update link (#15756)

I think this was meant to be a `3`

.github/workflows/ci.yml

@@ -155,30 +155,6 @@ 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

CODEOWNERS

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

Makefile

@@ -1,7 +1,7 @@
 default_target: local
 
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.15.0
+VERSION = 0.14.1
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
 CURRENT_UID := $(shell id -u)

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

@@ -1,10 +0,0 @@
-BOARDS += h8l
-
-local-h8l: version
-	docker buildx bake --load --file=docker/hailo8l/h8l.hcl --set h8l.tags=frigate:latest-h8l h8l
-
-build-h8l: version
-	docker buildx bake --file=docker/hailo8l/h8l.hcl --set h8l.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-h8l h8l
-
-push-h8l: build-h8l
-	docker buildx bake --push --file=docker/hailo8l/h8l.hcl --set h8l.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-h8l h8l

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==0.18.3
-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/*
@@ -163,10 +161,6 @@ 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
 
@@ -194,13 +188,6 @@ 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/lib/btbn-ffmpeg/bin:/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PATH}"
 
 # Install dependencies

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/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.*
@@ -30,13 +30,3 @@ ws4py == 0.5.*
 unidecode == 1.3.*
 onnxruntime == 1.18.*
 openvino == 2024.1.*
-# Embeddings
-onnx_clip == 4.0.*
-chromadb == 0.5.0
-# Generative AI
-google-generativeai == 0.6.*
-ollama == 0.2.*
-openai == 1.30.*
-# push notifications
-py-vapid == 1.9.*
-pywebpush == 2.0.*

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/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/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))

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)
@@ -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.
@@ -59,6 +65,18 @@ ffmpeg:
 
 ## Model/vendor specific setup
 
+### Amcrest & Dahua
+
+Amcrest & Dahua cameras should be connected to via RTSP using the following format:
+
+```
+rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=0 # this is the main stream
+rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=1 # this is the sub stream, typically supporting low resolutions only
+rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=2 # higher end cameras support a third stream with a mid resolution (1280x720, 1920x1080)
+rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=3 # new higher end cameras support a fourth stream with another mid resolution (1280x720, 1920x1080)
+
+```
+
 ### Annke C800
 
 This camera is H.265 only. To be able to play clips on some devices (like MacOs or iPhone) the H.265 stream has to be repackaged and the audio stream has to be converted to aac. Unfortunately direct playback of in the browser is not working (yet), but the downloaded clip can be played locally.
@@ -71,7 +89,7 @@ cameras:
         record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v copy -tag:v hvc1 -bsf:v hevc_mp4toannexb -c:a aac
 
       inputs:
-        - path: rtsp://user:password@camera-ip:554/H264/ch1/main/av_stream # <----- Update for your camera
+        - path: rtsp://USERNAME:PASSWORD@CAMERA-IP/H264/ch1/main/av_stream # <----- Update for your camera
           roles:
             - detect
             - record
@@ -89,6 +107,29 @@ ffmpeg:
   input_args: preset-rtsp-blue-iris
 ```
 
+### Hikvision Cameras
+
+Hikvision cameras should be connected to via RTSP using the following format:
+
+```
+rtsp://USERNAME:PASSWORD@CAMERA-IP/streaming/channels/101 # this is the main stream
+rtsp://USERNAME:PASSWORD@CAMERA-IP/streaming/channels/102 # this is the sub stream, typically supporting low resolutions only
+rtsp://USERNAME:PASSWORD@CAMERA-IP/streaming/channels/103 # higher end cameras support a third stream with a mid resolution (1280x720, 1920x1080)
+```
+
+:::note
+
+[Some users have reported](https://www.reddit.com/r/frigate_nvr/comments/1hg4ze7/hikvision_security_settings) that newer Hikvision cameras require adjustments to the security settings:
+
+```
+RTSP Authentication - digest/basic
+RTSP Digest Algorithm - MD5
+WEB Authentication - digest/basic
+WEB Digest Algorithm  - MD5
+```
+
+:::
+
 ### Reolink Cameras
 
 Reolink has older cameras (ex: 410 & 520) as well as newer camera (ex: 520a & 511wa) which support different subsets of options. In both cases using the http stream is recommended.

docs/docs/configuration/cameras.md

@@ -46,6 +46,14 @@ cameras:
   side: ...
 ```
 
+:::note
+
+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 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.
+
+:::
+
 For camera model specific settings check the [camera specific](camera_specific.md) infos.
 
 ## Setting up camera PTZ controls
@@ -71,29 +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                                                                                                                                           |
+| 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                      |      ✅      |      ✅      | ⛔️ Generally, Amcrest should work, but some older models (like the common IP2M-841) don't support autotracking                                 |
-| Amcrest ASH21            |      ❌      |      ❌      | No ONVIF support                                                                                                                                |
+| Amcrest ASH21                |      ✅      |      ❌      | ONVIF service port: 80                                                                                                                          |
-| Ctronics PTZ             |      ✅      |      ❌      |                                                                                                                                                 |
+| Amcrest IP4M-S2112EW-AI      |      ✅      |      ❌      | FOV relative movement not supported.                                                                                                            |
-| Dahua                    |      ✅      |      ✅      |                                                                                                                                                 |
+| Amcrest IP5M-1190EW          |      ✅      |      ❌      | ONVIF Port: 80. FOV relative movement not supported.                                                                                            |
-| Foscam R5                |      ✅      |      ❌      |                                                                                                                                                 |
+| Ctronics PTZ                 |      ✅      |      ❌      |                                                                                                                                                 |
-| Hanwha XNP-6550RH        |      ✅      |      ❌      |                                                                                                                                                 |
+| Dahua                        |      ✅      |      ✅      |                                                                                                                                                 |
-| Hikvision                |      ✅      |      ❌      | Incomplete ONVIF support (MoveStatus won't update even on latest firmware) - reported with HWP-N4215IH-DE and DS-2DE3304W-DE, but likely others |
+| Dahua DH-SD2A500HB           |      ✅      |      ❌      |                                                                                                                                                 |
-| Reolink 511WA            |      ✅      |      ❌      | Zoom only                                                                                                                                       |
+| Foscam R5                    |      ✅      |      ❌      |                                                                                                                                                 |
-| Reolink E1 Pro           |      ✅      |      ❌      |                                                                                                                                                 |
+| Hanwha XNP-6550RH            |      ✅      |      ❌      |                                                                                                                                                 |
-| Reolink E1 Zoom          |      ✅      |      ❌      |                                                                                                                                                 |
+| 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 RLC-823A 16x     |      ✅      |      ❌      |                                                                                                                                                 |
+| Hikvision DS-2DE3A404IWG-E/W |      ✅      |      ✅      |                                                                                                                                                 |
-| Sunba 405-D20X           |      ✅      |      ❌      |                                                                                                                                                 |
+| Reolink 511WA                |      ✅      |      ❌      | Zoom only                                                                                                                                       |
-| Tapo                     |      ✅      |      ❌      | Many models supported, ONVIF Service Port: 2020                                                                                                 |
+| Reolink E1 Pro               |      ✅      |      ❌      |                                                                                                                                                 |
-| Uniview IPC672LR-AX4DUPK |      ✅      |      ❌      | Firmware says FOV relative movement is supported, but camera doesn't actually move when sending ONVIF commands                                  |
+| Reolink E1 Zoom              |      ✅      |      ❌      |                                                                                                                                                 |
-| Vikylin PTZ-2804X-I2     |      ❌      |      ❌      | Incomplete ONVIF support                                                                                                                        |
+| 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

@@ -1,135 +0,0 @@
----
-id: genai
-title: Generative AI
----
-
-Generative AI can be used to automatically generate descriptions based on the thumbnails of your events. This helps with [semantic search](/configuration/semantic_search) in Frigate by providing detailed text descriptions as a basis of the search query.
-
-## 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 detection 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 detection 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). If delivering a package, include the company the package is from."
-    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."
-```
-
-### Experiment with prompts
-
-Providers also has a public facing chat interface for their models. Download a couple 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

@@ -370,7 +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.

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                                                                    |
+| Source | Frame Rate                            | Resolution | Audio                        | Requires go2rtc | Notes                                                                                                                                                               |
-| ------ | ------- | ------------------------------------- | ---------- | ---------------------------- | --------------- | ------------------------------------------------------------------------------------ |
+| ------ | ------------------------------------- | ---------- | ---------------------------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| jsmpeg | low     | same as `detect -> fps`, capped at 10 | 720p       | no                           | no              | resolution is configurable, but go2rtc is recommended if you want higher resolutions |
+| 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    | low     | native                                | native     | yes (depends on audio codec) | yes             | iPhone requires iOS 17.1+, Firefox is h.264 only                                     |
+| 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 | lowest  | native                                | native     | yes (depends on audio codec) | yes             | requires extra config, doesn't support h.265                                         |
+| 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/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

@@ -5,7 +5,7 @@ title: Object Detectors
 
 # 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)
 
@@ -149,7 +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
 
@@ -386,25 +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 if you are using the Raspberry Pi 5 with Hailo-8L AI Kit. This has not been tested using the Hailo-8L with other hardware.
-
-### 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/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/reference.md

@@ -320,6 +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
@@ -329,12 +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.
@@ -372,14 +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:
@@ -473,43 +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 events (default: shown below)
-  reindex: False
-
-# Optional: Configuration for AI generated event 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 Google Gemini 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
+  # Optional: Set the name of the stream configured in go2rtc
-  # in frigate WebUI. (default: name of camera)
+  # 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
@@ -756,7 +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/review.md

@@ -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

@@ -1,38 +0,0 @@
----
-id: semantic_search
-title: Using Semantic Search
----
-
-Semantic search works by embedding images and/or text into a vector representation identified by numbers. Frigate has support for two such models which both 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).
-
-## Configuration
-
-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 detections in your database by adding `reindex: True` to your `semantic_search` configuration. Depending on the number of detections you have, it can take up to 30 minutes to complete and may max out your CPU while indexing. Make sure to set the config back to `False` before restarting Frigate again.
-
-:::
-
-### 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 detections to encode the thumbnail image and store it in Chroma. When searching detections via text in the search box, frigate will perform a `text -> image` similarity search against this embedding. When clicking "FIND SIMILAR" next to a detection, 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 detection descriptions and perform searches against them. Descriptions can be created and/or modified on the search page when clicking on the info icon next to a detection. See [the Generative AI docs](/configuration/genai.md) for more information on how to automatically generate event descriptions.
-
-## Usage Tips
-
-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 detection you want to test and start typing keywords to see what works for you.

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/4fwoNWA" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T549M-ALED-S3</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/3YXpcMw" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T54IR-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/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                                                                                                                                   |
+| 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.                                               |
+| Beelink EQ13 (<a href="https://amzn.to/4iQaBKu" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms                | USB                 | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras. |
-| 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. |
 
 ## 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
 
@@ -107,12 +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

@@ -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
+    - 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.
-camera's audio stream is not AAC.
+    - If possible, update your camera's audio settings to AAC in your camera's firmware.
-    - If possible, update your camera's audio settings to AAC.
     - 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

@@ -294,11 +294,21 @@ cameras:
 
 If you don't have separate streams for detect and record, you would just add the record role to the list on the first input.
 
+:::note
+
+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 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 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
 
@@ -309,4 +319,3 @@ Now that you have a working install, you can use the following documentation for
 3. [Review](../configuration/review.md)
 4. [Masks](../configuration/masks.md)
 5. [Home Assistant Integration](../integrations/home-assistant.md) - Integrate with Home Assistant
-

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.
+This guide outlines the basic configuration steps needed to set up a reverse proxy in front of your Frigate instance.
-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.
 
-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
+Before setting up a reverse proxy, check if any of the built-in functionality in Frigate suits your needs:
-- **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
+|Topic|Docs|
-- **Restrict** access to your Frigate instance at the proxy using, for example, password authentication
+|-|-|
+|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
+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.
-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,
+## Proxies
-and the community is invited to document others through a contribution to this page.
+
+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

@@ -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.
 
@@ -381,6 +381,14 @@ 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.
@@ -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.

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`
 

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 `SEND TO FRIGATE+` buttons on the events 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 events page in Frigate using the `SEND TO 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|
+| Shortcut Key      | Description                   |
-|-----|--------|
+| ----------------- | ----------------------------- |
-|`?`|Show all keyboard shortcuts|
+| `?`               | Show all keyboard shortcuts   |
-|`w`|Add box|
+| `w`               | Add box                       |
-|`d`|Toggle difficult|
+| `d`               | Toggle difficult              |
-|`s`|Switch to the next label|
+| `s`               | Switch to the next label      |
-|`tab`|Select next largest box|
+| `tab`             | Select next largest box       |
-|`del`|Delete current box|
+| `del`             | Delete current box            |
-|`esc`|Deselect/Cancel|
+| `esc`             | Deselect/Cancel               |
-|`← ↑ → ↓`|Move box|
+| `← ↑ → ↓`         | Move box                      |
-|`Shift + ← ↑ → ↓`|Resize box|
+| `Shift + ← ↑ → ↓` | Resize box                    |
-|`-`|Zoom out|
+| `scrollwheel`     | Zoom in/out                   |
-|`=`|Zoom in|
+| `f`               | Hide/show all but current box |
-|`f`|Hide/show all but current box|
+| `spacebar`        | Verify and save               |
-|`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 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.
+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/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/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
@@ -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 peewee import DoesNotExist, fn, operator
-from PIL import Image
 from playhouse.shortcuts import model_to_dict
 
 from frigate.const import (
     CLIPS_DIR,
 )
-from frigate.embeddings import EmbeddingsContext
+from frigate.models import Event, Timeline
-from frigate.embeddings.embeddings import get_metadata
-from frigate.models import Event, ReviewSegment, Timeline
 from frigate.object_processing import TrackedObject
 from frigate.util.builtin import get_tz_modifiers
 
@@ -251,209 +245,6 @@ def events():
     return jsonify(list(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", "text", 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)
-
-    if not query:
-        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.data,
-        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 == "thumbnail":
-        # Grab the ids of events that match the thumbnail image embeddings
-        try:
-            search_event: Event = Event.get(Event.id == query)
-        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], thumb_result["distances"][0]))
-    else:
-        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]),
-            )
-        )
-        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"}
-        | {
-            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)
@@ -813,52 +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:
@@ -880,11 +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),
         (

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/app.py

@@ -22,12 +22,11 @@ from pydantic import ValidationError
 from frigate.api.app import create_app
 from frigate.api.auth import hash_password
 from frigate.comms.config_updater import ConfigPublisher
+from frigate.comms.detections_updater import DetectionProxy
 from frigate.comms.dispatcher import Communicator, Dispatcher
 from frigate.comms.inter_process import InterProcessCommunicator
 from frigate.comms.mqtt import MqttClient
-from frigate.comms.webpush import WebPushClient
 from frigate.comms.ws import WebSocketClient
-from frigate.comms.zmq_proxy import ZmqProxy
 from frigate.config import FrigateConfig
 from frigate.const import (
     CACHE_DIR,
@@ -38,7 +37,6 @@ from frigate.const import (
     MODEL_CACHE_DIR,
     RECORD_DIR,
 )
-from frigate.embeddings import EmbeddingsContext, manage_embeddings
 from frigate.events.audio import listen_to_audio
 from frigate.events.cleanup import EventCleanup
 from frigate.events.external import ExternalEventProcessor
@@ -318,25 +316,7 @@ class FrigateApp:
         self.review_segment_process = review_segment_process
         review_segment_process.start()
         self.processes["review_segment"] = review_segment_process.pid or 0
-        logger.info(f"Review process started: {review_segment_process.pid}")
+        logger.info(f"Recording process started: {review_segment_process.pid}")
-
-    def init_embeddings_manager(self) -> None:
-        if not self.config.semantic_search.enabled:
-            self.embeddings = None
-            return
-
-        # Create a client for other processes to use
-        self.embeddings = EmbeddingsContext()
-        embedding_process = mp.Process(
-            target=manage_embeddings,
-            name="embeddings_manager",
-            args=(self.config,),
-        )
-        embedding_process.daemon = True
-        self.embedding_process = embedding_process
-        embedding_process.start()
-        self.processes["embeddings"] = embedding_process.pid or 0
-        logger.info(f"Embedding process started: {embedding_process.pid}")
 
     def bind_database(self) -> None:
         """Bind db to the main process."""
@@ -382,13 +362,12 @@ class FrigateApp:
     def init_inter_process_communicator(self) -> None:
         self.inter_process_communicator = InterProcessCommunicator()
         self.inter_config_updater = ConfigPublisher()
-        self.inter_zmq_proxy = ZmqProxy()
+        self.inter_detection_proxy = DetectionProxy()
 
     def init_web_server(self) -> None:
         self.flask_app = create_app(
             self.config,
             self.db,
-            self.embeddings,
             self.detected_frames_processor,
             self.storage_maintainer,
             self.onvif_controller,
@@ -406,9 +385,6 @@ class FrigateApp:
         if self.config.mqtt.enabled:
             comms.append(MqttClient(self.config))
 
-        if self.config.notifications.enabled:
-            comms.append(WebPushClient(self.config))
-
         comms.append(WebSocketClient(self.config))
         comms.append(self.inter_process_communicator)
 
@@ -702,7 +678,6 @@ class FrigateApp:
             self.init_onvif()
             self.init_recording_manager()
             self.init_review_segment_manager()
-            self.init_embeddings_manager()
             self.init_go2rtc()
             self.bind_database()
             self.check_db_data_migrations()
@@ -819,14 +794,10 @@ class FrigateApp:
         self.frigate_watchdog.join()
         self.db.stop()
 
-        # Save embeddings stats to disk
-        if self.embeddings:
-            self.embeddings.save_stats()
-
         # Stop Communicators
         self.inter_process_communicator.stop()
         self.inter_config_updater.stop()
-        self.inter_zmq_proxy.stop()
+        self.inter_detection_proxy.stop()
 
         while len(self.detection_shms) > 0:
             shm = self.detection_shms.pop()

frigate/comms/detections_updater.py

@@ -1,9 +1,14 @@
 """Facilitates communication between processes."""
 
+import threading
 from enum import Enum
 from typing import Optional
 
-from .zmq_proxy import Publisher, Subscriber
+import zmq
+
+SOCKET_CONTROL = "inproc://control.detections_updater"
+SOCKET_PUB = "ipc:///tmp/cache/detect_pub"
+SOCKET_SUB = "ipc:///tmp/cache/detect_sub"
 
 
 class DetectionTypeEnum(str, Enum):
@@ -13,31 +18,85 @@ class DetectionTypeEnum(str, Enum):
     audio = "audio"
 
 
-class DetectionPublisher(Publisher):
+class DetectionProxyRunner(threading.Thread):
+    def __init__(self, context: zmq.Context[zmq.Socket]) -> None:
+        threading.Thread.__init__(self)
+        self.name = "detection_proxy"
+        self.context = context
+
+    def run(self) -> None:
+        """Run the proxy."""
+        control = self.context.socket(zmq.REP)
+        control.connect(SOCKET_CONTROL)
+        incoming = self.context.socket(zmq.XSUB)
+        incoming.bind(SOCKET_PUB)
+        outgoing = self.context.socket(zmq.XPUB)
+        outgoing.bind(SOCKET_SUB)
+
+        zmq.proxy_steerable(
+            incoming, outgoing, None, control
+        )  # blocking, will unblock terminate message is received
+        incoming.close()
+        outgoing.close()
+
+
+class DetectionProxy:
+    """Proxies video and audio detections."""
+
+    def __init__(self) -> None:
+        self.context = zmq.Context()
+        self.control = self.context.socket(zmq.REQ)
+        self.control.bind(SOCKET_CONTROL)
+        self.runner = DetectionProxyRunner(self.context)
+        self.runner.start()
+
+    def stop(self) -> None:
+        self.control.send("TERMINATE".encode())  # tell the proxy to stop
+        self.runner.join()
+        self.context.destroy()
+
+
+class DetectionPublisher:
     """Simplifies receiving video and audio detections."""
 
-    topic_base = "detection/"
-
     def __init__(self, topic: DetectionTypeEnum) -> None:
-        topic = topic.value
+        self.topic = topic
-        super().__init__(topic)
+        self.context = zmq.Context()
+        self.socket = self.context.socket(zmq.PUB)
+        self.socket.connect(SOCKET_PUB)
+
+    def send_data(self, payload: any) -> None:
+        """Publish detection."""
+        self.socket.send_string(self.topic.value, flags=zmq.SNDMORE)
+        self.socket.send_json(payload)
+
+    def stop(self) -> None:
+        self.socket.close()
+        self.context.destroy()
 
 
-class DetectionSubscriber(Subscriber):
+class DetectionSubscriber:
     """Simplifies receiving video and audio detections."""
 
-    topic_base = "detection/"
-
     def __init__(self, topic: DetectionTypeEnum) -> None:
-        topic = topic.value
+        self.context = zmq.Context()
-        super().__init__(topic)
+        self.socket = self.context.socket(zmq.SUB)
+        self.socket.setsockopt_string(zmq.SUBSCRIBE, topic.value)
+        self.socket.connect(SOCKET_SUB)
 
-    def check_for_update(
+    def get_data(self, timeout: float = None) -> Optional[tuple[str, any]]:
-        self, timeout: float = None
+        """Returns detections or None if no update."""
-    ) -> Optional[tuple[DetectionTypeEnum, any]]:
+        try:
-        return super().check_for_update(timeout)
+            has_update, _, _ = zmq.select([self.socket], [], [], timeout)
 
-    def _return_object(self, topic: str, payload: any) -> any:
+            if has_update:
-        if payload is None:
+                topic = DetectionTypeEnum[self.socket.recv_string(flags=zmq.NOBLOCK)]
-            return (None, None)
+                return (topic, self.socket.recv_json())
-        return (DetectionTypeEnum[topic[len(self.topic_base) :]], payload)
+        except zmq.ZMQError:
+            pass
+
+        return (None, None)
+
+    def stop(self) -> None:
+        self.socket.close()
+        self.context.destroy()

frigate/comms/dispatcher.py

@@ -14,10 +14,9 @@ from frigate.const import (
     INSERT_PREVIEW,
     REQUEST_REGION_GRID,
     UPDATE_CAMERA_ACTIVITY,
-    UPDATE_EVENT_DESCRIPTION,
     UPSERT_REVIEW_SEGMENT,
 )
-from frigate.models import Event, Previews, Recordings, ReviewSegment
+from frigate.models import Previews, Recordings, ReviewSegment
 from frigate.ptz.onvif import OnvifCommandEnum, OnvifController
 from frigate.types import PTZMetricsTypes
 from frigate.util.object import get_camera_regions_grid
@@ -129,10 +128,6 @@ class Dispatcher:
             ).execute()
         elif topic == UPDATE_CAMERA_ACTIVITY:
             self.camera_activity = payload
-        elif topic == UPDATE_EVENT_DESCRIPTION:
-            event: Event = Event.get(Event.id == payload["id"])
-            event.data["description"] = payload["description"]
-            event.save()
         elif topic == "onConnect":
             camera_status = self.camera_activity.copy()
 

frigate/comms/events_updater.py

@@ -1,51 +1,100 @@
 """Facilitates communication between processes."""
 
+import zmq
+
 from frigate.events.types import EventStateEnum, EventTypeEnum
 
-from .zmq_proxy import Publisher, Subscriber
+SOCKET_PUSH_PULL = "ipc:///tmp/cache/events"
+SOCKET_PUSH_PULL_END = "ipc:///tmp/cache/events_ended"
 
 
-class EventUpdatePublisher(Publisher):
+class EventUpdatePublisher:
     """Publishes events (objects, audio, manual)."""
 
-    topic_base = "event/"
-
     def __init__(self) -> None:
-        super().__init__("update")
+        self.context = zmq.Context()
+        self.socket = self.context.socket(zmq.PUSH)
+        self.socket.connect(SOCKET_PUSH_PULL)
 
     def publish(
         self, payload: tuple[EventTypeEnum, EventStateEnum, str, dict[str, any]]
     ) -> None:
-        super().publish(payload)
+        """There is no communication back to the processes."""
+        self.socket.send_json(payload)
+
+    def stop(self) -> None:
+        self.socket.close()
+        self.context.destroy()
 
 
-class EventUpdateSubscriber(Subscriber):
+class EventUpdateSubscriber:
     """Receives event updates."""
 
-    topic_base = "event/"
-
     def __init__(self) -> None:
-        super().__init__("update")
+        self.context = zmq.Context()
+        self.socket = self.context.socket(zmq.PULL)
+        self.socket.bind(SOCKET_PUSH_PULL)
+
+    def check_for_update(
+        self, timeout=1
+    ) -> tuple[EventTypeEnum, EventStateEnum, str, dict[str, any]]:
+        """Returns events or None if no update."""
+        try:
+            has_update, _, _ = zmq.select([self.socket], [], [], timeout)
+
+            if has_update:
+                return self.socket.recv_json()
+        except zmq.ZMQError:
+            pass
+
+        return None
+
+    def stop(self) -> None:
+        self.socket.close()
+        self.context.destroy()
 
 
-class EventEndPublisher(Publisher):
+class EventEndPublisher:
     """Publishes events that have ended."""
 
-    topic_base = "event/"
-
     def __init__(self) -> None:
-        super().__init__("finalized")
+        self.context = zmq.Context()
+        self.socket = self.context.socket(zmq.PUSH)
+        self.socket.connect(SOCKET_PUSH_PULL_END)
 
     def publish(
         self, payload: tuple[EventTypeEnum, EventStateEnum, str, dict[str, any]]
     ) -> None:
-        super().publish(payload)
+        """There is no communication back to the processes."""
+        self.socket.send_json(payload)
+
+    def stop(self) -> None:
+        self.socket.close()
+        self.context.destroy()
 
 
-class EventEndSubscriber(Subscriber):
+class EventEndSubscriber:
     """Receives events that have ended."""
 
-    topic_base = "event/"
-
     def __init__(self) -> None:
-        super().__init__("finalized")
+        self.context = zmq.Context()
+        self.socket = self.context.socket(zmq.PULL)
+        self.socket.bind(SOCKET_PUSH_PULL_END)
+
+    def check_for_update(
+        self, timeout=1
+    ) -> tuple[EventTypeEnum, EventStateEnum, str, dict[str, any]]:
+        """Returns events ended or None if no update."""
+        try:
+            has_update, _, _ = zmq.select([self.socket], [], [], timeout)
+
+            if has_update:
+                return self.socket.recv_json()
+        except zmq.ZMQError:
+            pass
+
+        return None
+
+    def stop(self) -> None:
+        self.socket.close()
+        self.context.destroy()

frigate/comms/webpush.py

@@ -1,190 +0,0 @@
-"""Handle sending notifications for Frigate via Firebase."""
-
-import datetime
-import json
-import logging
-import os
-from typing import Any, Callable
-
-from py_vapid import Vapid01
-from pywebpush import WebPusher
-
-from frigate.comms.dispatcher import Communicator
-from frigate.config import FrigateConfig
-from frigate.const import CONFIG_DIR
-from frigate.models import User
-
-logger = logging.getLogger(__name__)
-
-
-class WebPushClient(Communicator):  # type: ignore[misc]
-    """Frigate wrapper for webpush client."""
-
-    def __init__(self, config: FrigateConfig) -> None:
-        self.config = config
-        self.claim_headers: dict[str, dict[str, str]] = {}
-        self.refresh: int = 0
-        self.web_pushers: dict[str, list[WebPusher]] = {}
-        self.expired_subs: dict[str, list[str]] = {}
-
-        if not self.config.notifications.email:
-            logger.warning("Email must be provided for push notifications to be sent.")
-
-        # Pull keys from PEM or generate if they do not exist
-        self.vapid = Vapid01.from_file(os.path.join(CONFIG_DIR, "notifications.pem"))
-
-        users: list[User] = (
-            User.select(User.username, User.notification_tokens).dicts().iterator()
-        )
-        for user in users:
-            self.web_pushers[user["username"]] = []
-            for sub in user["notification_tokens"]:
-                self.web_pushers[user["username"]].append(WebPusher(sub))
-
-    def subscribe(self, receiver: Callable) -> None:
-        """Wrapper for allowing dispatcher to subscribe."""
-        pass
-
-    def check_registrations(self) -> None:
-        # check for valid claim or create new one
-        now = datetime.datetime.now().timestamp()
-        if len(self.claim_headers) == 0 or self.refresh < now:
-            self.refresh = int(
-                (datetime.datetime.now() + datetime.timedelta(hours=1)).timestamp()
-            )
-            endpoints: set[str] = set()
-
-            # get a unique set of push endpoints
-            for pushers in self.web_pushers.values():
-                for push in pushers:
-                    endpoint: str = push.subscription_info["endpoint"]
-                    endpoints.add(endpoint[0 : endpoint.index("/", 10)])
-
-            # create new claim
-            for endpoint in endpoints:
-                claim = {
-                    "sub": f"mailto:{self.config.notifications.email}",
-                    "aud": endpoint,
-                    "exp": self.refresh,
-                }
-                self.claim_headers[endpoint] = self.vapid.sign(claim)
-
-    def cleanup_registrations(self) -> None:
-        # delete any expired subs
-        if len(self.expired_subs) > 0:
-            for user, expired in self.expired_subs.items():
-                user_subs = []
-
-                # get all subscriptions, removing ones that are expired
-                stored_user: User = User.get_by_id(user)
-                for token in stored_user.notification_tokens:
-                    if token["endpoint"] in expired:
-                        continue
-
-                    user_subs.append(token)
-
-                # overwrite the database and reset web pushers
-                User.update(notification_tokens=user_subs).where(
-                    User.username == user
-                ).execute()
-
-                self.web_pushers[user] = []
-
-                for sub in user_subs:
-                    self.web_pushers[user].append(WebPusher(sub))
-
-                logger.info(
-                    f"Cleaned up {len(expired)} notification subscriptions for {user}"
-                )
-
-        self.expired_subs = {}
-
-    def publish(self, topic: str, payload: Any, retain: bool = False) -> None:
-        """Wrapper for publishing when client is in valid state."""
-        if topic == "reviews":
-            self.send_alert(json.loads(payload))
-
-    def send_alert(self, payload: dict[str, any]) -> None:
-        if not self.config.notifications.email:
-            return
-
-        self.check_registrations()
-
-        # Only notify for alerts
-        if payload["after"]["severity"] != "alert":
-            return
-
-        state = payload["type"]
-
-        # Don't notify if message is an update and important fields don't have an update
-        if (
-            state == "update"
-            and len(payload["before"]["data"]["objects"])
-            == len(payload["after"]["data"]["objects"])
-            and len(payload["before"]["data"]["zones"])
-            == len(payload["after"]["data"]["zones"])
-        ):
-            return
-
-        reviewId = payload["after"]["id"]
-        sorted_objects: set[str] = set()
-
-        for obj in payload["after"]["data"]["objects"]:
-            if "-verified" not in obj:
-                sorted_objects.add(obj)
-
-        sorted_objects.update(payload["after"]["data"]["sub_labels"])
-
-        camera: str = payload["after"]["camera"]
-        title = f"{', '.join(sorted_objects).replace('_', ' ').title()}{' was' if state == 'end' else ''} detected in {', '.join(payload['after']['data']['zones']).replace('_', ' ').title()}"
-        message = f"Detected on {camera.replace('_', ' ').title()}"
-        image = f'{payload["after"]["thumb_path"].replace("/media/frigate", "")}'
-
-        # if event is ongoing open to live view otherwise open to recordings view
-        direct_url = f"/review?id={reviewId}" if state == "end" else f"/#{camera}"
-
-        for user, pushers in self.web_pushers.items():
-            for pusher in pushers:
-                endpoint = pusher.subscription_info["endpoint"]
-
-                # set headers for notification behavior
-                headers = self.claim_headers[
-                    endpoint[0 : endpoint.index("/", 10)]
-                ].copy()
-                headers["urgency"] = "high"
-                ttl = 3600 if state == "end" else 0
-
-                # send message
-                resp = pusher.send(
-                    headers=headers,
-                    ttl=ttl,
-                    data=json.dumps(
-                        {
-                            "title": title,
-                            "message": message,
-                            "direct_url": direct_url,
-                            "image": image,
-                            "id": reviewId,
-                            "type": "alert",
-                        }
-                    ),
-                )
-
-                if resp.status_code == 201:
-                    pass
-                elif resp.status_code == 404 or resp.status_code == 410:
-                    # subscription is not found or has been unsubscribed
-                    if not self.expired_subs.get(user):
-                        self.expired_subs[user] = []
-
-                    self.expired_subs[user].append(pusher.subscription_info["endpoint"])
-                    # the subscription no longer exists and should be removed
-                else:
-                    logger.warning(
-                        f"Failed to send notification to {user} :: {resp.headers}"
-                    )
-
-        self.cleanup_registrations()
-
-    def stop(self) -> None:
-        pass

frigate/comms/zmq_proxy.py

@@ -1,99 +0,0 @@
-"""Facilitates communication over zmq proxy."""
-
-import json
-import threading
-from typing import Optional
-
-import zmq
-
-SOCKET_PUB = "ipc:///tmp/cache/proxy_pub"
-SOCKET_SUB = "ipc:///tmp/cache/proxy_sub"
-
-
-class ZmqProxyRunner(threading.Thread):
-    def __init__(self, context: zmq.Context[zmq.Socket]) -> None:
-        threading.Thread.__init__(self)
-        self.name = "detection_proxy"
-        self.context = context
-
-    def run(self) -> None:
-        """Run the proxy."""
-        incoming = self.context.socket(zmq.XSUB)
-        incoming.bind(SOCKET_PUB)
-        outgoing = self.context.socket(zmq.XPUB)
-        outgoing.bind(SOCKET_SUB)
-
-        # Blocking: This will unblock (via exception) when we destroy the context
-        # The incoming and outgoing sockets will be closed automatically
-        # when the context is destroyed as well.
-        try:
-            zmq.proxy(incoming, outgoing)
-        except zmq.ZMQError:
-            pass
-
-
-class ZmqProxy:
-    """Proxies video and audio detections."""
-
-    def __init__(self) -> None:
-        self.context = zmq.Context()
-        self.runner = ZmqProxyRunner(self.context)
-        self.runner.start()
-
-    def stop(self) -> None:
-        # destroying the context will tell the proxy to stop
-        self.context.destroy()
-        self.runner.join()
-
-
-class Publisher:
-    """Publishes messages."""
-
-    topic_base: str = ""
-
-    def __init__(self, topic: str = "") -> None:
-        self.topic = f"{self.topic_base}{topic}"
-        self.context = zmq.Context()
-        self.socket = self.context.socket(zmq.PUB)
-        self.socket.connect(SOCKET_PUB)
-
-    def publish(self, payload: any, sub_topic: str = "") -> None:
-        """Publish message."""
-        self.socket.send_string(f"{self.topic}{sub_topic} {json.dumps(payload)}")
-
-    def stop(self) -> None:
-        self.socket.close()
-        self.context.destroy()
-
-
-class Subscriber:
-    """Receives messages."""
-
-    topic_base: str = ""
-
-    def __init__(self, topic: str = "") -> None:
-        self.topic = f"{self.topic_base}{topic}"
-        self.context = zmq.Context()
-        self.socket = self.context.socket(zmq.SUB)
-        self.socket.setsockopt_string(zmq.SUBSCRIBE, self.topic)
-        self.socket.connect(SOCKET_SUB)
-
-    def check_for_update(self, timeout: float = 1) -> Optional[tuple[str, any]]:
-        """Returns message or None if no update."""
-        try:
-            has_update, _, _ = zmq.select([self.socket], [], [], timeout)
-
-            if has_update:
-                parts = self.socket.recv_string(flags=zmq.NOBLOCK).split(maxsplit=1)
-                return self._return_object(parts[0], json.loads(parts[1]))
-        except zmq.ZMQError:
-            pass
-
-        return self._return_object("", None)
-
-    def stop(self) -> None:
-        self.socket.close()
-        self.context.destroy()
-
-    def _return_object(self, topic: str, payload: any) -> any:
-        return payload

frigate/config.py

@@ -169,11 +169,6 @@ class AuthConfig(FrigateBaseModel):
     hash_iterations: int = Field(default=600000, title="Password hash iterations")
 
 
-class NotificationConfig(FrigateBaseModel):
-    enabled: bool = Field(default=False, title="Enable notifications")
-    email: Optional[str] = Field(default=None, title="Email required for push.")
-
-
 class StatsConfig(FrigateBaseModel):
     amd_gpu_stats: bool = Field(default=True, title="Enable AMD GPU stats.")
     intel_gpu_stats: bool = Field(default=True, title="Enable Intel GPU stats.")
@@ -735,38 +730,6 @@ class ReviewConfig(FrigateBaseModel):
     )
 
 
-class SemanticSearchConfig(FrigateBaseModel):
-    enabled: bool = Field(default=False, title="Enable semantic search.")
-    reindex: Optional[bool] = Field(
-        default=False, title="Reindex all detections on startup."
-    )
-
-
-class GenAIProviderEnum(str, Enum):
-    openai = "openai"
-    gemini = "gemini"
-    ollama = "ollama"
-
-
-class GenAIConfig(FrigateBaseModel):
-    enabled: bool = Field(default=False, title="Enable GenAI.")
-    provider: GenAIProviderEnum = Field(
-        default=GenAIProviderEnum.openai, title="GenAI provider."
-    )
-    base_url: Optional[str] = Field(None, title="Provider base url.")
-    api_key: Optional[str] = Field(None, title="Provider API key.")
-    model: str = Field(default="gpt-4o", title="GenAI model.")
-    prompt: str = Field(
-        default="Describe the {label} in the sequence of images with as much detail as possible. Do not describe the background.",
-        title="Default caption prompt.",
-    )
-    object_prompts: Dict[str, str] = Field(default={}, title="Object specific prompts.")
-
-
-class GenAICameraConfig(FrigateBaseModel):
-    enabled: bool = Field(default=False, title="Enable GenAI for camera.")
-
-
 class AudioConfig(FrigateBaseModel):
     enabled: bool = Field(default=False, title="Enable audio events.")
     max_not_heard: int = Field(
@@ -1048,9 +1011,6 @@ class CameraConfig(FrigateBaseModel):
     review: ReviewConfig = Field(
         default_factory=ReviewConfig, title="Review configuration."
     )
-    genai: GenAICameraConfig = Field(
-        default_factory=GenAICameraConfig, title="Generative AI configuration."
-    )
     audio: AudioConfig = Field(
         default_factory=AudioConfig, title="Audio events configuration."
     )
@@ -1366,9 +1326,6 @@ class FrigateConfig(FrigateBaseModel):
         default_factory=dict, title="Frigate environment variables."
     )
     ui: UIConfig = Field(default_factory=UIConfig, title="UI configuration.")
-    notifications: NotificationConfig = Field(
-        default_factory=NotificationConfig, title="Notification Config"
-    )
     telemetry: TelemetryConfig = Field(
         default_factory=TelemetryConfig, title="Telemetry configuration."
     )
@@ -1406,12 +1363,6 @@ class FrigateConfig(FrigateBaseModel):
     review: ReviewConfig = Field(
         default_factory=ReviewConfig, title="Review configuration."
     )
-    semantic_search: SemanticSearchConfig = Field(
-        default_factory=SemanticSearchConfig, title="Semantic search configuration."
-    )
-    genai: GenAIConfig = Field(
-        default_factory=GenAIConfig, title="Generative AI configuration."
-    )
     audio: AudioConfig = Field(
         default_factory=AudioConfig, title="Global Audio events configuration."
     )
@@ -1446,10 +1397,6 @@ class FrigateConfig(FrigateBaseModel):
             config.mqtt.user = config.mqtt.user.format(**FRIGATE_ENV_VARS)
             config.mqtt.password = config.mqtt.password.format(**FRIGATE_ENV_VARS)
 
-        # GenAI substitution
-        if config.genai.api_key:
-            config.genai.api_key = config.genai.api_key.format(**FRIGATE_ENV_VARS)
-
         # set default min_score for object attributes
         for attribute in ALL_ATTRIBUTE_LABELS:
             if not config.objects.filters.get(attribute):
@@ -1471,7 +1418,6 @@ class FrigateConfig(FrigateBaseModel):
                 "live": ...,
                 "objects": ...,
                 "review": ...,
-                "genai": {"enabled"},
                 "motion": ...,
                 "detect": ...,
                 "ffmpeg": ...,

frigate/const.py

@@ -81,7 +81,6 @@ REQUEST_REGION_GRID = "request_region_grid"
 UPSERT_REVIEW_SEGMENT = "upsert_review_segment"
 CLEAR_ONGOING_REVIEW_SEGMENTS = "clear_ongoing_review_segments"
 UPDATE_CAMERA_ACTIVITY = "update_camera_activity"
-UPDATE_EVENT_DESCRIPTION = "update_event_description"
 
 # Stats Values
 

frigate/detectors/plugins/hailo8l.py

@@ -1,294 +0,0 @@
-import logging
-import os
-import urllib.request
-
-import numpy as np
-
-try:
-    from hailo_platform import (
-        HEF,
-        ConfigureParams,
-        FormatType,
-        HailoRTException,
-        HailoStreamInterface,
-        InferVStreams,
-        InputVStreamParams,
-        OutputVStreamParams,
-        VDevice,
-    )
-except ModuleNotFoundError:
-    pass
-
-from pydantic import BaseModel, Field
-from typing_extensions import Literal
-
-from frigate.detectors.detection_api import DetectionApi
-from frigate.detectors.detector_config import BaseDetectorConfig
-from frigate.detectors.util import preprocess  # Assuming this function is available
-
-# Set up logging
-logger = logging.getLogger(__name__)
-
-# Define the detector key for Hailo
-DETECTOR_KEY = "hailo8l"
-
-
-# Configuration class for model settings
-class ModelConfig(BaseModel):
-    path: str = Field(default=None, title="Model Path")  # Path to the HEF file
-
-
-# Configuration class for Hailo detector
-class HailoDetectorConfig(BaseDetectorConfig):
-    type: Literal[DETECTOR_KEY]  # Type of the detector
-    device: str = Field(default="PCIe", title="Device Type")  # Device type (e.g., PCIe)
-
-
-# Hailo detector class implementation
-class HailoDetector(DetectionApi):
-    type_key = DETECTOR_KEY  # Set the type key to the Hailo detector key
-
-    def __init__(self, detector_config: HailoDetectorConfig):
-        # Initialize device type and model path from the configuration
-        self.h8l_device_type = detector_config.device
-        self.h8l_model_path = detector_config.model.path
-        self.h8l_model_height = detector_config.model.height
-        self.h8l_model_width = detector_config.model.width
-        self.h8l_model_type = detector_config.model.model_type
-        self.h8l_tensor_format = detector_config.model.input_tensor
-        self.h8l_pixel_format = detector_config.model.input_pixel_format
-        self.model_url = "https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.11.0/hailo8l/ssd_mobilenet_v1.hef"
-        self.cache_dir = "/config/model_cache/h8l_cache"
-        self.expected_model_filename = "ssd_mobilenet_v1.hef"
-        output_type = "FLOAT32"
-
-        logger.info(f"Initializing Hailo device as {self.h8l_device_type}")
-        self.check_and_prepare_model()
-        try:
-            # Validate device type
-            if self.h8l_device_type not in ["PCIe", "M.2"]:
-                raise ValueError(f"Unsupported device type: {self.h8l_device_type}")
-
-            # Initialize the Hailo device
-            self.target = VDevice()
-            # Load the HEF (Hailo's binary format for neural networks)
-            self.hef = HEF(self.h8l_model_path)
-            # Create configuration parameters from the HEF
-            self.configure_params = ConfigureParams.create_from_hef(
-                hef=self.hef, interface=HailoStreamInterface.PCIe
-            )
-            # Configure the device with the HEF
-            self.network_groups = self.target.configure(self.hef, self.configure_params)
-            self.network_group = self.network_groups[0]
-            self.network_group_params = self.network_group.create_params()
-
-            # Create input and output virtual stream parameters
-            self.input_vstreams_params = InputVStreamParams.make(
-                self.network_group,
-                format_type=self.hef.get_input_vstream_infos()[0].format.type,
-            )
-            self.output_vstreams_params = OutputVStreamParams.make(
-                self.network_group, format_type=getattr(FormatType, output_type)
-            )
-
-            # Get input and output stream information from the HEF
-            self.input_vstream_info = self.hef.get_input_vstream_infos()
-            self.output_vstream_info = self.hef.get_output_vstream_infos()
-
-            logger.info("Hailo device initialized successfully")
-            logger.debug(f"[__init__] Model Path: {self.h8l_model_path}")
-            logger.debug(f"[__init__] Input Tensor Format: {self.h8l_tensor_format}")
-            logger.debug(f"[__init__] Input Pixel Format: {self.h8l_pixel_format}")
-            logger.debug(f"[__init__] Input VStream Info: {self.input_vstream_info[0]}")
-            logger.debug(
-                f"[__init__] Output VStream Info: {self.output_vstream_info[0]}"
-            )
-        except HailoRTException as e:
-            logger.error(f"HailoRTException during initialization: {e}")
-            raise
-        except Exception as e:
-            logger.error(f"Failed to initialize Hailo device: {e}")
-            raise
-
-    def check_and_prepare_model(self):
-        # Ensure cache directory exists
-        if not os.path.exists(self.cache_dir):
-            os.makedirs(self.cache_dir)
-
-        # Check for the expected model file
-        model_file_path = os.path.join(self.cache_dir, self.expected_model_filename)
-        if not os.path.isfile(model_file_path):
-            logger.info(
-                f"A model file was not found at {model_file_path}, Downloading one from {self.model_url}."
-            )
-            urllib.request.urlretrieve(self.model_url, model_file_path)
-            logger.info(f"A model file was downloaded to {model_file_path}.")
-        else:
-            logger.info(
-                f"A model file already exists at {model_file_path} not downloading one."
-            )
-
-    def detect_raw(self, tensor_input):
-        logger.debug("[detect_raw] Entering function")
-        logger.debug(
-            f"[detect_raw] The `tensor_input` = {tensor_input} tensor_input shape = {tensor_input.shape}"
-        )
-
-        if tensor_input is None:
-            raise ValueError(
-                "[detect_raw] The 'tensor_input' argument must be provided"
-            )
-
-        # Ensure tensor_input is a numpy array
-        if isinstance(tensor_input, list):
-            tensor_input = np.array(tensor_input)
-            logger.debug(
-                f"[detect_raw] Converted tensor_input to numpy array: shape {tensor_input.shape}"
-            )
-
-        # Preprocess the tensor input using Frigate's preprocess function
-        processed_tensor = preprocess(
-            tensor_input, (1, self.h8l_model_height, self.h8l_model_width, 3), np.uint8
-        )
-        logger.debug(
-            f"[detect_raw] Tensor data and shape after preprocessing: {processed_tensor} {processed_tensor.shape}"
-        )
-
-        input_data = processed_tensor
-        logger.debug(
-            f"[detect_raw] Input data for inference shape: {processed_tensor.shape}, dtype: {processed_tensor.dtype}"
-        )
-
-        try:
-            with InferVStreams(
-                self.network_group,
-                self.input_vstreams_params,
-                self.output_vstreams_params,
-            ) as infer_pipeline:
-                input_dict = {}
-                if isinstance(input_data, dict):
-                    input_dict = input_data
-                    logger.debug("[detect_raw] it a dictionary.")
-                elif isinstance(input_data, (list, tuple)):
-                    for idx, layer_info in enumerate(self.input_vstream_info):
-                        input_dict[layer_info.name] = input_data[idx]
-                        logger.debug("[detect_raw] converted from list/tuple.")
-                else:
-                    if len(input_data.shape) == 3:
-                        input_data = np.expand_dims(input_data, axis=0)
-                        logger.debug("[detect_raw] converted from an array.")
-                    input_dict[self.input_vstream_info[0].name] = input_data
-
-                logger.debug(
-                    f"[detect_raw] Input dictionary for inference keys: {input_dict.keys()}"
-                )
-
-                with self.network_group.activate(self.network_group_params):
-                    raw_output = infer_pipeline.infer(input_dict)
-                    logger.debug(f"[detect_raw] Raw inference output: {raw_output}")
-
-                    if self.output_vstream_info[0].name not in raw_output:
-                        logger.error(
-                            f"[detect_raw] Missing output stream {self.output_vstream_info[0].name} in inference results"
-                        )
-                        return np.zeros((20, 6), np.float32)
-
-                    raw_output = raw_output[self.output_vstream_info[0].name][0]
-                    logger.debug(
-                        f"[detect_raw] Raw output for stream {self.output_vstream_info[0].name}: {raw_output}"
-                    )
-
-            # Process the raw output
-            detections = self.process_detections(raw_output)
-            if len(detections) == 0:
-                logger.debug(
-                    "[detect_raw] No detections found after processing. Setting default values."
-                )
-                return np.zeros((20, 6), np.float32)
-            else:
-                formatted_detections = detections
-                if (
-                    formatted_detections.shape[1] != 6
-                ):  # Ensure the formatted detections have 6 columns
-                    logger.error(
-                        f"[detect_raw] Unexpected shape for formatted detections: {formatted_detections.shape}. Expected (20, 6)."
-                    )
-                    return np.zeros((20, 6), np.float32)
-                return formatted_detections
-        except HailoRTException as e:
-            logger.error(f"[detect_raw] HailoRTException during inference: {e}")
-            return np.zeros((20, 6), np.float32)
-        except Exception as e:
-            logger.error(f"[detect_raw] Exception during inference: {e}")
-            return np.zeros((20, 6), np.float32)
-        finally:
-            logger.debug("[detect_raw] Exiting function")
-
-    def process_detections(self, raw_detections, threshold=0.5):
-        boxes, scores, classes = [], [], []
-        num_detections = 0
-
-        logger.debug(f"[process_detections] Raw detections: {raw_detections}")
-
-        for i, detection_set in enumerate(raw_detections):
-            if not isinstance(detection_set, np.ndarray) or detection_set.size == 0:
-                logger.debug(
-                    f"[process_detections] Detection set {i} is empty or not an array, skipping."
-                )
-                continue
-
-            logger.debug(
-                f"[process_detections] Detection set {i} shape: {detection_set.shape}"
-            )
-
-            for detection in detection_set:
-                if detection.shape[0] == 0:
-                    logger.debug(
-                        f"[process_detections] Detection in set {i} is empty, skipping."
-                    )
-                    continue
-
-                ymin, xmin, ymax, xmax = detection[:4]
-                score = np.clip(detection[4], 0, 1)  # Use np.clip for clarity
-
-                if score < threshold:
-                    logger.debug(
-                        f"[process_detections] Detection in set {i} has a score {score} below threshold {threshold}. Skipping."
-                    )
-                    continue
-
-                logger.debug(
-                    f"[process_detections] Adding detection with coordinates: ({xmin}, {ymin}), ({xmax}, {ymax}) and score: {score}"
-                )
-                boxes.append([ymin, xmin, ymax, xmax])
-                scores.append(score)
-                classes.append(i)
-                num_detections += 1
-
-        logger.debug(
-            f"[process_detections] Boxes: {boxes}, Scores: {scores}, Classes: {classes}, Num detections: {num_detections}"
-        )
-
-        if num_detections == 0:
-            logger.debug("[process_detections] No valid detections found.")
-            return np.zeros((20, 6), np.float32)
-
-        combined = np.hstack(
-            (
-                np.array(classes)[:, np.newaxis],
-                np.array(scores)[:, np.newaxis],
-                np.array(boxes),
-            )
-        )
-
-        if combined.shape[0] < 20:
-            padding = np.zeros(
-                (20 - combined.shape[0], combined.shape[1]), dtype=combined.dtype
-            )
-            combined = np.vstack((combined, padding))
-
-        logger.debug(
-            f"[process_detections] Combined detections (padded to 20 if necessary): {np.array_str(combined, precision=4, suppress_small=True)}"
-        )
-
-        return combined[:20, :6]

frigate/embeddings/__init__.py

@@ -1,91 +0,0 @@
-"""ChromaDB embeddings database."""
-
-import json
-import logging
-import multiprocessing as mp
-import signal
-import threading
-from types import FrameType
-from typing import Optional
-
-from playhouse.sqliteq import SqliteQueueDatabase
-from setproctitle import setproctitle
-
-from frigate.config import FrigateConfig
-from frigate.const import CONFIG_DIR
-from frigate.models import Event
-from frigate.util.services import listen
-
-from .embeddings import Embeddings
-from .maintainer import EmbeddingMaintainer
-from .util import ZScoreNormalization
-
-logger = logging.getLogger(__name__)
-
-
-def manage_embeddings(config: FrigateConfig) -> None:
-    # Only initialize embeddings if semantic search is enabled
-    if not config.semantic_search.enabled:
-        return
-
-    stop_event = mp.Event()
-
-    def receiveSignal(signalNumber: int, frame: Optional[FrameType]) -> None:
-        stop_event.set()
-
-    signal.signal(signal.SIGTERM, receiveSignal)
-    signal.signal(signal.SIGINT, receiveSignal)
-
-    threading.current_thread().name = "process:embeddings_manager"
-    setproctitle("frigate.embeddings_manager")
-    listen()
-
-    # Configure Frigate DB
-    db = SqliteQueueDatabase(
-        config.database.path,
-        pragmas={
-            "auto_vacuum": "FULL",  # Does not defragment database
-            "cache_size": -512 * 1000,  # 512MB of cache
-            "synchronous": "NORMAL",  # Safe when using WAL https://www.sqlite.org/pragma.html#pragma_synchronous
-        },
-        timeout=max(60, 10 * len([c for c in config.cameras.values() if c.enabled])),
-    )
-    models = [Event]
-    db.bind(models)
-
-    embeddings = Embeddings()
-
-    # Check if we need to re-index events
-    if config.semantic_search.reindex:
-        embeddings.reindex()
-
-    maintainer = EmbeddingMaintainer(
-        config,
-        stop_event,
-    )
-    maintainer.start()
-
-
-class EmbeddingsContext:
-    def __init__(self):
-        self.embeddings = Embeddings()
-        self.thumb_stats = ZScoreNormalization()
-        self.desc_stats = ZScoreNormalization()
-
-        # load stats from disk
-        try:
-            with open(f"{CONFIG_DIR}/.search_stats.json", "r") as f:
-                data = json.loads(f.read())
-                self.thumb_stats.from_dict(data["thumb_stats"])
-                self.desc_stats.from_dict(data["desc_stats"])
-        except FileNotFoundError:
-            pass
-
-    def save_stats(self):
-        """Write the stats to disk as JSON on exit."""
-        contents = {
-            "thumb_stats": self.thumb_stats.to_dict(),
-            "desc_stats": self.desc_stats.to_dict(),
-        }
-        with open(f"{CONFIG_DIR}/.search_stats.json", "w") as f:
-            f.write(json.dumps(contents))

frigate/embeddings/embeddings.py

@@ -1,163 +0,0 @@
-"""ChromaDB embeddings database."""
-
-import base64
-import io
-import logging
-import sys
-import time
-
-import numpy as np
-from PIL import Image
-from playhouse.shortcuts import model_to_dict
-
-from frigate.models import Event
-
-# Squelch posthog logging
-logging.getLogger("chromadb.telemetry.product.posthog").setLevel(logging.CRITICAL)
-
-# Hotsawp the sqlite3 module for Chroma compatibility
-try:
-    from chromadb import Collection
-    from chromadb import HttpClient as ChromaClient
-    from chromadb.config import Settings
-
-    from .functions.clip import ClipEmbedding
-    from .functions.minilm_l6_v2 import MiniLMEmbedding
-except RuntimeError:
-    __import__("pysqlite3")
-    sys.modules["sqlite3"] = sys.modules.pop("pysqlite3")
-    from chromadb import Collection
-    from chromadb import HttpClient as ChromaClient
-    from chromadb.config import Settings
-
-    from .functions.clip import ClipEmbedding
-    from .functions.minilm_l6_v2 import MiniLMEmbedding
-
-logger = logging.getLogger(__name__)
-
-
-def get_metadata(event: Event) -> dict:
-    """Extract valid event metadata."""
-    event_dict = model_to_dict(event)
-    return (
-        {
-            k: v
-            for k, v in event_dict.items()
-            if k not in ["id", "thumbnail"]
-            and v is not None
-            and isinstance(v, (str, int, float, bool))
-        }
-        | {
-            k: v
-            for k, v in event_dict["data"].items()
-            if k not in ["description"]
-            and v is not None
-            and isinstance(v, (str, int, float, bool))
-        }
-        | {
-            # Metadata search doesn't support $contains
-            # and an event can have multiple zones, so
-            # we need to create a key for each zone
-            f"{k}_{x}": True
-            for k, v in event_dict.items()
-            if isinstance(v, list) and len(v) > 0
-            for x in v
-            if isinstance(x, str)
-        }
-    )
-
-
-class Embeddings:
-    """ChromaDB embeddings database."""
-
-    def __init__(self) -> None:
-        self.client: ChromaClient = ChromaClient(
-            host="127.0.0.1",
-            settings=Settings(anonymized_telemetry=False),
-        )
-
-    @property
-    def thumbnail(self) -> Collection:
-        return self.client.get_or_create_collection(
-            name="event_thumbnail", embedding_function=ClipEmbedding()
-        )
-
-    @property
-    def description(self) -> Collection:
-        return self.client.get_or_create_collection(
-            name="event_description", embedding_function=MiniLMEmbedding()
-        )
-
-    def reindex(self) -> None:
-        """Reindex all event embeddings."""
-        logger.info("Indexing event embeddings...")
-        self.client.reset()
-
-        st = time.time()
-        totals = {
-            "thumb": 0,
-            "desc": 0,
-        }
-
-        batch_size = 100
-        current_page = 1
-        events = (
-            Event.select()
-            .where(
-                (Event.has_clip == True | Event.has_snapshot == True)
-                & Event.thumbnail.is_null(False)
-            )
-            .order_by(Event.start_time.desc())
-            .paginate(current_page, batch_size)
-        )
-
-        while len(events) > 0:
-            thumbnails = {"ids": [], "images": [], "metadatas": []}
-            descriptions = {"ids": [], "documents": [], "metadatas": []}
-
-            event: Event
-            for event in events:
-                metadata = get_metadata(event)
-                thumbnail = base64.b64decode(event.thumbnail)
-                img = np.array(Image.open(io.BytesIO(thumbnail)).convert("RGB"))
-                thumbnails["ids"].append(event.id)
-                thumbnails["images"].append(img)
-                thumbnails["metadatas"].append(metadata)
-                if event.data.get("description") is not None:
-                    descriptions["ids"].append(event.id)
-                    descriptions["documents"].append(event.data["description"])
-                    descriptions["metadatas"].append(metadata)
-
-            if len(thumbnails["ids"]) > 0:
-                totals["thumb"] += len(thumbnails["ids"])
-                self.thumbnail.upsert(
-                    images=thumbnails["images"],
-                    metadatas=thumbnails["metadatas"],
-                    ids=thumbnails["ids"],
-                )
-
-            if len(descriptions["ids"]) > 0:
-                totals["desc"] += len(descriptions["ids"])
-                self.description.upsert(
-                    documents=descriptions["documents"],
-                    metadatas=descriptions["metadatas"],
-                    ids=descriptions["ids"],
-                )
-
-            current_page += 1
-            events = (
-                Event.select()
-                .where(
-                    (Event.has_clip == True | Event.has_snapshot == True)
-                    & Event.thumbnail.is_null(False)
-                )
-                .order_by(Event.start_time.desc())
-                .paginate(current_page, batch_size)
-            )
-
-        logger.info(
-            "Embedded %d thumbnails and %d descriptions in %s seconds",
-            totals["thumb"],
-            totals["desc"],
-            time.time() - st,
-        )

frigate/embeddings/functions/clip.py

@@ -1,63 +0,0 @@
-"""CLIP Embeddings for Frigate."""
-
-import os
-from typing import Tuple, Union
-
-import onnxruntime as ort
-from chromadb import EmbeddingFunction, Embeddings
-from chromadb.api.types import (
-    Documents,
-    Images,
-    is_document,
-    is_image,
-)
-from onnx_clip import OnnxClip
-
-from frigate.const import MODEL_CACHE_DIR
-
-
-class Clip(OnnxClip):
-    """Override load models to download to cache directory."""
-
-    @staticmethod
-    def _load_models(
-        model: str,
-        silent: bool,
-    ) -> Tuple[ort.InferenceSession, ort.InferenceSession]:
-        """
-        These models are a part of the container. Treat as as such.
-        """
-        if model == "ViT-B/32":
-            IMAGE_MODEL_FILE = "clip_image_model_vitb32.onnx"
-            TEXT_MODEL_FILE = "clip_text_model_vitb32.onnx"
-        elif model == "RN50":
-            IMAGE_MODEL_FILE = "clip_image_model_rn50.onnx"
-            TEXT_MODEL_FILE = "clip_text_model_rn50.onnx"
-        else:
-            raise ValueError(f"Unexpected model {model}. No `.onnx` file found.")
-
-        models = []
-        for model_file in [IMAGE_MODEL_FILE, TEXT_MODEL_FILE]:
-            path = os.path.join(MODEL_CACHE_DIR, "clip", model_file)
-            models.append(OnnxClip._load_model(path, silent))
-
-        return models[0], models[1]
-
-
-class ClipEmbedding(EmbeddingFunction):
-    """Embedding function for CLIP model used in Chroma."""
-
-    def __init__(self, model: str = "ViT-B/32"):
-        """Initialize CLIP Embedding function."""
-        self.model = Clip(model)
-
-    def __call__(self, input: Union[Documents, Images]) -> Embeddings:
-        embeddings: Embeddings = []
-        for item in input:
-            if is_image(item):
-                result = self.model.get_image_embeddings([item])
-                embeddings.append(result[0, :].tolist())
-            elif is_document(item):
-                result = self.model.get_text_embeddings([item])
-                embeddings.append(result[0, :].tolist())
-        return embeddings

frigate/embeddings/functions/minilm_l6_v2.py

@@ -1,11 +0,0 @@
-"""Embedding function for ONNX MiniLM-L6 model used in Chroma."""
-
-from chromadb.utils.embedding_functions import ONNXMiniLM_L6_V2
-
-from frigate.const import MODEL_CACHE_DIR
-
-
-class MiniLMEmbedding(ONNXMiniLM_L6_V2):
-    """Override DOWNLOAD_PATH to download to cache directory."""
-
-    DOWNLOAD_PATH = f"{MODEL_CACHE_DIR}/all-MiniLM-L6-v2"

frigate/embeddings/maintainer.py

@@ -1,197 +0,0 @@
-"""Maintain embeddings in Chroma."""
-
-import base64
-import io
-import logging
-import threading
-from multiprocessing.synchronize import Event as MpEvent
-from typing import Optional
-
-import cv2
-import numpy as np
-from peewee import DoesNotExist
-from PIL import Image
-
-from frigate.comms.events_updater import EventEndSubscriber, EventUpdateSubscriber
-from frigate.comms.inter_process import InterProcessRequestor
-from frigate.config import FrigateConfig
-from frigate.const import UPDATE_EVENT_DESCRIPTION
-from frigate.events.types import EventTypeEnum
-from frigate.genai import get_genai_client
-from frigate.models import Event
-from frigate.util.image import SharedMemoryFrameManager, calculate_region
-
-from .embeddings import Embeddings, get_metadata
-
-logger = logging.getLogger(__name__)
-
-
-class EmbeddingMaintainer(threading.Thread):
-    """Handle embedding queue and post event updates."""
-
-    def __init__(
-        self,
-        config: FrigateConfig,
-        stop_event: MpEvent,
-    ) -> None:
-        threading.Thread.__init__(self)
-        self.name = "embeddings_maintainer"
-        self.config = config
-        self.embeddings = Embeddings()
-        self.event_subscriber = EventUpdateSubscriber()
-        self.event_end_subscriber = EventEndSubscriber()
-        self.frame_manager = SharedMemoryFrameManager()
-        # create communication for updating event descriptions
-        self.requestor = InterProcessRequestor()
-        self.stop_event = stop_event
-        self.tracked_events = {}
-        self.genai_client = get_genai_client(config.genai)
-
-    def run(self) -> None:
-        """Maintain a Chroma vector database for semantic search."""
-        while not self.stop_event.is_set():
-            self._process_updates()
-            self._process_finalized()
-
-        self.event_subscriber.stop()
-        self.event_end_subscriber.stop()
-        self.requestor.stop()
-        logger.info("Exiting embeddings maintenance...")
-
-    def _process_updates(self) -> None:
-        """Process event updates"""
-        update = self.event_subscriber.check_for_update()
-
-        if update is None:
-            return
-
-        source_type, _, camera, data = update
-
-        if not camera or source_type != EventTypeEnum.tracked_object:
-            return
-
-        camera_config = self.config.cameras[camera]
-        if data["id"] not in self.tracked_events:
-            self.tracked_events[data["id"]] = []
-
-        # Create our own thumbnail based on the bounding box and the frame time
-        try:
-            frame_id = f"{camera}{data['frame_time']}"
-            yuv_frame = self.frame_manager.get(frame_id, camera_config.frame_shape_yuv)
-            data["thumbnail"] = self._create_thumbnail(yuv_frame, data["box"])
-            self.tracked_events[data["id"]].append(data)
-            self.frame_manager.close(frame_id)
-        except FileNotFoundError:
-            pass
-
-    def _process_finalized(self) -> None:
-        """Process the end of an event."""
-        while True:
-            ended = self.event_end_subscriber.check_for_update()
-
-            if ended == None:
-                break
-
-            event_id, camera, updated_db = ended
-            camera_config = self.config.cameras[camera]
-
-            if updated_db:
-                try:
-                    event: Event = Event.get(Event.id == event_id)
-                except DoesNotExist:
-                    continue
-
-                # Skip the event if not an object
-                if event.data.get("type") != "object":
-                    continue
-
-                # Extract valid event metadata
-                metadata = get_metadata(event)
-                thumbnail = base64.b64decode(event.thumbnail)
-
-                # Embed the thumbnail
-                self._embed_thumbnail(event_id, thumbnail, metadata)
-
-                if (
-                    camera_config.genai.enabled
-                    and self.genai_client is not None
-                    and event.data.get("description") is None
-                ):
-                    # Generate the description. Call happens in a thread since it is network bound.
-                    threading.Thread(
-                        target=self._embed_description,
-                        name=f"_embed_description_{event.id}",
-                        daemon=True,
-                        args=(
-                            event,
-                            [
-                                data["thumbnail"]
-                                for data in self.tracked_events[event_id]
-                            ]
-                            if len(self.tracked_events.get(event_id, [])) > 0
-                            else [thumbnail],
-                            metadata,
-                        ),
-                    ).start()
-
-            # Delete tracked events based on the event_id
-            if event_id in self.tracked_events:
-                del self.tracked_events[event_id]
-
-    def _create_thumbnail(self, yuv_frame, box, height=500) -> Optional[bytes]:
-        """Return jpg thumbnail of a region of the frame."""
-        frame = cv2.cvtColor(yuv_frame, cv2.COLOR_YUV2BGR_I420)
-        region = calculate_region(
-            frame.shape, box[0], box[1], box[2], box[3], height, multiplier=1.4
-        )
-        frame = frame[region[1] : region[3], region[0] : region[2]]
-        width = int(height * frame.shape[1] / frame.shape[0])
-        frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
-        ret, jpg = cv2.imencode(".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), 100])
-
-        if ret:
-            return jpg.tobytes()
-
-        return None
-
-    def _embed_thumbnail(self, event_id: str, thumbnail: bytes, metadata: dict) -> None:
-        """Embed the thumbnail for an event."""
-
-        # Encode the thumbnail
-        img = np.array(Image.open(io.BytesIO(thumbnail)).convert("RGB"))
-        self.embeddings.thumbnail.upsert(
-            images=[img],
-            metadatas=[metadata],
-            ids=[event_id],
-        )
-
-    def _embed_description(
-        self, event: Event, thumbnails: list[bytes], metadata: dict
-    ) -> None:
-        """Embed the description for an event."""
-
-        description = self.genai_client.generate_description(thumbnails, metadata)
-
-        if description is None:
-            logger.debug("Failed to generate description for %s", event.id)
-            return
-
-        # fire and forget description update
-        self.requestor.send_data(
-            UPDATE_EVENT_DESCRIPTION,
-            {"id": event.id, "description": description},
-        )
-
-        # Encode the description
-        self.embeddings.description.upsert(
-            documents=[description],
-            metadatas=[metadata],
-            ids=[event.id],
-        )
-
-        logger.debug(
-            "Generated description for %s (%d images): %s",
-            event.id,
-            len(thumbnails),
-            description,
-        )

frigate/embeddings/util.py

@@ -1,47 +0,0 @@
-"""Z-score normalization for search distance."""
-
-import math
-
-
-class ZScoreNormalization:
-    """Running Z-score normalization for search distance."""
-
-    def __init__(self):
-        self.n = 0
-        self.mean = 0
-        self.m2 = 0
-
-    @property
-    def variance(self):
-        return self.m2 / (self.n - 1) if self.n > 1 else 0.0
-
-    @property
-    def stddev(self):
-        return math.sqrt(self.variance)
-
-    def normalize(self, distances: list[float]):
-        self._update(distances)
-        if self.stddev == 0:
-            return distances
-        return [(x - self.mean) / self.stddev for x in distances]
-
-    def _update(self, distances: list[float]):
-        for x in distances:
-            self.n += 1
-            delta = x - self.mean
-            self.mean += delta / self.n
-            delta2 = x - self.mean
-            self.m2 += delta * delta2
-
-    def to_dict(self):
-        return {
-            "n": self.n,
-            "mean": self.mean,
-            "m2": self.m2,
-        }
-
-    def from_dict(self, data: dict):
-        self.n = data["n"]
-        self.mean = data["mean"]
-        self.m2 = data["m2"]
-        return self

frigate/events/audio.py

@@ -223,7 +223,7 @@ class AudioEventMaintainer(threading.Thread):
                     audio_detections.append(label)
 
             # send audio detection data
-            self.detection_publisher.publish(
+            self.detection_publisher.send_data(
                 (
                     self.config.name,
                     datetime.datetime.now().timestamp(),

frigate/events/cleanup.py

@@ -10,7 +10,6 @@ from pathlib import Path
 
 from frigate.config import FrigateConfig
 from frigate.const import CLIPS_DIR
-from frigate.embeddings.embeddings import Embeddings
 from frigate.models import Event, Timeline
 
 logger = logging.getLogger(__name__)
@@ -31,9 +30,6 @@ class EventCleanup(threading.Thread):
         self.removed_camera_labels: list[str] = None
         self.camera_labels: dict[str, dict[str, any]] = {}
 
-        if self.config.semantic_search.enabled:
-            self.embeddings = Embeddings()
-
     def get_removed_camera_labels(self) -> list[Event]:
         """Get a list of distinct labels for removed cameras."""
         if self.removed_camera_labels is None:
@@ -194,31 +190,16 @@ class EventCleanup(threading.Thread):
             events_with_expired_clips = self.expire(EventCleanupType.clips)
 
             # delete timeline entries for events that have expired recordings
-            # delete up to 100,000 at a time
+            Timeline.delete().where(
-            max_deletes = 100000
+                Timeline.source_id << events_with_expired_clips
-            deleted_events_list = list(events_with_expired_clips)
+            ).execute()
-            for i in range(0, len(deleted_events_list), max_deletes):
-                Timeline.delete().where(
-                    Timeline.source_id << deleted_events_list[i : i + max_deletes]
-                ).execute()
 
             self.expire(EventCleanupType.snapshots)
 
             # drop events from db where has_clip and has_snapshot are false
-            events = (
+            delete_query = Event.delete().where(
-                Event.select()
+                Event.has_clip == False, Event.has_snapshot == False
-                .where(Event.has_clip == False, Event.has_snapshot == False)
-                .iterator()
             )
-            events_to_delete = [e.id for e in events]
+            delete_query.execute()
-            if len(events_to_delete) > 0:
-                chunk_size = 50
-                for i in range(0, len(events_to_delete), chunk_size):
-                    chunk = events_to_delete[i : i + chunk_size]
-                    Event.delete().where(Event.id << chunk).execute()
-
-                    if self.config.semantic_search.enabled:
-                        self.embeddings.thumbnail.delete(ids=chunk)
-                        self.embeddings.description.delete(ids=chunk)
 
         logger.info("Exiting event cleanup...")

frigate/events/external.py

@@ -86,7 +86,7 @@ class ExternalEventProcessor:
 
         if source_type == "api":
             self.event_camera[event_id] = camera
-            self.detection_updater.publish(
+            self.detection_updater.send_data(
                 (
                     camera,
                     now,
@@ -115,7 +115,7 @@ class ExternalEventProcessor:
         )
 
         if event_id in self.event_camera:
-            self.detection_updater.publish(
+            self.detection_updater.send_data(
                 (
                     self.event_camera[event_id],
                     end_time,

frigate/events/maintainer.py

@@ -237,7 +237,7 @@ class EventProcessor(threading.Thread):
 
         if event_type == EventStateEnum.end:
             del self.events_in_process[event_data["id"]]
-            self.event_end_publisher.publish((event_data["id"], camera, updated_db))
+            self.event_end_publisher.publish((event_data["id"], camera))
 
     def handle_external_detection(
         self, event_type: EventStateEnum, event_data: Event

frigate/genai/__init__.py

@@ -1,63 +0,0 @@
-"""Generative AI module for Frigate."""
-
-import importlib
-import os
-from typing import Optional
-
-from frigate.config import GenAIConfig, GenAIProviderEnum
-
-PROVIDERS = {}
-
-
-def register_genai_provider(key: GenAIProviderEnum):
-    """Register a GenAI provider."""
-
-    def decorator(cls):
-        PROVIDERS[key] = cls
-        return cls
-
-    return decorator
-
-
-class GenAIClient:
-    """Generative AI client for Frigate."""
-
-    def __init__(self, genai_config: GenAIConfig, timeout: int = 60) -> None:
-        self.genai_config: GenAIConfig = genai_config
-        self.timeout = timeout
-        self.provider = self._init_provider()
-
-    def generate_description(
-        self, thumbnails: list[bytes], metadata: dict[str, any]
-    ) -> Optional[str]:
-        """Generate a description for the frame."""
-        prompt = self.genai_config.object_prompts.get(
-            metadata["label"], self.genai_config.prompt
-        ).format(**metadata)
-        return self._send(prompt, thumbnails)
-
-    def _init_provider(self):
-        """Initialize the client."""
-        return None
-
-    def _send(self, prompt: str, images: list[bytes]) -> Optional[str]:
-        """Submit a request to the provider."""
-        return None
-
-
-def get_genai_client(genai_config: GenAIConfig) -> Optional[GenAIClient]:
-    """Get the GenAI client."""
-    if genai_config.enabled:
-        load_providers()
-        provider = PROVIDERS.get(genai_config.provider)
-        if provider:
-            return provider(genai_config)
-    return None
-
-
-def load_providers():
-    package_dir = os.path.dirname(__file__)
-    for filename in os.listdir(package_dir):
-        if filename.endswith(".py") and filename != "__init__.py":
-            module_name = f"frigate.genai.{filename[:-3]}"
-            importlib.import_module(module_name)

frigate/genai/gemini.py

@@ -1,49 +0,0 @@
-"""Gemini Provider for Frigate AI."""
-
-from typing import Optional
-
-import google.generativeai as genai
-from google.api_core.exceptions import GoogleAPICallError
-
-from frigate.config import GenAIProviderEnum
-from frigate.genai import GenAIClient, register_genai_provider
-
-
-@register_genai_provider(GenAIProviderEnum.gemini)
-class GeminiClient(GenAIClient):
-    """Generative AI client for Frigate using Gemini."""
-
-    provider: genai.GenerativeModel
-
-    def _init_provider(self):
-        """Initialize the client."""
-        genai.configure(api_key=self.genai_config.api_key)
-        return genai.GenerativeModel(self.genai_config.model)
-
-    def _send(self, prompt: str, images: list[bytes]) -> Optional[str]:
-        """Submit a request to Gemini."""
-        data = [
-            {
-                "mime_type": "image/jpeg",
-                "data": img,
-            }
-            for img in images
-        ] + [prompt]
-        try:
-            response = self.provider.generate_content(
-                data,
-                generation_config=genai.types.GenerationConfig(
-                    candidate_count=1,
-                ),
-                request_options=genai.types.RequestOptions(
-                    timeout=self.timeout,
-                ),
-            )
-        except GoogleAPICallError:
-            return None
-        try:
-            description = response.text.strip()
-        except ValueError:
-            # No description was generated
-            return None
-        return description

frigate/genai/ollama.py

@@ -1,41 +0,0 @@
-"""Ollama Provider for Frigate AI."""
-
-import logging
-from typing import Optional
-
-from httpx import TimeoutException
-from ollama import Client as ApiClient
-from ollama import ResponseError
-
-from frigate.config import GenAIProviderEnum
-from frigate.genai import GenAIClient, register_genai_provider
-
-logger = logging.getLogger(__name__)
-
-
-@register_genai_provider(GenAIProviderEnum.ollama)
-class OllamaClient(GenAIClient):
-    """Generative AI client for Frigate using Ollama."""
-
-    provider: ApiClient
-
-    def _init_provider(self):
-        """Initialize the client."""
-        client = ApiClient(host=self.genai_config.base_url, timeout=self.timeout)
-        response = client.pull(self.genai_config.model)
-        if response["status"] != "success":
-            logger.error("Failed to pull %s model from Ollama", self.genai_config.model)
-            return None
-        return client
-
-    def _send(self, prompt: str, images: list[bytes]) -> Optional[str]:
-        """Submit a request to Ollama"""
-        try:
-            result = self.provider.generate(
-                self.genai_config.model,
-                prompt,
-                images=images,
-            )
-            return result["response"].strip()
-        except (TimeoutException, ResponseError):
-            return None

frigate/genai/openai.py

@@ -1,51 +0,0 @@
-"""OpenAI Provider for Frigate AI."""
-
-import base64
-from typing import Optional
-
-from httpx import TimeoutException
-from openai import OpenAI
-
-from frigate.config import GenAIProviderEnum
-from frigate.genai import GenAIClient, register_genai_provider
-
-
-@register_genai_provider(GenAIProviderEnum.openai)
-class OpenAIClient(GenAIClient):
-    """Generative AI client for Frigate using OpenAI."""
-
-    provider: OpenAI
-
-    def _init_provider(self):
-        """Initialize the client."""
-        return OpenAI(api_key=self.genai_config.api_key)
-
-    def _send(self, prompt: str, images: list[bytes]) -> Optional[str]:
-        """Submit a request to OpenAI."""
-        encoded_images = [base64.b64encode(image).decode("utf-8") for image in images]
-        try:
-            result = self.provider.chat.completions.create(
-                model=self.genai_config.model,
-                messages=[
-                    {
-                        "role": "user",
-                        "content": [
-                            {
-                                "type": "image_url",
-                                "image_url": {
-                                    "url": f"data:image/jpeg;base64,{image}",
-                                    "detail": "low",
-                                },
-                            }
-                            for image in encoded_images
-                        ]
-                        + [prompt],
-                    },
-                ],
-                timeout=self.timeout,
-            )
-        except TimeoutException:
-            return None
-        if len(result.choices) > 0:
-            return result.choices[0].message.content.strip()
-        return None

frigate/models.py

@@ -118,4 +118,3 @@ class RecordingsToDelete(Model):  # type: ignore[misc]
 class User(Model):  # type: ignore[misc]
     username = CharField(null=False, primary_key=True, max_length=30)
     password_hash = CharField(null=False, max_length=120)
-    notification_tokens = JSONField()

frigate/object_processing.py

@@ -1187,7 +1187,7 @@ class TrackedObjectProcessor(threading.Thread):
             ]
 
             # publish info on this frame
-            self.detection_publisher.publish(
+            self.detection_publisher.send_data(
                 (
                     camera,
                     frame_time,
@@ -1274,7 +1274,7 @@ class TrackedObjectProcessor(threading.Thread):
                 if not update:
                     break
 
-                event_id, camera, _ = update
+                event_id, camera = update
                 self.camera_states[camera].finished(event_id)
 
         self.requestor.stop()

frigate/output/output.py

@@ -80,7 +80,7 @@ def output_frames(
     websocket_thread.start()
 
     while not stop_event.is_set():
-        (topic, data) = detection_subscriber.check_for_update(timeout=1)
+        (topic, data) = detection_subscriber.get_data(timeout=1)
 
         if not topic:
             continue
@@ -134,7 +134,7 @@ def output_frames(
     move_preview_frames("clips")
 
     while True:
-        (topic, data) = detection_subscriber.check_for_update(timeout=0)
+        (topic, data) = detection_subscriber.get_data(timeout=0)
 
         if not topic:
             break

frigate/record/export.py

@@ -10,7 +10,6 @@ import subprocess as sp
 import threading
 from enum import Enum
 from pathlib import Path
-from typing import Optional
 
 from peewee import DoesNotExist
 
@@ -50,8 +49,7 @@ class RecordingExporter(threading.Thread):
         self,
         config: FrigateConfig,
         camera: str,
-        name: Optional[str],
+        name: str,
-        image: Optional[str],
         start_time: int,
         end_time: int,
         playback_factor: PlaybackFactorEnum,
@@ -60,7 +58,6 @@ class RecordingExporter(threading.Thread):
         self.config = config
         self.camera = camera
         self.user_provided_name = name
-        self.user_provided_image = image
         self.start_time = start_time
         self.end_time = end_time
         self.playback_factor = playback_factor
@@ -75,12 +72,6 @@ class RecordingExporter(threading.Thread):
     def save_thumbnail(self, id: str) -> str:
         thumb_path = os.path.join(CLIPS_DIR, f"export/{id}.webp")
 
-        if self.user_provided_image is not None and os.path.isfile(
-            self.user_provided_image
-        ):
-            shutil.copy(self.user_provided_image, thumb_path)
-            return thumb_path
-
         if (
             self.start_time
             < datetime.datetime.now(datetime.timezone.utc)

frigate/record/maintainer.py

@@ -470,7 +470,7 @@ class RecordingMaintainer(threading.Thread):
             stale_frame_count_threshold = 10
             # empty the object recordings info queue
             while True:
-                (topic, data) = self.detection_subscriber.check_for_update(
+                (topic, data) = self.detection_subscriber.get_data(
                     timeout=QUEUE_READ_TIMEOUT
                 )
 

frigate/review/maintainer.py

@@ -424,7 +424,7 @@ class ReviewSegmentMaintainer(threading.Thread):
                 camera_name = updated_topic.rpartition("/")[-1]
                 self.config.cameras[camera_name].record = updated_record_config
 
-            (topic, data) = self.detection_subscriber.check_for_update(timeout=1)
+            (topic, data) = self.detection_subscriber.get_data(timeout=1)
 
             if not topic:
                 continue

frigate/test/test_http.py

@@ -120,7 +120,6 @@ class TestHttp(unittest.TestCase):
             None,
             None,
             None,
-            None,
             PlusApi(),
             None,
         )
@@ -157,7 +156,6 @@ class TestHttp(unittest.TestCase):
             None,
             None,
             None,
-            None,
             PlusApi(),
             None,
         )
@@ -179,7 +177,6 @@ class TestHttp(unittest.TestCase):
             None,
             None,
             None,
-            None,
             PlusApi(),
             None,
         )
@@ -200,7 +197,6 @@ class TestHttp(unittest.TestCase):
             None,
             None,
             None,
-            None,
             PlusApi(),
             None,
         )
@@ -223,7 +219,6 @@ class TestHttp(unittest.TestCase):
             None,
             None,
             None,
-            None,
             PlusApi(),
             None,
         )
@@ -250,7 +245,6 @@ class TestHttp(unittest.TestCase):
             None,
             None,
             None,
-            None,
             PlusApi(),
             None,
         )
@@ -289,7 +283,6 @@ class TestHttp(unittest.TestCase):
             None,
             None,
             None,
-            None,
             PlusApi(),
             None,
         )
@@ -325,7 +318,6 @@ class TestHttp(unittest.TestCase):
             None,
             None,
             None,
-            None,
             PlusApi(),
             None,
         )
@@ -351,7 +343,6 @@ class TestHttp(unittest.TestCase):
             None,
             None,
             None,
-            None,
             PlusApi(),
             None,
         )
@@ -369,7 +360,6 @@ class TestHttp(unittest.TestCase):
             None,
             None,
             None,
-            None,
             PlusApi(),
             None,
         )
@@ -391,7 +381,6 @@ class TestHttp(unittest.TestCase):
             None,
             None,
             None,
-            None,
             PlusApi(),
             stats,
         )

migrations/026_add_notification_tokens.py

@@ -1,40 +0,0 @@
-"""Peewee migrations
-
-Some examples (model - class or model name)::
-
-    > Model = migrator.orm['model_name']            # Return model in current state by name
-
-    > migrator.sql(sql)                             # Run custom SQL
-    > migrator.python(func, *args, **kwargs)        # Run python code
-    > migrator.create_model(Model)                  # Create a model (could be used as decorator)
-    > migrator.remove_model(model, cascade=True)    # Remove a model
-    > migrator.add_fields(model, **fields)          # Add fields to a model
-    > migrator.change_fields(model, **fields)       # Change fields
-    > migrator.remove_fields(model, *field_names, cascade=True)
-    > migrator.rename_field(model, old_field_name, new_field_name)
-    > migrator.rename_table(model, new_table_name)
-    > migrator.add_index(model, *col_names, unique=False)
-    > migrator.drop_index(model, *col_names)
-    > migrator.add_not_null(model, *field_names)
-    > migrator.drop_not_null(model, *field_names)
-    > migrator.add_default(model, field_name, default)
-
-"""
-
-import peewee as pw
-from playhouse.sqlite_ext import JSONField
-
-from frigate.models import User
-
-SQL = pw.SQL
-
-
-def migrate(migrator, database, fake=False, **kwargs):
-    migrator.add_fields(
-        User,
-        notification_tokens=JSONField(default=[]),
-    )
-
-
-def rollback(migrator, database, fake=False, **kwargs):
-    pass

notebooks/YOLO_NAS_Pretrained_Export.ipynb

@@ -11,6 +11,18 @@
         "! pip install -q super_gradients==3.7.1"
       ]
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.10/dist-packages/super_gradients/training/pretrained_models.py\n",
+        "! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.10/dist-packages/super_gradients/training/utils/checkpoint_utils.py"
+      ],
+      "metadata": {
+        "id": "NiRCt917KKcL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "execution_count": null,

package-lock.json

@@ -1,6 +0,0 @@
-{
-  "name": "frigate",
-  "lockfileVersion": 3,
-  "requires": true,
-  "packages": {}
-}

web/public/notifications-worker.js

@@ -1,66 +0,0 @@
-// Notifications Worker
-
-self.addEventListener("push", function (event) {
-  // @ts-expect-error we know this exists
-  if (event.data) {
-    // @ts-expect-error we know this exists
-    const data = event.data.json();
-
-    let actions = [];
-
-    switch (data.type ?? "unknown") {
-      case "alert":
-        actions = [
-          {
-            action: "markReviewed",
-            title: "Mark as Reviewed",
-          },
-        ];
-        break;
-    }
-
-    // @ts-expect-error we know this exists
-    self.registration.showNotification(data.title, {
-      body: data.message,
-      icon: "/images/maskable-icon.png",
-      image: data.image,
-      badge: "/images/maskable-badge.png",
-      tag: data.id,
-      data: { id: data.id, link: data.direct_url },
-      actions,
-    });
-  } else {
-    // pass
-    // This push event has no data
-  }
-});
-
-self.addEventListener("notificationclick", (event) => {
-  // @ts-expect-error we know this exists
-  if (event.notification) {
-    // @ts-expect-error we know this exists
-    event.notification.close();
-
-    switch (event.action ?? "default") {
-      case "markReviewed":
-        if (event.notification.data) {
-          fetch("/api/reviews/viewed", {
-            method: "POST",
-            headers: { "Content-Type": "application/json", "X-CSRF-TOKEN": 1 },
-            body: JSON.stringify({ ids: [event.notification.data.id] }),
-          });
-        }
-        break;
-      default:
-        // @ts-expect-error we know this exists
-        if (event.notification.data) {
-          const url = event.notification.data.link;
-          // eslint-disable-next-line no-undef
-          if (clients.openWindow) {
-            // eslint-disable-next-line no-undef
-            return clients.openWindow(url);
-          }
-        }
-    }
-  }
-});

web/site.webmanifest

@@ -20,12 +20,6 @@
       "sizes": "180x180",
       "type": "image/png",
       "purpose": "maskable"
-    },
-    {
-      "src": "/images/maskable-badge.png",
-      "sizes": "96x96",
-      "type": "image/png",
-      "purpose": "maskable"
     }
   ],
   "theme_color": "#ffffff",