty

docker/hailo8l/Dockerfile

@@ -1,40 +0,0 @@
-# syntax=docker/dockerfile:1.6
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-# Build Python wheels
-FROM wheels AS h8l-wheels
-
-COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
-COPY docker/hailo8l/requirements-wheels-h8l.txt /requirements-wheels-h8l.txt
-
-RUN sed -i "/https:\/\//d" /requirements-wheels.txt
-
-# Create a directory to store the built wheels
-RUN mkdir /h8l-wheels
-
-# Build the wheels
-RUN pip3 wheel --wheel-dir=/h8l-wheels -c /requirements-wheels.txt -r /requirements-wheels-h8l.txt
-
-FROM wget AS hailort
-ARG TARGETARCH
-RUN --mount=type=bind,source=docker/hailo8l/install_hailort.sh,target=/deps/install_hailort.sh \
-    /deps/install_hailort.sh
-
-# Use deps as the base image
-FROM deps AS h8l-frigate
-
-# Copy the wheels from the wheels stage
-COPY --from=h8l-wheels /h8l-wheels /deps/h8l-wheels
-COPY --from=hailort /hailo-wheels /deps/hailo-wheels
-COPY --from=hailort /rootfs/ /
-
-# Install the wheels
-RUN pip3 install -U /deps/h8l-wheels/*.whl
-RUN pip3 install -U /deps/hailo-wheels/*.whl
-
-# Copy base files from the rootfs stage
-COPY --from=rootfs / /
-
-# Set workdir
-WORKDIR /opt/frigate/

docker/hailo8l/h8l.hcl

@@ -1,34 +0,0 @@
-target wget {
-  dockerfile = "docker/main/Dockerfile"
-  platforms = ["linux/arm64","linux/amd64"]
-  target = "wget"
-}
-
-target wheels {
-  dockerfile = "docker/main/Dockerfile"
-  platforms = ["linux/arm64","linux/amd64"]
-  target = "wheels"
-}
-
-target deps {
-  dockerfile = "docker/main/Dockerfile"
-  platforms = ["linux/arm64","linux/amd64"]
-  target = "deps"
-}
-
-target rootfs {
-  dockerfile = "docker/main/Dockerfile"
-  platforms = ["linux/arm64","linux/amd64"]
-  target = "rootfs"
-}
-
-target h8l {
-  dockerfile = "docker/hailo8l/Dockerfile"
-  contexts = {
-    wget = "target:wget"
-    wheels = "target:wheels"
-    deps = "target:deps"
-    rootfs = "target:rootfs"
-  }
-  platforms = ["linux/arm64","linux/amd64"]
-}

docker/hailo8l/h8l.mk

@@ -1,15 +0,0 @@
-BOARDS += h8l
-
-local-h8l: version
-	docker buildx bake --file=docker/hailo8l/h8l.hcl h8l \
-		--set h8l.tags=frigate:latest-h8l \
-		--load
-
-build-h8l: version
-	docker buildx bake --file=docker/hailo8l/h8l.hcl h8l \
-		--set h8l.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-h8l
-
-push-h8l: build-h8l
-	docker buildx bake --file=docker/hailo8l/h8l.hcl h8l \
-	 	--set h8l.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-h8l \
-		--push

docker/hailo8l/install_hailort.sh

@@ -1,19 +0,0 @@
-#!/bin/bash
-
-set -euxo pipefail
-
-hailo_version="4.19.0"
-
-if [[ "${TARGETARCH}" == "amd64" ]]; then
-    arch="x86_64"
-elif [[ "${TARGETARCH}" == "arm64" ]]; then
-    arch="aarch64"
-fi
-
-wget -qO- "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-${TARGETARCH}.tar.gz" |
-    tar -C / -xzf -
-
-mkdir -p /hailo-wheels
-
-wget -P /hailo-wheels/ "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-${hailo_version}-cp39-cp39-linux_${arch}.whl"
-

docker/hailo8l/requirements-wheels-h8l.txt

@@ -1,12 +0,0 @@
-appdirs==1.4.*
-argcomplete==2.0.*
-contextlib2==0.6.*
-distlib==0.3.*
-filelock==3.8.*
-future==0.18.*
-importlib-metadata==5.1.*
-importlib-resources==5.1.*
-netaddr==0.8.*
-netifaces==0.10.*
-verboselogs==1.7.*
-virtualenv==20.17.*

docker/hailo8l/user_installation.sh

@@ -4,6 +4,7 @@
 sudo apt-get update
 sudo apt-get install -y build-essential cmake git wget
 
+hailo_version="4.20.0"
 arch=$(uname -m)
 
 if [[ $arch == "x86_64" ]]; then
@@ -13,7 +14,7 @@ else
 fi
 
 # Clone the HailoRT driver repository
-git clone --depth 1 --branch v4.19.0 https://github.com/hailo-ai/hailort-drivers.git
+git clone --depth 1 --branch v${hailo_version} https://github.com/hailo-ai/hailort-drivers.git
 
 # Build and install the HailoRT driver
 cd hailort-drivers/linux/pcie

docker/main/Dockerfile

@@ -3,12 +3,12 @@
 # https://askubuntu.com/questions/972516/debian-frontend-environment-variable
 ARG DEBIAN_FRONTEND=noninteractive
 
-ARG BASE_IMAGE=debian:11
-ARG SLIM_BASE=debian:11-slim
+ARG BASE_IMAGE=debian:12
+ARG SLIM_BASE=debian:12-slim
 
 FROM ${BASE_IMAGE} AS base
 
-FROM --platform=${BUILDPLATFORM} debian:11 AS base_host
+FROM --platform=${BUILDPLATFORM} debian:12 AS base_host
 
 FROM ${SLIM_BASE} AS slim-base
 
@@ -66,8 +66,8 @@ COPY docker/main/requirements-ov.txt /requirements-ov.txt
 RUN apt-get -qq update \
     && apt-get -qq install -y wget python3 python3-dev python3-distutils gcc pkg-config libhdf5-dev \
     && wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
-    && python3 get-pip.py "pip" \
-    && pip install -r /requirements-ov.txt
+    && python3 get-pip.py "pip" --break-system-packages \
+    && pip install --break-system-packages -r /requirements-ov.txt
 
 # Get OpenVino Model
 RUN --mount=type=bind,source=docker/main/build_ov_model.py,target=/build_ov_model.py \
@@ -139,24 +139,17 @@ ARG TARGETARCH
 # Use a separate container to build wheels to prevent build dependencies in final image
 RUN apt-get -qq update \
     && apt-get -qq install -y \
-    apt-transport-https \
-    gnupg \
-    wget \
-    # the key fingerprint can be obtained from https://ftp-master.debian.org/keys.html
-    && wget -qO- "https://keyserver.ubuntu.com/pks/lookup?op=get&search=0xA4285295FC7B1A81600062A9605C66F00D6C9793" | \
-    gpg --dearmor > /usr/share/keyrings/debian-archive-bullseye-stable.gpg \
-    && echo "deb [signed-by=/usr/share/keyrings/debian-archive-bullseye-stable.gpg] http://deb.debian.org/debian bullseye main contrib non-free" | \
-    tee /etc/apt/sources.list.d/debian-bullseye-nonfree.list \
+    apt-transport-https wget \
     && apt-get -qq update \
     && apt-get -qq install -y \
-    python3.9 \
-    python3.9-dev \
+    python3 \
+    python3-dev \
     # opencv dependencies
     build-essential cmake git pkg-config libgtk-3-dev \
     libavcodec-dev libavformat-dev libswscale-dev libv4l-dev \
     libxvidcore-dev libx264-dev libjpeg-dev libpng-dev libtiff-dev \
     gfortran openexr libatlas-base-dev libssl-dev\
-    libtbb2 libtbb-dev libdc1394-22-dev libopenexr-dev \
+    libtbbmalloc2 libtbb-dev libdc1394-dev libopenexr-dev \
     libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev \
     # sqlite3 dependencies
     tclsh \
@@ -164,14 +157,11 @@ RUN apt-get -qq update \
     gcc gfortran libopenblas-dev liblapack-dev && \
     rm -rf /var/lib/apt/lists/*
 
-# Ensure python3 defaults to python3.9
-RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1
-
 RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
-    && python3 get-pip.py "pip"
+    && python3 get-pip.py "pip" --break-system-packages
 
 COPY docker/main/requirements.txt /requirements.txt
-RUN pip3 install -r /requirements.txt
+RUN pip3 install -r /requirements.txt --break-system-packages
 
 # Build pysqlite3 from source
 COPY docker/main/build_pysqlite3.sh /build_pysqlite3.sh
@@ -180,6 +170,9 @@ RUN /build_pysqlite3.sh
 COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
 RUN pip3 wheel --wheel-dir=/wheels -r /requirements-wheels.txt
 
+# Install HailoRT & Wheels
+RUN --mount=type=bind,source=docker/main/install_hailort.sh,target=/deps/install_hailort.sh \
+    /deps/install_hailort.sh
 
 # Collect deps in a single layer
 FROM scratch AS deps-rootfs
@@ -190,6 +183,7 @@ COPY --from=libusb-build /usr/local/lib /usr/local/lib
 COPY --from=tempio /rootfs/ /
 COPY --from=s6-overlay /rootfs/ /
 COPY --from=models /rootfs/ /
+COPY --from=wheels /rootfs/ /
 COPY docker/main/rootfs/ /
 
 
@@ -221,8 +215,8 @@ RUN --mount=type=bind,source=docker/main/install_deps.sh,target=/deps/install_de
     /deps/install_deps.sh
 
 RUN --mount=type=bind,from=wheels,source=/wheels,target=/deps/wheels \
-    python3 -m pip install --upgrade pip && \
-    pip3 install -U /deps/wheels/*.whl
+    python3 -m pip install --upgrade pip --break-system-packages && \
+    pip3 install -U /deps/wheels/*.whl --break-system-packages
 
 COPY --from=deps-rootfs / /
 
@@ -269,7 +263,7 @@ RUN apt-get update \
     && rm -rf /var/lib/apt/lists/*
 
 RUN --mount=type=bind,source=./docker/main/requirements-dev.txt,target=/workspace/frigate/requirements-dev.txt \
-    pip3 install -r requirements-dev.txt
+    pip3 install -r requirements-dev.txt --break-system-packages
 
 HEALTHCHECK NONE
 

docker/main/build_nginx.sh

@@ -8,10 +8,16 @@ SECURE_TOKEN_MODULE_VERSION="1.5"
 SET_MISC_MODULE_VERSION="v0.33"
 NGX_DEVEL_KIT_VERSION="v0.3.3"
 
-cp /etc/apt/sources.list /etc/apt/sources.list.d/sources-src.list
-sed -i 's|deb http|deb-src http|g' /etc/apt/sources.list.d/sources-src.list
-apt-get update
+source /etc/os-release
 
+if [[ "$VERSION_ID" == "12" ]]; then
+    sed -i '/^Types:/s/deb/& deb-src/' /etc/apt/sources.list.d/debian.sources
+else
+    cp /etc/apt/sources.list /etc/apt/sources.list.d/sources-src.list
+    sed -i 's|deb http|deb-src http|g' /etc/apt/sources.list.d/sources-src.list
+fi
+
+apt-get update
 apt-get -yqq build-dep nginx
 
 apt-get -yqq install --no-install-recommends ca-certificates wget

docker/main/build_ov_model.py

@@ -4,7 +4,7 @@ from openvino.tools import mo
 ov_model = mo.convert_model(
     "/models/ssdlite_mobilenet_v2_coco_2018_05_09/frozen_inference_graph.pb",
     compress_to_fp16=True,
-    transformations_config="/usr/local/lib/python3.9/dist-packages/openvino/tools/mo/front/tf/ssd_v2_support.json",
+    transformations_config="/usr/local/lib/python3.11/dist-packages/openvino/tools/mo/front/tf/ssd_v2_support.json",
     tensorflow_object_detection_api_pipeline_config="/models/ssdlite_mobilenet_v2_coco_2018_05_09/pipeline.config",
     reverse_input_channels=True,
 )

docker/main/build_sqlite_vec.sh

@@ -4,8 +4,15 @@ set -euxo pipefail
 
 SQLITE_VEC_VERSION="0.1.3"
 
-cp /etc/apt/sources.list /etc/apt/sources.list.d/sources-src.list
-sed -i 's|deb http|deb-src http|g' /etc/apt/sources.list.d/sources-src.list
+source /etc/os-release
+
+if [[ "$VERSION_ID" == "12" ]]; then
+    sed -i '/^Types:/s/deb/& deb-src/' /etc/apt/sources.list.d/debian.sources
+else
+    cp /etc/apt/sources.list /etc/apt/sources.list.d/sources-src.list
+    sed -i 's|deb http|deb-src http|g' /etc/apt/sources.list.d/sources-src.list
+fi
+
 apt-get update
 apt-get -yqq build-dep sqlite3 gettext git
 

docker/main/install_deps.sh

@@ -11,33 +11,34 @@ apt-get -qq install --no-install-recommends -y \
     lbzip2 \
     procps vainfo \
     unzip locales tzdata libxml2 xz-utils \
-    python3.9 \
+    python3 \
     python3-pip \
     curl \
     lsof \
     jq \
-    nethogs
-
-# ensure python3 defaults to python3.9
-update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1
+    nethogs \
+    libgl1 \
+    libglib2.0-0 \
+    libusb-1.0.0
 
 mkdir -p -m 600 /root/.gnupg
 
-# add coral repo
-curl -fsSLo - https://packages.cloud.google.com/apt/doc/apt-key.gpg | \
-    gpg --dearmor -o /etc/apt/trusted.gpg.d/google-cloud-packages-archive-keyring.gpg
-echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | tee /etc/apt/sources.list.d/coral-edgetpu.list
-echo "libedgetpu1-max libedgetpu/accepted-eula select true" | debconf-set-selections
+# install coral runtime
+wget -q -O /tmp/libedgetpu1-max.deb "https://github.com/feranick/libedgetpu/releases/download/16.0TF2.17.0-1/libedgetpu1-max_16.0tf2.17.0-1.bookworm_${TARGETARCH}.deb"
+unset DEBIAN_FRONTEND
+yes | dpkg -i /tmp/libedgetpu1-max.deb && export DEBIAN_FRONTEND=noninteractive
+rm /tmp/libedgetpu1-max.deb
 
-# enable non-free repo in Debian
-if grep -q "Debian" /etc/issue; then
-    sed -i -e's/ main/ main contrib non-free/g' /etc/apt/sources.list
+# install python3 & tflite runtime
+if [[ "${TARGETARCH}" == "amd64" ]]; then
+    pip3 install --break-system-packages https://github.com/feranick/TFlite-builds/releases/download/v2.17.0/tflite_runtime-2.17.0-cp311-cp311-linux_x86_64.whl
+    pip3 install --break-system-packages https://github.com/feranick/pycoral/releases/download/2.0.2TF2.17.0/pycoral-2.0.2-cp311-cp311-linux_x86_64.whl
 fi
 
-# coral drivers
-apt-get -qq update
-apt-get -qq install --no-install-recommends --no-install-suggests -y \
-    libedgetpu1-max python3-tflite-runtime python3-pycoral
+if [[ "${TARGETARCH}" == "arm64" ]]; then
+    pip3 install --break-system-packages https://github.com/feranick/TFlite-builds/releases/download/v2.17.0/tflite_runtime-2.17.0-cp311-cp311-linux_aarch64.whl
+    pip3 install --break-system-packages https://github.com/feranick/pycoral/releases/download/2.0.2TF2.17.0/pycoral-2.0.2-cp311-cp311-linux_aarch64.whl
+fi
 
 # btbn-ffmpeg -> amd64
 if [[ "${TARGETARCH}" == "amd64" ]]; then
@@ -65,23 +66,15 @@ fi
 
 # arch specific packages
 if [[ "${TARGETARCH}" == "amd64" ]]; then
-    # use debian bookworm for amd / intel-i965 driver packages
-    echo 'deb https://deb.debian.org/debian bookworm main contrib non-free' >/etc/apt/sources.list.d/debian-bookworm.list
-    apt-get -qq update
+    # install amd / intel-i965 driver packages
     apt-get -qq install --no-install-recommends --no-install-suggests -y \
         i965-va-driver intel-gpu-tools onevpl-tools \
         libva-drm2 \
         mesa-va-drivers radeontop
 
-    # something about this dependency requires it to be installed in a separate call rather than in the line above
-    apt-get -qq install --no-install-recommends --no-install-suggests -y \
-        i965-va-driver-shaders
-
     # intel packages use zst compression so we need to update dpkg
     apt-get install -y dpkg
 
-    rm -f /etc/apt/sources.list.d/debian-bookworm.list
-
     # use intel apt intel packages
     wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg
     echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | tee /etc/apt/sources.list.d/intel-gpu-jammy.list

docker/main/install_hailort.sh

@@ -0,0 +1,14 @@
+#!/bin/bash
+
+set -euxo pipefail
+
+hailo_version="4.20.0"
+
+if [[ "${TARGETARCH}" == "amd64" ]]; then
+    arch="x86_64"
+elif [[ "${TARGETARCH}" == "arm64" ]]; then
+    arch="aarch64"
+fi
+
+wget -qO- "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-${TARGETARCH}.tar.gz" | tar -C / -xzf -
+wget -P /wheels/ "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-${hailo_version}-cp311-cp311-linux_${arch}.whl"

docker/main/requirements-wheels.txt

@@ -1,3 +1,4 @@
+aiofiles == 24.1.*
 click == 8.1.*
 # FastAPI
 aiohttp == 3.11.2
@@ -10,10 +11,10 @@ imutils == 0.5.*
 joserfc == 1.0.*
 pathvalidate == 3.2.*
 markupsafe == 2.1.*
+python-multipart == 0.0.12
+# General
 mypy == 1.6.1
-numpy == 1.26.*
-onvif_zeep == 0.2.12
-opencv-python-headless == 4.9.0.*
+onvif-zeep-async == 3.1.*
 paho-mqtt == 2.1.*
 pandas == 2.2.*
 peewee == 3.17.*
@@ -27,15 +28,19 @@ ruamel.yaml == 0.18.*
 tzlocal == 5.2
 requests == 2.32.*
 types-requests == 2.32.*
-scipy == 1.13.*
 norfair == 2.2.*
 setproctitle == 1.3.*
 ws4py == 0.5.*
 unidecode == 1.3.*
+# Image Manipulation
+numpy == 1.26.*
+opencv-python-headless == 4.10.0.*
+opencv-contrib-python == 4.9.0.*
+scipy == 1.14.*
 # OpenVino & ONNX
-openvino == 2024.3.*
-onnxruntime-openvino == 1.19.* ; platform_machine == 'x86_64'
-onnxruntime == 1.19.* ; platform_machine == 'aarch64'
+openvino == 2024.4.*
+onnxruntime-openvino == 1.20.* ; platform_machine == 'x86_64'
+onnxruntime == 1.20.* ; platform_machine == 'aarch64'
 # Embeddings
 transformers == 4.45.*
 # Generative AI
@@ -45,3 +50,21 @@ openai == 1.51.*
 # push notifications
 py-vapid == 1.9.*
 pywebpush == 2.0.*
+# alpr
+pyclipper == 1.3.*
+shapely == 2.0.*
+Levenshtein==0.26.*
+prometheus-client == 0.21.*
+# HailoRT Wheels
+appdirs==1.4.*
+argcomplete==2.0.*
+contextlib2==0.6.*
+distlib==0.3.*
+filelock==3.8.*
+future==0.18.*
+importlib-metadata==5.1.*
+importlib-resources==5.1.*
+netaddr==0.8.*
+netifaces==0.10.*
+verboselogs==1.7.*
+virtualenv==20.17.*

docker/main/requirements.txt

@@ -1,2 +1,2 @@
-scikit-build == 0.17.*
+scikit-build == 0.18.*
 nvidia-pyindex

docker/main/rootfs/usr/local/nginx/conf/nginx.conf

@@ -81,6 +81,9 @@ http {
         open_file_cache_errors on;
         aio on;
 
+        # file upload size
+        client_max_body_size 10M;
+
         # https://github.com/kaltura/nginx-vod-module#vod_open_file_thread_pool
         vod_open_file_thread_pool default;
 
@@ -106,6 +109,14 @@ http {
             expires off;
 
             keepalive_disable safari;
+
+            # vod module returns 502 for non-existent media
+            # https://github.com/kaltura/nginx-vod-module/issues/468
+            error_page 502 =404 /vod-not-found;
+        }
+
+        location = /vod-not-found {
+            return 404;
         }
 
         location /stream/ {

docker/rockchip/COCO/coco_subset_20.txt

@@ -0,0 +1,20 @@
+./subset/000000005001.jpg
+./subset/000000038829.jpg
+./subset/000000052891.jpg
+./subset/000000075612.jpg
+./subset/000000098261.jpg
+./subset/000000181542.jpg
+./subset/000000215245.jpg
+./subset/000000277005.jpg
+./subset/000000288685.jpg
+./subset/000000301421.jpg
+./subset/000000334371.jpg
+./subset/000000348481.jpg
+./subset/000000373353.jpg
+./subset/000000397681.jpg
+./subset/000000414673.jpg
+./subset/000000419312.jpg
+./subset/000000465822.jpg
+./subset/000000475732.jpg
+./subset/000000559707.jpg
+./subset/000000574315.jpg

docker/rockchip/Dockerfile

@@ -7,18 +7,23 @@ FROM wheels as rk-wheels
 COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
 COPY docker/rockchip/requirements-wheels-rk.txt /requirements-wheels-rk.txt
 RUN sed -i "/https:\/\//d" /requirements-wheels.txt
+RUN sed -i "/onnxruntime/d" /requirements-wheels.txt
+RUN python3 -m pip config set global.break-system-packages true
 RUN pip3 wheel --wheel-dir=/rk-wheels -c /requirements-wheels.txt -r /requirements-wheels-rk.txt
+RUN rm -rf /rk-wheels/opencv_python-*
 
 FROM deps AS rk-frigate
 ARG TARGETARCH
 
 RUN --mount=type=bind,from=rk-wheels,source=/rk-wheels,target=/deps/rk-wheels \
-    pip3 install -U /deps/rk-wheels/*.whl
+    pip3 install --no-deps -U /deps/rk-wheels/*.whl --break-system-packages
 
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
+COPY docker/rockchip/COCO /COCO
+COPY docker/rockchip/conv2rknn.py /opt/conv2rknn.py
 
-ADD https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.0.0/librknnrt.so /usr/lib/
+ADD https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.0/librknnrt.so /usr/lib/
 
 RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffmpeg
 RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffprobe

docker/rockchip/conv2rknn.py

@@ -0,0 +1,82 @@
+import os
+
+import rknn
+import yaml
+from rknn.api import RKNN
+
+try:
+    with open(rknn.__path__[0] + "/VERSION") as file:
+        tk_version = file.read().strip()
+except FileNotFoundError:
+    pass
+
+try:
+    with open("/config/conv2rknn.yaml", "r") as config_file:
+        configuration = yaml.safe_load(config_file)
+except FileNotFoundError:
+    raise Exception("Please place a config.yaml file in /config/conv2rknn.yaml")
+
+if configuration["config"] != None:
+    rknn_config = configuration["config"]
+else:
+    rknn_config = {}
+
+if not os.path.isdir("/config/model_cache/rknn_cache/onnx"):
+    raise Exception(
+        "Place the onnx models you want to convert to rknn format in /config/model_cache/rknn_cache/onnx"
+    )
+
+if "soc" not in configuration:
+    try:
+        with open("/proc/device-tree/compatible") as file:
+            soc = file.read().split(",")[-1].strip("\x00")
+    except FileNotFoundError:
+        raise Exception("Make sure to run docker in privileged mode.")
+
+    configuration["soc"] = [
+        soc,
+    ]
+
+if "quantization" not in configuration:
+    configuration["quantization"] = False
+
+if "output_name" not in configuration:
+    configuration["output_name"] = "{{input_basename}}"
+
+for input_filename in os.listdir("/config/model_cache/rknn_cache/onnx"):
+    for soc in configuration["soc"]:
+        quant = "i8" if configuration["quantization"] else "fp16"
+
+        input_path = "/config/model_cache/rknn_cache/onnx/" + input_filename
+        input_basename = input_filename[: input_filename.rfind(".")]
+
+        output_filename = (
+            configuration["output_name"].format(
+                quant=quant,
+                input_basename=input_basename,
+                soc=soc,
+                tk_version=tk_version,
+            )
+            + ".rknn"
+        )
+        output_path = "/config/model_cache/rknn_cache/" + output_filename
+
+        rknn_config["target_platform"] = soc
+
+        rknn = RKNN(verbose=True)
+        rknn.config(**rknn_config)
+
+        if rknn.load_onnx(model=input_path) != 0:
+            raise Exception("Error loading model.")
+
+        if (
+            rknn.build(
+                do_quantization=configuration["quantization"],
+                dataset="/COCO/coco_subset_20.txt",
+            )
+            != 0
+        ):
+            raise Exception("Error building model.")
+
+        if rknn.export_rknn(output_path) != 0:
+            raise Exception("Error exporting rknn model.")

docker/rockchip/requirements-wheels-rk.txt

@@ -1 +1,2 @@
-rknn-toolkit-lite2 @ https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.0.0/rknn_toolkit_lite2-2.0.0b0-cp39-cp39-linux_aarch64.whl
+rknn-toolkit2 == 2.3.0
+rknn-toolkit-lite2 == 2.3.0

docker/rocm/Dockerfile

@@ -34,7 +34,7 @@ RUN mkdir -p /opt/rocm-dist/etc/ld.so.conf.d/
 RUN echo /opt/rocm/lib|tee /opt/rocm-dist/etc/ld.so.conf.d/rocm.conf
 
 #######################################################################
-FROM --platform=linux/amd64 debian:11 as debian-base
+FROM --platform=linux/amd64 debian:12 as debian-base
 
 RUN apt-get update && apt-get -y upgrade
 RUN apt-get -y install --no-install-recommends libelf1 libdrm2 libdrm-amdgpu1 libnuma1 kmod
@@ -51,7 +51,7 @@ COPY --from=rocm /opt/rocm-$ROCM /opt/rocm-$ROCM
 RUN ln -s /opt/rocm-$ROCM /opt/rocm
 
 RUN apt-get -y install g++ cmake
-RUN apt-get -y install python3-pybind11 python3.9-distutils python3-dev
+RUN apt-get -y install python3-pybind11 python3-distutils python3-dev
 
 WORKDIR /opt/build
 
@@ -70,10 +70,11 @@ RUN apt-get -y install libnuma1
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
 
-COPY docker/rocm/requirements-wheels-rocm.txt /requirements.txt
-RUN python3 -m pip install --upgrade pip \
-    && pip3 uninstall -y onnxruntime-openvino \
-    && pip3 install -r /requirements.txt
+# Temporarily disabled to see if a new wheel can be built to support py3.11
+#COPY docker/rocm/requirements-wheels-rocm.txt /requirements.txt
+#RUN python3 -m pip install --upgrade pip \
+#    && pip3 uninstall -y onnxruntime-openvino \
+#    && pip3 install -r /requirements.txt
 
 #######################################################################
 FROM scratch AS rocm-dist
@@ -86,12 +87,12 @@ COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*$AMDGPU* /opt/rocm-$ROCM/share
 COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*gfx908* /opt/rocm-$ROCM/share/miopen/db/
 COPY --from=rocm /opt/rocm-$ROCM/lib/rocblas/library/*$AMDGPU* /opt/rocm-$ROCM/lib/rocblas/library/
 COPY --from=rocm /opt/rocm-dist/ /
-COPY --from=debian-build /opt/rocm/lib/migraphx.cpython-39-x86_64-linux-gnu.so /opt/rocm-$ROCM/lib/
+COPY --from=debian-build /opt/rocm/lib/migraphx.cpython-311-x86_64-linux-gnu.so /opt/rocm-$ROCM/lib/
 
 #######################################################################
 FROM deps-prelim AS rocm-prelim-hsa-override0
-
-ENV HSA_ENABLE_SDMA=0
+\
+    ENV HSA_ENABLE_SDMA=0
 
 COPY --from=rocm-dist / /
 

docker/rpi/install_deps.sh

@@ -18,13 +18,14 @@ apt-get -qq install --no-install-recommends -y \
 mkdir -p -m 600 /root/.gnupg
 
 # enable non-free repo
-sed -i -e's/ main/ main contrib non-free/g' /etc/apt/sources.list
+echo "deb http://deb.debian.org/debian bookworm main contrib non-free non-free-firmware" | tee -a /etc/apt/sources.list
+apt update
 
 # ffmpeg -> arm64
 if [[ "${TARGETARCH}" == "arm64" ]]; then
     # add raspberry pi repo
     gpg --no-default-keyring --keyring /usr/share/keyrings/raspbian.gpg --keyserver keyserver.ubuntu.com --recv-keys 82B129927FA3303E
-    echo "deb [signed-by=/usr/share/keyrings/raspbian.gpg] https://archive.raspberrypi.org/debian/ bullseye main" | tee /etc/apt/sources.list.d/raspi.list
+    echo "deb [signed-by=/usr/share/keyrings/raspbian.gpg] https://archive.raspberrypi.org/debian/ bookworm main" | tee /etc/apt/sources.list.d/raspi.list
     apt-get -qq update
     apt-get -qq install --no-install-recommends --no-install-suggests -y ffmpeg
 fi

docker/tensorrt/Dockerfile.amd64

@@ -7,18 +7,19 @@ ARG DEBIAN_FRONTEND=noninteractive
 FROM wheels as trt-wheels
 ARG DEBIAN_FRONTEND
 ARG TARGETARCH
+RUN python3 -m pip config set global.break-system-packages true
 
 # Add TensorRT wheels to another folder
 COPY docker/tensorrt/requirements-amd64.txt /requirements-tensorrt.txt
 RUN mkdir -p /trt-wheels && pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt
 
 FROM tensorrt-base AS frigate-tensorrt
-ENV TRT_VER=8.5.3
+ENV TRT_VER=8.6.1
+RUN python3 -m pip config set global.break-system-packages true
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
-    pip3 install -U /deps/trt-wheels/*.whl && \
+    pip3 install -U /deps/trt-wheels/*.whl --break-system-packages && \
     ldconfig
 
-ENV LD_LIBRARY_PATH=/usr/local/lib/python3.9/dist-packages/tensorrt:/usr/local/cuda/lib64:/usr/local/lib/python3.9/dist-packages/nvidia/cufft/lib
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
 
@@ -31,4 +32,4 @@ COPY --from=trt-deps /usr/local/cuda-12.1 /usr/local/cuda
 COPY docker/tensorrt/detector/rootfs/ /
 COPY --from=trt-deps /usr/local/lib/libyolo_layer.so /usr/local/lib/libyolo_layer.so
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
-    pip3 install -U /deps/trt-wheels/*.whl
+    pip3 install -U /deps/trt-wheels/*.whl --break-system-packages

docker/tensorrt/Dockerfile.arm64

@@ -41,11 +41,11 @@ RUN --mount=type=bind,source=docker/tensorrt/detector/build_python_tensorrt.sh,t
     && TENSORRT_VER=$(cat /etc/TENSORRT_VER) /deps/build_python_tensorrt.sh
 
 COPY docker/tensorrt/requirements-arm64.txt /requirements-tensorrt.txt
-ADD https://nvidia.box.com/shared/static/9aemm4grzbbkfaesg5l7fplgjtmswhj8.whl /tmp/onnxruntime_gpu-1.15.1-cp39-cp39-linux_aarch64.whl
+ADD https://nvidia.box.com/shared/static/psl23iw3bh7hlgku0mjo1xekxpego3e3.whl /tmp/onnxruntime_gpu-1.15.1-cp311-cp311-linux_aarch64.whl
 
 RUN pip3 uninstall -y onnxruntime-openvino \
     && pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt \
-    && pip3 install --no-deps /tmp/onnxruntime_gpu-1.15.1-cp39-cp39-linux_aarch64.whl
+    && pip3 install --no-deps /tmp/onnxruntime_gpu-1.15.1-cp311-cp311-linux_aarch64.whl
 
 FROM build-wheels AS trt-model-wheels
 ARG DEBIAN_FRONTEND

docker/tensorrt/Dockerfile.base

@@ -3,7 +3,7 @@
 # https://askubuntu.com/questions/972516/debian-frontend-environment-variable
 ARG DEBIAN_FRONTEND=noninteractive
 
-ARG TRT_BASE=nvcr.io/nvidia/tensorrt:23.03-py3
+ARG TRT_BASE=nvcr.io/nvidia/tensorrt:23.12-py3
 
 # Build TensorRT-specific library
 FROM ${TRT_BASE} AS trt-deps

docker/tensorrt/detector/rootfs/etc/ld.so.conf.d/cuda_tensorrt.conf

@@ -1,6 +1,8 @@
 /usr/local/lib
-/usr/local/lib/python3.9/dist-packages/nvidia/cudnn/lib
-/usr/local/lib/python3.9/dist-packages/nvidia/cuda_runtime/lib
-/usr/local/lib/python3.9/dist-packages/nvidia/cublas/lib
-/usr/local/lib/python3.9/dist-packages/nvidia/cuda_nvrtc/lib
-/usr/local/lib/python3.9/dist-packages/tensorrt
+/usr/local/cuda/lib64
+/usr/local/lib/python3.11/dist-packages/nvidia/cudnn/lib
+/usr/local/lib/python3.11/dist-packages/nvidia/cuda_runtime/lib
+/usr/local/lib/python3.11/dist-packages/nvidia/cublas/lib
+/usr/local/lib/python3.11/dist-packages/nvidia/cuda_nvrtc/lib
+/usr/local/lib/python3.11/dist-packages/tensorrt
+/usr/local/lib/python3.11/dist-packages/nvidia/cufft/lib

docker/tensorrt/requirements-amd64.txt

@@ -1,14 +1,14 @@
 # NVidia TensorRT Support (amd64 only)
 --extra-index-url 'https://pypi.nvidia.com'
 numpy < 1.24; platform_machine == 'x86_64'
-tensorrt == 8.5.3.*; platform_machine == 'x86_64'
-cuda-python == 11.8; platform_machine == 'x86_64'
-cython == 0.29.*; platform_machine == 'x86_64'
+tensorrt == 8.6.1.*; platform_machine == 'x86_64'
+cuda-python == 11.8.*; platform_machine == 'x86_64'
+cython == 3.0.*; platform_machine == 'x86_64'
 nvidia-cuda-runtime-cu12 == 12.1.*; platform_machine == 'x86_64'
 nvidia-cuda-runtime-cu11 == 11.8.*; platform_machine == 'x86_64'
 nvidia-cublas-cu11 == 11.11.3.6; platform_machine == 'x86_64'
-nvidia-cudnn-cu11 == 8.6.0.*; platform_machine == 'x86_64'
+nvidia-cudnn-cu12 == 9.5.0.*; platform_machine == 'x86_64'
 nvidia-cufft-cu11==10.*; platform_machine == 'x86_64'
 onnx==1.16.*; platform_machine == 'x86_64'
-onnxruntime-gpu==1.18.*; platform_machine == 'x86_64'
+onnxruntime-gpu==1.20.*; platform_machine == 'x86_64'
 protobuf==3.20.3; platform_machine == 'x86_64'

docs/docs/configuration/advanced.md

@@ -4,7 +4,9 @@ title: Advanced Options
 sidebar_label: Advanced Options
 ---
 
-### `logger`
+### Logging
+
+#### Frigate `logger`
 
 Change the default log level for troubleshooting purposes.
 
@@ -28,6 +30,18 @@ Examples of available modules are:
 - `watchdog.<camera_name>`
 - `ffmpeg.<camera_name>.<sorted_roles>` NOTE: All FFmpeg logs are sent as `error` level.
 
+#### Go2RTC Logging
+
+See [the go2rtc docs](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#module-log) for logging configuration
+
+```yaml
+go2rtc:
+  streams:
+    ...
+  log:
+    exec: trace
+```
+
 ### `environment_vars`
 
 This section can be used to set environment variables for those unable to modify the environment of the container (ie. within HassOS)
@@ -189,16 +203,16 @@ When frigate starts up, it checks whether your config file is valid, and if it i
 
 ### Via API
 
-Frigate can accept a new configuration file as JSON at the `/config/save` endpoint. When updating the config this way, Frigate will validate the config before saving it, and return a `400` if the config is not valid.
+Frigate can accept a new configuration file as JSON at the `/api/config/save` endpoint. When updating the config this way, Frigate will validate the config before saving it, and return a `400` if the config is not valid.
 
 ```bash
-curl -X POST http://frigate_host:5000/config/save -d @config.json
+curl -X POST http://frigate_host:5000/api/config/save -d @config.json
 ```
 
 if you'd like you can use your yaml config directly by using [`yq`](https://github.com/mikefarah/yq) to convert it to json:
 
 ```bash
-yq r -j config.yml | curl -X POST http://frigate_host:5000/config/save -d @-
+yq r -j config.yml | curl -X POST http://frigate_host:5000/api/config/save -d @-
 ```
 
 ### Via Command Line

docs/docs/configuration/authentication.md

@@ -24,6 +24,11 @@ On startup, an admin user and password are generated and printed in the logs. It
 
 In the event that you are locked out of your instance, you can tell Frigate to reset the admin password and print it in the logs on next startup using the `reset_admin_password` setting in your config file.
 
+```yaml
+auth:
+  reset_admin_password: true
+```
+
 ## Login failure rate limiting
 
 In order to limit the risk of brute force attacks, rate limiting is available for login failures. This is implemented with SlowApi, and the string notation for valid values is available in [the documentation](https://limits.readthedocs.io/en/stable/quickstart.html#examples).

docs/docs/configuration/autotracking.md

@@ -167,3 +167,7 @@ To maintain object tracking during PTZ moves, Frigate tracks the motion of your
 ### Calibration seems to have completed, but the camera is not actually moving to track my object. Why?
 
 Some cameras have firmware that reports that FOV RelativeMove, the ONVIF command that Frigate uses for autotracking, is supported. However, if the camera does not pan or tilt when an object comes into the required zone, your camera's firmware does not actually support FOV RelativeMove. One such camera is the Uniview IPC672LR-AX4DUPK. It actually moves its zoom motor instead of panning and tilting and does not follow the ONVIF standard whatsoever.
+
+### Frigate reports an error saying that calibration has failed. Why?
+
+Calibration measures the amount of time it takes for Frigate to make a series of movements with your PTZ. This error message is recorded in the log if these values are too high for Frigate to support calibrated autotracking. This is often the case when your camera's motor or network connection is too slow or your camera's firmware doesn't report the motor status in a timely manner. You can try running without calibration (just remove the `movement_weights` line from your config and restart), but if calibration fails, this often means that autotracking will behave unpredictably.

docs/docs/configuration/camera_specific.md

@@ -22,7 +22,7 @@ Note that mjpeg cameras require encoding the video into h264 for recording, and
 ```yaml
 go2rtc:
   streams:
-    mjpeg_cam: "ffmpeg:{your_mjpeg_stream_url}#video=h264#hardware" # <- use hardware acceleration to create an h264 stream usable for other components.
+    mjpeg_cam: "ffmpeg:http://your_mjpeg_stream_url#video=h264#hardware" # <- use hardware acceleration to create an h264 stream usable for other components.
 
 cameras:
   ...
@@ -65,19 +65,32 @@ ffmpeg:
 
 ## Model/vendor specific setup
 
+### Amcrest & Dahua
+
+Amcrest & Dahua cameras should be connected to via RTSP using the following format:
+
+```
+rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=0 # this is the main stream
+rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=1 # this is the sub stream, typically supporting low resolutions only
+rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=2 # higher end cameras support a third stream with a mid resolution (1280x720, 1920x1080)
+rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=3 # new higher end cameras support a fourth stream with another mid resolution (1280x720, 1920x1080)
+
+```
+
 ### Annke C800
 
-This camera is H.265 only. To be able to play clips on some devices (like MacOs or iPhone) the H.265 stream has to be repackaged and the audio stream has to be converted to aac. Unfortunately direct playback of in the browser is not working (yet), but the downloaded clip can be played locally.
+This camera is H.265 only. To be able to play clips on some devices (like MacOs or iPhone) the H.265 stream has to be adjusted using the `apple_compatibility` config.
 
 ```yaml
 cameras:
   annkec800: # <------ Name the camera
     ffmpeg:
+      apple_compatibility: true # <- Adds compatibility with MacOS and iPhone
       output_args:
-        record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v copy -tag:v hvc1 -bsf:v hevc_mp4toannexb -c:a aac
+        record: preset-record-generic-audio-aac
 
       inputs:
-        - path: rtsp://user:password@camera-ip:554/H264/ch1/main/av_stream # <----- Update for your camera
+        - path: rtsp://USERNAME:PASSWORD@CAMERA-IP/H264/ch1/main/av_stream # <----- Update for your camera
           roles:
             - detect
             - record
@@ -95,6 +108,29 @@ ffmpeg:
   input_args: preset-rtsp-blue-iris
 ```
 
+### Hikvision Cameras
+
+Hikvision cameras should be connected to via RTSP using the following format:
+
+```
+rtsp://USERNAME:PASSWORD@CAMERA-IP/streaming/channels/101 # this is the main stream
+rtsp://USERNAME:PASSWORD@CAMERA-IP/streaming/channels/102 # this is the sub stream, typically supporting low resolutions only
+rtsp://USERNAME:PASSWORD@CAMERA-IP/streaming/channels/103 # higher end cameras support a third stream with a mid resolution (1280x720, 1920x1080)
+```
+
+:::note
+
+[Some users have reported](https://www.reddit.com/r/frigate_nvr/comments/1hg4ze7/hikvision_security_settings) that newer Hikvision cameras require adjustments to the security settings:
+
+```
+RTSP Authentication - digest/basic
+RTSP Digest Algorithm - MD5
+WEB Authentication - digest/basic
+WEB Digest Algorithm  - MD5
+```
+
+:::
+
 ### Reolink Cameras
 
 Reolink has older cameras (ex: 410 & 520) as well as newer camera (ex: 520a & 511wa) which support different subsets of options. In both cases using the http stream is recommended.

docs/docs/configuration/face_recognition.md

@@ -0,0 +1,59 @@
+---
+id: face_recognition
+title: Face Recognition
+---
+
+Face recognition allows people to be assigned names and when their face is recognized Frigate will assign the person's name as a sub label. This information is included in the UI, filters, as well as in notifications.
+
+Frigate has support for FaceNet to create face embeddings, which runs locally. Embeddings are then saved to Frigate's database.
+
+## Minimum System Requirements
+
+Face recognition works by running a large AI model locally on your system. Systems without a GPU will not run Face Recognition reliably or at all.
+
+## Configuration
+
+Face recognition is disabled by default and requires semantic search to be enabled, face recognition must be enabled in your config file before it can be used. Semantic Search and face recognition are global configuration settings.
+
+```yaml
+face_recognition:
+  enabled: true
+```
+
+## Dataset
+
+The number of images needed for a sufficient training set for face recognition varies depending on several factors:
+
+- Diversity of the dataset: A dataset with diverse images, including variations in lighting, pose, and facial expressions, will require fewer images per person than a less diverse dataset.
+- Desired accuracy: The higher the desired accuracy, the more images are typically needed.
+
+However, here are some general guidelines:
+
+- Minimum: For basic face recognition tasks, a minimum of 10-20 images per person is often recommended.
+- Recommended: For more robust and accurate systems, 30-50 images per person is a good starting point.
+- Ideal: For optimal performance, especially in challenging conditions, 100 or more images per person can be beneficial.
+
+## Creating a Robust Training Set
+
+The accuracy of face recognition is heavily dependent on the quality of data given to it for training. It is recommended to build the face training library in phases.
+
+:::tip
+
+When choosing images to include in the face training set it is recommended to always follow these recommendations:
+- If it is difficult to make out details in a persons face it will not be helpful in training.
+- Avoid images with under/over-exposure.
+- Avoid blurry / pixelated images.
+- Be careful when uploading images of people when they are wearing clothing that covers a lot of their face as this may confuse the training.
+- Do not upload too many images at the same time, it is recommended to train 4-6 images for each person each day so it is easier to know if the previously added images helped or hurt performance.
+
+:::
+
+### Step 1 - Building a Strong Foundation
+
+When first enabling face recognition it is important to build a foundation of strong images. It is recommended to start by uploading 1-2 photos taken by a smartphone for each person. It is important that the person's face in the photo is straight-on and not turned which will ensure a good starting point.
+
+Then it is recommended to use the `Face Library` tab in Frigate to select and train images for each person as they are detected. When building a strong foundation it is strongly recommended to only train on images that are straight-on. Ignore images from cameras that recognize faces from an angle. Once a person starts to be consistently recognized correctly on images that are straight-on, it is time to move on to the next step.
+
+### Step 2 - Expanding The Dataset
+
+Once straight-on images are performing well, start choosing slightly off-angle images to include for training. It is important to still choose images where enough face detail is visible to recognize someone.

docs/docs/configuration/genai.md

@@ -15,9 +15,9 @@ Semantic Search must be enabled to use Generative AI.
 
 ## Configuration
 
-Generative AI can be enabled for all cameras or only for specific cameras. There are currently 3 providers available to integrate with Frigate.
+Generative AI can be enabled for all cameras or only for specific cameras. There are currently 3 native providers available to integrate with Frigate. Other providers that support the OpenAI standard API can also be used. See the OpenAI section below.
 
-If the provider you choose requires an API key, you may either directly paste it in your configuration, or store it in an environment variable prefixed with `FRIGATE_`.
+To use Generative AI, you must define a single provider at the global level of your Frigate configuration. If the provider you choose requires an API key, you may either directly paste it in your configuration, or store it in an environment variable prefixed with `FRIGATE_`.
 
 ```yaml
 genai:
@@ -116,7 +116,7 @@ genai:
   model: gpt-4o
 ```
 
-::: note
+:::note
 
 To use a different OpenAI-compatible API endpoint, set the `OPENAI_BASE_URL` environment variable to your provider's API URL.
 

docs/docs/configuration/hardware_acceleration.md

@@ -175,6 +175,16 @@ For more information on the various values across different distributions, see h
 
 Depending on your OS and kernel configuration, you may need to change the `/proc/sys/kernel/perf_event_paranoid` kernel tunable. You can test the change by running `sudo sh -c 'echo 2 >/proc/sys/kernel/perf_event_paranoid'` which will persist until a reboot. Make it permanent by running `sudo sh -c 'echo kernel.perf_event_paranoid=2 >> /etc/sysctl.d/local.conf'`
 
+#### Stats for SR-IOV devices
+
+When using virtualized GPUs via SR-IOV, additional args are needed for GPU stats to function. This can be enabled with the following config:
+
+```yaml
+telemetry:
+  stats:
+    sriov: True
+```
+
 ## AMD/ATI GPUs (Radeon HD 2000 and newer GPUs) via libva-mesa-driver
 
 VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams.

docs/docs/configuration/license_plate_recognition.md

@@ -0,0 +1,88 @@
+---
+id: license_plate_recognition
+title: License Plate Recognition (LPR)
+---
+
+Frigate can recognize license plates on vehicles and automatically add the detected characters as a `sub_label` to objects that are of type `car`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street with a dedicated LPR camera.
+
+Users running a Frigate+ model (or any custom model that natively detects license plates) should ensure that `license_plate` is added to the [list of objects to track](https://docs.frigate.video/plus/#available-label-types) either globally or for a specific camera. This will improve the accuracy and performance of the LPR model.
+
+Users without a model that detects license plates can still run LPR. A small, CPU inference, YOLOv9 license plate detection model will be used instead. You should _not_ define `license_plate` in your list of objects to track.
+
+LPR is most effective when the vehicle’s license plate is fully visible to the camera. For moving vehicles, Frigate will attempt to read the plate continuously, refining recognition and keeping the most confident result. LPR will not run on stationary vehicles.
+
+## Minimum System Requirements
+
+License plate recognition works by running AI models locally on your system. The models are relatively lightweight and run on your CPU. At least 4GB of RAM is required.
+
+## Configuration
+
+License plate recognition is disabled by default. Enable it in your config file:
+
+```yaml
+lpr:
+  enabled: True
+```
+
+## Advanced Configuration
+
+Fine-tune the LPR feature using these optional parameters:
+
+### Detection
+
+- **`detection_threshold`**: License plate object detection confidence score required before recognition runs.
+  - Default: `0.7`
+  - Note: If you are using a Frigate+ model and you set the `threshold` in your objects config for `license_plate` higher than this value, recognition will never run. It's best to ensure these values match, or this `detection_threshold` is lower than your object config `threshold`.
+- **`min_area`**: Defines the minimum size (in pixels) a license plate must be before recognition runs.
+  - Default: `1000` pixels.
+  - Depending on the resolution of your cameras, you can increase this value to ignore small or distant plates.
+
+### Recognition
+
+- **`recognition_threshold`**: Recognition confidence score required to add the plate to the object as a sub label.
+  - Default: `0.9`.
+- **`min_plate_length`**: Specifies the minimum number of characters a detected license plate must have to be added as a sub-label to an object.
+  - Use this to filter out short, incomplete, or incorrect detections.
+- **`format`**: A regular expression defining the expected format of detected plates. Plates that do not match this format will be discarded.
+  - `"^[A-Z]{1,3} [A-Z]{1,2} [0-9]{1,4}$"` matches plates like "B AB 1234" or "M X 7"
+  - `"^[A-Z]{2}[0-9]{2} [A-Z]{3}$"` matches plates like "AB12 XYZ" or "XY68 ABC"
+
+### Matching
+
+- **`known_plates`**: List of strings or regular expressions that assign custom a `sub_label` to `car` objects when a recognized plate matches a known value.
+  - These labels appear in the UI, filters, and notifications.
+- **`match_distance`**: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate.
+  - For example, setting `match_distance: 1` allows a plate `ABCDE` to match `ABCBE` or `ABCD`.
+  - This parameter will not operate on known plates that are defined as regular expressions. You should define the full string of your plate in `known_plates` in order to use `match_distance`.
+
+### Examples
+
+```yaml
+lpr:
+  enabled: True
+  min_area: 1500 # Ignore plates smaller than 1500 pixels
+  min_plate_length: 4 # Only recognize plates with 4 or more characters
+  known_plates:
+    Wife's Car:
+      - "ABC-1234"
+      - "ABC-I234" # Accounts for potential confusion between the number one (1) and capital letter I
+    Johnny:
+      - "J*N-*234" # Matches JHN-1234 and JMN-I234, but also note that "*" matches any number of characters
+    Sally:
+      - "[S5]LL-1234" # Matches both SLL-1234 and 5LL-1234
+```
+
+```yaml
+lpr:
+  enabled: True
+  min_area: 4000 # Run recognition on larger plates only
+  recognition_threshold: 0.85
+  format: "^[A-Z]{3}-[0-9]{4}$" # Only recognize plates that are three letters, followed by a dash, followed by 4 numbers
+  match_distance: 1 # Allow one character variation in plate matching
+  known_plates:
+    Delivery Van:
+      - "RJK-5678"
+      - "UPS-1234"
+    Employee Parking:
+      - "EMP-[0-9]{3}[A-Z]" # Matches plates like EMP-123A, EMP-456Z
+```

docs/docs/configuration/live.md

@@ -3,9 +3,9 @@ id: live
 title: Live View
 ---
 
-Frigate intelligently displays your camera streams on the Live view dashboard. Your camera images update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any motion is detected, cameras seamlessly switch to a live stream.
+Frigate intelligently displays your camera streams on the Live view dashboard. By default, Frigate employs "smart streaming" where camera images update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any motion or active objects are detected, cameras seamlessly switch to a live stream.
 
-## Live View technologies
+### Live View technologies
 
 Frigate intelligently uses three different streaming technologies to display your camera streams on the dashboard and the single camera view, switching between available modes based on network bandwidth, player errors, or required features like two-way talk. The highest quality and fluency of the Live view requires the bundled `go2rtc` to be configured as shown in the [step by step guide](/guides/configuring_go2rtc).
 
@@ -51,19 +51,32 @@ go2rtc:
       - ffmpeg:rtsp://192.168.1.5:554/live0#video=copy
 ```
 
-### Setting Stream For Live UI
+### Setting Streams For Live UI
 
-There may be some cameras that you would prefer to use the sub stream for live view, but the main stream for recording. This can be done via `live -> stream_name`.
+You can configure Frigate to allow manual selection of the stream you want to view in the Live UI. For example, you may want to view your camera's substream on mobile devices, but the full resolution stream on desktop devices. Setting the `live -> streams` list will populate a dropdown in the UI's Live view that allows you to choose between the streams. This stream setting is _per device_ and is saved in your browser's local storage.
+
+Additionally, when creating and editing camera groups in the UI, you can choose the stream you want to use for your camera group's Live dashboard.
+
+:::note
+
+Frigate's default dashboard ("All Cameras") will always use the first entry you've defined in `streams:` when playing live streams from your cameras.
+
+:::
+
+Configure the `streams` option with a "friendly name" for your stream followed by the go2rtc stream name.
+
+Using Frigate's internal version of go2rtc is required to use this feature. You cannot specify paths in the `streams` configuration, only go2rtc stream names.
 
 ```yaml
 go2rtc:
   streams:
     test_cam:
-      - rtsp://192.168.1.5:554/live0 # <- stream which supports video & aac audio.
+      - rtsp://192.168.1.5:554/live_main # <- stream which supports video & aac audio.
       - "ffmpeg:test_cam#audio=opus" # <- copy of the stream which transcodes audio to opus for webrtc
     test_cam_sub:
-      - rtsp://192.168.1.5:554/substream # <- stream which supports video & aac audio.
-      - "ffmpeg:test_cam_sub#audio=opus" # <- copy of the stream which transcodes audio to opus for webrtc
+      - rtsp://192.168.1.5:554/live_sub # <- stream which supports video & aac audio.
+    test_cam_another_sub:
+      - rtsp://192.168.1.5:554/live_alt # <- stream which supports video & aac audio.
 
 cameras:
   test_cam:
@@ -80,7 +93,10 @@ cameras:
           roles:
             - detect
     live:
-      stream_name: test_cam_sub
+      streams: # <--- Multiple streams for Frigate 0.16 and later
+        Main Stream: test_cam # <--- Specify a "friendly name" followed by the go2rtc stream name
+        Sub Stream: test_cam_sub
+        Special Stream: test_cam_another_sub
 ```
 
 ### WebRTC extra configuration:
@@ -101,6 +117,7 @@ WebRTC works by creating a TCP or UDP connection on port `8555`. However, it req
   ```
 
 - For access through Tailscale, the Frigate system's Tailscale IP must be added as a WebRTC candidate. Tailscale IPs all start with `100.`, and are reserved within the `100.64.0.0/10` CIDR block.
+- Note that WebRTC does not support H.265.
 
 :::tip
 
@@ -148,3 +165,50 @@ For devices that support two way talk, Frigate can be configured to use the feat
 - For the Home Assistant Frigate card, [follow the docs](https://github.com/dermotduffy/frigate-hass-card?tab=readme-ov-file#using-2-way-audio) for the correct source.
 
 To use the Reolink Doorbell with two way talk, you should use the [recommended Reolink configuration](/configuration/camera_specific#reolink-doorbell)
+
+### Streaming options on camera group dashboards
+
+Frigate provides a dialog in the Camera Group Edit pane with several options for streaming on a camera group's dashboard. These settings are _per device_ and are saved in your device's local storage.
+
+- Stream selection using the `live -> streams` configuration option (see _Setting Streams For Live UI_ above)
+- Streaming type:
+  - _No streaming_: Camera images will only update once per minute and no live streaming will occur.
+  - _Smart Streaming_ (default, recommended setting): Smart streaming will update your camera image once per minute when no detectable activity is occurring to conserve bandwidth and resources, since a static picture is the same as a streaming image with no motion or objects. When motion or objects are detected, the image seamlessly switches to a live stream.
+  - _Continuous Streaming_: Camera image will always be a live stream when visible on the dashboard, even if no activity is being detected. Continuous streaming may cause high bandwidth usage and performance issues. **Use with caution.**
+- _Compatibility mode_: Enable this option only if your camera's live stream is displaying color artifacts and has a diagonal line on the right side of the image. Before enabling this, try setting your camera's `detect` width and height to a standard aspect ratio (for example: 640x352 becomes 640x360, and 800x443 becomes 800x450, 2688x1520 becomes 2688x1512, etc). Depending on your browser and device, more than a few cameras in compatibility mode may not be supported, so only use this option if changing your config fails to resolve the color artifacts and diagonal line.
+
+:::note
+
+The default dashboard ("All Cameras") will always use Smart Streaming and the first entry set in your `streams` configuration, if defined. Use a camera group if you want to change any of these settings from the defaults.
+
+:::
+
+## Live view FAQ
+
+1. Why don't I have audio in my Live view?
+   You must use go2rtc to hear audio in your live streams. If you have go2rtc already configured, you need to ensure your camera is sending PCMA/PCMU or AAC audio. If you can't change your camera's audio codec, you need to [transcode the audio](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg) using go2rtc.
+
+   Note that the low bandwidth mode player is a video-only stream. You should not expect to hear audio when in low bandwidth mode, even if you've set up go2rtc.
+
+2. Frigate shows that my live stream is in "low bandwidth mode". What does this mean?
+   Frigate intelligently selects the live streaming technology based on a number of factors (user-selected modes like two-way talk, camera settings, browser capabilities, available bandwidth) and prioritizes showing an actual up-to-date live view of your camera's stream as quickly as possible.
+
+   When you have go2rtc configured, Live view initially attempts to load and play back your stream with a clearer, fluent stream technology (MSE). An initial timeout, a low bandwidth condition that would cause buffering of the stream, or decoding errors in the stream will cause Frigate to switch to the stream defined by the `detect` role, using the jsmpeg format. This is what the UI labels as "low bandwidth mode". On Live dashboards, the mode will automatically reset when smart streaming is configured and activity stops. You can also try using the _Reset_ button to force a reload of your stream.
+
+   If you are still experiencing Frigate falling back to low bandwidth mode, you may need to adjust your camera's settings per the recommendations above or ensure you have enough bandwidth available.
+
+3. It doesn't seem like my cameras are streaming on the Live dashboard. Why?
+   On the default Live dashboard ("All Cameras"), your camera images will update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity is detected, cameras seamlessly switch to a full-resolution live stream. If you want to customize this behavior, use a camera group.
+
+4. I see a strange diagonal line on my live view, but my recordings look fine. How can I fix it?
+   This is caused by incorrect dimensions set in your detect width or height (or incorrectly auto-detected), causing the jsmpeg player's rendering engine to display a slightly distorted image. You should enlarge the width and height of your `detect` resolution up to a standard aspect ratio (example: 640x352 becomes 640x360, and 800x443 becomes 800x450, 2688x1520 becomes 2688x1512, etc). If changing the resolution to match a standard (4:3, 16:9, or 32:9, etc) aspect ratio does not solve the issue, you can enable "compatibility mode" in your camera group dashboard's stream settings. Depending on your browser and device, more than a few cameras in compatibility mode may not be supported, so only use this option if changing your `detect` width and height fails to resolve the color artifacts and diagonal line.
+
+5. How does "smart streaming" work?
+   Because a static image of a scene looks exactly the same as a live stream with no motion or activity, smart streaming updates your camera images once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity (motion or object/audio detection) occurs, cameras seamlessly switch to a live stream.
+
+   This static image is pulled from the stream defined in your config with the `detect` role. When activity is detected, images from the `detect` stream immediately begin updating at ~5 frames per second so you can see the activity until the live player is loaded and begins playing. This usually only takes a second or two. If the live player times out, buffers, or has streaming errors, the jsmpeg player is loaded and plays a video-only stream from the `detect` role. When activity ends, the players are destroyed and a static image is displayed until activity is detected again, and the process repeats.
+
+   This is Frigate's default and recommended setting because it results in a significant bandwidth savings, especially for high resolution cameras.
+
+6. I have unmuted some cameras on my dashboard, but I do not hear sound. Why?
+   If your camera is streaming (as indicated by a red dot in the upper right, or if it has been set to continuous streaming mode), your browser may be blocking audio until you interact with the page. This is an intentional browser limitation. See [this article](https://developer.mozilla.org/en-US/docs/Web/Media/Autoplay_guide#autoplay_availability). Many browsers have a whitelist feature to change this behavior.

docs/docs/configuration/metrics.md

@@ -0,0 +1,99 @@
+---
+id: metrics
+title: Metrics
+---
+
+# Metrics
+
+Frigate exposes Prometheus metrics at the `/api/metrics` endpoint that can be used to monitor the performance and health of your Frigate instance.
+
+## Available Metrics
+
+### System Metrics
+- `frigate_cpu_usage_percent{pid="", name="", process="", type="", cmdline=""}` - Process CPU usage percentage
+- `frigate_mem_usage_percent{pid="", name="", process="", type="", cmdline=""}` - Process memory usage percentage
+- `frigate_gpu_usage_percent{gpu_name=""}` - GPU utilization percentage
+- `frigate_gpu_mem_usage_percent{gpu_name=""}` - GPU memory usage percentage
+
+### Camera Metrics
+- `frigate_camera_fps{camera_name=""}` - Frames per second being consumed from your camera
+- `frigate_detection_fps{camera_name=""}` - Number of times detection is run per second
+- `frigate_process_fps{camera_name=""}` - Frames per second being processed
+- `frigate_skipped_fps{camera_name=""}` - Frames per second skipped for processing
+- `frigate_detection_enabled{camera_name=""}` - Detection enabled status for camera
+- `frigate_audio_dBFS{camera_name=""}` - Audio dBFS for camera
+- `frigate_audio_rms{camera_name=""}` - Audio RMS for camera
+
+### Detector Metrics
+- `frigate_detector_inference_speed_seconds{name=""}` - Time spent running object detection in seconds
+- `frigate_detection_start{name=""}` - Detector start time (unix timestamp)
+
+### Storage Metrics
+- `frigate_storage_free_bytes{storage=""}` - Storage free bytes
+- `frigate_storage_total_bytes{storage=""}` - Storage total bytes
+- `frigate_storage_used_bytes{storage=""}` - Storage used bytes
+- `frigate_storage_mount_type{mount_type="", storage=""}` - Storage mount type info
+
+### Service Metrics
+- `frigate_service_uptime_seconds` - Uptime in seconds
+- `frigate_service_last_updated_timestamp` - Stats recorded time (unix timestamp)
+- `frigate_device_temperature{device=""}` - Device Temperature
+
+### Event Metrics
+- `frigate_camera_events{camera="", label=""}` - Count of camera events since exporter started
+
+## Configuring Prometheus
+
+To scrape metrics from Frigate, add the following to your Prometheus configuration:
+
+```yaml
+scrape_configs:
+  - job_name: 'frigate'
+    metrics_path: '/api/metrics'
+    static_configs:
+      - targets: ['frigate:5000']
+    scrape_interval: 15s
+```
+
+## Example Queries
+
+Here are some example PromQL queries that might be useful:
+
+```promql
+# Average CPU usage across all processes
+avg(frigate_cpu_usage_percent)
+
+# Total GPU memory usage
+sum(frigate_gpu_mem_usage_percent)
+
+# Detection FPS by camera
+rate(frigate_detection_fps{camera_name="front_door"}[5m])
+
+# Storage usage percentage
+(frigate_storage_used_bytes / frigate_storage_total_bytes) * 100
+
+# Event count by camera in last hour
+increase(frigate_camera_events[1h])
+```
+
+## Grafana Dashboard
+
+You can use these metrics to create Grafana dashboards to monitor your Frigate instance. Here's an example of metrics you might want to track:
+
+- CPU, Memory and GPU usage over time
+- Camera FPS and detection rates
+- Storage usage and trends
+- Event counts by camera
+- System temperatures
+
+A sample Grafana dashboard JSON will be provided in a future update.
+
+## Metric Types
+
+The metrics exposed by Frigate use the following Prometheus metric types:
+
+- **Counter**: Cumulative values that only increase (e.g., `frigate_camera_events`)
+- **Gauge**: Values that can go up and down (e.g., `frigate_cpu_usage_percent`)
+- **Info**: Key-value pairs for metadata (e.g., `frigate_storage_mount_type`)
+
+For more information about Prometheus metric types, see the [Prometheus documentation](https://prometheus.io/docs/concepts/metric_types/).

docs/docs/configuration/object_detectors.md

@@ -33,6 +33,14 @@ Frigate supports multiple different detectors that work on different types of ha
 
 :::
 
+:::note
+
+Multiple detectors can not be mixed for object detection (ex: OpenVINO and Coral EdgeTPU can not be used for object detection at the same time).
+
+This does not affect using hardware for accelerating other tasks such as [semantic search](./semantic_search.md)
+
+:::
+
 # Officially Supported Detectors
 
 Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `hailo8l`, `onnx`, `openvino`, `rknn`, `rocm`, and `tensorrt`. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
@@ -116,6 +124,30 @@ detectors:
     device: pci
 ```
 
+## Hailo-8l
+
+This detector is available for use with Hailo-8 AI Acceleration Module.
+
+See the [installation docs](../frigate/installation.md#hailo-8l) for information on configuring the hailo8.
+
+### Configuration
+
+```yaml
+detectors:
+  hailo8l:
+    type: hailo8l
+    device: PCIe
+
+model:
+  width: 300
+  height: 300
+  input_tensor: nhwc
+  input_pixel_format: bgr
+  model_type: ssd
+  path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
+```
+
+
 ## OpenVINO Detector
 
 The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel VPU hardware. To configure an OpenVINO detector, set the `"type"` attribute to `"openvino"`.
@@ -169,15 +201,7 @@ This detector also supports YOLOX. Frigate does not come with any YOLOX models p
 
 #### YOLO-NAS
 
-[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
-
-:::warning
-
-The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
-
-:::
-
-The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.
+[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. See [the models section](#downloading-yolo-nas-model) for more information on downloading the YOLO-NAS model for use in Frigate.
 
 After placing the downloaded onnx model in your config folder, you can use the following configuration:
 
@@ -199,13 +223,43 @@ model:
 
 Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
+#### YOLOv9
+
+[YOLOv9](https://github.com/MultimediaTechLab/YOLO) models are supported, but not included by default.
+
+:::tip
+
+The YOLOv9 detector has been designed to support YOLOv9 models, but may support other YOLO model architectures as well.
+
+:::
+
+After placing the downloaded onnx model in your config folder, you can use the following configuration:
+
+```yaml
+detectors:
+  ov:
+    type: openvino
+    device: GPU
+
+model:
+  model_type: yolov9
+  width: 640 # <--- should match the imgsize set during model export
+  height: 640 # <--- should match the imgsize set during model export
+  input_tensor: nchw
+  input_dtype: float
+  path: /config/model_cache/yolov9-t.onnx
+  labelmap_path: /labelmap/coco-80.txt
+```
+
+Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
+
 ## NVidia TensorRT Detector
 
 Nvidia GPUs may be used for object detection using the TensorRT libraries. Due to the size of the additional libraries, this detector is only provided in images with the `-tensorrt` tag suffix, e.g. `ghcr.io/blakeblackshear/frigate:stable-tensorrt`. This detector is designed to work with Yolo models for object detection.
 
 ### Minimum Hardware Support
 
-The TensorRT detector uses the 12.x series of CUDA libraries which have minor version compatibility. The minimum driver version on the host system must be `>=530`. Also the GPU must support a Compute Capability of `5.0` or greater. This generally correlates to a Maxwell-era GPU or newer, check the NVIDIA GPU Compute Capability table linked below.
+The TensorRT detector uses the 12.x series of CUDA libraries which have minor version compatibility. The minimum driver version on the host system must be `>=545`. Also the GPU must support a Compute Capability of `5.0` or greater. This generally correlates to a Maxwell-era GPU or newer, check the NVIDIA GPU Compute Capability table linked below.
 
 To use the TensorRT detector, make sure your host system has the [nvidia-container-runtime](https://docs.docker.com/config/containers/resource_constraints/#access-an-nvidia-gpu) installed to pass through the GPU to the container and the host system has a compatible driver installed for your GPU.
 
@@ -233,6 +287,8 @@ If your GPU does not support FP16 operations, you can pass the environment varia
 
 Specific models can be selected by passing an environment variable to the `docker run` command or in your `docker-compose.yml` file. Use the form `-e YOLO_MODELS=yolov4-416,yolov4-tiny-416` to select one or more model names. The models available are shown below.
 
+<details>
+<summary>Available Models</summary>
 ```
 yolov3-288
 yolov3-416
@@ -261,6 +317,7 @@ yolov7-320
 yolov7x-640
 yolov7x-320
 ```
+</details>
 
 An example `docker-compose.yml` fragment that converts the `yolov4-608` and `yolov7x-640` models for a Pascal card would look something like this:
 
@@ -388,15 +445,7 @@ There is no default model provided, the following formats are supported:
 
 #### YOLO-NAS
 
-[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
-
-:::warning
-
-The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
-
-:::
-
-The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.
+[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. See [the models section](#downloading-yolo-nas-model) for more information on downloading the YOLO-NAS model for use in Frigate.
 
 After placing the downloaded onnx model in your config folder, you can use the following configuration:
 
@@ -418,7 +467,7 @@ Note that the labelmap uses a subset of the complete COCO label set that has onl
 
 ## ONNX
 
-ONNX is an open format for building machine learning models, Frigate supports running ONNX models on CPU, OpenVINO, and TensorRT. On startup Frigate will automatically try to use a GPU if one is available.
+ONNX is an open format for building machine learning models, Frigate supports running ONNX models on CPU, OpenVINO, ROCm, and TensorRT. On startup Frigate will automatically try to use a GPU if one is available.
 
 :::info
 
@@ -458,15 +507,7 @@ There is no default model provided, the following formats are supported:
 
 #### YOLO-NAS
 
-[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
-
-:::warning
-
-The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
-
-:::
-
-The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.
+[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. See [the models section](#downloading-yolo-nas-model) for more information on downloading the YOLO-NAS model for use in Frigate.
 
 After placing the downloaded onnx model in your config folder, you can use the following configuration:
 
@@ -485,6 +526,33 @@ model:
   labelmap_path: /labelmap/coco-80.txt
 ```
 
+#### YOLOv9
+
+[YOLOv9](https://github.com/MultimediaTechLab/YOLO) models are supported, but not included by default.
+
+:::tip
+
+The YOLOv9 detector has been designed to support YOLOv9 models, but may support other YOLO model architectures as well.
+
+:::
+
+After placing the downloaded onnx model in your config folder, you can use the following configuration:
+
+```yaml
+detectors:
+  onnx:
+    type: onnx
+
+model:
+  model_type: yolov9
+  width: 640 # <--- should match the imgsize set during model export
+  height: 640 # <--- should match the imgsize set during model export
+  input_tensor: nchw
+  input_dtype: float
+  path: /config/model_cache/yolov9-t.onnx
+  labelmap_path: /labelmap/coco-80.txt
+```
+
 Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
 ## CPU Detector (not recommended)
@@ -550,7 +618,7 @@ Hardware accelerated object detection is supported on the following SoCs:
 - RK3576
 - RK3588
 
-This implementation uses the [Rockchip's RKNN-Toolkit2](https://github.com/airockchip/rknn-toolkit2/), version v2.0.0.beta0. Currently, only [Yolo-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) is supported as object detection model.
+This implementation uses the [Rockchip's RKNN-Toolkit2](https://github.com/airockchip/rknn-toolkit2/), version v2.3.0. Currently, only [Yolo-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) is supported as object detection model.
 
 ### Prerequisites
 
@@ -625,25 +693,56 @@ $ cat /sys/kernel/debug/rknpu/load
 - All models are automatically downloaded and stored in the folder `config/model_cache/rknn_cache`. After upgrading Frigate, you should remove older models to free up space.
 - You can also provide your own `.rknn` model. You should not save your own models in the `rknn_cache` folder, store them directly in the `model_cache` folder or another subfolder. To convert a model to `.rknn` format see the `rknn-toolkit2` (requires a x86 machine). Note, that there is only post-processing for the supported models.
 
-## Hailo-8l
+### Converting your own onnx model to rknn format
 
-This detector is available for use with Hailo-8 AI Acceleration Module.
+To convert a onnx model to the rknn format using the [rknn-toolkit2](https://github.com/airockchip/rknn-toolkit2/) you have to:
 
-See the [installation docs](../frigate/installation.md#hailo-8l) for information on configuring the hailo8.
+- Place one ore more models in onnx format in the directory `config/model_cache/rknn_cache/onnx` on your docker host (this might require `sudo` privileges).
+- Save the configuration file under `config/conv2rknn.yaml` (see below for details).
+- Run `docker exec <frigate_container_id> python3 /opt/conv2rknn.py`. If the conversion was successful, the rknn models will be placed in `config/model_cache/rknn_cache`.
 
-### Configuration
+This is an example configuration file that you need to adjust to your specific onnx model:
 
 ```yaml
-detectors:
-  hailo8l:
-    type: hailo8l
-    device: PCIe
+soc: ["rk3562","rk3566", "rk3568", "rk3576", "rk3588"]
+quantization: false
 
-model:
-  width: 300
-  height: 300
-  input_tensor: nhwc
-  input_pixel_format: bgr
-  model_type: ssd
-  path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
+output_name: "{input_basename}"
+
+config:
+  mean_values: [[0, 0, 0]]
+  std_values: [[255, 255, 255]]
+  quant_img_rgb2bgr: true
 ```
+
+Explanation of the paramters:
+
+- `soc`: A list of all SoCs you want to build the rknn model for. If you don't specify this parameter, the script tries to find out your SoC and builds the rknn model for this one.
+- `quantization`: true: 8 bit integer (i8) quantization, false: 16 bit float (fp16). Default: false.
+- `output_name`: The output name of the model. The following variables are available:
+  - `quant`: "i8" or "fp16" depending on the config
+  - `input_basename`: the basename of the input model (e.g. "my_model" if the input model is calles "my_model.onnx")
+  - `soc`: the SoC this model was build for (e.g. "rk3588")
+  - `tk_version`: Version of `rknn-toolkit2` (e.g. "2.3.0")
+  - **example**: Specifying `output_name = "frigate-{quant}-{input_basename}-{soc}-v{tk_version}"` could result in a model called `frigate-i8-my_model-rk3588-v2.3.0.rknn`.
+- `config`: Configuration passed to `rknn-toolkit2` for model conversion. For an explanation of all available parameters have a look at section "2.2. Model configuration" of [this manual](https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.0/03_Rockchip_RKNPU_API_Reference_RKNN_Toolkit2_V2.3.0_EN.pdf).
+
+# Models
+
+Some model types are not included in Frigate by default.
+
+## Downloading Models
+
+Here are some tips for getting different model types
+
+### Downloading YOLO-NAS Model
+
+You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
+
+:::warning
+
+The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
+
+:::
+
+The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.

docs/docs/configuration/object_filters.md

@@ -34,7 +34,7 @@ False positives can also be reduced by filtering a detection based on its shape.
 
 ### Object Area
 
-`min_area` and `max_area` filter on the area of an objects bounding box in pixels and can be used to reduce false positives that are outside the range of expected sizes. For example when a leaf is detected as a dog or when a large tree is detected as a person, these can be reduced by adding a `min_area` / `max_area` filter.
+`min_area` and `max_area` filter on the area of an objects bounding box and can be used to reduce false positives that are outside the range of expected sizes. For example when a leaf is detected as a dog or when a large tree is detected as a person, these can be reduced by adding a `min_area` / `max_area` filter. These values can either be in pixels or as a percentage of the frame (for example, 0.12 represents 12% of the frame).
 
 ### Object Proportions
 

docs/docs/configuration/reference.md

@@ -46,6 +46,11 @@ mqtt:
   tls_insecure: false
   # Optional: interval in seconds for publishing stats (default: shown below)
   stats_interval: 60
+  # Optional: QoS level for subscriptions and publishing (default: shown below)
+  # 0 = at most once
+  # 1 = at least once
+  # 2 = exactly once
+  qos: 0
 
 # Optional: Detectors configuration. Defaults to a single CPU detector
 detectors:
@@ -244,6 +249,8 @@ ffmpeg:
   # If set too high, then if a ffmpeg crash or camera stream timeout occurs, you could potentially lose up to a maximum of retry_interval second(s) of footage
   # NOTE: this can be a useful setting for Wireless / Battery cameras to reduce how much footage is potentially lost during a connection timeout.
   retry_interval: 10
+  # Optional: Set tag on HEVC (H.265) recording stream to improve compatibility with Apple players. (default: shown below)
+  apple_compatibility: false
 
 # Optional: Detect configuration
 # NOTE: Can be overridden at the camera level
@@ -310,9 +317,11 @@ objects:
   # Optional: filters to reduce false positives for specific object types
   filters:
     person:
-      # Optional: minimum width*height of the bounding box for the detected object (default: 0)
+      # Optional: minimum size of the bounding box for the detected object (default: 0).
+      # Can be specified as an integer for width*height in pixels or as a decimal representing the percentage of the frame (0.000001 to 0.99).
       min_area: 5000
-      # Optional: maximum width*height of the bounding box for the detected object (default: 24000000)
+      # Optional: maximum size of the bounding box for the detected object (default: 24000000).
+      # Can be specified as an integer for width*height in pixels or as a decimal representing the percentage of the frame (0.000001 to 0.99).
       max_area: 100000
       # Optional: minimum width/height of the bounding box for the detected object (default: 0)
       min_ratio: 0.5
@@ -331,6 +340,8 @@ objects:
 review:
   # Optional: alerts configuration
   alerts:
+    # Optional: enables alerts for the camera (default: shown below)
+    enabled: True
     # Optional: labels that qualify as an alert (default: shown below)
     labels:
       - car
@@ -343,6 +354,8 @@ review:
       - driveway
   # Optional: detections configuration
   detections:
+    # Optional: enables detections for the camera (default: shown below)
+    enabled: True
     # Optional: labels that qualify as a detection (default: all labels that are tracked / listened to)
     labels:
       - car
@@ -400,6 +413,7 @@ motion:
   mqtt_off_delay: 30
 
 # Optional: Notification Configuration
+# NOTE: Can be overridden at the camera level (except email)
 notifications:
   # Optional: Enable notification service (default: shown below)
   enabled: False
@@ -524,6 +538,33 @@ semantic_search:
   # NOTE: small model runs on CPU and large model runs on GPU
   model_size: "small"
 
+# Optional: Configuration for face recognition capability
+face_recognition:
+  # Optional: Enable semantic search (default: shown below)
+  enabled: False
+  # Optional: Set the model size used for embeddings. (default: shown below)
+  # NOTE: small model runs on CPU and large model runs on GPU
+  model_size: "small"
+
+# Optional: Configuration for license plate recognition capability
+lpr:
+  # Optional: Enable license plate recognition (default: shown below)
+  enabled: False
+  # Optional: License plate object confidence score required to begin running recognition (default: shown below)
+  detection_threshold: 0.7
+  # Optional: Minimum area of license plate to begin running recognition (default: shown below)
+  min_area: 1000
+  # Optional: Recognition confidence score required to add the plate to the object as a sub label (default: shown below)
+  recognition_threshold: 0.9
+  # Optional: Minimum number of characters a license plate must have to be added to the object as a sub label (default: shown below)
+  min_plate_length: 4
+  # Optional: Regular expression for the expected format of a license plate (default: shown below)
+  format: None
+  # Optional: Allow this number of missing/incorrect characters to still cause a detected plate to match a known plate
+  match_distance: 1
+  # Optional: Known plates to track (strings or regular expressions) (default: shown below)
+  known_plates: {}
+
 # Optional: Configuration for AI generated tracked object descriptions
 # NOTE: Semantic Search must be enabled for this to do anything.
 # WARNING: Depending on the provider, this will send thumbnails over the internet
@@ -549,16 +590,18 @@ genai:
 # Optional: Restream configuration
 # Uses https://github.com/AlexxIT/go2rtc (v1.9.2)
 # NOTE: The default go2rtc API port (1984) must be used,
-#       changing this port for the integrated go2rtc instance is not supported. 
+#       changing this port for the integrated go2rtc instance is not supported.
 go2rtc:
 
 # Optional: Live stream configuration for WebUI.
 # NOTE: Can be overridden at the camera level
 live:
-  # Optional: Set the name of the stream configured in go2rtc
+  # Optional: Set the streams configured in go2rtc
   # that should be used for live view in frigate WebUI. (default: name of camera)
   # NOTE: In most cases this should be set at the camera level only.
-  stream_name: camera_name
+  streams:
+    main_stream: main_stream_name
+    sub_stream: sub_stream_name
   # Optional: Set the height of the jsmpeg stream. (default: 720)
   # This must be less than or equal to the height of the detect stream. Lower resolutions
   # reduce bandwidth required for viewing the jsmpeg stream. Width is computed to match known aspect ratio.
@@ -643,7 +686,10 @@ cameras:
       front_steps:
         # Required: List of x,y coordinates to define the polygon of the zone.
         # NOTE: Presence in a zone is evaluated only based on the bottom center of the objects bounding box.
-        coordinates: 0.284,0.997,0.389,0.869,0.410,0.745
+        coordinates: 0.033,0.306,0.324,0.138,0.439,0.185,0.042,0.428
+        # Optional: The real-world distances of a 4-sided zone used for zones with speed estimation enabled (default: none)
+        # List distances in order of the zone points coordinates and use the unit system defined in the ui config
+        distances: 10,15,12,11
         # Optional: Number of consecutive frames required for object to be considered present in the zone (default: shown below).
         inertia: 3
         # Optional: Number of seconds that an object must loiter to be considered in the zone (default: shown below)
@@ -794,6 +840,9 @@ ui:
   # https://www.gnu.org/software/libc/manual/html_node/Formatting-Calendar-Time.html
   # possible values are shown above (default: not set)
   strftime_fmt: "%Y/%m/%d %H:%M"
+  # Optional: Set the unit system to either "imperial" or "metric" (default: metric)
+  # Used in the UI and in MQTT topics
+  unit_system: metric
 
 # Optional: Telemetry configuration
 telemetry:
@@ -807,11 +856,13 @@ telemetry:
     - lo
   # Optional: Configure system stats
   stats:
-    # Enable AMD GPU stats (default: shown below)
+    # Optional: Enable AMD GPU stats (default: shown below)
     amd_gpu_stats: True
-    # Enable Intel GPU stats (default: shown below)
+    # Optional: Enable Intel GPU stats (default: shown below)
     intel_gpu_stats: True
-    # Enable network bandwidth stats monitoring for camera ffmpeg processes, go2rtc, and object detectors. (default: shown below)
+    # Optional: Treat GPU as SR-IOV to fix GPU stats (default: shown below)
+    sriov: False
+    # Optional: Enable network bandwidth stats monitoring for camera ffmpeg processes, go2rtc, and object detectors. (default: shown below)
     # NOTE: The container must either be privileged or have cap_net_admin, cap_net_raw capabilities enabled.
     network_bandwidth: False
   # Optional: Enable the latest version outbound check (default: shown below)

docs/docs/configuration/semantic_search.md

@@ -1,6 +1,6 @@
 ---
 id: semantic_search
-title: Using Semantic Search
+title: Semantic Search
 ---
 
 Semantic Search in Frigate allows you to find tracked objects within your review items using either the image itself, a user-defined text description, or an automatically generated one. This feature works by creating _embeddings_ — numerical vector representations — for both the images and text descriptions of your tracked objects. By comparing these embeddings, Frigate assesses their similarities to deliver relevant search results.

docs/docs/configuration/zones.md

@@ -122,16 +122,59 @@ cameras:
           - car
 ```
 
-### Loitering Time
+### Speed Estimation
 
-Zones support a `loitering_time` configuration which can be used to only consider an object as part of a zone if they loiter in the zone for the specified number of seconds. This can be used, for example, to create alerts for cars that stop on the street but not cars that just drive past your camera.
+Frigate can be configured to estimate the speed of objects moving through a zone. This works by combining data from Frigate's object tracker and "real world" distance measurements of the edges of the zone. The recommended use case for this feature is to track the speed of vehicles on a road as they move through the zone.
+
+Your zone must be defined with exactly 4 points and should be aligned to the ground where objects are moving.
+
+![Ground plane 4-point zone](/img/ground-plane.jpg)
+
+Speed estimation requires a minimum number of frames for your object to be tracked before a valid estimate can be calculated, so create your zone away from places where objects enter and exit for the best results. _Your zone should not take up the full frame._ An object's speed is tracked while it is in the zone and then saved to Frigate's database.
+
+Accurate real-world distance measurements are required to estimate speeds. These distances can be specified in your zone config through the `distances` field.
 
 ```yaml
 cameras:
   name_of_your_camera:
     zones:
-      front_yard:
-        loitering_time: 5 # unit is in seconds
-        objects:
-          - person
+      street:
+        coordinates: 0.033,0.306,0.324,0.138,0.439,0.185,0.042,0.428
+        distances: 10,12,11,13.5
+```
+
+Each number in the `distance` field represents the real-world distance between the points in the `coordinates` list. So in the example above, the distance between the first two points ([0.033,0.306] and [0.324,0.138]) is 10. The distance between the second and third set of points ([0.324,0.138] and [0.439,0.185]) is 12, and so on. The fastest and most accurate way to configure this is through the Zone Editor in the Frigate UI.
+
+The `distance` values are measured in meters or feet, depending on how `unit_system` is configured in your `ui` config:
+
+```yaml
+ui:
+  # can be "metric" or "imperial", default is metric
+  unit_system: metric
+```
+
+The average speed of your object as it moved through your zone is saved in Frigate's database and can be seen in the UI in the Tracked Object Details pane in Explore. Current estimated speed can also be seen on the debug view as the third value in the object label (see the caveats below). Current estimated speed, average estimated speed, and velocity angle (the angle of the direction the object is moving relative to the frame) of tracked objects is also sent through the `events` MQTT topic. See the [MQTT docs](../integrations/mqtt.md#frigateevents). These speed values are output as a number in miles per hour (mph) or kilometers per hour (kph), depending on how `unit_system` is configured in your `ui` config.
+
+#### Best practices and caveats
+
+- Speed estimation works best with a straight road or path when your object travels in a straight line across that path. Avoid creating your zone near intersections or anywhere that objects would make a turn. If the bounding box changes shape (either because the object made a turn or became partially obscured, for example), speed estimation will not be accurate.
+- Create a zone where the bottom center of your object's bounding box travels directly through it and does not become obscured at any time. See the photo example above.
+- Depending on the size and location of your zone, you may want to decrease the zone's `inertia` value from the default of 3.
+- The more accurate your real-world dimensions can be measured, the more accurate speed estimation will be. However, due to the way Frigate's tracking algorithm works, you may need to tweak the real-world distance values so that estimated speeds better match real-world speeds.
+- Once an object leaves the zone, speed accuracy will likely decrease due to perspective distortion and misalignment with the calibrated area. Therefore, speed values will show as a zero through MQTT and will not be visible on the debug view when an object is outside of a speed tracking zone.
+- The speeds are only an _estimation_ and are highly dependent on camera position, zone points, and real-world measurements. This feature should not be used for law enforcement.
+
+### Speed Threshold
+
+Zones can be configured with a minimum speed requirement, meaning an object must be moving at or above this speed to be considered inside the zone. Zone `distances` must be defined as described above.
+
+```yaml
+cameras:
+  name_of_your_camera:
+    zones:
+      sidewalk:
+        coordinates: ...
+        distances: ...
+        inertia: 1
+        speed_threshold: 20 # unit is in kph or mph, depending on how unit_system is set (see above)
 ```

docs/docs/frigate/hardware.md

@@ -13,20 +13,19 @@ Many users have reported various issues with Reolink cameras, so I do not recomm
 
 Here are some of the camera's I recommend:
 
-- <a href="https://amzn.to/3uFLtxB" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) T5442TM-AS-LED</a> (affiliate link)
-- <a href="https://amzn.to/3isJ3gU" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T5442TM-AS</a> (affiliate link)
-- <a href="https://amzn.to/2ZWNWIA" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-28MM</a> (affiliate link)
+- <a href="https://amzn.to/4fwoNWA" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T549M-ALED-S3</a> (affiliate link)
+- <a href="https://amzn.to/3YXpcMw" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T54IR-AS</a> (affiliate link)
+- <a href="https://amzn.to/3AvBHoY" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-AI-V3</a> (affiliate link)
 
 I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
 
 ## Server
 
-My current favorite is the Beelink EQ12 because of the efficient N100 CPU and dual NICs that allow you to setup a dedicated private network for your cameras where they can be blocked from accessing the internet. There are many used workstation options on eBay that work very well. Anything with an Intel CPU and capable of running Debian should work fine. As a bonus, you may want to look for devices with a M.2 or PCIe express slot that is compatible with the Google Coral. I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
+My current favorite is the Beelink EQ13 because of the efficient N100 CPU and dual NICs that allow you to setup a dedicated private network for your cameras where they can be blocked from accessing the internet. There are many used workstation options on eBay that work very well. Anything with an Intel CPU and capable of running Debian should work fine. As a bonus, you may want to look for devices with a M.2 or PCIe express slot that is compatible with the Google Coral. I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
 
-| Name                                                                                                          | Coral Inference Speed | Coral Compatibility | Notes                                                                                                                                   |
-| ------------------------------------------------------------------------------------------------------------- | --------------------- | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
-| Beelink EQ12 (<a href="https://amzn.to/3OlTMJY" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms                | USB                 | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras.                                               |
-| Intel NUC (<a href="https://amzn.to/3psFlHi" target="_blank" rel="nofollow noopener sponsored">Amazon</a>)    | 5-10ms                | USB                 | Overkill for most, but great performance. Can handle many cameras at 5fps depending on typical amounts of motion. Requires extra parts. |
+| Name                                                                                                          | Coral Inference Speed | Coral Compatibility | Notes                                                                                     |
+| ------------------------------------------------------------------------------------------------------------- | --------------------- | ------------------- | ----------------------------------------------------------------------------------------- |
+| Beelink EQ13 (<a href="https://amzn.to/4iQaBKu" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms                | USB                 | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras. |
 
 ## Detectors
 
@@ -52,24 +51,25 @@ The OpenVINO detector type is able to run on:
 
 More information is available [in the detector docs](/configuration/object_detectors#openvino-detector)
 
-Inference speeds vary greatly depending on the CPU, GPU, or VPU used, some known examples are below:
+Inference speeds vary greatly depending on the CPU or GPU used, some known examples of GPU inference times are below:
 
-| Name                 | Inference Speed | Notes                                                                 |
-| -------------------- | --------------- | --------------------------------------------------------------------- |
-| Intel NCS2 VPU       | 60 - 65 ms      | May vary based on host device                                         |
-| Intel Celeron J4105  | ~ 25 ms         | Inference speeds on CPU were 150 - 200 ms                             |
-| Intel Celeron N3060  | 130 - 150 ms    | Inference speeds on CPU were ~ 550 ms                                 |
-| Intel Celeron N3205U | ~ 120 ms        | Inference speeds on CPU were ~ 380 ms                                 |
-| Intel Celeron N4020  | 50 - 200 ms     | Inference speeds on CPU were ~ 800 ms, greatly depends on other loads |
-| Intel i3 6100T       | 15 - 35 ms      | Inference speeds on CPU were 60 - 120 ms                              |
-| Intel i3 8100        | ~ 15 ms         | Inference speeds on CPU were ~ 65 ms                                  |
-| Intel i5 4590        | ~ 20 ms         | Inference speeds on CPU were ~ 230 ms                                 |
-| Intel i5 6500        | ~ 15 ms         | Inference speeds on CPU were ~ 150 ms                                 |
-| Intel i5 7200u       | 15 - 25 ms      | Inference speeds on CPU were ~ 150 ms                                 |
-| Intel i5 7500        | ~ 15 ms         | Inference speeds on CPU were ~ 260 ms                                 |
-| Intel i5 1135G7      | 10 - 15 ms      |                                                                       |
-| Intel i5 12600K      | ~ 15 ms         | Inference speeds on CPU were ~ 35 ms                                  |
-| Intel Arc A750       | ~ 4 ms          |                                                                       |
+| Name                 | MobileNetV2 Inference Time | YOLO-NAS Inference Time   | Notes                                  |
+| -------------------- | -------------------------- | ------------------------- | -------------------------------------- |
+| Intel Celeron J4105  | ~ 25 ms                    |                           | Can only run one detector instance     |
+| Intel Celeron N3060  | 130 - 150 ms               |                           | Can only run one detector instance     |
+| Intel Celeron N3205U | ~ 120 ms                   |                           | Can only run one detector instance     |
+| Intel Celeron N4020  | 50 - 200 ms                |                           | Inference speed depends on other loads |
+| Intel i3 6100T       | 15 - 35 ms                 |                           | Can only run one detector instance     |
+| Intel i3 8100        | ~ 15 ms                    |                           |                                        |
+| Intel i5 4590        | ~ 20 ms                    |                           |                                        |
+| Intel i5 6500        | ~ 15 ms                    |                           |                                        |
+| Intel i5 7200u       | 15 - 25 ms                 |                           |                                        |
+| Intel i5 7500        | ~ 15 ms                    |                           |                                        |
+| Intel i5 1135G7      | 10 - 15 ms                 |                           |                                        |
+| Intel i3 12000       |                            | 320: ~ 19 ms 640: ~ 54 ms |                                        |
+| Intel i5 12600K      | ~ 15 ms                    | 320: ~ 20 ms 640: ~ 46 ms |                                        |
+| Intel Arc A380       | ~ 6 ms                     | 320: ~ 10 ms              |                                        |
+| Intel Arc A750       | ~ 4 ms                     | 320: ~ 8 ms               |                                        |
 
 ### TensorRT - Nvidia GPU
 
@@ -78,29 +78,35 @@ The TensortRT detector is able to run on x86 hosts that have an Nvidia GPU which
 Inference speeds will vary greatly depending on the GPU and the model used.
 `tiny` variants are faster than the equivalent non-tiny model, some known examples are below:
 
-| Name            | Inference Speed |
-| --------------- | --------------- |
-| GTX 1060 6GB    | ~ 7 ms          |
-| GTX 1070        | ~ 6 ms          |
-| GTX 1660 SUPER  | ~ 4 ms          |
-| RTX 3050        | 5 - 7 ms        |
-| RTX 3070 Mobile | ~ 5 ms          |
-| Quadro P400 2GB | 20 - 25 ms      |
-| Quadro P2000    | ~ 12 ms         |
+| Name            | YoloV7 Inference Time | YOLO-NAS Inference Time   |
+| --------------- | --------------------- | ------------------------- |
+| GTX 1060 6GB    | ~ 7 ms                |                           |
+| GTX 1070        | ~ 6 ms                |                           |
+| GTX 1660 SUPER  | ~ 4 ms                |                           |
+| RTX 3050        | 5 - 7 ms              | 320: ~ 10 ms 640: ~ 16 ms |
+| RTX 3070 Mobile | ~ 5 ms                |                           |
+| Quadro P400 2GB | 20 - 25 ms            |                           |
+| Quadro P2000    | ~ 12 ms               |                           |
 
-#### AMD GPUs
+### AMD GPUs
 
-With the [rocm](../configuration/object_detectors.md#amdrocm-gpu-detector) detector Frigate can take advantage of many AMD GPUs.
+With the [rocm](../configuration/object_detectors.md#amdrocm-gpu-detector) detector Frigate can take advantage of many discrete AMD GPUs.
 
-### Community Supported:
+### Hailo-8l PCIe
 
-#### Nvidia Jetson
+Frigate supports the Hailo-8l M.2 card on any hardware but currently it is only tested on the Raspberry Pi5 PCIe hat from the AI kit.
+
+The inference time for the Hailo-8L chip at time of writing is around 17-21 ms for the SSD MobileNet Version 1 model.
+
+## Community Supported Detectors
+
+### Nvidia Jetson
 
 Frigate supports all Jetson boards, from the inexpensive Jetson Nano to the powerful Jetson Orin AGX. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration#nvidia-jetson-orin-agx-orin-nx-orin-nano-xavier-agx-xavier-nx-tx2-tx1-nano) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets), and will make use of the Jetson's GPU and DLA for object detection when configured with the [TensorRT detector](/configuration/object_detectors#nvidia-tensorrt-detector).
 
 Inference speed will vary depending on the YOLO model, jetson platform and jetson nvpmodel (GPU/DLA/EMC clock speed). It is typically 20-40 ms for most models. The DLA is more efficient than the GPU, but not faster, so using the DLA will reduce power consumption but will slightly increase inference time.
 
-#### Rockchip platform
+### Rockchip platform
 
 Frigate supports hardware video processing on all Rockchip boards. However, hardware object detection is only supported on these boards:
 
@@ -112,12 +118,6 @@ Frigate supports hardware video processing on all Rockchip boards. However, hard
 
 The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms for yolo-nas s.
 
-#### Hailo-8l PCIe
-
-Frigate supports the Hailo-8l M.2 card on any hardware but currently it is only tested on the Raspberry Pi5 PCIe hat from the AI kit.
-
-The inference time for the Hailo-8L chip at time of writing is around 17-21 ms for the SSD MobileNet Version 1 model.
-
 ## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
 
 This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.

docs/docs/frigate/installation.md

@@ -111,13 +111,13 @@ For Raspberry Pi 5 users with the AI Kit, installation is straightforward. Simpl
 For other installations, follow these steps for installation:
 
 1. Install the driver from the [Hailo GitHub repository](https://github.com/hailo-ai/hailort-drivers). A convenient script for Linux is available to clone the repository, build the driver, and install it.
-2. Copy or download [this script](https://github.com/blakeblackshear/frigate/blob/41c9b13d2fffce508b32dfc971fa529b49295fbd/docker/hailo8l/user_installation.sh).
+2. Copy or download [this script](https://github.com/blakeblackshear/frigate/blob/dev/docker/hailo8l/user_installation.sh).
 3. Ensure it has execution permissions with `sudo chmod +x user_installation.sh`
 4. Run the script with `./user_installation.sh`
 
 #### Setup
 
-To set up Frigate, follow the default installation instructions, but use a Docker image with the `-h8l` suffix, for example: `ghcr.io/blakeblackshear/frigate:stable-h8l`
+To set up Frigate, follow the default installation instructions, for example: `ghcr.io/blakeblackshear/frigate:stable`
 
 Next, grant Docker permissions to access your hardware by adding the following lines to your `docker-compose.yml` file:
 

docs/docs/integrations/home-assistant.md

@@ -97,13 +97,13 @@ services:
 
 If you are using HassOS with the addon, the URL should be one of the following depending on which addon version you are using. Note that if you are using the Proxy Addon, you do NOT point the integration at the proxy URL. Just enter the URL used to access Frigate directly from your network.
 
-| Addon Version                  | URL                                    |
-| ------------------------------ | -------------------------------------- |
-| Frigate NVR                    | `http://ccab4aaf-frigate:5000`         |
-| Frigate NVR (Full Access)      | `http://ccab4aaf-frigate-fa:5000`      |
-| Frigate NVR Beta               | `http://ccab4aaf-frigate-beta:5000`    |
-| Frigate NVR Beta (Full Access) | `http://ccab4aaf-frigate-fa-beta:5000` |
-| Frigate NVR HailoRT Beta       | `http://ccab4aaf-frigate-hailo-beta:5000`    |
+| Addon Version                  | URL                                       |
+| ------------------------------ | ----------------------------------------- |
+| Frigate NVR                    | `http://ccab4aaf-frigate:5000`            |
+| Frigate NVR (Full Access)      | `http://ccab4aaf-frigate-fa:5000`         |
+| Frigate NVR Beta               | `http://ccab4aaf-frigate-beta:5000`       |
+| Frigate NVR Beta (Full Access) | `http://ccab4aaf-frigate-fa-beta:5000`    |
+| Frigate NVR HailoRT Beta       | `http://ccab4aaf-frigate-hailo-beta:5000` |
 
 ### Frigate running on a separate machine
 
@@ -301,3 +301,7 @@ which server they are referring to.
 #### If I am detecting multiple objects, how do I assign the correct `binary_sensor` to the camera in HomeKit?
 
 The [HomeKit integration](https://www.home-assistant.io/integrations/homekit/) randomly links one of the binary sensors (motion sensor entities) grouped with the camera device in Home Assistant. You can specify a `linked_motion_sensor` in the Home Assistant [HomeKit configuration](https://www.home-assistant.io/integrations/homekit/#linked_motion_sensor) for each camera.
+
+#### I have set up automations based on the occupancy sensors. Sometimes the automation runs because the sensors are turned on, but then I look at Frigate I can't find the object that triggered the sensor. Is this a bug?
+
+No. The occupancy sensors have fewer checks in place because they are often used for things like turning the lights on where latency needs to be as low as possible. So false positives can sometimes trigger these sensors. If you want false positive filtering, you should use an mqtt sensor on the `frigate/events` or `frigate/reviews` topic.

docs/docs/integrations/mqtt.md

@@ -52,7 +52,9 @@ Message published for each changed tracked object. The first message is publishe
     "attributes": {
       "face": 0.64
     }, // attributes with top score that have been identified on the object at any point
-    "current_attributes": [] // detailed data about the current attributes in this frame
+    "current_attributes": [], // detailed data about the current attributes in this frame
+    "current_estimated_speed": 0.71, // current estimated speed (mph or kph) for objects moving through zones with speed estimation enabled
+    "velocity_angle": 180 // direction of travel relative to the frame for objects moving through zones with speed estimation enabled
   },
   "after": {
     "id": "1607123955.475377-mxklsc",
@@ -89,7 +91,9 @@ Message published for each changed tracked object. The first message is publishe
         "box": [442, 506, 534, 524],
         "score": 0.86
       }
-    ]
+    ],
+    "current_estimated_speed": 0.77, // current estimated speed (mph or kph) for objects moving through zones with speed estimation enabled
+    "velocity_angle": 180 // direction of travel relative to the frame for objects moving through zones with speed estimation enabled
   }
 }
 ```
@@ -312,6 +316,22 @@ Topic with current state of the PTZ autotracker for a camera. Published values a
 
 Topic to determine if PTZ autotracker is actively tracking an object. Published values are `ON` and `OFF`.
 
+### `frigate/<camera_name>/review_alerts/set`
+
+Topic to turn review alerts for a camera on or off. Expected values are `ON` and `OFF`.
+
+### `frigate/<camera_name>/review_alerts/state`
+
+Topic with current state of review alerts for a camera. Published values are `ON` and `OFF`.
+
+### `frigate/<camera_name>/review_detections/set`
+
+Topic to turn review detections for a camera on or off. Expected values are `ON` and `OFF`.
+
+### `frigate/<camera_name>/review_detections/state`
+
+Topic with current state of review detections for a camera. Published values are `ON` and `OFF`.
+
 ### `frigate/<camera_name>/birdseye/set`
 
 Topic to turn Birdseye for a camera on and off. Expected values are `ON` and `OFF`. Birdseye mode
@@ -337,3 +357,19 @@ the camera to be removed from the view._
 ### `frigate/<camera_name>/birdseye_mode/state`
 
 Topic with current state of the Birdseye mode for a camera. Published values are `CONTINUOUS`, `MOTION`, `OBJECTS`.
+
+### `frigate/<camera_name>/notifications/set`
+
+Topic to turn notifications on and off. Expected values are `ON` and `OFF`.
+
+### `frigate/<camera_name>/notifications/state`
+
+Topic with current state of notifications. Published values are `ON` and `OFF`.
+
+### `frigate/<camera_name>/notifications/suspend`
+
+Topic to suspend notifications for a certain number of minutes. Expected value is an integer.
+
+### `frigate/<camera_name>/notifications/suspended`
+
+Topic with timestamp that notifications are suspended until. Published value is a UNIX timestamp, or 0 if notifications are not suspended.

docs/docs/integrations/third_party_extensions.md

@@ -19,6 +19,10 @@ Please use your own knowledge to assess and vet them before you install anything
 It supports automatically setting the sub labels in Frigate for person objects that are detected and recognized.
 This is a fork (with fixed errors and new features) of [original Double Take](https://github.com/jakowenko/double-take) project which, unfortunately, isn't being maintained by author.
 
+## [Frigate Notify](https://github.com/0x2142/frigate-notify)
+
+[Frigate Notify](https://github.com/0x2142/frigate-notify) is a simple app designed to send notifications from Frigate NVR to your favorite platforms. Intended to be used with standalone Frigate installations - Home Assistant not required, MQTT is optional but recommended.
+
 ## [Frigate telegram](https://github.com/OldTyT/frigate-telegram)
 
 [Frigate telegram](https://github.com/OldTyT/frigate-telegram) makes it possible to send events from Frigate to Telegram. Events are sent as a message with a text description, video, and thumbnail.

docs/docs/plus/first_model.md

@@ -5,7 +5,7 @@ title: Requesting your first model
 
 ## Step 1: Upload and annotate your images
 
-Before requesting your first model, you will need to upload and verify at least 1 image to Frigate+. The more images you upload, annotate, and verify the better your results will be. Most users start to see very good results once they have at least 100 verified images per camera. Keep in mind that varying conditions should be included. You will want images from cloudy days, sunny days, dawn, dusk, and night. Refer to the [integration docs](../integrations/plus.md#generate-an-api-key) for instructions on how to easily submit images to Frigate+ directly from Frigate.
+Before requesting your first model, you will need to upload and verify at least 10 images to Frigate+. The more images you upload, annotate, and verify the better your results will be. Most users start to see very good results once they have at least 100 verified images per camera. Keep in mind that varying conditions should be included. You will want images from cloudy days, sunny days, dawn, dusk, and night. Refer to the [integration docs](../integrations/plus.md#generate-an-api-key) for instructions on how to easily submit images to Frigate+ directly from Frigate.
 
 It is recommended to submit **both** true positives and false positives. This will help the model differentiate between what is and isn't correct. You should aim for a target of 80% true positive submissions and 20% false positives across all of your images. If you are experiencing false positives in a specific area, submitting true positives for any object type near that area in similar lighting conditions will help teach the model what that area looks like when no objects are present.
 

docs/docs/plus/improving_model.md

@@ -13,7 +13,7 @@ You may find that Frigate+ models result in more false positives initially, but
 
 For the best results, follow the following guidelines.
 
-**Label every object in the image**: It is important that you label all objects in each image before verifying. If you don't label a car for example, the model will be taught that part of the image is _not_ a car and it will start to get confused.
+**Label every object in the image**: It is important that you label all objects in each image before verifying. If you don't label a car for example, the model will be taught that part of the image is _not_ a car and it will start to get confused. You can exclude labels that you don't want detected on any of your cameras.
 
 **Make tight bounding boxes**: Tighter bounding boxes improve the recognition and ensure that accurate bounding boxes are predicted at runtime.
 
@@ -21,7 +21,7 @@ For the best results, follow the following guidelines.
 
 **Label objects hard to identify as difficult**: When objects are truly difficult to make out, such as a car barely visible through a bush, or a dog that is hard to distinguish from the background at night, flag it as 'difficult'. This is not used in the model training as of now, but will in the future.
 
-**`amazon`, `ups`, and `fedex` should label the logo**: For a Fedex truck, label the truck as a `car` and make a different bounding box just for the Fedex logo. If there are multiple logos, label each of them.
+**Delivery logos such as `amazon`, `ups`, and `fedex` should label the logo**: For a Fedex truck, label the truck as a `car` and make a different bounding box just for the Fedex logo. If there are multiple logos, label each of them.
 
 ![Fedex Logo](/img/plus/fedex-logo.jpg)
 

docs/docs/plus/index.md

@@ -17,7 +17,7 @@ Information on how to integrate Frigate+ with Frigate can be found in the [integ
 
 ## Available model types
 
-There are two model types offered in Frigate+: `mobiledet` and `yolonas`. Both of these models are object detection models and are trained to detect the same set of labels [listed below](#available-label-types).
+There are two model types offered in Frigate+, `mobiledet` and `yolonas`. Both of these models are object detection models and are trained to detect the same set of labels [listed below](#available-label-types).
 
 Not all model types are supported by all detectors, so it's important to choose a model type to match your detector as shown in the table under [supported detector types](#supported-detector-types).
 
@@ -32,7 +32,7 @@ Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVi
 
 :::warning
 
-Using Frigate+ models with `onnx` and `rocm` is only available with Frigate 0.15, which is still under development.
+Using Frigate+ models with `onnx` and `rocm` is only available with Frigate 0.15 and later.
 
 :::
 
@@ -48,11 +48,19 @@ _\* Requires Frigate 0.15_
 
 ## Available label types
 
-Frigate+ models support a more relevant set of objects for security cameras. Currently, only the following objects are supported: `person`, `face`, `car`, `license_plate`, `amazon`, `ups`, `fedex`, `package`, `dog`, `cat`, `deer`. Other object types available in the default Frigate model are not available. Additional object types will be added in future releases.
+Frigate+ models support a more relevant set of objects for security cameras. Currently, the following objects are supported:
+
+- **People**: `person`, `face`
+- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `license_plate`
+- **Delivery Logos**: `amazon`, `usps`, `ups`, `fedex`, `dhl`, `an_post`, `purolator`, `postnl`, `nzpost`, `postnord`, `gls`, `dpd`
+- **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`
+- **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`
+
+Other object types available in the default Frigate model are not available. Additional object types will be added in future releases.
 
 ### Label attributes
 
-Frigate has special handling for some labels when using Frigate+ models. `face`, `license_plate`, `amazon`, `ups`, and `fedex` are considered attribute labels which are not tracked like regular objects and do not generate review items directly. In addition, the `threshold` filter will have no effect on these labels. You should adjust the `min_score` and other filter values as needed.
+Frigate has special handling for some labels when using Frigate+ models. `face`, `license_plate`, and delivery logos such as `amazon`, `ups`, and `fedex` are considered attribute labels which are not tracked like regular objects and do not generate review items directly. In addition, the `threshold` filter will have no effect on these labels. You should adjust the `min_score` and other filter values as needed.
 
 In order to have Frigate start using these attribute labels, you will need to add them to the list of objects to track:
 
@@ -75,6 +83,6 @@ When using Frigate+ models, Frigate will choose the snapshot of a person object
 
 ![Face Attribute](/img/plus/attribute-example-face.jpg)
 
-`amazon`, `ups`, and `fedex` labels are used to automatically assign a sub label to car objects.
+Delivery logos such as `amazon`, `ups`, and `fedex` labels are used to automatically assign a sub label to car objects.
 
 ![Fedex Attribute](/img/plus/attribute-example-fedex.jpg)

docs/docs/troubleshooting/edgetpu.md

@@ -54,6 +54,21 @@ The most common reason for the PCIe Coral not being detected is that the driver
 - In most cases [the Coral docs](https://coral.ai/docs/m2/get-started/#2-install-the-pcie-driver-and-edge-tpu-runtime) show how to install the driver for the PCIe based Coral.
 - For Ubuntu 22.04+ https://github.com/jnicolson/gasket-builder can be used to build and install the latest version of the driver.
 
+## Attempting to load TPU as pci & Fatal Python error: Illegal instruction
+
+This is an issue due to outdated gasket driver when being used with new linux kernels. Installing an updated driver from https://github.com/jnicolson/gasket-builder has been reported to fix the issue.
+
+### Not detected on Raspberry Pi5
+
+A kernel update to the RPi5 means an upate to config.txt is required, see [the raspberry pi forum for more info](https://forums.raspberrypi.com/viewtopic.php?t=363682&sid=cb59b026a412f0dc041595951273a9ca&start=25)
+
+Specifically, add the following to config.txt
+
+```
+dtoverlay=pciex1-compat-pi5,no-mip
+dtoverlay=pcie-32bit-dma-pi5
+```
+
 ## Only One PCIe Coral Is Detected With Coral Dual EdgeTPU
 
 Coral Dual EdgeTPU is one card with two identical TPU cores. Each core has it's own PCIe interface and motherboard needs to have two PCIe busses on the m.2 slot to make them both work.

docs/sidebars.ts

@@ -33,9 +33,11 @@ const sidebars: SidebarsConfig = {
         'configuration/object_detectors',
         'configuration/audio_detectors',
       ],
-      'Semantic Search': [
+      Classifiers: [
         'configuration/semantic_search',
         'configuration/genai',
+        'configuration/face_recognition',
+        'configuration/license_plate_recognition',
       ],
       Cameras: [
         'configuration/cameras',
@@ -82,6 +84,7 @@ const sidebars: SidebarsConfig = {
         items: frigateHttpApiSidebar,
       },
       'integrations/mqtt',
+      'configuration/metrics',
       'integrations/third_party_extensions',
     ],
     'Frigate+': [

frigate/__main__.py

@@ -3,12 +3,15 @@ import faulthandler
 import signal
 import sys
 import threading
+from typing import Union
 
+import ruamel.yaml
 from pydantic import ValidationError
 
 from frigate.app import FrigateApp
 from frigate.config import FrigateConfig
 from frigate.log import setup_logging
+from frigate.util.config import find_config_file
 
 
 def main() -> None:
@@ -42,10 +45,51 @@ def main() -> None:
         print("*************************************************************")
         print("*************************************************************")
         print("***    Config Validation Errors                           ***")
-        print("*************************************************************")
+        print("*************************************************************\n")
+        # Attempt to get the original config file for line number tracking
+        config_path = find_config_file()
+        with open(config_path, "r") as f:
+            yaml_config = ruamel.yaml.YAML()
+            yaml_config.preserve_quotes = True
+            full_config = yaml_config.load(f)
+
         for error in e.errors():
-            location = ".".join(str(item) for item in error["loc"])
-            print(f"{location}: {error['msg']}")
+            error_path = error["loc"]
+
+            current = full_config
+            line_number = "Unknown"
+            last_line_number = "Unknown"
+
+            try:
+                for i, part in enumerate(error_path):
+                    key: Union[int, str] = (
+                        int(part) if isinstance(part, str) and part.isdigit() else part
+                    )
+
+                    if isinstance(current, ruamel.yaml.comments.CommentedMap):
+                        current = current[key]
+                    elif isinstance(current, list):
+                        if isinstance(key, int):
+                            current = current[key]
+
+                    if hasattr(current, "lc"):
+                        last_line_number = current.lc.line
+
+                    if i == len(error_path) - 1:
+                        if hasattr(current, "lc"):
+                            line_number = current.lc.line
+                        else:
+                            line_number = last_line_number
+
+            except Exception as traverse_error:
+                print(f"Could not determine exact line number: {traverse_error}")
+
+            if current != full_config:
+                print(f"Line #  : {line_number}")
+                print(f"Key     : {' -> '.join(map(str, error_path))}")
+                print(f"Value   : {error.get('input', '-')}")
+            print(f"Message : {error.get('msg', error.get('type', 'Unknown'))}\n")
+
         print("*************************************************************")
         print("***    End Config Validation Errors                       ***")
         print("*************************************************************")

frigate/api/app.py

@@ -1,5 +1,6 @@
 """Main api runner."""
 
+import asyncio
 import copy
 import json
 import logging
@@ -7,15 +8,20 @@ import os
 import traceback
 from datetime import datetime, timedelta
 from functools import reduce
+from io import StringIO
 from typing import Any, Optional
 
+import aiofiles
 import requests
+import ruamel.yaml
 from fastapi import APIRouter, Body, Path, Request, Response
 from fastapi.encoders import jsonable_encoder
 from fastapi.params import Depends
-from fastapi.responses import JSONResponse, PlainTextResponse
+from fastapi.responses import JSONResponse, PlainTextResponse, StreamingResponse
 from markupsafe import escape
 from peewee import operator
+from prometheus_client import CONTENT_TYPE_LATEST, generate_latest
+from pydantic import ValidationError
 
 from frigate.api.defs.query.app_query_parameters import AppTimelineHourlyQueryParameters
 from frigate.api.defs.request.app_body import AppConfigSetBody
@@ -31,6 +37,7 @@ from frigate.util.config import find_config_file
 from frigate.util.services import (
     ffprobe_stream,
     get_nvidia_driver_info,
+    process_logs,
     restart_frigate,
     vainfo_hwaccel,
 )
@@ -105,6 +112,12 @@ def stats_history(request: Request, keys: str = None):
     return JSONResponse(content=request.app.stats_emitter.get_stats_history(keys))
 
 
+@router.get("/metrics")
+def metrics():
+    """Expose Prometheus metrics endpoint"""
+    return Response(content=generate_latest(), media_type=CONTENT_TYPE_LATEST)
+
+
 @router.get("/config")
 def config(request: Request):
     config_obj: FrigateConfig = request.app.frigate_config
@@ -154,6 +167,7 @@ def config(request: Request):
     config["plus"] = {"enabled": request.app.frigate_config.plus_api.is_active()}
     config["model"]["colormap"] = config_obj.model.colormap
     config["model"]["all_attributes"] = config_obj.model.all_attributes
+    config["model"]["non_logo_attributes"] = config_obj.model.non_logo_attributes
 
     # use merged labelamp
     for detector_config in config["detectors"].values():
@@ -186,7 +200,6 @@ def config_raw():
 @router.post("/config/save")
 def config_save(save_option: str, body: Any = Body(media_type="text/plain")):
     new_config = body.decode()
-
     if not new_config:
         return JSONResponse(
             content=(
@@ -197,13 +210,64 @@ def config_save(save_option: str, body: Any = Body(media_type="text/plain")):
 
     # Validate the config schema
     try:
+        # Use ruamel to parse and preserve line numbers
+        yaml_config = ruamel.yaml.YAML()
+        yaml_config.preserve_quotes = True
+        full_config = yaml_config.load(StringIO(new_config))
+
         FrigateConfig.parse_yaml(new_config)
+
+    except ValidationError as e:
+        error_message = []
+
+        for error in e.errors():
+            error_path = error["loc"]
+            current = full_config
+            line_number = "Unknown"
+            last_line_number = "Unknown"
+
+            try:
+                for i, part in enumerate(error_path):
+                    key = int(part) if part.isdigit() else part
+
+                    if isinstance(current, ruamel.yaml.comments.CommentedMap):
+                        current = current[key]
+                    elif isinstance(current, list):
+                        current = current[key]
+
+                    if hasattr(current, "lc"):
+                        last_line_number = current.lc.line
+
+                    if i == len(error_path) - 1:
+                        if hasattr(current, "lc"):
+                            line_number = current.lc.line
+                        else:
+                            line_number = last_line_number
+
+            except Exception:
+                line_number = "Unable to determine"
+
+            error_message.append(
+                f"Line {line_number}: {' -> '.join(map(str, error_path))} - {error.get('msg', error.get('type', 'Unknown'))}"
+            )
+
+        return JSONResponse(
+            content=(
+                {
+                    "success": False,
+                    "message": "Your configuration is invalid.\nSee the official documentation at docs.frigate.video.\n\n"
+                    + "\n".join(error_message),
+                }
+            ),
+            status_code=400,
+        )
+
     except Exception:
         return JSONResponse(
             content=(
                 {
                     "success": False,
-                    "message": f"\nConfig Error:\n\n{escape(str(traceback.format_exc()))}",
+                    "message": f"\nYour configuration is invalid.\nSee the official documentation at docs.frigate.video.\n\n{escape(str(traceback.format_exc()))}",
                 }
             ),
             status_code=400,
@@ -394,9 +458,10 @@ def nvinfo():
 
 
 @router.get("/logs/{service}", tags=[Tags.logs])
-def logs(
+async def logs(
     service: str = Path(enum=["frigate", "nginx", "go2rtc"]),
     download: Optional[str] = None,
+    stream: Optional[bool] = False,
     start: Optional[int] = 0,
     end: Optional[int] = None,
 ):
@@ -415,6 +480,27 @@ def logs(
                 status_code=500,
             )
 
+    async def stream_logs(file_path: str):
+        """Asynchronously stream log lines."""
+        buffer = ""
+        try:
+            async with aiofiles.open(file_path, "r") as file:
+                await file.seek(0, 2)
+                while True:
+                    line = await file.readline()
+                    if line:
+                        buffer += line
+                        # Process logs only when there are enough lines in the buffer
+                        if "\n" in buffer:
+                            _, processed_lines = process_logs(buffer, service)
+                            buffer = ""
+                            for processed_line in processed_lines:
+                                yield f"{processed_line}\n"
+                    else:
+                        await asyncio.sleep(0.1)
+        except FileNotFoundError:
+            yield "Log file not found.\n"
+
     log_locations = {
         "frigate": "/dev/shm/logs/frigate/current",
         "go2rtc": "/dev/shm/logs/go2rtc/current",
@@ -431,48 +517,17 @@ def logs(
     if download:
         return download_logs(service_location)
 
+    if stream:
+        return StreamingResponse(stream_logs(service_location), media_type="text/plain")
+
+    # For full logs initially
     try:
-        file = open(service_location, "r")
-        contents = file.read()
-        file.close()
-
-        # use the start timestamp to group logs together``
-        logLines = []
-        keyLength = 0
-        dateEnd = 0
-        currentKey = ""
-        currentLine = ""
-
-        for rawLine in contents.splitlines():
-            cleanLine = rawLine.strip()
-
-            if len(cleanLine) < 10:
-                continue
-
-            # handle cases where S6 does not include date in log line
-            if "  " not in cleanLine:
-                cleanLine = f"{datetime.now()}  {cleanLine}"
-
-            if dateEnd == 0:
-                dateEnd = cleanLine.index("  ")
-                keyLength = dateEnd - (6 if service_location == "frigate" else 0)
-
-            newKey = cleanLine[0:keyLength]
-
-            if newKey == currentKey:
-                currentLine += f"\n{cleanLine[dateEnd:].strip()}"
-                continue
-            else:
-                if len(currentLine) > 0:
-                    logLines.append(currentLine)
-
-                currentKey = newKey
-                currentLine = cleanLine
-
-        logLines.append(currentLine)
+        async with aiofiles.open(service_location, "r") as file:
+            contents = await file.read()
 
+        total_lines, log_lines = process_logs(contents, service, start, end)
         return JSONResponse(
-            content={"totalLines": len(logLines), "lines": logLines[start:end]},
+            content={"totalLines": total_lines, "lines": log_lines},
             status_code=200,
         )
     except FileNotFoundError as e:

frigate/api/classification.py

@@ -0,0 +1,178 @@
+"""Object classification APIs."""
+
+import logging
+import os
+import random
+import shutil
+import string
+
+from fastapi import APIRouter, Request, UploadFile
+from fastapi.responses import JSONResponse
+from pathvalidate import sanitize_filename
+
+from frigate.api.defs.tags import Tags
+from frigate.const import FACE_DIR
+from frigate.embeddings import EmbeddingsContext
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(tags=[Tags.events])
+
+
+@router.get("/faces")
+def get_faces():
+    face_dict: dict[str, list[str]] = {}
+
+    for name in os.listdir(FACE_DIR):
+        face_dir = os.path.join(FACE_DIR, name)
+
+        if not os.path.isdir(face_dir):
+            continue
+
+        face_dict[name] = []
+
+        for file in sorted(
+            os.listdir(face_dir),
+            key=lambda f: os.path.getctime(os.path.join(face_dir, f)),
+            reverse=True,
+        ):
+            face_dict[name].append(file)
+
+    return JSONResponse(status_code=200, content=face_dict)
+
+
+@router.post("/faces/reprocess")
+def reclassify_face(request: Request, body: dict = None):
+    if not request.app.frigate_config.face_recognition.enabled:
+        return JSONResponse(
+            status_code=400,
+            content={"message": "Face recognition is not enabled.", "success": False},
+        )
+
+    json: dict[str, any] = body or {}
+    training_file = os.path.join(
+        FACE_DIR, f"train/{sanitize_filename(json.get('training_file', ''))}"
+    )
+
+    if not training_file or not os.path.isfile(training_file):
+        return JSONResponse(
+            content=(
+                {
+                    "success": False,
+                    "message": f"Invalid filename or no file exists: {training_file}",
+                }
+            ),
+            status_code=404,
+        )
+
+    context: EmbeddingsContext = request.app.embeddings
+    response = context.reprocess_face(training_file)
+
+    return JSONResponse(
+        content=response,
+        status_code=200,
+    )
+
+
+@router.post("/faces/train/{name}/classify")
+def train_face(request: Request, name: str, body: dict = None):
+    if not request.app.frigate_config.face_recognition.enabled:
+        return JSONResponse(
+            status_code=400,
+            content={"message": "Face recognition is not enabled.", "success": False},
+        )
+
+    json: dict[str, any] = body or {}
+    training_file = os.path.join(
+        FACE_DIR, f"train/{sanitize_filename(json.get('training_file', ''))}"
+    )
+
+    if not training_file or not os.path.isfile(training_file):
+        return JSONResponse(
+            content=(
+                {
+                    "success": False,
+                    "message": f"Invalid filename or no file exists: {training_file}",
+                }
+            ),
+            status_code=404,
+        )
+
+    sanitized_name = sanitize_filename(name)
+    rand_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
+    new_name = f"{sanitized_name}-{rand_id}.webp"
+    new_file = os.path.join(FACE_DIR, f"{sanitized_name}/{new_name}")
+    shutil.move(training_file, new_file)
+
+    context: EmbeddingsContext = request.app.embeddings
+    context.clear_face_classifier()
+
+    return JSONResponse(
+        content=(
+            {
+                "success": True,
+                "message": f"Successfully saved {training_file} as {new_name}.",
+            }
+        ),
+        status_code=200,
+    )
+
+
+@router.post("/faces/{name}/create")
+async def create_face(request: Request, name: str):
+    if not request.app.frigate_config.face_recognition.enabled:
+        return JSONResponse(
+            status_code=400,
+            content={"message": "Face recognition is not enabled.", "success": False},
+        )
+
+    os.makedirs(
+        os.path.join(FACE_DIR, sanitize_filename(name.replace(" ", "_"))), exist_ok=True
+    )
+    return JSONResponse(
+        status_code=200,
+        content={"success": False, "message": "Successfully created face folder."},
+    )
+
+
+@router.post("/faces/{name}/register")
+async def register_face(request: Request, name: str, file: UploadFile):
+    if not request.app.frigate_config.face_recognition.enabled:
+        return JSONResponse(
+            status_code=400,
+            content={"message": "Face recognition is not enabled.", "success": False},
+        )
+
+    context: EmbeddingsContext = request.app.embeddings
+    result = context.register_face(name, await file.read())
+    return JSONResponse(
+        status_code=200 if result.get("success", True) else 400,
+        content=result,
+    )
+
+
+@router.post("/faces/{name}/delete")
+def deregister_faces(request: Request, name: str, body: dict = None):
+    if not request.app.frigate_config.face_recognition.enabled:
+        return JSONResponse(
+            status_code=400,
+            content={"message": "Face recognition is not enabled.", "success": False},
+        )
+
+    json: dict[str, any] = body or {}
+    list_of_ids = json.get("ids", "")
+
+    if not list_of_ids or len(list_of_ids) == 0:
+        return JSONResponse(
+            content=({"success": False, "message": "Not a valid list of ids"}),
+            status_code=404,
+        )
+
+    context: EmbeddingsContext = request.app.embeddings
+    context.delete_face_ids(
+        name, map(lambda file: sanitize_filename(file), list_of_ids)
+    )
+    return JSONResponse(
+        content=({"success": True, "message": "Successfully deleted faces."}),
+        status_code=200,
+    )

frigate/api/defs/query/events_query_parameters.py

@@ -25,6 +25,8 @@ class EventsQueryParams(BaseModel):
     favorites: Optional[int] = None
     min_score: Optional[float] = None
     max_score: Optional[float] = None
+    min_speed: Optional[float] = None
+    max_speed: Optional[float] = None
     is_submitted: Optional[int] = None
     min_length: Optional[float] = None
     max_length: Optional[float] = None
@@ -51,6 +53,8 @@ class EventsSearchQueryParams(BaseModel):
     timezone: Optional[str] = "utc"
     min_score: Optional[float] = None
     max_score: Optional[float] = None
+    min_speed: Optional[float] = None
+    max_speed: Optional[float] = None
     sort: Optional[str] = None
 
 

frigate/api/defs/query/media_query_parameters.py

@@ -20,6 +20,7 @@ class MediaLatestFrameQueryParams(BaseModel):
     regions: Optional[int] = None
     quality: Optional[int] = 70
     height: Optional[int] = None
+    store: Optional[int] = None
 
 
 class MediaEventsSnapshotQueryParams(BaseModel):
@@ -40,3 +41,8 @@ class MediaMjpegFeedQueryParams(BaseModel):
     mask: Optional[int] = None
     motion: Optional[int] = None
     regions: Optional[int] = None
+
+
+class MediaRecordingsSummaryQueryParams(BaseModel):
+    timezone: str = "utc"
+    cameras: Optional[str] = "all"

frigate/api/defs/request/events_body.py

@@ -8,6 +8,9 @@ class EventsSubLabelBody(BaseModel):
     subLabelScore: Optional[float] = Field(
         title="Score for sub label", default=None, gt=0.0, le=1.0
     )
+    camera: Optional[str] = Field(
+        title="Camera this object is detected on.", default=None
+    )
 
 
 class EventsDescriptionBody(BaseModel):

frigate/api/defs/request/export_rename_body.py

@@ -0,0 +1,5 @@
+from pydantic import BaseModel, Field
+
+
+class ExportRenameBody(BaseModel):
+    name: str = Field(title="Friendly name", max_length=256)

frigate/api/defs/tags.py

@@ -10,4 +10,5 @@ class Tags(Enum):
     review = "Review"
     export = "Export"
     events = "Events"
+    classification = "classification"
     auth = "Auth"

frigate/api/event.py

@@ -92,6 +92,8 @@ def events(params: EventsQueryParams = Depends()):
     favorites = params.favorites
     min_score = params.min_score
     max_score = params.max_score
+    min_speed = params.min_speed
+    max_speed = params.max_speed
     is_submitted = params.is_submitted
     min_length = params.min_length
     max_length = params.max_length
@@ -226,6 +228,12 @@ def events(params: EventsQueryParams = Depends()):
     if min_score is not None:
         clauses.append((Event.data["score"] >= min_score))
 
+    if max_speed is not None:
+        clauses.append((Event.data["average_estimated_speed"] <= max_speed))
+
+    if min_speed is not None:
+        clauses.append((Event.data["average_estimated_speed"] >= min_speed))
+
     if min_length is not None:
         clauses.append(((Event.end_time - Event.start_time) >= min_length))
 
@@ -249,6 +257,10 @@ def events(params: EventsQueryParams = Depends()):
             order_by = Event.data["score"].asc()
         elif sort == "score_desc":
             order_by = Event.data["score"].desc()
+        elif sort == "speed_asc":
+            order_by = Event.data["average_estimated_speed"].asc()
+        elif sort == "speed_desc":
+            order_by = Event.data["average_estimated_speed"].desc()
         elif sort == "date_asc":
             order_by = Event.start_time.asc()
         elif sort == "date_desc":
@@ -316,7 +328,15 @@ def events_explore(limit: int = 10):
                     k: v
                     for k, v in event.data.items()
                     if k
-                    in ["type", "score", "top_score", "description", "sub_label_score"]
+                    in [
+                        "type",
+                        "score",
+                        "top_score",
+                        "description",
+                        "sub_label_score",
+                        "average_estimated_speed",
+                        "velocity_angle",
+                    ]
                 },
                 "event_count": label_counts[event.label],
             }
@@ -367,6 +387,8 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
     before = params.before
     min_score = params.min_score
     max_score = params.max_score
+    min_speed = params.min_speed
+    max_speed = params.max_speed
     time_range = params.time_range
     has_clip = params.has_clip
     has_snapshot = params.has_snapshot
@@ -466,6 +488,16 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
         if max_score is not None:
             event_filters.append((Event.data["score"] <= max_score))
 
+    if min_speed is not None and max_speed is not None:
+        event_filters.append(
+            (Event.data["average_estimated_speed"].between(min_speed, max_speed))
+        )
+    else:
+        if min_speed is not None:
+            event_filters.append((Event.data["average_estimated_speed"] >= min_speed))
+        if max_speed is not None:
+            event_filters.append((Event.data["average_estimated_speed"] <= max_speed))
+
     if time_range != DEFAULT_TIME_RANGE:
         tz_name = params.timezone
         hour_modifier, minute_modifier, _ = get_tz_modifiers(tz_name)
@@ -581,7 +613,16 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
         processed_event["data"] = {
             k: v
             for k, v in event["data"].items()
-            if k in ["type", "score", "top_score", "description"]
+            if k
+            in [
+                "type",
+                "score",
+                "top_score",
+                "description",
+                "sub_label_score",
+                "average_estimated_speed",
+                "velocity_angle",
+            ]
         }
 
         if event["id"] in search_results:
@@ -596,6 +637,10 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
         processed_events.sort(key=lambda x: x["score"])
     elif min_score is not None and max_score is not None and sort == "score_desc":
         processed_events.sort(key=lambda x: x["score"], reverse=True)
+    elif min_speed is not None and max_speed is not None and sort == "speed_asc":
+        processed_events.sort(key=lambda x: x["average_estimated_speed"])
+    elif min_speed is not None and max_speed is not None and sort == "speed_desc":
+        processed_events.sort(key=lambda x: x["average_estimated_speed"], reverse=True)
     elif sort == "date_asc":
         processed_events.sort(key=lambda x: x["start_time"])
     else:
@@ -909,38 +954,59 @@ def set_sub_label(
     try:
         event: Event = Event.get(Event.id == event_id)
     except DoesNotExist:
+        if not body.camera:
+            return JSONResponse(
+                content=(
+                    {
+                        "success": False,
+                        "message": "Event "
+                        + event_id
+                        + " not found and camera is not provided.",
+                    }
+                ),
+                status_code=404,
+            )
+
+        event = None
+
+    if request.app.detected_frames_processor:
+        tracked_obj: TrackedObject = (
+            request.app.detected_frames_processor.camera_states[
+                event.camera if event else body.camera
+            ].tracked_objects.get(event_id)
+        )
+    else:
+        tracked_obj = None
+
+    if not event and not tracked_obj:
         return JSONResponse(
-            content=({"success": False, "message": "Event " + event_id + " not found"}),
+            content=(
+                {"success": False, "message": "Event " + event_id + " not found."}
+            ),
             status_code=404,
         )
 
     new_sub_label = body.subLabel
     new_score = body.subLabelScore
 
-    if not event.end_time:
-        # update tracked object
-        tracked_obj: TrackedObject = (
-            request.app.detected_frames_processor.camera_states[
-                event.camera
-            ].tracked_objects.get(event.id)
-        )
-
-        if tracked_obj:
-            tracked_obj.obj_data["sub_label"] = (new_sub_label, new_score)
+    if tracked_obj:
+        tracked_obj.obj_data["sub_label"] = (new_sub_label, new_score)
 
         # update timeline items
         Timeline.update(
             data=Timeline.data.update({"sub_label": (new_sub_label, new_score)})
         ).where(Timeline.source_id == event_id).execute()
 
-    event.sub_label = new_sub_label
+    if event:
+        event.sub_label = new_sub_label
 
-    if new_score:
-        data = event.data
-        data["sub_label_score"] = new_score
-        event.data = data
+        if new_score:
+            data = event.data
+            data["sub_label_score"] = new_score
+            event.data = data
+
+        event.save()
 
-    event.save()
     return JSONResponse(
         content=(
             {

frigate/api/export.py

@@ -12,6 +12,7 @@ from peewee import DoesNotExist
 from playhouse.shortcuts import model_to_dict
 
 from frigate.api.defs.request.export_recordings_body import ExportRecordingsBody
+from frigate.api.defs.request.export_rename_body import ExportRenameBody
 from frigate.api.defs.tags import Tags
 from frigate.const import EXPORT_DIR
 from frigate.models import Export, Previews, Recordings
@@ -129,8 +130,8 @@ def export_recording(
     )
 
 
-@router.patch("/export/{event_id}/{new_name}")
-def export_rename(event_id: str, new_name: str):
+@router.patch("/export/{event_id}/rename")
+def export_rename(event_id: str, body: ExportRenameBody):
     try:
         export: Export = Export.get(Export.id == event_id)
     except DoesNotExist:
@@ -144,7 +145,7 @@ def export_rename(event_id: str, new_name: str):
             status_code=404,
         )
 
-    export.name = new_name
+    export.name = body.name
     export.save()
     return JSONResponse(
         content=(

frigate/api/fastapi_app.py

@@ -11,7 +11,16 @@ from starlette_context import middleware, plugins
 from starlette_context.plugins import Plugin
 
 from frigate.api import app as main_app
-from frigate.api import auth, event, export, media, notification, preview, review
+from frigate.api import (
+    auth,
+    classification,
+    event,
+    export,
+    media,
+    notification,
+    preview,
+    review,
+)
 from frigate.api.auth import get_jwt_secret, limiter
 from frigate.comms.event_metadata_updater import (
     EventMetadataPublisher,
@@ -103,6 +112,7 @@ def create_fastapi_app(
     # Routes
     # Order of include_router matters: https://fastapi.tiangolo.com/tutorial/path-params/#order-matters
     app.include_router(auth.router)
+    app.include_router(classification.router)
     app.include_router(review.router)
     app.include_router(main_app.router)
     app.include_router(preview.router)

frigate/api/media.py

@@ -25,6 +25,7 @@ from frigate.api.defs.query.media_query_parameters import (
     MediaEventsSnapshotQueryParams,
     MediaLatestFrameQueryParams,
     MediaMjpegFeedQueryParams,
+    MediaRecordingsSummaryQueryParams,
 )
 from frigate.api.defs.tags import Tags
 from frigate.config import FrigateConfig
@@ -182,11 +183,16 @@ def latest_frame(
 
         frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
 
-        ret, img = cv2.imencode(f".{extension}", frame, quality_params)
+        _, img = cv2.imencode(f".{extension}", frame, quality_params)
         return Response(
             content=img.tobytes(),
             media_type=f"image/{mime_type}",
-            headers={"Content-Type": f"image/{mime_type}", "Cache-Control": "no-store"},
+            headers={
+                "Content-Type": f"image/{mime_type}",
+                "Cache-Control": "no-store"
+                if not params.store
+                else "private, max-age=60",
+            },
         )
     elif camera_name == "birdseye" and request.app.frigate_config.birdseye.restream:
         frame = cv2.cvtColor(
@@ -199,11 +205,16 @@ def latest_frame(
 
         frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
 
-        ret, img = cv2.imencode(f".{extension}", frame, quality_params)
+        _, img = cv2.imencode(f".{extension}", frame, quality_params)
         return Response(
             content=img.tobytes(),
             media_type=f"image/{mime_type}",
-            headers={"Content-Type": f"image/{mime_type}", "Cache-Control": "no-store"},
+            headers={
+                "Content-Type": f"image/{mime_type}",
+                "Cache-Control": "no-store"
+                if not params.store
+                else "private, max-age=60",
+            },
         )
     else:
         return JSONResponse(
@@ -362,6 +373,48 @@ def get_recordings_storage_usage(request: Request):
     return JSONResponse(content=camera_usages)
 
 
+@router.get("/recordings/summary")
+def all_recordings_summary(params: MediaRecordingsSummaryQueryParams = Depends()):
+    """Returns true/false by day indicating if recordings exist"""
+    hour_modifier, minute_modifier, seconds_offset = get_tz_modifiers(params.timezone)
+
+    cameras = params.cameras
+
+    query = (
+        Recordings.select(
+            fn.strftime(
+                "%Y-%m-%d",
+                fn.datetime(
+                    Recordings.start_time + seconds_offset,
+                    "unixepoch",
+                    hour_modifier,
+                    minute_modifier,
+                ),
+            ).alias("day")
+        )
+        .group_by(
+            fn.strftime(
+                "%Y-%m-%d",
+                fn.datetime(
+                    Recordings.start_time + seconds_offset,
+                    "unixepoch",
+                    hour_modifier,
+                    minute_modifier,
+                ),
+            )
+        )
+        .order_by(Recordings.start_time.desc())
+    )
+
+    if cameras != "all":
+        query = query.where(Recordings.camera << cameras.split(","))
+
+    recording_days = query.namedtuples()
+    days = {day.day: True for day in recording_days}
+
+    return JSONResponse(content=days)
+
+
 @router.get("/{camera_name}/recordings/summary")
 def recordings_summary(camera_name: str, timezone: str = "utc"):
     """Returns hourly summary for recordings of given camera"""
@@ -1035,30 +1088,8 @@ def event_clip(request: Request, event_id: str):
             content={"success": False, "message": "Clip not available"}, status_code=404
         )
 
-    file_name = f"{event.camera}-{event.id}.mp4"
-    clip_path = os.path.join(CLIPS_DIR, file_name)
-
-    if not os.path.isfile(clip_path):
-        end_ts = (
-            datetime.now().timestamp() if event.end_time is None else event.end_time
-        )
-        return recording_clip(request, event.camera, event.start_time, end_ts)
-
-    headers = {
-        "Content-Description": "File Transfer",
-        "Cache-Control": "no-cache",
-        "Content-Type": "video/mp4",
-        "Content-Length": str(os.path.getsize(clip_path)),
-        # nginx: https://nginx.org/en/docs/http/ngx_http_proxy_module.html#proxy_ignore_headers
-        "X-Accel-Redirect": f"/clips/{file_name}",
-    }
-
-    return FileResponse(
-        clip_path,
-        media_type="video/mp4",
-        filename=file_name,
-        headers=headers,
-    )
+    end_ts = datetime.now().timestamp() if event.end_time is None else event.end_time
+    return recording_clip(request, event.camera, event.start_time, end_ts)
 
 
 @router.get("/events/{event_id}/preview.gif")

frigate/api/review.py

@@ -110,6 +110,28 @@ def review(params: ReviewQueryParams = Depends()):
     return JSONResponse(content=[r for r in review])
 
 
+@router.get("/review_ids", response_model=list[ReviewSegmentResponse])
+def review_ids(ids: str):
+    ids = ids.split(",")
+
+    if not ids:
+        return JSONResponse(
+            content=({"success": False, "message": "Valid list of ids must be sent"}),
+            status_code=400,
+        )
+
+    try:
+        reviews = (
+            ReviewSegment.select().where(ReviewSegment.id << ids).dicts().iterator()
+        )
+        return JSONResponse(list(reviews))
+    except Exception:
+        return JSONResponse(
+            content=({"success": False, "message": "Review segments not found"}),
+            status_code=400,
+        )
+
+
 @router.get("/review/summary", response_model=ReviewSummaryResponse)
 def review_summary(params: ReviewSummaryQueryParams = Depends()):
     hour_modifier, minute_modifier, seconds_offset = get_tz_modifiers(params.timezone)
@@ -490,8 +512,6 @@ def set_not_reviewed(review_id: str):
     review.save()
 
     return JSONResponse(
-        content=(
-            {"success": True, "message": "Set Review " + review_id + " as not viewed"}
-        ),
+        content=({"success": True, "message": f"Set Review {review_id} as not viewed"}),
         status_code=200,
     )

frigate/app.py

@@ -17,8 +17,9 @@ import frigate.util as util
 from frigate.api.auth import hash_password
 from frigate.api.fastapi_app import create_fastapi_app
 from frigate.camera import CameraMetrics, PTZMetrics
+from frigate.comms.base_communicator import Communicator
 from frigate.comms.config_updater import ConfigPublisher
-from frigate.comms.dispatcher import Communicator, Dispatcher
+from frigate.comms.dispatcher import Dispatcher
 from frigate.comms.event_metadata_updater import (
     EventMetadataPublisher,
     EventMetadataTypeEnum,
@@ -34,10 +35,12 @@ from frigate.const import (
     CLIPS_DIR,
     CONFIG_DIR,
     EXPORT_DIR,
+    FACE_DIR,
     MODEL_CACHE_DIR,
     RECORD_DIR,
     SHM_FRAMES_VAR,
 )
+from frigate.data_processing.types import DataProcessorMetrics
 from frigate.db.sqlitevecq import SqliteVecQueueDatabase
 from frigate.embeddings import EmbeddingsContext, manage_embeddings
 from frigate.events.audio import AudioProcessor
@@ -88,6 +91,9 @@ class FrigateApp:
         self.detection_shms: list[mp.shared_memory.SharedMemory] = []
         self.log_queue: Queue = mp.Queue()
         self.camera_metrics: dict[str, CameraMetrics] = {}
+        self.embeddings_metrics: DataProcessorMetrics | None = (
+            DataProcessorMetrics() if config.semantic_search.enabled else None
+        )
         self.ptz_metrics: dict[str, PTZMetrics] = {}
         self.processes: dict[str, int] = {}
         self.embeddings: Optional[EmbeddingsContext] = None
@@ -96,14 +102,19 @@ class FrigateApp:
         self.config = config
 
     def ensure_dirs(self) -> None:
-        for d in [
+        dirs = [
             CONFIG_DIR,
             RECORD_DIR,
             f"{CLIPS_DIR}/cache",
             CACHE_DIR,
             MODEL_CACHE_DIR,
             EXPORT_DIR,
-        ]:
+        ]
+
+        if self.config.face_recognition.enabled:
+            dirs.append(FACE_DIR)
+
+        for d in dirs:
             if not os.path.exists(d) and not os.path.islink(d):
                 logger.info(f"Creating directory: {d}")
                 os.makedirs(d)
@@ -229,7 +240,10 @@ class FrigateApp:
         embedding_process = util.Process(
             target=manage_embeddings,
             name="embeddings_manager",
-            args=(self.config,),
+            args=(
+                self.config,
+                self.embeddings_metrics,
+            ),
         )
         embedding_process.daemon = True
         self.embedding_process = embedding_process
@@ -301,8 +315,14 @@ class FrigateApp:
         if self.config.mqtt.enabled:
             comms.append(MqttClient(self.config))
 
-        if self.config.notifications.enabled_in_config:
-            comms.append(WebPushClient(self.config))
+        notification_cameras = [
+            c
+            for c in self.config.cameras.values()
+            if c.enabled and c.notifications.enabled_in_config
+        ]
+
+        if notification_cameras:
+            comms.append(WebPushClient(self.config, self.stop_event))
 
         comms.append(WebSocketClient(self.config))
         comms.append(self.inter_process_communicator)
@@ -491,7 +511,11 @@ class FrigateApp:
         self.stats_emitter = StatsEmitter(
             self.config,
             stats_init(
-                self.config, self.camera_metrics, self.detectors, self.processes
+                self.config,
+                self.camera_metrics,
+                self.embeddings_metrics,
+                self.detectors,
+                self.processes,
             ),
             self.stop_event,
         )

frigate/camera/activity_manager.py

@@ -0,0 +1,130 @@
+"""Manage camera activity and updating listeners."""
+
+from collections import Counter
+from typing import Callable
+
+from frigate.config.config import FrigateConfig
+
+
+class CameraActivityManager:
+    def __init__(
+        self, config: FrigateConfig, publish: Callable[[str, any], None]
+    ) -> None:
+        self.config = config
+        self.publish = publish
+        self.last_camera_activity: dict[str, dict[str, any]] = {}
+        self.camera_all_object_counts: dict[str, Counter] = {}
+        self.camera_active_object_counts: dict[str, Counter] = {}
+        self.zone_all_object_counts: dict[str, Counter] = {}
+        self.zone_active_object_counts: dict[str, Counter] = {}
+        self.all_zone_labels: dict[str, set[str]] = {}
+
+        for camera_config in config.cameras.values():
+            if not camera_config.enabled:
+                continue
+
+            self.last_camera_activity[camera_config.name] = {}
+            self.camera_all_object_counts[camera_config.name] = Counter()
+            self.camera_active_object_counts[camera_config.name] = Counter()
+
+            for zone, zone_config in camera_config.zones.items():
+                if zone not in self.all_zone_labels:
+                    self.zone_all_object_counts[zone] = Counter()
+                    self.zone_active_object_counts[zone] = Counter()
+                    self.all_zone_labels[zone] = set()
+
+                self.all_zone_labels[zone].update(zone_config.objects)
+
+    def update_activity(self, new_activity: dict[str, dict[str, any]]) -> None:
+        all_objects: list[dict[str, any]] = []
+
+        for camera in new_activity.keys():
+            new_objects = new_activity[camera].get("objects", [])
+            all_objects.extend(new_objects)
+
+            if self.last_camera_activity.get(camera, {}).get("objects") != new_objects:
+                self.compare_camera_activity(camera, new_objects)
+
+        # run through every zone, getting a count of objects in that zone right now
+        for zone, labels in self.all_zone_labels.items():
+            all_zone_objects = Counter(
+                obj["label"].replace("-verified", "")
+                for obj in all_objects
+                if zone in obj["current_zones"]
+            )
+            active_zone_objects = Counter(
+                obj["label"].replace("-verified", "")
+                for obj in all_objects
+                if zone in obj["current_zones"] and not obj["stationary"]
+            )
+            any_changed = False
+
+            # run through each object and check what topics need to be updated for this zone
+            for label in labels:
+                new_count = all_zone_objects[label]
+                new_active_count = active_zone_objects[label]
+
+                if (
+                    new_count != self.zone_all_object_counts[zone][label]
+                    or label not in self.zone_all_object_counts[zone]
+                ):
+                    any_changed = True
+                    self.publish(f"{zone}/{label}", new_count)
+                    self.zone_all_object_counts[zone][label] = new_count
+
+                if (
+                    new_active_count != self.zone_active_object_counts[zone][label]
+                    or label not in self.zone_active_object_counts[zone]
+                ):
+                    any_changed = True
+                    self.publish(f"{zone}/{label}/active", new_active_count)
+                    self.zone_active_object_counts[zone][label] = new_active_count
+
+            if any_changed:
+                self.publish(f"{zone}/all", sum(list(all_zone_objects.values())))
+                self.publish(
+                    f"{zone}/all/active", sum(list(active_zone_objects.values()))
+                )
+
+        self.last_camera_activity = new_activity
+
+    def compare_camera_activity(
+        self, camera: str, new_activity: dict[str, any]
+    ) -> None:
+        all_objects = Counter(
+            obj["label"].replace("-verified", "") for obj in new_activity
+        )
+        active_objects = Counter(
+            obj["label"].replace("-verified", "")
+            for obj in new_activity
+            if not obj["stationary"]
+        )
+        any_changed = False
+
+        # run through each object and check what topics need to be updated
+        for label in self.config.cameras[camera].objects.track:
+            if label in self.config.model.non_logo_attributes:
+                continue
+
+            new_count = all_objects[label]
+            new_active_count = active_objects[label]
+
+            if (
+                new_count != self.camera_all_object_counts[camera][label]
+                or label not in self.camera_all_object_counts[camera]
+            ):
+                any_changed = True
+                self.publish(f"{camera}/{label}", new_count)
+                self.camera_all_object_counts[camera][label] = new_count
+
+            if (
+                new_active_count != self.camera_active_object_counts[camera][label]
+                or label not in self.camera_active_object_counts[camera]
+            ):
+                any_changed = True
+                self.publish(f"{camera}/{label}/active", new_active_count)
+                self.camera_active_object_counts[camera][label] = new_active_count
+
+        if any_changed:
+            self.publish(f"{camera}/all", sum(list(all_objects.values())))
+            self.publish(f"{camera}/all/active", sum(list(active_objects.values())))

frigate/comms/base_communicator.py

@@ -0,0 +1,21 @@
+from abc import ABC, abstractmethod
+from typing import Any, Callable
+
+
+class Communicator(ABC):
+    """pub/sub model via specific protocol."""
+
+    @abstractmethod
+    def publish(self, topic: str, payload: Any, retain: bool = False) -> None:
+        """Send data via specific protocol."""
+        pass
+
+    @abstractmethod
+    def subscribe(self, receiver: Callable) -> None:
+        """Pass receiver so communicators can pass commands."""
+        pass
+
+    @abstractmethod
+    def stop(self) -> None:
+        """Stop the communicator."""
+        pass

frigate/comms/dispatcher.py

@@ -3,16 +3,19 @@
 import datetime
 import json
 import logging
-from abc import ABC, abstractmethod
 from typing import Any, Callable, Optional
 
 from frigate.camera import PTZMetrics
+from frigate.camera.activity_manager import CameraActivityManager
+from frigate.comms.base_communicator import Communicator
 from frigate.comms.config_updater import ConfigPublisher
+from frigate.comms.webpush import WebPushClient
 from frigate.config import BirdseyeModeEnum, FrigateConfig
 from frigate.const import (
     CLEAR_ONGOING_REVIEW_SEGMENTS,
     INSERT_MANY_RECORDINGS,
     INSERT_PREVIEW,
+    NOTIFICATION_TEST,
     REQUEST_REGION_GRID,
     UPDATE_CAMERA_ACTIVITY,
     UPDATE_EMBEDDINGS_REINDEX_PROGRESS,
@@ -29,25 +32,6 @@ from frigate.util.services import restart_frigate
 logger = logging.getLogger(__name__)
 
 
-class Communicator(ABC):
-    """pub/sub model via specific protocol."""
-
-    @abstractmethod
-    def publish(self, topic: str, payload: Any, retain: bool = False) -> None:
-        """Send data via specific protocol."""
-        pass
-
-    @abstractmethod
-    def subscribe(self, receiver: Callable) -> None:
-        """Pass receiver so communicators can pass commands."""
-        pass
-
-    @abstractmethod
-    def stop(self) -> None:
-        """Stop the communicator."""
-        pass
-
-
 class Dispatcher:
     """Handle communication between Frigate and communicators."""
 
@@ -64,7 +48,7 @@ class Dispatcher:
         self.onvif = onvif
         self.ptz_metrics = ptz_metrics
         self.comms = communicators
-        self.camera_activity = {}
+        self.camera_activity = CameraActivityManager(config, self.publish)
         self.model_state = {}
         self.embeddings_reindex = {}
 
@@ -76,18 +60,25 @@ class Dispatcher:
             "motion": self._on_motion_command,
             "motion_contour_area": self._on_motion_contour_area_command,
             "motion_threshold": self._on_motion_threshold_command,
+            "notifications": self._on_camera_notification_command,
             "recordings": self._on_recordings_command,
             "snapshots": self._on_snapshots_command,
             "birdseye": self._on_birdseye_command,
             "birdseye_mode": self._on_birdseye_mode_command,
+            "review_alerts": self._on_alerts_command,
+            "review_detections": self._on_detections_command,
         }
         self._global_settings_handlers: dict[str, Callable] = {
-            "notifications": self._on_notification_command,
+            "notifications": self._on_global_notification_command,
         }
 
         for comm in self.comms:
             comm.subscribe(self._receive)
 
+        self.web_push_client = next(
+            (comm for comm in communicators if isinstance(comm, WebPushClient)), None
+        )
+
     def _receive(self, topic: str, payload: str) -> Optional[Any]:
         """Handle receiving of payload from communicators."""
 
@@ -130,7 +121,7 @@ class Dispatcher:
             ).execute()
 
         def handle_update_camera_activity():
-            self.camera_activity = payload
+            self.camera_activity.update_activity(payload)
 
         def handle_update_event_description():
             event: Event = Event.get(Event.id == payload["id"])
@@ -171,7 +162,7 @@ class Dispatcher:
             )
 
         def handle_on_connect():
-            camera_status = self.camera_activity.copy()
+            camera_status = self.camera_activity.last_camera_activity.copy()
 
             for camera in camera_status.keys():
                 camera_status[camera]["config"] = {
@@ -179,9 +170,18 @@ class Dispatcher:
                     "snapshots": self.config.cameras[camera].snapshots.enabled,
                     "record": self.config.cameras[camera].record.enabled,
                     "audio": self.config.cameras[camera].audio.enabled,
+                    "notifications": self.config.cameras[camera].notifications.enabled,
+                    "notifications_suspended": int(
+                        self.web_push_client.suspended_cameras.get(camera, 0)
+                    )
+                    if self.web_push_client
+                    and camera in self.web_push_client.suspended_cameras
+                    else 0,
                     "autotracking": self.config.cameras[
                         camera
                     ].onvif.autotracking.enabled,
+                    "alerts": self.config.cameras[camera].review.alerts.enabled,
+                    "detections": self.config.cameras[camera].review.detections.enabled,
                 }
 
             self.publish("camera_activity", json.dumps(camera_status))
@@ -191,6 +191,9 @@ class Dispatcher:
                 json.dumps(self.embeddings_reindex.copy()),
             )
 
+        def handle_notification_test():
+            self.publish("notification_test", "Test notification")
+
         # Dictionary mapping topic to handlers
         topic_handlers = {
             INSERT_MANY_RECORDINGS: handle_insert_many_recordings,
@@ -202,13 +205,14 @@ class Dispatcher:
             UPDATE_EVENT_DESCRIPTION: handle_update_event_description,
             UPDATE_MODEL_STATE: handle_update_model_state,
             UPDATE_EMBEDDINGS_REINDEX_PROGRESS: handle_update_embeddings_reindex_progress,
+            NOTIFICATION_TEST: handle_notification_test,
             "restart": handle_restart,
             "embeddingsReindexProgress": handle_embeddings_reindex_progress,
             "modelState": handle_model_state,
             "onConnect": handle_on_connect,
         }
 
-        if topic.endswith("set") or topic.endswith("ptz"):
+        if topic.endswith("set") or topic.endswith("ptz") or topic.endswith("suspend"):
             try:
                 parts = topic.split("/")
                 if len(parts) == 3 and topic.endswith("set"):
@@ -223,6 +227,11 @@ class Dispatcher:
                     # example /cam_name/ptz payload=MOVE_UP|MOVE_DOWN|STOP...
                     camera_name = parts[-2]
                     handle_camera_command("ptz", camera_name, "", payload)
+                elif len(parts) == 3 and topic.endswith("suspend"):
+                    # example /cam_name/notifications/suspend payload=duration
+                    camera_name = parts[-3]
+                    command = parts[-2]
+                    self._on_camera_notification_suspend(camera_name, payload)
             except IndexError:
                 logger.error(
                     f"Received invalid {topic.split('/')[-1]} command: {topic}"
@@ -364,16 +373,18 @@ class Dispatcher:
         self.config_updater.publish(f"config/motion/{camera_name}", motion_settings)
         self.publish(f"{camera_name}/motion_threshold/state", payload, retain=True)
 
-    def _on_notification_command(self, payload: str) -> None:
-        """Callback for notification topic."""
+    def _on_global_notification_command(self, payload: str) -> None:
+        """Callback for global notification topic."""
         if payload != "ON" and payload != "OFF":
-            f"Received unsupported value for notification: {payload}"
+            f"Received unsupported value for all notification: {payload}"
             return
 
         notification_settings = self.config.notifications
-        logger.info(f"Setting notifications: {payload}")
+        logger.info(f"Setting all notifications: {payload}")
         notification_settings.enabled = payload == "ON"  # type: ignore[union-attr]
-        self.config_updater.publish("config/notifications", notification_settings)
+        self.config_updater.publish(
+            "config/notifications", {"_global_notifications": notification_settings}
+        )
         self.publish("notifications/state", payload, retain=True)
 
     def _on_audio_command(self, camera_name: str, payload: str) -> None:
@@ -490,3 +501,115 @@ class Dispatcher:
 
         self.config_updater.publish(f"config/birdseye/{camera_name}", birdseye_settings)
         self.publish(f"{camera_name}/birdseye_mode/state", payload, retain=True)
+
+    def _on_camera_notification_command(self, camera_name: str, payload: str) -> None:
+        """Callback for camera level notifications topic."""
+        notification_settings = self.config.cameras[camera_name].notifications
+
+        if payload == "ON":
+            if not self.config.cameras[camera_name].notifications.enabled_in_config:
+                logger.error(
+                    "Notifications must be enabled in the config to be turned on via MQTT."
+                )
+                return
+
+            if not notification_settings.enabled:
+                logger.info(f"Turning on notifications for {camera_name}")
+                notification_settings.enabled = True
+            if (
+                self.web_push_client
+                and camera_name in self.web_push_client.suspended_cameras
+            ):
+                self.web_push_client.suspended_cameras[camera_name] = 0
+        elif payload == "OFF":
+            if notification_settings.enabled:
+                logger.info(f"Turning off notifications for {camera_name}")
+                notification_settings.enabled = False
+            if (
+                self.web_push_client
+                and camera_name in self.web_push_client.suspended_cameras
+            ):
+                self.web_push_client.suspended_cameras[camera_name] = 0
+
+        self.config_updater.publish(
+            "config/notifications", {camera_name: notification_settings}
+        )
+        self.publish(f"{camera_name}/notifications/state", payload, retain=True)
+        self.publish(f"{camera_name}/notifications/suspended", "0", retain=True)
+
+    def _on_camera_notification_suspend(self, camera_name: str, payload: str) -> None:
+        """Callback for camera level notifications suspend topic."""
+        try:
+            duration = int(payload)
+        except ValueError:
+            logger.error(f"Invalid suspension duration: {payload}")
+            return
+
+        if self.web_push_client is None:
+            logger.error("WebPushClient not available for suspension")
+            return
+
+        notification_settings = self.config.cameras[camera_name].notifications
+
+        if not notification_settings.enabled:
+            logger.error(f"Notifications are not enabled for {camera_name}")
+            return
+
+        if duration != 0:
+            self.web_push_client.suspend_notifications(camera_name, duration)
+        else:
+            self.web_push_client.unsuspend_notifications(camera_name)
+
+        self.publish(
+            f"{camera_name}/notifications/suspended",
+            str(
+                int(self.web_push_client.suspended_cameras.get(camera_name, 0))
+                if camera_name in self.web_push_client.suspended_cameras
+                else 0
+            ),
+            retain=True,
+        )
+
+    def _on_alerts_command(self, camera_name: str, payload: str) -> None:
+        """Callback for alerts topic."""
+        review_settings = self.config.cameras[camera_name].review
+
+        if payload == "ON":
+            if not self.config.cameras[camera_name].review.alerts.enabled_in_config:
+                logger.error(
+                    "Alerts must be enabled in the config to be turned on via MQTT."
+                )
+                return
+
+            if not review_settings.alerts.enabled:
+                logger.info(f"Turning on alerts for {camera_name}")
+                review_settings.alerts.enabled = True
+        elif payload == "OFF":
+            if review_settings.alerts.enabled:
+                logger.info(f"Turning off alerts for {camera_name}")
+                review_settings.alerts.enabled = False
+
+        self.config_updater.publish(f"config/review/{camera_name}", review_settings)
+        self.publish(f"{camera_name}/review_alerts/state", payload, retain=True)
+
+    def _on_detections_command(self, camera_name: str, payload: str) -> None:
+        """Callback for detections topic."""
+        review_settings = self.config.cameras[camera_name].review
+
+        if payload == "ON":
+            if not self.config.cameras[camera_name].review.detections.enabled_in_config:
+                logger.error(
+                    "Detections must be enabled in the config to be turned on via MQTT."
+                )
+                return
+
+            if not review_settings.detections.enabled:
+                logger.info(f"Turning on detections for {camera_name}")
+                review_settings.detections.enabled = True
+        elif payload == "OFF":
+            if review_settings.detections.enabled:
+                logger.info(f"Turning off detections for {camera_name}")
+                review_settings.detections.enabled = False
+
+        self.config_updater.publish(f"config/review/{camera_name}", review_settings)
+        self.publish(f"{camera_name}/review_detections/state", payload, retain=True)

frigate/comms/embeddings_updater.py

@@ -9,9 +9,12 @@ SOCKET_REP_REQ = "ipc:///tmp/cache/embeddings"
 
 
 class EmbeddingsRequestEnum(Enum):
+    clear_face_classifier = "clear_face_classifier"
     embed_description = "embed_description"
     embed_thumbnail = "embed_thumbnail"
     generate_search = "generate_search"
+    register_face = "register_face"
+    reprocess_face = "reprocess_face"
 
 
 class EmbeddingsResponder:
@@ -22,7 +25,7 @@ class EmbeddingsResponder:
 
     def check_for_request(self, process: Callable) -> None:
         while True:  # load all messages that are queued
-            has_message, _, _ = zmq.select([self.socket], [], [], 0.1)
+            has_message, _, _ = zmq.select([self.socket], [], [], 0.01)
 
             if not has_message:
                 break

frigate/comms/inter_process.py

@@ -7,7 +7,7 @@ from typing import Callable
 
 import zmq
 
-from frigate.comms.dispatcher import Communicator
+from frigate.comms.base_communicator import Communicator
 
 SOCKET_REP_REQ = "ipc:///tmp/cache/comms"
 

frigate/comms/mqtt.py

@@ -5,7 +5,7 @@ from typing import Any, Callable
 import paho.mqtt.client as mqtt
 from paho.mqtt.enums import CallbackAPIVersion
 
-from frigate.comms.dispatcher import Communicator
+from frigate.comms.base_communicator import Communicator
 from frigate.config import FrigateConfig
 
 logger = logging.getLogger(__name__)
@@ -31,7 +31,10 @@ class MqttClient(Communicator):  # type: ignore[misc]
             return
 
         self.client.publish(
-            f"{self.mqtt_config.topic_prefix}/{topic}", payload, retain=retain
+            f"{self.mqtt_config.topic_prefix}/{topic}",
+            payload,
+            qos=self.config.mqtt.qos,
+            retain=retain,
         )
 
     def stop(self) -> None:
@@ -104,6 +107,16 @@ class MqttClient(Communicator):  # type: ignore[misc]
                 ),
                 retain=True,
             )
+            self.publish(
+                f"{camera_name}/review_alerts/state",
+                "ON" if camera.review.alerts.enabled_in_config else "OFF",
+                retain=True,
+            )
+            self.publish(
+                f"{camera_name}/review_detections/state",
+                "ON" if camera.review.detections.enabled_in_config else "OFF",
+                retain=True,
+            )
 
         if self.config.notifications.enabled_in_config:
             self.publish(
@@ -151,7 +164,7 @@ class MqttClient(Communicator):  # type: ignore[misc]
 
         self.connected = True
         logger.debug("MQTT connected")
-        client.subscribe(f"{self.mqtt_config.topic_prefix}/#")
+        client.subscribe(f"{self.mqtt_config.topic_prefix}/#", qos=self.config.mqtt.qos)
         self._set_initial_topics()
 
     def _on_disconnect(

frigate/comms/webpush.py

@@ -4,13 +4,17 @@ import datetime
 import json
 import logging
 import os
+import queue
+import threading
+from dataclasses import dataclass
+from multiprocessing.synchronize import Event as MpEvent
 from typing import Any, Callable
 
 from py_vapid import Vapid01
 from pywebpush import WebPusher
 
+from frigate.comms.base_communicator import Communicator
 from frigate.comms.config_updater import ConfigSubscriber
-from frigate.comms.dispatcher import Communicator
 from frigate.config import FrigateConfig
 from frigate.const import CONFIG_DIR
 from frigate.models import User
@@ -18,15 +22,36 @@ from frigate.models import User
 logger = logging.getLogger(__name__)
 
 
+@dataclass
+class PushNotification:
+    user: str
+    payload: dict[str, Any]
+    title: str
+    message: str
+    direct_url: str = ""
+    image: str = ""
+    notification_type: str = "alert"
+    ttl: int = 0
+
+
 class WebPushClient(Communicator):  # type: ignore[misc]
     """Frigate wrapper for webpush client."""
 
-    def __init__(self, config: FrigateConfig) -> None:
+    def __init__(self, config: FrigateConfig, stop_event: MpEvent) -> None:
         self.config = config
+        self.stop_event = stop_event
         self.claim_headers: dict[str, dict[str, str]] = {}
         self.refresh: int = 0
         self.web_pushers: dict[str, list[WebPusher]] = {}
         self.expired_subs: dict[str, list[str]] = {}
+        self.suspended_cameras: dict[str, int] = {
+            c.name: 0 for c in self.config.cameras.values()
+        }
+        self.notification_queue: queue.Queue[PushNotification] = queue.Queue()
+        self.notification_thread = threading.Thread(
+            target=self._process_notifications, daemon=True
+        )
+        self.notification_thread.start()
 
         if not self.config.notifications.email:
             logger.warning("Email must be provided for push notifications to be sent.")
@@ -103,30 +128,144 @@ class WebPushClient(Communicator):  # type: ignore[misc]
 
         self.expired_subs = {}
 
+    def suspend_notifications(self, camera: str, minutes: int) -> None:
+        """Suspend notifications for a specific camera."""
+        suspend_until = int(
+            (datetime.datetime.now() + datetime.timedelta(minutes=minutes)).timestamp()
+        )
+        self.suspended_cameras[camera] = suspend_until
+        logger.info(
+            f"Notifications for {camera} suspended until {datetime.datetime.fromtimestamp(suspend_until).strftime('%Y-%m-%d %H:%M:%S')}"
+        )
+
+    def unsuspend_notifications(self, camera: str) -> None:
+        """Unsuspend notifications for a specific camera."""
+        self.suspended_cameras[camera] = 0
+        logger.info(f"Notifications for {camera} unsuspended")
+
+    def is_camera_suspended(self, camera: str) -> bool:
+        return datetime.datetime.now().timestamp() <= self.suspended_cameras[camera]
+
     def publish(self, topic: str, payload: Any, retain: bool = False) -> None:
         """Wrapper for publishing when client is in valid state."""
         # check for updated notification config
         _, updated_notification_config = self.config_subscriber.check_for_update()
 
         if updated_notification_config:
-            self.config.notifications = updated_notification_config
+            for key, value in updated_notification_config.items():
+                if key == "_global_notifications":
+                    self.config.notifications = value
 
-        if not self.config.notifications.enabled:
-            return
+                elif key in self.config.cameras:
+                    self.config.cameras[key].notifications = value
 
         if topic == "reviews":
-            self.send_alert(json.loads(payload))
+            decoded = json.loads(payload)
+            camera = decoded["before"]["camera"]
+            if not self.config.cameras[camera].notifications.enabled:
+                return
+            if self.is_camera_suspended(camera):
+                logger.debug(f"Notifications for {camera} are currently suspended.")
+                return
+            self.send_alert(decoded)
+        elif topic == "notification_test":
+            if not self.config.notifications.enabled:
+                return
+            self.send_notification_test()
 
-    def send_alert(self, payload: dict[str, any]) -> None:
+    def send_push_notification(
+        self,
+        user: str,
+        payload: dict[str, Any],
+        title: str,
+        message: str,
+        direct_url: str = "",
+        image: str = "",
+        notification_type: str = "alert",
+        ttl: int = 0,
+    ) -> None:
+        notification = PushNotification(
+            user=user,
+            payload=payload,
+            title=title,
+            message=message,
+            direct_url=direct_url,
+            image=image,
+            notification_type=notification_type,
+            ttl=ttl,
+        )
+        self.notification_queue.put(notification)
+
+    def _process_notifications(self) -> None:
+        while not self.stop_event.is_set():
+            try:
+                notification = self.notification_queue.get(timeout=1.0)
+                self.check_registrations()
+
+                for pusher in self.web_pushers[notification.user]:
+                    endpoint = pusher.subscription_info["endpoint"]
+                    headers = self.claim_headers[
+                        endpoint[: endpoint.index("/", 10)]
+                    ].copy()
+                    headers["urgency"] = "high"
+
+                    resp = pusher.send(
+                        headers=headers,
+                        ttl=notification.ttl,
+                        data=json.dumps(
+                            {
+                                "title": notification.title,
+                                "message": notification.message,
+                                "direct_url": notification.direct_url,
+                                "image": notification.image,
+                                "id": notification.payload.get("after", {}).get(
+                                    "id", ""
+                                ),
+                                "type": notification.notification_type,
+                            }
+                        ),
+                        timeout=10,
+                    )
+
+                    if resp.status_code in (404, 410):
+                        self.expired_subs.setdefault(notification.user, []).append(
+                            endpoint
+                        )
+                    elif resp.status_code != 201:
+                        logger.warning(
+                            f"Failed to send notification to {notification.user} :: {resp.status_code}"
+                        )
+
+            except queue.Empty:
+                continue
+            except Exception as e:
+                logger.error(f"Error processing notification: {str(e)}")
+
+    def send_notification_test(self) -> None:
         if not self.config.notifications.email:
             return
 
         self.check_registrations()
 
-        # Only notify for alerts
-        if payload["after"]["severity"] != "alert":
+        for user in self.web_pushers:
+            self.send_push_notification(
+                user=user,
+                payload={},
+                title="Test Notification",
+                message="This is a test notification from Frigate.",
+                direct_url="/",
+                notification_type="test",
+            )
+
+    def send_alert(self, payload: dict[str, Any]) -> None:
+        if (
+            not self.config.notifications.email
+            or payload["after"]["severity"] != "alert"
+        ):
             return
 
+        self.check_registrations()
+
         state = payload["type"]
 
         # Don't notify if message is an update and important fields don't have an update
@@ -155,49 +294,21 @@ class WebPushClient(Communicator):  # type: ignore[misc]
 
         # if event is ongoing open to live view otherwise open to recordings view
         direct_url = f"/review?id={reviewId}" if state == "end" else f"/#{camera}"
+        ttl = 3600 if state == "end" else 0
 
-        for user, pushers in self.web_pushers.items():
-            for pusher in pushers:
-                endpoint = pusher.subscription_info["endpoint"]
-
-                # set headers for notification behavior
-                headers = self.claim_headers[
-                    endpoint[0 : endpoint.index("/", 10)]
-                ].copy()
-                headers["urgency"] = "high"
-                ttl = 3600 if state == "end" else 0
-
-                # send message
-                resp = pusher.send(
-                    headers=headers,
-                    ttl=ttl,
-                    data=json.dumps(
-                        {
-                            "title": title,
-                            "message": message,
-                            "direct_url": direct_url,
-                            "image": image,
-                            "id": reviewId,
-                            "type": "alert",
-                        }
-                    ),
-                )
-
-                if resp.status_code == 201:
-                    pass
-                elif resp.status_code == 404 or resp.status_code == 410:
-                    # subscription is not found or has been unsubscribed
-                    if not self.expired_subs.get(user):
-                        self.expired_subs[user] = []
-
-                    self.expired_subs[user].append(pusher.subscription_info["endpoint"])
-                    # the subscription no longer exists and should be removed
-                else:
-                    logger.warning(
-                        f"Failed to send notification to {user} :: {resp.headers}"
-                    )
+        for user in self.web_pushers:
+            self.send_push_notification(
+                user=user,
+                payload=payload,
+                title=title,
+                message=message,
+                direct_url=direct_url,
+                image=image,
+                ttl=ttl,
+            )
 
         self.cleanup_registrations()
 
     def stop(self) -> None:
-        pass
+        logger.info("Closing notification queue")
+        self.notification_thread.join()

frigate/comms/ws.py

@@ -15,7 +15,7 @@ from ws4py.server.wsgirefserver import (
 from ws4py.server.wsgiutils import WebSocketWSGIApplication
 from ws4py.websocket import WebSocket as WebSocket_
 
-from frigate.comms.dispatcher import Communicator
+from frigate.comms.base_communicator import Communicator
 from frigate.config import FrigateConfig
 
 logger = logging.getLogger(__name__)

frigate/config/__init__.py

@@ -3,13 +3,12 @@ from frigate.detectors import DetectorConfig, ModelConfig  # noqa: F401
 from .auth import *  # noqa: F403
 from .camera import *  # noqa: F403
 from .camera_group import *  # noqa: F403
+from .classification import *  # noqa: F403
 from .config import *  # noqa: F403
 from .database import *  # noqa: F403
 from .logger import *  # noqa: F403
 from .mqtt import *  # noqa: F403
-from .notification import *  # noqa: F403
 from .proxy import *  # noqa: F403
-from .semantic_search import *  # noqa: F403
 from .telemetry import *  # noqa: F403
 from .tls import *  # noqa: F403
 from .ui import *  # noqa: F403

frigate/config/camera/camera.py

@@ -25,6 +25,7 @@ from .genai import GenAICameraConfig
 from .live import CameraLiveConfig
 from .motion import MotionConfig
 from .mqtt import CameraMqttConfig
+from .notification import NotificationConfig
 from .objects import ObjectConfig
 from .onvif import OnvifConfig
 from .record import RecordConfig
@@ -85,6 +86,9 @@ class CameraConfig(FrigateBaseModel):
     mqtt: CameraMqttConfig = Field(
         default_factory=CameraMqttConfig, title="MQTT configuration."
     )
+    notifications: NotificationConfig = Field(
+        default_factory=NotificationConfig, title="Notifications configuration."
+    )
     onvif: OnvifConfig = Field(
         default_factory=OnvifConfig, title="Camera Onvif Configuration."
     )
@@ -167,7 +171,7 @@ class CameraConfig(FrigateBaseModel):
             record_args = get_ffmpeg_arg_list(
                 parse_preset_output_record(
                     self.ffmpeg.output_args.record,
-                    self.ffmpeg.output_args._force_record_hvc1,
+                    self.ffmpeg.apple_compatibility,
                 )
                 or self.ffmpeg.output_args.record
             )