Update hardware recs

add detail to reset_admin_password setting

.cspell/frigate-dictionary.txt

@@ -1,303 +1,168 @@
-aarch
-absdiff
-airockchip
-Alloc
-Amcrest
-amdgpu
-analyzeduration
-Annke
-apexcharts
-arange
-argmax
-argmin
-argpartition
-ascontiguousarray
-authelia
-authentik
-autodetected
-automations
-autotrack
-autotracked
-autotracker
-autotracking
-balena
-Beelink
-BGRA
-BHWC
-blackshear
-blakeblackshear
-bottombar
-buildx
-castable
-cdist
-Celeron
-cgroups
-chipset
-chromadb
-Chromecast
-cmdline
-codeowner
-CODEOWNERS
-codeproject
-colormap
-colorspace
-comms
-ctypeslib
-CUDA
-Cuvid
-Dahua
-datasheet
-debconf
-deci
-deepstack
-defragment
-devcontainer
-DEVICEMAP
-discardcorrupt
-dpkg
-dsize
-dtype
-ECONNRESET
-edgetpu
-faststart
-fflags
-ffprobe
-fillna
-flac
-foscam
-fourcc
-framebuffer
-fregate
-frégate
-fromarray
-frombuffer
-frontdoor
-fstype
-fullchain
-fullscreen
-genai
-generativeai
-genpts
-getpid
-gpuload
-HACS
-Hailo
-hass
-hconcat
-healthcheck
-hideable
-Hikvision
-homeassistant
-homekit
-homography
-hsize
-hstack
-httpx
-hwaccel
-hwdownload
-hwmap
-hwupload
-iloc
-imagestream
-imdecode
-imencode
-imread
-imutils
-imwrite
-interp
-iostat
-iotop
-itemsize
-Jellyfin
-jetson
-jetsons
-joserfc
-jsmpeg
-jsonify
-Kalman
-keepalive
-keepdims
-labelmap
-letsencrypt
-levelname
-LIBAVFORMAT
-libedgetpu
-libnvinfer
-libva
-libwebp
-libx
-libyolo
-linalg
-localzone
-logpipe
-Loryta
-lstsq
-lsusb
-markupsafe
-maxsplit
-MEMHOSTALLOC
-memlimit
-meshgrid
-metadatas
-migraphx
-minilm
-mjpeg
-mkfifo
-mobiledet
-mobilenet
-modelpath
-mosquitto
-mountpoint
-movflags
-mpegts
-mqtt
-mse
-msenc
-namedtuples
-nbytes
-nchw
-ndarray
-ndimage
-nethogs
-newaxis
-nhwc
-NOBLOCK
-nobuffer
-nokey
-NONBLOCK
-noninteractive
-noprint
-Norfair
-nptype
-NTSC
-numpy
-nvenc
-nvhost
-nvml
-nvmpi
-ollama
-onnx
-onnxruntime
-onvif
-ONVIF
-openai
-opencv
-openvino
-OWASP
-paho
-passwordless
-popleft
-posthog
-postprocess
-poweroff
-preexec
-probesize
-protobuf
-psutil
-pubkey
-putenv
-pycache
-pydantic
-pyobj
-pysqlite
-pytz
-pywebpush
-qnap
-quantisation
-Radeon
-radeonsi
-radeontop
-rawvideo
-rcond
-RDONLY
-rebranded
-referer
-Reolink
-restream
-restreamed
-restreaming
-rkmpp
-rknn
-rkrga
-rockchip
-rocm
-rocminfo
-rootfs
 rtmp
+edgetpu
+labelmap
+rockchip
+jetson
+rocm
+vaapi
+CUDA
+hwaccel
 RTSP
-ruamel
-scroller
-setproctitle
-setpts
-shms
-SIGUSR
-skylake
-sleeptime
-SNDMORE
-socs
-sqliteq
-ssdlite
-statm
-stimeout
-stylelint
-subclassing
-substream
-superfast
-surveillance
-svscan
-Swipeable
-sysconf
-tailscale
-Tapo
-tensorrt
+Hikvision
+Dahua
+Amcrest
+Reolink
+Loryta
+Beelink
+Celeron
+vaapi
+blakeblackshear
+workdir
+onvif
+autotracking
+openvino
 tflite
+deepstack
+codeproject
+udev
+tailscale
+restream
+restreaming
+webrtc
+ssdlite
+mobilenet
+mosquitto
+datasheet
+Jellyfin
+Radeon
+libva
+Ubiquiti
+Unifi
+Tapo
+Annke
+autotracker
+autotracked
+variations
+ONVIF
+traefik
+devcontainer
+rootfs
+ffprobe
+autotrack
+logpipe
+imread
+imwrite
+imencode
+imutils
 thresholded
 timelapse
-tmpfs
-tobytes
-toggleable
-traefik
-tzlocal
-Ubiquiti
-udev
-udevadm
 ultrafast
-unichip
-unidecode
-Unifi
-unixepoch
-unraid
-unreviewed
-userdata
-usermod
-vaapi
+sleeptime
+radeontop
 vainfo
-variations
-vconcat
-vitb
-vstream
-vsync
-wallclock
-webp
-webpush
-webrtc
+tmpfs
+homography
 websockets
-webui
-werkzeug
-workdir
-WRONLY
-wsgirefserver
-wsgiutils
-wsize
-xaddr
-xmaxs
-xmins
-XPUB
-XSUB
-ymaxs
-ymins
-yolo
-yolonas
-yolox
+LIBAVFORMAT
+NTSC
+onnxruntime
+fourcc
+radeonsi
+paho
+imagestream
+jsonify
+cgroups
+sysconf
+memlimit
+gpuload
+nvml
+setproctitle
+psutil
+Kalman
+frontdoor
+namedtuples
 zeep
-zerolatency
+fflags
+probesize
+wallclock
+rknn
+socs
+pydantic
+shms
+imdecode
+colormap
+webui
+mse
+jsmpeg
+unreviewed
+Chromecast
+Swipeable
+flac
+scroller
+cmdline
+toggleable
+bottombar
+opencv
+apexcharts
+buildx
+mqtt
+rawvideo
+defragment
+Norfair
+subclassing
+yolo
+tensorrt
+blackshear
+stylelint
+HACS
+homeassistant
+hass
+castable
+mobiledet
+framebuffer
+mjpeg
+substream
+codeowner
+noninteractive
+restreamed
+mountpoint
+fstype
+OWASP
+iotop
+letsencrypt
+fullchain
+lsusb
+iostat
+usermod
+balena
+passwordless
+debconf
+dpkg
+poweroff
+surveillance
+qnap
+homekit
+colorspace
+quantisation
+skylake
+Cuvid
+foscam
+onnx
+numpy
+protobuf
+aarch
+amdgpu
+chipset
+referer
+mpegts
+webp
+authelia
+authentik
+unichip
+rebranded
+udevadm
+automations
+unraid
+hideable
+healthcheck
+keepalive

.devcontainer/post_create.sh

@@ -17,7 +17,7 @@ sudo chown -R "$(id -u):$(id -g)" /media/frigate
 # When started as a service, LIBAVFORMAT_VERSION_MAJOR is defined in the
 # s6 service file. For dev, where frigate is started from an interactive
 # shell, we define it in .bashrc instead.
-echo 'export LIBAVFORMAT_VERSION_MAJOR=$(/usr/lib/ffmpeg/7.0/bin/ffmpeg -version | grep -Po "libavformat\W+\K\d+")' >> $HOME/.bashrc
+echo 'export LIBAVFORMAT_VERSION_MAJOR=$(ffmpeg -version | grep -Po "libavformat\W+\K\d+")' >> $HOME/.bashrc
 
 make version
 

.github/workflows/ci.yml

@@ -155,42 +155,57 @@ 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
-  combined_extra_builds:
-    runs-on: ubuntu-latest
-    name: Combined Extra Builds
-    needs:
-      - amd64_build
-      - arm64_build
-    steps:
-      - name: Check out code
-        uses: actions/checkout@v4
-      - name: Set up QEMU and Buildx
-        id: setup
-        uses: ./.github/actions/setup
-        with:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      - name: Build and push Hailo-8l build
-        uses: docker/bake-action@v4
-        with:
-          push: true
-          targets: h8l
-          files: docker/hailo8l/h8l.hcl
-          set: |
-            h8l.tags=${{ 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@v3
-        with:
-          push: true
-          targets: rocm
-          files: docker/rocm/rocm.hcl
-          set: |
-            rocm.tags=${{ steps.setup.outputs.image-name }}-rocm
-            *.cache-from=type=gha
+      #- name: AMD/ROCm general build
+      #  env:
+      #    AMDGPU: gfx
+      #    HSA_OVERRIDE: 0
+      #  uses: docker/bake-action@v3
+      #  with:
+      #    push: true
+      #    targets: rocm
+      #    files: docker/rocm/rocm.hcl
+      #    set: |
+      #      rocm.tags=${{ steps.setup.outputs.image-name }}-rocm
+      #      *.cache-from=type=gha
+      #- name: AMD/ROCm gfx900
+      #  env:
+      #    AMDGPU: gfx900
+      #    HSA_OVERRIDE: 1
+      #    HSA_OVERRIDE_GFX_VERSION: 9.0.0
+      #  uses: docker/bake-action@v3
+      #  with:
+      #    push: true
+      #    targets: rocm
+      #    files: docker/rocm/rocm.hcl
+      #    set: |
+      #      rocm.tags=${{ steps.setup.outputs.image-name }}-rocm-gfx900
+      #      *.cache-from=type=gha
+      #- name: AMD/ROCm gfx1030
+      #  env:
+      #    AMDGPU: gfx1030
+      #    HSA_OVERRIDE: 1
+      #    HSA_OVERRIDE_GFX_VERSION: 10.3.0
+      #  uses: docker/bake-action@v3
+      #  with:
+      #    push: true
+      #    targets: rocm
+      #    files: docker/rocm/rocm.hcl
+      #    set: |
+      #      rocm.tags=${{ steps.setup.outputs.image-name }}-rocm-gfx1030
+      #      *.cache-from=type=gha
+      #- name: AMD/ROCm gfx1100
+      #  env:
+      #    AMDGPU: gfx1100
+      #    HSA_OVERRIDE: 1
+      #    HSA_OVERRIDE_GFX_VERSION: 11.0.0
+      #  uses: docker/bake-action@v3
+      #  with:
+      #    push: true
+      #    targets: rocm
+      #    files: docker/rocm/rocm.hcl
+      #    set: |
+      #      rocm.tags=${{ steps.setup.outputs.image-name }}-rocm-gfx1100
+      #      *.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:

.vscode/launch.json

@@ -3,9 +3,10 @@
   "configurations": [
     {
       "name": "Python: Launch Frigate",
-      "type": "debugpy",
+      "type": "python",
       "request": "launch",
-      "module": "frigate"
+      "module": "frigate",
+      "justMyCode": true
     }
   ]
 }

CODEOWNERS

@@ -4,4 +4,3 @@
 /docker/tensorrt/*jetson* @madsciencetist
 /docker/rockchip/ @MarcA711
 /docker/rocm/ @harakas
-/docker/hailo8l/ @spanner3003

Makefile

@@ -1,9 +1,11 @@
 default_target: local
 
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.15.0
+VERSION = 0.14.1
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
+CURRENT_UID := $(shell id -u)
+CURRENT_GID := $(shell id -g)
 BOARDS= #Initialized empty
 
 include docker/*/*.mk
@@ -16,38 +18,25 @@ version:
 	echo 'VERSION = "$(VERSION)-$(COMMIT_HASH)"' > frigate/version.py
 
 local: version
-	docker buildx build --target=frigate --file docker/main/Dockerfile . \
-		--tag frigate:latest \
-		--load
+	docker buildx build --target=frigate --tag frigate:latest --load --file docker/main/Dockerfile .
 
 amd64:
-	docker buildx build --target=frigate --file docker/main/Dockerfile . \
-		--tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) \
-		--platform linux/amd64
+	docker buildx build --platform linux/amd64 --target=frigate --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) --file docker/main/Dockerfile .
 
 arm64:
-	docker buildx build --target=frigate --file docker/main/Dockerfile . \
-		--tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) \
-		--platform linux/arm64
+	docker buildx build --platform linux/arm64 --target=frigate --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) --file docker/main/Dockerfile .
 
 build: version amd64 arm64
-	docker buildx build --target=frigate --file docker/main/Dockerfile . \
-		--tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) \
-		--platform linux/arm64/v8,linux/amd64
+	docker buildx build --platform linux/arm64/v8,linux/amd64 --target=frigate --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) --file docker/main/Dockerfile .
 
 push: push-boards
-	docker buildx build --target=frigate --file docker/main/Dockerfile . \
-		--tag $(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH) \
-		--platform linux/arm64/v8,linux/amd64 \
-		--push
+	docker buildx build --push --platform linux/arm64/v8,linux/amd64 --target=frigate --tag $(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH) --file docker/main/Dockerfile .
 
 run: local
 	docker run --rm --publish=5000:5000 --volume=${PWD}/config:/config frigate:latest
 
 run_tests: local
-	docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest \
-		python3 -u -m unittest
-	docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest \
-		python3 -u -m mypy --config-file frigate/mypy.ini frigate
+	docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest python3 -u -m unittest
+	docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest python3 -u -m mypy --config-file frigate/mypy.ini frigate
 
 .PHONY: run_tests

cspell.json

@@ -7,8 +7,7 @@
     "*.db",
     "node_modules",
     "__pycache__",
-    "dist",
-    "/audio-labelmap.txt"
+    "dist"
   ],
   "language": "en",
   "dictionaryDefinitions": [

docker/hailo8l/Dockerfile

@@ -1,104 +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
-
-# Build HailoRT and create wheel
-FROM wheels AS build-hailort
-ARG TARGETARCH
-
-SHELL ["/bin/bash", "-c"]
-
-# Install necessary APT packages
-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-get -qq update \
-    && apt-get -qq install -y \
-    python3.9 \
-    python3.9-dev \
-    build-essential cmake git \
-    && rm -rf /var/lib/apt/lists/*
-
-# Extract Python version and set environment variables
-RUN PYTHON_VERSION=$(python3 --version 2>&1 | awk '{print $2}' | cut -d. -f1,2) && \
-    PYTHON_VERSION_NO_DOT=$(echo $PYTHON_VERSION | sed 's/\.//') && \
-    echo "PYTHON_VERSION=$PYTHON_VERSION" > /etc/environment && \
-    echo "PYTHON_VERSION_NO_DOT=$PYTHON_VERSION_NO_DOT" >> /etc/environment
-
-# Clone and build HailoRT
-RUN . /etc/environment && \
-    git clone https://github.com/hailo-ai/hailort.git /opt/hailort && \
-    cd /opt/hailort && \
-    git checkout v4.17.0 && \
-    cmake -H. -Bbuild -DCMAKE_BUILD_TYPE=Release -DHAILO_BUILD_PYBIND=1 -DPYBIND11_PYTHON_VERSION=${PYTHON_VERSION} && \
-    cmake --build build --config release --target libhailort && \
-    cmake --build build --config release --target _pyhailort && \
-    cp build/hailort/libhailort/bindings/python/src/_pyhailort.cpython-${PYTHON_VERSION_NO_DOT}-$(if [ $TARGETARCH == "amd64" ]; then echo 'x86_64'; else echo 'aarch64'; fi )-linux-gnu.so hailort/libhailort/bindings/python/platform/hailo_platform/pyhailort/ && \
-    cp build/hailort/libhailort/src/libhailort.so hailort/libhailort/bindings/python/platform/hailo_platform/pyhailort/
-
-RUN ls -ahl /opt/hailort/build/hailort/libhailort/src/
-RUN ls -ahl /opt/hailort/hailort/libhailort/bindings/python/platform/hailo_platform/pyhailort/
-
-# Remove the existing setup.py if it exists in the target directory
-RUN rm -f /opt/hailort/hailort/libhailort/bindings/python/platform/setup.py
-
-# Copy generate_wheel_conf.py and setup.py
-COPY docker/hailo8l/pyhailort_build_scripts/generate_wheel_conf.py /opt/hailort/hailort/libhailort/bindings/python/platform/generate_wheel_conf.py
-COPY docker/hailo8l/pyhailort_build_scripts/setup.py /opt/hailort/hailort/libhailort/bindings/python/platform/setup.py
-
-# Run the generate_wheel_conf.py script
-RUN python3 /opt/hailort/hailort/libhailort/bindings/python/platform/generate_wheel_conf.py
-
-# Create a wheel file using pip3 wheel
-RUN cd /opt/hailort/hailort/libhailort/bindings/python/platform && \
-    python3 setup.py bdist_wheel --dist-dir /hailo-wheels
-
-# Use deps as the base image
-FROM deps AS h8l-frigate
-
-# Copy the wheels from the wheels stage
-COPY --from=h8l-wheels /h8l-wheels /deps/h8l-wheels
-COPY --from=build-hailort /hailo-wheels /deps/hailo-wheels
-COPY --from=build-hailort /etc/environment /etc/environment
-RUN CC=$(python3 -c "import sysconfig; import shlex; cc = sysconfig.get_config_var('CC'); cc_cmd = shlex.split(cc)[0]; print(cc_cmd[:-4] if cc_cmd.endswith('-gcc') else cc_cmd)") && \
-    echo "CC=$CC" >> /etc/environment
-
-# Install the wheels
-RUN pip3 install -U /deps/h8l-wheels/*.whl
-RUN pip3 install -U /deps/hailo-wheels/*.whl
-
-RUN . /etc/environment && \
-    mv /usr/local/lib/python${PYTHON_VERSION}/dist-packages/hailo_platform/pyhailort/libhailort.so /usr/lib/${CC} && \
-    cd /usr/lib/${CC}/ && \
-    ln -s libhailort.so libhailort.so.4.17.0
-
-# Copy base files from the rootfs stage
-COPY --from=rootfs / /
-
-# Set environment variables for Hailo SDK
-ENV PATH="/opt/hailort/bin:${PATH}"
-ENV LD_LIBRARY_PATH="/usr/lib/$(if [ $TARGETARCH == "amd64" ]; then echo 'x86_64'; else echo 'aarch64'; fi )-linux-gnu:${LD_LIBRARY_PATH}"
-
-# Set workdir
-WORKDIR /opt/frigate/

docker/hailo8l/h8l.hcl

@@ -1,27 +0,0 @@
-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 = {
-    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/pyhailort_build_scripts/generate_wheel_conf.py

@@ -1,67 +0,0 @@
-import json
-import os
-import platform
-import sys
-import sysconfig
-
-
-def extract_toolchain_info(compiler):
-    # Remove the "-gcc" or "-g++" suffix if present
-    if compiler.endswith("-gcc") or compiler.endswith("-g++"):
-        compiler = compiler.rsplit("-", 1)[0]
-
-    # Extract the toolchain and ABI part (e.g., "gnu")
-    toolchain_parts = compiler.split("-")
-    abi_conventions = next(
-        (part for part in toolchain_parts if part in ["gnu", "musl", "eabi", "uclibc"]),
-        "",
-    )
-
-    return abi_conventions
-
-
-def generate_wheel_conf():
-    conf_file_path = os.path.join(
-        os.path.abspath(os.path.dirname(__file__)), "wheel_conf.json"
-    )
-
-    # Extract current system and Python version information
-    py_version = f"cp{sys.version_info.major}{sys.version_info.minor}"
-    arch = platform.machine()
-    system = platform.system().lower()
-    libc_version = platform.libc_ver()[1]
-
-    # Get the compiler information
-    compiler = sysconfig.get_config_var("CC")
-    abi_conventions = extract_toolchain_info(compiler)
-
-    # Create the new configuration data
-    new_conf_data = {
-        "py_version": py_version,
-        "arch": arch,
-        "system": system,
-        "libc_version": libc_version,
-        "abi": abi_conventions,
-        "extension": {
-            "posix": "so",
-            "nt": "pyd",  # Windows
-        }[os.name],
-    }
-
-    # If the file exists, load the existing data
-    if os.path.isfile(conf_file_path):
-        with open(conf_file_path, "r") as conf_file:
-            conf_data = json.load(conf_file)
-        # Update the existing data with the new data
-        conf_data.update(new_conf_data)
-    else:
-        # If the file does not exist, use the new data
-        conf_data = new_conf_data
-
-    # Write the updated data to the file
-    with open(conf_file_path, "w") as conf_file:
-        json.dump(conf_data, conf_file, indent=4)
-
-
-if __name__ == "__main__":
-    generate_wheel_conf()

docker/hailo8l/pyhailort_build_scripts/setup.py

@@ -1,111 +0,0 @@
-import json
-import os
-
-from setuptools import find_packages, setup
-from wheel.bdist_wheel import bdist_wheel as orig_bdist_wheel
-
-
-class NonPurePythonBDistWheel(orig_bdist_wheel):
-    """Makes the wheel platform-dependent so it can be based on the _pyhailort architecture"""
-
-    def finalize_options(self):
-        orig_bdist_wheel.finalize_options(self)
-        self.root_is_pure = False
-
-
-def _get_hailort_lib_path():
-    lib_filename = "libhailort.so"
-    lib_path = os.path.join(
-        os.path.abspath(os.path.dirname(__file__)),
-        f"hailo_platform/pyhailort/{lib_filename}",
-    )
-    if os.path.exists(lib_path):
-        print(f"Found libhailort shared library at: {lib_path}")
-    else:
-        print(f"Error: libhailort shared library not found at: {lib_path}")
-        raise FileNotFoundError(f"libhailort shared library not found at: {lib_path}")
-    return lib_path
-
-
-def _get_pyhailort_lib_path():
-    conf_file_path = os.path.join(
-        os.path.abspath(os.path.dirname(__file__)), "wheel_conf.json"
-    )
-    if not os.path.isfile(conf_file_path):
-        raise FileNotFoundError(f"Configuration file not found: {conf_file_path}")
-
-    with open(conf_file_path, "r") as conf_file:
-        content = json.load(conf_file)
-        py_version = content["py_version"]
-        arch = content["arch"]
-        system = content["system"]
-        extension = content["extension"]
-        abi = content["abi"]
-
-        # Construct the filename directly
-        lib_filename = f"_pyhailort.cpython-{py_version.split('cp')[1]}-{arch}-{system}-{abi}.{extension}"
-        lib_path = os.path.join(
-            os.path.abspath(os.path.dirname(__file__)),
-            f"hailo_platform/pyhailort/{lib_filename}",
-        )
-
-        if os.path.exists(lib_path):
-            print(f"Found _pyhailort shared library at: {lib_path}")
-        else:
-            print(f"Error: _pyhailort shared library not found at: {lib_path}")
-            raise FileNotFoundError(
-                f"_pyhailort shared library not found at: {lib_path}"
-            )
-
-        return lib_path
-
-
-def _get_package_paths():
-    packages = []
-    pyhailort_lib = _get_pyhailort_lib_path()
-    hailort_lib = _get_hailort_lib_path()
-    if pyhailort_lib:
-        packages.append(pyhailort_lib)
-    if hailort_lib:
-        packages.append(hailort_lib)
-    packages.append(os.path.abspath("hailo_tutorials/notebooks/*"))
-    packages.append(os.path.abspath("hailo_tutorials/hefs/*"))
-    return packages
-
-
-if __name__ == "__main__":
-    setup(
-        author="Hailo team",
-        author_email="contact@hailo.ai",
-        cmdclass={
-            "bdist_wheel": NonPurePythonBDistWheel,
-        },
-        description="HailoRT",
-        entry_points={
-            "console_scripts": [
-                "hailo=hailo_platform.tools.hailocli.main:main",
-            ]
-        },
-        install_requires=[
-            "argcomplete",
-            "contextlib2",
-            "future",
-            "netaddr",
-            "netifaces",
-            "verboselogs",
-            "numpy==1.23.3",
-        ],
-        name="hailort",
-        package_data={
-            "hailo_platform": _get_package_paths(),
-        },
-        packages=find_packages(),
-        platforms=[
-            "linux_x86_64",
-            "linux_aarch64",
-            "win_amd64",
-        ],
-        url="https://hailo.ai/",
-        version="4.17.0",
-        zip_safe=False,
-    )

docker/hailo8l/requirements-wheels-h8l.txt

@@ -1,12 +0,0 @@
-appdirs==1.4.4
-argcomplete==2.0.0
-contextlib2==0.6.0.post1
-distlib==0.3.6
-filelock==3.8.0
-future==0.18.2
-importlib-metadata==5.1.0
-importlib-resources==5.1.2
-netaddr==0.8.0
-netifaces==0.10.9
-verboselogs==1.7
-virtualenv==20.17.0

docker/hailo8l/user_installation.sh

@@ -1,35 +0,0 @@
-#!/bin/bash
-
-# Update package list and install dependencies
-sudo apt-get update
-sudo apt-get install -y build-essential cmake git wget linux-modules-extra-$(uname -r)
-
-arch=$(uname -m)
-
-if [[ $arch == "x86_64" ]]; then
-    sudo apt install -y linux-headers-$(uname -r);
-else
-    sudo apt install -y linux-modules-extra-$(uname -r);
-fi
-
-# Clone the HailoRT driver repository
-git clone --depth 1 --branch v4.17.0 https://github.com/hailo-ai/hailort-drivers.git
-
-# Build and install the HailoRT driver
-cd hailort-drivers/linux/pcie
-sudo make all
-sudo make install
-
-# Load the Hailo PCI driver
-sudo modprobe hailo_pci
-
-# Download and install the firmware
-cd ../../
-./download_firmware.sh
-sudo mv hailo8_fw.4.17.0.bin /lib/firmware/hailo/hailo8_fw.bin
-
-# Install udev rules
-sudo cp ./linux/pcie/51-hailo-udev.rules /etc/udev/rules.d/
-sudo udevadm control --reload-rules && sudo udevadm trigger
-
-echo "HailoRT driver installation complete."

docker/main/Dockerfile

@@ -148,8 +148,6 @@ RUN apt-get -qq update \
     gfortran openexr libatlas-base-dev libssl-dev\
     libtbb2 libtbb-dev libdc1394-22-dev libopenexr-dev \
     libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev \
-    # sqlite3 dependencies
-    tclsh \
     # scipy dependencies
     gcc gfortran libopenblas-dev liblapack-dev && \
     rm -rf /var/lib/apt/lists/*
@@ -163,16 +161,9 @@ RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
 COPY docker/main/requirements.txt /requirements.txt
 RUN pip3 install -r /requirements.txt
 
-# Build pysqlite3 from source to support ChromaDB
-COPY docker/main/build_pysqlite3.sh /build_pysqlite3.sh
-RUN /build_pysqlite3.sh
-
 COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
 RUN pip3 wheel --wheel-dir=/wheels -r /requirements-wheels.txt
 
-COPY docker/main/requirements-wheels-post.txt /requirements-wheels-post.txt
-RUN pip3 wheel --no-deps --wheel-dir=/wheels-post -r /requirements-wheels-post.txt
-
 
 # Collect deps in a single layer
 FROM scratch AS deps-rootfs
@@ -197,15 +188,7 @@ ARG APT_KEY_DONT_WARN_ON_DANGEROUS_USAGE=DontWarn
 ENV NVIDIA_VISIBLE_DEVICES=all
 ENV NVIDIA_DRIVER_CAPABILITIES="compute,video,utility"
 
-# Turn off Chroma Telemetry: https://docs.trychroma.com/telemetry#opting-out
-ENV ANONYMIZED_TELEMETRY=False
-# Allow resetting the chroma database
-ENV ALLOW_RESET=True
-# Disable tokenizer parallelism warning
-ENV TOKENIZERS_PARALLELISM=true
-
-ENV PATH="/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PATH}"
-ENV LIBAVFORMAT_VERSION_MAJOR=60
+ENV PATH="/usr/lib/btbn-ffmpeg/bin:/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PATH}"
 
 # Install dependencies
 RUN --mount=type=bind,source=docker/main/install_deps.sh,target=/deps/install_deps.sh \
@@ -215,14 +198,6 @@ RUN --mount=type=bind,from=wheels,source=/wheels,target=/deps/wheels \
     python3 -m pip install --upgrade pip && \
     pip3 install -U /deps/wheels/*.whl
 
-# We have to uninstall this dependency specifically
-# as it will break onnxruntime-openvino
-RUN pip3 uninstall -y onnxruntime
-
-RUN --mount=type=bind,from=wheels,source=/wheels-post,target=/deps/wheels \
-    python3 -m pip install --upgrade pip && \
-    pip3 install -U /deps/wheels/*.whl
-
 COPY --from=deps-rootfs / /
 
 RUN ldconfig

docker/main/build_pysqlite3.sh

@@ -1,35 +0,0 @@
-#!/bin/bash
-
-set -euxo pipefail
-
-SQLITE3_VERSION="96c92aba00c8375bc32fafcdf12429c58bd8aabfcadab6683e35bbb9cdebf19e" # 3.46.0
-PYSQLITE3_VERSION="0.5.3"
-
-# Fetch the source code for the latest release of Sqlite.
-if [[ ! -d "sqlite" ]]; then
-  wget https://www.sqlite.org/src/tarball/sqlite.tar.gz?r=${SQLITE3_VERSION} -O sqlite.tar.gz
-  tar xzf sqlite.tar.gz
-  cd sqlite/
-  LIBS="-lm" ./configure --disable-tcl --enable-tempstore=always
-  make sqlite3.c
-  cd ../
-  rm sqlite.tar.gz
-fi
-
-# Grab the pysqlite3 source code.
-if [[ ! -d "./pysqlite3" ]]; then
-  git clone https://github.com/coleifer/pysqlite3.git
-fi
-
-cd pysqlite3/
-git checkout ${PYSQLITE3_VERSION}
-
-# Copy the sqlite3 source amalgamation into the pysqlite3 directory so we can
-# create a self-contained extension module.
-cp "../sqlite/sqlite3.c" ./
-cp "../sqlite/sqlite3.h" ./
-
-# Create the wheel and put it in the /wheels dir.
-sed -i "s|name='pysqlite3-binary'|name=PACKAGE_NAME|g" setup.py
-python3 setup.py build_static
-pip3 wheel . -w /wheels

docker/main/install_deps.sh

@@ -39,54 +39,32 @@ apt-get -qq install --no-install-recommends --no-install-suggests -y \
 
 # btbn-ffmpeg -> amd64
 if [[ "${TARGETARCH}" == "amd64" ]]; then
-    mkdir -p /usr/lib/ffmpeg/5.0
-    mkdir -p /usr/lib/ffmpeg/7.0
+    mkdir -p /usr/lib/btbn-ffmpeg
     wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linux64-gpl-5.1.tar.xz"
-    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1
-    rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/5.0/doc /usr/lib/ffmpeg/5.0/bin/ffplay
-    wget -qO btbn-ffmpeg.tar.xz "https://github.com/BtbN/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linux64-gpl-7.0.tar.xz"
-    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1
-    rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/7.0/doc /usr/lib/ffmpeg/7.0/bin/ffplay
+    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/btbn-ffmpeg --strip-components 1
+    rm -rf btbn-ffmpeg.tar.xz /usr/lib/btbn-ffmpeg/doc /usr/lib/btbn-ffmpeg/bin/ffplay
 fi
 
 # ffmpeg -> arm64
 if [[ "${TARGETARCH}" == "arm64" ]]; then
-    mkdir -p /usr/lib/ffmpeg/5.0
-    mkdir -p /usr/lib/ffmpeg/7.0
+    mkdir -p /usr/lib/btbn-ffmpeg
     wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linuxarm64-gpl-5.1.tar.xz"
-    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1
-    rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/5.0/doc /usr/lib/ffmpeg/5.0/bin/ffplay
-    wget -qO btbn-ffmpeg.tar.xz "https://github.com/BtbN/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linuxarm64-gpl-7.0.tar.xz"
-    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1
-    rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/7.0/doc /usr/lib/ffmpeg/7.0/bin/ffplay
+    tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/btbn-ffmpeg --strip-components 1
+    rm -rf btbn-ffmpeg.tar.xz /usr/lib/btbn-ffmpeg/doc /usr/lib/btbn-ffmpeg/bin/ffplay
 fi
 
 # arch specific packages
 if [[ "${TARGETARCH}" == "amd64" ]]; then
-    # use debian bookworm for amd / intel-i965 driver packages
+    # use debian bookworm for hwaccel 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
     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
-
+        intel-opencl-icd \
+        mesa-va-drivers radeontop libva-drm2 intel-media-va-driver-non-free i965-va-driver libmfx1 intel-gpu-tools
     # 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
-
     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
-    apt-get -qq update
-    apt-get -qq install --no-install-recommends --no-install-suggests -y \
-        intel-opencl-icd intel-level-zero-gpu intel-media-va-driver-non-free \
-        libmfx1 libmfxgen1 libvpl2
-
-    rm -f /usr/share/keyrings/intel-graphics.gpg
-    rm -f /etc/apt/sources.list.d/intel-gpu-jammy.list
 fi
 
 if [[ "${TARGETARCH}" == "arm64" ]]; then
@@ -94,10 +72,6 @@ if [[ "${TARGETARCH}" == "arm64" ]]; then
         libva-drm2 mesa-va-drivers
 fi
 
-# install vulkan
-apt-get -qq install --no-install-recommends --no-install-suggests -y \
-    libvulkan1 mesa-vulkan-drivers
-
 apt-get purge gnupg apt-transport-https xz-utils -y
 apt-get clean autoclean -y
 apt-get autoremove --purge -y

docker/main/requirements-wheels-post.txt

@@ -1,3 +0,0 @@
-# ONNX
-onnxruntime-openvino == 1.19.* ; platform_machine == 'x86_64'
-onnxruntime == 1.19.* ; platform_machine == 'aarch64'

docker/main/requirements-wheels.txt

@@ -1,8 +1,8 @@
 click == 8.1.*
 Flask == 3.0.*
-Flask_Limiter == 3.8.*
+Flask_Limiter == 3.7.*
 imutils == 0.5.*
-joserfc == 1.0.*
+joserfc == 0.11.*
 markupsafe == 2.1.*
 mypy == 1.6.1
 numpy == 1.26.*
@@ -11,13 +11,13 @@ opencv-python-headless == 4.9.0.*
 paho-mqtt == 2.1.*
 pandas == 2.2.*
 peewee == 3.17.*
-peewee_migrate == 1.13.*
+peewee_migrate == 1.12.*
 psutil == 5.9.*
-pydantic == 2.8.*
+pydantic == 2.7.*
 git+https://github.com/fbcotter/py3nvml#egg=py3nvml
 PyYAML == 6.0.*
 pytz == 2024.1
-pyzmq == 26.2.*
+pyzmq == 26.0.*
 ruamel.yaml == 0.18.*
 tzlocal == 5.2
 types-PyYAML == 6.0.*
@@ -28,15 +28,5 @@ norfair == 2.2.*
 setproctitle == 1.3.*
 ws4py == 0.5.*
 unidecode == 1.3.*
-# OpenVino (ONNX installed in wheels-post)
-openvino == 2024.3.*
-# Embeddings
-chromadb == 0.5.0
-onnx_clip == 4.0.*
-# Generative AI
-google-generativeai == 0.6.*
-ollama == 0.2.*
-openai == 1.30.*
-# push notifications
-py-vapid == 1.9.*
-pywebpush == 2.0.*
+onnxruntime == 1.18.*
+openvino == 2024.1.*

docker/main/rootfs/etc/s6-overlay/s6-rc.d/chroma-log/consumer-for

@@ -1 +0,0 @@
-chroma

docker/main/rootfs/etc/s6-overlay/s6-rc.d/chroma-log/pipeline-name

@@ -1 +0,0 @@
-chroma-pipeline

docker/main/rootfs/etc/s6-overlay/s6-rc.d/chroma-log/run

@@ -1,4 +0,0 @@
-#!/command/with-contenv bash
-# shellcheck shell=bash
-
-exec logutil-service /dev/shm/logs/chroma

docker/main/rootfs/etc/s6-overlay/s6-rc.d/chroma-log/type

@@ -1 +0,0 @@
-longrun

docker/main/rootfs/etc/s6-overlay/s6-rc.d/chroma/finish

@@ -1,28 +0,0 @@
-#!/command/with-contenv bash
-# shellcheck shell=bash
-# Take down the S6 supervision tree when the service exits
-
-set -o errexit -o nounset -o pipefail
-
-# Logs should be sent to stdout so that s6 can collect them
-
-declare exit_code_container
-exit_code_container=$(cat /run/s6-linux-init-container-results/exitcode)
-readonly exit_code_container
-readonly exit_code_service="${1}"
-readonly exit_code_signal="${2}"
-readonly service="ChromaDB"
-
-echo "[INFO] Service ${service} exited with code ${exit_code_service} (by signal ${exit_code_signal})"
-
-if [[ "${exit_code_service}" -eq 256 ]]; then
-  if [[ "${exit_code_container}" -eq 0 ]]; then
-    echo $((128 + exit_code_signal)) >/run/s6-linux-init-container-results/exitcode
-  fi
-elif [[ "${exit_code_service}" -ne 0 ]]; then
-  if [[ "${exit_code_container}" -eq 0 ]]; then
-    echo "${exit_code_service}" >/run/s6-linux-init-container-results/exitcode
-  fi
-fi
-
-exec /run/s6/basedir/bin/halt

docker/main/rootfs/etc/s6-overlay/s6-rc.d/chroma/producer-for

@@ -1 +0,0 @@
-chroma-log

docker/main/rootfs/etc/s6-overlay/s6-rc.d/chroma/run

@@ -1,27 +0,0 @@
-#!/command/with-contenv bash
-# shellcheck shell=bash
-# Start the Frigate service
-
-set -o errexit -o nounset -o pipefail
-
-# Logs should be sent to stdout so that s6 can collect them
-
-# Tell S6-Overlay not to restart this service
-s6-svc -O .
-
-search_enabled=`python3 /usr/local/semantic_search/get_search_settings.py | jq -r .enabled`
-
-# Replace the bash process with the Frigate process, redirecting stderr to stdout
-exec 2>&1
-
-if [[ "$search_enabled" == 'true' ]]; then
-    echo "[INFO] Starting ChromaDB..."
-    exec /usr/local/chroma run --path /config/chroma --host 127.0.0.1
-else
-    while true
-    do
-        sleep 9999
-        continue
-    done
-    exit 0
-fi

docker/main/rootfs/etc/s6-overlay/s6-rc.d/chroma/timeout-kill

@@ -1 +0,0 @@
-120000

docker/main/rootfs/etc/s6-overlay/s6-rc.d/chroma/type

@@ -1 +0,0 @@
-longrun

docker/main/rootfs/etc/s6-overlay/s6-rc.d/frigate/run

@@ -44,6 +44,8 @@ function migrate_db_path() {
 
 echo "[INFO] Preparing Frigate..."
 migrate_db_path
+export LIBAVFORMAT_VERSION_MAJOR=$(ffmpeg -version | grep -Po 'libavformat\W+\K\d+')
+
 echo "[INFO] Starting Frigate..."
 
 cd /opt/frigate || echo "[ERROR] Failed to change working directory to /opt/frigate"

docker/main/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/run

@@ -43,6 +43,8 @@ function get_ip_and_port_from_supervisor() {
     export FRIGATE_GO2RTC_WEBRTC_CANDIDATE_INTERNAL="${ip_address}:${webrtc_port}"
 }
 
+export LIBAVFORMAT_VERSION_MAJOR=$(ffmpeg -version | grep -Po 'libavformat\W+\K\d+')
+
 if [[ -f "/dev/shm/go2rtc.yaml" ]]; then
     echo "[INFO] Removing stale config from last run..."
     rm /dev/shm/go2rtc.yaml

docker/main/rootfs/etc/s6-overlay/s6-rc.d/log-prepare/run

@@ -4,7 +4,7 @@
 
 set -o errexit -o nounset -o pipefail
 
-dirs=(/dev/shm/logs/frigate /dev/shm/logs/go2rtc /dev/shm/logs/nginx /dev/shm/logs/certsync /dev/shm/logs/chroma)
+dirs=(/dev/shm/logs/frigate /dev/shm/logs/go2rtc /dev/shm/logs/nginx /dev/shm/logs/certsync)
 
 mkdir -p "${dirs[@]}"
 chown nobody:nogroup "${dirs[@]}"

docker/main/rootfs/usr/local/chroma

@@ -1,14 +0,0 @@
-#!/usr/bin/python3
-# -*- coding: utf-8 -*-s
-__import__("pysqlite3")
-
-import re
-import sys
-
-sys.modules["sqlite3"] = sys.modules.pop("pysqlite3")
-
-from chromadb.cli.cli import app
-
-if __name__ == "__main__":
-    sys.argv[0] = re.sub(r"(-script\.pyw|\.exe)?$", "", sys.argv[0])
-    sys.exit(app())

docker/main/rootfs/usr/local/go2rtc/create_config.py

@@ -2,19 +2,16 @@
 
 import json
 import os
-import shutil
 import sys
 from pathlib import Path
 
 import yaml
 
 sys.path.insert(0, "/opt/frigate")
-from frigate.const import (
-    BIRDSEYE_PIPE,
-    DEFAULT_FFMPEG_VERSION,
-    INCLUDED_FFMPEG_VERSIONS,
+from frigate.const import BIRDSEYE_PIPE  # noqa: E402
+from frigate.ffmpeg_presets import (  # noqa: E402
+    parse_preset_hardware_acceleration_encode,
 )
-from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_encode
 
 sys.path.remove("/opt/frigate")
 
@@ -108,32 +105,16 @@ else:
             **FRIGATE_ENV_VARS
         )
 
-# ensure ffmpeg path is set correctly
-path = config.get("ffmpeg", {}).get("path", "default")
-if path == "default":
-    if shutil.which("ffmpeg") is None:
-        ffmpeg_path = f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg"
-    else:
-        ffmpeg_path = "ffmpeg"
-elif path in INCLUDED_FFMPEG_VERSIONS:
-    ffmpeg_path = f"/usr/lib/ffmpeg/{path}/bin/ffmpeg"
-else:
-    ffmpeg_path = f"{path}/bin/ffmpeg"
-
-if go2rtc_config.get("ffmpeg") is None:
-    go2rtc_config["ffmpeg"] = {"bin": ffmpeg_path}
-elif go2rtc_config["ffmpeg"].get("bin") is None:
-    go2rtc_config["ffmpeg"]["bin"] = ffmpeg_path
-
 # need to replace ffmpeg command when using ffmpeg4
-if int(os.environ.get("LIBAVFORMAT_VERSION_MAJOR", "59") or "59") < 59:
-    if go2rtc_config["ffmpeg"].get("rtsp") is None:
+if int(os.environ["LIBAVFORMAT_VERSION_MAJOR"]) < 59:
+    if go2rtc_config.get("ffmpeg") is None:
+        go2rtc_config["ffmpeg"] = {
+            "rtsp": "-fflags nobuffer -flags low_delay -stimeout 5000000 -user_agent go2rtc/ffmpeg -rtsp_transport tcp -i {input}"
+        }
+    elif go2rtc_config["ffmpeg"].get("rtsp") is None:
         go2rtc_config["ffmpeg"]["rtsp"] = (
             "-fflags nobuffer -flags low_delay -stimeout 5000000 -user_agent go2rtc/ffmpeg -rtsp_transport tcp -i {input}"
         )
-else:
-    if go2rtc_config.get("ffmpeg") is None:
-        go2rtc_config["ffmpeg"] = {"path": ""}
 
 for name in go2rtc_config.get("streams", {}):
     stream = go2rtc_config["streams"][name]
@@ -164,7 +145,7 @@ if config.get("birdseye", {}).get("restream", False):
     birdseye: dict[str, any] = config.get("birdseye")
 
     input = f"-f rawvideo -pix_fmt yuv420p -video_size {birdseye.get('width', 1280)}x{birdseye.get('height', 720)} -r 10 -i {BIRDSEYE_PIPE}"
-    ffmpeg_cmd = f"exec:{parse_preset_hardware_acceleration_encode(ffmpeg_path, config.get('ffmpeg', {}).get('hwaccel_args'), input, '-rtsp_transport tcp -f rtsp {output}')}"
+    ffmpeg_cmd = f"exec:{parse_preset_hardware_acceleration_encode(config.get('ffmpeg', {}).get('hwaccel_args'), input, '-rtsp_transport tcp -f rtsp {output}')}"
 
     if go2rtc_config.get("streams"):
         go2rtc_config["streams"]["birdseye"] = ffmpeg_cmd

docker/main/rootfs/usr/local/semantic_search/get_search_settings.py

@@ -1,28 +0,0 @@
-"""Prints the semantic_search config as json to stdout."""
-
-import json
-import os
-
-import yaml
-
-config_file = os.environ.get("CONFIG_FILE", "/config/config.yml")
-
-# Check if we can use .yaml instead of .yml
-config_file_yaml = config_file.replace(".yml", ".yaml")
-if os.path.isfile(config_file_yaml):
-    config_file = config_file_yaml
-
-try:
-    with open(config_file) as f:
-        raw_config = f.read()
-
-    if config_file.endswith((".yaml", ".yml")):
-        config: dict[str, any] = yaml.safe_load(raw_config)
-    elif config_file.endswith(".json"):
-        config: dict[str, any] = json.loads(raw_config)
-except FileNotFoundError:
-    config: dict[str, any] = {}
-
-search_config: dict[str, any] = config.get("semantic_search", {"enabled": False})
-
-print(json.dumps(search_config))

docker/rockchip/Dockerfile

@@ -22,6 +22,5 @@ ADD https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.0.0/librknnrt
 
 RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffmpeg
 RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffprobe
-ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-5/ffmpeg /usr/lib/ffmpeg/6.0/bin/
-ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-5/ffprobe /usr/lib/ffmpeg/6.0/bin/
-ENV PATH="/usr/lib/ffmpeg/6.0/bin/:${PATH}"
+ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-5/ffmpeg /usr/lib/btbn-ffmpeg/bin/
+ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-5/ffprobe /usr/lib/btbn-ffmpeg/bin/

docker/rockchip/rk.mk

@@ -1,15 +1,10 @@
 BOARDS += rk
 
 local-rk: version
-	docker buildx bake --file=docker/rockchip/rk.hcl rk \
-		--set rk.tags=frigate:latest-rk \
-		--load
+	docker buildx bake --load --file=docker/rockchip/rk.hcl --set rk.tags=frigate:latest-rk rk
 
 build-rk: version
-	docker buildx bake --file=docker/rockchip/rk.hcl rk \
-		--set rk.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rk
+	docker buildx bake --file=docker/rockchip/rk.hcl --set rk.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rk rk
 
 push-rk: build-rk
-	docker buildx bake --file=docker/rockchip/rk.hcl rk \
-		--set rk.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rk \
-		--push
+	docker buildx bake --push --file=docker/rockchip/rk.hcl --set rk.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rk rk

docker/rocm/Dockerfile

@@ -23,11 +23,11 @@ COPY docker/rocm/rocm-pin-600 /etc/apt/preferences.d/
 
 RUN apt-get update
 
-RUN apt-get -y install --no-install-recommends migraphx hipfft roctracer
+RUN apt-get -y install --no-install-recommends migraphx
 RUN apt-get -y install --no-install-recommends migraphx-dev
 
 RUN mkdir -p /opt/rocm-dist/opt/rocm-$ROCM/lib
-RUN cd /opt/rocm-$ROCM/lib && cp -dpr libMIOpen*.so* libamd*.so* libhip*.so* libhsa*.so* libmigraphx*.so* librocm*.so* librocblas*.so* libroctracer*.so* librocfft*.so* /opt/rocm-dist/opt/rocm-$ROCM/lib/
+RUN cd /opt/rocm-$ROCM/lib && cp -dpr libMIOpen*.so* libamd*.so* libhip*.so* libhsa*.so* libmigraphx*.so* librocm*.so* librocblas*.so* /opt/rocm-dist/opt/rocm-$ROCM/lib/
 RUN cd /opt/rocm-dist/opt/ && ln -s rocm-$ROCM rocm
 
 RUN mkdir -p /opt/rocm-dist/etc/ld.so.conf.d/
@@ -69,11 +69,7 @@ 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
+COPY docker/rocm/rootfs/ /
 
 #######################################################################
 FROM scratch AS rocm-dist
@@ -105,3 +101,6 @@ ENV HSA_OVERRIDE_GFX_VERSION=$HSA_OVERRIDE_GFX_VERSION
 #######################################################################
 FROM rocm-prelim-hsa-override$HSA_OVERRIDE as rocm-deps
 
+# Request yolov8 download at startup
+ENV DOWNLOAD_YOLOV8=1
+

docker/rocm/requirements-wheels-rocm.txt

@@ -1 +0,0 @@
-onnxruntime-rocm @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v1.0.0/onnxruntime_rocm-1.17.3-cp39-cp39-linux_x86_64.whl

docker/rocm/rocm.mk

@@ -4,50 +4,14 @@ BOARDS += rocm
 ROCM_CHIPSETS:=gfx900:9.0.0 gfx1030:10.3.0 gfx1100:11.0.0
 
 local-rocm: version
-	$(foreach chipset,$(ROCM_CHIPSETS), \
-		AMDGPU=$(word 1,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE=1 \
-		docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-			--set rocm.tags=frigate:latest-rocm-$(word 1,$(subst :, ,$(chipset))) \
-			--load \
-	&&) true
-	
-	unset HSA_OVERRIDE_GFX_VERSION && \
-	HSA_OVERRIDE=0 \
-	AMDGPU=gfx \
-	docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-		--set rocm.tags=frigate:latest-rocm \
-		--load
+	$(foreach chipset,$(ROCM_CHIPSETS),AMDGPU=$(word 1,$(subst :, ,$(chipset))) HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) HSA_OVERRIDE=1 docker buildx bake --load --file=docker/rocm/rocm.hcl --set rocm.tags=frigate:latest-rocm-$(word 1,$(subst :, ,$(chipset))) rocm;)
+	unset HSA_OVERRIDE_GFX_VERSION && HSA_OVERRIDE=0 AMDGPU=gfx docker buildx bake --load --file=docker/rocm/rocm.hcl --set rocm.tags=frigate:latest-rocm rocm
 
 build-rocm: version
-	$(foreach chipset,$(ROCM_CHIPSETS), \
-		AMDGPU=$(word 1,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE=1 \
-		docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-			--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm-$(chipset) \
-	&&) true
-
-	unset HSA_OVERRIDE_GFX_VERSION && \
-	HSA_OVERRIDE=0 \
-	AMDGPU=gfx \
-	docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-		--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm
+	$(foreach chipset,$(ROCM_CHIPSETS),AMDGPU=$(word 1,$(subst :, ,$(chipset))) HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) HSA_OVERRIDE=1 docker buildx bake --file=docker/rocm/rocm.hcl --set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm-$(chipset) rocm;)
+	unset HSA_OVERRIDE_GFX_VERSION && HSA_OVERRIDE=0 AMDGPU=gfx docker buildx bake --file=docker/rocm/rocm.hcl --set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm rocm
 
 push-rocm: build-rocm
-	$(foreach chipset,$(ROCM_CHIPSETS), \
-		AMDGPU=$(word 1,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) \
-		HSA_OVERRIDE=1 \
-		docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-			--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm-$(chipset) \
-			--push \
-	&&) true
+	$(foreach chipset,$(ROCM_CHIPSETS),AMDGPU=$(word 1,$(subst :, ,$(chipset))) HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) HSA_OVERRIDE=1 docker buildx bake --push --file=docker/rocm/rocm.hcl --set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm-$(chipset) rocm;)
+	unset HSA_OVERRIDE_GFX_VERSION && HSA_OVERRIDE=0 AMDGPU=gfx docker buildx bake --push --file=docker/rocm/rocm.hcl --set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm rocm
 
-	unset HSA_OVERRIDE_GFX_VERSION && \
-	HSA_OVERRIDE=0 \
-	AMDGPU=gfx \
-	docker buildx bake --file=docker/rocm/rocm.hcl rocm \
-		--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm \
-		--push

docker/rocm/rootfs/etc/s6-overlay/s6-rc.d/compile-rocm-models/run

@@ -0,0 +1,20 @@
+#!/command/with-contenv bash
+# shellcheck shell=bash
+# Compile YoloV8 ONNX files into ROCm MIGraphX files
+
+OVERRIDE=$(cd /opt/frigate && python3 -c 'import frigate.detectors.plugins.rocm as rocm; print(rocm.auto_override_gfx_version())')
+
+if ! test -z "$OVERRIDE"; then
+  echo "Using HSA_OVERRIDE_GFX_VERSION=${OVERRIDE}"
+  export HSA_OVERRIDE_GFX_VERSION=$OVERRIDE
+fi
+
+for onnx in /config/model_cache/yolov8/*.onnx
+do
+  mxr="${onnx%.onnx}.mxr"
+  if ! test -f $mxr; then
+    echo "processing $onnx into $mxr"
+    /opt/rocm/bin/migraphx-driver compile $onnx --optimize --gpu --enable-offload-copy --binary -o $mxr
+  fi
+done
+

docker/rocm/rootfs/etc/s6-overlay/s6-rc.d/compile-rocm-models/type

@@ -0,0 +1 @@
+oneshot

docker/rocm/rootfs/etc/s6-overlay/s6-rc.d/compile-rocm-models/up

@@ -0,0 +1 @@
+/etc/s6-overlay/s6-rc.d/compile-rocm-models/run

docker/rpi/Dockerfile

@@ -12,7 +12,5 @@ RUN rm -rf /usr/lib/btbn-ffmpeg/
 RUN --mount=type=bind,source=docker/rpi/install_deps.sh,target=/deps/install_deps.sh \
     /deps/install_deps.sh
 
-ENV LIBAVFORMAT_VERSION_MAJOR=58
-
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /

docker/rpi/rpi.mk

@@ -1,15 +1,10 @@
 BOARDS += rpi
 
 local-rpi: version
-	docker buildx bake --file=docker/rpi/rpi.hcl rpi \
-		--set rpi.tags=frigate:latest-rpi \
-		--load
+	docker buildx bake --load --file=docker/rpi/rpi.hcl --set rpi.tags=frigate:latest-rpi rpi
 
 build-rpi: version
-	docker buildx bake --file=docker/rpi/rpi.hcl rpi \
-		--set rpi.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rpi
+	docker buildx bake --file=docker/rpi/rpi.hcl --set rpi.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rpi rpi
 
 push-rpi: build-rpi
-	docker buildx bake --file=docker/rpi/rpi.hcl rpi \
-		--set rpi.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rpi \
-		--push
+	docker buildx bake --push --file=docker/rpi/rpi.hcl --set rpi.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rpi rpi

docker/tensorrt/Dockerfile.amd64

@@ -12,28 +12,12 @@ ARG TARGETARCH
 COPY docker/tensorrt/requirements-amd64.txt /requirements-tensorrt.txt
 RUN mkdir -p /trt-wheels && pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt
 
-# Build CuDNN
-FROM wget AS cudnn-deps
-
-ARG COMPUTE_LEVEL
-
-RUN apt-get update \
-    && apt-get install -y git build-essential
-
-RUN wget https://developer.download.nvidia.com/compute/cuda/repos/debian11/x86_64/cuda-keyring_1.1-1_all.deb \
-&& dpkg -i cuda-keyring_1.1-1_all.deb \
-&& apt-get update \
-&& apt-get -y install cuda-toolkit \
-&& rm -rf /var/lib/apt/lists/*
-
 FROM tensorrt-base AS frigate-tensorrt
 ENV TRT_VER=8.5.3
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
     pip3 install -U /deps/trt-wheels/*.whl && \
     ldconfig
-COPY --from=cudnn-deps /usr/local/cuda-12.6 /usr/local/cuda
 
-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 / /
 
@@ -42,7 +26,6 @@ FROM devcontainer AS devcontainer-trt
 
 COPY --from=trt-deps /usr/local/lib/libyolo_layer.so /usr/local/lib/libyolo_layer.so
 COPY --from=trt-deps /usr/local/src/tensorrt_demos /usr/local/src/tensorrt_demos
-COPY --from=cudnn-deps /usr/local/cuda-12.6 /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 \

docker/tensorrt/requirements-amd64.txt

@@ -8,7 +8,5 @@ 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-cufft-cu11==10.*; platform_machine == 'x86_64'
 onnx==1.14.0; platform_machine == 'x86_64'
-onnxruntime-gpu==1.17.*; platform_machine == 'x86_64'
-protobuf==3.20.3; platform_machine == 'x86_64'
+protobuf==3.20.3; platform_machine == 'x86_64'

docker/tensorrt/trt.mk

@@ -7,35 +7,20 @@ JETPACK4_ARGS := ARCH=arm64 BASE_IMAGE=$(JETPACK4_BASE) SLIM_BASE=$(JETPACK4_BAS
 JETPACK5_ARGS := ARCH=arm64 BASE_IMAGE=$(JETPACK5_BASE) SLIM_BASE=$(JETPACK5_BASE) TRT_BASE=$(JETPACK5_BASE)
 
 local-trt: version
-	$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=frigate:latest-tensorrt \
-		--load
+	$(X86_DGPU_ARGS) docker buildx bake --load --file=docker/tensorrt/trt.hcl --set tensorrt.tags=frigate:latest-tensorrt tensorrt
 
 local-trt-jp4: version
-	$(JETPACK4_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=frigate:latest-tensorrt-jp4 \
-		--load
+	$(JETPACK4_ARGS) docker buildx bake --load --file=docker/tensorrt/trt.hcl --set tensorrt.tags=frigate:latest-tensorrt-jp4 tensorrt
 
 local-trt-jp5: version
-	$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=frigate:latest-tensorrt-jp5 \
-		--load
+	$(JETPACK5_ARGS) docker buildx bake --load --file=docker/tensorrt/trt.hcl --set tensorrt.tags=frigate:latest-tensorrt-jp5 tensorrt
 
 build-trt:
-	$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt
-	$(JETPACK4_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp4
-	$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp5
+	$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt tensorrt
+	$(JETPACK4_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp4 tensorrt
+	$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp5 tensorrt
 
 push-trt: build-trt
-	$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt \
-		--push
-	$(JETPACK4_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp4 \
-		--push
-	$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
-		--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp5 \
-		--push
+	$(X86_DGPU_ARGS) docker buildx bake --push --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt tensorrt
+	$(JETPACK4_ARGS) docker buildx bake --push --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp4 tensorrt
+	$(JETPACK5_ARGS) docker buildx bake --push --file=docker/tensorrt/trt.hcl --set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp5 tensorrt

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](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)
@@ -41,7 +55,7 @@ environment_vars:
 
 ### `database`
 
-Tracked object and recording information is managed in a sqlite database at `/config/frigate.db`. If that database is deleted, recordings will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within Home Assistant.
+Event and recording information is managed in a sqlite database at `/config/frigate.db`. If that database is deleted, recordings will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within Home Assistant.
 
 If you are storing your database on a network share (SMB, NFS, etc), you may get a `database is locked` error message on startup. You can customize the location of the database in the config if necessary.
 
@@ -162,15 +176,15 @@ listen [::]:5000 ipv6only=off;
 
 ### Custom ffmpeg build
 
-Included with Frigate is a build of ffmpeg that works for the vast majority of users. However, there exists some hardware setups which have incompatibilities with the included build. In this case, statically built ffmpeg binary can be downloaded to /config and used.
+Included with Frigate is a build of ffmpeg that works for the vast majority of users. However, there exists some hardware setups which have incompatibilities with the included build. In this case, a docker volume mapping can be used to overwrite the included ffmpeg build with an ffmpeg build that works for your specific hardware setup.
 
 To do this:
 
-1. Download your ffmpeg build and uncompress to the Frigate config folder.
+1. Download your ffmpeg build and uncompress to a folder on the host (let's use `/home/appdata/frigate/custom-ffmpeg` for this example).
 2. Update your docker-compose or docker CLI to include `'/home/appdata/frigate/custom-ffmpeg':'/usr/lib/btbn-ffmpeg':'ro'` in the volume mappings.
 3. Restart Frigate and the custom version will be used if the mapping was done correctly.
 
-NOTE: The folder that is set for the config needs to be the folder that contains `/bin`. So if the full structure is `/home/appdata/frigate/custom-ffmpeg/bin/ffmpeg` then the `ffmpeg -> path` field should be `/config/custom-ffmpeg/bin`.
+NOTE: The folder that is mapped from the host needs to be the folder that contains `/bin`. So if the full structure is `/home/appdata/frigate/custom-ffmpeg/bin/ffmpeg` then `/home/appdata/frigate/custom-ffmpeg` needs to be mapped to `/usr/lib/btbn-ffmpeg`.
 
 ### Custom go2rtc version
 
@@ -183,7 +197,7 @@ To do this:
 3. Give `go2rtc` execute permission.
 4. Restart Frigate and the custom version will be used, you can verify by checking go2rtc logs.
 
-## Validating your config.yaml file updates
+## Validating your config.yml file updates
 
 When frigate starts up, it checks whether your config file is valid, and if it is not, the process exits. To minimize interruptions when updating your config, you have three options -- you can edit the config via the WebUI which has built in validation, use the config API, or you can validate on the command line using the frigate docker container.
 

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 Flask-Limiter, and the string notation for valid values is available in [the documentation](https://flask-limiter.readthedocs.io/en/stable/configuration.html#rate-limit-string-notation).

docs/docs/configuration/camera_specific.md

@@ -9,6 +9,12 @@ This page makes use of presets of FFmpeg args. For more information on presets,
 
 :::
 
+:::note
+
+Many cameras support encoding options which greatly affect the live view experience, see the [Live view](/configuration/live) page for more info.
+
+:::
+
 ## MJPEG Cameras
 
 Note that mjpeg cameras require encoding the video into h264 for recording, and restream roles. This will use significantly more CPU than if the cameras supported h264 feeds directly. It is recommended to use the restream role to create an h264 restream and then use that as the source for ffmpeg.
@@ -187,4 +193,4 @@ ffmpeg:
 
 ### TP-Link VIGI Cameras
 
-TP-Link VIGI cameras need some adjustments to the main stream settings on the camera itself to avoid issues. The stream needs to be configured as `H264` with `Smart Coding` set to `off`. Without these settings you may have problems when trying to watch recorded footage. For example Firefox will stop playback after a few seconds and show the following error message: `The media playback was aborted due to a corruption problem or because the media used features your browser did not support.`.
+TP-Link VIGI cameras need some adjustments to the main stream settings on the camera itself to avoid issues. The stream needs to be configured as `H264` with `Smart Coding` set to `off`. Without these settings you may have problems when trying to watch recorded events. For example Firefox will stop playback after a few seconds and show the following error message: `The media playback was aborted due to a corruption problem or because the media used features your browser did not support.`.

docs/docs/configuration/cameras.md

@@ -7,7 +7,7 @@ title: Camera Configuration
 
 Several inputs can be configured for each camera and the role of each input can be mixed and matched based on your needs. This allows you to use a lower resolution stream for object detection, but create recordings from a higher resolution stream, or vice versa.
 
-A camera is enabled by default but can be temporarily disabled by using `enabled: False`. Existing tracked objects and recordings can still be accessed. Live streams, recording and detecting are not working. Camera specific configurations will be used.
+A camera is enabled by default but can be temporarily disabled by using `enabled: False`. Existing events and recordings can still be accessed. Live streams, recording and detecting are not working. Camera specific configurations will be used.
 
 Each role can only be assigned to one input per camera. The options for roles are as follows:
 
@@ -79,29 +79,41 @@ cameras:
 
 If the ONVIF connection is successful, PTZ controls will be available in the camera's WebUI.
 
+:::tip
+
+If your ONVIF camera does not require authentication credentials, you may still need to specify an empty string for `user` and `password`, eg: `user: ""` and `password: ""`.
+
+:::
+
 An ONVIF-capable camera that supports relative movement within the field of view (FOV) can also be configured to automatically track moving objects and keep them in the center of the frame. For autotracking setup, see the [autotracking](autotracking.md) docs.
 
 ## ONVIF PTZ camera recommendations
 
 This list of working and non-working PTZ cameras is based on user feedback.
 
-| Brand or specific camera | PTZ Controls | Autotracking | Notes                                                                                                                                           |
-| ------------------------ | :----------: | :----------: | ----------------------------------------------------------------------------------------------------------------------------------------------- |
-| Amcrest                  |      ✅      |      ✅      | ⛔️ Generally, Amcrest should work, but some older models (like the common IP2M-841) don't support autotracking                                 |
-| Amcrest ASH21            |      ❌      |      ❌      | No ONVIF support                                                                                                                                |
-| Ctronics PTZ             |      ✅      |      ❌      |                                                                                                                                                 |
-| Dahua                    |      ✅      |      ✅      |                                                                                                                                                 |
-| Foscam R5                |      ✅      |      ❌      |                                                                                                                                                 |
-| Hanwha XNP-6550RH        |      ✅      |      ❌      |                                                                                                                                                 |
-| Hikvision                |      ✅      |      ❌      | Incomplete ONVIF support (MoveStatus won't update even on latest firmware) - reported with HWP-N4215IH-DE and DS-2DE3304W-DE, but likely others |
-| Reolink 511WA            |      ✅      |      ❌      | Zoom only                                                                                                                                       |
-| Reolink E1 Pro           |      ✅      |      ❌      |                                                                                                                                                 |
-| Reolink E1 Zoom          |      ✅      |      ❌      |                                                                                                                                                 |
-| Reolink RLC-823A 16x     |      ✅      |      ❌      |                                                                                                                                                 |
-| Sunba 405-D20X           |      ✅      |      ❌      |                                                                                                                                                 |
-| Tapo                     |      ✅      |      ❌      | Many models supported, ONVIF Service Port: 2020                                                                                                 |
-| Uniview IPC672LR-AX4DUPK |      ✅      |      ❌      | Firmware says FOV relative movement is supported, but camera doesn't actually move when sending ONVIF commands                                  |
-| Vikylin PTZ-2804X-I2     |      ❌      |      ❌      | Incomplete ONVIF support                                                                                                                        |
+| Brand or specific camera     | PTZ Controls | Autotracking | Notes                                                                                                                                           |
+| ---------------------------- | :----------: | :----------: | ----------------------------------------------------------------------------------------------------------------------------------------------- |
+| Amcrest                      |      ✅      |      ✅      | ⛔️ Generally, Amcrest should work, but some older models (like the common IP2M-841) don't support autotracking                                 |
+| Amcrest ASH21                |      ✅      |      ❌      | ONVIF service port: 80                                                                                                                          |
+| Amcrest IP4M-S2112EW-AI      |      ✅      |      ❌      | FOV relative movement not supported.                                                                                                            |
+| Amcrest IP5M-1190EW          |      ✅      |      ❌      | ONVIF Port: 80. FOV relative movement not supported.                                                                                            |
+| Ctronics PTZ                 |      ✅      |      ❌      |                                                                                                                                                 |
+| Dahua                        |      ✅      |      ✅      |                                                                                                                                                 |
+| Dahua DH-SD2A500HB           |      ✅      |      ❌      |                                                                                                                                                 |
+| Foscam R5                    |      ✅      |      ❌      |                                                                                                                                                 |
+| Hanwha XNP-6550RH            |      ✅      |      ❌      |                                                                                                                                                 |
+| Hikvision                    |      ✅      |      ❌      | Incomplete ONVIF support (MoveStatus won't update even on latest firmware) - reported with HWP-N4215IH-DE and DS-2DE3304W-DE, but likely others |
+| Hikvision DS-2DE3A404IWG-E/W |      ✅      |      ✅      |                                                                                                                                                 |
+| Reolink 511WA                |      ✅      |      ❌      | Zoom only                                                                                                                                       |
+| Reolink E1 Pro               |      ✅      |      ❌      |                                                                                                                                                 |
+| Reolink E1 Zoom              |      ✅      |      ❌      |                                                                                                                                                 |
+| Reolink RLC-823A 16x         |      ✅      |      ❌      |                                                                                                                                                 |
+| Speco O8P32X                 |      ✅      |      ❌      |                                                                                                                                                 |
+| Sunba 405-D20X               |      ✅      |      ❌      |                                                                                                                                                 |
+| Tapo                         |      ✅      |      ❌      | Many models supported, ONVIF Service Port: 2020                                                                                                 |
+| Uniview IPC672LR-AX4DUPK     |      ✅      |      ❌      | Firmware says FOV relative movement is supported, but camera doesn't actually move when sending ONVIF commands                                  |
+| Uniview IPC6612SR-X33-VG     |      ✅      |      ✅      | Leave `calibrate_on_startup` as `False`. A user has reported that zooming with `absolute` is working.                                           |
+| Vikylin PTZ-2804X-I2         |      ❌      |      ❌      | Incomplete ONVIF support                                                                                                                        |
 
 ## Setting up camera groups
 

docs/docs/configuration/genai.md

@@ -1,149 +0,0 @@
----
-id: genai
-title: Generative AI
----
-
-Generative AI can be used to automatically generate descriptions based on the thumbnails of your tracked objects. This helps with [Semantic Search](/configuration/semantic_search) in Frigate by providing detailed text descriptions as a basis of the search query.
-
-Semantic Search must be enabled to use Generative AI. Descriptions are accessed via the _Explore_ view in the Frigate UI by clicking on a tracked object's thumbnail.
-
-## Configuration
-
-Generative AI can be enabled for all cameras or only for specific cameras. There are currently 3 providers available to integrate with Frigate.
-
-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:
-  enabled: True
-  provider: gemini
-  api_key: "{FRIGATE_GEMINI_API_KEY}"
-  model: gemini-1.5-flash
-
-cameras:
-  front_camera: ...
-  indoor_camera:
-    genai: # <- disable GenAI for your indoor camera
-      enabled: False
-```
-
-## Ollama
-
-[Ollama](https://ollama.com/) allows you to self-host large language models and keep everything running locally. It provides a nice API over [llama.cpp](https://github.com/ggerganov/llama.cpp). It is highly recommended to host this server on a machine with an Nvidia graphics card, or on a Apple silicon Mac for best performance. Most of the 7b parameter 4-bit vision models will fit inside 8GB of VRAM. There is also a [docker container](https://hub.docker.com/r/ollama/ollama) available.
-
-### Supported Models
-
-You must use a vision capable model with Frigate. Current model variants can be found [in their model library](https://ollama.com/library). At the time of writing, this includes `llava`, `llava-llama3`, `llava-phi3`, and `moondream`.
-
-:::note
-
-You should have at least 8 GB of RAM available (or VRAM if running on GPU) to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
-
-:::
-
-### Configuration
-
-```yaml
-genai:
-  enabled: True
-  provider: ollama
-  base_url: http://localhost:11434
-  model: llava
-```
-
-## Google Gemini
-
-Google Gemini has a free tier allowing [15 queries per minute](https://ai.google.dev/pricing) to the API, which is more than sufficient for standard Frigate usage.
-
-### Supported Models
-
-You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://ai.google.dev/gemini-api/docs/models/gemini). At the time of writing, this includes `gemini-1.5-pro` and `gemini-1.5-flash`.
-
-### Get API Key
-
-To start using Gemini, you must first get an API key from [Google AI Studio](https://aistudio.google.com).
-
-1. Accept the Terms of Service
-2. Click "Get API Key" from the right hand navigation
-3. Click "Create API key in new project"
-4. Copy the API key for use in your config
-
-### Configuration
-
-```yaml
-genai:
-  enabled: True
-  provider: gemini
-  api_key: "{FRIGATE_GEMINI_API_KEY}"
-  model: gemini-1.5-flash
-```
-
-## OpenAI
-
-OpenAI does not have a free tier for their API. With the release of gpt-4o, pricing has been reduced and each generation should cost fractions of a cent if you choose to go this route.
-
-### Supported Models
-
-You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://platform.openai.com/docs/models). At the time of writing, this includes `gpt-4o` and `gpt-4-turbo`.
-
-### Get API Key
-
-To start using OpenAI, you must first [create an API key](https://platform.openai.com/api-keys) and [configure billing](https://platform.openai.com/settings/organization/billing/overview).
-
-### Configuration
-
-```yaml
-genai:
-  enabled: True
-  provider: openai
-  api_key: "{FRIGATE_OPENAI_API_KEY}"
-  model: gpt-4o
-```
-
-## Custom Prompts
-
-Frigate sends multiple frames from the tracked object along with a prompt to your Generative AI provider asking it to generate a description. The default prompt is as follows:
-
-```
-Describe the {label} in the sequence of images with as much detail as possible. Do not describe the background.
-```
-
-:::tip
-
-Prompts can use variable replacements like `{label}`, `{sub_label}`, and `{camera}` to substitute information from the tracked object as part of the prompt.
-
-:::
-
-You are also able to define custom prompts in your configuration.
-
-```yaml
-genai:
-  enabled: True
-  provider: ollama
-  base_url: http://localhost:11434
-  model: llava
-  prompt: "Describe the {label} in these images from the {camera} security camera."
-  object_prompts:
-    person: "Describe the main person in these images (gender, age, clothing, activity, etc). Do not include where the activity is occurring (sidewalk, concrete, driveway, etc)."
-    car: "Label the primary vehicle in these images with just the name of the company if it is a delivery vehicle, or the color make and model."
-```
-
-Prompts can also be overriden at the camera level to provide a more detailed prompt to the model about your specific camera, if you desire.
-
-```yaml
-cameras:
-  front_door:
-    genai:
-      prompt: "Describe the {label} in these images from the {camera} security camera at the front door of a house, aimed outward toward the street."
-      object_prompts:
-        person: "Describe the main person in these images (gender, age, clothing, activity, etc). Do not include where the activity is occurring (sidewalk, concrete, driveway, etc). If delivering a package, include the company the package is from."
-        cat: "Describe the cat in these images (color, size, tail). Indicate whether or not the cat is by the flower pots. If the cat is chasing a mouse, make up a name for the mouse."
-```
-
-### Experiment with prompts
-
-Many providers also have a public facing chat interface for their models. Download a couple of different thumbnails or snapshots from Frigate and try new things in the playground to get descriptions to your liking before updating the prompt in Frigate.
-
-- OpenAI - [ChatGPT](https://chatgpt.com)
-- Gemini - [Google AI Studio](https://aistudio.google.com)
-- Ollama - [Open WebUI](https://docs.openwebui.com/)

docs/docs/configuration/hardware_acceleration.md

@@ -65,33 +65,24 @@ Or map in all the `/dev/video*` devices.
 
 ## Intel-based CPUs
 
-**Recommended hwaccel Preset**
-
-| CPU Generation | Intel Driver | Recommended Preset | Notes                               |
-| -------------- | ------------ | ------------------ | ----------------------------------- |
-| gen1 - gen7    | i965         | preset-vaapi       | qsv is not supported                |
-| gen8 - gen12   | iHD          | preset-vaapi       | preset-intel-qsv-* can also be used |
-| gen13+         | iHD / Xe     | preset-intel-qsv-* |                                     |
-| Intel Arc GPU  | iHD / Xe     | preset-intel-qsv-* |                                     |
-
-:::note
-
-The default driver is `iHD`. You may need to change the driver to `i965` by adding the following environment variable `LIBVA_DRIVER_NAME=i965` to your docker-compose file or [in the `frigate.yaml` for HA OS users](advanced.md#environment_vars).
-
-See [The Intel Docs](https://www.intel.com/content/www/us/en/support/articles/000005505/processors.html to figure out what generation your CPU is.)
-
-:::
-
 ### Via VAAPI
 
-VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams.
+VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams. VAAPI is recommended for all generations of Intel-based CPUs.
 
 ```yaml
 ffmpeg:
   hwaccel_args: preset-vaapi
 ```
 
-### Via Quicksync
+:::note
+
+With some of the processors, like the J4125, the default driver `iHD` doesn't seem to work correctly for hardware acceleration. You may need to change the driver to `i965` by adding the following environment variable `LIBVA_DRIVER_NAME=i965` to your docker-compose file or [in the `frigate.yaml` for HA OS users](advanced.md#environment_vars).
+
+:::
+
+### Via Quicksync (>=10th Generation only)
+
+If VAAPI does not work for you, you can try QSV if your processor supports it. QSV must be set specifically based on the video encoding of the stream.
 
 #### H.264 streams
 
@@ -379,7 +370,7 @@ Make sure to follow the [Rockchip specific installation instructions](/frigate/i
 
 ### Configuration
 
-Add one of the following FFmpeg presets to your `config.yaml` to enable hardware video processing:
+Add one of the following FFmpeg presets to your `config.yml` to enable hardware video processing:
 
 ```yaml
 # if you try to decode a h264 encoded stream

docs/docs/configuration/index.md

@@ -56,11 +56,6 @@ go2rtc:
     password: "{FRIGATE_GO2RTC_RTSP_PASSWORD}"
 ```
 
-```yaml
-genai:
-  api_key: "{FRIGATE_GENAI_API_KEY}"
-```
-
 ## Common configuration examples
 
 Here are some common starter configuration examples. Refer to the [reference config](./reference.md) for detailed information about all the config values.
@@ -72,7 +67,7 @@ Here are some common starter configuration examples. Refer to the [reference con
 - Hardware acceleration for decoding video
 - USB Coral detector
 - Save all video with any detectable motion for 7 days regardless of whether any objects were detected or not
-- Continue to keep all video if it qualified as an alert or detection for 30 days
+- Continue to keep all video if it was during any event for 30 days
 - Save snapshots for 30 days
 - Motion mask for the camera timestamp
 
@@ -95,12 +90,10 @@ record:
   retain:
     days: 7
     mode: motion
-  alerts:
+  events:
     retain:
-      days: 30
-  detections:
-    retain:
-      days: 30
+      default: 30
+      mode: motion
 
 snapshots:
   enabled: True
@@ -130,7 +123,7 @@ cameras:
 - VAAPI hardware acceleration for decoding video
 - USB Coral detector
 - Save all video with any detectable motion for 7 days regardless of whether any objects were detected or not
-- Continue to keep all video if it qualified as an alert or detection for 30 days
+- Continue to keep all video if it was during any event for 30 days
 - Save snapshots for 30 days
 - Motion mask for the camera timestamp
 
@@ -151,12 +144,10 @@ record:
   retain:
     days: 7
     mode: motion
-  alerts:
+  events:
     retain:
-      days: 30
-  detections:
-    retain:
-      days: 30
+      default: 30
+      mode: motion
 
 snapshots:
   enabled: True
@@ -186,7 +177,7 @@ cameras:
 - VAAPI hardware acceleration for decoding video
 - OpenVino detector
 - Save all video with any detectable motion for 7 days regardless of whether any objects were detected or not
-- Continue to keep all video if it qualified as an alert or detection for 30 days
+- Continue to keep all video if it was during any event for 30 days
 - Save snapshots for 30 days
 - Motion mask for the camera timestamp
 
@@ -218,12 +209,10 @@ record:
   retain:
     days: 7
     mode: motion
-  alerts:
+  events:
     retain:
-      days: 30
-  detections:
-    retain:
-      days: 30
+      default: 30
+      mode: motion
 
 snapshots:
   enabled: True

docs/docs/configuration/live.md

@@ -11,11 +11,21 @@ Frigate intelligently uses three different streaming technologies to display you
 
 The jsmpeg live view will use more browser and client GPU resources. Using go2rtc is highly recommended and will provide a superior experience.
 
-| Source | Latency | Frame Rate                            | Resolution | Audio                        | Requires go2rtc | Other Limitations                                                                    |
-| ------ | ------- | ------------------------------------- | ---------- | ---------------------------- | --------------- | ------------------------------------------------------------------------------------ |
-| jsmpeg | low     | same as `detect -> fps`, capped at 10 | 720p       | no                           | no              | resolution is configurable, but go2rtc is recommended if you want higher resolutions |
-| mse    | low     | native                                | native     | yes (depends on audio codec) | yes             | iPhone requires iOS 17.1+, Firefox is h.264 only                                     |
-| webrtc | lowest  | native                                | native     | yes (depends on audio codec) | yes             | requires extra config, doesn't support h.265                                         |
+| Source | Frame Rate                            | Resolution | Audio                        | Requires go2rtc | Notes                                                                                                                                                               |
+| ------ | ------------------------------------- | ---------- | ---------------------------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| jsmpeg | same as `detect -> fps`, capped at 10 | 720p       | no                           | no              | Resolution is configurable, but go2rtc is recommended if you want higher resolutions and better frame rates. jsmpeg is Frigate's default without go2rtc configured. |
+| mse    | native                                | native     | yes (depends on audio codec) | yes             | iPhone requires iOS 17.1+, Firefox is h.264 only. This is Frigate's default when go2rtc is configured.                                                              |
+| webrtc | native                                | native     | yes (depends on audio codec) | yes             | Requires extra configuration, doesn't support h.265. Frigate attempts to use WebRTC when MSE fails or when using a camera's two-way talk feature.                   |
+
+### Camera Settings Recommendations
+
+If you are using go2rtc, you should adjust the following settings in your camera's firmware for the best experience with Live view:
+
+- Video codec: **H.264** - provides the most compatible video codec with all Live view technologies and browsers. Avoid any kind of "smart codec" or "+" codec like _H.264+_ or _H.265+_. as these non-standard codecs remove keyframes (see below).
+- Audio codec: **AAC** - provides the most compatible audio codec with all Live view technologies and browsers that support audio.
+- I-frame interval (sometimes called the keyframe interval, the interframe space, or the GOP length): match your camera's frame rate, or choose "1x" (for interframe space on Reolink cameras). For example, if your stream outputs 20fps, your i-frame interval should be 20 (or 1x on Reolink). Values higher than the frame rate will cause the stream to take longer to begin playback. See [this page](https://gardinal.net/understanding-the-keyframe-interval/) for more on keyframes.
+
+The default video and audio codec on your camera may not always be compatible with your browser, which is why setting them to H.264 and AAC is recommended. See the [go2rtc docs](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#codecs-madness) for codec support information.
 
 ### Audio Support
 
@@ -32,6 +42,15 @@ go2rtc:
       - "ffmpeg:http_cam#audio=opus" # <- copy of the stream which transcodes audio to the missing codec (usually will be opus)
 ```
 
+If your camera does not have audio and you are having problems with Live view, you should have go2rtc send video only:
+
+```yaml
+go2rtc:
+  streams:
+    no_audio_camera:
+      - ffmpeg:rtsp://192.168.1.5:554/live0#video=copy
+```
+
 ### Setting Stream 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`.

docs/docs/configuration/motion_detection.md

@@ -13,11 +13,11 @@ Once motion is detected, it tries to group up nearby areas of motion together in
 
 The default motion settings should work well for the majority of cameras, however there are cases where tuning motion detection can lead to better and more optimal results. Each camera has its own environment with different variables that affect motion, this means that the same motion settings will not fit all of your cameras.
 
-Before tuning motion it is important to understand the goal. In an optimal configuration, motion from people and cars would be detected, but not grass moving, lighting changes, timestamps, etc. If your motion detection is too sensitive, you will experience higher CPU loads and greater false positives from the increased rate of object detection. If it is not sensitive enough, you will miss objects that you want to track.
+Before tuning motion it is important to understand the goal. In an optimal configuration, motion from people and cars would be detected, but not grass moving, lighting changes, timestamps, etc. If your motion detection is too sensitive, you will experience higher CPU loads and greater false positives from the increased rate of object detection. If it is not sensitive enough, you will miss events.
 
 ## Create Motion Masks
 
-First, mask areas with regular motion not caused by the objects you want to detect. The best way to find candidates for motion masks is by watching the debug stream with motion boxes enabled. Good use cases for motion masks are timestamps or tree limbs and large bushes that regularly move due to wind. When possible, avoid creating motion masks that would block motion detection for objects you want to track **even if they are in locations where you don't want alerts or detections**. Motion masks should not be used to avoid detecting objects in specific areas. More details can be found [in the masks docs.](/configuration/masks.md).
+First, mask areas with regular motion not caused by the objects you want to detect. The best way to find candidates for motion masks is by watching the debug stream with motion boxes enabled. Good use cases for motion masks are timestamps or tree limbs and large bushes that regularly move due to wind. When possible, avoid creating motion masks that would block motion detection for objects you want to track **even if they are in locations where you don't want events**. Motion masks should not be used to avoid detecting objects in specific areas. More details can be found [in the masks docs.](/configuration/masks.md).
 
 ## Prepare For Testing
 
@@ -29,7 +29,7 @@ Now that things are set up, find a time to tune that represents normal circumsta
 
 :::note
 
-Remember that motion detection is just used to determine when object detection should be used. You should aim to have motion detection sensitive enough that you won't miss objects you want to detect with object detection. The goal is to prevent object detection from running constantly for every small pixel change in the image. Windy days are still going to result in lots of motion being detected.
+Remember that motion detection is just used to determine when object detection should be used. You should aim to have motion detection sensitive enough that you won't miss events from objects you want to detect with object detection. The goal is to prevent object detection from running constantly for every small pixel change in the image. Windy days are still going to result in lots of motion being detected.
 
 :::
 
@@ -94,7 +94,7 @@ motion:
 
 :::tip
 
-Some cameras like doorbell cameras may have missed detections when someone walks directly in front of the camera and the lightning_threshold causes motion detection to be re-calibrated. In this case, it may be desirable to increase the `lightning_threshold` to ensure these objects are not missed.
+Some cameras like doorbell cameras may have missed detections when someone walks directly in front of the camera and the lightning_threshold causes motion detection to be re-calibrated. In this case, it may be desirable to increase the `lightning_threshold` to ensure these events are not missed.
 
 :::
 

docs/docs/configuration/notifications.md

@@ -1,42 +0,0 @@
----
-id: notifications
-title: Notifications
----
-
-# Notifications
-
-Frigate offers native notifications using the [WebPush Protocol](https://web.dev/articles/push-notifications-web-push-protocol) which uses the [VAPID spec](https://tools.ietf.org/html/draft-thomson-webpush-vapid) to deliver notifications to web apps using encryption.
-
-## Setting up Notifications
-
-In order to use notifications the following requirements must be met:
-
-- Frigate must be accessed via a secure https connection
-- A supported browser must be used. Currently Chrome, Firefox, and Safari are known to be supported.
-- In order for notifications to be usable externally, Frigate must be accessible externally
-
-### Configuration
-
-To configure notifications, go to the Frigate WebUI -> Settings -> Notifications and enable, then fill out the fields and save.
-
-### Registration
-
-Once notifications are enabled, press the `Register for Notifications` button on all devices that you would like to receive notifications on. This will register the background worker. After this Frigate must be restarted and then notifications will begin to be sent.
-
-## Supported Notifications
-
-Currently notifications are only supported for review alerts. More notifications will be supported in the future.
-
-:::note
-
-Currently, only Chrome supports images in notifications. Safari and Firefox will only show a title and message in the notification.
-
-:::
-
-## Reduce Notification Latency
-
-Different platforms handle notifications differently, some settings changes may be required to get optimal notification delivery.
-
-### Android
-
-Most Android phones have battery optimization settings. To get reliable Notification delivery the browser (Chrome, Firefox) should have battery optimizations disabled. If Frigate is running as a PWA then the Frigate app should have battery optimizations disabled as well.

docs/docs/configuration/object_detectors.md

@@ -3,32 +3,9 @@ id: object_detectors
 title: Object Detectors
 ---
 
-# Supported Hardware
-
-Frigate supports multiple different detectors that work on different types of hardware:
-
-**Most Hardware**
-- [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB and m.2 format allowing for a wide range of compatibility with devices.
-- [Hailo](#hailo-8l): The Hailo8 AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
-
-**AMD**
-- [ROCm](#amdrocm-gpu-detector): ROCm can run on AMD Discrete GPUs to provide efficient object detection.
-- [ONNX](#onnx): ROCm will automatically be detected and used as a detector in the `-rocm` Frigate image when a supported ONNX model is configured.
-
-**Intel**
-- [OpenVino](#openvino-detector): OpenVino can run on Intel Arc GPUs, Intel integrated GPUs, and Intel CPUs to provide efficient object detection.
-- [ONNX](#onnx): OpenVINO will automatically be detected and used as a detector in the default Frigate image when a supported ONNX model is configured.
-
-**Nvidia**
-- [TensortRT](#nvidia-tensorrt-detector): TensorRT can run on Nvidia GPUs, using one of many default models.
-- [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt` Frigate image when a supported ONNX model is configured.
-
-**Rockchip**
-- [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs.
-
 # Officially Supported Detectors
 
-Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `openvino`, `tensorrt`, `rknn`, and `hailo8l`. 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.
+Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `openvino`, `tensorrt`, and `rknn`. 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.
 
 ## CPU Detector (not recommended)
 
@@ -145,22 +122,6 @@ The OpenVINO device to be used is specified using the `"device"` attribute accor
 
 OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will also run on AMD CPUs despite having no official support for it. A supported Intel platform is required to use the `GPU` device with OpenVINO. For detailed system requirements, see [OpenVINO System Requirements](https://docs.openvino.ai/2024/about-openvino/release-notes-openvino/system-requirements.html)
 
-:::tip
-
-When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
-
-```yaml
-detectors:
-  ov_0:
-    type: openvino
-    device: GPU
-  ov_1:
-    type: openvino
-    device: GPU
-```
-
-:::
-
 ### Supported Models
 
 #### SSDLite MobileNet v2
@@ -188,7 +149,7 @@ 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/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).
+[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
 
@@ -317,173 +278,6 @@ model:
   height: 320
 ```
 
-## AMD/ROCm GPU detector
-
-### Setup
-
-The `rocm` detector supports running YOLO-NAS models on AMD GPUs. Use a frigate docker image with `-rocm` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-rocm`.
-
-### Docker settings for GPU access
-
-ROCm needs access to the `/dev/kfd` and `/dev/dri` devices. When docker or frigate is not run under root then also `video` (and possibly `render` and `ssl/_ssl`) groups should be added.
-
-When running docker directly the following flags should be added for device access:
-
-```bash
-$ docker run --device=/dev/kfd --device=/dev/dri  \
-    ...
-```
-
-When using docker compose:
-
-```yaml
-services:
-  frigate:
----
-devices:
-  - /dev/dri
-  - /dev/kfd
-```
-
-For reference on recommended settings see [running ROCm/pytorch in Docker](https://rocm.docs.amd.com/projects/install-on-linux/en/develop/how-to/3rd-party/pytorch-install.html#using-docker-with-pytorch-pre-installed).
-
-### Docker settings for overriding the GPU chipset
-
-Your GPU might work just fine without any special configuration but in many cases they need manual settings. AMD/ROCm software stack comes with a limited set of GPU drivers and for newer or missing models you will have to override the chipset version to an older/generic version to get things working.
-
-Also AMD/ROCm does not "officially" support integrated GPUs. It still does work with most of them just fine but requires special settings. One has to configure the `HSA_OVERRIDE_GFX_VERSION` environment variable. See the [ROCm bug report](https://github.com/ROCm/ROCm/issues/1743) for context and examples.
-
-For the rocm frigate build there is some automatic detection:
-
-- gfx90c -> 9.0.0
-- gfx1031 -> 10.3.0
-- gfx1103 -> 11.0.0
-
-If you have something else you might need to override the `HSA_OVERRIDE_GFX_VERSION` at Docker launch. Suppose the version you want is `9.0.0`, then you should configure it from command line as:
-
-```bash
-$ docker run -e HSA_OVERRIDE_GFX_VERSION=9.0.0 \
-    ...
-```
-
-When using docker compose:
-
-```yaml
-services:
-  frigate:
-...
-environment:
-  HSA_OVERRIDE_GFX_VERSION: "9.0.0"
-```
-
-Figuring out what version you need can be complicated as you can't tell the chipset name and driver from the AMD brand name.
-
-- first make sure that rocm environment is running properly by running `/opt/rocm/bin/rocminfo` in the frigate container -- it should list both the CPU and the GPU with their properties
-- find the chipset version you have (gfxNNN) from the output of the `rocminfo` (see below)
-- use a search engine to query what `HSA_OVERRIDE_GFX_VERSION` you need for the given gfx name ("gfxNNN ROCm HSA_OVERRIDE_GFX_VERSION")
-- override the `HSA_OVERRIDE_GFX_VERSION` with relevant value
-- if things are not working check the frigate docker logs
-
-#### Figuring out if AMD/ROCm is working and found your GPU
-
-```bash
-$ docker exec -it frigate /opt/rocm/bin/rocminfo
-```
-
-#### Figuring out your AMD GPU chipset version:
-
-We unset the `HSA_OVERRIDE_GFX_VERSION` to prevent an existing override from messing up the result:
-
-```bash
-$ docker exec -it frigate /bin/bash -c '(unset HSA_OVERRIDE_GFX_VERSION && /opt/rocm/bin/rocminfo |grep gfx)'
-```
-
-### Supported Models
-
-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.
-
-After placing the downloaded onnx model in your config folder, you can use the following configuration:
-
-```yaml
-detectors:
-  onnx:
-    type: rocm
-
-model:
-  model_type: yolonas
-  width: 320 # <--- should match whatever was set in notebook
-  height: 320 # <--- should match whatever was set in notebook
-  input_pixel_format: bgr
-  path: /config/yolo_nas_s.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.
-
-## 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.
-
-:::tip
-
-When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
-
-```yaml
-detectors:
-  onnx_0:
-    type: onnx
-  onnx_1:
-    type: onnx
-```
-
-:::
-
-### Supported Models
-
-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.
-
-After placing the downloaded onnx model in your config folder, you can use the following configuration:
-
-```yaml
-detectors:
-  onnx:
-    type: onnx
-
-model:
-  model_type: yolonas
-  width: 320 # <--- should match whatever was set in notebook
-  height: 320 # <--- should match whatever was set in notebook
-  input_pixel_format: bgr
-  path: /config/yolo_nas_s.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.
-
 ## Deepstack / CodeProject.AI Server Detector
 
 The Deepstack / CodeProject.AI Server detector for Frigate allows you to integrate Deepstack and CodeProject.AI object detection capabilities into Frigate. CodeProject.AI and DeepStack are open-source AI platforms that can be run on various devices such as the Raspberry Pi, Nvidia Jetson, and other compatible hardware. It is important to note that the integration is performed over the network, so the inference times may not be as fast as native Frigate detectors, but it still provides an efficient and reliable solution for object detection and tracking.
@@ -592,25 +386,3 @@ $ 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
-
-This detector is available for use with Hailo-8 AI Acceleration Module.
-
-### Configuration
-
-```yaml
-detectors:
-  hailo8l:
-    type: hailo8l
-    device: PCIe
-    model:
-      path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
-
-model:
-  width: 300
-  height: 300
-  input_tensor: nhwc
-  input_pixel_format: bgr
-  model_type: ssd
-```

docs/docs/configuration/object_filters.md

@@ -20,13 +20,15 @@ For object filters in your configuration, any single detection below `min_score`
 
 In frame 2, the score is below the `min_score` value, so Frigate ignores it and it becomes a 0.0. The computed score is the median of the score history (padding to at least 3 values), and only when that computed score crosses the `threshold` is the object marked as a true positive. That happens in frame 4 in the example.
 
+show image of snapshot vs event with differing scores
+
 ### Minimum Score
 
-Any detection below `min_score` will be immediately thrown out and never tracked because it is considered a false positive. If `min_score` is too low then false positives may be detected and tracked which can confuse the object tracker and may lead to wasted resources. If `min_score` is too high then lower scoring true positives like objects that are further away or partially occluded may be thrown out which can also confuse the tracker and cause valid tracked objects to be lost or disjointed.
+Any detection below `min_score` will be immediately thrown out and never tracked because it is considered a false positive. If `min_score` is too low then false positives may be detected and tracked which can confuse the object tracker and may lead to wasted resources. If `min_score` is too high then lower scoring true positives like objects that are further away or partially occluded may be thrown out which can also confuse the tracker and cause valid events to be lost or disjointed.
 
 ### Threshold
 
-`threshold` is used to determine that the object is a true positive. Once an object is detected with a score >= `threshold` object is considered a true positive. If `threshold` is too low then some higher scoring false positives may create an tracked object. If `threshold` is too high then true positive tracked objects may be missed due to the object never scoring high enough.
+`threshold` is used to determine that the object is a true positive. Once an object is detected with a score >= `threshold` object is considered a true positive. If `threshold` is too low then some higher scoring false positives may create an event. If `threshold` is too high then true positive events may be missed due to the object never scoring high enough.
 
 ## Object Shape
 
@@ -50,7 +52,7 @@ Conceptually, a ratio of 1 is a square, 0.5 is a "tall skinny" box, and 2 is a "
 
 ### Zones
 
-[Required zones](/configuration/zones.md) can be a great tool to reduce false positives that may be detected in the sky or other areas that are not of interest. The required zones will only create tracked objects for objects that enter the zone.
+[Required zones](/configuration/zones.md) can be a great tool to reduce false positives that may be detected in the sky or other areas that are not of interest. The required zones will only create events for objects that enter the zone.
 
 ### Object Masks
 

docs/docs/configuration/objects.md

@@ -5,7 +5,7 @@ title: Available Objects
 
 import labels from "../../../labelmap.txt";
 
-Frigate includes the object models listed below from the Google Coral test data.
+Frigate includes the object labels listed below from the Google Coral test data.
 
 Please note:
 

docs/docs/configuration/pwa.md

@@ -0,0 +1,24 @@
+---
+id: pwa
+title: Installing Frigate App
+---
+
+Frigate supports being installed as a [Progressive Web App](https://web.dev/explore/progressive-web-apps) on Desktop, Android, and iOS.
+
+This adds features including the ability to deep link directly into the app.
+
+## Requirements
+
+In order to install Frigate as a PWA, the following requirements must be met:
+
+- Frigate must be accessed via a secure context (localhost, secure https, etc.)
+- On Android, Firefox, Chrome, Edge, Opera, and Samsung Internet Browser all support installing PWAs.
+- On iOS 16.4 and later, PWAs can be installed from the Share menu in Safari, Chrome, Edge, Firefox, and Orion.
+
+## Installation
+
+Installation varies slightly based on the device that is being used:
+
+- Desktop: Use the install button typically found in right edge of the address bar
+- Android: Use the `Install as App` button in the more options menu
+- iOS: Use the `Add to Homescreen` button in the share menu

docs/docs/configuration/record.md

@@ -3,7 +3,7 @@ id: record
 title: Recording
 ---
 
-Recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM-DD/HH/<camera_name>/MM.SS.mp4` in **UTC time**. These recordings are written directly from your camera stream without re-encoding. Each camera supports a configurable retention policy in the config. Frigate chooses the largest matching retention value between the recording retention and the tracked object retention when determining if a recording should be removed.
+Recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM-DD/HH/<camera_name>/MM.SS.mp4` in **UTC time**. These recordings are written directly from your camera stream without re-encoding. Each camera supports a configurable retention policy in the config. Frigate chooses the largest matching retention value between the recording retention and the event retention when determining if a recording should be removed.
 
 New recording segments are written from the camera stream to cache, they are only moved to disk if they match the setup recording retention policy.
 
@@ -13,7 +13,7 @@ H265 recordings can be viewed in Chrome 108+, Edge and Safari only. All other br
 
 ### Most conservative: Ensure all video is saved
 
-For users deploying Frigate in environments where it is important to have contiguous video stored even if there was no detectable motion, the following config will store all video for 3 days. After 3 days, only video containing motion and overlapping with alerts or detections will be retained until 30 days have passed.
+For users deploying Frigate in environments where it is important to have contiguous video stored even if there was no detectable motion, the following config will store all video for 3 days. After 3 days, only video containing motion and overlapping with events will be retained until 30 days have passed.
 
 ```yaml
 record:
@@ -21,13 +21,9 @@ record:
   retain:
     days: 3
     mode: all
-  alerts:
+  events:
     retain:
-      days: 30
-      mode: motion
-  detections:
-    retain:
-      days: 30
+      default: 30
       mode: motion
 ```
 
@@ -41,28 +37,25 @@ record:
   retain:
     days: 3
     mode: motion
-  alerts:
+  events:
     retain:
-      days: 30
-      mode: motion
-  detections:
-    retain:
-      days: 30
+      default: 30
       mode: motion
 ```
 
-### Minimum: Alerts only
+### Minimum: Events only
 
-If you only want to retain video that occurs during a tracked object, this config will discard video unless an alert is ongoing.
+If you only want to retain video that occurs during an event, this config will discard video unless an event is ongoing.
 
 ```yaml
 record:
   enabled: True
   retain:
     days: 0
-  alerts:
+    mode: all
+  events:
     retain:
-      days: 30
+      default: 30
       mode: motion
 ```
 
@@ -72,7 +65,7 @@ As of Frigate 0.12 if there is less than an hour left of storage, the oldest 2 h
 
 ## Configuring Recording Retention
 
-Frigate supports both continuous and tracked object based recordings with separate retention modes and retention periods.
+Frigate supports both continuous and event based recordings with separate retention modes and retention periods.
 
 :::tip
 
@@ -93,28 +86,25 @@ record:
 
 Continuous recording supports different retention modes [which are described below](#what-do-the-different-retain-modes-mean)
 
-### Object Recording
+### Event Recording
 
-The number of days to record review items can be specified for review items classified as alerts as well as tracked objects.
+If you only used clips in previous versions with recordings disabled, you can use the following config to get the same behavior. This is also the default behavior when recordings are enabled.
 
 ```yaml
 record:
   enabled: True
-  alerts:
+  events:
     retain:
-      days: 10 # <- number of days to keep alert recordings
-  detections:
-    retain:
-      days: 10 # <- number of days to keep detections recordings
+      default: 10 # <- number of days to keep event recordings
 ```
 
-This configuration will retain recording segments that overlap with alerts and detections for 10 days. Because multiple tracked objects can reference the same recording segments, this avoids storing duplicate footage for overlapping tracked objects and reduces overall storage needs.
+This configuration will retain recording segments that overlap with events and have active tracked objects for 10 days. Because multiple events can reference the same recording segments, this avoids storing duplicate footage for overlapping events and reduces overall storage needs.
 
 **WARNING**: Recordings still must be enabled in the config. If a camera has recordings disabled in the config, enabling via the methods listed above will have no effect.
 
 ## What do the different retain modes mean?
 
-Frigate saves from the stream with the `record` role in 10 second segments. These options determine which recording segments are kept for continuous recording (but can also affect tracked objects).
+Frigate saves from the stream with the `record` role in 10 second segments. These options determine which recording segments are kept for continuous recording (but can also affect events).
 
 Let's say you have Frigate configured so that your doorbell camera would retain the last **2** days of continuous recording.
 
@@ -122,7 +112,11 @@ Let's say you have Frigate configured so that your doorbell camera would retain
 - With the `motion` option the only parts of those 48 hours would be segments that Frigate detected motion. This is the middle ground option that won't keep all 48 hours, but will likely keep all segments of interest along with the potential for some extra segments.
 - With the `active_objects` option the only segments that would be kept are those where there was a true positive object that was not considered stationary.
 
-The same options are available with alerts and detections, except it will only save the recordings when it overlaps with a review item of that type.
+The same options are available with events. Let's consider a scenario where you drive up and park in your driveway, go inside, then come back out 4 hours later.
+
+- With the `all` option all segments for the duration of the event would be saved for the event. This event would have 4 hours of footage.
+- With the `motion` option all segments for the duration of the event with motion would be saved. This means any segment where a car drove by in the street, person walked by, lighting changed, etc. would be saved.
+- With the `active_objects` it would only keep segments where the object was active. In this case the only segments that would be saved would be the ones where the car was driving up, you going inside, you coming outside, and the car driving away. Essentially reducing the 4 hours to a minute or two of event footage.
 
 A configuration example of the above retain modes where all `motion` segments are stored for 7 days and `active objects` are stored for 14 days would be as follows:
 
@@ -132,18 +126,33 @@ record:
   retain:
     days: 7
     mode: motion
-  alerts:
+  events:
     retain:
-      days: 14
-      mode: active_objects
-  detections:
-    retain:
-      days: 14
+      default: 14
       mode: active_objects
 ```
 
 The above configuration example can be added globally or on a per camera basis.
 
+### Object Specific Retention
+
+You can also set specific retention length for an object type. The below configuration example builds on from above but also specifies that recordings of dogs only need to be kept for 2 days and recordings of cars should be kept for 7 days.
+
+```yaml
+record:
+  enabled: True
+  retain:
+    days: 7
+    mode: motion
+  events:
+    retain:
+      default: 14
+      mode: active_objects
+      objects:
+        dog: 2
+        car: 7
+```
+
 ## Can I have "continuous" recordings, but only at certain times?
 
 Using Frigate UI, HomeAssistant, or MQTT, cameras can be automated to only record in certain situations or at certain times.

docs/docs/configuration/reference.md

@@ -210,10 +210,6 @@ birdseye:
 # Optional: ffmpeg configuration
 # More information about presets at https://docs.frigate.video/configuration/ffmpeg_presets
 ffmpeg:
-  # Optional: ffmpeg binry path (default: shown below)
-  # can also be set to `7.0` or `5.0` to specify one of the included versions
-  # or can be set to any path that holds `bin/ffmpeg` & `bin/ffprobe`
-  path: "default"
   # Optional: global ffmpeg args (default: shown below)
   global_args: -hide_banner -loglevel warning -threads 2
   # Optional: global hwaccel args (default: auto detect)
@@ -275,13 +271,13 @@ detect:
   # especially when using separate streams for detect and record.
   # Use this setting to make the timeline bounding boxes more closely align
   # with the recording. The value can be positive or negative.
-  # TIP: Imagine there is an tracked object clip with a person walking from left to right.
-  #      If the tracked object lifecycle bounding box is consistently to the left of the person
+  # TIP: Imagine there is an event clip with a person walking from left to right.
+  #      If the event timeline bounding box is consistently to the left of the person
   #      then the value should be decreased. Similarly, if a person is walking from
   #      left to right and the bounding box is consistently ahead of the person
   #      then the value should be increased.
   # TIP: This offset is dynamic so you can change the value and it will update existing
-  #      tracked objects, this makes it easy to tune.
+  #      events, this makes it easy to tune.
   # WARNING: Fast moving objects will likely not have the bounding box align.
   annotation_offset: 0
 
@@ -324,6 +320,9 @@ review:
       - car
       - person
     # Optional: required zones for an object to be marked as an alert (default: none)
+    # NOTE: when settings required zones globally, this zone must exist on all cameras
+    #       or the config will be considered invalid. In that case the required_zones
+    #       should be configured at the camera level.
     required_zones:
       - driveway
   # Optional: detections configuration
@@ -333,12 +332,20 @@ review:
       - car
       - person
     # Optional: required zones for an object to be marked as a detection (default: none)
+    # NOTE: when settings required zones globally, this zone must exist on all cameras
+    #       or the config will be considered invalid. In that case the required_zones
+    #       should be configured at the camera level.
     required_zones:
       - driveway
 
 # Optional: Motion configuration
 # NOTE: Can be overridden at the camera level
 motion:
+  # Optional: enables detection for the camera (default: True)
+  # NOTE: Motion detection is required for object detection,
+  #       setting this to False and leaving detect enabled
+  #       will result in an error on startup.
+  enabled: False
   # Optional: The threshold passed to cv2.threshold to determine if a pixel is different enough to be counted as motion. (default: shown below)
   # Increasing this value will make motion detection less sensitive and decreasing it will make motion detection more sensitive.
   # The value should be between 1 and 255.
@@ -376,14 +383,6 @@ motion:
   # Optional: Delay when updating camera motion through MQTT from ON -> OFF (default: shown below).
   mqtt_off_delay: 30
 
-# Optional: Notification Configuration
-notifications:
-  # Optional: Enable notification service (default: shown below)
-  enabled: False
-  # Optional: Email for push service to reach out to
-  # NOTE: This is required to use notifications
-  email: "admin@example.com"
-
 # Optional: Record configuration
 # NOTE: Can be overridden at the camera level
 record:
@@ -398,9 +397,9 @@ record:
   sync_recordings: False
   # Optional: Retention settings for recording
   retain:
-    # Optional: Number of days to retain recordings regardless of tracked objects (default: shown below)
-    # NOTE: This should be set to 0 and retention should be defined in alerts and detections section below
-    #       if you only want to retain recordings of alerts and detections.
+    # Optional: Number of days to retain recordings regardless of events (default: shown below)
+    # NOTE: This should be set to 0 and retention should be defined in events section below
+    #       if you only want to retain recordings of events.
     days: 0
     # Optional: Mode for retention. Available options are: all, motion, and active_objects
     #   all - save all recording segments regardless of activity
@@ -423,48 +422,34 @@ record:
     # Optional: Quality of recording preview (default: shown below).
     # Options are: very_low, low, medium, high, very_high
     quality: medium
-  # Optional: alert recording settings
-  alerts:
-    # Optional: Number of seconds before the alert to include (default: shown below)
+  # Optional: Event recording settings
+  events:
+    # Optional: Number of seconds before the event to include (default: shown below)
     pre_capture: 5
-    # Optional: Number of seconds after the alert to include (default: shown below)
+    # Optional: Number of seconds after the event to include (default: shown below)
     post_capture: 5
-    # Optional: Retention settings for recordings of alerts
+    # Optional: Objects to save recordings for. (default: all tracked objects)
+    objects:
+      - person
+    # Optional: Retention settings for recordings of events
     retain:
-      # Required: Retention days (default: shown below)
-      days: 14
+      # Required: Default retention days (default: shown below)
+      default: 10
       # Optional: Mode for retention. (default: shown below)
-      #   all - save all recording segments for alerts regardless of activity
-      #   motion - save all recordings segments for alerts with any detected motion
-      #   active_objects - save all recording segments for alerts with active/moving objects
-      #
-      # NOTE: If the retain mode for the camera is more restrictive than the mode configured
-      #       here, the segments will already be gone by the time this mode is applied.
-      #       For example, if the camera retain mode is "motion", the segments without motion are
-      #       never stored, so setting the mode to "all" here won't bring them back.
-      mode: motion
-  # Optional: detection recording settings
-  detections:
-    # Optional: Number of seconds before the detection to include (default: shown below)
-    pre_capture: 5
-    # Optional: Number of seconds after the detection to include (default: shown below)
-    post_capture: 5
-    # Optional: Retention settings for recordings of detections
-    retain:
-      # Required: Retention days (default: shown below)
-      days: 14
-      # Optional: Mode for retention. (default: shown below)
-      #   all - save all recording segments for detections regardless of activity
-      #   motion - save all recordings segments for detections with any detected motion
-      #   active_objects - save all recording segments for detections with active/moving objects
+      #   all - save all recording segments for events regardless of activity
+      #   motion - save all recordings segments for events with any detected motion
+      #   active_objects - save all recording segments for event with active/moving objects
       #
       # NOTE: If the retain mode for the camera is more restrictive than the mode configured
       #       here, the segments will already be gone by the time this mode is applied.
       #       For example, if the camera retain mode is "motion", the segments without motion are
       #       never stored, so setting the mode to "all" here won't bring them back.
       mode: motion
+      # Optional: Per object retention days
+      objects:
+        person: 15
 
-# Optional: Configuration for the jpg snapshots written to the clips directory for each tracked object
+# Optional: Configuration for the jpg snapshots written to the clips directory for each event
 # NOTE: Can be overridden at the camera level
 snapshots:
   # Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
@@ -491,43 +476,16 @@ snapshots:
   # Optional: quality of the encoded jpeg, 0-100 (default: shown below)
   quality: 70
 
-# Optional: Configuration for semantic search capability
-semantic_search:
-  # Optional: Enable semantic search (default: shown below)
-  enabled: False
-  # Optional: Re-index embeddings database from historical tracked objects (default: shown below)
-  reindex: False
-
-# 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
-# to Google or OpenAI's LLMs to generate descriptions. It can be overridden at
-# the camera level (enabled: False) to enhance privacy for indoor cameras.
-genai:
-  # Optional: Enable AI description generation (default: shown below)
-  enabled: False
-  # Required if enabled: Provider must be one of ollama, gemini, or openai
-  provider: ollama
-  # Required if provider is ollama. May also be used for an OpenAI API compatible backend with the openai provider.
-  base_url: http://localhost::11434
-  # Required if gemini or openai
-  api_key: "{FRIGATE_GENAI_API_KEY}"
-  # Optional: The default prompt for generating descriptions. Can use replacement
-  # variables like "label", "sub_label", "camera" to make more dynamic. (default: shown below)
-  prompt: "Describe the {label} in the sequence of images with as much detail as possible. Do not describe the background."
-  # Optional: Object specific prompts to customize description results
-  # Format: {label}: {prompt}
-  object_prompts:
-    person: "My special person prompt."
-
 # Optional: Restream configuration
 # Uses https://github.com/AlexxIT/go2rtc (v1.9.2)
 go2rtc:
 
-# Optional: jsmpeg stream configuration for WebUI
+# Optional: Live stream configuration for WebUI.
+# NOTE: Can be overridden at the camera level
 live:
-  # Optional: Set the name of the stream that should be used for live view
-  # in frigate WebUI. (default: name of camera)
+  # Optional: Set the name of the stream 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
   # 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
@@ -712,18 +670,6 @@ cameras:
       # By default the cameras are sorted alphabetically.
       order: 0
 
-    # Optional: Configuration for AI generated tracked object descriptions
-    genai:
-      # Optional: Enable AI description generation (default: shown below)
-      enabled: False
-      # Optional: The default prompt for generating descriptions. Can use replacement
-      # variables like "label", "sub_label", "camera" to make more dynamic. (default: shown below)
-      prompt: "Describe the {label} in the sequence of images with as much detail as possible. Do not describe the background."
-      # Optional: Object specific prompts to customize description results
-      # Format: {label}: {prompt}
-      object_prompts:
-        person: "My special person prompt."
-
 # Optional
 ui:
   # Optional: Set a timezone to use in the UI (default: use browser local time)
@@ -786,7 +732,7 @@ camera_groups:
       - side_cam
       - front_doorbell_cam
     # Required: icon used for group
-    icon: car
+    icon: LuCar
     # Required: index of this group
     order: 0
 ```

docs/docs/configuration/restream.md

@@ -21,7 +21,7 @@ Birdseye RTSP restream can be accessed at `rtsp://<frigate_host>:8554/birdseye`.
 
 ```yaml
 birdseye:
-  restream: True
+  restream: true
 ```
 
 ### Securing Restream With Authentication

docs/docs/configuration/review.md

@@ -7,13 +7,13 @@ The Review page of the Frigate UI is for quickly reviewing historical footage of
 
 Review items are filterable by date, object type, and camera.
 
-### Review items vs. tracked objects (formerly "events")
+### Review items vs. events
 
 In Frigate 0.13 and earlier versions, the UI presented "events". An event was synonymous with a tracked or detected object. In Frigate 0.14 and later, a review item is a time period where any number of tracked objects were active.
 
 For example, consider a situation where two people walked past your house. One was walking a dog. At the same time, a car drove by on the street behind them.
 
-In this scenario, Frigate 0.13 and earlier would show 4 "events" in the UI - one for each person, another for the dog, and yet another for the car. You would have had 4 separate videos to watch even though they would have all overlapped.
+In this scenario, Frigate 0.13 and earlier would show 4 events in the UI - one for each person, another for the dog, and yet another for the car. You would have had 4 separate videos to watch even though they would have all overlapped.
 
 In 0.14 and later, all of that is bundled into a single review item which starts and ends to capture all of that activity. Reviews for a single camera cannot overlap. Once you have watched that time period on that camera, it is marked as reviewed.
 
@@ -41,8 +41,6 @@ review:
 
 By default all detections that do not qualify as an alert qualify as a detection. However, detections can further be filtered to only include certain labels or certain zones.
 
-By default a review item will only be marked as an alert if a person or car is detected. This can be configured to include any object or audio label using the following config:
-
 ```yaml
 # can be overridden at the camera level
 review:

docs/docs/configuration/semantic_search.md

@@ -1,44 +0,0 @@
----
-id: semantic_search
-title: Using 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.
-
-Frigate has support for two models to create embeddings, both of which run locally: [OpenAI CLIP](https://openai.com/research/clip) and [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). Embeddings are then saved to a local instance of [ChromaDB](https://trychroma.com).
-
-Semantic Search is accessed via the _Explore_ view in the Frigate UI.
-
-## Configuration
-
-Semantic search is disabled by default, and must be enabled in your config file before it can be used. Semantic Search is a global configuration setting.
-
-```yaml
-semantic_search:
-  enabled: True
-  reindex: False
-```
-
-:::tip
-
-The embeddings database can be re-indexed from the existing tracked objects in your database by adding `reindex: True` to your `semantic_search` configuration. Depending on the number of tracked objects you have, it can take a long while to complete and may max out your CPU while indexing. Make sure to set the config back to `False` before restarting Frigate again.
-
-If you are enabling the Search feature for the first time, be advised that Frigate does not automatically index older tracked objects. You will need to enable the `reindex` feature in order to do that.
-
-:::
-
-### OpenAI CLIP
-
-This model is able to embed both images and text into the same vector space, which allows `image -> image` and `text -> image` similarity searches. Frigate uses this model on tracked objects to encode the thumbnail image and store it in Chroma. When searching for tracked objects via text in the search box, Frigate will perform a `text -> image` similarity search against this embedding. When clicking "Find Similar" in the tracked object detail pane, Frigate will perform an `image -> image` similarity search to retrieve the closest matching thumbnails.
-
-### all-MiniLM-L6-v2
-
-This is a sentence embedding model that has been fine tuned on over 1 billion sentence pairs. This model is used to embed tracked object descriptions and perform searches against them. Descriptions can be created, viewed, and modified on the Search page when clicking on the gray tracked object chip at the top left of each review item. See [the Generative AI docs](/configuration/genai.md) for more information on how to automatically generate tracked object descriptions.
-
-## Usage
-
-1. Semantic search is used in conjunction with the other filters available on the Search page. Use a combination of traditional filtering and semantic search for the best results.
-2. The comparison between text and image embedding distances generally means that results matching `description` will appear first, even if a `thumbnail` embedding may be a better match. Play with the "Search Type" filter to help find what you are looking for.
-3. Make your search language and tone closely match your descriptions. If you are using thumbnail search, phrase your query as an image caption.
-4. Semantic search on thumbnails tends to return better results when matching large subjects that take up most of the frame. Small things like "cat" tend to not work well.
-5. Experiment! Find a tracked object you want to test and start typing keywords to see what works for you.

docs/docs/configuration/zones.md

@@ -64,7 +64,7 @@ cameras:
 
 ### Restricting zones to specific objects
 
-Sometimes you want to limit a zone to specific object types to have more granular control of when alerts, detections, and snapshots are saved. The following example will limit one zone to person objects and the other to cars.
+Sometimes you want to limit a zone to specific object types to have more granular control of when events/snapshots are saved. The following example will limit one zone to person objects and the other to cars.
 
 ```yaml
 cameras:
@@ -80,7 +80,7 @@ cameras:
           - car
 ```
 
-Only car objects can trigger the `front_yard_street` zone and only person can trigger the `entire_yard`. Objects will be tracked for any `person` that enter anywhere in the yard, and for cars only if they enter the street.
+Only car objects can trigger the `front_yard_street` zone and only person can trigger the `entire_yard`. You will get events for person objects that enter anywhere in the yard, and events for cars only if they enter the street.
 
 ### Zone Loitering
 

docs/docs/frigate/glossary.md

@@ -16,6 +16,10 @@ A box returned from the object detection model that outlines an object in the fr
 - A gray thin line indicates that object is detected as being stationary
 - A thick line indicates that object is the subject of autotracking (when enabled).
 
+## Event
+
+The time period starting when a tracked object entered the frame and ending when it left the frame, including any time that the object remained still. Events are saved when it is considered a [true positive](#threshold) and meets the requirements for a snapshot or recording to be saved.
+
 ## False Positive
 
 An incorrect detection of an object type. For example a dog being detected as a person, a chair being detected as a dog, etc. A person being detected in an area you want to ignore is not a false positive.
@@ -60,10 +64,6 @@ The threshold is the median score that an object must reach in order to be consi
 
 The top score for an object is the highest median score for an object.
 
-## Tracked Object ("event" in previous versions)
-
-The time period starting when a tracked object entered the frame and ending when it left the frame, including any time that the object remained still. Tracked objects are saved when it is considered a [true positive](#threshold) and meets the requirements for a snapshot or recording to be saved.
-
 ## Zone
 
 Zones are areas of interest, zones can be used for notifications and for limiting the areas where Frigate will create an [event](#event). [See the zone docs for more info](/configuration/zones)

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/4ejU0ew" 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
 
@@ -69,6 +68,7 @@ Inference speeds vary greatly depending on the CPU, GPU, or VPU used, some known
 | 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          |                                                                       |
 
 ### TensorRT - Nvidia GPU
 
@@ -87,10 +87,6 @@ Inference speeds will vary greatly depending on the GPU and the model used.
 | Quadro P400 2GB | 20 - 25 ms      |
 | Quadro P2000    | ~ 12 ms         |
 
-#### AMD GPUs
-
-With the [rocm](../configuration/object_detectors.md#amdrocm-gpu-detector) detector Frigate can take advantage of many AMD GPUs.
-
 ### Community Supported:
 
 #### Nvidia Jetson
@@ -111,12 +107,6 @@ 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

@@ -73,23 +73,23 @@ Users of the Snapcraft build of Docker cannot use storage locations outside your
 
 Frigate utilizes shared memory to store frames during processing. The default `shm-size` provided by Docker is **64MB**.
 
-The default shm size of **128MB** is fine for setups with **2 cameras** detecting at **720p**. If Frigate is exiting with "Bus error" messages, it is likely because you have too many high resolution cameras and you need to specify a higher shm size, using [`--shm-size`](https://docs.docker.com/engine/reference/run/#runtime-constraints-on-resources) (or [`service.shm_size`](https://docs.docker.com/compose/compose-file/compose-file-v2/#shm_size) in docker-compose).
+The default shm size of **64MB** is fine for setups with **2 cameras** detecting at **720p**. If Frigate is exiting with "Bus error" messages, it is likely because you have too many high resolution cameras and you need to specify a higher shm size, using [`--shm-size`](https://docs.docker.com/engine/reference/run/#runtime-constraints-on-resources) (or [`service.shm_size`](https://docs.docker.com/compose/compose-file/compose-file-v2/#shm_size) in docker-compose).
 
-The Frigate container also stores logs in shm, which can take up to **40MB**, so make sure to take this into account in your math as well.
+The Frigate container also stores logs in shm, which can take up to **30MB**, so make sure to take this into account in your math as well.
 
-You can calculate the **minimum** shm size for each camera with the following formula using the resolution specified for detect:
+You can calculate the necessary shm size for each camera with the following formula using the resolution specified for detect:
 
 ```console
 # Replace <width> and <height>
-$ python -c 'print("{:.2f}MB".format((<width> * <height> * 1.5 * 10 + 270480) / 1048576))'
+$ python -c 'print("{:.2f}MB".format((<width> * <height> * 1.5 * 9 + 270480) / 1048576))'
 
 # Example for 1280x720
-$ python -c 'print("{:.2f}MB".format((1280 * 720 * 1.5 * 10 + 270480) / 1048576))'
-13.44MB
+$ python -c 'print("{:.2f}MB".format((1280 * 720 * 1.5 * 9 + 270480) / 1048576))'
+12.12MB
 
 # Example for eight cameras detecting at 1280x720, including logs
-$ python -c 'print("{:.2f}MB".format(((1280 * 720 * 1.5 * 10 + 270480) / 1048576) * 8 + 40))'
-136.99MB
+$ python -c 'print("{:.2f}MB".format(((1280 * 720 * 1.5 * 9 + 270480) / 1048576) * 8 + 30))'
+126.99MB
 ```
 
 The shm size cannot be set per container for Home Assistant add-ons. However, this is probably not required since by default Home Assistant Supervisor allocates `/dev/shm` with half the size of your total memory. If your machine has 8GB of memory, chances are that Frigate will have access to up to 4GB without any additional configuration.
@@ -100,38 +100,6 @@ By default, the Raspberry Pi limits the amount of memory available to the GPU. I
 
 Additionally, the USB Coral draws a considerable amount of power. If using any other USB devices such as an SSD, you will experience instability due to the Pi not providing enough power to USB devices. You will need to purchase an external USB hub with it's own power supply. Some have reported success with <a href="https://amzn.to/3a2mH0P" target="_blank" rel="nofollow noopener sponsored">this</a> (affiliate link).
 
-### Hailo-8L
-
-The Hailo-8L is an M.2 card typically connected to a carrier board for PCIe, which then connects to the Raspberry Pi 5 as part of the AI Kit. However, it can also be used on other boards equipped with an M.2 M key edge connector.
-
-#### Installation
-
-For Raspberry Pi 5 users with the AI Kit, installation is straightforward. Simply follow this [guide](https://www.raspberrypi.com/documentation/accessories/ai-kit.html#ai-kit-installation) to install the driver and software.
-
-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).
-3. Ensure it has execution permissions with `sudo chmod +x install_hailo8l_driver.sh`
-4. Run the script with `./install_hailo8l_driver.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`
-
-Next, grant Docker permissions to access your hardware by adding the following lines to your `docker-compose.yml` file:
-
-```yaml
-devices:
-  - /dev/hailo0
-```
-
-If you are using `docker run`, add this option to your command `--device /dev/hailo0`
-
-#### Configuration
-
-Finally, configure [hardware object detection](/configuration/object_detectors#hailo-8l) to complete the setup.
-
 ### Rockchip platform
 
 Make sure that you use a linux distribution that comes with the rockchip BSP kernel 5.10 or 6.1 and necessary drivers (especially rkvdec2 and rknpu). To check, enter the following commands:
@@ -254,7 +222,6 @@ The community supported docker image tags for the current stable version are:
   - `stable-rocm-gfx900` - AMD gfx900 driver only
   - `stable-rocm-gfx1030` - AMD gfx1030 driver only
   - `stable-rocm-gfx1100` - AMD gfx1100 driver only
-  - `stable-h8l` - Frigate build for the Hailo-8L M.2 PICe Raspberry Pi 5 hat
 
 ## Home Assistant Addon
 

docs/docs/guides/configuring_go2rtc.md

@@ -13,7 +13,15 @@ Use of the bundled go2rtc is optional. You can still configure FFmpeg to connect
 
 # Setup a go2rtc stream
 
-First, you will want to configure go2rtc to connect to your camera stream by adding the stream you want to use for live view in your Frigate config file. If you set the stream name under go2rtc to match the name of your camera, it will automatically be mapped and you will get additional live view options for the camera. Avoid changing any other parts of your config at this step. Note that go2rtc supports [many different stream types](https://github.com/AlexxIT/go2rtc/tree/v1.9.4#module-streams), not just rtsp.
+First, you will want to configure go2rtc to connect to your camera stream by adding the stream you want to use for live view in your Frigate config file. Avoid changing any other parts of your config at this step. Note that go2rtc supports [many different stream types](https://github.com/AlexxIT/go2rtc/tree/v1.9.4#module-streams), not just rtsp.
+
+:::tip
+
+For the best experience, you should set the stream name under `go2rtc` to match the name of your camera so that Frigate will automatically map it and be able to use better live view options for the camera.
+
+See [the live view docs](../configuration/live.md#setting-stream-for-live-ui) for more information.
+
+:::
 
 ```yaml
 go2rtc:
@@ -22,7 +30,7 @@ go2rtc:
       - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
 ```
 
-The easiest live view to get working is MSE. After adding this to the config, restart Frigate and try to watch the live stream by selecting MSE in the dropdown after clicking on the camera.
+After adding this to the config, restart Frigate and try to watch the live stream for a single camera by clicking on it from the dashboard. It should look much clearer and more fluent than the original jsmpeg stream.
 
 
 ### What if my video doesn't play?
@@ -46,7 +54,7 @@ The easiest live view to get working is MSE. After adding this to the config, re
           streams:
             back:
               - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-              - "ffmpeg:back#video=h264"
+              - "ffmpeg:back#video=h264#hardware"
         ```
 
 - Switch to FFmpeg if needed: 
@@ -58,9 +66,8 @@ The easiest live view to get working is MSE. After adding this to the config, re
               - ffmpeg:rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
         ```
 
-- If you can see the video but do not have audio, this is most likely because your
-camera's audio stream is not AAC.
-    - If possible, update your camera's audio settings to AAC.
+    - If you can see the video but do not have audio, this is most likely because your camera's audio stream codec is not AAC.
+    - If possible, update your camera's audio settings to AAC in your camera's firmware.
     - If your cameras do not support AAC audio, you will need to tell go2rtc to re-encode the audio to AAC on demand if you want audio. This will use additional CPU and add some latency. To add AAC audio on demand, you can update your go2rtc config as follows:
         ```yaml
         go2rtc:
@@ -77,7 +84,7 @@ camera's audio stream is not AAC.
           streams:
             back:
               - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-              - "ffmpeg:back#video=h264#audio=aac"
+              - "ffmpeg:back#video=h264#audio=aac#hardware"
         ```
 
         When using the ffmpeg module, you would add AAC audio like this:
@@ -86,7 +93,7 @@ camera's audio stream is not AAC.
         go2rtc:
           streams:
             back:
-              - "ffmpeg:rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2#video=copy#audio=copy#audio=aac"
+              - "ffmpeg:rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2#video=copy#audio=copy#audio=aac#hardware"
         ```
 
 :::warning
@@ -102,4 +109,4 @@ section.
 ## Next steps
 
 1. If the stream you added to go2rtc is also used by Frigate for the `record` or `detect` role, you can migrate your config to pull from the RTSP restream to reduce the number of connections to your camera as shown [here](/configuration/restream#reduce-connections-to-camera).
-1. You may also prefer to [setup WebRTC](/configuration/live#webrtc-extra-configuration) for slightly lower latency than MSE. Note that WebRTC only supports h264 and specific audio formats.
+2. You may also prefer to [setup WebRTC](/configuration/live#webrtc-extra-configuration) for slightly lower latency than MSE. Note that WebRTC only supports h264 and specific audio formats and may require opening ports on your router.

docs/docs/guides/getting_started.md

@@ -238,7 +238,7 @@ Now that you know where you need to mask, use the "Mask & Zone creator" in the o
 
 :::warning
 
-Note that motion masks should not be used to mark out areas where you do not want objects to be detected or to reduce false positives. They do not alter the image sent to object detection, so you can still get tracked objects, alerts, and detections in areas with motion masks. These only prevent motion in these areas from initiating object detection.
+Note that motion masks should not be used to mark out areas where you do not want objects to be detected or to reduce false positives. They do not alter the image sent to object detection, so you can still get events and detections in areas with motion masks. These only prevent motion in these areas from initiating object detection.
 
 :::
 
@@ -298,15 +298,17 @@ If you don't have separate streams for detect and record, you would just add the
 
 If you only define one stream in your `inputs` and do not assign a `detect` role to it, Frigate will automatically assign it the `detect` role. Frigate will always decode a stream to support motion detection, Birdseye, the API image endpoints, and other features, even if you have disabled object detection with `enabled: False` in your config's `detect` section.
 
-If you only plan to use Frigate for recording, it is still recommended to define a `detect` role for a low resolution stream to minimize resource usage from the required stream decoding.
+If you plan to use Frigate for recording only, it is still recommended to define a `detect` role for a low resolution stream to minimize resource usage from the required stream decoding.
 
 :::
 
-By default, Frigate will retain video of all tracked objects for 10 days. The full set of options for recording can be found [here](../configuration/reference.md).
+By default, Frigate will retain video of all events for 10 days. The full set of options for recording can be found [here](../configuration/reference.md).
 
 ### Step 7: Complete config
 
-At this point you have a complete config with basic functionality. You can see the [full config reference](../configuration/reference.md) for a complete list of configuration options.
+At this point you have a complete config with basic functionality. 
+- View [common configuration examples](../configuration/index.md#common-configuration-examples) for a list of common configuration examples.
+- View [full config reference](../configuration/reference.md) for a complete list of configuration options.
 
 ### Follow up
 

docs/docs/guides/ha_notifications.md

@@ -7,11 +7,11 @@ The best way to get started with notifications for Frigate is to use the [Bluepr
 
 It is generally recommended to trigger notifications based on the `frigate/reviews` mqtt topic. This provides the event_id(s) needed to fetch [thumbnails/snapshots/clips](../integrations/home-assistant.md#notification-api) and other useful information to customize when and where you want to receive alerts. The data is published in the form of a change feed, which means you can reference the "previous state" of the object in the `before` section and the "current state" of the object in the `after` section. You can see an example [here](../integrations/mqtt.md#frigateevents).
 
-Here is a simple example of a notification automation of tracked objects which will update the existing notification for each change. This means the image you see in the notification will update as Frigate finds a "better" image.
+Here is a simple example of a notification automation of events which will update the existing notification for each change. This means the image you see in the notification will update as Frigate finds a "better" image.
 
 ```yaml
 automation:
-  - alias: Notify of tracked object
+  - alias: Notify of events
     trigger:
       platform: mqtt
       topic: frigate/events

docs/docs/guides/reverse_proxy.md

@@ -3,25 +3,38 @@ id: reverse_proxy
 title: Setting up a reverse proxy
 ---
 
-This guide outlines the basic configuration steps needed to expose your Frigate UI to the internet.
-A common way of accomplishing this is to use a reverse proxy webserver between your router and your Frigate instance.
-A reverse proxy accepts HTTP requests from the public internet and redirects them transparently to internal webserver(s) on your network.
+This guide outlines the basic configuration steps needed to set up a reverse proxy in front of your Frigate instance.
 
-The suggested steps are:
+A reverse proxy is typically needed if you want to set up Frigate on a custom URL, on a subdomain, or on a host serving multiple sites. It could also be used to set up your own authentication provider or for more advanced HTTP routing.
 
-- **Configure** a 'proxy' HTTP webserver (such as [Apache2](https://httpd.apache.org/docs/current/) or [NPM](https://github.com/NginxProxyManager/nginx-proxy-manager)) and only expose ports 80/443 from this webserver to the internet
-- **Encrypt** content from the proxy webserver by installing SSL (such as with [Let's Encrypt](https://letsencrypt.org/)). Note that SSL is then not required on your Frigate webserver as the proxy encrypts all requests for you
-- **Restrict** access to your Frigate instance at the proxy using, for example, password authentication
+Before setting up a reverse proxy, check if any of the built-in functionality in Frigate suits your needs:
+|Topic|Docs|
+|-|-|
+|TLS|Please see the  `tls` [configuration option](../configuration/tls.md)|
+|Authentication|Please see the [authentication](../configuration/authentication.md) documentation|
+|IPv6|[Enabling IPv6](../configuration/advanced.md#enabling-ipv6)
+
+**Note about TLS**  
+When using a reverse proxy, the TLS session is usually terminated at the proxy, sending the internal request over plain HTTP. If this is the desired behavior, TLS must first be disabled in Frigate, or you will encounter an HTTP 400 error: "The plain HTTP request was sent to HTTPS port."  
+To disable TLS, set the following in your Frigate configuration:
+```yml
+tls:
+  enabled: false
+```
 
 :::warning
-A reverse proxy can be used to secure access to an internal webserver but the user will be entirely reliant
-on the steps they have taken. You must ensure you are following security best practices.
-This page does not attempt to outline the specific steps needed to secure your internal website.
+A reverse proxy can be used to secure access to an internal web server, but the user will be entirely reliant on the steps they have taken. You must ensure you are following security best practices.
+This page does not attempt to outline the specific steps needed to secure your internal website.  
 Please use your own knowledge to assess and vet the reverse proxy software before you install anything on your system.
 :::
 
-There are several technologies available to implement reverse proxies. This document currently suggests one, using Apache2,
-and the community is invited to document others through a contribution to this page.
+## Proxies
+
+There are many solutions available to implement reverse proxies and the community is invited to help out documenting others through a contribution to this page.
+
+* [Apache2](#apache2-reverse-proxy)
+* [Nginx](#nginx-reverse-proxy)
+* [Traefik](#traefik-reverse-proxy)
 
 ## Apache2 Reverse Proxy
 
@@ -141,3 +154,26 @@ The settings below enabled connection upgrade, sets up logging (optional) and pr
   }
 
 ```
+
+## Traefik Reverse Proxy
+
+This example shows how to add a `label` to the Frigate Docker compose file, enabling Traefik to automatically discover your Frigate instance.  
+Before using the example below, you must first set up Traefik with the [Docker provider](https://doc.traefik.io/traefik/providers/docker/)
+
+```yml
+services:
+  frigate:
+    container_name: frigate
+    image: ghcr.io/blakeblackshear/frigate:stable
+    ...
+    ...
+    labels:
+      - "traefik.enable=true"
+      - "traefik.http.services.frigate.loadbalancer.server.port=8971"
+      - "traefik.http.routers.frigate.rule=Host(`traefik.example.com`)"
+```
+
+The above configuration will create a "service" in Traefik, automatically adding your container's IP on port 8971 as a backend.
+It will also add a router, routing requests to "traefik.example.com" to your local container.
+
+Note that with this approach, you don't need to expose any ports for the Frigate instance since all traffic will be routed over the internal Docker network.

docs/docs/integrations/api.md

@@ -189,15 +189,15 @@ Example parameters:
 
 ### `GET /api/<camera_name>/<label>/thumbnail.jpg`
 
-Returns the thumbnail from the latest tracked object for the given camera and label combo. Using `any` as the label will return the latest thumbnail regardless of type.
+Returns the thumbnail from the latest event for the given camera and label combo. Using `any` as the label will return the latest thumbnail regardless of type.
 
 ### `GET /api/<camera_name>/<label>/clip.mp4`
 
-Returns the clip from the latest tracked object for the given camera and label combo. Using `any` as the label will return the latest clip regardless of type.
+Returns the clip from the latest event for the given camera and label combo. Using `any` as the label will return the latest clip regardless of type.
 
 ### `GET /api/<camera_name>/<label>/snapshot.jpg`
 
-Returns the snapshot image from the latest tracked object for the given camera and label combo. Using `any` as the label will return the latest thumbnail regardless of type.
+Returns the snapshot image from the latest event for the given camera and label combo. Using `any` as the label will return the latest thumbnail regardless of type.
 
 ### `GET /api/<camera_name>/grid.jpg`
 
@@ -373,7 +373,7 @@ Metadata about previews for this time range.
 
 Metadata about previews for this hour
 
-### `GET /api/preview/<camera>/start/<start-timestamp>/end/<end-timestamp>`
+### `GET /api/preview/<camera>/start/<start-timestamp>/end/<end-timestamp>/frames`
 
 List of frames in the preview cache for the time range. Previews are only kept in the cache until they are combined into an mp4 at the end of the hour.
 
@@ -381,13 +381,21 @@ List of frames in the preview cache for the time range. Previews are only kept i
 
 Specific preview frame from preview cache.
 
+### `GET /review/<review_id>/preview`
+
+Looping image made from preview video / frames during this review item.
+
+| param     | Type | Description                      |
+| --------- | ---- | -------------------------------- |
+| `format`  | str  | Format of preview [`gif`, `mp4`] |
+
 ### `GET /<camera>/start/<start-timestamp>/end/<end-timestamp>/preview`
 
 Looping image made from preview video / frames during this time range.
 
-| param    | Type | Description                      |
-| -------- | ---- | -------------------------------- |
-| `format` | str  | Format of preview [`gif`, `mp4`] |
+| param     | Type | Description                      |
+| --------- | ---- | -------------------------------- |
+| `format`  | str  | Format of preview [`gif`, `mp4`] |
 
 ## Recordings
 
@@ -403,17 +411,37 @@ HTTP Live Streaming Video on Demand URL for the specified event. Can be viewed i
 
 HTTP Live Streaming Video on Demand URL for the camera with the specified time range. Can be viewed in an application like VLC.
 
+### `GET /api/exports`
+
+Fetch a list of all export recordings
+
+Sample response:
+```json
+[
+  {
+    "camera": "doorbell",
+    "date": 12800057,
+    "id": "doorbell_pjis54",
+    "in_progress": false,
+    "name": "2024-10-04 fox visit",
+    "thumb_path": "/media/frigate/clips/export/doorbell_pjis54.webp",
+    "video_path": "/media/frigate/exports/doorbell_pjis54.mp4"
+  }
+]
+```
+
 ### `POST /api/export/<camera>/start/<start-timestamp>/end/<end-timestamp>`
 
 Export recordings from `start-timestamp` to `end-timestamp` for `camera` as a single mp4 file. These recordings will be exported to the `/media/frigate/exports` folder.
 
-It is also possible to export this recording as a time-lapse.
+It is also possible to export this recording as a time-lapse using the "playback" key in the json body, or specify a custom export filename, using the "name" key.
 
 **Optional Body:**
 
 ```json
 {
-  "playback": "realtime" // playback factor: realtime or timelapse_25x
+  "playback": "realtime", // playback factor: realtime or timelapse_25x
+  "name": "custom export name" // override the default export filename with a custom name
 }
 ```
 

docs/docs/integrations/home-assistant.md

@@ -25,7 +25,7 @@ Available via HACS as a default repository. To install:
 - Use [HACS](https://hacs.xyz/) to install the integration:
 
 ```
-Home Assistant > HACS > Integrations > "Explore & Add Integrations" > Frigate
+Home Assistant > HACS > Click in the Search bar and type "Frigate" > Frigate
 ```
 
 - Restart Home Assistant.
@@ -148,19 +148,19 @@ Home Assistant > Configuration > Integrations > Frigate > Options
 
 ## Entities Provided
 
-| Platform        | Description                                                                     |
-| --------------- | ------------------------------------------------------------------------------- |
-| `camera`        | Live camera stream (requires RTSP).                                             |
-| `image`         | Image of the latest detected object for each camera.                            |
-| `sensor`        | States to monitor Frigate performance, object counts for all zones and cameras. |
-| `switch`        | Switch entities to toggle detection, recordings and snapshots.                  |
-| `binary_sensor` | A "motion" binary sensor entity per camera/zone/object.                         |
+| Platform        | Description                                                                       |
+| --------------- | --------------------------------------------------------------------------------- |
+| `camera`        | Live camera stream (requires RTSP).                                               |
+| `image`         | Image of the latest detected object for each camera.                              |
+| `sensor`        | States to monitor Frigate performance, object counts for all zones and cameras.   |
+| `switch`        | Switch entities to toggle detection, recordings and snapshots.                    |
+| `binary_sensor` | A "motion" binary sensor entity per camera/zone/object.                           |
 
 ## Media Browser Support
 
 The integration provides:
 
-- Browsing tracked object recordings with thumbnails
+- Browsing event recordings with thumbnails
 - Browsing snapshots
 - Browsing recordings by month, day, camera, time
 
@@ -183,19 +183,19 @@ For clips to be castable to media devices, audio is required and may need to be
 
 Many people do not want to expose Frigate to the web, so the integration creates some public API endpoints that can be used for notifications.
 
-To load a thumbnail for a tracked object:
+To load a thumbnail for an event:
 
 ```
 https://HA_URL/api/frigate/notifications/<event-id>/thumbnail.jpg
 ```
 
-To load a snapshot for a tracked object:
+To load a snapshot for an event:
 
 ```
 https://HA_URL/api/frigate/notifications/<event-id>/snapshot.jpg
 ```
 
-To load a video clip of a tracked object:
+To load a video clip of an event:
 
 ```
 https://HA_URL/api/frigate/notifications/<event-id>/clip.mp4

docs/docs/integrations/mqtt.md

@@ -11,7 +11,7 @@ These are the MQTT messages generated by Frigate. The default topic_prefix is `f
 
 Designed to be used as an availability topic with Home Assistant. Possible message are:
 "online": published when Frigate is running (on startup)
-"offline": published right before Frigate stops
+"offline": published after Frigate has stopped
 
 ### `frigate/restart`
 
@@ -19,7 +19,7 @@ Causes Frigate to exit. Docker should be configured to automatically restart the
 
 ### `frigate/events`
 
-Message published for each changed tracked object. The first message is published when the tracked object is no longer marked as a false_positive. When Frigate finds a better snapshot of the tracked object or when a zone change occurs, it will publish a message with the same id. When the tracked object ends, a final message is published with `end_time` set.
+Message published for each changed event. The first message is published when the tracked object is no longer marked as a false_positive. When Frigate finds a better snapshot of the tracked object or when a zone change occurs, it will publish a message with the same id. When the event ends, a final message is published with `end_time` set.
 
 ```json
 {
@@ -45,7 +45,6 @@ Message published for each changed tracked object. The first message is publishe
     "thumbnail": null,
     "has_snapshot": false,
     "has_clip": false,
-    "active": true, // convenience attribute, this is strictly opposite of "stationary"
     "stationary": false, // whether or not the object is considered stationary
     "motionless_count": 0, // number of frames the object has been motionless
     "position_changes": 2, // number of times the object has moved from a stationary position
@@ -75,7 +74,6 @@ Message published for each changed tracked object. The first message is publishe
     "thumbnail": null,
     "has_snapshot": false,
     "has_clip": false,
-    "active": true, // convenience attribute, this is strictly opposite of "stationary"
     "stationary": false, // whether or not the object is considered stationary
     "motionless_count": 0, // number of frames the object has been motionless
     "position_changes": 2, // number of times the object has changed position
@@ -100,22 +98,24 @@ Message published for each changed review item. The first message is published w
 
 ```json
 {
-  "type": "update", // new, update, end
+  "type": "update",  // new, update, end
   "before": {
-    "id": "1718987129.308396-fqk5ka", // review_id
+    "id": "1718987129.308396-fqk5ka",  // review_id
     "camera": "front_cam",
     "start_time": 1718987129.308396,
     "end_time": null,
     "severity": "detection",
     "thumb_path": "/media/frigate/clips/review/thumb-front_cam-1718987129.308396-fqk5ka.webp",
     "data": {
-      "detections": [
-        // list of event IDs
+      "detections": [  // list of event IDs
         "1718987128.947436-g92ztx",
         "1718987148.879516-d7oq7r",
         "1718987126.934663-q5ywpt"
       ],
-      "objects": ["person", "car"],
+      "objects": [
+        "person",
+        "car"
+      ],
       "sub_labels": [],
       "zones": [],
       "audio": []
@@ -134,9 +134,14 @@ Message published for each changed review item. The first message is published w
         "1718987148.879516-d7oq7r",
         "1718987126.934663-q5ywpt"
       ],
-      "objects": ["person", "car"],
+      "objects": [
+        "person",
+        "car"
+      ],
       "sub_labels": ["Bob"],
-      "zones": ["front_yard"],
+      "zones": [
+        "front_yard"
+      ],
       "audio": []
     }
   }
@@ -147,14 +152,6 @@ Message published for each changed review item. The first message is published w
 
 Same data available at `/api/stats` published at a configurable interval.
 
-### `frigate/notifications/set`
-
-Topic to turn notifications on and off. Expected values are `ON` and `OFF`.
-
-### `frigate/notifications/state`
-
-Topic with current state of notifications. Published values are `ON` and `OFF`.
-
 ## Frigate Camera Topics
 
 ### `frigate/<camera_name>/<object_name>`
@@ -162,23 +159,11 @@ Topic with current state of notifications. Published values are `ON` and `OFF`.
 Publishes the count of objects for the camera for use as a sensor in Home Assistant.
 `all` can be used as the object_name for the count of all objects for the camera.
 
-### `frigate/<camera_name>/<object_name>/active`
-
-Publishes the count of active objects for the camera for use as a sensor in Home
-Assistant. `all` can be used as the object_name for the count of all active objects
-for the camera.
-
 ### `frigate/<zone_name>/<object_name>`
 
 Publishes the count of objects for the zone for use as a sensor in Home Assistant.
 `all` can be used as the object_name for the count of all objects for the zone.
 
-### `frigate/<zone_name>/<object_name>/active`
-
-Publishes the count of active objects for the zone for use as a sensor in Home
-Assistant. `all` can be used as the object_name for the count of all objects for the
-zone.
-
 ### `frigate/<camera_name>/<object_name>/snapshot`
 
 Publishes a jpeg encoded frame of the detected object type. When the object is no longer detected, the highest confidence image is published or the original image

docs/docs/integrations/plus.md

@@ -19,17 +19,17 @@ Once logged in, you can generate an API key for Frigate in Settings.
 
 ### Set your API key
 
-In Frigate, you can use an environment variable or a docker secret named `PLUS_API_KEY` to enable the `Frigate+` buttons on the Explore page. Home Assistant Addon users can set it under Settings > Addons > Frigate NVR > Configuration > Options (be sure to toggle the "Show unused optional configuration options" switch).
+In Frigate, you can use an environment variable or a docker secret named `PLUS_API_KEY` to enable the Frigate+ page. Home Assistant Addon users can set it under Settings > Addons > Frigate NVR > Configuration > Options (be sure to toggle the "Show unused optional configuration options" switch).
 
 :::warning
 
-You cannot use the `environment_vars` section of your configuration file to set this environment variable.
+You cannot use the `environment_vars` section of your Frigate configuration file to set this environment variable. It must be defined as an environment variable in the docker config or HA addon config.
 
 :::
 
 ## Submit examples
 
-Once your API key is configured, you can submit examples directly from the Explore page in Frigate using the `Frigate+` button.
+Once your API key is configured, you can submit examples directly from the Frigate+ page.
 
 :::note
 

docs/docs/integrations/third_party_extensions.md

@@ -18,3 +18,7 @@ Please use your own knowledge to assess and vet them before you install anything
 [Double Take](https://github.com/skrashevich/double-take) provides an unified UI and API for processing and training images for facial recognition.
 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 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 at least 10 images to Frigate+. But for the best results, you should provide 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.
 
@@ -13,7 +13,7 @@ For more detailed recommendations, you can refer to the docs on [improving your
 
 ## Step 2: Submit a model request
 
-Once you have an initial set of verified images, you can request a model on the Models page. Each model request requires 1 of the 12 trainings that you receive with your annual subscription. This model will support all [label types available](./index.md#available-label-types) even if you do not submit any examples for those labels. Model creation can take up to 36 hours.
+Once you have an initial set of verified images, you can request a model on the Models page. For guidance on choosing a model type, refer to [this part of the documentation](./index.md#available-model-types). Each model request requires 1 of the 12 trainings that you receive with your annual subscription. This model will support all [label types available](./index.md#available-label-types) even if you do not submit any examples for those labels. Model creation can take up to 36 hours.
 ![Plus Models Page](/img/plus/plus-models.jpg)
 
 ## Step 3: Set your model id in the config

docs/docs/plus/improving_model.md

@@ -3,7 +3,7 @@ id: improving_model
 title: Improving your model
 ---
 
-You may find that Frigate+ models result in more false positives initially, but by submitting true and false positives, the model will improve. Because a limited number of users submitted images to Frigate+ prior to this launch, you may need to submit several hundred images per camera to see good results. With all the new images now being submitted, future base models will improve as more and more users (including you) submit examples to Frigate+. Note that only verified images will be used when training your model. Submitting an image from Frigate as a true or false positive will not verify the image. You still must verify the image in Frigate+ in order for it to be used in training.
+You may find that Frigate+ models result in more false positives initially, but by submitting true and false positives, the model will improve. With all the new images now being submitted by subscribers, future base models will improve as more and more examples are incorporated. Note that only images with at least one verified label will be used when training your model. Submitting an image from Frigate as a true or false positive will not verify the image. You still must verify the image in Frigate+ in order for it to be used in training.
 
 - **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.
 - **Lower your thresholds a little in order to generate more false/true positives near the threshold value**. For example, if you have some false positives that are scoring at 68% and some true positives scoring at 72%, you can try lowering your threshold to 65% and submitting both true and false positives within that range. This will help the model learn and widen the gap between true and false positive scores.
@@ -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)
 
@@ -36,18 +36,17 @@ Misidentified objects should have a correct label added. For example, if a perso
 
 ## Shortcuts for a faster workflow
 
-|Shortcut Key|Description|
-|-----|--------|
-|`?`|Show all keyboard shortcuts|
-|`w`|Add box|
-|`d`|Toggle difficult|
-|`s`|Switch to the next label|
-|`tab`|Select next largest box|
-|`del`|Delete current box|
-|`esc`|Deselect/Cancel|
-|`← ↑ → ↓`|Move box|
-|`Shift + ← ↑ → ↓`|Resize box|
-|`-`|Zoom out|
-|`=`|Zoom in|
-|`f`|Hide/show all but current box|
-|`spacebar`|Verify and save|
+| Shortcut Key      | Description                   |
+| ----------------- | ----------------------------- |
+| `?`               | Show all keyboard shortcuts   |
+| `w`               | Add box                       |
+| `d`               | Toggle difficult              |
+| `s`               | Switch to the next label      |
+| `tab`             | Select next largest box       |
+| `del`             | Delete current box            |
+| `esc`             | Deselect/Cancel               |
+| `← ↑ → ↓`         | Move box                      |
+| `Shift + ← ↑ → ↓` | Resize box                    |
+| `scrollwheel`     | Zoom in/out                   |
+| `f`               | Hide/show all but current box |
+| `spacebar`        | Verify and save               |

docs/docs/plus/index.md

@@ -15,25 +15,52 @@ With a subscription, 12 model trainings per year are included. If you cancel you
 
 Information on how to integrate Frigate+ with Frigate can be found in the [integration docs](../integrations/plus.md).
 
+## 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).
+
+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).
+
+| Model Type  | Description                                                                                                                                  |
+| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------- |
+| `mobiledet` | Based on the same architecture as the default model included with Frigate. Runs on Google Coral devices and CPUs.                            |
+| `yolonas`   | A newer architecture that offers slightly higher accuracy and improved detection of small objects. Runs on Intel, NVidia GPUs, and AMD GPUs. |
+
 ## Supported detector types
 
+Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVino (`openvino`), ONNX (`onnx`), and ROCm (`rocm`) detectors.
+
 :::warning
 
-Frigate+ models are not supported for TensorRT or OpenVino yet.
+Using Frigate+ models with `onnx` and `rocm` is only available with Frigate 0.15, which is still under development.
 
 :::
 
-Currently, Frigate+ models only support CPU (`cpu`) and Coral (`edgetpu`) models. OpenVino is next in line to gain support.
+| Hardware                                                                                                                     | Recommended Detector Type | Recommended Model Type |
+| ---------------------------------------------------------------------------------------------------------------------------- | ------------------------- | ---------------------- |
+| [CPU](/configuration/object_detectors.md#cpu-detector-not-recommended)                                                       | `cpu`                     | `mobiledet`            |
+| [Coral (all form factors)](/configuration/object_detectors.md#edge-tpu-detector)                                             | `edgetpu`                 | `mobiledet`            |
+| [Intel](/configuration/object_detectors.md#openvino-detector)                                                                | `openvino`                | `yolonas`              |
+| [NVidia GPU](https://deploy-preview-13787--frigate-docs.netlify.app/configuration/object_detectors#onnx)\*                   | `onnx`                    | `yolonas`              |
+| [AMD ROCm GPU](https://deploy-preview-13787--frigate-docs.netlify.app/configuration/object_detectors#amdrocm-gpu-detector)\* | `rocm`                    | `yolonas`              |
 
-The models are created using the same MobileDet architecture as the default model. Additional architectures will be added in future releases as needed.
+_\* 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 events. 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:
 
@@ -56,6 +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

@@ -28,6 +28,18 @@ The USB coral has different IDs when it is uninitialized and initialized.
 - When running Frigate in a VM, Proxmox lxc, etc. you must ensure both device IDs are mapped.
 - When running HA OS you may need to run the Full Access version of the Frigate addon with the `Protected Mode` switch disabled so that the coral can be accessed.
 
+### Synology 716+II running DSM 7.2.1-69057 Update 5
+
+Some users have reported that this older device runs an older kernel causing issues with the coral not being detected. The following steps allowed it to be detected correctly:
+
+1. Plug in the coral TPU in any of the USB ports on the NAS
+2. Open the control panel - info screen. The coral TPU would be shown as a generic device.
+3. Start the docker container with Coral TPU enabled in the config
+4. The TPU would be detected but a few moments later it would disconnect.
+5. While leaving the TPU device plugged in, restart the NAS using the reboot command in the UI. Do NOT unplug the NAS/power it off etc.
+6. Open the control panel - info scree. The coral TPU will now be recognised as a USB Device - google inc
+7. Start the frigate container. Everything should work now!
+
 ## USB Coral Detection Appears to be Stuck
 
 The USB Coral can become stuck and need to be restarted, this can happen for a number of reasons depending on hardware and software setup. Some common reasons are:
@@ -37,7 +49,21 @@ The USB Coral can become stuck and need to be restarted, this can happen for a n
 
 ## PCIe Coral Not Detected
 
-The most common reason for the PCIe coral not being detected is that the driver has not been installed. See [the coral docs](https://coral.ai/docs/m2/get-started/#2-install-the-pcie-driver-and-edge-tpu-runtime) for how to install the driver for the PCIe based coral.
+The most common reason for the PCIe Coral not being detected is that the driver has not been installed. This process varies based on what OS and kernel that is being run. 
+
+- 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.
+
+### 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
 

docs/docs/troubleshooting/faqs.md

@@ -17,7 +17,7 @@ ffmpeg:
     record: preset-record-generic-audio-aac
 ```
 
-### I can't view recordings in the Web UI.
+### I can't view events or recordings in the Web UI.
 
 Ensure your cameras send h264 encoded video, or [transcode them](/configuration/restream.md).
 

docs/package.json

@@ -14,15 +14,15 @@
     "write-heading-ids": "docusaurus write-heading-ids"
   },
   "dependencies": {
-    "@docusaurus/core": "^3.5.2",
-    "@docusaurus/preset-classic": "^3.5.2",
-    "@docusaurus/theme-mermaid": "^3.5.2",
+    "@docusaurus/core": "^3.4.0",
+    "@docusaurus/preset-classic": "^3.4.0",
+    "@docusaurus/theme-mermaid": "^3.4.0",
     "@mdx-js/react": "^3.0.0",
     "clsx": "^2.0.0",
-    "prism-react-renderer": "^2.4.0",
+    "prism-react-renderer": "^2.1.0",
     "raw-loader": "^4.0.2",
-    "react": "^18.3.1",
-    "react-dom": "^18.3.1"
+    "react": "^18.2.0",
+    "react-dom": "^18.2.0"
   },
   "browserslist": {
     "production": [
@@ -39,7 +39,7 @@
   "devDependencies": {
     "@docusaurus/module-type-aliases": "^3.4.0",
     "@docusaurus/types": "^3.4.0",
-    "@types/react": "^18.3.7"
+    "@types/react": "^18.2.79"
   },
   "engines": {
     "node": ">=18.0"

docs/sidebars.js

@@ -29,10 +29,6 @@ module.exports = {
         "configuration/object_detectors",
         "configuration/audio_detectors",
       ],
-      "Semantic Search": [
-        "configuration/semantic_search",
-        "configuration/genai",
-      ],
       Cameras: [
         "configuration/cameras",
         "configuration/review",
@@ -54,9 +50,9 @@ module.exports = {
       ],
       "Extra Configuration": [
         "configuration/authentication",
-        "configuration/notifications",
         "configuration/hardware_acceleration",
         "configuration/ffmpeg_presets",
+        "configuration/pwa",
         "configuration/tls",
         "configuration/advanced",
       ],

frigate/__main__.py

@@ -1,28 +1,17 @@
 import faulthandler
-import logging
 import threading
 
 from flask import cli
 
 from frigate.app import FrigateApp
 
+faulthandler.enable()
 
-def main() -> None:
-    faulthandler.enable()
-
-    # Clear all existing handlers.
-    logging.basicConfig(
-        level=logging.INFO,
-        handlers=[],
-        force=True,
-    )
-
-    threading.current_thread().name = "frigate"
-    cli.show_server_banner = lambda *x: None
-
-    # Run the main application.
-    FrigateApp().start()
+threading.current_thread().name = "frigate"
 
+cli.show_server_banner = lambda *x: None
 
 if __name__ == "__main__":
-    main()
+    frigate_app = FrigateApp()
+
+    frigate_app.start()

frigate/api/app.py

@@ -7,7 +7,6 @@ import os
 import traceback
 from datetime import datetime, timedelta
 from functools import reduce
-from typing import Optional
 
 import requests
 from flask import Blueprint, Flask, current_app, jsonify, make_response, request
@@ -20,12 +19,10 @@ from frigate.api.auth import AuthBp, get_jwt_secret, limiter
 from frigate.api.event import EventBp
 from frigate.api.export import ExportBp
 from frigate.api.media import MediaBp
-from frigate.api.notification import NotificationBp
 from frigate.api.preview import PreviewBp
 from frigate.api.review import ReviewBp
 from frigate.config import FrigateConfig
 from frigate.const import CONFIG_DIR
-from frigate.embeddings import EmbeddingsContext
 from frigate.events.external import ExternalEventProcessor
 from frigate.models import Event, Timeline
 from frigate.plus import PlusApi
@@ -50,13 +47,11 @@ bp.register_blueprint(MediaBp)
 bp.register_blueprint(PreviewBp)
 bp.register_blueprint(ReviewBp)
 bp.register_blueprint(AuthBp)
-bp.register_blueprint(NotificationBp)
 
 
 def create_app(
     frigate_config,
     database: SqliteQueueDatabase,
-    embeddings: Optional[EmbeddingsContext],
     detected_frames_processor,
     storage_maintainer: StorageMaintainer,
     onvif: OnvifController,
@@ -84,7 +79,6 @@ def create_app(
             database.close()
 
     app.frigate_config = frigate_config
-    app.embeddings = embeddings
     app.detected_frames_processor = detected_frames_processor
     app.storage_maintainer = storage_maintainer
     app.onvif = onvif
@@ -414,7 +408,7 @@ def ffprobe():
     output = []
 
     for path in paths:
-        ffprobe = ffprobe_stream(current_app.frigate_config.ffmpeg, path.strip())
+        ffprobe = ffprobe_stream(path.strip())
         output.append(
             {
                 "return_code": ffprobe.returncode,
@@ -456,24 +450,10 @@ def vainfo():
 
 @bp.route("/logs/<service>", methods=["GET"])
 def logs(service: str):
-    def download_logs(service_location: str):
-        try:
-            file = open(service_location, "r")
-            contents = file.read()
-            file.close()
-            return jsonify(contents)
-        except FileNotFoundError as e:
-            logger.error(e)
-            return make_response(
-                jsonify({"success": False, "message": "Could not find log file"}),
-                500,
-            )
-
     log_locations = {
         "frigate": "/dev/shm/logs/frigate/current",
         "go2rtc": "/dev/shm/logs/go2rtc/current",
         "nginx": "/dev/shm/logs/nginx/current",
-        "chroma": "/dev/shm/logs/chroma/current",
     }
     service_location = log_locations.get(service)
 
@@ -483,9 +463,6 @@ def logs(service: str):
             404,
         )
 
-    if request.args.get("download", type=bool, default=False):
-        return download_logs(service_location)
-
     start = request.args.get("start", type=int, default=0)
     end = request.args.get("end", type=int)
 

frigate/api/event.py

@@ -1,7 +1,5 @@
 """Event apis."""
 
-import base64
-import io
 import logging
 import os
 from datetime import datetime
@@ -10,7 +8,6 @@ from pathlib import Path
 from urllib.parse import unquote
 
 import cv2
-import numpy as np
 from flask import (
     Blueprint,
     current_app,
@@ -18,16 +15,13 @@ from flask import (
     make_response,
     request,
 )
-from peewee import JOIN, DoesNotExist, fn, operator
-from PIL import Image
+from peewee import DoesNotExist, fn, operator
 from playhouse.shortcuts import model_to_dict
 
 from frigate.const import (
     CLIPS_DIR,
 )
-from frigate.embeddings import EmbeddingsContext
-from frigate.embeddings.embeddings import get_metadata
-from frigate.models import Event, ReviewSegment, Timeline
+from frigate.models import Event, Timeline
 from frigate.object_processing import TrackedObject
 from frigate.util.builtin import get_tz_modifiers
 
@@ -251,295 +245,6 @@ def events():
     return jsonify(list(events))
 
 
-@EventBp.route("/events/explore")
-def events_explore():
-    limit = request.args.get("limit", 10, type=int)
-
-    subquery = Event.select(
-        Event.id,
-        Event.camera,
-        Event.label,
-        Event.zones,
-        Event.start_time,
-        Event.end_time,
-        Event.has_clip,
-        Event.has_snapshot,
-        Event.plus_id,
-        Event.retain_indefinitely,
-        Event.sub_label,
-        Event.top_score,
-        Event.false_positive,
-        Event.box,
-        Event.data,
-        fn.rank()
-        .over(partition_by=[Event.label], order_by=[Event.start_time.desc()])
-        .alias("rank"),
-        fn.COUNT(Event.id).over(partition_by=[Event.label]).alias("event_count"),
-    ).alias("subquery")
-
-    query = (
-        Event.select(
-            subquery.c.id,
-            subquery.c.camera,
-            subquery.c.label,
-            subquery.c.zones,
-            subquery.c.start_time,
-            subquery.c.end_time,
-            subquery.c.has_clip,
-            subquery.c.has_snapshot,
-            subquery.c.plus_id,
-            subquery.c.retain_indefinitely,
-            subquery.c.sub_label,
-            subquery.c.top_score,
-            subquery.c.false_positive,
-            subquery.c.box,
-            subquery.c.data,
-            subquery.c.event_count,
-        )
-        .from_(subquery)
-        .where(subquery.c.rank <= limit)
-        .order_by(subquery.c.event_count.desc(), subquery.c.start_time.desc())
-        .dicts()
-    )
-
-    events = list(query.iterator())
-
-    processed_events = [
-        {k: v for k, v in event.items() if k != "data"}
-        | {
-            "data": {
-                k: v
-                for k, v in event["data"].items()
-                if k in ["type", "score", "top_score", "description"]
-            }
-        }
-        for event in events
-    ]
-
-    return jsonify(processed_events)
-
-
-@EventBp.route("/event_ids")
-def event_ids():
-    idString = request.args.get("ids")
-    ids = idString.split(",")
-
-    if not ids:
-        return make_response(
-            jsonify({"success": False, "message": "Valid list of ids must be sent"}),
-            400,
-        )
-
-    try:
-        events = Event.select().where(Event.id << ids).dicts().iterator()
-        return jsonify(list(events))
-    except Exception:
-        return make_response(
-            jsonify({"success": False, "message": "Events not found"}), 400
-        )
-
-
-@EventBp.route("/events/search")
-def events_search():
-    query = request.args.get("query", type=str)
-    search_type = request.args.get("search_type", "thumbnail,description", type=str)
-    include_thumbnails = request.args.get("include_thumbnails", default=1, type=int)
-    limit = request.args.get("limit", 50, type=int)
-
-    # Filters
-    cameras = request.args.get("cameras", "all", type=str)
-    labels = request.args.get("labels", "all", type=str)
-    zones = request.args.get("zones", "all", type=str)
-    after = request.args.get("after", type=float)
-    before = request.args.get("before", type=float)
-
-    # for similarity search
-    event_id = request.args.get("event_id", type=str)
-
-    if not query and not event_id:
-        return make_response(
-            jsonify(
-                {
-                    "success": False,
-                    "message": "A search query must be supplied",
-                }
-            ),
-            400,
-        )
-
-    if not current_app.frigate_config.semantic_search.enabled:
-        return make_response(
-            jsonify(
-                {
-                    "success": False,
-                    "message": "Semantic search is not enabled",
-                }
-            ),
-            400,
-        )
-
-    context: EmbeddingsContext = current_app.embeddings
-
-    selected_columns = [
-        Event.id,
-        Event.camera,
-        Event.label,
-        Event.sub_label,
-        Event.zones,
-        Event.start_time,
-        Event.end_time,
-        Event.has_clip,
-        Event.has_snapshot,
-        Event.data,
-        Event.plus_id,
-        ReviewSegment.thumb_path,
-    ]
-
-    if include_thumbnails:
-        selected_columns.append(Event.thumbnail)
-
-    # Build the where clause for the embeddings query
-    embeddings_filters = []
-
-    if cameras != "all":
-        camera_list = cameras.split(",")
-        embeddings_filters.append({"camera": {"$in": camera_list}})
-
-    if labels != "all":
-        label_list = labels.split(",")
-        embeddings_filters.append({"label": {"$in": label_list}})
-
-    if zones != "all":
-        filtered_zones = zones.split(",")
-        zone_filters = [{f"zones_{zone}": {"$eq": True}} for zone in filtered_zones]
-        if len(zone_filters) > 1:
-            embeddings_filters.append({"$or": zone_filters})
-        else:
-            embeddings_filters.append(zone_filters[0])
-
-    if after:
-        embeddings_filters.append({"start_time": {"$gt": after}})
-
-    if before:
-        embeddings_filters.append({"start_time": {"$lt": before}})
-
-    where = None
-    if len(embeddings_filters) > 1:
-        where = {"$and": embeddings_filters}
-    elif len(embeddings_filters) == 1:
-        where = embeddings_filters[0]
-
-    thumb_ids = {}
-    desc_ids = {}
-
-    if search_type == "similarity":
-        # Grab the ids of events that match the thumbnail image embeddings
-        try:
-            search_event: Event = Event.get(Event.id == event_id)
-        except DoesNotExist:
-            return make_response(
-                jsonify(
-                    {
-                        "success": False,
-                        "message": "Event not found",
-                    }
-                ),
-                404,
-            )
-        thumbnail = base64.b64decode(search_event.thumbnail)
-        img = np.array(Image.open(io.BytesIO(thumbnail)).convert("RGB"))
-        thumb_result = context.embeddings.thumbnail.query(
-            query_images=[img],
-            n_results=limit,
-            where=where,
-        )
-        thumb_ids = dict(
-            zip(
-                thumb_result["ids"][0],
-                context.thumb_stats.normalize(thumb_result["distances"][0]),
-            )
-        )
-    else:
-        search_types = search_type.split(",")
-
-        if "thumbnail" in search_types:
-            thumb_result = context.embeddings.thumbnail.query(
-                query_texts=[query],
-                n_results=limit,
-                where=where,
-            )
-            # Do a rudimentary normalization of the difference in distances returned by CLIP and MiniLM.
-            thumb_ids = dict(
-                zip(
-                    thumb_result["ids"][0],
-                    context.thumb_stats.normalize(thumb_result["distances"][0]),
-                )
-            )
-
-        if "description" in search_types:
-            desc_result = context.embeddings.description.query(
-                query_texts=[query],
-                n_results=limit,
-                where=where,
-            )
-            desc_ids = dict(
-                zip(
-                    desc_result["ids"][0],
-                    context.desc_stats.normalize(desc_result["distances"][0]),
-                )
-            )
-
-    results = {}
-    for event_id in thumb_ids.keys() | desc_ids:
-        min_distance = min(
-            i
-            for i in (thumb_ids.get(event_id), desc_ids.get(event_id))
-            if i is not None
-        )
-        results[event_id] = {
-            "distance": min_distance,
-            "source": "thumbnail"
-            if min_distance == thumb_ids.get(event_id)
-            else "description",
-        }
-
-    if not results:
-        return jsonify([])
-
-    # Get the event data
-    events = (
-        Event.select(*selected_columns)
-        .join(
-            ReviewSegment,
-            JOIN.LEFT_OUTER,
-            on=(fn.json_extract(ReviewSegment.data, "$.detections").contains(Event.id)),
-        )
-        .where(Event.id << list(results.keys()))
-        .dicts()
-        .iterator()
-    )
-    events = list(events)
-
-    events = [
-        {k: v for k, v in event.items() if k != "data"}
-        | {
-            "data": {
-                k: v
-                for k, v in event["data"].items()
-                if k in ["type", "score", "top_score", "description"]
-            }
-        }
-        | {
-            "search_distance": results[event["id"]]["distance"],
-            "search_source": results[event["id"]]["source"],
-        }
-        for event in events
-    ]
-    events = sorted(events, key=lambda x: x["search_distance"])[:limit]
-
-    return jsonify(events)
-
-
 @EventBp.route("/events/summary")
 def events_summary():
     tz_name = request.args.get("timezone", default="utc", type=str)
@@ -899,52 +604,6 @@ def set_sub_label(id):
     )
 
 
-@EventBp.route("/events/<id>/description", methods=("POST",))
-def set_description(id):
-    try:
-        event: Event = Event.get(Event.id == id)
-    except DoesNotExist:
-        return make_response(
-            jsonify({"success": False, "message": "Event " + id + " not found"}), 404
-        )
-
-    json: dict[str, any] = request.get_json(silent=True) or {}
-    new_description = json.get("description")
-
-    if new_description is None or len(new_description) == 0:
-        return make_response(
-            jsonify(
-                {
-                    "success": False,
-                    "message": "description cannot be empty",
-                }
-            ),
-            400,
-        )
-
-    event.data["description"] = new_description
-    event.save()
-
-    # If semantic search is enabled, update the index
-    if current_app.frigate_config.semantic_search.enabled:
-        context: EmbeddingsContext = current_app.embeddings
-        context.embeddings.description.upsert(
-            documents=[new_description],
-            metadatas=[get_metadata(event)],
-            ids=[id],
-        )
-
-    return make_response(
-        jsonify(
-            {
-                "success": True,
-                "message": "Event " + id + " description set to " + new_description,
-            }
-        ),
-        200,
-    )
-
-
 @EventBp.route("/events/<id>", methods=("DELETE",))
 def delete_event(id):
     try:
@@ -966,11 +625,6 @@ def delete_event(id):
 
     event.delete_instance()
     Timeline.delete().where(Timeline.source_id == id).execute()
-    # If semantic search is enabled, update the index
-    if current_app.frigate_config.semantic_search.enabled:
-        context: EmbeddingsContext = current_app.embeddings
-        context.embeddings.thumbnail.delete(ids=[id])
-        context.embeddings.description.delete(ids=[id])
     return make_response(
         jsonify({"success": True, "message": "Event " + id + " deleted"}), 200
     )

frigate/api/export.py

@@ -55,8 +55,6 @@ def export_recording(camera_name: str, start_time, end_time):
             401,
         )
 
-    existing_image = json.get("image_path")
-
     recordings_count = (
         Recordings.select()
         .where(
@@ -80,7 +78,6 @@ def export_recording(camera_name: str, start_time, end_time):
         current_app.frigate_config,
         camera_name,
         friendly_name,
-        existing_image,
         int(start_time),
         int(end_time),
         (
@@ -149,9 +146,9 @@ def export_delete(id: str):
         try:
             if process.name() != "ffmpeg":
                 continue
-            file_list = process.open_files()
-            if file_list:
-                for nt in file_list:
+            flist = process.open_files()
+            if flist:
+                for nt in flist:
                     if nt.path.startswith(EXPORT_DIR):
                         files_in_use.append(nt.path.split("/")[-1])
         except psutil.Error:

frigate/api/media.py

@@ -17,7 +17,6 @@ from peewee import DoesNotExist, fn
 from tzlocal import get_localzone_name
 from werkzeug.utils import secure_filename
 
-from frigate.config import FrigateConfig
 from frigate.const import (
     CACHE_DIR,
     CLIPS_DIR,
@@ -180,20 +179,14 @@ def latest_frame(camera_name):
         )
 
 
-@MediaBp.route("/<camera_name>/recordings/<frame_time>/snapshot.<format>")
-def get_snapshot_from_recording(camera_name: str, frame_time: str, format: str):
+@MediaBp.route("/<camera_name>/recordings/<frame_time>/snapshot.png")
+def get_snapshot_from_recording(camera_name: str, frame_time: str):
     if camera_name not in current_app.frigate_config.cameras:
         return make_response(
             jsonify({"success": False, "message": "Camera not found"}),
             404,
         )
 
-    if format not in ["png", "jpg"]:
-        return make_response(
-            jsonify({"success": False, "message": "Invalid format"}),
-            400,
-        )
-
     frame_time = float(frame_time)
     recording_query = (
         Recordings.select(
@@ -214,14 +207,7 @@ def get_snapshot_from_recording(camera_name: str, frame_time: str, format: str):
     try:
         recording: Recordings = recording_query.get()
         time_in_segment = frame_time - recording.start_time
-
-        height = request.args.get("height", type=int)
-        codec = "png" if format == "png" else "mjpeg"
-        config: FrigateConfig = current_app.frigate_config
-
-        image_data = get_image_from_recording(
-            config.ffmpeg, recording.path, time_in_segment, codec, height
-        )
+        image_data = get_image_from_recording(recording.path, time_in_segment)
 
         if not image_data:
             return make_response(
@@ -235,7 +221,7 @@ def get_snapshot_from_recording(camera_name: str, frame_time: str, format: str):
             )
 
         response = make_response(image_data)
-        response.headers["Content-Type"] = f"image/{format}"
+        response.headers["Content-Type"] = "image/png"
         return response
     except DoesNotExist:
         return make_response(
@@ -275,12 +261,9 @@ def submit_recording_snapshot_to_plus(camera_name: str, frame_time: str):
     )
 
     try:
-        config: FrigateConfig = current_app.frigate_config
         recording: Recordings = recording_query.get()
         time_in_segment = frame_time - recording.start_time
-        image_data = get_image_from_recording(
-            config.ffmpeg, recording.path, time_in_segment, "png"
-        )
+        image_data = get_image_from_recording(recording.path, time_in_segment)
 
         if not image_data:
             return make_response(
@@ -479,11 +462,9 @@ def recording_clip(camera_name, start_ts, end_ts):
     file_name = secure_filename(file_name)
     path = os.path.join(CLIPS_DIR, f"cache/{file_name}")
 
-    config: FrigateConfig = current_app.frigate_config
-
     if not os.path.exists(path):
         ffmpeg_cmd = [
-            config.ffmpeg.ffmpeg_path,
+            "ffmpeg",
             "-hide_banner",
             "-y",
             "-protocol_whitelist",
@@ -604,8 +585,7 @@ def vod_ts(camera_name, start_ts, end_ts):
     )
 
 
-@MediaBp.route("/vod/<year_month>/<int:day>/<int:hour>/<camera_name>")
-@MediaBp.route("/vod/<year_month>/<float:day>/<float:hour>/<camera_name>")
+@MediaBp.route("/vod/<year_month>/<day>/<hour>/<camera_name>")
 def vod_hour_no_timezone(year_month, day, hour, camera_name):
     return vod_hour(
         year_month, day, hour, camera_name, get_localzone_name().replace("/", ",")
@@ -1148,9 +1128,8 @@ def preview_gif(camera_name: str, start_ts, end_ts, max_cache_age=2592000):
         diff = start_ts - preview.start_time
         minutes = int(diff / 60)
         seconds = int(diff % 60)
-        config: FrigateConfig = current_app.frigate_config
         ffmpeg_cmd = [
-            config.ffmpeg.ffmpeg_path,
+            "ffmpeg",
             "-hide_banner",
             "-loglevel",
             "warning",
@@ -1214,10 +1193,9 @@ def preview_gif(camera_name: str, start_ts, end_ts, max_cache_age=2592000):
 
         last_file = selected_previews[-2]
         selected_previews.append(last_file)
-        config: FrigateConfig = current_app.frigate_config
 
         ffmpeg_cmd = [
-            config.ffmpeg.ffmpeg_path,
+            "ffmpeg",
             "-hide_banner",
             "-loglevel",
             "warning",
@@ -1310,9 +1288,8 @@ def preview_mp4(camera_name: str, start_ts, end_ts, max_cache_age=604800):
         diff = start_ts - preview.start_time
         minutes = int(diff / 60)
         seconds = int(diff % 60)
-        config: FrigateConfig = current_app.frigate_config
         ffmpeg_cmd = [
-            config.ffmpeg.ffmpeg_path,
+            "ffmpeg",
             "-hide_banner",
             "-loglevel",
             "warning",
@@ -1374,10 +1351,9 @@ def preview_mp4(camera_name: str, start_ts, end_ts, max_cache_age=604800):
 
         last_file = selected_previews[-2]
         selected_previews.append(last_file)
-        config: FrigateConfig = current_app.frigate_config
 
         ffmpeg_cmd = [
-            config.ffmpeg.ffmpeg_path,
+            "ffmpeg",
             "-hide_banner",
             "-loglevel",
             "warning",

frigate/api/notification.py

@@ -1,65 +0,0 @@
-"""Notification apis."""
-
-import logging
-import os
-
-from cryptography.hazmat.primitives import serialization
-from flask import (
-    Blueprint,
-    current_app,
-    jsonify,
-    make_response,
-    request,
-)
-from peewee import DoesNotExist
-from py_vapid import Vapid01, utils
-
-from frigate.const import CONFIG_DIR
-from frigate.models import User
-
-logger = logging.getLogger(__name__)
-
-NotificationBp = Blueprint("notifications", __name__)
-
-
-@NotificationBp.route("/notifications/pubkey", methods=["GET"])
-def get_vapid_pub_key():
-    if not current_app.frigate_config.notifications.enabled:
-        return make_response(
-            jsonify({"success": False, "message": "Notifications are not enabled."}),
-            400,
-        )
-
-    key = Vapid01.from_file(os.path.join(CONFIG_DIR, "notifications.pem"))
-    raw_pub = key.public_key.public_bytes(
-        serialization.Encoding.X962, serialization.PublicFormat.UncompressedPoint
-    )
-    return jsonify(utils.b64urlencode(raw_pub)), 200
-
-
-@NotificationBp.route("/notifications/register", methods=["POST"])
-def register_notifications():
-    if current_app.frigate_config.auth.enabled:
-        username = request.headers.get("remote-user", type=str) or "admin"
-    else:
-        username = "admin"
-
-    json: dict[str, any] = request.get_json(silent=True) or {}
-    sub = json.get("sub")
-
-    if not sub:
-        return jsonify(
-            {"success": False, "message": "Subscription must be provided."}
-        ), 400
-
-    try:
-        User.update(notification_tokens=User.notification_tokens.append(sub)).where(
-            User.username == username
-        ).execute()
-        return make_response(
-            jsonify({"success": True, "message": "Successfully saved token."}), 200
-        )
-    except DoesNotExist:
-        return make_response(
-            jsonify({"success": False, "message": "Could not find user."}), 404
-        )

frigate/api/preview.py

@@ -75,10 +75,7 @@ def preview_ts(camera_name, start_ts, end_ts):
     return make_response(jsonify(clips), 200)
 
 
-@PreviewBp.route("/preview/<year_month>/<int:day>/<int:hour>/<camera_name>/<tz_name>")
-@PreviewBp.route(
-    "/preview/<year_month>/<float:day>/<float:hour>/<camera_name>/<tz_name>"
-)
+@PreviewBp.route("/preview/<year_month>/<day>/<hour>/<camera_name>/<tz_name>")
 def preview_hour(year_month, day, hour, camera_name, tz_name):
     parts = year_month.split("-")
     start_date = (

frigate/api/review.py

@@ -94,18 +94,6 @@ def review():
     return jsonify([r for r in review])
 
 
-@ReviewBp.route("/review/event/<id>")
-def get_review_from_event(id: str):
-    try:
-        return model_to_dict(
-            ReviewSegment.get(
-                ReviewSegment.data["detections"].cast("text") % f'*"{id}"*'
-            )
-        )
-    except DoesNotExist:
-        return "Review item not found", 404
-
-
 @ReviewBp.route("/review/<id>")
 def get_review(id: str):
     try: