diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 63adae73d..c782fb32f 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -8,9 +8,25 @@ "overrideCommand": false, "remoteUser": "vscode", "features": { - "ghcr.io/devcontainers/features/common-utils:1": {} + "ghcr.io/devcontainers/features/common-utils:2": {} + // Uncomment the following lines to use ONNX Runtime with CUDA support + // "ghcr.io/devcontainers/features/nvidia-cuda:1": { + // "installCudnn": true, + // "installNvtx": true, + // "installToolkit": true, + // "cudaVersion": "12.5", + // "cudnnVersion": "9.4.0.58" + // }, + // "./features/onnxruntime-gpu": {} }, - "forwardPorts": [8971, 5000, 5001, 5173, 8554, 8555], + "forwardPorts": [ + 8971, + 5000, + 5001, + 5173, + 8554, + 8555 + ], "portsAttributes": { "8971": { "label": "External NGINX", @@ -64,10 +80,18 @@ "editor.formatOnType": true, "python.testing.pytestEnabled": false, "python.testing.unittestEnabled": true, - "python.testing.unittestArgs": ["-v", "-s", "./frigate/test"], + "python.testing.unittestArgs": [ + "-v", + "-s", + "./frigate/test" + ], "files.trimTrailingWhitespace": true, - "eslint.workingDirectories": ["./web"], - "isort.args": ["--settings-path=./pyproject.toml"], + "eslint.workingDirectories": [ + "./web" + ], + "isort.args": [ + "--settings-path=./pyproject.toml" + ], "[python]": { "editor.defaultFormatter": "charliermarsh.ruff", "editor.formatOnSave": true, @@ -86,9 +110,16 @@ ], "editor.tabSize": 2 }, - "cSpell.ignoreWords": ["rtmp"], - "cSpell.words": ["preact", "astype", "hwaccel", "mqtt"] + "cSpell.ignoreWords": [ + "rtmp" + ], + "cSpell.words": [ + "preact", + "astype", + "hwaccel", + "mqtt" + ] } } } -} +} \ No newline at end of file diff --git a/.devcontainer/features/onnxruntime-gpu/devcontainer-feature.json b/.devcontainer/features/onnxruntime-gpu/devcontainer-feature.json new file mode 100644 index 000000000..30514442b --- /dev/null +++ b/.devcontainer/features/onnxruntime-gpu/devcontainer-feature.json @@ -0,0 +1,22 @@ +{ + "id": "onnxruntime-gpu", + "version": "0.0.1", + "name": "ONNX Runtime GPU (Nvidia)", + "description": "Installs ONNX Runtime for Nvidia GPUs.", + "documentationURL": "", + "options": { + "version": { + "type": "string", + "proposals": [ + "latest", + "1.20.1", + "1.20.0" + ], + "default": "latest", + "description": "Version of ONNX Runtime to install" + } + }, + "installsAfter": [ + "ghcr.io/devcontainers/features/nvidia-cuda" + ] +} \ No newline at end of file diff --git a/.devcontainer/features/onnxruntime-gpu/install.sh b/.devcontainer/features/onnxruntime-gpu/install.sh new file mode 100644 index 000000000..0c090beec --- /dev/null +++ b/.devcontainer/features/onnxruntime-gpu/install.sh @@ -0,0 +1,15 @@ +#!/usr/bin/env bash + +set -e + +VERSION=${VERSION} + +python3 -m pip config set global.break-system-packages true +# if VERSION == "latest" or VERSION is empty, install the latest version +if [ "$VERSION" == "latest" ] || [ -z "$VERSION" ]; then + python3 -m pip install onnxruntime-gpu +else + python3 -m pip install onnxruntime-gpu==$VERSION +fi + +echo "Done!" \ No newline at end of file diff --git a/.devcontainer/post_create.sh b/.devcontainer/post_create.sh index ec33ffb86..fcf7ca693 100755 --- a/.devcontainer/post_create.sh +++ b/.devcontainer/post_create.sh @@ -19,7 +19,7 @@ sudo chown -R "$(id -u):$(id -g)" /media/frigate # When started as a service, LIBAVFORMAT_VERSION_MAJOR is defined in the # s6 service file. For dev, where frigate is started from an interactive # shell, we define it in .bashrc instead. -echo 'export LIBAVFORMAT_VERSION_MAJOR=$(/usr/lib/ffmpeg/7.0/bin/ffmpeg -version | grep -Po "libavformat\W+\K\d+")' >> $HOME/.bashrc +echo 'export LIBAVFORMAT_VERSION_MAJOR=$("$(python3 /usr/local/ffmpeg/get_ffmpeg_path.py)" -version | grep -Po "libavformat\W+\K\d+")' >> "$HOME/.bashrc" make version diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 398d7fc8c..5b787b273 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -42,7 +42,7 @@ jobs: tags: ${{ steps.setup.outputs.image-name }}-amd64 cache-from: type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64 arm64_build: - runs-on: ubuntu-22.04 + runs-on: ubuntu-22.04-arm name: ARM Build steps: - name: Check out code @@ -107,7 +107,7 @@ jobs: *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp5 *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp5,mode=max jetson_jp6_build: - runs-on: ubuntu-22.04 + runs-on: ubuntu-22.04-arm name: Jetson Jetpack 6 steps: - name: Check out code @@ -175,9 +175,10 @@ jobs: files: docker/rocm/rocm.hcl set: | rocm.tags=${{ steps.setup.outputs.image-name }}-rocm + *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-rocm,mode=max *.cache-from=type=gha arm64_extra_builds: - runs-on: ubuntu-22.04 + runs-on: ubuntu-22.04-arm name: ARM Extra Build needs: - arm64_build diff --git a/.github/workflows/pull_request.yml b/.github/workflows/pull_request.yml index 0d0492d0c..02fde5861 100644 --- a/.github/workflows/pull_request.yml +++ b/.github/workflows/pull_request.yml @@ -24,7 +24,7 @@ jobs: persist-credentials: false - uses: actions/setup-node@master with: - node-version: 16.x + node-version: 20.x - name: Install devcontainer cli run: npm install --global @devcontainers/cli - name: Build devcontainer @@ -64,6 +64,9 @@ jobs: node-version: 20.x - run: npm install working-directory: ./web + - name: Build web + run: npm run build + working-directory: ./web # - name: Test # run: npm run test # working-directory: ./web @@ -77,7 +80,7 @@ jobs: with: persist-credentials: false - name: Set up Python ${{ env.DEFAULT_PYTHON }} - uses: actions/setup-python@v5.3.0 + uses: actions/setup-python@v5.4.0 with: python-version: ${{ env.DEFAULT_PYTHON }} - name: Install requirements @@ -99,14 +102,6 @@ jobs: uses: actions/checkout@v4 with: persist-credentials: false - - uses: actions/setup-node@master - with: - node-version: 16.x - - run: npm install - working-directory: ./web - - name: Build web - run: npm run build - working-directory: ./web - name: Set up QEMU uses: docker/setup-qemu-action@v3 - name: Set up Docker Buildx diff --git a/docker/main/Dockerfile b/docker/main/Dockerfile index 0bafeab80..7a0351240 100644 --- a/docker/main/Dockerfile +++ b/docker/main/Dockerfile @@ -14,16 +14,18 @@ ARG BASE_HOOK= FROM ${BASE_IMAGE} AS base ARG PIP_BREAK_SYSTEM_PACKAGES +ARG BASE_HOOK -RUN ${BASE_HOOK} +RUN sh -c "$BASE_HOOK" FROM --platform=${BUILDPLATFORM} debian:12 AS base_host ARG PIP_BREAK_SYSTEM_PACKAGES FROM ${SLIM_BASE} AS slim-base ARG PIP_BREAK_SYSTEM_PACKAGES +ARG BASE_HOOK -RUN ${BASE_HOOK} +RUN sh -c "$BASE_HOOK" FROM slim-base AS wget ARG DEBIAN_FRONTEND @@ -37,10 +39,7 @@ ARG DEBIAN_FRONTEND ENV CCACHE_DIR /root/.ccache ENV CCACHE_MAXSIZE 2G -# bind /var/cache/apt to tmpfs to speed up nginx build -RUN --mount=type=tmpfs,target=/tmp --mount=type=tmpfs,target=/var/cache/apt \ - --mount=type=bind,source=docker/main/build_nginx.sh,target=/deps/build_nginx.sh \ - --mount=type=cache,target=/root/.ccache \ +RUN --mount=type=bind,source=docker/main/build_nginx.sh,target=/deps/build_nginx.sh \ /deps/build_nginx.sh FROM wget AS sqlite-vec @@ -223,14 +222,22 @@ ENV TRANSFORMERS_NO_ADVISORY_WARNINGS=1 # Set OpenCV ffmpeg loglevel to fatal: https://ffmpeg.org/doxygen/trunk/log_8h.html ENV OPENCV_FFMPEG_LOGLEVEL=8 +# Set HailoRT to disable logging +ENV HAILORT_LOGGER_PATH=NONE + ENV PATH="/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PATH}" # Install dependencies RUN --mount=type=bind,source=docker/main/install_deps.sh,target=/deps/install_deps.sh \ /deps/install_deps.sh +ENV DEFAULT_FFMPEG_VERSION="7.0" +ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:5.0" + +RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \ + && python3 get-pip.py "pip" + RUN --mount=type=bind,from=wheels,source=/wheels,target=/deps/wheels \ - python3 -m pip install --upgrade pip && \ pip3 install -U /deps/wheels/*.whl COPY --from=deps-rootfs / / diff --git a/docker/main/install_deps.sh b/docker/main/install_deps.sh index a7b7789c0..9684199f8 100755 --- a/docker/main/install_deps.sh +++ b/docker/main/install_deps.sh @@ -6,13 +6,13 @@ apt-get -qq update apt-get -qq install --no-install-recommends -y \ apt-transport-https \ + ca-certificates \ gnupg \ wget \ lbzip2 \ procps vainfo \ unzip locales tzdata libxml2 xz-utils \ python3.11 \ - python3-pip \ curl \ lsof \ jq \ @@ -31,28 +31,28 @@ unset DEBIAN_FRONTEND yes | dpkg -i /tmp/libedgetpu1-max.deb && export DEBIAN_FRONTEND=noninteractive rm /tmp/libedgetpu1-max.deb -# btbn-ffmpeg -> amd64 +# ffmpeg -> amd64 if [[ "${TARGETARCH}" == "amd64" ]]; then mkdir -p /usr/lib/ffmpeg/5.0 + wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linux64-gpl-5.1.tar.xz" + tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1 amd64/bin/ffmpeg amd64/bin/ffprobe + rm -rf ffmpeg.tar.xz mkdir -p /usr/lib/ffmpeg/7.0 - wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linux64-gpl-5.1.tar.xz" - tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1 - rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/5.0/doc /usr/lib/ffmpeg/5.0/bin/ffplay - wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linux64-gpl-7.0.tar.xz" - tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1 - rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/7.0/doc /usr/lib/ffmpeg/7.0/bin/ffplay + wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linux64-gpl-7.0.tar.xz" + tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1 amd64/bin/ffmpeg amd64/bin/ffprobe + rm -rf ffmpeg.tar.xz fi # ffmpeg -> arm64 if [[ "${TARGETARCH}" == "arm64" ]]; then mkdir -p /usr/lib/ffmpeg/5.0 + wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linuxarm64-gpl-5.1.tar.xz" + tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1 arm64/bin/ffmpeg arm64/bin/ffprobe + rm -f ffmpeg.tar.xz mkdir -p /usr/lib/ffmpeg/7.0 - wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linuxarm64-gpl-5.1.tar.xz" - tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1 - rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/5.0/doc /usr/lib/ffmpeg/5.0/bin/ffplay - wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linuxarm64-gpl-7.0.tar.xz" - tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1 - rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/7.0/doc /usr/lib/ffmpeg/7.0/bin/ffplay + wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linuxarm64-gpl-7.0.tar.xz" + tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1 arm64/bin/ffmpeg arm64/bin/ffprobe + rm -f ffmpeg.tar.xz fi # arch specific packages diff --git a/docker/main/requirements-wheels.txt b/docker/main/requirements-wheels.txt index f06f82d88..795007e86 100644 --- a/docker/main/requirements-wheels.txt +++ b/docker/main/requirements-wheels.txt @@ -1,7 +1,7 @@ aiofiles == 24.1.* click == 8.1.* # FastAPI -aiohttp == 3.11.2 +aiohttp == 3.11.3 starlette == 0.41.2 starlette-context == 0.3.6 fastapi == 0.115.* @@ -20,9 +20,9 @@ pandas == 2.2.* peewee == 3.17.* peewee_migrate == 1.13.* psutil == 6.1.* -pydantic == 2.8.* +pydantic == 2.10.* git+https://github.com/fbcotter/py3nvml#egg=py3nvml -pytz == 2024.* +pytz == 2025.* pyzmq == 26.2.* ruamel.yaml == 0.18.* tzlocal == 5.2 @@ -34,8 +34,8 @@ ws4py == 0.5.* unidecode == 1.3.* # Image Manipulation numpy == 1.26.* -opencv-python-headless == 4.10.0.* -opencv-contrib-python == 4.9.0.* +opencv-python-headless == 4.11.0.* +opencv-contrib-python == 4.11.0.* scipy == 1.14.* # OpenVino & ONNX openvino == 2024.4.* @@ -46,7 +46,7 @@ transformers == 4.45.* # Generative AI google-generativeai == 0.8.* ollama == 0.3.* -openai == 1.51.* +openai == 1.65.* # push notifications py-vapid == 1.9.* pywebpush == 2.0.* @@ -54,7 +54,6 @@ pywebpush == 2.0.* pyclipper == 1.3.* shapely == 2.0.* Levenshtein==0.26.* -prometheus-client == 0.21.* # HailoRT Wheels appdirs==1.4.* argcomplete==2.0.* @@ -70,5 +69,5 @@ verboselogs==1.7.* virtualenv==20.17.* prometheus-client == 0.21.* # TFLite -tflite_runtime @ https://github.com/feranick/TFlite-builds/releases/download/v2.17.1/tflite_runtime-2.17.1-cp311-cp311-linux_x86_64.whl; platform_machine == 'x86_64' +tflite_runtime @ https://github.com/frigate-nvr/TFlite-builds/releases/download/v2.17.1/tflite_runtime-2.17.1-cp311-cp311-linux_x86_64.whl; platform_machine == 'x86_64' tflite_runtime @ https://github.com/feranick/TFlite-builds/releases/download/v2.17.1/tflite_runtime-2.17.1-cp311-cp311-linux_aarch64.whl; platform_machine == 'aarch64' diff --git a/docker/main/rootfs/etc/s6-overlay/s6-rc.d/frigate/run b/docker/main/rootfs/etc/s6-overlay/s6-rc.d/frigate/run index a683e7ff2..c27f62d63 100755 --- a/docker/main/rootfs/etc/s6-overlay/s6-rc.d/frigate/run +++ b/docker/main/rootfs/etc/s6-overlay/s6-rc.d/frigate/run @@ -82,8 +82,10 @@ function migrate_db_from_media_to_config() { } function set_libva_version() { - local ffmpeg_path=$(python3 /usr/local/ffmpeg/get_ffmpeg_path.py) - export LIBAVFORMAT_VERSION_MAJOR=$($ffmpeg_path -version | grep -Po "libavformat\W+\K\d+") + local ffmpeg_path + ffmpeg_path=$(python3 /usr/local/ffmpeg/get_ffmpeg_path.py) + LIBAVFORMAT_VERSION_MAJOR=$("$ffmpeg_path" -version | grep -Po "libavformat\W+\K\d+") + export LIBAVFORMAT_VERSION_MAJOR } echo "[INFO] Preparing Frigate..." diff --git a/docker/main/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/run b/docker/main/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/run index 90c26ceff..2c3a7ab6f 100755 --- a/docker/main/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/run +++ b/docker/main/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/run @@ -44,10 +44,14 @@ function get_ip_and_port_from_supervisor() { } function set_libva_version() { - local ffmpeg_path=$(python3 /usr/local/ffmpeg/get_ffmpeg_path.py) - export LIBAVFORMAT_VERSION_MAJOR=$($ffmpeg_path -version | grep -Po "libavformat\W+\K\d+") + local ffmpeg_path + ffmpeg_path=$(python3 /usr/local/ffmpeg/get_ffmpeg_path.py) + LIBAVFORMAT_VERSION_MAJOR=$("$ffmpeg_path" -version | grep -Po "libavformat\W+\K\d+") + export LIBAVFORMAT_VERSION_MAJOR } +set_libva_version + if [[ -f "/dev/shm/go2rtc.yaml" ]]; then echo "[INFO] Removing stale config from last run..." rm /dev/shm/go2rtc.yaml @@ -66,8 +70,6 @@ else echo "[WARNING] Unable to remove existing go2rtc config. Changes made to your frigate config file may not be recognized. Please remove the /dev/shm/go2rtc.yaml from your docker host manually." fi -set_libva_version - readonly config_path="/config" if [[ -x "${config_path}/go2rtc" ]]; then diff --git a/docker/main/rootfs/usr/local/ffmpeg/get_ffmpeg_path.py b/docker/main/rootfs/usr/local/ffmpeg/get_ffmpeg_path.py index 27034bff9..ed7f6a891 100644 --- a/docker/main/rootfs/usr/local/ffmpeg/get_ffmpeg_path.py +++ b/docker/main/rootfs/usr/local/ffmpeg/get_ffmpeg_path.py @@ -1,6 +1,5 @@ import json import os -import shutil import sys from ruamel.yaml import YAML @@ -35,10 +34,7 @@ except FileNotFoundError: path = config.get("ffmpeg", {}).get("path", "default") if path == "default": - if shutil.which("ffmpeg") is None: - print(f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg") - else: - print("ffmpeg") + print(f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg") elif path in INCLUDED_FFMPEG_VERSIONS: print(f"/usr/lib/ffmpeg/{path}/bin/ffmpeg") else: diff --git a/docker/main/rootfs/usr/local/go2rtc/create_config.py b/docker/main/rootfs/usr/local/go2rtc/create_config.py index 0e4aa7bd1..4fe26775e 100644 --- a/docker/main/rootfs/usr/local/go2rtc/create_config.py +++ b/docker/main/rootfs/usr/local/go2rtc/create_config.py @@ -2,7 +2,6 @@ import json import os -import shutil import sys from pathlib import Path @@ -13,6 +12,7 @@ from frigate.const import ( BIRDSEYE_PIPE, DEFAULT_FFMPEG_VERSION, INCLUDED_FFMPEG_VERSIONS, + LIBAVFORMAT_VERSION_MAJOR, ) from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_encode @@ -115,10 +115,7 @@ else: # ensure ffmpeg path is set correctly path = config.get("ffmpeg", {}).get("path", "default") if path == "default": - if shutil.which("ffmpeg") is None: - ffmpeg_path = f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg" - else: - ffmpeg_path = "ffmpeg" + ffmpeg_path = f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg" elif path in INCLUDED_FFMPEG_VERSIONS: ffmpeg_path = f"/usr/lib/ffmpeg/{path}/bin/ffmpeg" else: @@ -130,14 +127,12 @@ elif go2rtc_config["ffmpeg"].get("bin") is None: go2rtc_config["ffmpeg"]["bin"] = ffmpeg_path # need to replace ffmpeg command when using ffmpeg4 -if int(os.environ.get("LIBAVFORMAT_VERSION_MAJOR", "59") or "59") < 59: - if go2rtc_config["ffmpeg"].get("rtsp") is None: - go2rtc_config["ffmpeg"]["rtsp"] = ( - "-fflags nobuffer -flags low_delay -stimeout 5000000 -user_agent go2rtc/ffmpeg -rtsp_transport tcp -i {input}" - ) -else: +if LIBAVFORMAT_VERSION_MAJOR < 59: + rtsp_args = "-fflags nobuffer -flags low_delay -stimeout 5000000 -user_agent go2rtc/ffmpeg -rtsp_transport tcp -i {input}" if go2rtc_config.get("ffmpeg") is None: - go2rtc_config["ffmpeg"] = {"path": ""} + go2rtc_config["ffmpeg"] = {"rtsp": rtsp_args} + elif go2rtc_config["ffmpeg"].get("rtsp") is None: + go2rtc_config["ffmpeg"]["rtsp"] = rtsp_args for name in go2rtc_config.get("streams", {}): stream = go2rtc_config["streams"][name] diff --git a/docker/main/rootfs/usr/local/nginx/conf/auth_request.conf b/docker/main/rootfs/usr/local/nginx/conf/auth_request.conf index b054a6b97..9e745b6dc 100644 --- a/docker/main/rootfs/usr/local/nginx/conf/auth_request.conf +++ b/docker/main/rootfs/usr/local/nginx/conf/auth_request.conf @@ -1,14 +1,16 @@ ## Send a subrequest to verify if the user is authenticated and has permission to access the resource. auth_request /auth; -## Save the upstream metadata response headers from Authelia to variables. +## Save the upstream metadata response headers from the auth request to variables auth_request_set $user $upstream_http_remote_user; +auth_request_set $role $upstream_http_remote_role; auth_request_set $groups $upstream_http_remote_groups; auth_request_set $name $upstream_http_remote_name; auth_request_set $email $upstream_http_remote_email; ## Inject the metadata response headers from the variables into the request made to the backend. proxy_set_header Remote-User $user; +proxy_set_header Remote-Role $role; proxy_set_header Remote-Groups $groups; proxy_set_header Remote-Email $email; proxy_set_header Remote-Name $name; diff --git a/docker/rockchip/Dockerfile b/docker/rockchip/Dockerfile index 09380dfb3..59c8ad791 100644 --- a/docker/rockchip/Dockerfile +++ b/docker/rockchip/Dockerfile @@ -3,6 +3,9 @@ # https://askubuntu.com/questions/972516/debian-frontend-environment-variable ARG DEBIAN_FRONTEND=noninteractive +# Globally set pip break-system-packages option to avoid having to specify it every time +ARG PIP_BREAK_SYSTEM_PACKAGES=1 + FROM wheels as rk-wheels COPY docker/main/requirements-wheels.txt /requirements-wheels.txt COPY docker/rockchip/requirements-wheels-rk.txt /requirements-wheels-rk.txt @@ -13,6 +16,7 @@ RUN rm -rf /rk-wheels/opencv_python-* FROM deps AS rk-frigate ARG TARGETARCH +ARG PIP_BREAK_SYSTEM_PACKAGES RUN --mount=type=bind,from=rk-wheels,source=/rk-wheels,target=/deps/rk-wheels \ pip3 install --no-deps -U /deps/rk-wheels/*.whl @@ -24,8 +28,7 @@ COPY docker/rockchip/conv2rknn.py /opt/conv2rknn.py ADD https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.0/librknnrt.so /usr/lib/ -RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffmpeg -RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffprobe ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-7/ffmpeg /usr/lib/ffmpeg/6.0/bin/ ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-7/ffprobe /usr/lib/ffmpeg/6.0/bin/ -ENV PATH="/usr/lib/ffmpeg/6.0/bin/:${PATH}" +ENV DEFAULT_FFMPEG_VERSION="6.0" +ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:${INCLUDED_FFMPEG_VERSIONS}" diff --git a/docker/rocm/Dockerfile b/docker/rocm/Dockerfile index 34c7efffb..78f91b96f 100644 --- a/docker/rocm/Dockerfile +++ b/docker/rocm/Dockerfile @@ -2,79 +2,49 @@ # https://askubuntu.com/questions/972516/debian-frontend-environment-variable ARG DEBIAN_FRONTEND=noninteractive -ARG ROCM=5.7.3 +ARG ROCM=6.3.3 ARG AMDGPU=gfx900 ARG HSA_OVERRIDE_GFX_VERSION ARG HSA_OVERRIDE ####################################################################### -FROM ubuntu:focal as rocm +FROM wget AS rocm ARG ROCM +ARG AMDGPU -RUN apt-get update && apt-get -y upgrade -RUN apt-get -y install gnupg wget - -RUN mkdir --parents --mode=0755 /etc/apt/keyrings - -RUN wget https://repo.radeon.com/rocm/rocm.gpg.key -O - | gpg --dearmor | tee /etc/apt/keyrings/rocm.gpg > /dev/null -COPY docker/rocm/rocm.list /etc/apt/sources.list.d/ -COPY docker/rocm/rocm-pin-600 /etc/apt/preferences.d/ - -RUN apt-get update - -RUN apt-get -y install --no-install-recommends migraphx hipfft roctracer -RUN apt-get -y install --no-install-recommends migraphx-dev +RUN apt update && \ + apt install -y wget gpg && \ + wget -O rocm.deb https://repo.radeon.com/amdgpu-install/$ROCM/ubuntu/jammy/amdgpu-install_6.3.60303-1_all.deb && \ + apt install -y ./rocm.deb && \ + apt update && \ + apt install -y rocm RUN mkdir -p /opt/rocm-dist/opt/rocm-$ROCM/lib -RUN cd /opt/rocm-$ROCM/lib && cp -dpr libMIOpen*.so* libamd*.so* libhip*.so* libhsa*.so* libmigraphx*.so* librocm*.so* librocblas*.so* libroctracer*.so* librocfft*.so* /opt/rocm-dist/opt/rocm-$ROCM/lib/ +RUN cd /opt/rocm-$ROCM/lib && \ + cp -dpr libMIOpen*.so* libamd*.so* libhip*.so* libhsa*.so* libmigraphx*.so* librocm*.so* librocblas*.so* libroctracer*.so* librocfft*.so* librocprofiler*.so* libroctx*.so* /opt/rocm-dist/opt/rocm-$ROCM/lib/ && \ + mkdir -p /opt/rocm-dist/opt/rocm-$ROCM/lib/migraphx/lib && \ + cp -dpr migraphx/lib/* /opt/rocm-dist/opt/rocm-$ROCM/lib/migraphx/lib RUN cd /opt/rocm-dist/opt/ && ln -s rocm-$ROCM rocm RUN mkdir -p /opt/rocm-dist/etc/ld.so.conf.d/ RUN echo /opt/rocm/lib|tee /opt/rocm-dist/etc/ld.so.conf.d/rocm.conf -####################################################################### -FROM --platform=linux/amd64 debian:12 as debian-base - -RUN apt-get update && apt-get -y upgrade -RUN apt-get -y install --no-install-recommends libelf1 libdrm2 libdrm-amdgpu1 libnuma1 kmod - -RUN apt-get -y install python3 - -####################################################################### -# ROCm does not come with migraphx wrappers for python 3.9, so we build it here -FROM debian-base as debian-build - -ARG ROCM - -COPY --from=rocm /opt/rocm-$ROCM /opt/rocm-$ROCM -RUN ln -s /opt/rocm-$ROCM /opt/rocm - -RUN apt-get -y install g++ cmake -RUN apt-get -y install python3-pybind11 python3-distutils python3-dev - -WORKDIR /opt/build - -COPY docker/rocm/migraphx . - -RUN mkdir build && cd build && cmake .. && make install - ####################################################################### FROM deps AS deps-prelim -# need this to install libnuma1 -RUN apt-get update -# no ugprade?!?! -RUN apt-get -y install libnuma1 +RUN apt-get update && apt-get install -y libnuma1 -WORKDIR /opt/frigate/ +WORKDIR /opt/frigate COPY --from=rootfs / / -# Temporarily disabled to see if a new wheel can be built to support py3.11 -#COPY docker/rocm/requirements-wheels-rocm.txt /requirements.txt -#RUN python3 -m pip install --upgrade pip \ -# && pip3 uninstall -y onnxruntime-openvino \ -# && pip3 install -r /requirements.txt +RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \ + && python3 get-pip.py "pip" --break-system-packages +RUN python3 -m pip config set global.break-system-packages true + +COPY docker/rocm/requirements-wheels-rocm.txt /requirements.txt +RUN pip3 uninstall -y onnxruntime-openvino \ + && pip3 install -r /requirements.txt ####################################################################### FROM scratch AS rocm-dist @@ -87,12 +57,11 @@ COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*$AMDGPU* /opt/rocm-$ROCM/share COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*gfx908* /opt/rocm-$ROCM/share/miopen/db/ COPY --from=rocm /opt/rocm-$ROCM/lib/rocblas/library/*$AMDGPU* /opt/rocm-$ROCM/lib/rocblas/library/ COPY --from=rocm /opt/rocm-dist/ / -COPY --from=debian-build /opt/rocm/lib/migraphx.cpython-311-x86_64-linux-gnu.so /opt/rocm-$ROCM/lib/ ####################################################################### FROM deps-prelim AS rocm-prelim-hsa-override0 -\ - ENV HSA_ENABLE_SDMA=0 +ENV HSA_ENABLE_SDMA=0 +ENV MIGRAPHX_ENABLE_NHWC=1 COPY --from=rocm-dist / / diff --git a/docker/rocm/migraphx/CMakeLists.txt b/docker/rocm/migraphx/CMakeLists.txt deleted file mode 100644 index 271dd094b..000000000 --- a/docker/rocm/migraphx/CMakeLists.txt +++ /dev/null @@ -1,26 +0,0 @@ - -cmake_minimum_required(VERSION 3.1) - -set(CMAKE_CXX_STANDARD 17) -set(CMAKE_CXX_STANDARD_REQUIRED ON) -set(CMAKE_CXX_EXTENSIONS OFF) - -if(NOT CMAKE_BUILD_TYPE) - set(CMAKE_BUILD_TYPE Release) -endif() - -SET(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE) - -project(migraphx_py) - -include_directories(/opt/rocm/include) - -find_package(pybind11 REQUIRED) -pybind11_add_module(migraphx migraphx_py.cpp) - -target_link_libraries(migraphx PRIVATE /opt/rocm/lib/libmigraphx.so /opt/rocm/lib/libmigraphx_tf.so /opt/rocm/lib/libmigraphx_onnx.so) - -install(TARGETS migraphx - COMPONENT python - LIBRARY DESTINATION /opt/rocm/lib -) diff --git a/docker/rocm/migraphx/migraphx_py.cpp b/docker/rocm/migraphx/migraphx_py.cpp deleted file mode 100644 index 894c9d186..000000000 --- a/docker/rocm/migraphx/migraphx_py.cpp +++ /dev/null @@ -1,582 +0,0 @@ -/* - * The MIT License (MIT) - * - * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved. - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to deal - * in the Software without restriction, including without limitation the rights - * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell - * copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in - * all copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN - * THE SOFTWARE. - */ - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#ifdef HAVE_GPU -#include -#endif - -using half = half_float::half; -namespace py = pybind11; - -#ifdef __clang__ -#define MIGRAPHX_PUSH_UNUSED_WARNING \ - _Pragma("clang diagnostic push") \ - _Pragma("clang diagnostic ignored \"-Wused-but-marked-unused\"") -#define MIGRAPHX_POP_WARNING _Pragma("clang diagnostic pop") -#else -#define MIGRAPHX_PUSH_UNUSED_WARNING -#define MIGRAPHX_POP_WARNING -#endif -#define MIGRAPHX_PYBIND11_MODULE(...) \ - MIGRAPHX_PUSH_UNUSED_WARNING \ - PYBIND11_MODULE(__VA_ARGS__) \ - MIGRAPHX_POP_WARNING - -#define MIGRAPHX_PYTHON_GENERATE_SHAPE_ENUM(x, t) .value(#x, migraphx::shape::type_t::x) -namespace migraphx { - -migraphx::value to_value(py::kwargs kwargs); -migraphx::value to_value(py::list lst); - -template -void visit_py(T x, F f) -{ - if(py::isinstance(x)) - { - f(to_value(x.template cast())); - } - else if(py::isinstance(x)) - { - f(to_value(x.template cast())); - } - else if(py::isinstance(x)) - { - f(x.template cast()); - } - else if(py::isinstance(x) or py::hasattr(x, "__index__")) - { - f(x.template cast()); - } - else if(py::isinstance(x)) - { - f(x.template cast()); - } - else if(py::isinstance(x)) - { - f(x.template cast()); - } - else if(py::isinstance(x)) - { - f(migraphx::to_value(x.template cast())); - } - else - { - MIGRAPHX_THROW("VISIT_PY: Unsupported data type!"); - } -} - -migraphx::value to_value(py::list lst) -{ - migraphx::value v = migraphx::value::array{}; - for(auto val : lst) - { - visit_py(val, [&](auto py_val) { v.push_back(py_val); }); - } - - return v; -} - -migraphx::value to_value(py::kwargs kwargs) -{ - migraphx::value v = migraphx::value::object{}; - - for(auto arg : kwargs) - { - auto&& key = py::str(arg.first); - auto&& val = arg.second; - visit_py(val, [&](auto py_val) { v[key] = py_val; }); - } - return v; -} -} // namespace migraphx - -namespace pybind11 { -namespace detail { - -template <> -struct npy_format_descriptor -{ - static std::string format() - { - // following: https://docs.python.org/3/library/struct.html#format-characters - return "e"; - } - static constexpr auto name() { return _("half"); } -}; - -} // namespace detail -} // namespace pybind11 - -template -void visit_type(const migraphx::shape& s, F f) -{ - s.visit_type(f); -} - -template -void visit(const migraphx::raw_data& x, F f) -{ - x.visit(f); -} - -template -void visit_types(F f) -{ - migraphx::shape::visit_types(f); -} - -template -py::buffer_info to_buffer_info(T& x) -{ - migraphx::shape s = x.get_shape(); - assert(s.type() != migraphx::shape::tuple_type); - if(s.dynamic()) - MIGRAPHX_THROW("MIGRAPHX PYTHON: dynamic shape argument passed to to_buffer_info"); - auto strides = s.strides(); - std::transform( - strides.begin(), strides.end(), strides.begin(), [&](auto i) { return i * s.type_size(); }); - py::buffer_info b; - visit_type(s, [&](auto as) { - // migraphx use int8_t data to store bool type, we need to - // explicitly specify the data type as bool for python - if(s.type() == migraphx::shape::bool_type) - { - b = py::buffer_info(x.data(), - as.size(), - py::format_descriptor::format(), - s.ndim(), - s.lens(), - strides); - } - else - { - b = py::buffer_info(x.data(), - as.size(), - py::format_descriptor::format(), - s.ndim(), - s.lens(), - strides); - } - }); - return b; -} - -migraphx::shape to_shape(const py::buffer_info& info) -{ - migraphx::shape::type_t t; - std::size_t n = 0; - visit_types([&](auto as) { - if(info.format == py::format_descriptor::format() or - (info.format == "l" and py::format_descriptor::format() == "q") or - (info.format == "L" and py::format_descriptor::format() == "Q")) - { - t = as.type_enum(); - n = sizeof(as()); - } - else if(info.format == "?" and py::format_descriptor::format() == "b") - { - t = migraphx::shape::bool_type; - n = sizeof(bool); - } - }); - - if(n == 0) - { - MIGRAPHX_THROW("MIGRAPHX PYTHON: Unsupported data type " + info.format); - } - - auto strides = info.strides; - std::transform(strides.begin(), strides.end(), strides.begin(), [&](auto i) -> std::size_t { - return n > 0 ? i / n : 0; - }); - - // scalar support - if(info.shape.empty()) - { - return migraphx::shape{t}; - } - else - { - return migraphx::shape{t, info.shape, strides}; - } -} - -MIGRAPHX_PYBIND11_MODULE(migraphx, m) -{ - py::class_ shape_cls(m, "shape"); - shape_cls - .def(py::init([](py::kwargs kwargs) { - auto v = migraphx::to_value(kwargs); - auto t = migraphx::shape::parse_type(v.get("type", "float")); - if(v.contains("dyn_dims")) - { - auto dyn_dims = - migraphx::from_value>( - v.at("dyn_dims")); - return migraphx::shape(t, dyn_dims); - } - auto lens = v.get("lens", {1}); - if(v.contains("strides")) - return migraphx::shape(t, lens, v.at("strides").to_vector()); - else - return migraphx::shape(t, lens); - })) - .def("type", &migraphx::shape::type) - .def("lens", &migraphx::shape::lens) - .def("strides", &migraphx::shape::strides) - .def("ndim", &migraphx::shape::ndim) - .def("elements", &migraphx::shape::elements) - .def("bytes", &migraphx::shape::bytes) - .def("type_string", &migraphx::shape::type_string) - .def("type_size", &migraphx::shape::type_size) - .def("dyn_dims", &migraphx::shape::dyn_dims) - .def("packed", &migraphx::shape::packed) - .def("transposed", &migraphx::shape::transposed) - .def("broadcasted", &migraphx::shape::broadcasted) - .def("standard", &migraphx::shape::standard) - .def("scalar", &migraphx::shape::scalar) - .def("dynamic", &migraphx::shape::dynamic) - .def("__eq__", std::equal_to{}) - .def("__ne__", std::not_equal_to{}) - .def("__repr__", [](const migraphx::shape& s) { return migraphx::to_string(s); }); - - py::enum_(shape_cls, "type_t") - MIGRAPHX_SHAPE_VISIT_TYPES(MIGRAPHX_PYTHON_GENERATE_SHAPE_ENUM); - - py::class_(shape_cls, "dynamic_dimension") - .def(py::init<>()) - .def(py::init()) - .def(py::init>()) - .def_readwrite("min", &migraphx::shape::dynamic_dimension::min) - .def_readwrite("max", &migraphx::shape::dynamic_dimension::max) - .def_readwrite("optimals", &migraphx::shape::dynamic_dimension::optimals) - .def("is_fixed", &migraphx::shape::dynamic_dimension::is_fixed); - - py::class_(m, "argument", py::buffer_protocol()) - .def_buffer([](migraphx::argument& x) -> py::buffer_info { return to_buffer_info(x); }) - .def(py::init([](py::buffer b) { - py::buffer_info info = b.request(); - return migraphx::argument(to_shape(info), info.ptr); - })) - .def("get_shape", &migraphx::argument::get_shape) - .def("data_ptr", - [](migraphx::argument& x) { return reinterpret_cast(x.data()); }) - .def("tolist", - [](migraphx::argument& x) { - py::list l{x.get_shape().elements()}; - visit(x, [&](auto data) { l = py::cast(data.to_vector()); }); - return l; - }) - .def("__eq__", std::equal_to{}) - .def("__ne__", std::not_equal_to{}) - .def("__repr__", [](const migraphx::argument& x) { return migraphx::to_string(x); }); - - py::class_(m, "target"); - - py::class_(m, "instruction_ref") - .def("shape", [](migraphx::instruction_ref i) { return i->get_shape(); }) - .def("op", [](migraphx::instruction_ref i) { return i->get_operator(); }); - - py::class_>(m, "module") - .def("print", [](const migraphx::module& mm) { std::cout << mm << std::endl; }) - .def( - "add_instruction", - [](migraphx::module& mm, - const migraphx::operation& op, - std::vector& args, - std::vector& mod_args) { - return mm.add_instruction(op, args, mod_args); - }, - py::arg("op"), - py::arg("args"), - py::arg("mod_args") = std::vector{}) - .def( - "add_literal", - [](migraphx::module& mm, py::buffer data) { - py::buffer_info info = data.request(); - auto literal_shape = to_shape(info); - return mm.add_literal(literal_shape, reinterpret_cast(info.ptr)); - }, - py::arg("data")) - .def( - "add_parameter", - [](migraphx::module& mm, const std::string& name, const migraphx::shape shape) { - return mm.add_parameter(name, shape); - }, - py::arg("name"), - py::arg("shape")) - .def( - "add_return", - [](migraphx::module& mm, std::vector& args) { - return mm.add_return(args); - }, - py::arg("args")) - .def("__repr__", [](const migraphx::module& mm) { return migraphx::to_string(mm); }); - - py::class_(m, "program") - .def(py::init([]() { return migraphx::program(); })) - .def("get_parameter_names", &migraphx::program::get_parameter_names) - .def("get_parameter_shapes", &migraphx::program::get_parameter_shapes) - .def("get_output_shapes", &migraphx::program::get_output_shapes) - .def("is_compiled", &migraphx::program::is_compiled) - .def( - "compile", - [](migraphx::program& p, - const migraphx::target& t, - bool offload_copy, - bool fast_math, - bool exhaustive_tune) { - migraphx::compile_options options; - options.offload_copy = offload_copy; - options.fast_math = fast_math; - options.exhaustive_tune = exhaustive_tune; - p.compile(t, options); - }, - py::arg("t"), - py::arg("offload_copy") = true, - py::arg("fast_math") = true, - py::arg("exhaustive_tune") = false) - .def("get_main_module", [](const migraphx::program& p) { return p.get_main_module(); }) - .def( - "create_module", - [](migraphx::program& p, const std::string& name) { return p.create_module(name); }, - py::arg("name")) - .def("run", - [](migraphx::program& p, py::dict params) { - migraphx::parameter_map pm; - for(auto x : params) - { - std::string key = x.first.cast(); - py::buffer b = x.second.cast(); - py::buffer_info info = b.request(); - pm[key] = migraphx::argument(to_shape(info), info.ptr); - } - return p.eval(pm); - }) - .def("run_async", - [](migraphx::program& p, - py::dict params, - std::uintptr_t stream, - std::string stream_name) { - migraphx::parameter_map pm; - for(auto x : params) - { - std::string key = x.first.cast(); - py::buffer b = x.second.cast(); - py::buffer_info info = b.request(); - pm[key] = migraphx::argument(to_shape(info), info.ptr); - } - migraphx::execution_environment exec_env{ - migraphx::any_ptr(reinterpret_cast(stream), stream_name), true}; - return p.eval(pm, exec_env); - }) - .def("sort", &migraphx::program::sort) - .def("print", [](const migraphx::program& p) { std::cout << p << std::endl; }) - .def("__eq__", std::equal_to{}) - .def("__ne__", std::not_equal_to{}) - .def("__repr__", [](const migraphx::program& p) { return migraphx::to_string(p); }); - - py::class_ op(m, "op"); - op.def(py::init([](const std::string& name, py::kwargs kwargs) { - migraphx::value v = migraphx::value::object{}; - if(kwargs) - { - v = migraphx::to_value(kwargs); - } - return migraphx::make_op(name, v); - })) - .def("name", &migraphx::operation::name); - - py::enum_(op, "pooling_mode") - .value("average", migraphx::op::pooling_mode::average) - .value("max", migraphx::op::pooling_mode::max) - .value("lpnorm", migraphx::op::pooling_mode::lpnorm); - - py::enum_(op, "rnn_direction") - .value("forward", migraphx::op::rnn_direction::forward) - .value("reverse", migraphx::op::rnn_direction::reverse) - .value("bidirectional", migraphx::op::rnn_direction::bidirectional); - - m.def( - "argument_from_pointer", - [](const migraphx::shape shape, const int64_t address) { - return migraphx::argument(shape, reinterpret_cast(address)); - }, - py::arg("shape"), - py::arg("address")); - - m.def( - "parse_tf", - [](const std::string& filename, - bool is_nhwc, - unsigned int batch_size, - std::unordered_map> map_input_dims, - std::vector output_names) { - return migraphx::parse_tf( - filename, migraphx::tf_options{is_nhwc, batch_size, map_input_dims, output_names}); - }, - "Parse tf protobuf (default format is nhwc)", - py::arg("filename"), - py::arg("is_nhwc") = true, - py::arg("batch_size") = 1, - py::arg("map_input_dims") = std::unordered_map>(), - py::arg("output_names") = std::vector()); - - m.def( - "parse_onnx", - [](const std::string& filename, - unsigned int default_dim_value, - migraphx::shape::dynamic_dimension default_dyn_dim_value, - std::unordered_map> map_input_dims, - std::unordered_map> - map_dyn_input_dims, - bool skip_unknown_operators, - bool print_program_on_error, - int64_t max_loop_iterations) { - migraphx::onnx_options options; - options.default_dim_value = default_dim_value; - options.default_dyn_dim_value = default_dyn_dim_value; - options.map_input_dims = map_input_dims; - options.map_dyn_input_dims = map_dyn_input_dims; - options.skip_unknown_operators = skip_unknown_operators; - options.print_program_on_error = print_program_on_error; - options.max_loop_iterations = max_loop_iterations; - return migraphx::parse_onnx(filename, options); - }, - "Parse onnx file", - py::arg("filename"), - py::arg("default_dim_value") = 0, - py::arg("default_dyn_dim_value") = migraphx::shape::dynamic_dimension{1, 1}, - py::arg("map_input_dims") = std::unordered_map>(), - py::arg("map_dyn_input_dims") = - std::unordered_map>(), - py::arg("skip_unknown_operators") = false, - py::arg("print_program_on_error") = false, - py::arg("max_loop_iterations") = 10); - - m.def( - "parse_onnx_buffer", - [](const std::string& onnx_buffer, - unsigned int default_dim_value, - migraphx::shape::dynamic_dimension default_dyn_dim_value, - std::unordered_map> map_input_dims, - std::unordered_map> - map_dyn_input_dims, - bool skip_unknown_operators, - bool print_program_on_error) { - migraphx::onnx_options options; - options.default_dim_value = default_dim_value; - options.default_dyn_dim_value = default_dyn_dim_value; - options.map_input_dims = map_input_dims; - options.map_dyn_input_dims = map_dyn_input_dims; - options.skip_unknown_operators = skip_unknown_operators; - options.print_program_on_error = print_program_on_error; - return migraphx::parse_onnx_buffer(onnx_buffer, options); - }, - "Parse onnx file", - py::arg("filename"), - py::arg("default_dim_value") = 0, - py::arg("default_dyn_dim_value") = migraphx::shape::dynamic_dimension{1, 1}, - py::arg("map_input_dims") = std::unordered_map>(), - py::arg("map_dyn_input_dims") = - std::unordered_map>(), - py::arg("skip_unknown_operators") = false, - py::arg("print_program_on_error") = false); - - m.def( - "load", - [](const std::string& name, const std::string& format) { - migraphx::file_options options; - options.format = format; - return migraphx::load(name, options); - }, - "Load MIGraphX program", - py::arg("filename"), - py::arg("format") = "msgpack"); - - m.def( - "save", - [](const migraphx::program& p, const std::string& name, const std::string& format) { - migraphx::file_options options; - options.format = format; - return migraphx::save(p, name, options); - }, - "Save MIGraphX program", - py::arg("p"), - py::arg("filename"), - py::arg("format") = "msgpack"); - - m.def("get_target", &migraphx::make_target); - m.def("create_argument", [](const migraphx::shape& s, const std::vector& values) { - if(values.size() != s.elements()) - MIGRAPHX_THROW("Values and shape elements do not match"); - migraphx::argument a{s}; - a.fill(values.begin(), values.end()); - return a; - }); - m.def("generate_argument", &migraphx::generate_argument, py::arg("s"), py::arg("seed") = 0); - m.def("fill_argument", &migraphx::fill_argument, py::arg("s"), py::arg("value")); - m.def("quantize_fp16", - &migraphx::quantize_fp16, - py::arg("prog"), - py::arg("ins_names") = std::vector{"all"}); - m.def("quantize_int8", - &migraphx::quantize_int8, - py::arg("prog"), - py::arg("t"), - py::arg("calibration") = std::vector{}, - py::arg("ins_names") = std::vector{"dot", "convolution"}); - -#ifdef HAVE_GPU - m.def("allocate_gpu", &migraphx::gpu::allocate_gpu, py::arg("s"), py::arg("host") = false); - m.def("to_gpu", &migraphx::gpu::to_gpu, py::arg("arg"), py::arg("host") = false); - m.def("from_gpu", &migraphx::gpu::from_gpu); - m.def("gpu_sync", [] { migraphx::gpu::gpu_sync(); }); -#endif - -#ifdef VERSION_INFO - m.attr("__version__") = VERSION_INFO; -#else - m.attr("__version__") = "dev"; -#endif -} diff --git a/docker/rocm/requirements-wheels-rocm.txt b/docker/rocm/requirements-wheels-rocm.txt index 89d0e6096..85450768e 100644 --- a/docker/rocm/requirements-wheels-rocm.txt +++ b/docker/rocm/requirements-wheels-rocm.txt @@ -1 +1 @@ -onnxruntime-rocm @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v1.0.0/onnxruntime_rocm-1.17.3-cp39-cp39-linux_x86_64.whl \ No newline at end of file +onnxruntime-rocm @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v6.3.3/onnxruntime_rocm-1.20.1-cp311-cp311-linux_x86_64.whl \ No newline at end of file diff --git a/docker/rocm/rocm-pin-600 b/docker/rocm/rocm-pin-600 deleted file mode 100644 index 88348a5c1..000000000 --- a/docker/rocm/rocm-pin-600 +++ /dev/null @@ -1,3 +0,0 @@ -Package: * -Pin: release o=repo.radeon.com -Pin-Priority: 600 diff --git a/docker/rocm/rocm.hcl b/docker/rocm/rocm.hcl index 33a2d2323..6a84b350d 100644 --- a/docker/rocm/rocm.hcl +++ b/docker/rocm/rocm.hcl @@ -2,7 +2,7 @@ variable "AMDGPU" { default = "gfx900" } variable "ROCM" { - default = "5.7.3" + default = "6.3.3" } variable "HSA_OVERRIDE_GFX_VERSION" { default = "" @@ -10,6 +10,13 @@ variable "HSA_OVERRIDE_GFX_VERSION" { variable "HSA_OVERRIDE" { default = "1" } + +target wget { + dockerfile = "docker/main/Dockerfile" + platforms = ["linux/amd64"] + target = "wget" +} + target deps { dockerfile = "docker/main/Dockerfile" platforms = ["linux/amd64"] @@ -26,6 +33,7 @@ target rocm { dockerfile = "docker/rocm/Dockerfile" contexts = { deps = "target:deps", + wget = "target:wget", rootfs = "target:rootfs" } platforms = ["linux/amd64"] diff --git a/docker/rocm/rocm.list b/docker/rocm/rocm.list deleted file mode 100644 index 0915b4094..000000000 --- a/docker/rocm/rocm.list +++ /dev/null @@ -1 +0,0 @@ -deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/5.7.3 focal main diff --git a/docker/rpi/Dockerfile b/docker/rpi/Dockerfile index 581ca7ff8..35a225227 100644 --- a/docker/rpi/Dockerfile +++ b/docker/rpi/Dockerfile @@ -6,11 +6,12 @@ ARG DEBIAN_FRONTEND=noninteractive FROM deps AS rpi-deps ARG TARGETARCH -RUN rm -rf /usr/lib/btbn-ffmpeg/ - # Install dependencies RUN --mount=type=bind,source=docker/rpi/install_deps.sh,target=/deps/install_deps.sh \ /deps/install_deps.sh +ENV DEFAULT_FFMPEG_VERSION="rpi" +ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:${INCLUDED_FFMPEG_VERSIONS}" + WORKDIR /opt/frigate/ COPY --from=rootfs / / diff --git a/docker/rpi/install_deps.sh b/docker/rpi/install_deps.sh index ed34389e5..bf537d503 100755 --- a/docker/rpi/install_deps.sh +++ b/docker/rpi/install_deps.sh @@ -28,4 +28,7 @@ if [[ "${TARGETARCH}" == "arm64" ]]; then echo "deb [signed-by=/usr/share/keyrings/raspbian.gpg] https://archive.raspberrypi.org/debian/ bookworm main" | tee /etc/apt/sources.list.d/raspi.list apt-get -qq update apt-get -qq install --no-install-recommends --no-install-suggests -y ffmpeg + mkdir -p /usr/lib/ffmpeg/rpi/bin + ln -svf /usr/bin/ffmpeg /usr/lib/ffmpeg/rpi/bin/ffmpeg + ln -svf /usr/bin/ffprobe /usr/lib/ffmpeg/rpi/bin/ffprobe fi diff --git a/docker/tensorrt/Dockerfile.amd64 b/docker/tensorrt/Dockerfile.amd64 index 6be11c210..e6429aa90 100644 --- a/docker/tensorrt/Dockerfile.amd64 +++ b/docker/tensorrt/Dockerfile.amd64 @@ -3,22 +3,16 @@ # https://askubuntu.com/questions/972516/debian-frontend-environment-variable ARG DEBIAN_FRONTEND=noninteractive -# Make this a separate target so it can be built/cached optionally -FROM wheels as trt-wheels -ARG DEBIAN_FRONTEND -ARG TARGETARCH -RUN python3 -m pip config set global.break-system-packages true - -# Add TensorRT wheels to another folder -COPY docker/tensorrt/requirements-amd64.txt /requirements-tensorrt.txt -RUN mkdir -p /trt-wheels && pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt +# Globally set pip break-system-packages option to avoid having to specify it every time +ARG PIP_BREAK_SYSTEM_PACKAGES=1 FROM tensorrt-base AS frigate-tensorrt +ARG PIP_BREAK_SYSTEM_PACKAGES ENV TRT_VER=8.6.1 -RUN python3 -m pip config set global.break-system-packages true -RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \ - pip3 install -U /deps/trt-wheels/*.whl && \ - ldconfig + +# Install TensorRT wheels +COPY docker/tensorrt/requirements-amd64.txt /requirements-tensorrt.txt +RUN pip3 install -U -r /requirements-tensorrt.txt && ldconfig WORKDIR /opt/frigate/ COPY --from=rootfs / / diff --git a/docker/tensorrt/Dockerfile.arm64 b/docker/tensorrt/Dockerfile.arm64 index 33fd8182a..7a88a03a6 100644 --- a/docker/tensorrt/Dockerfile.arm64 +++ b/docker/tensorrt/Dockerfile.arm64 @@ -76,8 +76,9 @@ RUN apt-get update \ && apt-get install -y python-is-python3 libprotobuf23 \ && rm -rf /var/lib/apt/lists/* -RUN rm -rf /usr/lib/btbn-ffmpeg/ COPY --from=jetson-ffmpeg /rootfs / +ENV DEFAULT_FFMPEG_VERSION="jetson" +ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:${INCLUDED_FFMPEG_VERSIONS}" # ffmpeg runtime dependencies RUN apt-get -qq update \ diff --git a/docker/tensorrt/Dockerfile.base b/docker/tensorrt/Dockerfile.base index f9cdde587..4305f1d74 100644 --- a/docker/tensorrt/Dockerfile.base +++ b/docker/tensorrt/Dockerfile.base @@ -8,6 +8,7 @@ ARG TRT_BASE=nvcr.io/nvidia/tensorrt:23.12-py3 # Build TensorRT-specific library FROM ${TRT_BASE} AS trt-deps +ARG TARGETARCH ARG COMPUTE_LEVEL RUN apt-get update \ @@ -16,15 +17,26 @@ RUN apt-get update \ RUN --mount=type=bind,source=docker/tensorrt/detector/tensorrt_libyolo.sh,target=/tensorrt_libyolo.sh \ /tensorrt_libyolo.sh +# COPY required individual CUDA deps +RUN mkdir -p /usr/local/cuda-deps +RUN if [ "$TARGETARCH" = "amd64" ]; then \ + cp /usr/local/cuda-12.3/targets/x86_64-linux/lib/libcurand.so.* /usr/local/cuda-deps/ && \ + cp /usr/local/cuda-12.3/targets/x86_64-linux/lib/libnvrtc.so.* /usr/local/cuda-deps/ ; \ + fi + # Frigate w/ TensorRT Support as separate image FROM deps AS tensorrt-base #Disable S6 Global timeout ENV S6_CMD_WAIT_FOR_SERVICES_MAXTIME=0 +# COPY TensorRT Model Generation Deps COPY --from=trt-deps /usr/local/lib/libyolo_layer.so /usr/local/lib/libyolo_layer.so COPY --from=trt-deps /usr/local/src/tensorrt_demos /usr/local/src/tensorrt_demos -COPY --from=trt-deps /usr/local/cuda-12.* /usr/local/cuda + +# COPY Individual CUDA deps folder +COPY --from=trt-deps /usr/local/cuda-deps /usr/local/cuda + COPY docker/tensorrt/detector/rootfs/ / ENV YOLO_MODELS="" diff --git a/docker/tensorrt/build_jetson_ffmpeg.sh b/docker/tensorrt/build_jetson_ffmpeg.sh index 692612137..fb29eb214 100755 --- a/docker/tensorrt/build_jetson_ffmpeg.sh +++ b/docker/tensorrt/build_jetson_ffmpeg.sh @@ -5,7 +5,7 @@ set -euxo pipefail -INSTALL_PREFIX=/rootfs/usr/local +INSTALL_PREFIX=/rootfs/usr/lib/ffmpeg/jetson apt-get -qq update apt-get -qq install -y --no-install-recommends build-essential ccache clang cmake pkg-config diff --git a/docker/tensorrt/detector/rootfs/etc/ld.so.conf.d/cuda_tensorrt.conf b/docker/tensorrt/detector/rootfs/etc/ld.so.conf.d/cuda_tensorrt.conf index 561b7bcd4..72eec56e0 100644 --- a/docker/tensorrt/detector/rootfs/etc/ld.so.conf.d/cuda_tensorrt.conf +++ b/docker/tensorrt/detector/rootfs/etc/ld.so.conf.d/cuda_tensorrt.conf @@ -1,5 +1,5 @@ /usr/local/lib -/usr/local/cuda/lib64 +/usr/local/cuda /usr/local/lib/python3.11/dist-packages/nvidia/cudnn/lib /usr/local/lib/python3.11/dist-packages/nvidia/cuda_runtime/lib /usr/local/lib/python3.11/dist-packages/nvidia/cublas/lib diff --git a/docker/tensorrt/detector/rootfs/etc/s6-overlay/s6-rc.d/trt-model-prepare/run b/docker/tensorrt/detector/rootfs/etc/s6-overlay/s6-rc.d/trt-model-prepare/run index a88da89d6..e3440e7ac 100755 --- a/docker/tensorrt/detector/rootfs/etc/s6-overlay/s6-rc.d/trt-model-prepare/run +++ b/docker/tensorrt/detector/rootfs/etc/s6-overlay/s6-rc.d/trt-model-prepare/run @@ -20,7 +20,7 @@ FIRST_MODEL=true MODEL_DOWNLOAD="" MODEL_CONVERT="" -if [ -z "$YOLO_MODELS"]; then +if [ -z "$YOLO_MODELS" ]; then echo "tensorrt model preparation disabled" exit 0 fi @@ -64,7 +64,7 @@ fi # order to run libyolo here. # On Jetpack 5.0, these libraries are not mounted by the runtime and are supplied by the image. if [[ "$(arch)" == "aarch64" ]]; then - if [[ ! -e /usr/lib/aarch64-linux-gnu/tegra ]]; then + if [[ ! -e /usr/lib/aarch64-linux-gnu/tegra && ! -e /usr/lib/aarch64-linux-gnu/tegra-egl ]]; then echo "ERROR: Container must be launched with nvidia runtime" exit 1 elif [[ ! -e /usr/lib/aarch64-linux-gnu/libnvinfer.so.8 || diff --git a/docker/tensorrt/requirements-amd64.txt b/docker/tensorrt/requirements-amd64.txt index c81851506..0e003ca3d 100644 --- a/docker/tensorrt/requirements-amd64.txt +++ b/docker/tensorrt/requirements-amd64.txt @@ -1,14 +1,17 @@ # NVidia TensorRT Support (amd64 only) --extra-index-url 'https://pypi.nvidia.com' numpy < 1.24; platform_machine == 'x86_64' -tensorrt == 8.6.1.*; platform_machine == 'x86_64' +tensorrt == 8.6.1; platform_machine == 'x86_64' +tensorrt_bindings == 8.6.1; platform_machine == 'x86_64' cuda-python == 11.8.*; platform_machine == 'x86_64' cython == 3.0.*; platform_machine == 'x86_64' nvidia-cuda-runtime-cu12 == 12.1.*; platform_machine == 'x86_64' nvidia-cuda-runtime-cu11 == 11.8.*; platform_machine == 'x86_64' nvidia-cublas-cu11 == 11.11.3.6; platform_machine == 'x86_64' +nvidia-cudnn-cu11 == 8.6.0.*; platform_machine == 'x86_64' nvidia-cudnn-cu12 == 9.5.0.*; platform_machine == 'x86_64' nvidia-cufft-cu11==10.*; platform_machine == 'x86_64' +nvidia-cufft-cu12==11.*; platform_machine == 'x86_64' onnx==1.16.*; platform_machine == 'x86_64' onnxruntime-gpu==1.20.*; platform_machine == 'x86_64' protobuf==3.20.3; platform_machine == 'x86_64' diff --git a/docker/tensorrt/trt.hcl b/docker/tensorrt/trt.hcl index 730f54053..80757ba6d 100644 --- a/docker/tensorrt/trt.hcl +++ b/docker/tensorrt/trt.hcl @@ -14,12 +14,17 @@ variable "COMPUTE_LEVEL" { default = "" } variable "BASE_HOOK" { - # Ensure an up-to-date python 3.11 is available in tensorrt/jetson image + # Ensure an up-to-date python 3.11 is available in jetson images default = <> /etc/apt/sources.list.d/deadsnakes.list + echo "deb-src https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu $VERSION_CODENAME main" >> /etc/apt/sources.list.d/deadsnakes.list + + # Add deadsnakes signing key + apt-key adv --keyserver keyserver.ubuntu.com --recv-keys F23C5A6CF475977595C89F51BA6932366A755776 fi EOT } @@ -90,7 +95,6 @@ target "tensorrt" { wget = "target:wget", tensorrt-base = "target:tensorrt-base", rootfs = "target:rootfs" - wheels = "target:wheels" } target = "frigate-tensorrt" inherits = ["_build_args"] diff --git a/docs/docs/configuration/advanced.md b/docs/docs/configuration/advanced.md index c1f12ee08..b037c0768 100644 --- a/docs/docs/configuration/advanced.md +++ b/docs/docs/configuration/advanced.md @@ -37,7 +37,7 @@ See [the go2rtc docs](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#modul ```yaml go2rtc: streams: - ... + # ... log: exec: trace ``` @@ -176,15 +176,13 @@ listen [::]:5000 ipv6only=off; ### Custom ffmpeg build -Included with Frigate is a build of ffmpeg that works for the vast majority of users. However, there exists some hardware setups which have incompatibilities with the included build. In this case, statically built ffmpeg binary can be downloaded to /config and used. +Included with Frigate is a build of ffmpeg that works for the vast majority of users. However, there exists some hardware setups which have incompatibilities with the included build. In this case, statically built `ffmpeg` and `ffprobe` binaries can be placed in `/config/custom-ffmpeg/bin` for Frigate to use. To do this: -1. Download your ffmpeg build and uncompress to the Frigate config folder. -2. Update your docker-compose or docker CLI to include `'/home/appdata/frigate/custom-ffmpeg':'/usr/lib/btbn-ffmpeg':'ro'` in the volume mappings. -3. Restart Frigate and the custom version will be used if the mapping was done correctly. - -NOTE: The folder that is set for the config needs to be the folder that contains `/bin`. So if the full structure is `/home/appdata/frigate/custom-ffmpeg/bin/ffmpeg` then the `ffmpeg -> path` field should be `/config/custom-ffmpeg/bin`. +1. Download your ffmpeg build and uncompress it to the `/config/custom-ffmpeg` folder. Verify that both the `ffmpeg` and `ffprobe` binaries are located in `/config/custom-ffmpeg/bin`. +2. Update the `ffmpeg.path` in your Frigate config to `/config/custom-ffmpeg`. +3. Restart Frigate and the custom version will be used if the steps above were done correctly. ### Custom go2rtc version @@ -192,7 +190,7 @@ Frigate currently includes go2rtc v1.9.2, there may be certain cases where you w To do this: -1. Download the go2rtc build to the /config folder. +1. Download the go2rtc build to the `/config` folder. 2. Rename the build to `go2rtc`. 3. Give `go2rtc` execute permission. 4. Restart Frigate and the custom version will be used, you can verify by checking go2rtc logs. diff --git a/docs/docs/configuration/cameras.md b/docs/docs/configuration/cameras.md index 50a8c6f93..f1a6b7bf5 100644 --- a/docs/docs/configuration/cameras.md +++ b/docs/docs/configuration/cameras.md @@ -7,7 +7,7 @@ title: Camera Configuration Several inputs can be configured for each camera and the role of each input can be mixed and matched based on your needs. This allows you to use a lower resolution stream for object detection, but create recordings from a higher resolution stream, or vice versa. -A camera is enabled by default but can be temporarily disabled by using `enabled: False`. Existing tracked objects and recordings can still be accessed. Live streams, recording and detecting are not working. Camera specific configurations will be used. +A camera is enabled by default but can be disabled by using `enabled: False`. Cameras that are disabled through the configuration file will not appear in the Frigate UI and will not consume system resources. Each role can only be assigned to one input per camera. The options for roles are as follows: diff --git a/docs/docs/configuration/face_recognition.md b/docs/docs/configuration/face_recognition.md index aaab92e6d..4d934afce 100644 --- a/docs/docs/configuration/face_recognition.md +++ b/docs/docs/configuration/face_recognition.md @@ -9,7 +9,7 @@ Frigate has support for CV2 Local Binary Pattern Face Recognizer to recognize fa ## Configuration -Face recognition is disabled by default and requires semantic search to be enabled, face recognition must be enabled in your config file before it can be used. Semantic Search and face recognition are global configuration settings. +Face recognition is disabled by default, face recognition must be enabled in your config file before it can be used. Face recognition is a global configuration setting. ```yaml face_recognition: @@ -36,6 +36,7 @@ The accuracy of face recognition is heavily dependent on the quality of data giv :::tip When choosing images to include in the face training set it is recommended to always follow these recommendations: + - If it is difficult to make out details in a persons face it will not be helpful in training. - Avoid images with under/over-exposure. - Avoid blurry / pixelated images. @@ -52,4 +53,4 @@ Then it is recommended to use the `Face Library` tab in Frigate to select and tr ### Step 2 - Expanding The Dataset -Once straight-on images are performing well, start choosing slightly off-angle images to include for training. It is important to still choose images where enough face detail is visible to recognize someone. \ No newline at end of file +Once straight-on images are performing well, start choosing slightly off-angle images to include for training. It is important to still choose images where enough face detail is visible to recognize someone. diff --git a/docs/docs/configuration/genai.md b/docs/docs/configuration/genai.md index 23f1c06be..ec733684f 100644 --- a/docs/docs/configuration/genai.md +++ b/docs/docs/configuration/genai.md @@ -5,13 +5,7 @@ title: Generative AI Generative AI can be used to automatically generate descriptive text based on the thumbnails of your tracked objects. This helps with [Semantic Search](/configuration/semantic_search) in Frigate to provide more context about your tracked objects. Descriptions are accessed via the _Explore_ view in the Frigate UI by clicking on a tracked object's thumbnail. -Requests for a description are sent off automatically to your AI provider at the end of the tracked object's lifecycle. Descriptions can also be regenerated manually via the Frigate UI. - -:::info - -Semantic Search must be enabled to use Generative AI. - -::: +Requests for a description are sent off automatically to your AI provider at the end of the tracked object's lifecycle, or can optionally be sent earlier after a number of significantly changed frames, for example in use in more real-time notifications. Descriptions can also be regenerated manually via the Frigate UI. Note that if you are manually entering a description for tracked objects prior to its end, this will be overwritten by the generated response. ## Configuration @@ -154,6 +148,15 @@ While generating simple descriptions of detected objects is useful, understandin Frigate provides an [MQTT topic](/integrations/mqtt), `frigate/tracked_object_update`, that is updated with a JSON payload containing `event_id` and `description` when your AI provider returns a description for a tracked object. This description could be used directly in notifications, such as sending alerts to your phone or making audio announcements. If additional details from the tracked object are needed, you can query the [HTTP API](/integrations/api/event-events-event-id-get) using the `event_id`, eg: `http://frigate_ip:5000/api/events/`. +If looking to get notifications earlier than when an object ceases to be tracked, an additional send trigger can be configured of `after_significant_updates`. + +```yaml +genai: + send_triggers: + tracked_object_end: true # default + after_significant_updates: 3 # how many updates to a tracked object before we should send an image +``` + ## Custom Prompts Frigate sends multiple frames from the tracked object along with a prompt to your Generative AI provider asking it to generate a description. The default prompt is as follows: diff --git a/docs/docs/configuration/license_plate_recognition.md b/docs/docs/configuration/license_plate_recognition.md index d2a130c5e..3fe1ee852 100644 --- a/docs/docs/configuration/license_plate_recognition.md +++ b/docs/docs/configuration/license_plate_recognition.md @@ -3,13 +3,28 @@ id: license_plate_recognition title: License Plate Recognition (LPR) --- -Frigate can recognize license plates on vehicles and automatically add the detected characters as a `sub_label` to objects that are of type `car`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street with a dedicated LPR camera. +Frigate can recognize license plates on vehicles and automatically add the detected characters or recognized name as a `sub_label` to objects that are of type `car`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street. + +LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. However, LPR does not run on stationary vehicles. + +When a plate is recognized, the detected characters or recognized name is: + +- Added as a `sub_label` to the `car` tracked object. +- Viewable in the Review Item Details pane in Review and the Tracked Object Details pane in Explore. +- Filterable through the More Filters menu in Explore. +- Published via the `frigate/events` MQTT topic as a `sub_label` for the tracked object. + +## Model Requirements Users running a Frigate+ model (or any custom model that natively detects license plates) should ensure that `license_plate` is added to the [list of objects to track](https://docs.frigate.video/plus/#available-label-types) either globally or for a specific camera. This will improve the accuracy and performance of the LPR model. -Users without a model that detects license plates can still run LPR. A small, CPU inference, YOLOv9 license plate detection model will be used instead. You should _not_ define `license_plate` in your list of objects to track. +Users without a model that detects license plates can still run LPR. Frigate uses a lightweight YOLOv9 license plate detection model that runs on your CPU. In this case, you should _not_ define `license_plate` in your list of objects to track. -LPR is most effective when the vehicle’s license plate is fully visible to the camera. For moving vehicles, Frigate will attempt to read the plate continuously, refining recognition and keeping the most confident result. LPR will not run on stationary vehicles. +:::note + +Frigate needs to first detect a `car` before it can recognize a license plate. If you're using a dedicated LPR camera or have a zoomed-in view, make sure the camera captures enough of the `car` for Frigate to detect it reliably. + +::: ## Minimum System Requirements @@ -24,6 +39,10 @@ lpr: enabled: True ``` +Ensure that your camera is configured to detect objects of type `car`, and that a car is actually being detected by Frigate. Otherwise, LPR will not run. + +Like the other real-time processors in Frigate, license plate recognition runs on the camera stream defined by the `detect` role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements. + ## Advanced Configuration Fine-tune the LPR feature using these optional parameters: @@ -35,17 +54,18 @@ Fine-tune the LPR feature using these optional parameters: - Note: If you are using a Frigate+ model and you set the `threshold` in your objects config for `license_plate` higher than this value, recognition will never run. It's best to ensure these values match, or this `detection_threshold` is lower than your object config `threshold`. - **`min_area`**: Defines the minimum size (in pixels) a license plate must be before recognition runs. - Default: `1000` pixels. - - Depending on the resolution of your cameras, you can increase this value to ignore small or distant plates. + - Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant plates. ### Recognition - **`recognition_threshold`**: Recognition confidence score required to add the plate to the object as a sub label. - Default: `0.9`. -- **`min_plate_length`**: Specifies the minimum number of characters a detected license plate must have to be added as a sub-label to an object. +- **`min_plate_length`**: Specifies the minimum number of characters a detected license plate must have to be added as a sub label to an object. - Use this to filter out short, incomplete, or incorrect detections. - **`format`**: A regular expression defining the expected format of detected plates. Plates that do not match this format will be discarded. - `"^[A-Z]{1,3} [A-Z]{1,2} [0-9]{1,4}$"` matches plates like "B AB 1234" or "M X 7" - `"^[A-Z]{2}[0-9]{2} [A-Z]{3}$"` matches plates like "AB12 XYZ" or "XY68 ABC" + - Websites like https://regex101.com/ can help test regular expressions for your plates. ### Matching @@ -53,9 +73,9 @@ Fine-tune the LPR feature using these optional parameters: - These labels appear in the UI, filters, and notifications. - **`match_distance`**: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate. - For example, setting `match_distance: 1` allows a plate `ABCDE` to match `ABCBE` or `ABCD`. - - This parameter will not operate on known plates that are defined as regular expressions. You should define the full string of your plate in `known_plates` in order to use `match_distance`. + - This parameter will _not_ operate on known plates that are defined as regular expressions. You should define the full string of your plate in `known_plates` in order to use `match_distance`. -### Examples +## Configuration Examples ```yaml lpr: @@ -69,7 +89,9 @@ lpr: Johnny: - "J*N-*234" # Matches JHN-1234 and JMN-I234, but also note that "*" matches any number of characters Sally: - - "[S5]LL-1234" # Matches both SLL-1234 and 5LL-1234 + - "[S5]LL 1234" # Matches both SLL 1234 and 5LL 1234 + Work Trucks: + - "EMP-[0-9]{3}[A-Z]" # Matches plates like EMP-123A, EMP-456Z ``` ```yaml @@ -77,12 +99,54 @@ lpr: enabled: True min_area: 4000 # Run recognition on larger plates only recognition_threshold: 0.85 - format: "^[A-Z]{3}-[0-9]{4}$" # Only recognize plates that are three letters, followed by a dash, followed by 4 numbers + format: "^[A-Z]{2} [A-Z][0-9]{4}$" # Only recognize plates that are two letters, followed by a space, followed by a single letter and 4 numbers match_distance: 1 # Allow one character variation in plate matching known_plates: Delivery Van: - - "RJK-5678" - - "UPS-1234" - Employee Parking: - - "EMP-[0-9]{3}[A-Z]" # Matches plates like EMP-123A, EMP-456Z + - "RJ K5678" + - "UP A1234" + Supervisor: + - "MN D3163" ``` + +## FAQ + +### Why isn't my license plate being detected and recognized? + +Ensure that: + +- Your camera has a clear, human-readable, well-lit view of the plate. If you can't read the plate, Frigate certainly won't be able to. This may require changing video size, quality, or frame rate settings on your camera, depending on your scene and how fast the vehicles are traveling. +- The plate is large enough in the image (try adjusting `min_area`) or increasing the resolution of your camera's stream. +- A `car` is detected first, as LPR only runs on recognized vehicles. + +If you are using a Frigate+ model or a custom model that detects license plates, ensure that `license_plate` is added to your list of objects to track. +If you are using the free model that ships with Frigate, you should _not_ add `license_plate` to the list of objects to track. + +### Can I run LPR without detecting `car` objects? + +No, Frigate requires a `car` to be detected first before recognizing a license plate. + +### How can I improve detection accuracy? + +- Use high-quality cameras with good resolution. +- Adjust `detection_threshold` and `recognition_threshold` values. +- Define a `format` regex to filter out invalid detections. + +### Does LPR work at night? + +Yes, but performance depends on camera quality, lighting, and infrared capabilities. Make sure your camera can capture clear images of plates at night. + +### How can I match known plates with minor variations? + +Use `match_distance` to allow small character mismatches. Alternatively, define multiple variations in `known_plates`. + +### How do I debug LPR issues? + +- View MQTT messages for `frigate/events` to verify detected plates. +- Adjust `detection_threshold` and `recognition_threshold` settings. +- If you are using a Frigate+ model or a model that detects license plates, watch the debug view (Settings --> Debug) to ensure that `license_plate` is being detected with a `car`. +- Enable debug logs for LPR by adding `frigate.data_processing.common.license_plate: debug` to your `logger` configuration. These logs are _very_ verbose, so only enable this when necessary. + +### Will LPR slow down my system? + +LPR runs on the CPU, so performance impact depends on your hardware. Ensure you have at least 4GB RAM and a capable CPU for optimal results. diff --git a/docs/docs/configuration/live.md b/docs/docs/configuration/live.md index 76fcf6826..42809739a 100644 --- a/docs/docs/configuration/live.md +++ b/docs/docs/configuration/live.md @@ -183,32 +183,46 @@ The default dashboard ("All Cameras") will always use Smart Streaming and the fi ::: +### Disabling cameras + +Cameras can be temporarily disabled through the Frigate UI and through [MQTT](/integrations/mqtt#frigatecamera_nameenabledset) to conserve system resources. When disabled, Frigate's ffmpeg processes are terminated — recording stops, object detection is paused, and the Live dashboard displays a blank image with a disabled message. Review items, tracked objects, and historical footage for disabled cameras can still be accessed via the UI. + +For restreamed cameras, go2rtc remains active but does not use system resources for decoding or processing unless there are active external consumers (such as the Advanced Camera Card in Home Assistant using a go2rtc source). + +Note that disabling a camera through the config file (`enabled: False`) removes all related UI elements, including historical footage access. To retain access while disabling the camera, keep it enabled in the config and use the UI or MQTT to disable it temporarily. + ## Live view FAQ -1. Why don't I have audio in my Live view? +1. **Why don't I have audio in my Live view?** + You must use go2rtc to hear audio in your live streams. If you have go2rtc already configured, you need to ensure your camera is sending PCMA/PCMU or AAC audio. If you can't change your camera's audio codec, you need to [transcode the audio](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg) using go2rtc. Note that the low bandwidth mode player is a video-only stream. You should not expect to hear audio when in low bandwidth mode, even if you've set up go2rtc. -2. Frigate shows that my live stream is in "low bandwidth mode". What does this mean? +2. **Frigate shows that my live stream is in "low bandwidth mode". What does this mean?** + Frigate intelligently selects the live streaming technology based on a number of factors (user-selected modes like two-way talk, camera settings, browser capabilities, available bandwidth) and prioritizes showing an actual up-to-date live view of your camera's stream as quickly as possible. When you have go2rtc configured, Live view initially attempts to load and play back your stream with a clearer, fluent stream technology (MSE). An initial timeout, a low bandwidth condition that would cause buffering of the stream, or decoding errors in the stream will cause Frigate to switch to the stream defined by the `detect` role, using the jsmpeg format. This is what the UI labels as "low bandwidth mode". On Live dashboards, the mode will automatically reset when smart streaming is configured and activity stops. You can also try using the _Reset_ button to force a reload of your stream. If you are still experiencing Frigate falling back to low bandwidth mode, you may need to adjust your camera's settings per the recommendations above or ensure you have enough bandwidth available. -3. It doesn't seem like my cameras are streaming on the Live dashboard. Why? +3. **It doesn't seem like my cameras are streaming on the Live dashboard. Why?** + On the default Live dashboard ("All Cameras"), your camera images will update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity is detected, cameras seamlessly switch to a full-resolution live stream. If you want to customize this behavior, use a camera group. -4. I see a strange diagonal line on my live view, but my recordings look fine. How can I fix it? +4. **I see a strange diagonal line on my live view, but my recordings look fine. How can I fix it?** + This is caused by incorrect dimensions set in your detect width or height (or incorrectly auto-detected), causing the jsmpeg player's rendering engine to display a slightly distorted image. You should enlarge the width and height of your `detect` resolution up to a standard aspect ratio (example: 640x352 becomes 640x360, and 800x443 becomes 800x450, 2688x1520 becomes 2688x1512, etc). If changing the resolution to match a standard (4:3, 16:9, or 32:9, etc) aspect ratio does not solve the issue, you can enable "compatibility mode" in your camera group dashboard's stream settings. Depending on your browser and device, more than a few cameras in compatibility mode may not be supported, so only use this option if changing your `detect` width and height fails to resolve the color artifacts and diagonal line. -5. How does "smart streaming" work? +5. **How does "smart streaming" work?** + Because a static image of a scene looks exactly the same as a live stream with no motion or activity, smart streaming updates your camera images once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity (motion or object/audio detection) occurs, cameras seamlessly switch to a live stream. This static image is pulled from the stream defined in your config with the `detect` role. When activity is detected, images from the `detect` stream immediately begin updating at ~5 frames per second so you can see the activity until the live player is loaded and begins playing. This usually only takes a second or two. If the live player times out, buffers, or has streaming errors, the jsmpeg player is loaded and plays a video-only stream from the `detect` role. When activity ends, the players are destroyed and a static image is displayed until activity is detected again, and the process repeats. This is Frigate's default and recommended setting because it results in a significant bandwidth savings, especially for high resolution cameras. -6. I have unmuted some cameras on my dashboard, but I do not hear sound. Why? +6. **I have unmuted some cameras on my dashboard, but I do not hear sound. Why?** + If your camera is streaming (as indicated by a red dot in the upper right, or if it has been set to continuous streaming mode), your browser may be blocking audio until you interact with the page. This is an intentional browser limitation. See [this article](https://developer.mozilla.org/en-US/docs/Web/Media/Autoplay_guide#autoplay_availability). Many browsers have a whitelist feature to change this behavior. diff --git a/docs/docs/configuration/notifications.md b/docs/docs/configuration/notifications.md index 8ae2f6d47..b5e1600e4 100644 --- a/docs/docs/configuration/notifications.md +++ b/docs/docs/configuration/notifications.md @@ -14,6 +14,7 @@ In order to use notifications the following requirements must be met: - Frigate must be accessed via a secure `https` connection ([see the authorization docs](/configuration/authentication)). - 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. +- For iOS devices, some users have also indicated that the Notifications switch needs to be enabled in iOS Settings --> Apps --> Safari --> Advanced --> Features. ### Configuration diff --git a/docs/docs/configuration/object_detectors.md b/docs/docs/configuration/object_detectors.md index 21ba46c2d..3423ac3c6 100644 --- a/docs/docs/configuration/object_detectors.md +++ b/docs/docs/configuration/object_detectors.md @@ -10,25 +10,31 @@ title: Object Detectors Frigate supports multiple different detectors that work on different types of hardware: **Most Hardware** + - [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB and m.2 format allowing for a wide range of compatibility with devices. - [Hailo](#hailo-8l): The Hailo8 AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices. **AMD** + - [ROCm](#amdrocm-gpu-detector): ROCm can run on AMD Discrete GPUs to provide efficient object detection. - [ONNX](#onnx): ROCm will automatically be detected and used as a detector in the `-rocm` Frigate image when a supported ONNX model is configured. **Intel** + - [OpenVino](#openvino-detector): OpenVino can run on Intel Arc GPUs, Intel integrated GPUs, and Intel CPUs to provide efficient object detection. - [ONNX](#onnx): OpenVINO will automatically be detected and used as a detector in the default Frigate image when a supported ONNX model is configured. **Nvidia** + - [TensortRT](#nvidia-tensorrt-detector): TensorRT can run on Nvidia GPUs and Jetson devices, using one of many default models. - [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt` or `-tensorrt-jp(4/5)` Frigate images when a supported ONNX model is configured. **Rockchip** + - [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs. **For Testing** + - [CPU Detector (not recommended for actual use](#cpu-detector-not-recommended): Use a CPU to run tflite model, this is not recommended and in most cases OpenVINO can be used in CPU mode with better results. ::: @@ -43,7 +49,7 @@ This does not affect using hardware for accelerating other tasks such as [semant # Officially Supported Detectors -Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `hailo8l`, `onnx`, `openvino`, `rknn`, `rocm`, and `tensorrt`. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras. +Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `hailo8l`, `onnx`, `openvino`, `rknn`, and `tensorrt`. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras. ## Edge TPU Detector @@ -147,7 +153,6 @@ model: path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef ``` - ## OpenVINO Detector The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel VPU hardware. To configure an OpenVINO detector, set the `"type"` attribute to `"openvino"`. @@ -225,7 +230,7 @@ Note that the labelmap uses a subset of the complete COCO label set that has onl #### YOLOv9 -[YOLOv9](https://github.com/MultimediaTechLab/YOLO) models are supported, but not included by default. +[YOLOv9](https://github.com/WongKinYiu/yolov9) models are supported, but not included by default. :::tip @@ -362,7 +367,7 @@ model: ### Setup -The `rocm` detector supports running YOLO-NAS models on AMD GPUs. Use a frigate docker image with `-rocm` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-rocm`. +Support for AMD GPUs is provided using the [ONNX detector](#ONNX). In order to utilize the AMD GPU for object detection use a frigate docker image with `-rocm` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-rocm`. ### Docker settings for GPU access @@ -412,7 +417,7 @@ When using docker compose: ```yaml services: frigate: -... + environment: HSA_OVERRIDE_GFX_VERSION: "9.0.0" ``` @@ -441,29 +446,9 @@ $ docker exec -it frigate /bin/bash -c '(unset HSA_OVERRIDE_GFX_VERSION && /opt/ ### Supported Models -There is no default model provided, the following formats are supported: - -#### YOLO-NAS - -[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. See [the models section](#downloading-yolo-nas-model) for more information on downloading the YOLO-NAS model for use in Frigate. - -After placing the downloaded onnx model in your config folder, you can use the following configuration: - -```yaml -detectors: - rocm: - type: rocm - -model: - model_type: yolonas - width: 320 # <--- should match whatever was set in notebook - height: 320 # <--- should match whatever was set in notebook - input_pixel_format: bgr - path: /config/yolo_nas_s.onnx - labelmap_path: /labelmap/coco-80.txt -``` - -Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects. +See [ONNX supported models](#supported-models) for supported models, there are some caveats: +- D-FINE models are not supported +- YOLO-NAS models are known to not run well on integrated GPUs ## ONNX @@ -528,7 +513,7 @@ model: #### YOLOv9 -[YOLOv9](https://github.com/MultimediaTechLab/YOLO) models are supported, but not included by default. +[YOLOv9](https://github.com/WongKinYiu/yolov9) models are supported, but not included by default. :::tip @@ -555,6 +540,35 @@ model: Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects. +#### D-FINE + +[D-FINE](https://github.com/Peterande/D-FINE) is the [current state of the art](https://paperswithcode.com/sota/real-time-object-detection-on-coco?p=d-fine-redefine-regression-task-in-detrs-as) at the time of writing. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate. + +:::warning + +D-FINE is currently not supported on OpenVINO + +::: + +After placing the downloaded onnx model in your config/model_cache folder, you can use the following configuration: + +```yaml +detectors: + onnx: + type: onnx + +model: + model_type: dfine + width: 640 + height: 640 + input_tensor: nchw + input_dtype: float + path: /config/model_cache/dfine_m_obj2coco.onnx + labelmap_path: /labelmap/coco-80.txt +``` + +Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects. + ## CPU Detector (not recommended) The CPU detector type runs a TensorFlow Lite model utilizing the CPU without hardware acceleration. It is recommended to use a hardware accelerated detector type instead for better performance. To configure a CPU based detector, set the `"type"` attribute to `"cpu"`. @@ -704,7 +718,7 @@ To convert a onnx model to the rknn format using the [rknn-toolkit2](https://git This is an example configuration file that you need to adjust to your specific onnx model: ```yaml -soc: ["rk3562","rk3566", "rk3568", "rk3576", "rk3588"] +soc: ["rk3562", "rk3566", "rk3568", "rk3576", "rk3588"] quantization: false output_name: "{input_basename}" @@ -735,6 +749,29 @@ Some model types are not included in Frigate by default. Here are some tips for getting different model types +### Downloading D-FINE Model + +To export as ONNX: + +1. Clone: https://github.com/Peterande/D-FINE and install all dependencies. +2. Select and download a checkpoint from the [readme](https://github.com/Peterande/D-FINE). +3. Modify line 58 of `tools/deployment/export_onnx.py` and change batch size to 1: `data = torch.rand(1, 3, 640, 640)` +4. Run the export, making sure you select the right config, for your checkpoint. + +Example: + +``` +python3 tools/deployment/export_onnx.py -c configs/dfine/objects365/dfine_hgnetv2_m_obj2coco.yml -r output/dfine_m_obj2coco.pth +``` + +:::tip + +Model export has only been tested on Linux (or WSL2). Not all dependencies are in `requirements.txt`. Some live in the deployment folder, and some are still missing entirely and must be installed manually. + +Make sure you change the batch size to 1 before exporting. + +::: + ### Downloading YOLO-NAS Model You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb). diff --git a/docs/docs/configuration/record.md b/docs/docs/configuration/record.md index fd7de42d0..f84d84cee 100644 --- a/docs/docs/configuration/record.md +++ b/docs/docs/configuration/record.md @@ -183,6 +183,8 @@ record: sync_recordings: True ``` +This feature is meant to fix variations in files, not completely delete entries in the database. If you delete all of your media, don't use `sync_recordings`, just stop Frigate, delete the `frigate.db` database, and restart. + :::warning The sync operation uses considerable CPU resources and in most cases is not needed, only enable when necessary. diff --git a/docs/docs/configuration/reference.md b/docs/docs/configuration/reference.md index b791e708a..37884259a 100644 --- a/docs/docs/configuration/reference.md +++ b/docs/docs/configuration/reference.md @@ -255,6 +255,8 @@ ffmpeg: # Optional: Detect configuration # NOTE: Can be overridden at the camera level detect: + # Optional: enables detection for the camera (default: shown below) + enabled: False # Optional: width of the frame for the input with the detect role (default: use native stream resolution) width: 1280 # Optional: height of the frame for the input with the detect role (default: use native stream resolution) @@ -262,8 +264,6 @@ detect: # Optional: desired fps for your camera for the input with the detect role (default: shown below) # NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera. fps: 5 - # Optional: enables detection for the camera (default: True) - enabled: True # Optional: Number of consecutive detection hits required for an object to be initialized in the tracker. (default: 1/2 the frame rate) min_initialized: 2 # Optional: Number of frames without a detection before Frigate considers an object to be gone. (default: 5x the frame rate) @@ -536,6 +536,8 @@ semantic_search: enabled: False # Optional: Re-index embeddings database from historical tracked objects (default: shown below) reindex: False + # Optional: Set the model used for embeddings. (default: shown below) + model: "jinav1" # Optional: Set the model size used for embeddings. (default: shown below) # NOTE: small model runs on CPU and large model runs on GPU model_size: "small" @@ -568,7 +570,6 @@ lpr: known_plates: {} # Optional: Configuration for AI generated tracked object descriptions -# NOTE: Semantic Search must be enabled for this to do anything. # WARNING: Depending on the provider, this will send thumbnails over the internet # 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. @@ -812,6 +813,12 @@ cameras: - cat # Optional: Restrict generation to objects that entered any of the listed zones (default: none, all zones qualify) required_zones: [] + # Optional: What triggers to use to send frames for a tracked object to generative AI (default: shown below) + send_triggers: + # Once the object is no longer tracked + tracked_object_end: True + # Optional: After X many significant updates are received (default: shown below) + after_significant_updates: None # Optional: Save thumbnails sent to generative AI for review/debugging purposes (default: shown below) debug_save_thumbnails: False diff --git a/docs/docs/configuration/semantic_search.md b/docs/docs/configuration/semantic_search.md index bd3d79cae..07e2cbfb2 100644 --- a/docs/docs/configuration/semantic_search.md +++ b/docs/docs/configuration/semantic_search.md @@ -5,7 +5,7 @@ title: Semantic Search Semantic Search in Frigate allows you to find tracked objects within your review items using either the image itself, a user-defined text description, or an automatically generated one. This feature works by creating _embeddings_ — numerical vector representations — for both the images and text descriptions of your tracked objects. By comparing these embeddings, Frigate assesses their similarities to deliver relevant search results. -Frigate uses [Jina AI's CLIP model](https://huggingface.co/jinaai/jina-clip-v1) to create and save embeddings to Frigate's database. All of this runs locally. +Frigate uses models from [Jina AI](https://huggingface.co/jinaai) to create and save embeddings to Frigate's database. All of this runs locally. Semantic Search is accessed via the _Explore_ view in the Frigate UI. @@ -35,23 +35,47 @@ If you are enabling Semantic Search for the first time, be advised that Frigate ::: -### Jina AI CLIP +### Jina AI CLIP (version 1) -The vision model is able to embed both images and text into the same vector space, which allows `image -> image` and `text -> image` similarity searches. Frigate uses this model on tracked objects to encode the thumbnail image and store it in the database. When searching for tracked objects via text in the search box, Frigate will perform a `text -> image` similarity search against this embedding. When clicking "Find Similar" in the tracked object detail pane, Frigate will perform an `image -> image` similarity search to retrieve the closest matching thumbnails. +The [V1 model from Jina](https://huggingface.co/jinaai/jina-clip-v1) has a vision model which is able to embed both images and text into the same vector space, which allows `image -> image` and `text -> image` similarity searches. Frigate uses this model on tracked objects to encode the thumbnail image and store it in the database. When searching for tracked objects via text in the search box, Frigate will perform a `text -> image` similarity search against this embedding. When clicking "Find Similar" in the tracked object detail pane, Frigate will perform an `image -> image` similarity search to retrieve the closest matching thumbnails. -The text model is used to embed tracked object descriptions and perform searches against them. Descriptions can be created, viewed, and modified on the Explore page when clicking on thumbnail of a tracked object. See [the Generative AI docs](/configuration/genai.md) for more information on how to automatically generate tracked object descriptions. +The V1 text model is used to embed tracked object descriptions and perform searches against them. Descriptions can be created, viewed, and modified on the Explore page when clicking on thumbnail of a tracked object. See [the Generative AI docs](/configuration/genai.md) for more information on how to automatically generate tracked object descriptions. -Differently weighted versions of the Jina model are available and can be selected by setting the `model_size` config option as `small` or `large`: +Differently weighted versions of the Jina models are available and can be selected by setting the `model_size` config option as `small` or `large`: ```yaml semantic_search: enabled: True + model: "jinav1" model_size: small ``` - Configuring the `large` model employs the full Jina model and will automatically run on the GPU if applicable. - Configuring the `small` model employs a quantized version of the Jina model that uses less RAM and runs on CPU with a very negligible difference in embedding quality. +### Jina AI CLIP (version 2) + +Frigate also supports the [V2 model from Jina](https://huggingface.co/jinaai/jina-clip-v2), which introduces multilingual support (89 languages). In contrast, the V1 model only supports English. + +V2 offers only a 3% performance improvement over V1 in both text-image and text-text retrieval tasks, an upgrade that is unlikely to yield noticeable real-world benefits. Additionally, V2 has _significantly_ higher RAM and GPU requirements, leading to increased inference time and memory usage. If you plan to use V2, ensure your system has ample RAM and a discrete GPU. CPU inference (with the `small` model) using V2 is not recommended. + +To use the V2 model, update the `model` parameter in your config: + +```yaml +semantic_search: + enabled: True + model: "jinav2" + model_size: large +``` + +For most users, especially native English speakers, the V1 model remains the recommended choice. + +:::note + +Switching between V1 and V2 requires reindexing your embeddings. To do this, set `reindex: True` in your Semantic Search configuration and restart Frigate. The embeddings from V1 and V2 are incompatible, and failing to reindex will result in incorrect search results. + +::: + ### GPU Acceleration The CLIP models are downloaded in ONNX format, and the `large` model can be accelerated using GPU hardware, when available. This depends on the Docker build that is used. diff --git a/docs/docs/configuration/zones.md b/docs/docs/configuration/zones.md index 8dd63f0f3..0c6793d58 100644 --- a/docs/docs/configuration/zones.md +++ b/docs/docs/configuration/zones.md @@ -140,12 +140,12 @@ cameras: zones: street: coordinates: 0.033,0.306,0.324,0.138,0.439,0.185,0.042,0.428 - distances: 10,12,11,13.5 + distances: 10,12,11,13.5 # in meters or feet ``` Each number in the `distance` field represents the real-world distance between the points in the `coordinates` list. So in the example above, the distance between the first two points ([0.033,0.306] and [0.324,0.138]) is 10. The distance between the second and third set of points ([0.324,0.138] and [0.439,0.185]) is 12, and so on. The fastest and most accurate way to configure this is through the Zone Editor in the Frigate UI. -The `distance` values are measured in meters or feet, depending on how `unit_system` is configured in your `ui` config: +The `distance` values are measured in meters (metric) or feet (imperial), depending on how `unit_system` is configured in your `ui` config: ```yaml ui: @@ -153,7 +153,9 @@ ui: unit_system: metric ``` -The average speed of your object as it moved through your zone is saved in Frigate's database and can be seen in the UI in the Tracked Object Details pane in Explore. Current estimated speed can also be seen on the debug view as the third value in the object label (see the caveats below). Current estimated speed, average estimated speed, and velocity angle (the angle of the direction the object is moving relative to the frame) of tracked objects is also sent through the `events` MQTT topic. See the [MQTT docs](../integrations/mqtt.md#frigateevents). These speed values are output as a number in miles per hour (mph) or kilometers per hour (kph), depending on how `unit_system` is configured in your `ui` config. +The average speed of your object as it moved through your zone is saved in Frigate's database and can be seen in the UI in the Tracked Object Details pane in Explore. Current estimated speed can also be seen on the debug view as the third value in the object label (see the caveats below). Current estimated speed, average estimated speed, and velocity angle (the angle of the direction the object is moving relative to the frame) of tracked objects is also sent through the `events` MQTT topic. See the [MQTT docs](../integrations/mqtt.md#frigateevents). + +These speed values are output as a number in miles per hour (mph) or kilometers per hour (kph). For miles per hour, set `unit_system` to `imperial`. For kilometers per hour, set `unit_system` to `metric`. #### Best practices and caveats diff --git a/docs/docs/frigate/installation.md b/docs/docs/frigate/installation.md index a1c8550b9..9b3920ebc 100644 --- a/docs/docs/frigate/installation.md +++ b/docs/docs/frigate/installation.md @@ -80,12 +80,12 @@ The Frigate container also stores logs in shm, which can take up to **40MB**, so You can calculate the **minimum** shm size for each camera with the following formula using the resolution specified for detect: ```console -# Replace and +# Template for one camera without logs, replace and $ python -c 'print("{:.2f}MB".format(( * * 1.5 * 20 + 270480) / 1048576))' # Example for 1280x720, including logs -$ python -c 'print("{:.2f}MB".format((1280 * 720 * 1.5 * 20 + 270480) / 1048576)) + 40' -46.63MB +$ python -c 'print("{:.2f}MB".format((1280 * 720 * 1.5 * 20 + 270480) / 1048576 + 40))' +66.63MB # Example for eight cameras detecting at 1280x720, including logs $ python -c 'print("{:.2f}MB".format(((1280 * 720 * 1.5 * 20 + 270480) / 1048576) * 8 + 40))' diff --git a/docs/docs/guides/getting_started.md b/docs/docs/guides/getting_started.md index bb880b8f0..6fe3a8e22 100644 --- a/docs/docs/guides/getting_started.md +++ b/docs/docs/guides/getting_started.md @@ -151,8 +151,6 @@ cameras: - path: rtsp://10.0.10.10:554/rtsp # <----- The stream you want to use for detection roles: - detect - detect: - enabled: False # <---- disable detection until you have a working camera feed ``` ### Step 2: Start Frigate @@ -177,7 +175,7 @@ services: frigate: ... devices: - - /dev/dri/renderD128 # for intel hwaccel, needs to be updated for your hardware + - /dev/dri/renderD128:/dev/dri/renderD128 # for intel hwaccel, needs to be updated for your hardware ... ``` @@ -307,7 +305,7 @@ By default, Frigate will retain video of all tracked objects for 10 days. The fu ### Step 7: Complete config -At this point you have a complete config with basic functionality. +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. diff --git a/docs/docs/integrations/mqtt.md b/docs/docs/integrations/mqtt.md index 4eaf61919..fc8888e40 100644 --- a/docs/docs/integrations/mqtt.md +++ b/docs/docs/integrations/mqtt.md @@ -222,6 +222,14 @@ Publishes the rms value for audio detected on this camera. **NOTE:** Requires audio detection to be enabled +### `frigate//enabled/set` + +Topic to turn Frigate's processing of a camera on and off. Expected values are `ON` and `OFF`. + +### `frigate//enabled/state` + +Topic with current state of processing for a camera. Published values are `ON` and `OFF`. + ### `frigate//detect/set` Topic to turn object detection for a camera on and off. Expected values are `ON` and `OFF`. diff --git a/docs/docs/plus/index.md b/docs/docs/plus/index.md index 37798badb..589adca72 100644 --- a/docs/docs/plus/index.md +++ b/docs/docs/plus/index.md @@ -28,11 +28,11 @@ Not all model types are supported by all detectors, so it's important to choose ## Supported detector types -Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVino (`openvino`), ONNX (`onnx`), and ROCm (`rocm`) detectors. +Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVino (`openvino`), and ONNX (`onnx`) detectors. :::warning -Using Frigate+ models with `onnx` and `rocm` is only available with Frigate 0.15 and later. +Using Frigate+ models with `onnx` is only available with Frigate 0.15 and later. ::: @@ -42,7 +42,7 @@ Using Frigate+ models with `onnx` and `rocm` is only available with Frigate 0.15 | [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` | +| [AMD ROCm GPU](https://deploy-preview-13787--frigate-docs.netlify.app/configuration/object_detectors#amdrocm-gpu-detector)\* | `onnx` | `yolonas` | _\* Requires Frigate 0.15_ diff --git a/docs/docs/troubleshooting/edgetpu.md b/docs/docs/troubleshooting/edgetpu.md index 2e10f0839..90006c41e 100644 --- a/docs/docs/troubleshooting/edgetpu.md +++ b/docs/docs/troubleshooting/edgetpu.md @@ -10,6 +10,12 @@ There are many possible causes for a USB coral not being detected and some are O 1. When the device is first plugged in and has not initialized it will appear as `1a6e:089a Global Unichip Corp.` when running `lsusb` or checking the hardware page in HA OS. 2. Once initialized, the device will appear as `18d1:9302 Google Inc.` when running `lsusb` or checking the hardware page in HA OS. +:::tip + +Using `lsusb` or checking the hardware page in HA OS will show as `1a6e:089a Global Unichip Corp.` until Frigate runs an inferance using the coral. So don't worry about the identification until after Frigate has attempted to detect the coral. + +::: + If the coral does not initialize then Frigate can not interface with it. Some common reasons for the USB based Coral not initializing are: ### Not Enough Power diff --git a/frigate/api/app.py b/frigate/api/app.py index c55e36a4b..5ce90130f 100644 --- a/frigate/api/app.py +++ b/frigate/api/app.py @@ -22,6 +22,7 @@ from markupsafe import escape from peewee import operator from pydantic import ValidationError +from frigate.api.auth import require_role from frigate.api.defs.query.app_query_parameters import AppTimelineHourlyQueryParameters from frigate.api.defs.request.app_body import AppConfigSetBody from frigate.api.defs.tags import Tags @@ -201,7 +202,7 @@ def config_raw(): ) -@router.post("/config/save") +@router.post("/config/save", dependencies=[Depends(require_role(["admin"]))]) def config_save(save_option: str, body: Any = Body(media_type="text/plain")): new_config = body.decode() if not new_config: @@ -326,7 +327,7 @@ def config_save(save_option: str, body: Any = Body(media_type="text/plain")): ) -@router.put("/config/set") +@router.put("/config/set", dependencies=[Depends(require_role(["admin"]))]) def config_set(request: Request, body: AppConfigSetBody): config_file = find_config_file() @@ -542,7 +543,7 @@ async def logs( ) -@router.post("/restart") +@router.post("/restart", dependencies=[Depends(require_role(["admin"]))]) def restart(): try: restart_frigate() diff --git a/frigate/api/auth.py b/frigate/api/auth.py index be5917450..1752b19c9 100644 --- a/frigate/api/auth.py +++ b/frigate/api/auth.py @@ -11,8 +11,9 @@ import secrets import time from datetime import datetime from pathlib import Path +from typing import List -from fastapi import APIRouter, Request, Response +from fastapi import APIRouter, Depends, HTTPException, Request, Response from fastapi.responses import JSONResponse, RedirectResponse from joserfc import jwt from peewee import DoesNotExist @@ -22,6 +23,7 @@ from frigate.api.defs.request.app_body import ( AppPostLoginBody, AppPostUsersBody, AppPutPasswordBody, + AppPutRoleBody, ) from frigate.api.defs.tags import Tags from frigate.config import AuthConfig, ProxyConfig @@ -169,8 +171,10 @@ def verify_password(password, password_hash): return secrets.compare_digest(password_hash, compare_hash) -def create_encoded_jwt(user, expiration, secret): - return jwt.encode({"alg": "HS256"}, {"sub": user, "exp": expiration}, secret) +def create_encoded_jwt(user, role, expiration, secret): + return jwt.encode( + {"alg": "HS256"}, {"sub": user, "role": role, "exp": expiration}, secret + ) def set_jwt_cookie(response: Response, cookie_name, encoded_jwt, expiration, secure): @@ -184,7 +188,48 @@ def set_jwt_cookie(response: Response, cookie_name, encoded_jwt, expiration, sec ) -# Endpoint for use with nginx auth_request +async def get_current_user(request: Request): + JWT_COOKIE_NAME = request.app.frigate_config.auth.cookie_name + encoded_token = request.cookies.get(JWT_COOKIE_NAME) + if not encoded_token: + return JSONResponse(content={"message": "No JWT token found"}, status_code=401) + + try: + token = jwt.decode(encoded_token, request.app.jwt_token) + if "sub" not in token.claims or "role" not in token.claims: + return JSONResponse( + content={"message": "Invalid JWT token"}, status_code=401 + ) + return {"username": token.claims["sub"], "role": token.claims["role"]} + except Exception as e: + logger.error(f"Error parsing JWT: {e}") + return JSONResponse(content={"message": "Invalid JWT token"}, status_code=401) + + +def require_role(required_roles: List[str]): + async def role_checker(request: Request): + # Get role from header (could be comma-separated) + role_header = request.headers.get("remote-role") + roles = [r.strip() for r in role_header.split(",")] if role_header else [] + + # Check if we have any roles + if not roles: + raise HTTPException(status_code=403, detail="Role not provided") + + # Check if any role matches required_roles + if not any(role in required_roles for role in roles): + raise HTTPException( + status_code=403, + detail=f"Role {', '.join(roles)} not authorized. Required: {', '.join(required_roles)}", + ) + + # Return the first matching role + return next((role for role in roles if role in required_roles), roles[0]) + + return role_checker + + +# Endpoints @router.get("/auth") def auth(request: Request): auth_config: AuthConfig = request.app.frigate_config.auth @@ -195,6 +240,8 @@ def auth(request: Request): # dont require auth if the request is on the internal port # this header is set by Frigate's nginx proxy, so it cant be spoofed if int(request.headers.get("x-server-port", default=0)) == 5000: + success_response.headers["remote-user"] = "anonymous" + success_response.headers["remote-role"] = "admin" return success_response fail_response = Response("", status_code=401) @@ -211,14 +258,18 @@ def auth(request: Request): if not auth_config.enabled: # pass the user header value from the upstream proxy if a mapping is specified # or use anonymous if none are specified - if proxy_config.header_map.user is not None: - upstream_user_header_value = request.headers.get( - proxy_config.header_map.user, - default="anonymous", - ) - success_response.headers["remote-user"] = upstream_user_header_value - else: - success_response.headers["remote-user"] = "anonymous" + user_header = proxy_config.header_map.user + role_header = proxy_config.header_map.get("role", "Remote-Role") + success_response.headers["remote-user"] = ( + request.headers.get(user_header, default="anonymous") + if user_header + else "anonymous" + ) + success_response.headers["remote-role"] = ( + request.headers.get(role_header, default="viewer") + if role_header + else "viewer" + ) return success_response # now apply authentication @@ -251,11 +302,15 @@ def auth(request: Request): if "sub" not in token.claims: logger.debug("user not set in jwt token") return fail_response + if "role" not in token.claims: + logger.debug("role not set in jwt token") + return fail_response if "exp" not in token.claims: logger.debug("exp not set in jwt token") return fail_response user = token.claims.get("sub") + role = token.claims.get("role") current_time = int(time.time()) # if the jwt is expired @@ -283,7 +338,7 @@ def auth(request: Request): return fail_response new_expiration = current_time + JWT_SESSION_LENGTH new_encoded_jwt = create_encoded_jwt( - user, new_expiration, request.app.jwt_token + user, role, new_expiration, request.app.jwt_token ) set_jwt_cookie( success_response, @@ -294,6 +349,7 @@ def auth(request: Request): ) success_response.headers["remote-user"] = user + success_response.headers["remote-role"] = role return success_response except Exception as e: logger.error(f"Error parsing jwt: {e}") @@ -302,8 +358,16 @@ def auth(request: Request): @router.get("/profile") def profile(request: Request): - username = request.headers.get("remote-user") - return JSONResponse(content={"username": username}) + username = request.headers.get("remote-user", "anonymous") + if username != "anonymous": + try: + user = User.get_by_id(username) + role = getattr(user, "role", "viewer") + except DoesNotExist: + role = "viewer" # Fallback if user deleted + else: + role = None + return JSONResponse(content={"username": username, "role": role}) @router.get("/logout") @@ -333,8 +397,11 @@ def login(request: Request, body: AppPostLoginBody): password_hash = db_user.password_hash if verify_password(password, password_hash): + role = getattr(db_user, "role", "viewer") + if role not in ["admin", "viewer"]: + role = "viewer" # Enforce valid roles expiration = int(time.time()) + JWT_SESSION_LENGTH - encoded_jwt = create_encoded_jwt(user, expiration, request.app.jwt_token) + encoded_jwt = create_encoded_jwt(user, role, expiration, request.app.jwt_token) response = Response("", 200) set_jwt_cookie( response, JWT_COOKIE_NAME, encoded_jwt, expiration, JWT_COOKIE_SECURE @@ -343,25 +410,31 @@ def login(request: Request, body: AppPostLoginBody): return JSONResponse(content={"message": "Login failed"}, status_code=401) -@router.get("/users") +@router.get("/users", dependencies=[Depends(require_role(["admin"]))]) def get_users(): - exports = User.select(User.username).order_by(User.username).dicts().iterator() + exports = ( + User.select(User.username, User.role).order_by(User.username).dicts().iterator() + ) return JSONResponse([e for e in exports]) -@router.post("/users") -def create_user(request: Request, body: AppPostUsersBody): +@router.post("/users", dependencies=[Depends(require_role(["admin"]))]) +def create_user( + request: Request, + body: AppPostUsersBody, +): HASH_ITERATIONS = request.app.frigate_config.auth.hash_iterations if not re.match("^[A-Za-z0-9._]+$", body.username): - JSONResponse(content={"message": "Invalid username"}, status_code=400) + return JSONResponse(content={"message": "Invalid username"}, status_code=400) + role = body.role if body.role in ["admin", "viewer"] else "viewer" password_hash = hash_password(body.password, iterations=HASH_ITERATIONS) - User.insert( { User.username: body.username, User.password_hash: password_hash, + User.role: role, User.notification_tokens: [], } ).execute() @@ -375,15 +448,61 @@ def delete_user(username: str): @router.put("/users/{username}/password") -def update_password(request: Request, username: str, body: AppPutPasswordBody): +async def update_password( + request: Request, + username: str, + body: AppPutPasswordBody, +): + current_user = await get_current_user(request) + if isinstance(current_user, JSONResponse): + # auth failed + return current_user + + current_username = current_user.get("username") + current_role = current_user.get("role") + + # viewers can only change their own password + if current_role == "viewer" and current_username != username: + raise HTTPException( + status_code=403, detail="Viewers can only update their own password" + ) + HASH_ITERATIONS = request.app.frigate_config.auth.hash_iterations password_hash = hash_password(body.password, iterations=HASH_ITERATIONS) + User.set_by_id(username, {User.password_hash: password_hash}) - User.set_by_id( - username, - { - User.password_hash: password_hash, - }, - ) + return JSONResponse(content={"success": True}) + + +@router.put( + "/users/{username}/role", + dependencies=[Depends(require_role(["admin"]))], +) +async def update_role( + request: Request, + username: str, + body: AppPutRoleBody, +): + current_user = await get_current_user(request) + if isinstance(current_user, JSONResponse): + # auth failed + return current_user + + current_role = current_user.get("role") + # viewers can't change anyone's role + if current_role == "viewer": + raise HTTPException( + status_code=403, detail="Admin role is required to change user roles" + ) + if username == "admin": + return JSONResponse( + content={"message": "Cannot modify admin user's role"}, status_code=403 + ) + if body.role not in ["admin", "viewer"]: + return JSONResponse( + content={"message": "Role must be 'admin' or 'viewer'"}, status_code=400 + ) + + User.set_by_id(username, {User.role: body.role}) return JSONResponse(content={"success": True}) diff --git a/frigate/api/classification.py b/frigate/api/classification.py index 7cd127d07..85b604379 100644 --- a/frigate/api/classification.py +++ b/frigate/api/classification.py @@ -6,13 +6,17 @@ import random import shutil import string -from fastapi import APIRouter, Request, UploadFile +from fastapi import APIRouter, Depends, Request, UploadFile from fastapi.responses import JSONResponse from pathvalidate import sanitize_filename +from peewee import DoesNotExist +from playhouse.shortcuts import model_to_dict +from frigate.api.auth import require_role from frigate.api.defs.tags import Tags from frigate.const import FACE_DIR from frigate.embeddings import EmbeddingsContext +from frigate.models import Event logger = logging.getLogger(__name__) @@ -41,7 +45,7 @@ def get_faces(): return JSONResponse(status_code=200, content=face_dict) -@router.post("/faces/reprocess") +@router.post("/faces/reprocess", dependencies=[Depends(require_role(["admin"]))]) def reclassify_face(request: Request, body: dict = None): if not request.app.frigate_config.face_recognition.enabled: return JSONResponse( @@ -118,7 +122,7 @@ def train_face(request: Request, name: str, body: dict = None): ) -@router.post("/faces/{name}/create") +@router.post("/faces/{name}/create", dependencies=[Depends(require_role(["admin"]))]) async def create_face(request: Request, name: str): if not request.app.frigate_config.face_recognition.enabled: return JSONResponse( @@ -135,7 +139,7 @@ async def create_face(request: Request, name: str): ) -@router.post("/faces/{name}/register") +@router.post("/faces/{name}/register", dependencies=[Depends(require_role(["admin"]))]) async def register_face(request: Request, name: str, file: UploadFile): if not request.app.frigate_config.face_recognition.enabled: return JSONResponse( @@ -151,7 +155,7 @@ async def register_face(request: Request, name: str, file: UploadFile): ) -@router.post("/faces/{name}/delete") +@router.post("/faces/{name}/delete", dependencies=[Depends(require_role(["admin"]))]) def deregister_faces(request: Request, name: str, body: dict = None): if not request.app.frigate_config.face_recognition.enabled: return JSONResponse( @@ -176,3 +180,36 @@ def deregister_faces(request: Request, name: str, body: dict = None): content=({"success": True, "message": "Successfully deleted faces."}), status_code=200, ) + + +@router.put("/lpr/reprocess") +def reprocess_license_plate(request: Request, event_id: str): + if not request.app.frigate_config.lpr.enabled: + message = "License plate recognition is not enabled." + logger.error(message) + return JSONResponse( + content=( + { + "success": False, + "message": message, + } + ), + status_code=400, + ) + + try: + event = Event.get(Event.id == event_id) + except DoesNotExist: + message = f"Event {event_id} not found" + logger.error(message) + return JSONResponse( + content=({"success": False, "message": message}), status_code=404 + ) + + context: EmbeddingsContext = request.app.embeddings + response = context.reprocess_plate(model_to_dict(event)) + + return JSONResponse( + content=response, + status_code=200, + ) diff --git a/frigate/api/defs/request/app_body.py b/frigate/api/defs/request/app_body.py index 85daa5631..1fc05db2f 100644 --- a/frigate/api/defs/request/app_body.py +++ b/frigate/api/defs/request/app_body.py @@ -1,3 +1,5 @@ +from typing import Optional + from pydantic import BaseModel @@ -12,8 +14,13 @@ class AppPutPasswordBody(BaseModel): class AppPostUsersBody(BaseModel): username: str password: str + role: Optional[str] = "viewer" class AppPostLoginBody(BaseModel): user: str password: str + + +class AppPutRoleBody(BaseModel): + role: str diff --git a/frigate/api/defs/response/event_response.py b/frigate/api/defs/response/event_response.py index 17b9b166f..083849706 100644 --- a/frigate/api/defs/response/event_response.py +++ b/frigate/api/defs/response/event_response.py @@ -12,7 +12,7 @@ class EventResponse(BaseModel): end_time: Optional[float] false_positive: Optional[bool] zones: list[str] - thumbnail: str + thumbnail: Optional[str] has_clip: bool has_snapshot: bool retain_indefinitely: bool diff --git a/frigate/api/event.py b/frigate/api/event.py index 2df32471e..100bdfd9e 100644 --- a/frigate/api/event.py +++ b/frigate/api/event.py @@ -14,6 +14,7 @@ from fastapi.responses import JSONResponse from peewee import JOIN, DoesNotExist, fn, operator from playhouse.shortcuts import model_to_dict +from frigate.api.auth import require_role from frigate.api.defs.query.events_query_parameters import ( DEFAULT_TIME_RANGE, EventsQueryParams, @@ -708,7 +709,11 @@ def event(event_id: str): return JSONResponse(content="Event not found", status_code=404) -@router.post("/events/{event_id}/retain", response_model=GenericResponse) +@router.post( + "/events/{event_id}/retain", + response_model=GenericResponse, + dependencies=[Depends(require_role(["admin"]))], +) def set_retain(event_id: str): try: event = Event.get(Event.id == event_id) @@ -928,7 +933,11 @@ def false_positive(request: Request, event_id: str): ) -@router.delete("/events/{event_id}/retain", response_model=GenericResponse) +@router.delete( + "/events/{event_id}/retain", + response_model=GenericResponse, + dependencies=[Depends(require_role(["admin"]))], +) def delete_retain(event_id: str): try: event = Event.get(Event.id == event_id) @@ -947,7 +956,11 @@ def delete_retain(event_id: str): ) -@router.post("/events/{event_id}/sub_label", response_model=GenericResponse) +@router.post( + "/events/{event_id}/sub_label", + response_model=GenericResponse, + dependencies=[Depends(require_role(["admin"]))], +) def set_sub_label( request: Request, event_id: str, @@ -991,6 +1004,10 @@ def set_sub_label( new_sub_label = body.subLabel new_score = body.subLabelScore + if new_sub_label == "": + new_sub_label = None + new_score = None + if tracked_obj: tracked_obj.obj_data["sub_label"] = (new_sub_label, new_score) @@ -1001,26 +1018,28 @@ def set_sub_label( if event: event.sub_label = new_sub_label - - if new_score: - data = event.data + data = event.data + if new_sub_label is None: + data["sub_label_score"] = None + elif new_score is not None: data["sub_label_score"] = new_score - event.data = data - + event.data = data event.save() return JSONResponse( - content=( - { - "success": True, - "message": "Event " + event_id + " sub label set to " + new_sub_label, - } - ), + content={ + "success": True, + "message": f"Event {event_id} sub label set to {new_sub_label if new_sub_label is not None else 'None'}", + }, status_code=200, ) -@router.post("/events/{event_id}/description", response_model=GenericResponse) +@router.post( + "/events/{event_id}/description", + response_model=GenericResponse, + dependencies=[Depends(require_role(["admin"]))], +) def set_description( request: Request, event_id: str, @@ -1067,7 +1086,11 @@ def set_description( ) -@router.put("/events/{event_id}/description/regenerate", response_model=GenericResponse) +@router.put( + "/events/{event_id}/description/regenerate", + response_model=GenericResponse, + dependencies=[Depends(require_role(["admin"]))], +) def regenerate_description( request: Request, event_id: str, params: RegenerateQueryParameters = Depends() ): @@ -1081,10 +1104,7 @@ def regenerate_description( camera_config = request.app.frigate_config.cameras[event.camera] - if ( - request.app.frigate_config.semantic_search.enabled - and camera_config.genai.enabled - ): + if camera_config.genai.enabled: request.app.event_metadata_updater.publish((event.id, params.source)) return JSONResponse( @@ -1138,14 +1158,22 @@ def delete_single_event(event_id: str, request: Request) -> dict: return {"success": True, "message": f"Event {event_id} deleted"} -@router.delete("/events/{event_id}", response_model=GenericResponse) +@router.delete( + "/events/{event_id}", + response_model=GenericResponse, + dependencies=[Depends(require_role(["admin"]))], +) def delete_event(request: Request, event_id: str): result = delete_single_event(event_id, request) status_code = 200 if result["success"] else 404 return JSONResponse(content=result, status_code=status_code) -@router.delete("/events/", response_model=EventMultiDeleteResponse) +@router.delete( + "/events/", + response_model=EventMultiDeleteResponse, + dependencies=[Depends(require_role(["admin"]))], +) def delete_events(request: Request, body: EventsDeleteBody): if not body.event_ids: return JSONResponse( @@ -1171,7 +1199,11 @@ def delete_events(request: Request, body: EventsDeleteBody): return JSONResponse(content=response, status_code=200) -@router.post("/events/{camera_name}/{label}/create", response_model=EventCreateResponse) +@router.post( + "/events/{camera_name}/{label}/create", + response_model=EventCreateResponse, + dependencies=[Depends(require_role(["admin"]))], +) def create_event( request: Request, camera_name: str, @@ -1227,7 +1259,11 @@ def create_event( ) -@router.put("/events/{event_id}/end", response_model=GenericResponse) +@router.put( + "/events/{event_id}/end", + response_model=GenericResponse, + dependencies=[Depends(require_role(["admin"]))], +) def end_event(request: Request, event_id: str, body: EventsEndBody): try: end_time = body.end_time or datetime.datetime.now().timestamp() diff --git a/frigate/api/export.py b/frigate/api/export.py index 2ccbc4beb..160434c68 100644 --- a/frigate/api/export.py +++ b/frigate/api/export.py @@ -6,11 +6,12 @@ import string from pathlib import Path import psutil -from fastapi import APIRouter, Request +from fastapi import APIRouter, Depends, Request from fastapi.responses import JSONResponse from peewee import DoesNotExist from playhouse.shortcuts import model_to_dict +from frigate.api.auth import require_role from frigate.api.defs.request.export_recordings_body import ExportRecordingsBody from frigate.api.defs.request.export_rename_body import ExportRenameBody from frigate.api.defs.tags import Tags @@ -130,7 +131,9 @@ def export_recording( ) -@router.patch("/export/{event_id}/rename") +@router.patch( + "/export/{event_id}/rename", dependencies=[Depends(require_role(["admin"]))] +) def export_rename(event_id: str, body: ExportRenameBody): try: export: Export = Export.get(Export.id == event_id) @@ -158,7 +161,7 @@ def export_rename(event_id: str, body: ExportRenameBody): ) -@router.delete("/export/{event_id}") +@router.delete("/export/{event_id}", dependencies=[Depends(require_role(["admin"]))]) def export_delete(event_id: str): try: export: Export = Export.get(Export.id == event_id) diff --git a/frigate/api/media.py b/frigate/api/media.py index 74e9e7aaa..e3f74ea98 100644 --- a/frigate/api/media.py +++ b/frigate/api/media.py @@ -31,6 +31,7 @@ from frigate.config import FrigateConfig from frigate.const import ( CACHE_DIR, CLIPS_DIR, + INSTALL_DIR, MAX_SEGMENT_DURATION, PREVIEW_FRAME_TYPE, RECORD_DIR, @@ -155,7 +156,9 @@ def latest_frame( frame_processor.get_current_frame_time(camera_name) + retry_interval ): if request.app.camera_error_image is None: - error_image = glob.glob("/opt/frigate/frigate/images/camera-error.jpg") + error_image = glob.glob( + os.path.join(INSTALL_DIR, "frigate/images/camera-error.jpg") + ) if len(error_image) > 0: request.app.camera_error_image = cv2.imread( @@ -550,7 +553,7 @@ def recording_clip( ) file_name = sanitize_filename(f"playlist_{camera_name}_{start_ts}-{end_ts}.txt") - file_path = f"/tmp/cache/{file_name}" + file_path = os.path.join(CACHE_DIR, file_name) with open(file_path, "w") as file: clip: Recordings for clip in recordings: diff --git a/frigate/api/preview.py b/frigate/api/preview.py index d14a15ff1..2db2326ab 100644 --- a/frigate/api/preview.py +++ b/frigate/api/preview.py @@ -9,7 +9,7 @@ from fastapi import APIRouter from fastapi.responses import JSONResponse from frigate.api.defs.tags import Tags -from frigate.const import CACHE_DIR, PREVIEW_FRAME_TYPE +from frigate.const import BASE_DIR, CACHE_DIR, PREVIEW_FRAME_TYPE from frigate.models import Previews logger = logging.getLogger(__name__) @@ -52,7 +52,7 @@ def preview_ts(camera_name: str, start_ts: float, end_ts: float): clips.append( { "camera": preview["camera"], - "src": preview["path"].replace("/media/frigate", ""), + "src": preview["path"].replace(BASE_DIR, ""), "type": "video/mp4", "start": preview["start_time"], "end": preview["end_time"], diff --git a/frigate/api/review.py b/frigate/api/review.py index 3e503d400..4788356f3 100644 --- a/frigate/api/review.py +++ b/frigate/api/review.py @@ -12,6 +12,7 @@ from fastapi.responses import JSONResponse from peewee import Case, DoesNotExist, fn, operator from playhouse.shortcuts import model_to_dict +from frigate.api.auth import require_role from frigate.api.defs.query.review_query_parameters import ( ReviewActivityMotionQueryParams, ReviewQueryParams, @@ -343,7 +344,11 @@ def set_multiple_reviewed(body: ReviewModifyMultipleBody): ) -@router.post("/reviews/delete", response_model=GenericResponse) +@router.post( + "/reviews/delete", + response_model=GenericResponse, + dependencies=[Depends(require_role(["admin"]))], +) def delete_reviews(body: ReviewModifyMultipleBody): list_of_ids = body.ids reviews = ( diff --git a/frigate/app.py b/frigate/app.py index 400d4bca0..cdb4877cc 100644 --- a/frigate/app.py +++ b/frigate/app.py @@ -93,7 +93,13 @@ class FrigateApp: self.log_queue: Queue = mp.Queue() self.camera_metrics: dict[str, CameraMetrics] = {} self.embeddings_metrics: DataProcessorMetrics | None = ( - DataProcessorMetrics() if config.semantic_search.enabled else None + DataProcessorMetrics() + if ( + config.semantic_search.enabled + or config.lpr.enabled + or config.face_recognition.enabled + ) + else None ) self.ptz_metrics: dict[str, PTZMetrics] = {} self.processes: dict[str, int] = {} @@ -236,7 +242,16 @@ class FrigateApp: logger.info(f"Review process started: {review_segment_process.pid}") def init_embeddings_manager(self) -> None: - if not self.config.semantic_search.enabled: + genai_cameras = [ + c for c in self.config.cameras.values() if c.enabled and c.genai.enabled + ] + + if ( + not self.config.semantic_search.enabled + and not genai_cameras + and not self.config.lpr.enabled + and not self.config.face_recognition.enabled + ): return embedding_process = util.Process( @@ -293,7 +308,16 @@ class FrigateApp: migrate_exports(self.config.ffmpeg, list(self.config.cameras.keys())) def init_embeddings_client(self) -> None: - if self.config.semantic_search.enabled: + genai_cameras = [ + c for c in self.config.cameras.values() if c.enabled and c.genai.enabled + ] + + if ( + self.config.semantic_search.enabled + or self.config.lpr.enabled + or genai_cameras + or self.config.face_recognition.enabled + ): # Create a client for other processes to use self.embeddings = EmbeddingsContext(self.db) @@ -596,6 +620,7 @@ class FrigateApp: ) User.replace( username="admin", + role="admin", password_hash=password_hash, notification_tokens=[], ).execute() diff --git a/frigate/camera/activity_manager.py b/frigate/camera/activity_manager.py index 9c06cf6f9..7f6354641 100644 --- a/frigate/camera/activity_manager.py +++ b/frigate/camera/activity_manager.py @@ -20,7 +20,7 @@ class CameraActivityManager: self.all_zone_labels: dict[str, set[str]] = {} for camera_config in config.cameras.values(): - if not camera_config.enabled: + if not camera_config.enabled_in_config: continue self.last_camera_activity[camera_config.name] = {} @@ -33,7 +33,11 @@ class CameraActivityManager: self.zone_active_object_counts[zone] = Counter() self.all_zone_labels[zone] = set() - self.all_zone_labels[zone].update(zone_config.objects) + self.all_zone_labels[zone].update( + zone_config.objects + if zone_config.objects + else camera_config.objects.track + ) def update_activity(self, new_activity: dict[str, dict[str, any]]) -> None: all_objects: list[dict[str, any]] = [] diff --git a/frigate/comms/config_updater.py b/frigate/comms/config_updater.py index 273103911..49be36c1e 100644 --- a/frigate/comms/config_updater.py +++ b/frigate/comms/config_updater.py @@ -32,7 +32,9 @@ class ConfigPublisher: class ConfigSubscriber: """Simplifies receiving an updated config.""" - def __init__(self, topic: str) -> None: + def __init__(self, topic: str, exact=False) -> None: + self.topic = topic + self.exact = exact self.context = zmq.Context() self.socket = self.context.socket(zmq.SUB) self.socket.setsockopt_string(zmq.SUBSCRIBE, topic) @@ -42,7 +44,12 @@ class ConfigSubscriber: """Returns updated config or None if no update.""" try: topic = self.socket.recv_string(flags=zmq.NOBLOCK) - return (topic, self.socket.recv_pyobj()) + obj = self.socket.recv_pyobj() + + if not self.exact or self.topic == topic: + return (topic, obj) + else: + return (None, None) except zmq.ZMQError: return (None, None) diff --git a/frigate/comms/dispatcher.py b/frigate/comms/dispatcher.py index 61530d086..586b70cbb 100644 --- a/frigate/comms/dispatcher.py +++ b/frigate/comms/dispatcher.py @@ -55,6 +55,7 @@ class Dispatcher: self._camera_settings_handlers: dict[str, Callable] = { "audio": self._on_audio_command, "detect": self._on_detect_command, + "enabled": self._on_enabled_command, "improve_contrast": self._on_motion_improve_contrast_command, "ptz_autotracker": self._on_ptz_autotracker_command, "motion": self._on_motion_command, @@ -167,6 +168,7 @@ class Dispatcher: for camera in camera_status.keys(): camera_status[camera]["config"] = { "detect": self.config.cameras[camera].detect.enabled, + "enabled": self.config.cameras[camera].enabled, "snapshots": self.config.cameras[camera].snapshots.enabled, "record": self.config.cameras[camera].record.enabled, "audio": self.config.cameras[camera].audio.enabled, @@ -278,6 +280,27 @@ class Dispatcher: self.config_updater.publish(f"config/detect/{camera_name}", detect_settings) self.publish(f"{camera_name}/detect/state", payload, retain=True) + def _on_enabled_command(self, camera_name: str, payload: str) -> None: + """Callback for camera topic.""" + camera_settings = self.config.cameras[camera_name] + + if payload == "ON": + if not self.config.cameras[camera_name].enabled_in_config: + logger.error( + "Camera must be enabled in the config to be turned on via MQTT." + ) + return + if not camera_settings.enabled: + logger.info(f"Turning on camera {camera_name}") + camera_settings.enabled = True + elif payload == "OFF": + if camera_settings.enabled: + logger.info(f"Turning off camera {camera_name}") + camera_settings.enabled = False + + self.config_updater.publish(f"config/enabled/{camera_name}", camera_settings) + self.publish(f"{camera_name}/enabled/state", payload, retain=True) + def _on_motion_command(self, camera_name: str, payload: str) -> None: """Callback for motion topic.""" detect_settings = self.config.cameras[camera_name].detect diff --git a/frigate/comms/embeddings_updater.py b/frigate/comms/embeddings_updater.py index 58f012e7d..61c2331cf 100644 --- a/frigate/comms/embeddings_updater.py +++ b/frigate/comms/embeddings_updater.py @@ -15,6 +15,7 @@ class EmbeddingsRequestEnum(Enum): generate_search = "generate_search" register_face = "register_face" reprocess_face = "reprocess_face" + reprocess_plate = "reprocess_plate" class EmbeddingsResponder: diff --git a/frigate/comms/mqtt.py b/frigate/comms/mqtt.py index 9e11a0af1..316813518 100644 --- a/frigate/comms/mqtt.py +++ b/frigate/comms/mqtt.py @@ -43,6 +43,11 @@ class MqttClient(Communicator): # type: ignore[misc] def _set_initial_topics(self) -> None: """Set initial state topics.""" for camera_name, camera in self.config.cameras.items(): + self.publish( + f"{camera_name}/enabled/state", + "ON" if camera.enabled_in_config else "OFF", + retain=True, + ) self.publish( f"{camera_name}/recordings/state", "ON" if camera.record.enabled_in_config else "OFF", @@ -196,6 +201,7 @@ class MqttClient(Communicator): # type: ignore[misc] # register callbacks callback_types = [ + "enabled", "recordings", "snapshots", "detect", diff --git a/frigate/comms/recordings_updater.py b/frigate/comms/recordings_updater.py new file mode 100644 index 000000000..862ec1041 --- /dev/null +++ b/frigate/comms/recordings_updater.py @@ -0,0 +1,36 @@ +"""Facilitates communication between processes.""" + +import logging +from enum import Enum + +from .zmq_proxy import Publisher, Subscriber + +logger = logging.getLogger(__name__) + + +class RecordingsDataTypeEnum(str, Enum): + all = "" + recordings_available_through = "recordings_available_through" + + +class RecordingsDataPublisher(Publisher): + """Publishes latest recording data.""" + + topic_base = "recordings/" + + def __init__(self, topic: RecordingsDataTypeEnum) -> None: + topic = topic.value + super().__init__(topic) + + def publish(self, payload: tuple[str, float]) -> None: + super().publish(payload) + + +class RecordingsDataSubscriber(Subscriber): + """Receives latest recording data.""" + + topic_base = "recordings/" + + def __init__(self, topic: RecordingsDataTypeEnum) -> None: + topic = topic.value + super().__init__(topic) diff --git a/frigate/config/camera/camera.py b/frigate/config/camera/camera.py index 50f61f33c..2d928661e 100644 --- a/frigate/config/camera/camera.py +++ b/frigate/config/camera/camera.py @@ -102,6 +102,9 @@ class CameraConfig(FrigateBaseModel): zones: dict[str, ZoneConfig] = Field( default_factory=dict, title="Zone configuration." ) + enabled_in_config: Optional[bool] = Field( + default=None, title="Keep track of original state of camera." + ) _ffmpeg_cmds: list[dict[str, list[str]]] = PrivateAttr() diff --git a/frigate/config/camera/detect.py b/frigate/config/camera/detect.py index 273364e61..99e02c2c8 100644 --- a/frigate/config/camera/detect.py +++ b/frigate/config/camera/detect.py @@ -32,6 +32,7 @@ class StationaryConfig(FrigateBaseModel): class DetectConfig(FrigateBaseModel): + enabled: bool = Field(default=False, title="Detection Enabled.") height: Optional[int] = Field( default=None, title="Height of the stream for the detect role." ) @@ -41,7 +42,6 @@ class DetectConfig(FrigateBaseModel): fps: int = Field( default=5, title="Number of frames per second to process through detection." ) - enabled: bool = Field(default=True, title="Detection Enabled.") min_initialized: Optional[int] = Field( default=None, title="Minimum number of consecutive hits for an object to be initialized by the tracker.", diff --git a/frigate/config/camera/ffmpeg.py b/frigate/config/camera/ffmpeg.py index 4ab93d7b9..0b1ec2331 100644 --- a/frigate/config/camera/ffmpeg.py +++ b/frigate/config/camera/ffmpeg.py @@ -1,4 +1,3 @@ -import shutil from enum import Enum from typing import Union @@ -71,10 +70,7 @@ class FfmpegConfig(FrigateBaseModel): @property def ffmpeg_path(self) -> str: if self.path == "default": - if shutil.which("ffmpeg") is None: - return f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg" - else: - return "ffmpeg" + return f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg" elif self.path in INCLUDED_FFMPEG_VERSIONS: return f"/usr/lib/ffmpeg/{self.path}/bin/ffmpeg" else: @@ -83,10 +79,7 @@ class FfmpegConfig(FrigateBaseModel): @property def ffprobe_path(self) -> str: if self.path == "default": - if shutil.which("ffprobe") is None: - return f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffprobe" - else: - return "ffprobe" + return f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffprobe" elif self.path in INCLUDED_FFMPEG_VERSIONS: return f"/usr/lib/ffmpeg/{self.path}/bin/ffprobe" else: diff --git a/frigate/config/camera/genai.py b/frigate/config/camera/genai.py index e6b327836..6ef93682b 100644 --- a/frigate/config/camera/genai.py +++ b/frigate/config/camera/genai.py @@ -16,6 +16,17 @@ class GenAIProviderEnum(str, Enum): ollama = "ollama" +class GenAISendTriggersConfig(BaseModel): + tracked_object_end: bool = Field( + default=True, title="Send once the object is no longer tracked." + ) + after_significant_updates: Optional[int] = Field( + default=None, + title="Send an early request to generative AI when X frames accumulated.", + ge=1, + ) + + # uses BaseModel because some global attributes are not available at the camera level class GenAICameraConfig(BaseModel): enabled: bool = Field(default=False, title="Enable GenAI for camera.") @@ -42,6 +53,10 @@ class GenAICameraConfig(BaseModel): default=False, title="Save thumbnails sent to generative AI for debugging purposes.", ) + send_triggers: GenAISendTriggersConfig = Field( + default_factory=GenAISendTriggersConfig, + title="What triggers to use to send frames to generative AI for a tracked object.", + ) @field_validator("required_zones", mode="before") @classmethod diff --git a/frigate/config/classification.py b/frigate/config/classification.py index 8a8e95861..f3416b009 100644 --- a/frigate/config/classification.py +++ b/frigate/config/classification.py @@ -1,3 +1,4 @@ +from enum import Enum from typing import Dict, List, Optional from pydantic import Field @@ -11,6 +12,11 @@ __all__ = [ ] +class SemanticSearchModelEnum(str, Enum): + jinav1 = "jinav1" + jinav2 = "jinav2" + + class BirdClassificationConfig(FrigateBaseModel): enabled: bool = Field(default=False, title="Enable bird classification.") threshold: float = Field( @@ -30,7 +36,11 @@ class ClassificationConfig(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." + default=False, title="Reindex all tracked objects on startup." + ) + model: Optional[SemanticSearchModelEnum] = Field( + default=SemanticSearchModelEnum.jinav1, + title="The CLIP model to use for semantic search.", ) model_size: str = Field( default="small", title="The size of the embeddings model used." diff --git a/frigate/config/config.py b/frigate/config/config.py index 39ee31411..633aef803 100644 --- a/frigate/config/config.py +++ b/frigate/config/config.py @@ -172,16 +172,6 @@ class RestreamConfig(BaseModel): model_config = ConfigDict(extra="allow") -def verify_semantic_search_dependent_configs(config: FrigateConfig) -> None: - """Verify that semantic search is enabled if required features are enabled.""" - if not config.semantic_search.enabled: - if config.genai.enabled: - raise ValueError("Genai requires semantic search to be enabled.") - - if config.face_recognition.enabled: - raise ValueError("Face recognition requires semantic to be enabled.") - - def verify_config_roles(camera_config: CameraConfig) -> None: """Verify that roles are setup in the config correctly.""" assigned_roles = list( @@ -526,6 +516,7 @@ class FrigateConfig(FrigateBaseModel): camera_config.detect.stationary.interval = stationary_threshold # set config pre-value + camera_config.enabled_in_config = camera_config.enabled camera_config.audio.enabled_in_config = camera_config.audio.enabled camera_config.record.enabled_in_config = camera_config.record.enabled camera_config.notifications.enabled_in_config = ( @@ -647,7 +638,6 @@ class FrigateConfig(FrigateBaseModel): detector_config.model = model self.detectors[key] = detector_config - verify_semantic_search_dependent_configs(self) return self @field_validator("cameras") diff --git a/frigate/config/proxy.py b/frigate/config/proxy.py index 3427f60a0..df8a665fb 100644 --- a/frigate/config/proxy.py +++ b/frigate/config/proxy.py @@ -12,6 +12,10 @@ class HeaderMappingConfig(FrigateBaseModel): user: str = Field( default=None, title="Header name from upstream proxy to identify user." ) + role: str = Field( + default=None, + title="Header name from upstream proxy to identify user role.", + ) class ProxyConfig(FrigateBaseModel): diff --git a/frigate/const.py b/frigate/const.py index eb48e9bf9..ffd1ca406 100644 --- a/frigate/const.py +++ b/frigate/const.py @@ -1,5 +1,7 @@ +import os import re +INSTALL_DIR = "/opt/frigate" CONFIG_DIR = "/config" DEFAULT_DB_PATH = f"{CONFIG_DIR}/frigate.db" MODEL_CACHE_DIR = f"{CONFIG_DIR}/model_cache" @@ -61,8 +63,9 @@ MAX_WAL_SIZE = 10 # MB # Ffmpeg constants -DEFAULT_FFMPEG_VERSION = "7.0" -INCLUDED_FFMPEG_VERSIONS = ["7.0", "5.0"] +DEFAULT_FFMPEG_VERSION = os.environ.get("DEFAULT_FFMPEG_VERSION", "") +INCLUDED_FFMPEG_VERSIONS = os.environ.get("INCLUDED_FFMPEG_VERSIONS", "").split(":") +LIBAVFORMAT_VERSION_MAJOR = int(os.environ.get("LIBAVFORMAT_VERSION_MAJOR", "59")) FFMPEG_HWACCEL_NVIDIA = "preset-nvidia" FFMPEG_HWACCEL_VAAPI = "preset-vaapi" FFMPEG_HWACCEL_VULKAN = "preset-vulkan" diff --git a/frigate/data_processing/real_time/license_plate_processor.py b/frigate/data_processing/common/license_plate/mixin.py similarity index 91% rename from frigate/data_processing/real_time/license_plate_processor.py rename to frigate/data_processing/common/license_plate/mixin.py index 2d64e5cdb..aa03bc985 100644 --- a/frigate/data_processing/real_time/license_plate_processor.py +++ b/frigate/data_processing/common/license_plate/mixin.py @@ -13,29 +13,21 @@ from Levenshtein import distance from pyclipper import ET_CLOSEDPOLYGON, JT_ROUND, PyclipperOffset from shapely.geometry import Polygon -from frigate.comms.inter_process import InterProcessRequestor -from frigate.config import FrigateConfig from frigate.const import FRIGATE_LOCALHOST -from frigate.embeddings.functions.onnx import GenericONNXEmbedding, ModelTypeEnum from frigate.util.image import area -from ..types import DataProcessorMetrics -from .api import RealTimeProcessorApi - logger = logging.getLogger(__name__) WRITE_DEBUG_IMAGES = False -class LicensePlateProcessor(RealTimeProcessorApi): - def __init__(self, config: FrigateConfig, metrics: DataProcessorMetrics): - super().__init__(config, metrics) - self.requestor = InterProcessRequestor() - self.lpr_config = config.lpr +class LicensePlateProcessingMixin: + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.requires_license_plate_detection = ( "license_plate" not in self.config.objects.all_objects ) - self.detected_license_plates: dict[str, dict[str, any]] = {} self.ctc_decoder = CTCDecoder() @@ -47,65 +39,6 @@ class LicensePlateProcessor(RealTimeProcessorApi): self.box_thresh = 0.8 self.mask_thresh = 0.8 - self.lpr_detection_model = None - self.lpr_classification_model = None - self.lpr_recognition_model = None - - if self.config.lpr.enabled: - self.detection_model = GenericONNXEmbedding( - model_name="paddleocr-onnx", - model_file="detection.onnx", - download_urls={ - "detection.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/detection.onnx" - }, - model_size="large", - model_type=ModelTypeEnum.lpr_detect, - requestor=self.requestor, - device="CPU", - ) - - self.classification_model = GenericONNXEmbedding( - model_name="paddleocr-onnx", - model_file="classification.onnx", - download_urls={ - "classification.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/classification.onnx" - }, - model_size="large", - model_type=ModelTypeEnum.lpr_classify, - requestor=self.requestor, - device="CPU", - ) - - self.recognition_model = GenericONNXEmbedding( - model_name="paddleocr-onnx", - model_file="recognition.onnx", - download_urls={ - "recognition.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/recognition.onnx" - }, - model_size="large", - model_type=ModelTypeEnum.lpr_recognize, - requestor=self.requestor, - device="CPU", - ) - self.yolov9_detection_model = GenericONNXEmbedding( - model_name="yolov9_license_plate", - model_file="yolov9-256-license-plates.onnx", - download_urls={ - "yolov9-256-license-plates.onnx": "https://github.com/hawkeye217/yolov9-license-plates/raw/refs/heads/master/models/yolov9-256-license-plates.onnx" - }, - model_size="large", - model_type=ModelTypeEnum.yolov9_lpr_detect, - requestor=self.requestor, - device="CPU", - ) - - if self.lpr_config.enabled: - # all models need to be loaded to run LPR - self.detection_model._load_model_and_utils() - self.classification_model._load_model_and_utils() - self.recognition_model._load_model_and_utils() - self.yolov9_detection_model._load_model_and_utils() - def _detect(self, image: np.ndarray) -> List[np.ndarray]: """ Detect possible license plates in the input image by first resizing and normalizing it, @@ -132,7 +65,7 @@ class LicensePlateProcessor(RealTimeProcessorApi): resized_image, ) - outputs = self.detection_model([normalized_image])[0] + outputs = self.model_runner.detection_model([normalized_image])[0] outputs = outputs[0, :, :] boxes, _ = self._boxes_from_bitmap(outputs, outputs > self.mask_thresh, w, h) @@ -161,7 +94,7 @@ class LicensePlateProcessor(RealTimeProcessorApi): norm_img = norm_img[np.newaxis, :] norm_images.append(norm_img) - outputs = self.classification_model(norm_images) + outputs = self.model_runner.classification_model(norm_images) return self._process_classification_output(images, outputs) @@ -201,7 +134,7 @@ class LicensePlateProcessor(RealTimeProcessorApi): norm_image = norm_image[np.newaxis, :] norm_images.append(norm_image) - outputs = self.recognition_model(norm_images) + outputs = self.model_runner.recognition_model(norm_images) return self.ctc_decoder(outputs) def _process_license_plate( @@ -217,9 +150,9 @@ class LicensePlateProcessor(RealTimeProcessorApi): Tuple[List[str], List[float], List[int]]: Detected license plate texts, confidence scores, and areas of the plates. """ if ( - self.detection_model.runner is None - or self.classification_model.runner is None - or self.recognition_model.runner is None + self.model_runner.detection_model.runner is None + or self.model_runner.classification_model.runner is None + or self.model_runner.recognition_model.runner is None ): # we might still be downloading the models logger.debug("Model runners not loaded") @@ -683,7 +616,9 @@ class LicensePlateProcessor(RealTimeProcessorApi): input_w = int(input_h * max_wh_ratio) # check for model-specific input width - model_input_w = self.recognition_model.runner.ort.get_inputs()[0].shape[3] + model_input_w = self.model_runner.recognition_model.runner.ort.get_inputs()[ + 0 + ].shape[3] if isinstance(model_input_w, int) and model_input_w > 0: input_w = model_input_w @@ -750,19 +685,13 @@ class LicensePlateProcessor(RealTimeProcessorApi): image = np.rot90(image, k=3) return image - def __update_metrics(self, duration: float) -> None: - """ - Update inference metrics. - """ - self.metrics.alpr_pps.value = (self.metrics.alpr_pps.value * 9 + duration) / 10 - def _detect_license_plate(self, input: np.ndarray) -> tuple[int, int, int, int]: """ Use a lightweight YOLOv9 model to detect license plates for users without Frigate+ Return the dimensions of the detected plate as [x1, y1, x2, y2]. """ - predictions = self.yolov9_detection_model(input) + predictions = self.model_runner.yolov9_detection_model(input) confidence_threshold = self.lpr_config.detection_threshold @@ -788,8 +717,8 @@ class LicensePlateProcessor(RealTimeProcessorApi): # Return the top scoring bounding box if found if top_box is not None: - # expand box by 15% to help with OCR - expansion = (top_box[2:] - top_box[:2]) * 0.1 + # expand box by 30% to help with OCR + expansion = (top_box[2:] - top_box[:2]) * 0.30 # Expand box expanded_box = np.array( @@ -887,9 +816,22 @@ class LicensePlateProcessor(RealTimeProcessorApi): # 5. Return True if we should keep the previous plate (i.e., if it scores higher) return prev_score > curr_score - def process_frame(self, obj_data: dict[str, any], frame: np.ndarray): + def __update_yolov9_metrics(self, duration: float) -> None: + """ + Update inference metrics. + """ + self.metrics.yolov9_lpr_fps.value = ( + self.metrics.yolov9_lpr_fps.value * 9 + duration + ) / 10 + + def __update_lpr_metrics(self, duration: float) -> None: + """ + Update inference metrics. + """ + self.metrics.alpr_pps.value = (self.metrics.alpr_pps.value * 9 + duration) / 10 + + def lpr_process(self, obj_data: dict[str, any], frame: np.ndarray): """Look for license plates in image.""" - start = datetime.datetime.now().timestamp() id = obj_data["id"] @@ -915,6 +857,7 @@ class LicensePlateProcessor(RealTimeProcessorApi): if self.requires_license_plate_detection: logger.debug("Running manual license_plate detection.") + car_box = obj_data.get("box") if not car_box: @@ -939,6 +882,9 @@ class LicensePlateProcessor(RealTimeProcessorApi): logger.debug( f"YOLOv9 LPD inference time: {(datetime.datetime.now().timestamp() - yolov9_start) * 1000:.2f} ms" ) + self.__update_yolov9_metrics( + datetime.datetime.now().timestamp() - yolov9_start + ) if not license_plate: logger.debug("Detected no license plates for car object.") @@ -952,7 +898,7 @@ class LicensePlateProcessor(RealTimeProcessorApi): # check that license plate is valid # double the value because we've doubled the size of the car - if license_plate_area < self.config.lpr.min_area * 2: + if license_plate_area < self.lpr_config.min_area * 2: logger.debug("License plate is less than min_area") return @@ -990,7 +936,7 @@ class LicensePlateProcessor(RealTimeProcessorApi): # check that license plate is valid if ( not license_plate_box - or area(license_plate_box) < self.config.lpr.min_area + or area(license_plate_box) < self.lpr_config.min_area ): logger.debug(f"Invalid license plate box {license_plate}") return @@ -1017,11 +963,15 @@ class LicensePlateProcessor(RealTimeProcessorApi): license_plate_frame, ) + start = datetime.datetime.now().timestamp() + # run detection, returns results sorted by confidence, best first license_plates, confidences, areas = self._process_license_plate( license_plate_frame ) + self.__update_lpr_metrics(datetime.datetime.now().timestamp() - start) + logger.debug(f"Text boxes: {license_plates}") logger.debug(f"Confidences: {confidences}") logger.debug(f"Areas: {areas}") @@ -1096,10 +1046,9 @@ class LicensePlateProcessor(RealTimeProcessorApi): "plate": top_plate, "char_confidences": top_char_confidences, "area": top_area, + "obj_data": obj_data, } - self.__update_metrics(datetime.datetime.now().timestamp() - start) - def handle_request(self, topic, request_data) -> dict[str, any] | None: return diff --git a/frigate/data_processing/common/license_plate/model.py b/frigate/data_processing/common/license_plate/model.py new file mode 100644 index 000000000..25e7b2caf --- /dev/null +++ b/frigate/data_processing/common/license_plate/model.py @@ -0,0 +1,31 @@ +from frigate.embeddings.onnx.lpr_embedding import ( + LicensePlateDetector, + PaddleOCRClassification, + PaddleOCRDetection, + PaddleOCRRecognition, +) + +from ...types import DataProcessorModelRunner + + +class LicensePlateModelRunner(DataProcessorModelRunner): + def __init__(self, requestor, device: str = "CPU", model_size: str = "large"): + super().__init__(requestor, device, model_size) + self.detection_model = PaddleOCRDetection( + model_size=model_size, requestor=requestor, device=device + ) + self.classification_model = PaddleOCRClassification( + model_size=model_size, requestor=requestor, device=device + ) + self.recognition_model = PaddleOCRRecognition( + model_size=model_size, requestor=requestor, device=device + ) + self.yolov9_detection_model = LicensePlateDetector( + model_size=model_size, requestor=requestor, device=device + ) + + # Load all models once + self.detection_model._load_model_and_utils() + self.classification_model._load_model_and_utils() + self.recognition_model._load_model_and_utils() + self.yolov9_detection_model._load_model_and_utils() diff --git a/frigate/data_processing/post/api.py b/frigate/data_processing/post/api.py index 5c88221c2..c40caef71 100644 --- a/frigate/data_processing/post/api.py +++ b/frigate/data_processing/post/api.py @@ -5,16 +5,22 @@ from abc import ABC, abstractmethod from frigate.config import FrigateConfig -from ..types import DataProcessorMetrics, PostProcessDataEnum +from ..types import DataProcessorMetrics, DataProcessorModelRunner, PostProcessDataEnum logger = logging.getLogger(__name__) class PostProcessorApi(ABC): @abstractmethod - def __init__(self, config: FrigateConfig, metrics: DataProcessorMetrics) -> None: + def __init__( + self, + config: FrigateConfig, + metrics: DataProcessorMetrics, + model_runner: DataProcessorModelRunner, + ) -> None: self.config = config self.metrics = metrics + self.model_runner = model_runner pass @abstractmethod diff --git a/frigate/data_processing/post/license_plate.py b/frigate/data_processing/post/license_plate.py new file mode 100644 index 000000000..2c80418c7 --- /dev/null +++ b/frigate/data_processing/post/license_plate.py @@ -0,0 +1,224 @@ +"""Handle post processing for license plate recognition.""" + +import datetime +import logging + +import cv2 +import numpy as np +from peewee import DoesNotExist + +from frigate.comms.embeddings_updater import EmbeddingsRequestEnum +from frigate.config import FrigateConfig +from frigate.data_processing.common.license_plate.mixin import ( + WRITE_DEBUG_IMAGES, + LicensePlateProcessingMixin, +) +from frigate.data_processing.common.license_plate.model import ( + LicensePlateModelRunner, +) +from frigate.data_processing.types import PostProcessDataEnum +from frigate.models import Recordings +from frigate.util.image import get_image_from_recording + +from ..types import DataProcessorMetrics +from .api import PostProcessorApi + +logger = logging.getLogger(__name__) + + +class LicensePlatePostProcessor(LicensePlateProcessingMixin, PostProcessorApi): + def __init__( + self, + config: FrigateConfig, + metrics: DataProcessorMetrics, + model_runner: LicensePlateModelRunner, + detected_license_plates: dict[str, dict[str, any]], + ): + self.detected_license_plates = detected_license_plates + self.model_runner = model_runner + self.lpr_config = config.lpr + self.config = config + super().__init__(config, metrics, model_runner) + + def process_data( + self, data: dict[str, any], data_type: PostProcessDataEnum + ) -> None: + """Look for license plates in recording stream image + Args: + data (dict): containing data about the input. + data_type (enum): Describing the data that is being processed. + + Returns: + None. + """ + event_id = data["event_id"] + camera_name = data["camera"] + + if data_type == PostProcessDataEnum.recording: + obj_data = data["obj_data"] + frame_time = obj_data["frame_time"] + recordings_available_through = data["recordings_available"] + + if frame_time > recordings_available_through: + logger.debug( + f"LPR post processing: No recordings available for this frame time {frame_time}, available through {recordings_available_through}" + ) + + elif data_type == PostProcessDataEnum.tracked_object: + # non-functional, need to think about snapshot time + obj_data = data["event"]["data"] + obj_data["id"] = data["event"]["id"] + obj_data["camera"] = data["event"]["camera"] + # TODO: snapshot time? + frame_time = data["event"]["start_time"] + + else: + logger.error("No data type passed to LPR postprocessing") + return + + recording_query = ( + Recordings.select( + Recordings.path, + Recordings.start_time, + ) + .where( + ( + (frame_time >= Recordings.start_time) + & (frame_time <= Recordings.end_time) + ) + ) + .where(Recordings.camera == camera_name) + .order_by(Recordings.start_time.desc()) + .limit(1) + ) + + try: + recording: Recordings = recording_query.get() + time_in_segment = frame_time - recording.start_time + codec = "mjpeg" + + image_data = get_image_from_recording( + self.config.ffmpeg, recording.path, time_in_segment, codec, None + ) + + if not image_data: + logger.debug( + "LPR post processing: Unable to fetch license plate from recording" + ) + + # Convert bytes to numpy array + image_array = np.frombuffer(image_data, dtype=np.uint8) + + if len(image_array) == 0: + logger.debug("LPR post processing: No image") + return + + image = cv2.imdecode(image_array, cv2.IMREAD_COLOR) + + except DoesNotExist: + logger.debug("Error fetching license plate for postprocessing") + return + + if WRITE_DEBUG_IMAGES: + cv2.imwrite( + f"debug/frames/lpr_post_{datetime.datetime.now().timestamp()}.jpg", + image, + ) + + # convert to yuv for processing + frame = cv2.cvtColor(image, cv2.COLOR_BGR2YUV_I420) + + detect_width = self.config.cameras[camera_name].detect.width + detect_height = self.config.cameras[camera_name].detect.height + + # Scale the boxes based on detect dimensions + scale_x = image.shape[1] / detect_width + scale_y = image.shape[0] / detect_height + + # Determine which box to enlarge based on detection mode + if self.requires_license_plate_detection: + # Scale and enlarge the car box + box = obj_data.get("box") + if not box: + return + + # Scale original car box to detection dimensions + left = int(box[0] * scale_x) + top = int(box[1] * scale_y) + right = int(box[2] * scale_x) + bottom = int(box[3] * scale_y) + box = [left, top, right, bottom] + else: + # Get the license plate box from attributes + if not obj_data.get("current_attributes"): + return + + license_plate = None + for attr in obj_data["current_attributes"]: + if attr.get("label") != "license_plate": + continue + if license_plate is None or attr.get("score", 0.0) > license_plate.get( + "score", 0.0 + ): + license_plate = attr + + if not license_plate or not license_plate.get("box"): + return + + # Scale license plate box to detection dimensions + orig_box = license_plate["box"] + left = int(orig_box[0] * scale_x) + top = int(orig_box[1] * scale_y) + right = int(orig_box[2] * scale_x) + bottom = int(orig_box[3] * scale_y) + box = [left, top, right, bottom] + + width_box = right - left + height_box = bottom - top + + # Enlarge box slightly to account for drift in detect vs recording stream + enlarge_factor = 0.3 + new_left = max(0, int(left - (width_box * enlarge_factor / 2))) + new_top = max(0, int(top - (height_box * enlarge_factor / 2))) + new_right = min(image.shape[1], int(right + (width_box * enlarge_factor / 2))) + new_bottom = min( + image.shape[0], int(bottom + (height_box * enlarge_factor / 2)) + ) + + keyframe_obj_data = obj_data.copy() + if self.requires_license_plate_detection: + # car box + keyframe_obj_data["box"] = [new_left, new_top, new_right, new_bottom] + else: + # Update the license plate box in the attributes + new_attributes = [] + for attr in obj_data["current_attributes"]: + if attr.get("label") == "license_plate": + new_attr = attr.copy() + new_attr["box"] = [new_left, new_top, new_right, new_bottom] + new_attributes.append(new_attr) + else: + new_attributes.append(attr) + keyframe_obj_data["current_attributes"] = new_attributes + + # run the frame through lpr processing + logger.debug(f"Post processing plate: {event_id}, {frame_time}") + self.lpr_process(keyframe_obj_data, frame) + + def handle_request(self, topic, request_data) -> dict[str, any] | None: + if topic == EmbeddingsRequestEnum.reprocess_plate.value: + event = request_data["event"] + + self.process_data( + { + "event_id": event["id"], + "camera": event["camera"], + "event": event, + }, + PostProcessDataEnum.tracked_object, + ) + + return { + "message": "Successfully requested reprocessing of license plate.", + "success": True, + } diff --git a/frigate/data_processing/real_time/api.py b/frigate/data_processing/real_time/api.py index 205431a36..1ba01d5da 100644 --- a/frigate/data_processing/real_time/api.py +++ b/frigate/data_processing/real_time/api.py @@ -14,7 +14,11 @@ logger = logging.getLogger(__name__) class RealTimeProcessorApi(ABC): @abstractmethod - def __init__(self, config: FrigateConfig, metrics: DataProcessorMetrics) -> None: + def __init__( + self, + config: FrigateConfig, + metrics: DataProcessorMetrics, + ) -> None: self.config = config self.metrics = metrics pass diff --git a/frigate/data_processing/real_time/bird_processor.py b/frigate/data_processing/real_time/bird.py similarity index 99% rename from frigate/data_processing/real_time/bird_processor.py rename to frigate/data_processing/real_time/bird.py index 1199f6124..01490d895 100644 --- a/frigate/data_processing/real_time/bird_processor.py +++ b/frigate/data_processing/real_time/bird.py @@ -22,7 +22,7 @@ except ModuleNotFoundError: logger = logging.getLogger(__name__) -class BirdProcessor(RealTimeProcessorApi): +class BirdRealTimeProcessor(RealTimeProcessorApi): def __init__(self, config: FrigateConfig, metrics: DataProcessorMetrics): super().__init__(config, metrics) self.interpreter: Interpreter = None diff --git a/frigate/data_processing/real_time/face_processor.py b/frigate/data_processing/real_time/face.py similarity index 98% rename from frigate/data_processing/real_time/face_processor.py rename to frigate/data_processing/real_time/face.py index 086c59658..e7cf622e9 100644 --- a/frigate/data_processing/real_time/face_processor.py +++ b/frigate/data_processing/real_time/face.py @@ -27,7 +27,7 @@ logger = logging.getLogger(__name__) MIN_MATCHING_FACES = 2 -class FaceProcessor(RealTimeProcessorApi): +class FaceRealTimeProcessor(RealTimeProcessorApi): def __init__(self, config: FrigateConfig, metrics: DataProcessorMetrics): super().__init__(config, metrics) self.face_config = config.face_recognition @@ -76,14 +76,16 @@ class FaceProcessor(RealTimeProcessorApi): def __build_detector(self) -> None: self.face_detector = cv2.FaceDetectorYN.create( - "/config/model_cache/facedet/facedet.onnx", + os.path.join(MODEL_CACHE_DIR, "facedet/facedet.onnx"), config="", input_size=(320, 320), score_threshold=0.8, nms_threshold=0.3, ) self.landmark_detector = cv2.face.createFacemarkLBF() - self.landmark_detector.loadModel("/config/model_cache/facedet/landmarkdet.yaml") + self.landmark_detector.loadModel( + os.path.join(MODEL_CACHE_DIR, "facedet/landmarkdet.yaml") + ) def __build_classifier(self) -> None: if not self.landmark_detector: diff --git a/frigate/data_processing/real_time/license_plate.py b/frigate/data_processing/real_time/license_plate.py new file mode 100644 index 000000000..c8f0efa11 --- /dev/null +++ b/frigate/data_processing/real_time/license_plate.py @@ -0,0 +1,44 @@ +"""Handle processing images for face detection and recognition.""" + +import logging + +import numpy as np + +from frigate.config import FrigateConfig +from frigate.data_processing.common.license_plate.mixin import ( + LicensePlateProcessingMixin, +) +from frigate.data_processing.common.license_plate.model import ( + LicensePlateModelRunner, +) + +from ..types import DataProcessorMetrics +from .api import RealTimeProcessorApi + +logger = logging.getLogger(__name__) + + +class LicensePlateRealTimeProcessor(LicensePlateProcessingMixin, RealTimeProcessorApi): + def __init__( + self, + config: FrigateConfig, + metrics: DataProcessorMetrics, + model_runner: LicensePlateModelRunner, + detected_license_plates: dict[str, dict[str, any]], + ): + self.detected_license_plates = detected_license_plates + self.model_runner = model_runner + self.lpr_config = config.lpr + self.config = config + super().__init__(config, metrics) + + def process_frame(self, obj_data: dict[str, any], frame: np.ndarray): + """Look for license plates in image.""" + self.lpr_process(obj_data, frame) + + def handle_request(self, topic, request_data) -> dict[str, any] | None: + return + + def expire_object(self, object_id: str): + if object_id in self.detected_license_plates: + self.detected_license_plates.pop(object_id) diff --git a/frigate/data_processing/types.py b/frigate/data_processing/types.py index 39f355667..29abb22d1 100644 --- a/frigate/data_processing/types.py +++ b/frigate/data_processing/types.py @@ -10,12 +10,21 @@ class DataProcessorMetrics: text_embeddings_sps: Synchronized face_rec_fps: Synchronized alpr_pps: Synchronized + yolov9_lpr_fps: Synchronized def __init__(self): self.image_embeddings_fps = mp.Value("d", 0.01) self.text_embeddings_sps = mp.Value("d", 0.01) self.face_rec_fps = mp.Value("d", 0.01) self.alpr_pps = mp.Value("d", 0.01) + self.yolov9_lpr_fps = mp.Value("d", 0.01) + + +class DataProcessorModelRunner: + def __init__(self, requestor, device: str = "CPU", model_size: str = "large"): + self.requestor = requestor + self.device = device + self.model_size = model_size class PostProcessDataEnum(str, Enum): diff --git a/frigate/detectors/detector_config.py b/frigate/detectors/detector_config.py index c8aea0a1d..fceab5a19 100644 --- a/frigate/detectors/detector_config.py +++ b/frigate/detectors/detector_config.py @@ -9,7 +9,7 @@ import requests from pydantic import BaseModel, ConfigDict, Field from pydantic.fields import PrivateAttr -from frigate.const import DEFAULT_ATTRIBUTE_LABEL_MAP +from frigate.const import DEFAULT_ATTRIBUTE_LABEL_MAP, MODEL_CACHE_DIR from frigate.plus import PlusApi from frigate.util.builtin import generate_color_palette, load_labels @@ -37,6 +37,7 @@ class ModelTypeEnum(str, Enum): yolox = "yolox" yolov9 = "yolov9" yolonas = "yolonas" + dfine = "dfine" class ModelConfig(BaseModel): @@ -122,7 +123,7 @@ class ModelConfig(BaseModel): return model_id = self.path[7:] - self.path = f"/config/model_cache/{model_id}" + self.path = os.path.join(MODEL_CACHE_DIR, model_id) model_info_path = f"{self.path}.json" # download the model if it doesn't exist diff --git a/frigate/detectors/plugins/hailo8l.py b/frigate/detectors/plugins/hailo8l.py index b66d78bd6..69e86bc5b 100644 --- a/frigate/detectors/plugins/hailo8l.py +++ b/frigate/detectors/plugins/hailo8l.py @@ -22,6 +22,7 @@ except ModuleNotFoundError: from pydantic import BaseModel, Field from typing_extensions import Literal +from frigate.const import MODEL_CACHE_DIR from frigate.detectors.detection_api import DetectionApi from frigate.detectors.detector_config import BaseDetectorConfig @@ -57,7 +58,7 @@ class HailoDetector(DetectionApi): 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.cache_dir = os.path.join(MODEL_CACHE_DIR, "h8l_cache") self.expected_model_filename = "ssd_mobilenet_v1.hef" output_type = "FLOAT32" diff --git a/frigate/detectors/plugins/onnx.py b/frigate/detectors/plugins/onnx.py index c8589145a..d94b4660f 100644 --- a/frigate/detectors/plugins/onnx.py +++ b/frigate/detectors/plugins/onnx.py @@ -9,7 +9,11 @@ from frigate.detectors.detector_config import ( BaseDetectorConfig, ModelTypeEnum, ) -from frigate.util.model import get_ort_providers, post_process_yolov9 +from frigate.util.model import ( + get_ort_providers, + post_process_dfine, + post_process_yolov9, +) logger = logging.getLogger(__name__) @@ -41,6 +45,7 @@ class ONNXDetector(DetectionApi): providers, options = get_ort_providers( detector_config.device == "CPU", detector_config.device ) + self.model = ort.InferenceSession( path, providers=providers, provider_options=options ) @@ -55,6 +60,16 @@ class ONNXDetector(DetectionApi): logger.info(f"ONNX: {path} loaded") def detect_raw(self, tensor_input: np.ndarray): + if self.onnx_model_type == ModelTypeEnum.dfine: + tensor_output = self.model.run( + None, + { + "images": tensor_input, + "orig_target_sizes": np.array([[self.h, self.w]], dtype=np.int64), + }, + ) + return post_process_dfine(tensor_output, self.w, self.h) + model_input_name = self.model.get_inputs()[0].name tensor_output = self.model.run(None, {model_input_name: tensor_input}) @@ -84,5 +99,5 @@ class ONNXDetector(DetectionApi): return post_process_yolov9(predictions, self.w, self.h) else: raise Exception( - f"{self.onnx_model_type} is currently not supported for rocm. See the docs for more info on supported models." + f"{self.onnx_model_type} is currently not supported for onnx. See the docs for more info on supported models." ) diff --git a/frigate/detectors/plugins/openvino.py b/frigate/detectors/plugins/openvino.py index 27be6b9bd..0f0b99a1f 100644 --- a/frigate/detectors/plugins/openvino.py +++ b/frigate/detectors/plugins/openvino.py @@ -7,6 +7,7 @@ import openvino.properties as props from pydantic import Field from typing_extensions import Literal +from frigate.const import MODEL_CACHE_DIR from frigate.detectors.detection_api import DetectionApi from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum from frigate.util.model import post_process_yolov9 @@ -41,8 +42,10 @@ class OvDetector(DetectionApi): logger.error(f"OpenVino model file {detector_config.model.path} not found.") raise FileNotFoundError - os.makedirs("/config/model_cache/openvino", exist_ok=True) - self.ov_core.set_property({props.cache_dir: "/config/model_cache/openvino"}) + os.makedirs(os.path.join(MODEL_CACHE_DIR, "openvino"), exist_ok=True) + self.ov_core.set_property( + {props.cache_dir: os.path.join(MODEL_CACHE_DIR, "openvino")} + ) self.interpreter = self.ov_core.compile_model( model=detector_config.model.path, device_name=detector_config.device ) diff --git a/frigate/detectors/plugins/rknn.py b/frigate/detectors/plugins/rknn.py index bfd7866e6..407c93917 100644 --- a/frigate/detectors/plugins/rknn.py +++ b/frigate/detectors/plugins/rknn.py @@ -6,6 +6,7 @@ from typing import Literal from pydantic import Field +from frigate.const import MODEL_CACHE_DIR from frigate.detectors.detection_api import DetectionApi from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum @@ -17,7 +18,7 @@ supported_socs = ["rk3562", "rk3566", "rk3568", "rk3576", "rk3588"] supported_models = {ModelTypeEnum.yolonas: "^deci-fp16-yolonas_[sml]$"} -model_cache_dir = "/config/model_cache/rknn_cache/" +model_cache_dir = os.path.join(MODEL_CACHE_DIR, "rknn_cache/") class RknnDetectorConfig(BaseDetectorConfig): diff --git a/frigate/detectors/plugins/rocm.py b/frigate/detectors/plugins/rocm.py deleted file mode 100644 index 60118d129..000000000 --- a/frigate/detectors/plugins/rocm.py +++ /dev/null @@ -1,169 +0,0 @@ -import ctypes -import logging -import os -import subprocess -import sys - -import cv2 -import numpy as np -from pydantic import Field -from typing_extensions import Literal - -from frigate.detectors.detection_api import DetectionApi -from frigate.detectors.detector_config import ( - BaseDetectorConfig, - ModelTypeEnum, - PixelFormatEnum, -) - -logger = logging.getLogger(__name__) - -DETECTOR_KEY = "rocm" - - -def detect_gfx_version(): - return subprocess.getoutput( - "unset HSA_OVERRIDE_GFX_VERSION && /opt/rocm/bin/rocminfo | grep gfx |head -1|awk '{print $2}'" - ) - - -def auto_override_gfx_version(): - # If environment variable already in place, do not override - gfx_version = detect_gfx_version() - old_override = os.getenv("HSA_OVERRIDE_GFX_VERSION") - if old_override not in (None, ""): - logger.warning( - f"AMD/ROCm: detected {gfx_version} but HSA_OVERRIDE_GFX_VERSION already present ({old_override}), not overriding!" - ) - return old_override - mapping = { - "gfx90c": "9.0.0", - "gfx1031": "10.3.0", - "gfx1103": "11.0.0", - } - override = mapping.get(gfx_version) - if override is not None: - logger.warning( - f"AMD/ROCm: detected {gfx_version}, overriding HSA_OVERRIDE_GFX_VERSION={override}" - ) - os.putenv("HSA_OVERRIDE_GFX_VERSION", override) - return override - return "" - - -class ROCmDetectorConfig(BaseDetectorConfig): - type: Literal[DETECTOR_KEY] - conserve_cpu: bool = Field( - default=True, - title="Conserve CPU at the expense of latency (and reduced max throughput)", - ) - auto_override_gfx: bool = Field( - default=True, title="Automatically detect and override gfx version" - ) - - -class ROCmDetector(DetectionApi): - type_key = DETECTOR_KEY - - def __init__(self, detector_config: ROCmDetectorConfig): - if detector_config.auto_override_gfx: - auto_override_gfx_version() - - try: - sys.path.append("/opt/rocm/lib") - import migraphx - - logger.info("AMD/ROCm: loaded migraphx module") - except ModuleNotFoundError: - logger.error("AMD/ROCm: module loading failed, missing ROCm environment?") - raise - - if detector_config.conserve_cpu: - logger.info("AMD/ROCm: switching HIP to blocking mode to conserve CPU") - ctypes.CDLL("/opt/rocm/lib/libamdhip64.so").hipSetDeviceFlags(4) - - self.h = detector_config.model.height - self.w = detector_config.model.width - self.rocm_model_type = detector_config.model.model_type - self.rocm_model_px = detector_config.model.input_pixel_format - path = detector_config.model.path - - mxr_path = os.path.splitext(path)[0] + ".mxr" - if path.endswith(".mxr"): - logger.info(f"AMD/ROCm: loading parsed model from {mxr_path}") - self.model = migraphx.load(mxr_path) - elif os.path.exists(mxr_path): - logger.info(f"AMD/ROCm: loading parsed model from {mxr_path}") - self.model = migraphx.load(mxr_path) - else: - logger.info(f"AMD/ROCm: loading model from {path}") - - if ( - path.endswith(".tf") - or path.endswith(".tf2") - or path.endswith(".tflite") - ): - # untested - self.model = migraphx.parse_tf(path) - else: - self.model = migraphx.parse_onnx(path) - - logger.info("AMD/ROCm: compiling the model") - - self.model.compile( - migraphx.get_target("gpu"), offload_copy=True, fast_math=True - ) - - logger.info(f"AMD/ROCm: saving parsed model into {mxr_path}") - - os.makedirs("/config/model_cache/rocm", exist_ok=True) - migraphx.save(self.model, mxr_path) - - logger.info("AMD/ROCm: model loaded") - - def detect_raw(self, tensor_input): - model_input_name = self.model.get_parameter_names()[0] - model_input_shape = tuple( - self.model.get_parameter_shapes()[model_input_name].lens() - ) - - tensor_input = cv2.dnn.blobFromImage( - tensor_input[0], - 1.0, - (model_input_shape[3], model_input_shape[2]), - None, - swapRB=self.rocm_model_px == PixelFormatEnum.bgr, - ).astype(np.uint8) - - detector_result = self.model.run({model_input_name: tensor_input})[0] - addr = ctypes.cast(detector_result.data_ptr(), ctypes.POINTER(ctypes.c_float)) - - tensor_output = np.ctypeslib.as_array( - addr, shape=detector_result.get_shape().lens() - ) - - if self.rocm_model_type == ModelTypeEnum.yolonas: - predictions = tensor_output - - detections = np.zeros((20, 6), np.float32) - - for i, prediction in enumerate(predictions): - if i == 20: - break - (_, x_min, y_min, x_max, y_max, confidence, class_id) = prediction - # when running in GPU mode, empty predictions in the output have class_id of -1 - if class_id < 0: - break - detections[i] = [ - class_id, - confidence, - y_min / self.h, - x_min / self.w, - y_max / self.h, - x_max / self.w, - ] - return detections - else: - raise Exception( - f"{self.rocm_model_type} is currently not supported for rocm. See the docs for more info on supported models." - ) diff --git a/frigate/embeddings/__init__.py b/frigate/embeddings/__init__.py index 185d5436b..56bd097d6 100644 --- a/frigate/embeddings/__init__.py +++ b/frigate/embeddings/__init__.py @@ -17,7 +17,7 @@ from frigate.config import FrigateConfig from frigate.const import CONFIG_DIR, FACE_DIR from frigate.data_processing.types import DataProcessorMetrics from frigate.db.sqlitevecq import SqliteVecQueueDatabase -from frigate.models import Event +from frigate.models import Event, Recordings from frigate.util.builtin import serialize from frigate.util.services import listen @@ -28,10 +28,6 @@ logger = logging.getLogger(__name__) def manage_embeddings(config: FrigateConfig, metrics: DataProcessorMetrics) -> 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: @@ -55,7 +51,7 @@ def manage_embeddings(config: FrigateConfig, metrics: DataProcessorMetrics) -> N timeout=max(60, 10 * len([c for c in config.cameras.values() if c.enabled])), load_vec_extension=True, ) - models = [Event] + models = [Event, Recordings] db.bind(models) maintainer = EmbeddingMaintainer( @@ -234,3 +230,8 @@ class EmbeddingsContext: EmbeddingsRequestEnum.embed_description.value, {"id": event_id, "description": description}, ) + + def reprocess_plate(self, event: dict[str, any]) -> dict[str, any]: + return self.requestor.send_data( + EmbeddingsRequestEnum.reprocess_plate.value, {"event": event} + ) diff --git a/frigate/embeddings/embeddings.py b/frigate/embeddings/embeddings.py index 5ce7ba86d..7e866d1fe 100644 --- a/frigate/embeddings/embeddings.py +++ b/frigate/embeddings/embeddings.py @@ -10,6 +10,7 @@ from playhouse.shortcuts import model_to_dict from frigate.comms.inter_process import InterProcessRequestor from frigate.config import FrigateConfig +from frigate.config.classification import SemanticSearchModelEnum from frigate.const import ( CONFIG_DIR, UPDATE_EMBEDDINGS_REINDEX_PROGRESS, @@ -22,7 +23,8 @@ from frigate.types import ModelStatusTypesEnum from frigate.util.builtin import serialize from frigate.util.path import get_event_thumbnail_bytes -from .functions.onnx import GenericONNXEmbedding, ModelTypeEnum +from .onnx.jina_v1_embedding import JinaV1ImageEmbedding, JinaV1TextEmbedding +from .onnx.jina_v2_embedding import JinaV2Embedding logger = logging.getLogger(__name__) @@ -75,18 +77,7 @@ class Embeddings: # Create tables if they don't exist self.db.create_embeddings_tables() - models = [ - "jinaai/jina-clip-v1-text_model_fp16.onnx", - "jinaai/jina-clip-v1-tokenizer", - "jinaai/jina-clip-v1-vision_model_fp16.onnx" - if config.semantic_search.model_size == "large" - else "jinaai/jina-clip-v1-vision_model_quantized.onnx", - "jinaai/jina-clip-v1-preprocessor_config.json", - "facenet-facenet.onnx", - "paddleocr-onnx-detection.onnx", - "paddleocr-onnx-classification.onnx", - "paddleocr-onnx-recognition.onnx", - ] + models = self.get_model_definitions() for model in models: self.requestor.send_data( @@ -97,39 +88,64 @@ class Embeddings: }, ) - self.text_embedding = GenericONNXEmbedding( - model_name="jinaai/jina-clip-v1", - model_file="text_model_fp16.onnx", - tokenizer_file="tokenizer", - download_urls={ - "text_model_fp16.onnx": "https://huggingface.co/jinaai/jina-clip-v1/resolve/main/onnx/text_model_fp16.onnx", - }, - model_size=config.semantic_search.model_size, - model_type=ModelTypeEnum.text, - requestor=self.requestor, - device="CPU", + if self.config.semantic_search.model == SemanticSearchModelEnum.jinav2: + # Single JinaV2Embedding instance for both text and vision + self.embedding = JinaV2Embedding( + model_size=self.config.semantic_search.model_size, + requestor=self.requestor, + device="GPU" + if self.config.semantic_search.model_size == "large" + else "CPU", + ) + self.text_embedding = lambda input_data: self.embedding( + input_data, embedding_type="text" + ) + self.vision_embedding = lambda input_data: self.embedding( + input_data, embedding_type="vision" + ) + else: # Default to jinav1 + self.text_embedding = JinaV1TextEmbedding( + model_size=config.semantic_search.model_size, + requestor=self.requestor, + device="CPU", + ) + self.vision_embedding = JinaV1ImageEmbedding( + model_size=config.semantic_search.model_size, + requestor=self.requestor, + device="GPU" if config.semantic_search.model_size == "large" else "CPU", + ) + + def get_model_definitions(self): + # Version-specific models + if self.config.semantic_search.model == SemanticSearchModelEnum.jinav2: + models = [ + "jinaai/jina-clip-v2-tokenizer", + "jinaai/jina-clip-v2-model_fp16.onnx" + if self.config.semantic_search.model_size == "large" + else "jinaai/jina-clip-v2-model_quantized.onnx", + "jinaai/jina-clip-v2-preprocessor_config.json", + ] + else: # Default to jinav1 + models = [ + "jinaai/jina-clip-v1-text_model_fp16.onnx", + "jinaai/jina-clip-v1-tokenizer", + "jinaai/jina-clip-v1-vision_model_fp16.onnx" + if self.config.semantic_search.model_size == "large" + else "jinaai/jina-clip-v1-vision_model_quantized.onnx", + "jinaai/jina-clip-v1-preprocessor_config.json", + ] + + # Add common models + models.extend( + [ + "facenet-facenet.onnx", + "paddleocr-onnx-detection.onnx", + "paddleocr-onnx-classification.onnx", + "paddleocr-onnx-recognition.onnx", + ] ) - model_file = ( - "vision_model_fp16.onnx" - if self.config.semantic_search.model_size == "large" - else "vision_model_quantized.onnx" - ) - - download_urls = { - model_file: f"https://huggingface.co/jinaai/jina-clip-v1/resolve/main/onnx/{model_file}", - "preprocessor_config.json": "https://huggingface.co/jinaai/jina-clip-v1/resolve/main/preprocessor_config.json", - } - - self.vision_embedding = GenericONNXEmbedding( - model_name="jinaai/jina-clip-v1", - model_file=model_file, - download_urls=download_urls, - model_size=config.semantic_search.model_size, - model_type=ModelTypeEnum.vision, - requestor=self.requestor, - device="GPU" if config.semantic_search.model_size == "large" else "CPU", - ) + return models def embed_thumbnail( self, event_id: str, thumbnail: bytes, upsert: bool = True @@ -266,7 +282,11 @@ class Embeddings: # Get total count of events to process total_events = Event.select().count() - batch_size = 32 + batch_size = ( + 4 + if self.config.semantic_search.model == SemanticSearchModelEnum.jinav2 + else 32 + ) current_page = 1 totals = { diff --git a/frigate/embeddings/functions/onnx.py b/frigate/embeddings/functions/onnx.py deleted file mode 100644 index a8d52922b..000000000 --- a/frigate/embeddings/functions/onnx.py +++ /dev/null @@ -1,325 +0,0 @@ -import logging -import os -import warnings -from enum import Enum -from io import BytesIO -from typing import Dict, List, Optional, Union - -import cv2 -import numpy as np -import requests -from PIL import Image - -# importing this without pytorch or others causes a warning -# https://github.com/huggingface/transformers/issues/27214 -# suppressed by setting env TRANSFORMERS_NO_ADVISORY_WARNINGS=1 -from transformers import AutoFeatureExtractor, AutoTokenizer -from transformers.utils.logging import disable_progress_bar - -from frigate.comms.inter_process import InterProcessRequestor -from frigate.const import MODEL_CACHE_DIR, UPDATE_MODEL_STATE -from frigate.types import ModelStatusTypesEnum -from frigate.util.downloader import ModelDownloader -from frigate.util.model import ONNXModelRunner - -warnings.filterwarnings( - "ignore", - category=FutureWarning, - message="The class CLIPFeatureExtractor is deprecated", -) - -# disables the progress bar for downloading tokenizers and feature extractors -disable_progress_bar() -logger = logging.getLogger(__name__) - -FACE_EMBEDDING_SIZE = 160 -LPR_EMBEDDING_SIZE = 256 - - -class ModelTypeEnum(str, Enum): - face = "face" - vision = "vision" - text = "text" - lpr_detect = "lpr_detect" - lpr_classify = "lpr_classify" - lpr_recognize = "lpr_recognize" - yolov9_lpr_detect = "yolov9_lpr_detect" - - -class GenericONNXEmbedding: - """Generic embedding function for ONNX models (text and vision).""" - - def __init__( - self, - model_name: str, - model_file: str, - download_urls: Dict[str, str], - model_size: str, - model_type: ModelTypeEnum, - requestor: InterProcessRequestor, - tokenizer_file: Optional[str] = None, - device: str = "AUTO", - ): - self.model_name = model_name - self.model_file = model_file - self.tokenizer_file = tokenizer_file - self.requestor = requestor - self.download_urls = download_urls - self.model_type = model_type - self.model_size = model_size - self.device = device - self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name) - self.tokenizer = None - self.feature_extractor = None - self.runner = None - files_names = list(self.download_urls.keys()) + ( - [self.tokenizer_file] if self.tokenizer_file else [] - ) - - if not all( - os.path.exists(os.path.join(self.download_path, n)) for n in files_names - ): - logger.debug(f"starting model download for {self.model_name}") - self.downloader = ModelDownloader( - model_name=self.model_name, - download_path=self.download_path, - file_names=files_names, - download_func=self._download_model, - ) - self.downloader.ensure_model_files() - else: - self.downloader = None - ModelDownloader.mark_files_state( - self.requestor, - self.model_name, - files_names, - ModelStatusTypesEnum.downloaded, - ) - self._load_model_and_utils() - logger.debug(f"models are already downloaded for {self.model_name}") - - def _download_model(self, path: str): - try: - file_name = os.path.basename(path) - - if file_name in self.download_urls: - ModelDownloader.download_from_url(self.download_urls[file_name], path) - elif ( - file_name == self.tokenizer_file - and self.model_type == ModelTypeEnum.text - ): - if not os.path.exists(path + "/" + self.model_name): - logger.info(f"Downloading {self.model_name} tokenizer") - - tokenizer = AutoTokenizer.from_pretrained( - self.model_name, - trust_remote_code=True, - cache_dir=f"{MODEL_CACHE_DIR}/{self.model_name}/tokenizer", - clean_up_tokenization_spaces=True, - ) - tokenizer.save_pretrained(path) - - self.downloader.requestor.send_data( - UPDATE_MODEL_STATE, - { - "model": f"{self.model_name}-{file_name}", - "state": ModelStatusTypesEnum.downloaded, - }, - ) - except Exception: - self.downloader.requestor.send_data( - UPDATE_MODEL_STATE, - { - "model": f"{self.model_name}-{file_name}", - "state": ModelStatusTypesEnum.error, - }, - ) - - def _load_model_and_utils(self): - if self.runner is None: - if self.downloader: - self.downloader.wait_for_download() - if self.model_type == ModelTypeEnum.text: - self.tokenizer = self._load_tokenizer() - elif self.model_type == ModelTypeEnum.vision: - self.feature_extractor = self._load_feature_extractor() - elif self.model_type == ModelTypeEnum.face: - self.feature_extractor = [] - elif self.model_type == ModelTypeEnum.lpr_detect: - self.feature_extractor = [] - elif self.model_type == ModelTypeEnum.lpr_classify: - self.feature_extractor = [] - elif self.model_type == ModelTypeEnum.lpr_recognize: - self.feature_extractor = [] - elif self.model_type == ModelTypeEnum.yolov9_lpr_detect: - self.feature_extractor = [] - - self.runner = ONNXModelRunner( - os.path.join(self.download_path, self.model_file), - self.device, - self.model_size, - ) - - def _load_tokenizer(self): - tokenizer_path = os.path.join(f"{MODEL_CACHE_DIR}/{self.model_name}/tokenizer") - return AutoTokenizer.from_pretrained( - self.model_name, - cache_dir=tokenizer_path, - trust_remote_code=True, - clean_up_tokenization_spaces=True, - ) - - def _load_feature_extractor(self): - return AutoFeatureExtractor.from_pretrained( - f"{MODEL_CACHE_DIR}/{self.model_name}", - ) - - def _preprocess_inputs(self, raw_inputs: any) -> any: - if self.model_type == ModelTypeEnum.text: - max_length = max(len(self.tokenizer.encode(text)) for text in raw_inputs) - return [ - self.tokenizer( - text, - padding="max_length", - truncation=True, - max_length=max_length, - return_tensors="np", - ) - for text in raw_inputs - ] - elif self.model_type == ModelTypeEnum.vision: - processed_images = [self._process_image(img) for img in raw_inputs] - return [ - self.feature_extractor(images=image, return_tensors="np") - for image in processed_images - ] - elif self.model_type == ModelTypeEnum.face: - if isinstance(raw_inputs, list): - raise ValueError("Face embedding does not support batch inputs.") - - pil = self._process_image(raw_inputs) - - # handle images larger than input size - width, height = pil.size - if width != FACE_EMBEDDING_SIZE or height != FACE_EMBEDDING_SIZE: - if width > height: - new_height = int(((height / width) * FACE_EMBEDDING_SIZE) // 4 * 4) - pil = pil.resize((FACE_EMBEDDING_SIZE, new_height)) - else: - new_width = int(((width / height) * FACE_EMBEDDING_SIZE) // 4 * 4) - pil = pil.resize((new_width, FACE_EMBEDDING_SIZE)) - - og = np.array(pil).astype(np.float32) - - # Image must be FACE_EMBEDDING_SIZExFACE_EMBEDDING_SIZE - og_h, og_w, channels = og.shape - frame = np.full( - (FACE_EMBEDDING_SIZE, FACE_EMBEDDING_SIZE, channels), - (0, 0, 0), - dtype=np.float32, - ) - - # compute center offset - x_center = (FACE_EMBEDDING_SIZE - og_w) // 2 - y_center = (FACE_EMBEDDING_SIZE - og_h) // 2 - - # copy img image into center of result image - frame[y_center : y_center + og_h, x_center : x_center + og_w] = og - frame = np.expand_dims(frame, axis=0) - return [{"input_2": frame}] - elif self.model_type == ModelTypeEnum.lpr_detect: - preprocessed = [] - for x in raw_inputs: - preprocessed.append(x) - return [{"x": preprocessed[0]}] - elif self.model_type == ModelTypeEnum.lpr_classify: - processed = [] - for img in raw_inputs: - processed.append({"x": img}) - return processed - elif self.model_type == ModelTypeEnum.lpr_recognize: - processed = [] - for img in raw_inputs: - processed.append({"x": img}) - return processed - elif self.model_type == ModelTypeEnum.yolov9_lpr_detect: - if isinstance(raw_inputs, list): - raise ValueError( - "License plate embedding does not support batch inputs." - ) - # Get image as numpy array - img = self._process_image(raw_inputs) - height, width, channels = img.shape - - # Resize maintaining aspect ratio - if width > height: - new_height = int(((height / width) * LPR_EMBEDDING_SIZE) // 4 * 4) - img = cv2.resize(img, (LPR_EMBEDDING_SIZE, new_height)) - else: - new_width = int(((width / height) * LPR_EMBEDDING_SIZE) // 4 * 4) - img = cv2.resize(img, (new_width, LPR_EMBEDDING_SIZE)) - - # Get new dimensions after resize - og_h, og_w, channels = img.shape - - # Create black square frame - frame = np.full( - (LPR_EMBEDDING_SIZE, LPR_EMBEDDING_SIZE, channels), - (0, 0, 0), - dtype=np.float32, - ) - - # Center the resized image in the square frame - x_center = (LPR_EMBEDDING_SIZE - og_w) // 2 - y_center = (LPR_EMBEDDING_SIZE - og_h) // 2 - frame[y_center : y_center + og_h, x_center : x_center + og_w] = img - - # Normalize to 0-1 - frame = frame / 255.0 - - # Convert from HWC to CHW format and add batch dimension - frame = np.transpose(frame, (2, 0, 1)) - frame = np.expand_dims(frame, axis=0) - return [{"images": frame}] - else: - raise ValueError(f"Unable to preprocess inputs for {self.model_type}") - - def _process_image(self, image, output: str = "RGB") -> Image.Image: - if isinstance(image, str): - if image.startswith("http"): - response = requests.get(image) - image = Image.open(BytesIO(response.content)).convert(output) - elif isinstance(image, bytes): - image = Image.open(BytesIO(image)).convert(output) - - return image - - def __call__( - self, inputs: Union[List[str], List[Image.Image], List[str]] - ) -> List[np.ndarray]: - self._load_model_and_utils() - if self.runner is None or ( - self.tokenizer is None and self.feature_extractor is None - ): - logger.error( - f"{self.model_name} model or tokenizer/feature extractor is not loaded." - ) - return [] - - processed_inputs = self._preprocess_inputs(inputs) - input_names = self.runner.get_input_names() - onnx_inputs = {name: [] for name in input_names} - input: dict[str, any] - for input in processed_inputs: - for key, value in input.items(): - if key in input_names: - onnx_inputs[key].append(value[0]) - - for key in input_names: - if onnx_inputs.get(key): - onnx_inputs[key] = np.stack(onnx_inputs[key]) - else: - logger.warning(f"Expected input '{key}' not found in onnx_inputs") - - embeddings = self.runner.run(onnx_inputs)[0] - return [embedding for embedding in embeddings] diff --git a/frigate/embeddings/maintainer.py b/frigate/embeddings/maintainer.py index 7925345b2..b3bd6c204 100644 --- a/frigate/embeddings/maintainer.py +++ b/frigate/embeddings/maintainer.py @@ -20,24 +20,39 @@ from frigate.comms.event_metadata_updater import ( ) from frigate.comms.events_updater import EventEndSubscriber, EventUpdateSubscriber from frigate.comms.inter_process import InterProcessRequestor +from frigate.comms.recordings_updater import ( + RecordingsDataSubscriber, + RecordingsDataTypeEnum, +) from frigate.config import FrigateConfig from frigate.const import ( CLIPS_DIR, UPDATE_EVENT_DESCRIPTION, ) -from frigate.data_processing.real_time.api import RealTimeProcessorApi -from frigate.data_processing.real_time.bird_processor import BirdProcessor -from frigate.data_processing.real_time.face_processor import FaceProcessor -from frigate.data_processing.real_time.license_plate_processor import ( - LicensePlateProcessor, +from frigate.data_processing.common.license_plate.model import ( + LicensePlateModelRunner, ) -from frigate.data_processing.types import DataProcessorMetrics +from frigate.data_processing.post.api import PostProcessorApi +from frigate.data_processing.post.license_plate import ( + LicensePlatePostProcessor, +) +from frigate.data_processing.real_time.api import RealTimeProcessorApi +from frigate.data_processing.real_time.bird import BirdRealTimeProcessor +from frigate.data_processing.real_time.face import FaceRealTimeProcessor +from frigate.data_processing.real_time.license_plate import ( + LicensePlateRealTimeProcessor, +) +from frigate.data_processing.types import DataProcessorMetrics, PostProcessDataEnum from frigate.events.types import EventTypeEnum from frigate.genai import get_genai_client from frigate.models import Event from frigate.types import TrackedObjectUpdateTypesEnum from frigate.util.builtin import serialize -from frigate.util.image import SharedMemoryFrameManager, calculate_region +from frigate.util.image import ( + SharedMemoryFrameManager, + calculate_region, + ensure_jpeg_bytes, +) from frigate.util.path import get_event_thumbnail_bytes from .embeddings import Embeddings @@ -60,46 +75,81 @@ class EmbeddingMaintainer(threading.Thread): super().__init__(name="embeddings_maintainer") self.config = config self.metrics = metrics - self.embeddings = Embeddings(config, db, metrics) + self.embeddings = None - # Check if we need to re-index events - if config.semantic_search.reindex: - self.embeddings.reindex() + if config.semantic_search.enabled: + self.embeddings = Embeddings(config, db, metrics) + + # Check if we need to re-index events + if config.semantic_search.reindex: + self.embeddings.reindex() + + # create communication for updating event descriptions + self.requestor = InterProcessRequestor() self.event_subscriber = EventUpdateSubscriber() self.event_end_subscriber = EventEndSubscriber() self.event_metadata_subscriber = EventMetadataSubscriber( EventMetadataTypeEnum.regenerate_description ) + self.recordings_subscriber = RecordingsDataSubscriber( + RecordingsDataTypeEnum.recordings_available_through + ) self.embeddings_responder = EmbeddingsResponder() self.frame_manager = SharedMemoryFrameManager() - self.processors: list[RealTimeProcessorApi] = [] + + self.detected_license_plates: dict[str, dict[str, any]] = {} + + # model runners to share between realtime and post processors + if self.config.lpr.enabled: + lpr_model_runner = LicensePlateModelRunner(self.requestor) + + # realtime processors + self.realtime_processors: list[RealTimeProcessorApi] = [] if self.config.face_recognition.enabled: - self.processors.append(FaceProcessor(self.config, metrics)) + self.realtime_processors.append(FaceRealTimeProcessor(self.config, metrics)) if self.config.classification.bird.enabled: - self.processors.append(BirdProcessor(self.config, metrics)) + self.realtime_processors.append(BirdRealTimeProcessor(self.config, metrics)) if self.config.lpr.enabled: - self.processors.append(LicensePlateProcessor(self.config, metrics)) + self.realtime_processors.append( + LicensePlateRealTimeProcessor( + self.config, metrics, lpr_model_runner, self.detected_license_plates + ) + ) + + # post processors + self.post_processors: list[PostProcessorApi] = [] + + if self.config.lpr.enabled: + self.post_processors.append( + LicensePlatePostProcessor( + self.config, metrics, lpr_model_runner, self.detected_license_plates + ) + ) - # create communication for updating event descriptions - self.requestor = InterProcessRequestor() self.stop_event = stop_event self.tracked_events: dict[str, list[any]] = {} + self.early_request_sent: dict[str, bool] = {} self.genai_client = get_genai_client(config) + # recordings data + self.recordings_available_through: dict[str, float] = {} + def run(self) -> None: """Maintain a SQLite-vec database for semantic search.""" while not self.stop_event.is_set(): self._process_requests() self._process_updates() + self._process_recordings_updates() self._process_finalized() self._process_event_metadata() self.event_subscriber.stop() self.event_end_subscriber.stop() + self.recordings_subscriber.stop() self.event_metadata_subscriber.stop() self.embeddings_responder.stop() self.requestor.stop() @@ -110,32 +160,34 @@ class EmbeddingMaintainer(threading.Thread): def _handle_request(topic: str, data: dict[str, any]) -> str: try: - if topic == EmbeddingsRequestEnum.embed_description.value: - return serialize( - self.embeddings.embed_description( - data["id"], data["description"] - ), - pack=False, - ) - elif topic == EmbeddingsRequestEnum.embed_thumbnail.value: - thumbnail = base64.b64decode(data["thumbnail"]) - return serialize( - self.embeddings.embed_thumbnail(data["id"], thumbnail), - pack=False, - ) - elif topic == EmbeddingsRequestEnum.generate_search.value: - return serialize( - self.embeddings.embed_description("", data, upsert=False), - pack=False, - ) - else: - for processor in self.processors: + # First handle the embedding-specific topics when semantic search is enabled + if self.config.semantic_search.enabled: + if topic == EmbeddingsRequestEnum.embed_description.value: + return serialize( + self.embeddings.embed_description( + data["id"], data["description"] + ), + pack=False, + ) + elif topic == EmbeddingsRequestEnum.embed_thumbnail.value: + thumbnail = base64.b64decode(data["thumbnail"]) + return serialize( + self.embeddings.embed_thumbnail(data["id"], thumbnail), + pack=False, + ) + elif topic == EmbeddingsRequestEnum.generate_search.value: + return serialize( + self.embeddings.embed_description("", data, upsert=False), + pack=False, + ) + processors = [self.realtime_processors, self.post_processors] + for processor_list in processors: + for processor in processor_list: resp = processor.handle_request(topic, data) - if resp is not None: return resp except Exception as e: - logger.error(f"Unable to handle embeddings request {e}") + logger.error(f"Unable to handle embeddings request {e}", exc_info=True) self.embeddings_responder.check_for_request(_handle_request) @@ -154,7 +206,7 @@ class EmbeddingMaintainer(threading.Thread): camera_config = self.config.cameras[camera] # no need to process updated objects if face recognition, lpr, genai are disabled - if not camera_config.genai.enabled and len(self.processors) == 0: + if not camera_config.genai.enabled and len(self.realtime_processors) == 0: return # Create our own thumbnail based on the bounding box and the frame time @@ -171,7 +223,7 @@ class EmbeddingMaintainer(threading.Thread): ) return - for processor in self.processors: + for processor in self.realtime_processors: processor.process_frame(data, yuv_frame) # no need to save our own thumbnails if genai is not enabled @@ -189,6 +241,43 @@ class EmbeddingMaintainer(threading.Thread): self.tracked_events[data["id"]].append(data) + # check if we're configured to send an early request after a minimum number of updates received + if ( + self.genai_client is not None + and camera_config.genai.send_triggers.after_significant_updates + ): + if ( + len(self.tracked_events.get(data["id"], [])) + >= camera_config.genai.send_triggers.after_significant_updates + and data["id"] not in self.early_request_sent + ): + if data["has_clip"] and data["has_snapshot"]: + event: Event = Event.get(Event.id == data["id"]) + + if ( + not camera_config.genai.objects + or event.label in camera_config.genai.objects + ) and ( + not camera_config.genai.required_zones + or set(data["entered_zones"]) + & set(camera_config.genai.required_zones) + ): + logger.debug(f"{camera} sending early request to GenAI") + + self.early_request_sent[data["id"]] = True + threading.Thread( + target=self._genai_embed_description, + name=f"_genai_embed_description_{event.id}", + daemon=True, + args=( + event, + [ + data["thumbnail"] + for data in self.tracked_events[data["id"]] + ], + ), + ).start() + self.frame_manager.close(frame_name) def _process_finalized(self) -> None: @@ -202,7 +291,32 @@ class EmbeddingMaintainer(threading.Thread): event_id, camera, updated_db = ended camera_config = self.config.cameras[camera] - for processor in self.processors: + # call any defined post processors + for processor in self.post_processors: + if isinstance(processor, LicensePlatePostProcessor): + recordings_available = self.recordings_available_through.get(camera) + if ( + recordings_available is not None + and event_id in self.detected_license_plates + ): + processor.process_data( + { + "event_id": event_id, + "camera": camera, + "recordings_available": self.recordings_available_through[ + camera + ], + "obj_data": self.detected_license_plates[event_id][ + "obj_data" + ], + }, + PostProcessDataEnum.recording, + ) + else: + processor.process_data(event_id, PostProcessDataEnum.event_id) + + # expire in realtime processors + for processor in self.realtime_processors: processor.expire_object(event_id) if updated_db: @@ -221,10 +335,11 @@ class EmbeddingMaintainer(threading.Thread): # Embed the thumbnail self._embed_thumbnail(event_id, thumbnail) + # Run GenAI if ( camera_config.genai.enabled + and camera_config.genai.send_triggers.tracked_object_end and self.genai_client is not None - and event.data.get("description") is None and ( not camera_config.genai.objects or event.label in camera_config.genai.objects @@ -234,87 +349,30 @@ class EmbeddingMaintainer(threading.Thread): or set(event.zones) & set(camera_config.genai.required_zones) ) ): - if event.has_snapshot and camera_config.genai.use_snapshot: - with open( - os.path.join(CLIPS_DIR, f"{event.camera}-{event.id}.jpg"), - "rb", - ) as image_file: - snapshot_image = image_file.read() - - img = cv2.imdecode( - np.frombuffer(snapshot_image, dtype=np.int8), - cv2.IMREAD_COLOR, - ) - - # crop snapshot based on region before sending off to genai - height, width = img.shape[:2] - x1_rel, y1_rel, width_rel, height_rel = event.data["region"] - - x1, y1 = int(x1_rel * width), int(y1_rel * height) - cropped_image = img[ - y1 : y1 + int(height_rel * height), - x1 : x1 + int(width_rel * width), - ] - - _, buffer = cv2.imencode(".jpg", cropped_image) - snapshot_image = buffer.tobytes() - - num_thumbnails = len(self.tracked_events.get(event_id, [])) - - embed_image = ( - [snapshot_image] - if event.has_snapshot and camera_config.genai.use_snapshot - else ( - [ - data["thumbnail"] - for data in self.tracked_events[event_id] - ] - if num_thumbnails > 0 - else [thumbnail] - ) - ) - - if camera_config.genai.debug_save_thumbnails and num_thumbnails > 0: - logger.debug( - f"Saving {num_thumbnails} thumbnails for event {event.id}" - ) - - Path( - os.path.join(CLIPS_DIR, f"genai-requests/{event.id}") - ).mkdir(parents=True, exist_ok=True) - - for idx, data in enumerate(self.tracked_events[event_id], 1): - jpg_bytes: bytes = data["thumbnail"] - - if jpg_bytes is None: - logger.warning( - f"Unable to save thumbnail {idx} for {event.id}." - ) - else: - with open( - os.path.join( - CLIPS_DIR, - f"genai-requests/{event.id}/{idx}.jpg", - ), - "wb", - ) as j: - j.write(jpg_bytes) - - # 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, - embed_image, - ), - ).start() + self._process_genai_description(event, camera_config, thumbnail) # Delete tracked events based on the event_id if event_id in self.tracked_events: del self.tracked_events[event_id] + def _process_recordings_updates(self) -> None: + """Process recordings updates.""" + while True: + recordings_data = self.recordings_subscriber.check_for_update(timeout=0.01) + + if recordings_data == None: + break + + camera, recordings_available_through_timestamp = recordings_data + + self.recordings_available_through[camera] = ( + recordings_available_through_timestamp + ) + + logger.debug( + f"{camera} now has recordings available through {recordings_available_through_timestamp}" + ) + def _process_event_metadata(self): # Check for regenerate description requests (topic, event_id, source) = self.event_metadata_subscriber.check_for_update( @@ -345,9 +403,66 @@ class EmbeddingMaintainer(threading.Thread): def _embed_thumbnail(self, event_id: str, thumbnail: bytes) -> None: """Embed the thumbnail for an event.""" + if not self.config.semantic_search.enabled: + return + self.embeddings.embed_thumbnail(event_id, thumbnail) - def _embed_description(self, event: Event, thumbnails: list[bytes]) -> None: + def _process_genai_description(self, event, camera_config, thumbnail) -> None: + if event.has_snapshot and camera_config.genai.use_snapshot: + snapshot_image = self._read_and_crop_snapshot(event, camera_config) + if not snapshot_image: + return + + num_thumbnails = len(self.tracked_events.get(event.id, [])) + + # ensure we have a jpeg to pass to the model + thumbnail = ensure_jpeg_bytes(thumbnail) + + embed_image = ( + [snapshot_image] + if event.has_snapshot and camera_config.genai.use_snapshot + else ( + [data["thumbnail"] for data in self.tracked_events[event.id]] + if num_thumbnails > 0 + else [thumbnail] + ) + ) + + if camera_config.genai.debug_save_thumbnails and num_thumbnails > 0: + logger.debug(f"Saving {num_thumbnails} thumbnails for event {event.id}") + + Path(os.path.join(CLIPS_DIR, f"genai-requests/{event.id}")).mkdir( + parents=True, exist_ok=True + ) + + for idx, data in enumerate(self.tracked_events[event.id], 1): + jpg_bytes: bytes = data["thumbnail"] + + if jpg_bytes is None: + logger.warning(f"Unable to save thumbnail {idx} for {event.id}.") + else: + with open( + os.path.join( + CLIPS_DIR, + f"genai-requests/{event.id}/{idx}.jpg", + ), + "wb", + ) as j: + j.write(jpg_bytes) + + # Generate the description. Call happens in a thread since it is network bound. + threading.Thread( + target=self._genai_embed_description, + name=f"_genai_embed_description_{event.id}", + daemon=True, + args=( + event, + embed_image, + ), + ).start() + + def _genai_embed_description(self, event: Event, thumbnails: list[bytes]) -> None: """Embed the description for an event.""" camera_config = self.config.cameras[event.camera] @@ -370,7 +485,8 @@ class EmbeddingMaintainer(threading.Thread): ) # Embed the description - self.embeddings.embed_description(event.id, description) + if self.config.semantic_search.enabled: + self.embeddings.embed_description(event.id, description) logger.debug( "Generated description for %s (%d images): %s", @@ -379,6 +495,45 @@ class EmbeddingMaintainer(threading.Thread): description, ) + def _read_and_crop_snapshot(self, event: Event, camera_config) -> bytes | None: + """Read, decode, and crop the snapshot image.""" + + snapshot_file = os.path.join(CLIPS_DIR, f"{event.camera}-{event.id}.jpg") + + if not os.path.isfile(snapshot_file): + logger.error( + f"Cannot load snapshot for {event.id}, file not found: {snapshot_file}" + ) + return None + + try: + with open(snapshot_file, "rb") as image_file: + snapshot_image = image_file.read() + + img = cv2.imdecode( + np.frombuffer(snapshot_image, dtype=np.int8), + cv2.IMREAD_COLOR, + ) + + # Crop snapshot based on region + # provide full image if region doesn't exist (manual events) + height, width = img.shape[:2] + x1_rel, y1_rel, width_rel, height_rel = event.data.get( + "region", [0, 0, 1, 1] + ) + x1, y1 = int(x1_rel * width), int(y1_rel * height) + + cropped_image = img[ + y1 : y1 + int(height_rel * height), + x1 : x1 + int(width_rel * width), + ] + + _, buffer = cv2.imencode(".jpg", cropped_image) + + return buffer.tobytes() + except Exception: + return None + def handle_regenerate_description(self, event_id: str, source: str) -> None: try: event: Event = Event.get(Event.id == event_id) @@ -393,39 +548,18 @@ class EmbeddingMaintainer(threading.Thread): thumbnail = get_event_thumbnail_bytes(event) + # ensure we have a jpeg to pass to the model + thumbnail = ensure_jpeg_bytes(thumbnail) + logger.debug( f"Trying {source} regeneration for {event}, has_snapshot: {event.has_snapshot}" ) if event.has_snapshot and source == "snapshot": - snapshot_file = os.path.join(CLIPS_DIR, f"{event.camera}-{event.id}.jpg") - - if not os.path.isfile(snapshot_file): - logger.error( - f"Cannot regenerate description for {event.id}, snapshot file not found: {snapshot_file}" - ) + snapshot_image = self._read_and_crop_snapshot(event, camera_config) + if not snapshot_image: return - with open(snapshot_file, "rb") as image_file: - snapshot_image = image_file.read() - img = cv2.imdecode( - np.frombuffer(snapshot_image, dtype=np.int8), cv2.IMREAD_COLOR - ) - - # crop snapshot based on region before sending off to genai - # provide full image if region doesn't exist (manual events) - region = event.data.get("region", [0, 0, 1, 1]) - height, width = img.shape[:2] - x1_rel, y1_rel, width_rel, height_rel = region - - x1, y1 = int(x1_rel * width), int(y1_rel * height) - cropped_image = img[ - y1 : y1 + int(height_rel * height), x1 : x1 + int(width_rel * width) - ] - - _, buffer = cv2.imencode(".jpg", cropped_image) - snapshot_image = buffer.tobytes() - embed_image = ( [snapshot_image] if event.has_snapshot and source == "snapshot" @@ -436,4 +570,4 @@ class EmbeddingMaintainer(threading.Thread): ) ) - self._embed_description(event, embed_image) + self._genai_embed_description(event, embed_image) diff --git a/frigate/embeddings/onnx/base_embedding.py b/frigate/embeddings/onnx/base_embedding.py new file mode 100644 index 000000000..a2ea92674 --- /dev/null +++ b/frigate/embeddings/onnx/base_embedding.py @@ -0,0 +1,100 @@ +"""Base class for onnx embedding implementations.""" + +import logging +import os +from abc import ABC, abstractmethod +from enum import Enum +from io import BytesIO + +import numpy as np +import requests +from PIL import Image + +from frigate.const import UPDATE_MODEL_STATE +from frigate.types import ModelStatusTypesEnum +from frigate.util.downloader import ModelDownloader + +logger = logging.getLogger(__name__) + + +class EmbeddingTypeEnum(str, Enum): + thumbnail = "thumbnail" + description = "description" + + +class BaseEmbedding(ABC): + """Base embedding class.""" + + def __init__(self, model_name: str, model_file: str, download_urls: dict[str, str]): + self.model_name = model_name + self.model_file = model_file + self.download_urls = download_urls + self.downloader: ModelDownloader = None + + def _download_model(self, path: str): + try: + file_name = os.path.basename(path) + + if file_name in self.download_urls: + ModelDownloader.download_from_url(self.download_urls[file_name], path) + + self.downloader.requestor.send_data( + UPDATE_MODEL_STATE, + { + "model": f"{self.model_name}-{file_name}", + "state": ModelStatusTypesEnum.downloaded, + }, + ) + except Exception: + self.downloader.requestor.send_data( + UPDATE_MODEL_STATE, + { + "model": f"{self.model_name}-{file_name}", + "state": ModelStatusTypesEnum.error, + }, + ) + + @abstractmethod + def _load_model_and_utils(self): + pass + + @abstractmethod + def _preprocess_inputs(self, raw_inputs: any) -> any: + pass + + def _process_image(self, image, output: str = "RGB") -> Image.Image: + if isinstance(image, str): + if image.startswith("http"): + response = requests.get(image) + image = Image.open(BytesIO(response.content)).convert(output) + elif isinstance(image, bytes): + image = Image.open(BytesIO(image)).convert(output) + + return image + + def _postprocess_outputs(self, outputs: any) -> any: + return outputs + + def __call__( + self, inputs: list[str] | list[Image.Image] | list[str] + ) -> list[np.ndarray]: + self._load_model_and_utils() + processed = self._preprocess_inputs(inputs) + input_names = self.runner.get_input_names() + onnx_inputs = {name: [] for name in input_names} + input: dict[str, any] + for input in processed: + for key, value in input.items(): + if key in input_names: + onnx_inputs[key].append(value[0]) + + for key in input_names: + if onnx_inputs.get(key): + onnx_inputs[key] = np.stack(onnx_inputs[key]) + else: + logger.warning(f"Expected input '{key}' not found in onnx_inputs") + + outputs = self.runner.run(onnx_inputs)[0] + embeddings = self._postprocess_outputs(outputs) + + return [embedding for embedding in embeddings] diff --git a/frigate/embeddings/onnx/jina_v1_embedding.py b/frigate/embeddings/onnx/jina_v1_embedding.py new file mode 100644 index 000000000..9924ff9e1 --- /dev/null +++ b/frigate/embeddings/onnx/jina_v1_embedding.py @@ -0,0 +1,216 @@ +"""JinaV1 Embeddings.""" + +import logging +import os +import warnings + +# importing this without pytorch or others causes a warning +# https://github.com/huggingface/transformers/issues/27214 +# suppressed by setting env TRANSFORMERS_NO_ADVISORY_WARNINGS=1 +from transformers import AutoFeatureExtractor, AutoTokenizer +from transformers.utils.logging import disable_progress_bar + +from frigate.comms.inter_process import InterProcessRequestor +from frigate.const import MODEL_CACHE_DIR, UPDATE_MODEL_STATE +from frigate.types import ModelStatusTypesEnum +from frigate.util.downloader import ModelDownloader + +from .base_embedding import BaseEmbedding +from .runner import ONNXModelRunner + +warnings.filterwarnings( + "ignore", + category=FutureWarning, + message="The class CLIPFeatureExtractor is deprecated", +) + +# disables the progress bar for downloading tokenizers and feature extractors +disable_progress_bar() +logger = logging.getLogger(__name__) + + +class JinaV1TextEmbedding(BaseEmbedding): + def __init__( + self, + model_size: str, + requestor: InterProcessRequestor, + device: str = "AUTO", + ): + super().__init__( + model_name="jinaai/jina-clip-v1", + model_file="text_model_fp16.onnx", + download_urls={ + "text_model_fp16.onnx": "https://huggingface.co/jinaai/jina-clip-v1/resolve/main/onnx/text_model_fp16.onnx", + }, + ) + self.tokenizer_file = "tokenizer" + self.requestor = requestor + self.model_size = model_size + self.device = device + self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name) + self.tokenizer = None + self.feature_extractor = None + self.runner = None + files_names = list(self.download_urls.keys()) + [self.tokenizer_file] + + if not all( + os.path.exists(os.path.join(self.download_path, n)) for n in files_names + ): + logger.debug(f"starting model download for {self.model_name}") + self.downloader = ModelDownloader( + model_name=self.model_name, + download_path=self.download_path, + file_names=files_names, + download_func=self._download_model, + ) + self.downloader.ensure_model_files() + else: + self.downloader = None + ModelDownloader.mark_files_state( + self.requestor, + self.model_name, + files_names, + ModelStatusTypesEnum.downloaded, + ) + self._load_model_and_utils() + logger.debug(f"models are already downloaded for {self.model_name}") + + def _download_model(self, path: str): + try: + file_name = os.path.basename(path) + + if file_name in self.download_urls: + ModelDownloader.download_from_url(self.download_urls[file_name], path) + elif file_name == self.tokenizer_file: + if not os.path.exists(path + "/" + self.model_name): + logger.info(f"Downloading {self.model_name} tokenizer") + + tokenizer = AutoTokenizer.from_pretrained( + self.model_name, + trust_remote_code=True, + cache_dir=f"{MODEL_CACHE_DIR}/{self.model_name}/tokenizer", + clean_up_tokenization_spaces=True, + ) + tokenizer.save_pretrained(path) + + self.downloader.requestor.send_data( + UPDATE_MODEL_STATE, + { + "model": f"{self.model_name}-{file_name}", + "state": ModelStatusTypesEnum.downloaded, + }, + ) + except Exception: + self.downloader.requestor.send_data( + UPDATE_MODEL_STATE, + { + "model": f"{self.model_name}-{file_name}", + "state": ModelStatusTypesEnum.error, + }, + ) + + def _load_model_and_utils(self): + if self.runner is None: + if self.downloader: + self.downloader.wait_for_download() + + tokenizer_path = os.path.join( + f"{MODEL_CACHE_DIR}/{self.model_name}/tokenizer" + ) + self.tokenizer = AutoTokenizer.from_pretrained( + self.model_name, + cache_dir=tokenizer_path, + trust_remote_code=True, + clean_up_tokenization_spaces=True, + ) + + self.runner = ONNXModelRunner( + os.path.join(self.download_path, self.model_file), + self.device, + self.model_size, + ) + + def _preprocess_inputs(self, raw_inputs): + max_length = max(len(self.tokenizer.encode(text)) for text in raw_inputs) + return [ + self.tokenizer( + text, + padding="max_length", + truncation=True, + max_length=max_length, + return_tensors="np", + ) + for text in raw_inputs + ] + + +class JinaV1ImageEmbedding(BaseEmbedding): + def __init__( + self, + model_size: str, + requestor: InterProcessRequestor, + device: str = "AUTO", + ): + model_file = ( + "vision_model_fp16.onnx" + if model_size == "large" + else "vision_model_quantized.onnx" + ) + super().__init__( + model_name="jinaai/jina-clip-v1", + model_file=model_file, + download_urls={ + model_file: f"https://huggingface.co/jinaai/jina-clip-v1/resolve/main/onnx/{model_file}", + "preprocessor_config.json": "https://huggingface.co/jinaai/jina-clip-v1/resolve/main/preprocessor_config.json", + }, + ) + self.requestor = requestor + self.model_size = model_size + self.device = device + self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name) + self.feature_extractor = None + self.runner: ONNXModelRunner | None = None + files_names = list(self.download_urls.keys()) + if not all( + os.path.exists(os.path.join(self.download_path, n)) for n in files_names + ): + logger.debug(f"starting model download for {self.model_name}") + self.downloader = ModelDownloader( + model_name=self.model_name, + download_path=self.download_path, + file_names=files_names, + download_func=self._download_model, + ) + self.downloader.ensure_model_files() + else: + self.downloader = None + ModelDownloader.mark_files_state( + self.requestor, + self.model_name, + files_names, + ModelStatusTypesEnum.downloaded, + ) + self._load_model_and_utils() + logger.debug(f"models are already downloaded for {self.model_name}") + + def _load_model_and_utils(self): + if self.runner is None: + if self.downloader: + self.downloader.wait_for_download() + + self.feature_extractor = AutoFeatureExtractor.from_pretrained( + f"{MODEL_CACHE_DIR}/{self.model_name}", + ) + + self.runner = ONNXModelRunner( + os.path.join(self.download_path, self.model_file), + self.device, + self.model_size, + ) + + def _preprocess_inputs(self, raw_inputs): + processed_images = [self._process_image(img) for img in raw_inputs] + return [ + self.feature_extractor(images=image, return_tensors="np") + for image in processed_images + ] diff --git a/frigate/embeddings/onnx/jina_v2_embedding.py b/frigate/embeddings/onnx/jina_v2_embedding.py new file mode 100644 index 000000000..be6573e50 --- /dev/null +++ b/frigate/embeddings/onnx/jina_v2_embedding.py @@ -0,0 +1,231 @@ +"""JinaV2 Embeddings.""" + +import io +import logging +import os + +import numpy as np +from PIL import Image +from transformers import AutoTokenizer +from transformers.utils.logging import disable_progress_bar, set_verbosity_error + +from frigate.comms.inter_process import InterProcessRequestor +from frigate.const import MODEL_CACHE_DIR, UPDATE_MODEL_STATE +from frigate.types import ModelStatusTypesEnum +from frigate.util.downloader import ModelDownloader + +from .base_embedding import BaseEmbedding +from .runner import ONNXModelRunner + +# disables the progress bar and download logging for downloading tokenizers and image processors +disable_progress_bar() +set_verbosity_error() +logger = logging.getLogger(__name__) + + +class JinaV2Embedding(BaseEmbedding): + def __init__( + self, + model_size: str, + requestor: InterProcessRequestor, + device: str = "AUTO", + embedding_type: str = None, + ): + model_file = ( + "model_fp16.onnx" if model_size == "large" else "model_quantized.onnx" + ) + super().__init__( + model_name="jinaai/jina-clip-v2", + model_file=model_file, + download_urls={ + model_file: f"https://huggingface.co/jinaai/jina-clip-v2/resolve/main/onnx/{model_file}", + "preprocessor_config.json": "https://huggingface.co/jinaai/jina-clip-v2/resolve/main/preprocessor_config.json", + }, + ) + self.tokenizer_file = "tokenizer" + self.embedding_type = embedding_type + self.requestor = requestor + self.model_size = model_size + self.device = device + self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name) + self.tokenizer = None + self.image_processor = None + self.runner = None + files_names = list(self.download_urls.keys()) + [self.tokenizer_file] + if not all( + os.path.exists(os.path.join(self.download_path, n)) for n in files_names + ): + logger.debug(f"starting model download for {self.model_name}") + self.downloader = ModelDownloader( + model_name=self.model_name, + download_path=self.download_path, + file_names=files_names, + download_func=self._download_model, + ) + self.downloader.ensure_model_files() + else: + self.downloader = None + ModelDownloader.mark_files_state( + self.requestor, + self.model_name, + files_names, + ModelStatusTypesEnum.downloaded, + ) + self._load_model_and_utils() + logger.debug(f"models are already downloaded for {self.model_name}") + + def _download_model(self, path: str): + try: + file_name = os.path.basename(path) + + if file_name in self.download_urls: + ModelDownloader.download_from_url(self.download_urls[file_name], path) + elif file_name == self.tokenizer_file: + if not os.path.exists(os.path.join(path, self.model_name)): + logger.info(f"Downloading {self.model_name} tokenizer") + + tokenizer = AutoTokenizer.from_pretrained( + self.model_name, + trust_remote_code=True, + cache_dir=os.path.join( + MODEL_CACHE_DIR, self.model_name, "tokenizer" + ), + clean_up_tokenization_spaces=True, + ) + tokenizer.save_pretrained(path) + self.requestor.send_data( + UPDATE_MODEL_STATE, + { + "model": f"{self.model_name}-{file_name}", + "state": ModelStatusTypesEnum.downloaded, + }, + ) + except Exception: + self.requestor.send_data( + UPDATE_MODEL_STATE, + { + "model": f"{self.model_name}-{file_name}", + "state": ModelStatusTypesEnum.error, + }, + ) + + def _load_model_and_utils(self): + if self.runner is None: + if self.downloader: + self.downloader.wait_for_download() + + tokenizer_path = os.path.join( + f"{MODEL_CACHE_DIR}/{self.model_name}/tokenizer" + ) + self.tokenizer = AutoTokenizer.from_pretrained( + self.model_name, + cache_dir=tokenizer_path, + trust_remote_code=True, + clean_up_tokenization_spaces=True, + ) + + self.runner = ONNXModelRunner( + os.path.join(self.download_path, self.model_file), + self.device, + self.model_size, + ) + + def _preprocess_image(self, image_data: bytes | Image.Image) -> np.ndarray: + """ + Manually preprocess a single image from bytes or PIL.Image to (3, 512, 512). + """ + if isinstance(image_data, bytes): + image = Image.open(io.BytesIO(image_data)) + else: + image = image_data + + if image.mode != "RGB": + image = image.convert("RGB") + + image = image.resize((512, 512), Image.Resampling.LANCZOS) + + # Convert to numpy array, normalize to [0, 1], and transpose to (channels, height, width) + image_array = np.array(image, dtype=np.float32) / 255.0 + image_array = np.transpose(image_array, (2, 0, 1)) # (H, W, C) -> (C, H, W) + + return image_array + + def _preprocess_inputs(self, raw_inputs): + """ + Preprocess inputs into a list of real input tensors (no dummies). + - For text: Returns list of input_ids. + - For vision: Returns list of pixel_values. + """ + if not isinstance(raw_inputs, list): + raw_inputs = [raw_inputs] + + processed = [] + if self.embedding_type == "text": + for text in raw_inputs: + input_ids = self.tokenizer([text], return_tensors="np")["input_ids"] + processed.append(input_ids) + elif self.embedding_type == "vision": + for img in raw_inputs: + pixel_values = self._preprocess_image(img) + processed.append( + pixel_values[np.newaxis, ...] + ) # Add batch dim: (1, 3, 512, 512) + else: + raise ValueError( + f"Invalid embedding_type: {self.embedding_type}. Must be 'text' or 'vision'." + ) + return processed + + def _postprocess_outputs(self, outputs): + """ + Process ONNX model outputs, truncating each embedding in the array to truncate_dim. + - outputs: NumPy array of embeddings. + - Returns: List of truncated embeddings. + """ + # size of vector in database + truncate_dim = 768 + + # jina v2 defaults to 1024 and uses Matryoshka representation, so + # truncating only causes an extremely minor decrease in retrieval accuracy + if outputs.shape[-1] > truncate_dim: + outputs = outputs[..., :truncate_dim] + + return outputs + + def __call__( + self, inputs: list[str] | list[Image.Image] | list[str], embedding_type=None + ) -> list[np.ndarray]: + self.embedding_type = embedding_type + if not self.embedding_type: + raise ValueError( + "embedding_type must be specified either in __init__ or __call__" + ) + + self._load_model_and_utils() + processed = self._preprocess_inputs(inputs) + batch_size = len(processed) + + # Prepare ONNX inputs with matching batch sizes + onnx_inputs = {} + if self.embedding_type == "text": + onnx_inputs["input_ids"] = np.stack([x[0] for x in processed]) + onnx_inputs["pixel_values"] = np.zeros( + (batch_size, 3, 512, 512), dtype=np.float32 + ) + elif self.embedding_type == "vision": + onnx_inputs["input_ids"] = np.zeros((batch_size, 16), dtype=np.int64) + onnx_inputs["pixel_values"] = np.stack([x[0] for x in processed]) + else: + raise ValueError("Invalid embedding type") + + # Run inference + outputs = self.runner.run(onnx_inputs) + if self.embedding_type == "text": + embeddings = outputs[2] # text embeddings + elif self.embedding_type == "vision": + embeddings = outputs[3] # image embeddings + else: + raise ValueError("Invalid embedding type") + + embeddings = self._postprocess_outputs(embeddings) + return [embedding for embedding in embeddings] diff --git a/frigate/embeddings/onnx/lpr_embedding.py b/frigate/embeddings/onnx/lpr_embedding.py new file mode 100644 index 000000000..c3b9a8771 --- /dev/null +++ b/frigate/embeddings/onnx/lpr_embedding.py @@ -0,0 +1,297 @@ +import logging +import os +import warnings + +import cv2 +import numpy as np + +from frigate.comms.inter_process import InterProcessRequestor +from frigate.const import MODEL_CACHE_DIR +from frigate.types import ModelStatusTypesEnum +from frigate.util.downloader import ModelDownloader + +from .base_embedding import BaseEmbedding +from .runner import ONNXModelRunner + +warnings.filterwarnings( + "ignore", + category=FutureWarning, + message="The class CLIPFeatureExtractor is deprecated", +) + +logger = logging.getLogger(__name__) + +LPR_EMBEDDING_SIZE = 256 + + +class PaddleOCRDetection(BaseEmbedding): + def __init__( + self, + model_size: str, + requestor: InterProcessRequestor, + device: str = "AUTO", + ): + super().__init__( + model_name="paddleocr-onnx", + model_file="detection.onnx", + download_urls={ + "detection.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/detection.onnx" + }, + ) + self.requestor = requestor + self.model_size = model_size + self.device = device + self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name) + self.runner: ONNXModelRunner | None = None + files_names = list(self.download_urls.keys()) + if not all( + os.path.exists(os.path.join(self.download_path, n)) for n in files_names + ): + logger.debug(f"starting model download for {self.model_name}") + self.downloader = ModelDownloader( + model_name=self.model_name, + download_path=self.download_path, + file_names=files_names, + download_func=self._download_model, + ) + self.downloader.ensure_model_files() + else: + self.downloader = None + ModelDownloader.mark_files_state( + self.requestor, + self.model_name, + files_names, + ModelStatusTypesEnum.downloaded, + ) + self._load_model_and_utils() + logger.debug(f"models are already downloaded for {self.model_name}") + + def _load_model_and_utils(self): + if self.runner is None: + if self.downloader: + self.downloader.wait_for_download() + + self.runner = ONNXModelRunner( + os.path.join(self.download_path, self.model_file), + self.device, + self.model_size, + ) + + def _preprocess_inputs(self, raw_inputs): + preprocessed = [] + for x in raw_inputs: + preprocessed.append(x) + return [{"x": preprocessed[0]}] + + +class PaddleOCRClassification(BaseEmbedding): + def __init__( + self, + model_size: str, + requestor: InterProcessRequestor, + device: str = "AUTO", + ): + super().__init__( + model_name="paddleocr-onnx", + model_file="classification.onnx", + download_urls={ + "classification.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/classification.onnx" + }, + ) + self.requestor = requestor + self.model_size = model_size + self.device = device + self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name) + self.runner: ONNXModelRunner | None = None + files_names = list(self.download_urls.keys()) + if not all( + os.path.exists(os.path.join(self.download_path, n)) for n in files_names + ): + logger.debug(f"starting model download for {self.model_name}") + self.downloader = ModelDownloader( + model_name=self.model_name, + download_path=self.download_path, + file_names=files_names, + download_func=self._download_model, + ) + self.downloader.ensure_model_files() + else: + self.downloader = None + ModelDownloader.mark_files_state( + self.requestor, + self.model_name, + files_names, + ModelStatusTypesEnum.downloaded, + ) + self._load_model_and_utils() + logger.debug(f"models are already downloaded for {self.model_name}") + + def _load_model_and_utils(self): + if self.runner is None: + if self.downloader: + self.downloader.wait_for_download() + + self.runner = ONNXModelRunner( + os.path.join(self.download_path, self.model_file), + self.device, + self.model_size, + ) + + def _preprocess_inputs(self, raw_inputs): + processed = [] + for img in raw_inputs: + processed.append({"x": img}) + return processed + + +class PaddleOCRRecognition(BaseEmbedding): + def __init__( + self, + model_size: str, + requestor: InterProcessRequestor, + device: str = "AUTO", + ): + super().__init__( + model_name="paddleocr-onnx", + model_file="recognition.onnx", + download_urls={ + "recognition.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/recognition.onnx" + }, + ) + self.requestor = requestor + self.model_size = model_size + self.device = device + self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name) + self.runner: ONNXModelRunner | None = None + files_names = list(self.download_urls.keys()) + if not all( + os.path.exists(os.path.join(self.download_path, n)) for n in files_names + ): + logger.debug(f"starting model download for {self.model_name}") + self.downloader = ModelDownloader( + model_name=self.model_name, + download_path=self.download_path, + file_names=files_names, + download_func=self._download_model, + ) + self.downloader.ensure_model_files() + else: + self.downloader = None + ModelDownloader.mark_files_state( + self.requestor, + self.model_name, + files_names, + ModelStatusTypesEnum.downloaded, + ) + self._load_model_and_utils() + logger.debug(f"models are already downloaded for {self.model_name}") + + def _load_model_and_utils(self): + if self.runner is None: + if self.downloader: + self.downloader.wait_for_download() + + self.runner = ONNXModelRunner( + os.path.join(self.download_path, self.model_file), + self.device, + self.model_size, + ) + + def _preprocess_inputs(self, raw_inputs): + processed = [] + for img in raw_inputs: + processed.append({"x": img}) + return processed + + +class LicensePlateDetector(BaseEmbedding): + def __init__( + self, + model_size: str, + requestor: InterProcessRequestor, + device: str = "AUTO", + ): + super().__init__( + model_name="yolov9_license_plate", + model_file="yolov9-256-license-plates.onnx", + download_urls={ + "yolov9-256-license-plates.onnx": "https://github.com/hawkeye217/yolov9-license-plates/raw/refs/heads/master/models/yolov9-256-license-plates.onnx" + }, + ) + + self.requestor = requestor + self.model_size = model_size + self.device = device + self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name) + self.runner: ONNXModelRunner | None = None + files_names = list(self.download_urls.keys()) + if not all( + os.path.exists(os.path.join(self.download_path, n)) for n in files_names + ): + logger.debug(f"starting model download for {self.model_name}") + self.downloader = ModelDownloader( + model_name=self.model_name, + download_path=self.download_path, + file_names=files_names, + download_func=self._download_model, + ) + self.downloader.ensure_model_files() + else: + self.downloader = None + ModelDownloader.mark_files_state( + self.requestor, + self.model_name, + files_names, + ModelStatusTypesEnum.downloaded, + ) + self._load_model_and_utils() + logger.debug(f"models are already downloaded for {self.model_name}") + + def _load_model_and_utils(self): + if self.runner is None: + if self.downloader: + self.downloader.wait_for_download() + + self.runner = ONNXModelRunner( + os.path.join(self.download_path, self.model_file), + self.device, + self.model_size, + ) + + def _preprocess_inputs(self, raw_inputs): + if isinstance(raw_inputs, list): + raise ValueError("License plate embedding does not support batch inputs.") + # Get image as numpy array + img = self._process_image(raw_inputs) + height, width, channels = img.shape + + # Resize maintaining aspect ratio + if width > height: + new_height = int(((height / width) * LPR_EMBEDDING_SIZE) // 4 * 4) + img = cv2.resize(img, (LPR_EMBEDDING_SIZE, new_height)) + else: + new_width = int(((width / height) * LPR_EMBEDDING_SIZE) // 4 * 4) + img = cv2.resize(img, (new_width, LPR_EMBEDDING_SIZE)) + + # Get new dimensions after resize + og_h, og_w, channels = img.shape + + # Create black square frame + frame = np.full( + (LPR_EMBEDDING_SIZE, LPR_EMBEDDING_SIZE, channels), + (0, 0, 0), + dtype=np.float32, + ) + + # Center the resized image in the square frame + x_center = (LPR_EMBEDDING_SIZE - og_w) // 2 + y_center = (LPR_EMBEDDING_SIZE - og_h) // 2 + frame[y_center : y_center + og_h, x_center : x_center + og_w] = img + + # Normalize to 0-1 + frame = frame / 255.0 + + # Convert from HWC to CHW format and add batch dimension + frame = np.transpose(frame, (2, 0, 1)) + frame = np.expand_dims(frame, axis=0) + return [{"images": frame}] diff --git a/frigate/embeddings/onnx/runner.py b/frigate/embeddings/onnx/runner.py new file mode 100644 index 000000000..7badae325 --- /dev/null +++ b/frigate/embeddings/onnx/runner.py @@ -0,0 +1,76 @@ +"""Convenience runner for onnx models.""" + +import logging +import os.path +from typing import Any + +import onnxruntime as ort + +from frigate.const import MODEL_CACHE_DIR +from frigate.util.model import get_ort_providers + +try: + import openvino as ov +except ImportError: + # openvino is not included + pass + +logger = logging.getLogger(__name__) + + +class ONNXModelRunner: + """Run onnx models optimally based on available hardware.""" + + def __init__(self, model_path: str, device: str, requires_fp16: bool = False): + self.model_path = model_path + self.ort: ort.InferenceSession = None + self.ov: ov.Core = None + providers, options = get_ort_providers(device == "CPU", device, requires_fp16) + self.interpreter = None + + if "OpenVINOExecutionProvider" in providers: + try: + # use OpenVINO directly + self.type = "ov" + self.ov = ov.Core() + self.ov.set_property( + {ov.properties.cache_dir: os.path.join(MODEL_CACHE_DIR, "openvino")} + ) + self.interpreter = self.ov.compile_model( + model=model_path, device_name=device + ) + except Exception as e: + logger.warning( + f"OpenVINO failed to build model, using CPU instead: {e}" + ) + self.interpreter = None + + # Use ONNXRuntime + if self.interpreter is None: + self.type = "ort" + self.ort = ort.InferenceSession( + model_path, + providers=providers, + provider_options=options, + ) + + def get_input_names(self) -> list[str]: + if self.type == "ov": + input_names = [] + + for input in self.interpreter.inputs: + input_names.extend(input.names) + + return input_names + elif self.type == "ort": + return [input.name for input in self.ort.get_inputs()] + + def run(self, input: dict[str, Any]) -> Any: + if self.type == "ov": + infer_request = self.interpreter.create_infer_request() + + outputs = infer_request.infer(input) + + return outputs + elif self.type == "ort": + return self.ort.run(None, input) diff --git a/frigate/events/audio.py b/frigate/events/audio.py index 7675f821b..505802b8c 100644 --- a/frigate/events/audio.py +++ b/frigate/events/audio.py @@ -135,8 +135,13 @@ class AudioEventMaintainer(threading.Thread): # create communication for audio detections self.requestor = InterProcessRequestor() self.config_subscriber = ConfigSubscriber(f"config/audio/{camera.name}") + self.enabled_subscriber = ConfigSubscriber( + f"config/enabled/{camera.name}", True + ) self.detection_publisher = DetectionPublisher(DetectionTypeEnum.audio) + self.was_enabled = camera.enabled + def detect_audio(self, audio) -> None: if not self.config.audio.enabled or self.stop_event.is_set(): return @@ -248,6 +253,23 @@ class AudioEventMaintainer(threading.Thread): f"Failed to end audio event {detection['id']} with status code {resp.status_code}" ) + def expire_all_detections(self) -> None: + """Immediately end all current detections""" + now = datetime.datetime.now().timestamp() + for label, detection in list(self.detections.items()): + if detection: + self.requestor.send_data(f"{self.config.name}/audio/{label}", "OFF") + resp = requests.put( + f"{FRIGATE_LOCALHOST}/api/events/{detection['id']}/end", + json={"end_time": now}, + ) + if resp.status_code == 200: + self.detections[label] = None + else: + self.logger.warning( + f"Failed to end audio event {detection['id']} with status code {resp.status_code}" + ) + def start_or_restart_ffmpeg(self) -> None: self.audio_listener = start_or_restart_ffmpeg( self.ffmpeg_cmd, @@ -283,10 +305,41 @@ class AudioEventMaintainer(threading.Thread): self.logger.error(f"Error reading audio data from ffmpeg process: {e}") log_and_restart() + def _update_enabled_state(self) -> bool: + """Fetch the latest config and update enabled state.""" + _, config_data = self.enabled_subscriber.check_for_update() + if config_data: + self.config.enabled = config_data.enabled + return config_data.enabled + + return self.config.enabled + def run(self) -> None: - self.start_or_restart_ffmpeg() + if self._update_enabled_state(): + self.start_or_restart_ffmpeg() while not self.stop_event.is_set(): + enabled = self._update_enabled_state() + if enabled != self.was_enabled: + if enabled: + self.logger.debug( + f"Enabling audio detections for {self.config.name}" + ) + self.start_or_restart_ffmpeg() + else: + self.logger.debug( + f"Disabling audio detections for {self.config.name}, ending events" + ) + self.expire_all_detections() + stop_ffmpeg(self.audio_listener, self.logger) + self.audio_listener = None + self.was_enabled = enabled + continue + + if not enabled: + time.sleep(0.1) + continue + # check if there is an updated config ( updated_topic, @@ -302,6 +355,7 @@ class AudioEventMaintainer(threading.Thread): self.logpipe.close() self.requestor.stop() self.config_subscriber.stop() + self.enabled_subscriber.stop() self.detection_publisher.stop() diff --git a/frigate/ffmpeg_presets.py b/frigate/ffmpeg_presets.py index 208948044..3c251b3b7 100644 --- a/frigate/ffmpeg_presets.py +++ b/frigate/ffmpeg_presets.py @@ -10,6 +10,7 @@ from frigate.const import ( FFMPEG_HWACCEL_NVIDIA, FFMPEG_HWACCEL_VAAPI, FFMPEG_HWACCEL_VULKAN, + LIBAVFORMAT_VERSION_MAJOR, ) from frigate.util.services import vainfo_hwaccel from frigate.version import VERSION @@ -51,9 +52,8 @@ class LibvaGpuSelector: return "" -LIBAV_VERSION = int(os.getenv("LIBAVFORMAT_VERSION_MAJOR", "59") or "59") -FPS_VFR_PARAM = "-fps_mode vfr" if LIBAV_VERSION >= 59 else "-vsync 2" -TIMEOUT_PARAM = "-timeout" if LIBAV_VERSION >= 59 else "-stimeout" +FPS_VFR_PARAM = "-fps_mode vfr" if LIBAVFORMAT_VERSION_MAJOR >= 59 else "-vsync 2" +TIMEOUT_PARAM = "-timeout" if LIBAVFORMAT_VERSION_MAJOR >= 59 else "-stimeout" _gpu_selector = LibvaGpuSelector() _user_agent_args = [ @@ -65,8 +65,8 @@ PRESETS_HW_ACCEL_DECODE = { "preset-rpi-64-h264": "-c:v:1 h264_v4l2m2m", "preset-rpi-64-h265": "-c:v:1 hevc_v4l2m2m", FFMPEG_HWACCEL_VAAPI: f"-hwaccel_flags allow_profile_mismatch -hwaccel vaapi -hwaccel_device {_gpu_selector.get_selected_gpu()} -hwaccel_output_format vaapi", - "preset-intel-qsv-h264": f"-hwaccel qsv -qsv_device {_gpu_selector.get_selected_gpu()} -hwaccel_output_format qsv -c:v h264_qsv{' -bsf:v dump_extra' if LIBAV_VERSION >= 61 else ''}", # https://trac.ffmpeg.org/ticket/9766#comment:17 - "preset-intel-qsv-h265": f"-load_plugin hevc_hw -hwaccel qsv -qsv_device {_gpu_selector.get_selected_gpu()} -hwaccel_output_format qsv{' -bsf:v dump_extra' if LIBAV_VERSION >= 61 else ''}", # https://trac.ffmpeg.org/ticket/9766#comment:17 + "preset-intel-qsv-h264": f"-hwaccel qsv -qsv_device {_gpu_selector.get_selected_gpu()} -hwaccel_output_format qsv -c:v h264_qsv{' -bsf:v dump_extra' if LIBAVFORMAT_VERSION_MAJOR >= 61 else ''}", # https://trac.ffmpeg.org/ticket/9766#comment:17 + "preset-intel-qsv-h265": f"-load_plugin hevc_hw -hwaccel qsv -qsv_device {_gpu_selector.get_selected_gpu()} -hwaccel_output_format qsv{' -bsf:v dump_extra' if LIBAVFORMAT_VERSION_MAJOR >= 61 else ''}", # https://trac.ffmpeg.org/ticket/9766#comment:17 FFMPEG_HWACCEL_NVIDIA: "-hwaccel cuda -hwaccel_output_format cuda", "preset-jetson-h264": "-c:v h264_nvmpi -resize {1}x{2}", "preset-jetson-h265": "-c:v hevc_nvmpi -resize {1}x{2}", diff --git a/frigate/genai/openai.py b/frigate/genai/openai.py index 4568905a3..4b1926099 100644 --- a/frigate/genai/openai.py +++ b/frigate/genai/openai.py @@ -26,23 +26,30 @@ class OpenAIClient(GenAIClient): 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] + messages_content = [] + for image in encoded_images: + messages_content.append( + { + "type": "image_url", + "image_url": { + "url": f"data:image/jpeg;base64,{image}", + "detail": "low", + }, + } + ) + messages_content.append( + { + "type": "text", + "text": prompt, + } + ) 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], + "content": messages_content, }, ], timeout=self.timeout, diff --git a/frigate/models.py b/frigate/models.py index 62bbf0bd3..26375432e 100644 --- a/frigate/models.py +++ b/frigate/models.py @@ -117,5 +117,9 @@ class RecordingsToDelete(Model): # type: ignore[misc] class User(Model): # type: ignore[misc] username = CharField(null=False, primary_key=True, max_length=30) + role = CharField( + max_length=20, + default="viewer", + ) password_hash = CharField(null=False, max_length=120) notification_tokens = JSONField() diff --git a/frigate/motion/improved_motion.py b/frigate/motion/improved_motion.py index d865cc92d..aae5167a4 100644 --- a/frigate/motion/improved_motion.py +++ b/frigate/motion/improved_motion.py @@ -49,7 +49,7 @@ class ImprovedMotionDetector(MotionDetector): self.contrast_values = np.zeros((contrast_frame_history, 2), np.uint8) self.contrast_values[:, 1:2] = 255 self.contrast_values_index = 0 - self.config_subscriber = ConfigSubscriber(f"config/motion/{name}") + self.config_subscriber = ConfigSubscriber(f"config/motion/{name}", True) self.ptz_metrics = ptz_metrics self.last_stop_time = None diff --git a/frigate/object_detection.py b/frigate/object_detection.py index 022e565f0..8e88ae578 100644 --- a/frigate/object_detection.py +++ b/frigate/object_detection.py @@ -17,7 +17,6 @@ from frigate.detectors.detector_config import ( InputDTypeEnum, InputTensorEnum, ) -from frigate.detectors.plugins.rocm import DETECTOR_KEY as ROCM_DETECTOR_KEY from frigate.util.builtin import EventsPerSecond, load_labels from frigate.util.image import SharedMemoryFrameManager, UntrackedSharedMemory from frigate.util.services import listen @@ -52,13 +51,7 @@ class LocalObjectDetector(ObjectDetector): self.labels = load_labels(labels) if detector_config: - if detector_config.type == ROCM_DETECTOR_KEY: - # ROCm requires NHWC as input - self.input_transform = None - else: - self.input_transform = tensor_transform( - detector_config.model.input_tensor - ) + self.input_transform = tensor_transform(detector_config.model.input_tensor) self.dtype = detector_config.model.input_dtype else: diff --git a/frigate/object_processing.py b/frigate/object_processing.py index 137883b2b..8faf91cb5 100644 --- a/frigate/object_processing.py +++ b/frigate/object_processing.py @@ -10,6 +10,7 @@ from typing import Callable, Optional import cv2 import numpy as np +from frigate.comms.config_updater import ConfigSubscriber from frigate.comms.detections_updater import DetectionPublisher, DetectionTypeEnum from frigate.comms.dispatcher import Dispatcher from frigate.comms.events_updater import EventEndSubscriber, EventUpdatePublisher @@ -61,6 +62,7 @@ class CameraState: self.previous_frame_id = None self.callbacks = defaultdict(list) self.ptz_autotracker_thread = ptz_autotracker_thread + self.prev_enabled = self.camera_config.enabled def get_current_frame(self, draw_options={}): with self.current_frame_lock: @@ -310,6 +312,7 @@ class CameraState: # TODO: can i switch to looking this up and only changing when an event ends? # maintain best objects camera_activity: dict[str, list[any]] = { + "enabled": True, "motion": len(motion_boxes) > 0, "objects": [], } @@ -437,6 +440,8 @@ class TrackedObjectProcessor(threading.Thread): self.last_motion_detected: dict[str, float] = {} self.ptz_autotracker_thread = ptz_autotracker_thread + self.config_enabled_subscriber = ConfigSubscriber("config/enabled/") + self.requestor = InterProcessRequestor() self.detection_publisher = DetectionPublisher(DetectionTypeEnum.video) self.event_sender = EventUpdatePublisher() @@ -679,8 +684,63 @@ class TrackedObjectProcessor(threading.Thread): """Returns the latest frame time for a given camera.""" return self.camera_states[camera].current_frame_time + def force_end_all_events(self, camera: str, camera_state: CameraState): + """Ends all active events on camera when disabling.""" + last_frame_name = camera_state.previous_frame_id + for obj_id, obj in list(camera_state.tracked_objects.items()): + if "end_time" not in obj.obj_data: + logger.debug(f"Camera {camera} disabled, ending active event {obj_id}") + obj.obj_data["end_time"] = datetime.datetime.now().timestamp() + # end callbacks + for callback in camera_state.callbacks["end"]: + callback(camera, obj, last_frame_name) + + # camera activity callbacks + for callback in camera_state.callbacks["camera_activity"]: + callback( + camera, + {"enabled": False, "motion": 0, "objects": []}, + ) + def run(self): while not self.stop_event.is_set(): + # check for config updates + while True: + ( + updated_enabled_topic, + updated_enabled_config, + ) = self.config_enabled_subscriber.check_for_update() + + if not updated_enabled_topic: + break + + camera_name = updated_enabled_topic.rpartition("/")[-1] + self.config.cameras[ + camera_name + ].enabled = updated_enabled_config.enabled + + if self.camera_states[camera_name].prev_enabled is None: + self.camera_states[ + camera_name + ].prev_enabled = updated_enabled_config.enabled + + # manage camera disabled state + for camera, config in self.config.cameras.items(): + if not config.enabled_in_config: + continue + + current_enabled = config.enabled + camera_state = self.camera_states[camera] + + if camera_state.prev_enabled and not current_enabled: + logger.debug(f"Not processing objects for disabled camera {camera}") + self.force_end_all_events(camera, camera_state) + + camera_state.prev_enabled = current_enabled + + if not current_enabled: + continue + try: ( camera, @@ -693,6 +753,10 @@ class TrackedObjectProcessor(threading.Thread): except queue.Empty: continue + if not self.config.cameras[camera].enabled: + logger.debug(f"Camera {camera} disabled, skipping update") + continue + camera_state = self.camera_states[camera] camera_state.update( @@ -735,4 +799,6 @@ class TrackedObjectProcessor(threading.Thread): self.detection_publisher.stop() self.event_sender.stop() self.event_end_subscriber.stop() + self.config_enabled_subscriber.stop() + logger.info("Exiting object processor...") diff --git a/frigate/output/birdseye.py b/frigate/output/birdseye.py index 00f17c8f4..9bbd3abee 100644 --- a/frigate/output/birdseye.py +++ b/frigate/output/birdseye.py @@ -10,13 +10,14 @@ import queue import subprocess as sp import threading import traceback +from typing import Optional import cv2 import numpy as np from frigate.comms.config_updater import ConfigSubscriber from frigate.config import BirdseyeModeEnum, FfmpegConfig, FrigateConfig -from frigate.const import BASE_DIR, BIRDSEYE_PIPE +from frigate.const import BASE_DIR, BIRDSEYE_PIPE, INSTALL_DIR from frigate.util.image import ( SharedMemoryFrameManager, copy_yuv_to_position, @@ -297,7 +298,9 @@ class BirdsEyeFrameManager: birdseye_logo = cv2.imread(custom_logo_files[0], cv2.IMREAD_UNCHANGED) if birdseye_logo is None: - logo_files = glob.glob("/opt/frigate/frigate/images/birdseye.png") + logo_files = glob.glob( + os.path.join(INSTALL_DIR, "frigate/images/birdseye.png") + ) if len(logo_files) > 0: birdseye_logo = cv2.imread(logo_files[0], cv2.IMREAD_UNCHANGED) @@ -378,8 +381,11 @@ class BirdsEyeFrameManager: if mode == BirdseyeModeEnum.objects and object_box_count > 0: return True - def update_frame(self, frame: np.ndarray): - """Update to a new frame for birdseye.""" + def update_frame(self, frame: Optional[np.ndarray] = None) -> bool: + """ + Update birdseye, optionally with a new frame. + When no frame is passed, check the layout and update for any disabled cameras. + """ # determine how many cameras are tracking objects within the last inactivity_threshold seconds active_cameras: set[str] = set( @@ -387,11 +393,14 @@ class BirdsEyeFrameManager: cam for cam, cam_data in self.cameras.items() if self.config.cameras[cam].birdseye.enabled + and self.config.cameras[cam].enabled_in_config + and self.config.cameras[cam].enabled and cam_data["last_active_frame"] > 0 and cam_data["current_frame_time"] - cam_data["last_active_frame"] < self.inactivity_threshold ] ) + logger.debug(f"Active cameras: {active_cameras}") max_cameras = self.config.birdseye.layout.max_cameras max_camera_refresh = False @@ -409,118 +418,125 @@ class BirdsEyeFrameManager: - self.cameras[active_camera]["last_active_frame"] ), ) - active_cameras = limited_active_cameras[ - : self.config.birdseye.layout.max_cameras - ] + active_cameras = limited_active_cameras[:max_cameras] max_camera_refresh = True self.last_refresh_time = now - # if there are no active cameras + # Track if the frame changes + frame_changed = False + + # If no active cameras and layout is already empty, no update needed if len(active_cameras) == 0: # if the layout is already cleared if len(self.camera_layout) == 0: return False # if the layout needs to be cleared - else: - self.camera_layout = [] - self.active_cameras = set() - self.clear_frame() - return True - - # check if we need to reset the layout because there is a different number of cameras - if len(self.active_cameras) - len(active_cameras) == 0: - if len(self.active_cameras) == 1 and self.active_cameras != active_cameras: - reset_layout = True - elif max_camera_refresh: - reset_layout = True - else: - reset_layout = False - else: - reset_layout = True - - # reset the layout if it needs to be different - if reset_layout: - logger.debug("Added new cameras, resetting layout...") + self.camera_layout = [] + self.active_cameras = set() self.clear_frame() - self.active_cameras = active_cameras - - # this also converts added_cameras from a set to a list since we need - # to pop elements in order - active_cameras_to_add = sorted( - active_cameras, - # sort cameras by order and by name if the order is the same - key=lambda active_camera: ( - self.config.cameras[active_camera].birdseye.order, - active_camera, - ), - ) - - if len(active_cameras) == 1: - # show single camera as fullscreen - camera = active_cameras_to_add[0] - camera_dims = self.cameras[camera]["dimensions"].copy() - scaled_width = int(self.canvas.height * camera_dims[0] / camera_dims[1]) - - # center camera view in canvas and ensure that it fits - if scaled_width < self.canvas.width: - coefficient = 1 - x_offset = int((self.canvas.width - scaled_width) / 2) + frame_changed = True + else: + # Determine if layout needs resetting + if len(self.active_cameras) - len(active_cameras) == 0: + if ( + len(self.active_cameras) == 1 + and self.active_cameras != active_cameras + ): + reset_layout = True + elif max_camera_refresh: + reset_layout = True else: - coefficient = self.canvas.width / scaled_width - x_offset = int( - (self.canvas.width - (scaled_width * coefficient)) / 2 - ) - - self.camera_layout = [ - [ - ( - camera, - ( - x_offset, - 0, - int(scaled_width * coefficient), - int(self.canvas.height * coefficient), - ), - ) - ] - ] + reset_layout = False else: - # calculate optimal layout - coefficient = self.canvas.get_coefficient(len(active_cameras)) - calculating = True + reset_layout = True - # decrease scaling coefficient until height of all cameras can fit into the birdseye canvas - while calculating: - if self.stop_event.is_set(): - return + if reset_layout: + logger.debug("Resetting Birdseye layout...") + self.clear_frame() + self.active_cameras = active_cameras - layout_candidate = self.calculate_layout( - active_cameras_to_add, - coefficient, + # this also converts added_cameras from a set to a list since we need + # to pop elements in order + active_cameras_to_add = sorted( + active_cameras, + # sort cameras by order and by name if the order is the same + key=lambda active_camera: ( + self.config.cameras[active_camera].birdseye.order, + active_camera, + ), + ) + if len(active_cameras) == 1: + # show single camera as fullscreen + camera = active_cameras_to_add[0] + camera_dims = self.cameras[camera]["dimensions"].copy() + scaled_width = int( + self.canvas.height * camera_dims[0] / camera_dims[1] ) - if not layout_candidate: - if coefficient < 10: - coefficient += 1 - continue - else: - logger.error("Error finding appropriate birdseye layout") + # center camera view in canvas and ensure that it fits + if scaled_width < self.canvas.width: + coefficient = 1 + x_offset = int((self.canvas.width - scaled_width) / 2) + else: + coefficient = self.canvas.width / scaled_width + x_offset = int( + (self.canvas.width - (scaled_width * coefficient)) / 2 + ) + + self.camera_layout = [ + [ + ( + camera, + ( + x_offset, + 0, + int(scaled_width * coefficient), + int(self.canvas.height * coefficient), + ), + ) + ] + ] + else: + # calculate optimal layout + coefficient = self.canvas.get_coefficient(len(active_cameras)) + calculating = True + + # decrease scaling coefficient until height of all cameras can fit into the birdseye canvas + while calculating: + if self.stop_event.is_set(): return - calculating = False - self.canvas.set_coefficient(len(active_cameras), coefficient) + layout_candidate = self.calculate_layout( + active_cameras_to_add, coefficient + ) - self.camera_layout = layout_candidate + if not layout_candidate: + if coefficient < 10: + coefficient += 1 + continue + else: + logger.error( + "Error finding appropriate birdseye layout" + ) + return + calculating = False + self.canvas.set_coefficient(len(active_cameras), coefficient) - for row in self.camera_layout: - for position in row: - self.copy_to_position( - position[1], - position[0], - self.cameras[position[0]]["current_frame"], - ) + self.camera_layout = layout_candidate + frame_changed = True - return True + # Draw the layout + for row in self.camera_layout: + for position in row: + src_frame = self.cameras[position[0]]["current_frame"] + if src_frame is None or src_frame.size == 0: + logger.debug(f"Skipping invalid frame for {position[0]}") + continue + self.copy_to_position(position[1], position[0], src_frame) + if frame is not None: # Frame presence indicates a potential change + frame_changed = True + + return frame_changed def calculate_layout( self, @@ -674,30 +690,29 @@ class BirdsEyeFrameManager: frame: np.ndarray, ) -> bool: # don't process if birdseye is disabled for this camera - camera_config = self.config.cameras[camera].birdseye - - if not camera_config.enabled: - return False + camera_config = self.config.cameras[camera] + force_update = False # disabling birdseye is a little tricky - if not camera_config.enabled: + if not camera_config.birdseye.enabled or not camera_config.enabled: # if we've rendered a frame (we have a value for last_active_frame) # then we need to set it to zero if self.cameras[camera]["last_active_frame"] > 0: self.cameras[camera]["last_active_frame"] = 0 - - return False + force_update = True + else: + return False # update the last active frame for the camera self.cameras[camera]["current_frame"] = frame.copy() self.cameras[camera]["current_frame_time"] = frame_time - if self.camera_active(camera_config.mode, object_count, motion_count): + if self.camera_active(camera_config.birdseye.mode, object_count, motion_count): self.cameras[camera]["last_active_frame"] = frame_time now = datetime.datetime.now().timestamp() # limit output to 10 fps - if (now - self.last_output_time) < 1 / 10: + if not force_update and (now - self.last_output_time) < 1 / 10: return False try: @@ -709,7 +724,7 @@ class BirdsEyeFrameManager: print(traceback.format_exc()) # if the frame was updated or the fps is too low, send frame - if updated_frame or (now - self.last_output_time) > 1: + if force_update or updated_frame or (now - self.last_output_time) > 1: self.last_output_time = now return True return False @@ -739,8 +754,10 @@ class Birdseye: "birdseye", self.converter, websocket_server, stop_event ) self.birdseye_manager = BirdsEyeFrameManager(config, stop_event) - self.config_subscriber = ConfigSubscriber("config/birdseye/") + self.config_enabled_subscriber = ConfigSubscriber("config/enabled/") + self.birdseye_subscriber = ConfigSubscriber("config/birdseye/") self.frame_manager = SharedMemoryFrameManager() + self.stop_event = stop_event if config.birdseye.restream: self.birdseye_buffer = self.frame_manager.create( @@ -751,6 +768,22 @@ class Birdseye: self.converter.start() self.broadcaster.start() + def __send_new_frame(self) -> None: + frame_bytes = self.birdseye_manager.frame.tobytes() + + if self.config.birdseye.restream: + self.birdseye_buffer[:] = frame_bytes + + try: + self.input.put_nowait(frame_bytes) + except queue.Full: + # drop frames if queue is full + pass + + def all_cameras_disabled(self) -> None: + self.birdseye_manager.clear_frame() + self.__send_new_frame() + def write_data( self, camera: str, @@ -762,15 +795,27 @@ class Birdseye: # check if there is an updated config while True: ( - updated_topic, + updated_birdseye_topic, updated_birdseye_config, - ) = self.config_subscriber.check_for_update() + ) = self.birdseye_subscriber.check_for_update() - if not updated_topic: + ( + updated_enabled_topic, + updated_enabled_config, + ) = self.config_enabled_subscriber.check_for_update() + + if not updated_birdseye_topic and not updated_enabled_topic: break - camera_name = updated_topic.rpartition("/")[-1] - self.config.cameras[camera_name].birdseye = updated_birdseye_config + if updated_birdseye_config: + camera_name = updated_birdseye_topic.rpartition("/")[-1] + self.config.cameras[camera_name].birdseye = updated_birdseye_config + + if updated_enabled_config: + camera_name = updated_enabled_topic.rpartition("/")[-1] + self.config.cameras[ + camera_name + ].enabled = updated_enabled_config.enabled if self.birdseye_manager.update( camera, @@ -779,18 +824,10 @@ class Birdseye: frame_time, frame, ): - frame_bytes = self.birdseye_manager.frame.tobytes() - - if self.config.birdseye.restream: - self.birdseye_buffer[:] = frame_bytes - - try: - self.input.put_nowait(frame_bytes) - except queue.Full: - # drop frames if queue is full - pass + self.__send_new_frame() def stop(self) -> None: - self.config_subscriber.stop() + self.birdseye_subscriber.stop() + self.config_enabled_subscriber.stop() self.converter.join() self.broadcaster.join() diff --git a/frigate/output/output.py b/frigate/output/output.py index bb2d73511..30900a5ab 100644 --- a/frigate/output/output.py +++ b/frigate/output/output.py @@ -1,12 +1,12 @@ """Handle outputting raw frigate frames""" +import datetime import logging import multiprocessing as mp import os import shutil import signal import threading -from typing import Optional from wsgiref.simple_server import make_server from setproctitle import setproctitle @@ -17,6 +17,7 @@ from ws4py.server.wsgirefserver import ( ) from ws4py.server.wsgiutils import WebSocketWSGIApplication +from frigate.comms.config_updater import ConfigSubscriber from frigate.comms.detections_updater import DetectionSubscriber, DetectionTypeEnum from frigate.comms.ws import WebSocket from frigate.config import FrigateConfig @@ -24,11 +25,51 @@ from frigate.const import CACHE_DIR, CLIPS_DIR from frigate.output.birdseye import Birdseye from frigate.output.camera import JsmpegCamera from frigate.output.preview import PreviewRecorder -from frigate.util.image import SharedMemoryFrameManager +from frigate.util.image import SharedMemoryFrameManager, get_blank_yuv_frame logger = logging.getLogger(__name__) +def check_disabled_camera_update( + config: FrigateConfig, + birdseye: Birdseye | None, + previews: dict[str, PreviewRecorder], + write_times: dict[str, float], +) -> None: + """Check if camera is disabled / offline and needs an update.""" + now = datetime.datetime.now().timestamp() + has_enabled_camera = False + + for camera, last_update in write_times.items(): + offline_time = now - last_update + + if config.cameras[camera].enabled: + has_enabled_camera = True + else: + # flag camera as offline when it is disabled + previews[camera].flag_offline(now) + + if offline_time > 1: + # last camera update was more than 1 second ago + # need to send empty data to birdseye because current + # frame is now out of date + if birdseye and offline_time < 10: + # we only need to send blank frames to birdseye at the beginning of a camera being offline + birdseye.write_data( + camera, + [], + [], + now, + get_blank_yuv_frame( + config.cameras[camera].detect.width, + config.cameras[camera].detect.height, + ), + ) + + if not has_enabled_camera and birdseye: + birdseye.all_cameras_disabled() + + def output_frames( config: FrigateConfig, ): @@ -59,11 +100,18 @@ def output_frames( detection_subscriber = DetectionSubscriber(DetectionTypeEnum.video) + enabled_subscribers = { + camera: ConfigSubscriber(f"config/enabled/{camera}", True) + for camera in config.cameras.keys() + if config.cameras[camera].enabled_in_config + } + jsmpeg_cameras: dict[str, JsmpegCamera] = {} - birdseye: Optional[Birdseye] = None + birdseye: Birdseye | None = None preview_recorders: dict[str, PreviewRecorder] = {} preview_write_times: dict[str, float] = {} failed_frame_requests: dict[str, int] = {} + last_disabled_cam_check = datetime.datetime.now().timestamp() move_preview_frames("cache") @@ -80,8 +128,25 @@ def output_frames( websocket_thread.start() + def get_enabled_state(camera: str) -> bool: + _, config_data = enabled_subscribers[camera].check_for_update() + + if config_data: + config.cameras[camera].enabled = config_data.enabled + return config_data.enabled + + return config.cameras[camera].enabled + while not stop_event.is_set(): (topic, data) = detection_subscriber.check_for_update(timeout=1) + now = datetime.datetime.now().timestamp() + + if now - last_disabled_cam_check > 5: + # check disabled cameras every 5 seconds + last_disabled_cam_check = now + check_disabled_camera_update( + config, birdseye, preview_recorders, preview_write_times + ) if not topic: continue @@ -95,6 +160,9 @@ def output_frames( _, ) = data + if not get_enabled_state(camera): + continue + frame = frame_manager.get(frame_name, config.cameras[camera].frame_shape_yuv) if frame is None: @@ -110,6 +178,12 @@ def output_frames( else: failed_frame_requests[camera] = 0 + # send frames for low fps recording + preview_recorders[camera].write_data( + current_tracked_objects, motion_boxes, frame_time, frame + ) + preview_write_times[camera] = frame_time + # send camera frame to ffmpeg process if websockets are connected if any( ws.environ["PATH_INFO"].endswith(camera) for ws in websocket_server.manager @@ -133,24 +207,6 @@ def output_frames( frame, ) - # send frames for low fps recording - generated_preview = preview_recorders[camera].write_data( - current_tracked_objects, motion_boxes, frame_time, frame - ) - preview_write_times[camera] = frame_time - - # if another camera generated a preview, - # check for any cameras that are currently offline - # and need to generate a preview - if generated_preview: - logger.debug( - "Checking for offline cameras because another camera generated a preview." - ) - for camera, time in preview_write_times.copy().items(): - if time != 0 and frame_time - time > 10: - preview_recorders[camera].flag_offline(frame_time) - preview_write_times[camera] = frame_time - frame_manager.close(frame_name) move_preview_frames("clips") @@ -184,6 +240,9 @@ def output_frames( if birdseye is not None: birdseye.stop() + for subscriber in enabled_subscribers.values(): + subscriber.stop() + websocket_server.manager.close_all() websocket_server.manager.stop() websocket_server.manager.join() diff --git a/frigate/output/preview.py b/frigate/output/preview.py index ae2ba4591..247886bfd 100644 --- a/frigate/output/preview.py +++ b/frigate/output/preview.py @@ -23,7 +23,7 @@ from frigate.ffmpeg_presets import ( ) from frigate.models import Previews from frigate.object_processing import TrackedObject -from frigate.util.image import copy_yuv_to_position, get_yuv_crop +from frigate.util.image import copy_yuv_to_position, get_blank_yuv_frame, get_yuv_crop logger = logging.getLogger(__name__) @@ -153,6 +153,7 @@ class PreviewRecorder: self.config = config self.start_time = 0 self.last_output_time = 0 + self.offline = False self.output_frames = [] if config.detect.width > config.detect.height: @@ -172,7 +173,9 @@ class PreviewRecorder: # create communication for finished previews self.requestor = InterProcessRequestor() - self.config_subscriber = ConfigSubscriber(f"config/record/{self.config.name}") + self.config_subscriber = ConfigSubscriber( + f"config/record/{self.config.name}", True + ) y, u1, u2, v1, v2 = get_yuv_crop( self.config.frame_shape_yuv, @@ -239,6 +242,17 @@ class PreviewRecorder: self.last_output_time = ts self.output_frames.append(ts) + def reset_frame_cache(self, frame_time: float) -> None: + self.segment_end = ( + (datetime.datetime.now() + datetime.timedelta(hours=1)) + .astimezone(datetime.timezone.utc) + .replace(minute=0, second=0, microsecond=0) + .timestamp() + ) + self.start_time = frame_time + self.last_output_time = frame_time + self.output_frames: list[float] = [] + def should_write_frame( self, current_tracked_objects: list[dict[str, any]], @@ -305,7 +319,9 @@ class PreviewRecorder: motion_boxes: list[list[int]], frame_time: float, frame: np.ndarray, - ) -> bool: + ) -> None: + self.offline = False + # check for updated record config _, updated_record_config = self.config_subscriber.check_for_update() @@ -317,7 +333,7 @@ class PreviewRecorder: self.start_time = frame_time self.output_frames.append(frame_time) self.write_frame_to_cache(frame_time, frame) - return False + return # check if PREVIEW clip should be generated and cached frames reset if frame_time >= self.segment_end: @@ -338,32 +354,35 @@ class PreviewRecorder: f"Not saving preview for {self.config.name} because there are no saved frames." ) - # reset frame cache - self.segment_end = ( - (datetime.datetime.now() + datetime.timedelta(hours=1)) - .astimezone(datetime.timezone.utc) - .replace(minute=0, second=0, microsecond=0) - .timestamp() - ) - self.start_time = frame_time - self.last_output_time = frame_time - self.output_frames: list[float] = [] + self.reset_frame_cache(frame_time) # include first frame to ensure consistent duration if self.config.record.enabled: self.output_frames.append(frame_time) self.write_frame_to_cache(frame_time, frame) - return True + return elif self.should_write_frame(current_tracked_objects, motion_boxes, frame_time): self.output_frames.append(frame_time) self.write_frame_to_cache(frame_time, frame) - return False + return def flag_offline(self, frame_time: float) -> None: + if not self.offline: + self.write_frame_to_cache( + frame_time, + get_blank_yuv_frame( + self.config.detect.width, self.config.detect.height + ), + ) + self.offline = True + # check if PREVIEW clip should be generated and cached frames reset if frame_time >= self.segment_end: if len(self.output_frames) == 0: + # camera has been offline for entire hour + # we have no preview to create + self.reset_frame_cache(frame_time) return old_frame_path = get_cache_image_name( @@ -380,16 +399,7 @@ class PreviewRecorder: self.requestor, ).start() - # reset frame cache - self.segment_end = ( - (datetime.datetime.now() + datetime.timedelta(hours=1)) - .astimezone(datetime.timezone.utc) - .replace(minute=0, second=0, microsecond=0) - .timestamp() - ) - self.start_time = frame_time - self.last_output_time = frame_time - self.output_frames = [] + self.reset_frame_cache(frame_time) def stop(self) -> None: self.requestor.stop() diff --git a/frigate/record/export.py b/frigate/record/export.py index f05fae6aa..0e64021b4 100644 --- a/frigate/record/export.py +++ b/frigate/record/export.py @@ -363,10 +363,13 @@ class RecordingExporter(threading.Thread): } ).execute() - if self.playback_source == PlaybackSourceEnum.recordings: - ffmpeg_cmd, playlist_lines = self.get_record_export_command(video_path) - else: - ffmpeg_cmd, playlist_lines = self.get_preview_export_command(video_path) + try: + if self.playback_source == PlaybackSourceEnum.recordings: + ffmpeg_cmd, playlist_lines = self.get_record_export_command(video_path) + else: + ffmpeg_cmd, playlist_lines = self.get_preview_export_command(video_path) + except DoesNotExist: + return p = sp.run( ffmpeg_cmd, diff --git a/frigate/record/maintainer.py b/frigate/record/maintainer.py index a4c23763d..1cabbfdda 100644 --- a/frigate/record/maintainer.py +++ b/frigate/record/maintainer.py @@ -19,6 +19,10 @@ import psutil from frigate.comms.config_updater import ConfigSubscriber from frigate.comms.detections_updater import DetectionSubscriber, DetectionTypeEnum from frigate.comms.inter_process import InterProcessRequestor +from frigate.comms.recordings_updater import ( + RecordingsDataPublisher, + RecordingsDataTypeEnum, +) from frigate.config import FrigateConfig, RetainModeEnum from frigate.const import ( CACHE_DIR, @@ -70,6 +74,9 @@ class RecordingMaintainer(threading.Thread): self.requestor = InterProcessRequestor() self.config_subscriber = ConfigSubscriber("config/record/") self.detection_subscriber = DetectionSubscriber(DetectionTypeEnum.all) + self.recordings_publisher = RecordingsDataPublisher( + RecordingsDataTypeEnum.recordings_available_through + ) self.stop_event = stop_event self.object_recordings_info: dict[str, list] = defaultdict(list) @@ -213,6 +220,16 @@ class RecordingMaintainer(threading.Thread): [self.validate_and_move_segment(camera, reviews, r) for r in recordings] ) + # publish most recently available recording time and None if disabled + self.recordings_publisher.publish( + ( + camera, + recordings[0]["start_time"].timestamp() + if self.config.cameras[camera].record.enabled + else None, + ) + ) + recordings_to_insert: list[Optional[Recordings]] = await asyncio.gather(*tasks) # fire and forget recordings entries @@ -456,7 +473,7 @@ class RecordingMaintainer(threading.Thread): # get the segment size of the cache file # file without faststart is same size segment_size = round( - float(os.path.getsize(cache_path)) / pow(2, 20), 1 + float(os.path.getsize(cache_path)) / pow(2, 20), 2 ) except OSError: segment_size = 0 @@ -582,4 +599,5 @@ class RecordingMaintainer(threading.Thread): self.requestor.stop() self.config_subscriber.stop() self.detection_subscriber.stop() + self.recordings_publisher.stop() logger.info("Exiting recording maintenance...") diff --git a/frigate/review/maintainer.py b/frigate/review/maintainer.py index 158bc3ac4..1c015d217 100644 --- a/frigate/review/maintainer.py +++ b/frigate/review/maintainer.py @@ -150,6 +150,7 @@ class ReviewSegmentMaintainer(threading.Thread): self.requestor = InterProcessRequestor() self.record_config_subscriber = ConfigSubscriber("config/record/") self.review_config_subscriber = ConfigSubscriber("config/review/") + self.enabled_config_subscriber = ConfigSubscriber("config/enabled/") self.detection_subscriber = DetectionSubscriber(DetectionTypeEnum.all) # manual events @@ -450,7 +451,16 @@ class ReviewSegmentMaintainer(threading.Thread): updated_review_config, ) = self.review_config_subscriber.check_for_update() - if not updated_record_topic and not updated_review_topic: + ( + updated_enabled_topic, + updated_enabled_config, + ) = self.enabled_config_subscriber.check_for_update() + + if ( + not updated_record_topic + and not updated_review_topic + and not updated_enabled_topic + ): break if updated_record_topic: @@ -461,6 +471,12 @@ class ReviewSegmentMaintainer(threading.Thread): camera_name = updated_review_topic.rpartition("/")[-1] self.config.cameras[camera_name].review = updated_review_config + if updated_enabled_config: + camera_name = updated_enabled_topic.rpartition("/")[-1] + self.config.cameras[ + camera_name + ].enabled = updated_enabled_config.enabled + (topic, data) = self.detection_subscriber.check_for_update(timeout=1) if not topic: @@ -494,7 +510,10 @@ class ReviewSegmentMaintainer(threading.Thread): current_segment = self.active_review_segments.get(camera) - if not self.config.cameras[camera].record.enabled: + if ( + not self.config.cameras[camera].enabled + or not self.config.cameras[camera].record.enabled + ): if current_segment: self.end_segment(camera) continue diff --git a/frigate/stats/util.py b/frigate/stats/util.py index 262cec3d2..287c384cd 100644 --- a/frigate/stats/util.py +++ b/frigate/stats/util.py @@ -282,16 +282,24 @@ def stats_snapshot( } stats["detection_fps"] = round(total_detection_fps, 2) - if config.semantic_search.enabled: - embeddings_metrics = stats_tracking["embeddings_metrics"] - stats["embeddings"] = { - "image_embedding_speed": round( - embeddings_metrics.image_embeddings_fps.value * 1000, 2 - ), - "text_embedding_speed": round( - embeddings_metrics.text_embeddings_sps.value * 1000, 2 - ), - } + stats["embeddings"] = {} + + # Get metrics if available + embeddings_metrics = stats_tracking.get("embeddings_metrics") + + if embeddings_metrics: + # Add metrics based on what's enabled + if config.semantic_search.enabled: + stats["embeddings"].update( + { + "image_embedding_speed": round( + embeddings_metrics.image_embeddings_fps.value * 1000, 2 + ), + "text_embedding_speed": round( + embeddings_metrics.text_embeddings_sps.value * 1000, 2 + ), + } + ) if config.face_recognition.enabled: stats["embeddings"]["face_recognition_speed"] = round( @@ -303,6 +311,11 @@ def stats_snapshot( embeddings_metrics.alpr_pps.value * 1000, 2 ) + if "license_plate" not in config.objects.all_objects: + stats["embeddings"]["yolov9_plate_detection_speed"] = round( + embeddings_metrics.yolov9_lpr_fps.value * 1000, 2 + ) + get_processing_stats(config, stats, hwaccel_errors) stats["service"] = { diff --git a/frigate/test/http_api/base_http_test.py b/frigate/test/http_api/base_http_test.py index c16ab9926..f5a0aca3c 100644 --- a/frigate/test/http_api/base_http_test.py +++ b/frigate/test/http_api/base_http_test.py @@ -10,6 +10,7 @@ from pydantic import Json from frigate.api.fastapi_app import create_fastapi_app from frigate.config import FrigateConfig +from frigate.const import BASE_DIR, CACHE_DIR from frigate.models import Event, Recordings, ReviewSegment from frigate.review.types import SeverityEnum from frigate.test.const import TEST_DB, TEST_DB_CLEANUPS @@ -73,19 +74,19 @@ class BaseTestHttp(unittest.TestCase): "total": 67.1, "used": 16.6, }, - "/media/frigate/clips": { + os.path.join(BASE_DIR, "clips"): { "free": 42429.9, "mount_type": "ext4", "total": 244529.7, "used": 189607.0, }, - "/media/frigate/recordings": { + os.path.join(BASE_DIR, "recordings"): { "free": 0.2, "mount_type": "ext4", "total": 8.0, "used": 7.8, }, - "/tmp/cache": { + CACHE_DIR: { "free": 976.8, "mount_type": "tmpfs", "total": 1000.0, diff --git a/frigate/test/http_api/test_http_review.py b/frigate/test/http_api/test_http_review.py index c8f2b1719..ee7d96bc5 100644 --- a/frigate/test/http_api/test_http_review.py +++ b/frigate/test/http_api/test_http_review.py @@ -504,7 +504,7 @@ class TestHttpReview(BaseTestHttp): def test_post_reviews_delete_no_body(self): with TestClient(self.app) as client: super().insert_mock_review_segment("123456.random") - response = client.post("/reviews/delete") + response = client.post("/reviews/delete", headers={"remote-role": "admin"}) # Missing ids assert response.status_code == 422 @@ -512,7 +512,9 @@ class TestHttpReview(BaseTestHttp): with TestClient(self.app) as client: super().insert_mock_review_segment("123456.random") body = {"ids": [""]} - response = client.post("/reviews/delete", json=body) + response = client.post( + "/reviews/delete", json=body, headers={"remote-role": "admin"} + ) # Missing ids assert response.status_code == 422 @@ -521,7 +523,9 @@ class TestHttpReview(BaseTestHttp): id = "123456.random" super().insert_mock_review_segment(id) body = {"ids": ["1"]} - response = client.post("/reviews/delete", json=body) + response = client.post( + "/reviews/delete", json=body, headers={"remote-role": "admin"} + ) assert response.status_code == 200 response_json = response.json() assert response_json["success"] == True @@ -536,7 +540,9 @@ class TestHttpReview(BaseTestHttp): id = "123456.random" super().insert_mock_review_segment(id) body = {"ids": [id]} - response = client.post("/reviews/delete", json=body) + response = client.post( + "/reviews/delete", json=body, headers={"remote-role": "admin"} + ) assert response.status_code == 200 response_json = response.json() assert response_json["success"] == True @@ -558,7 +564,9 @@ class TestHttpReview(BaseTestHttp): assert len(recordings_ids_in_db_before) == 2 body = {"ids": ids} - response = client.post("/reviews/delete", json=body) + response = client.post( + "/reviews/delete", json=body, headers={"remote-role": "admin"} + ) assert response.status_code == 200 response_json = response.json() assert response_json["success"] == True diff --git a/frigate/test/test_config.py b/frigate/test/test_config.py index e6cb1274e..5a3deefda 100644 --- a/frigate/test/test_config.py +++ b/frigate/test/test_config.py @@ -854,9 +854,9 @@ class TestConfig(unittest.TestCase): assert frigate_config.model.merged_labelmap[0] == "person" def test_plus_labelmap(self): - with open("/config/model_cache/test", "w") as f: + with open(os.path.join(MODEL_CACHE_DIR, "test"), "w") as f: json.dump(self.plus_model_info, f) - with open("/config/model_cache/test.json", "w") as f: + with open(os.path.join(MODEL_CACHE_DIR, "test.json"), "w") as f: json.dump(self.plus_model_info, f) config = { diff --git a/frigate/test/test_http.py b/frigate/test/test_http.py index 8c89e0433..d6ff91a83 100644 --- a/frigate/test/test_http.py +++ b/frigate/test/test_http.py @@ -11,6 +11,7 @@ from playhouse.sqliteq import SqliteQueueDatabase from frigate.api.fastapi_app import create_fastapi_app from frigate.config import FrigateConfig +from frigate.const import BASE_DIR, CACHE_DIR from frigate.models import Event, Recordings, Timeline from frigate.test.const import TEST_DB, TEST_DB_CLEANUPS @@ -74,19 +75,19 @@ class TestHttp(unittest.TestCase): "total": 67.1, "used": 16.6, }, - "/media/frigate/clips": { + os.path.join(BASE_DIR, "clips"): { "free": 42429.9, "mount_type": "ext4", "total": 244529.7, "used": 189607.0, }, - "/media/frigate/recordings": { + os.path.join(BASE_DIR, "recordings"): { "free": 0.2, "mount_type": "ext4", "total": 8.0, "used": 7.8, }, - "/tmp/cache": { + CACHE_DIR: { "free": 976.8, "mount_type": "tmpfs", "total": 1000.0, @@ -171,7 +172,7 @@ class TestHttp(unittest.TestCase): event = client.get(f"/events/{id}").json() assert event assert event["id"] == id - client.delete(f"/events/{id}") + client.delete(f"/events/{id}", headers={"remote-role": "admin"}) event = client.get(f"/events/{id}").json() assert event == "Event not found" @@ -191,12 +192,12 @@ class TestHttp(unittest.TestCase): with TestClient(app) as client: _insert_mock_event(id) - client.post(f"/events/{id}/retain") + client.post(f"/events/{id}/retain", headers={"remote-role": "admin"}) event = client.get(f"/events/{id}").json() assert event assert event["id"] == id assert event["retain_indefinitely"] is True - client.delete(f"/events/{id}/retain") + client.delete(f"/events/{id}/retain", headers={"remote-role": "admin"}) event = client.get(f"/events/{id}").json() assert event assert event["id"] == id @@ -261,6 +262,7 @@ class TestHttp(unittest.TestCase): new_sub_label_response = client.post( f"/events/{id}/sub_label", json={"subLabel": sub_label}, + headers={"remote-role": "admin"}, ) assert new_sub_label_response.status_code == 200 event = client.get(f"/events/{id}").json() @@ -270,12 +272,13 @@ class TestHttp(unittest.TestCase): empty_sub_label_response = client.post( f"/events/{id}/sub_label", json={"subLabel": ""}, + headers={"remote-role": "admin"}, ) assert empty_sub_label_response.status_code == 200 event = client.get(f"/events/{id}").json() assert event assert event["id"] == id - assert event["sub_label"] == "" + assert event["sub_label"] == None def test_sub_label_list(self): app = create_fastapi_app( @@ -297,6 +300,7 @@ class TestHttp(unittest.TestCase): client.post( f"/events/{id}/sub_label", json={"subLabel": sub_label}, + headers={"remote-role": "admin"}, ) sub_labels = client.get("/sub_labels").json() assert sub_labels diff --git a/frigate/util/config.py b/frigate/util/config.py index 5b40fe37b..7bdc0c3d6 100644 --- a/frigate/util/config.py +++ b/frigate/util/config.py @@ -14,7 +14,7 @@ from frigate.util.services import get_video_properties logger = logging.getLogger(__name__) CURRENT_CONFIG_VERSION = "0.16-0" -DEFAULT_CONFIG_FILE = "/config/config.yml" +DEFAULT_CONFIG_FILE = os.path.join(CONFIG_DIR, "config.yml") def find_config_file() -> str: @@ -300,6 +300,12 @@ def migrate_016_0(config: dict[str, dict[str, any]]) -> dict[str, dict[str, any] """Handle migrating frigate config to 0.16-0""" new_config = config.copy() + # migrate config that does not have detect -> enabled explicitly set to have it enabled + if new_config.get("detect", {}).get("enabled") is None: + detect_config = new_config.get("detect", {}) + detect_config["enabled"] = True + new_config["detect"] = detect_config + for name, camera in config.get("cameras", {}).items(): camera_config: dict[str, dict[str, any]] = camera.copy() diff --git a/frigate/util/image.py b/frigate/util/image.py index 7e4915821..0b80efe88 100644 --- a/frigate/util/image.py +++ b/frigate/util/image.py @@ -632,6 +632,22 @@ def copy_yuv_to_position( ) +def get_blank_yuv_frame(width: int, height: int) -> np.ndarray: + """Creates a black YUV 4:2:0 frame.""" + yuv_height = height * 3 // 2 + yuv_frame = np.zeros((yuv_height, width), dtype=np.uint8) + + uv_height = height // 2 + + # The U and V planes are stored after the Y plane. + u_start = height # U plane starts right after Y plane + v_start = u_start + uv_height // 2 # V plane starts after U plane + yuv_frame[u_start : u_start + uv_height, :width] = 128 + yuv_frame[v_start : v_start + uv_height, :width] = 128 + + return yuv_frame + + def yuv_region_2_yuv(frame, region): try: # TODO: does this copy the numpy array? @@ -959,3 +975,22 @@ def get_histogram(image, x_min, y_min, x_max, y_max): [image_bgr], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256] ) return cv2.normalize(hist, hist).flatten() + + +def ensure_jpeg_bytes(image_data): + """Ensure image data is jpeg bytes for genai""" + try: + img_array = np.frombuffer(image_data, dtype=np.uint8) + img = cv2.imdecode(img_array, cv2.IMREAD_COLOR) + + if img is None: + return image_data + + success, encoded_img = cv2.imencode(".jpg", img) + + if success: + return encoded_img.tobytes() + except Exception as e: + logger.warning(f"Error when converting thumbnail to jpeg for genai: {e}") + + return image_data diff --git a/frigate/util/model.py b/frigate/util/model.py index 75b545cfb..d96493ee6 100644 --- a/frigate/util/model.py +++ b/frigate/util/model.py @@ -2,21 +2,43 @@ import logging import os -from typing import Any import cv2 import numpy as np import onnxruntime as ort -try: - import openvino as ov -except ImportError: - # openvino is not included - pass +from frigate.const import MODEL_CACHE_DIR logger = logging.getLogger(__name__) + ### Post Processing +def post_process_dfine(tensor_output: np.ndarray, width, height) -> np.ndarray: + class_ids = tensor_output[0][tensor_output[2] > 0.4] + boxes = tensor_output[1][tensor_output[2] > 0.4] + scores = tensor_output[2][tensor_output[2] > 0.4] + + input_shape = np.array([height, width, height, width]) + boxes = np.divide(boxes, input_shape, dtype=np.float32) + indices = cv2.dnn.NMSBoxes(boxes, scores, score_threshold=0.4, nms_threshold=0.4) + detections = np.zeros((20, 6), np.float32) + + for i, (bbox, confidence, class_id) in enumerate( + zip(boxes[indices], scores[indices], class_ids[indices]) + ): + if i == 20: + break + + detections[i] = [ + class_id, + confidence, + bbox[1], + bbox[0], + bbox[3], + bbox[2], + ] + + return detections def post_process_yolov9(predictions: np.ndarray, width, height) -> np.ndarray: @@ -85,7 +107,8 @@ def get_ort_providers( # so it is not enabled by default if device == "Tensorrt": os.makedirs( - "/config/model_cache/tensorrt/ort/trt-engines", exist_ok=True + os.path.join(MODEL_CACHE_DIR, "tensorrt/ort/trt-engines"), + exist_ok=True, ) device_id = 0 if not device.isdigit() else int(device) providers.append(provider) @@ -96,19 +119,23 @@ def get_ort_providers( and os.environ.get("USE_FP_16", "True") != "False", "trt_timing_cache_enable": True, "trt_engine_cache_enable": True, - "trt_timing_cache_path": "/config/model_cache/tensorrt/ort", - "trt_engine_cache_path": "/config/model_cache/tensorrt/ort/trt-engines", + "trt_timing_cache_path": os.path.join( + MODEL_CACHE_DIR, "tensorrt/ort" + ), + "trt_engine_cache_path": os.path.join( + MODEL_CACHE_DIR, "tensorrt/ort/trt-engines" + ), } ) else: continue elif provider == "OpenVINOExecutionProvider": - os.makedirs("/config/model_cache/openvino/ort", exist_ok=True) + os.makedirs(os.path.join(MODEL_CACHE_DIR, "openvino/ort"), exist_ok=True) providers.append(provider) options.append( { "arena_extend_strategy": "kSameAsRequested", - "cache_dir": "/config/model_cache/openvino/ort", + "cache_dir": os.path.join(MODEL_CACHE_DIR, "openvino/ort"), "device_type": device, } ) @@ -124,66 +151,3 @@ def get_ort_providers( options.append({}) return (providers, options) - - -class ONNXModelRunner: - """Run onnx models optimally based on available hardware.""" - - def __init__(self, model_path: str, device: str, requires_fp16: bool = False): - self.model_path = model_path - self.ort: ort.InferenceSession = None - self.ov: ov.Core = None - providers, options = get_ort_providers(device == "CPU", device, requires_fp16) - self.interpreter = None - - if "OpenVINOExecutionProvider" in providers: - try: - # use OpenVINO directly - self.type = "ov" - self.ov = ov.Core() - self.ov.set_property( - {ov.properties.cache_dir: "/config/model_cache/openvino"} - ) - self.interpreter = self.ov.compile_model( - model=model_path, device_name=device - ) - except Exception as e: - logger.warning( - f"OpenVINO failed to build model, using CPU instead: {e}" - ) - self.interpreter = None - - # Use ONNXRuntime - if self.interpreter is None: - self.type = "ort" - self.ort = ort.InferenceSession( - model_path, - providers=providers, - provider_options=options, - ) - - def get_input_names(self) -> list[str]: - if self.type == "ov": - input_names = [] - - for input in self.interpreter.inputs: - input_names.extend(input.names) - - return input_names - elif self.type == "ort": - return [input.name for input in self.ort.get_inputs()] - - def run(self, input: dict[str, Any]) -> Any: - if self.type == "ov": - infer_request = self.interpreter.create_infer_request() - input_tensor = list(input.values()) - - if len(input_tensor) == 1: - input_tensor = ov.Tensor(array=input_tensor[0]) - else: - input_tensor = ov.Tensor(array=input_tensor) - - infer_request.infer(input_tensor) - return [infer_request.get_output_tensor().data] - elif self.type == "ort": - return self.ort.run(None, input) diff --git a/frigate/util/services.py b/frigate/util/services.py index d7966bd00..ce7041c26 100644 --- a/frigate/util/services.py +++ b/frigate/util/services.py @@ -362,7 +362,7 @@ def get_intel_gpu_stats(sriov: bool) -> dict[str, str]: if video_frame is not None: video[key].append(float(video_frame)) - if render["global"]: + if render["global"] and video["global"]: results["gpu"] = ( f"{round(((sum(render['global']) / len(render['global'])) + (sum(video['global']) / len(video['global']))) / 2, 2)}%" ) @@ -659,25 +659,42 @@ def process_logs( if " " not in clean_line: clean_line = f"{datetime.now()} {clean_line}" - # Find the position of the first double space to extract timestamp and message - date_end = clean_line.index(" ") - timestamp = clean_line[:date_end] - message_part = clean_line[date_end:].strip() + try: + # Find the position of the first double space to extract timestamp and message + date_end = clean_line.index(" ") + timestamp = clean_line[:date_end] + full_message = clean_line[date_end:].strip() - if message_part == last_message: - repeat_count += 1 - continue - else: - if repeat_count > 0: - # Insert a deduplication message formatted the same way as logs - dedup_message = f"{last_timestamp} [LOGGING] Last message repeated {repeat_count} times" - log_lines.append(dedup_message) - repeat_count = 0 + # For frigate, remove the date part from message comparison + if service == "frigate": + # Skip the date at the start of the message if it exists + date_parts = full_message.split("]", 1) + if len(date_parts) > 1: + message_part = date_parts[1].strip() + else: + message_part = full_message + else: + message_part = full_message + if message_part == last_message: + repeat_count += 1 + continue + else: + if repeat_count > 0: + # Insert a deduplication message formatted the same way as logs + dedup_message = f"{last_timestamp} [LOGGING] Last message repeated {repeat_count} times" + log_lines.append(dedup_message) + repeat_count = 0 + + log_lines.append(clean_line) + last_timestamp = timestamp + + last_message = message_part + + except ValueError: + # If we can't parse the line properly, just add it as is log_lines.append(clean_line) - last_timestamp = timestamp - - last_message = message_part + continue # If there were repeated messages at the end, log the count if repeat_count > 0: diff --git a/frigate/video.py b/frigate/video.py index f82d86648..89543e21a 100755 --- a/frigate/video.py +++ b/frigate/video.py @@ -108,8 +108,20 @@ def capture_frames( frame_rate.start() skipped_eps = EventsPerSecond() skipped_eps.start() + config_subscriber = ConfigSubscriber(f"config/enabled/{config.name}", True) + + def get_enabled_state(): + """Fetch the latest enabled state from ZMQ.""" + _, config_data = config_subscriber.check_for_update() + if config_data: + return config_data.enabled + return config.enabled + + while not stop_event.is_set(): + if not get_enabled_state(): + logger.debug(f"Stopping capture thread for disabled {config.name}") + break - while True: fps.value = frame_rate.eps() skipped_fps.value = skipped_eps.eps() current_frame.value = datetime.datetime.now().timestamp() @@ -178,26 +190,38 @@ class CameraWatchdog(threading.Thread): self.stop_event = stop_event self.sleeptime = self.config.ffmpeg.retry_interval - def run(self): - self.start_ffmpeg_detect() + self.config_subscriber = ConfigSubscriber(f"config/enabled/{camera_name}", True) + self.was_enabled = self.config.enabled - for c in self.config.ffmpeg_cmds: - if "detect" in c["roles"]: - continue - logpipe = LogPipe( - f"ffmpeg.{self.camera_name}.{'_'.join(sorted(c['roles']))}" - ) - self.ffmpeg_other_processes.append( - { - "cmd": c["cmd"], - "roles": c["roles"], - "logpipe": logpipe, - "process": start_or_restart_ffmpeg(c["cmd"], self.logger, logpipe), - } - ) + def _update_enabled_state(self) -> bool: + """Fetch the latest config and update enabled state.""" + _, config_data = self.config_subscriber.check_for_update() + if config_data: + self.config.enabled = config_data.enabled + return config_data.enabled + + return self.config.enabled + + def run(self): + if self._update_enabled_state(): + self.start_all_ffmpeg() time.sleep(self.sleeptime) while not self.stop_event.wait(self.sleeptime): + enabled = self._update_enabled_state() + if enabled != self.was_enabled: + if enabled: + self.logger.debug(f"Enabling camera {self.camera_name}") + self.start_all_ffmpeg() + else: + self.logger.debug(f"Disabling camera {self.camera_name}") + self.stop_all_ffmpeg() + self.was_enabled = enabled + continue + + if not enabled: + continue + now = datetime.datetime.now().timestamp() if not self.capture_thread.is_alive(): @@ -279,11 +303,9 @@ class CameraWatchdog(threading.Thread): p["cmd"], self.logger, p["logpipe"], ffmpeg_process=p["process"] ) - stop_ffmpeg(self.ffmpeg_detect_process, self.logger) - for p in self.ffmpeg_other_processes: - stop_ffmpeg(p["process"], self.logger) - p["logpipe"].close() + self.stop_all_ffmpeg() self.logpipe.close() + self.config_subscriber.stop() def start_ffmpeg_detect(self): ffmpeg_cmd = [ @@ -306,6 +328,43 @@ class CameraWatchdog(threading.Thread): ) self.capture_thread.start() + def start_all_ffmpeg(self): + """Start all ffmpeg processes (detection and others).""" + logger.debug(f"Starting all ffmpeg processes for {self.camera_name}") + self.start_ffmpeg_detect() + for c in self.config.ffmpeg_cmds: + if "detect" in c["roles"]: + continue + logpipe = LogPipe( + f"ffmpeg.{self.camera_name}.{'_'.join(sorted(c['roles']))}" + ) + self.ffmpeg_other_processes.append( + { + "cmd": c["cmd"], + "roles": c["roles"], + "logpipe": logpipe, + "process": start_or_restart_ffmpeg(c["cmd"], self.logger, logpipe), + } + ) + + def stop_all_ffmpeg(self): + """Stop all ffmpeg processes (detection and others).""" + logger.debug(f"Stopping all ffmpeg processes for {self.camera_name}") + if self.capture_thread is not None and self.capture_thread.is_alive(): + self.capture_thread.join(timeout=5) + if self.capture_thread.is_alive(): + self.logger.warning( + f"Capture thread for {self.camera_name} did not stop gracefully." + ) + if self.ffmpeg_detect_process is not None: + stop_ffmpeg(self.ffmpeg_detect_process, self.logger) + self.ffmpeg_detect_process = None + for p in self.ffmpeg_other_processes[:]: + if p["process"] is not None: + stop_ffmpeg(p["process"], self.logger) + p["logpipe"].close() + self.ffmpeg_other_processes.clear() + def get_latest_segment_datetime(self, latest_segment: datetime.datetime) -> int: """Checks if ffmpeg is still writing recording segments to cache.""" cache_files = sorted( @@ -539,7 +598,8 @@ def process_frames( exit_on_empty: bool = False, ): next_region_update = get_tomorrow_at_time(2) - config_subscriber = ConfigSubscriber(f"config/detect/{camera_name}") + detect_config_subscriber = ConfigSubscriber(f"config/detect/{camera_name}", True) + enabled_config_subscriber = ConfigSubscriber(f"config/enabled/{camera_name}", True) fps_tracker = EventsPerSecond() fps_tracker.start() @@ -549,9 +609,43 @@ def process_frames( region_min_size = get_min_region_size(model_config) + prev_enabled = None + while not stop_event.is_set(): + _, enabled_config = enabled_config_subscriber.check_for_update() + current_enabled = ( + enabled_config.enabled + if enabled_config + else (prev_enabled if prev_enabled is not None else True) + ) + if prev_enabled is None: + prev_enabled = current_enabled + + if prev_enabled and not current_enabled and camera_metrics.frame_queue.empty(): + logger.debug(f"Camera {camera_name} disabled, clearing tracked objects") + + # Clear norfair's dictionaries + object_tracker.tracked_objects.clear() + object_tracker.disappeared.clear() + object_tracker.stationary_box_history.clear() + object_tracker.positions.clear() + object_tracker.track_id_map.clear() + + # Clear internal norfair states + for trackers_by_type in object_tracker.trackers.values(): + for tracker in trackers_by_type.values(): + tracker.tracked_objects = [] + for tracker in object_tracker.default_tracker.values(): + tracker.tracked_objects = [] + + prev_enabled = current_enabled + + if not current_enabled: + time.sleep(0.1) + continue + # check for updated detect config - _, updated_detect_config = config_subscriber.check_for_update() + _, updated_detect_config = detect_config_subscriber.check_for_update() if updated_detect_config: detect_config = updated_detect_config @@ -845,4 +939,5 @@ def process_frames( motion_detector.stop() requestor.stop() - config_subscriber.stop() + detect_config_subscriber.stop() + enabled_config_subscriber.stop() diff --git a/migrations/029_add_user_role.py b/migrations/029_add_user_role.py new file mode 100644 index 000000000..484e0c548 --- /dev/null +++ b/migrations/029_add_user_role.py @@ -0,0 +1,37 @@ +"""Peewee migrations -- 029_add_user_role.py. + +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 + +SQL = pw.SQL + + +def migrate(migrator, database, fake=False, **kwargs): + migrator.sql( + 'ALTER TABLE "user" ADD COLUMN "role" VARCHAR(20) NOT NULL DEFAULT \'admin\'' + ) + migrator.sql('UPDATE "user" SET "role" = \'admin\' WHERE "role" IS NULL') + + +def rollback(migrator, database, fake=False, **kwargs): + migrator.sql('ALTER TABLE "user" DROP COLUMN "role"') diff --git a/web/package-lock.json b/web/package-lock.json index 119fc79ea..97a0d991b 100644 --- a/web/package-lock.json +++ b/web/package-lock.json @@ -8,45 +8,45 @@ "name": "web-new", "version": "0.0.0", "dependencies": { - "@cycjimmy/jsmpeg-player": "^6.1.1", + "@cycjimmy/jsmpeg-player": "^6.1.2", "@hookform/resolvers": "^3.9.0", "@melloware/react-logviewer": "^6.1.2", - "@radix-ui/react-alert-dialog": "^1.1.2", - "@radix-ui/react-aspect-ratio": "^1.1.0", - "@radix-ui/react-checkbox": "^1.1.2", - "@radix-ui/react-context-menu": "^2.2.2", - "@radix-ui/react-dialog": "^1.1.2", - "@radix-ui/react-dropdown-menu": "^2.1.2", - "@radix-ui/react-hover-card": "^1.1.2", - "@radix-ui/react-label": "^2.1.0", - "@radix-ui/react-popover": "^1.1.2", - "@radix-ui/react-radio-group": "^1.2.1", - "@radix-ui/react-scroll-area": "^1.2.0", - "@radix-ui/react-select": "^2.1.2", - "@radix-ui/react-separator": "^1.1.0", - "@radix-ui/react-slider": "^1.2.1", - "@radix-ui/react-slot": "^1.1.0", - "@radix-ui/react-switch": "^1.1.1", - "@radix-ui/react-tabs": "^1.1.1", - "@radix-ui/react-toggle": "^1.1.0", - "@radix-ui/react-toggle-group": "^1.1.0", - "@radix-ui/react-tooltip": "^1.1.3", + "@radix-ui/react-alert-dialog": "^1.1.6", + "@radix-ui/react-aspect-ratio": "^1.1.2", + "@radix-ui/react-checkbox": "^1.1.4", + "@radix-ui/react-context-menu": "^2.2.6", + "@radix-ui/react-dialog": "^1.1.6", + "@radix-ui/react-dropdown-menu": "^2.1.6", + "@radix-ui/react-hover-card": "^1.1.6", + "@radix-ui/react-label": "^2.1.2", + "@radix-ui/react-popover": "^1.1.6", + "@radix-ui/react-radio-group": "^1.2.3", + "@radix-ui/react-scroll-area": "^1.2.3", + "@radix-ui/react-select": "^2.1.6", + "@radix-ui/react-separator": "^1.1.2", + "@radix-ui/react-slider": "^1.2.3", + "@radix-ui/react-slot": "^1.1.2", + "@radix-ui/react-switch": "^1.1.3", + "@radix-ui/react-tabs": "^1.1.3", + "@radix-ui/react-toggle": "^1.1.2", + "@radix-ui/react-toggle-group": "^1.1.2", + "@radix-ui/react-tooltip": "^1.1.8", "apexcharts": "^3.52.0", "axios": "^1.7.7", - "class-variance-authority": "^0.7.0", + "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "cmdk": "^1.0.0", "copy-to-clipboard": "^3.3.3", "date-fns": "^3.6.0", "embla-carousel-react": "^8.2.0", "framer-motion": "^11.5.4", - "hls.js": "^1.5.17", + "hls.js": "^1.5.20", "idb-keyval": "^6.2.1", "immer": "^10.1.1", - "konva": "^9.3.16", + "konva": "^9.3.18", "lodash": "^4.17.21", - "lucide-react": "^0.407.0", - "monaco-yaml": "^5.2.2", + "lucide-react": "^0.477.0", + "monaco-yaml": "^5.3.1", "next-themes": "^0.3.0", "nosleep.js": "^0.12.0", "react": "^18.3.1", @@ -56,10 +56,10 @@ "react-dom": "^18.3.1", "react-grid-layout": "^1.5.0", "react-hook-form": "^7.52.1", - "react-icons": "^5.2.1", + "react-icons": "^5.5.0", "react-konva": "^18.2.10", "react-router-dom": "^6.26.0", - "react-swipeable": "^7.0.1", + "react-swipeable": "^7.0.2", "react-tracked": "^2.0.1", "react-transition-group": "^4.4.5", "react-use-websocket": "^4.8.1", @@ -69,7 +69,7 @@ "sonner": "^1.5.0", "sort-by": "^1.2.0", "strftime": "^0.10.3", - "swr": "^2.2.5", + "swr": "^2.3.2", "tailwind-merge": "^2.4.0", "tailwind-scrollbar": "^3.1.0", "tailwindcss-animate": "^1.0.7", @@ -91,8 +91,8 @@ "@types/strftime": "^0.9.8", "@typescript-eslint/eslint-plugin": "^7.5.0", "@typescript-eslint/parser": "^7.5.0", - "@vitejs/plugin-react-swc": "^3.7.1", - "@vitest/coverage-v8": "^2.0.5", + "@vitejs/plugin-react-swc": "^3.8.0", + "@vitest/coverage-v8": "^3.0.7", "autoprefixer": "^10.4.20", "eslint": "^8.57.0", "eslint-config-prettier": "^9.1.0", @@ -109,9 +109,9 @@ "prettier": "^3.3.3", "prettier-plugin-tailwindcss": "^0.6.5", "tailwindcss": "^3.4.9", - "typescript": "^5.5.4", - "vite": "^5.4.0", - "vitest": "^2.0.5" + "typescript": "^5.8.2", + "vite": "^6.2.0", + "vitest": "^3.0.7" } }, "node_modules/@aashutoshrathi/word-wrap": { @@ -156,9 +156,9 @@ } }, "node_modules/@babel/helper-string-parser": { - "version": 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"https://registry.npmjs.org/use-sync-external-store/-/use-sync-external-store-1.2.0.tgz", - "integrity": "sha512-eEgnFxGQ1Ife9bzYs6VLi8/4X6CObHMw9Qr9tPY43iKwsPw8xE8+EFsf/2cFZ5S3esXgpWgtSCtLNS41F+sKPA==", + "version": "1.4.0", + "resolved": "https://registry.npmjs.org/use-sync-external-store/-/use-sync-external-store-1.4.0.tgz", + "integrity": "sha512-9WXSPC5fMv61vaupRkCKCxsPxBocVnwakBEkMIHHpkTTg6icbJtg6jzgtLDm4bl3cSHAca52rYWih0k4K3PfHw==", + "license": "MIT", "peerDependencies": { - "react": "^16.8.0 || ^17.0.0 || ^18.0.0" + "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0" } }, "node_modules/util-deprecate": { @@ -8841,21 +8682,21 @@ } }, "node_modules/vite": { - "version": "5.4.0", - "resolved": "https://registry.npmjs.org/vite/-/vite-5.4.0.tgz", - "integrity": "sha512-5xokfMX0PIiwCMCMb9ZJcMyh5wbBun0zUzKib+L65vAZ8GY9ePZMXxFrHbr/Kyll2+LSCY7xtERPpxkBDKngwg==", + "version": "6.2.0", + "resolved": "https://registry.npmjs.org/vite/-/vite-6.2.0.tgz", + "integrity": "sha512-7dPxoo+WsT/64rDcwoOjk76XHj+TqNTIvHKcuMQ1k4/SeHDaQt5GFAeLYzrimZrMpn/O6DtdI03WUjdxuPM0oQ==", "dev": true, "license": "MIT", "dependencies": { - "esbuild": "^0.21.3", - "postcss": "^8.4.40", - "rollup": "^4.13.0" + "esbuild": "^0.25.0", + "postcss": "^8.5.3", + "rollup": "^4.30.1" }, "bin": { "vite": "bin/vite.js" }, "engines": { - "node": "^18.0.0 || >=20.0.0" + "node": "^18.0.0 || ^20.0.0 || >=22.0.0" }, "funding": { "url": "https://github.com/vitejs/vite?sponsor=1" @@ -8864,19 +8705,25 @@ "fsevents": "~2.3.3" }, "peerDependencies": { - "@types/node": "^18.0.0 || >=20.0.0", + "@types/node": "^18.0.0 || ^20.0.0 || >=22.0.0", + "jiti": ">=1.21.0", "less": "*", "lightningcss": "^1.21.0", "sass": "*", "sass-embedded": "*", "stylus": "*", "sugarss": "*", - "terser": "^5.4.0" + "terser": "^5.16.0", + "tsx": "^4.8.1", + "yaml": "^2.4.2" }, "peerDependenciesMeta": { "@types/node": { "optional": true }, + "jiti": { + "optional": true + }, "less": { "optional": true }, @@ -8897,27 +8744,33 @@ }, "terser": { "optional": true + }, + "tsx": { + "optional": true + }, + "yaml": { + "optional": true } } }, "node_modules/vite-node": { - "version": "2.0.5", - "resolved": "https://registry.npmjs.org/vite-node/-/vite-node-2.0.5.tgz", - "integrity": "sha512-LdsW4pxj0Ot69FAoXZ1yTnA9bjGohr2yNBU7QKRxpz8ITSkhuDl6h3zS/tvgz4qrNjeRnvrWeXQ8ZF7Um4W00Q==", + "version": "3.0.7", + "resolved": "https://registry.npmjs.org/vite-node/-/vite-node-3.0.7.tgz", + "integrity": "sha512-2fX0QwX4GkkkpULXdT1Pf4q0tC1i1lFOyseKoonavXUNlQ77KpW2XqBGGNIm/J4Ows4KxgGJzDguYVPKwG/n5A==", "dev": true, "license": "MIT", "dependencies": { "cac": "^6.7.14", - "debug": "^4.3.5", - "pathe": "^1.1.2", - "tinyrainbow": "^1.2.0", - "vite": "^5.0.0" + "debug": "^4.4.0", + "es-module-lexer": "^1.6.0", + "pathe": "^2.0.3", + "vite": "^5.0.0 || ^6.0.0" }, "bin": { "vite-node": "vite-node.mjs" }, "engines": { - "node": "^18.0.0 || >=20.0.0" + "node": "^18.0.0 || ^20.0.0 || >=22.0.0" }, "funding": { "url": "https://opencollective.com/vitest" @@ -8932,46 +8785,48 @@ } }, "node_modules/vitest": { - "version": "2.0.5", - "resolved": "https://registry.npmjs.org/vitest/-/vitest-2.0.5.tgz", - "integrity": "sha512-8GUxONfauuIdeSl5f9GTgVEpg5BTOlplET4WEDaeY2QBiN8wSm68vxN/tb5z405OwppfoCavnwXafiaYBC/xOA==", + "version": "3.0.7", + "resolved": "https://registry.npmjs.org/vitest/-/vitest-3.0.7.tgz", + "integrity": "sha512-IP7gPK3LS3Fvn44x30X1dM9vtawm0aesAa2yBIZ9vQf+qB69NXC5776+Qmcr7ohUXIQuLhk7xQR0aSUIDPqavg==", "dev": true, "license": "MIT", "dependencies": { - "@ampproject/remapping": "^2.3.0", - "@vitest/expect": "2.0.5", - "@vitest/pretty-format": "^2.0.5", - "@vitest/runner": "2.0.5", - "@vitest/snapshot": "2.0.5", - "@vitest/spy": "2.0.5", - "@vitest/utils": "2.0.5", - "chai": "^5.1.1", - "debug": "^4.3.5", - "execa": "^8.0.1", - "magic-string": "^0.30.10", - "pathe": "^1.1.2", - "std-env": "^3.7.0", - "tinybench": "^2.8.0", - "tinypool": "^1.0.0", - "tinyrainbow": "^1.2.0", - "vite": "^5.0.0", - "vite-node": "2.0.5", + "@vitest/expect": "3.0.7", + "@vitest/mocker": "3.0.7", + "@vitest/pretty-format": "^3.0.7", + "@vitest/runner": "3.0.7", + "@vitest/snapshot": "3.0.7", + "@vitest/spy": "3.0.7", + "@vitest/utils": "3.0.7", + "chai": "^5.2.0", + "debug": "^4.4.0", + "expect-type": "^1.1.0", + "magic-string": "^0.30.17", + "pathe": "^2.0.3", + "std-env": "^3.8.0", + "tinybench": "^2.9.0", + "tinyexec": "^0.3.2", + "tinypool": "^1.0.2", + "tinyrainbow": "^2.0.0", + "vite": "^5.0.0 || ^6.0.0", + "vite-node": "3.0.7", "why-is-node-running": "^2.3.0" }, "bin": { "vitest": "vitest.mjs" }, "engines": { - "node": "^18.0.0 || >=20.0.0" + "node": "^18.0.0 || ^20.0.0 || >=22.0.0" }, "funding": { "url": "https://opencollective.com/vitest" }, "peerDependencies": { "@edge-runtime/vm": "*", - "@types/node": "^18.0.0 || >=20.0.0", - "@vitest/browser": "2.0.5", - "@vitest/ui": "2.0.5", + "@types/debug": "^4.1.12", + "@types/node": "^18.0.0 || ^20.0.0 || >=22.0.0", + "@vitest/browser": "3.0.7", + "@vitest/ui": "3.0.7", "happy-dom": "*", "jsdom": "*" }, @@ -8979,6 +8834,9 @@ "@edge-runtime/vm": { "optional": true }, + "@types/debug": { + "optional": true + }, "@types/node": { "optional": true }, @@ -8996,6 +8854,33 @@ } } }, + "node_modules/vitest/node_modules/@vitest/mocker": { + "version": "3.0.7", + "resolved": "https://registry.npmjs.org/@vitest/mocker/-/mocker-3.0.7.tgz", + "integrity": "sha512-qui+3BLz9Eonx4EAuR/i+QlCX6AUZ35taDQgwGkK/Tw6/WgwodSrjN1X2xf69IA/643ZX5zNKIn2svvtZDrs4w==", + "dev": true, + "license": "MIT", + "dependencies": { + "@vitest/spy": "3.0.7", + "estree-walker": "^3.0.3", + "magic-string": "^0.30.17" + }, + "funding": { + "url": "https://opencollective.com/vitest" + }, + "peerDependencies": { + "msw": "^2.4.9", + "vite": "^5.0.0 || ^6.0.0" + }, + "peerDependenciesMeta": { + "msw": { + "optional": true + }, + "vite": { + "optional": true + } + } + }, "node_modules/vscode-jsonrpc": { "version": "8.2.0", "resolved": "https://registry.npmjs.org/vscode-jsonrpc/-/vscode-jsonrpc-8.2.0.tgz", @@ -9136,6 +9021,7 @@ "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-6.2.0.tgz", "integrity": "sha512-r6lPcBGxZXlIcymEu7InxDMhdW0KDxpLgoFLcguasxCaJ/SOIZwINatK9KY/tf+ZrlywOKU0UDj3ATXUBfxJXA==", "dev": true, + "license": "MIT", "dependencies": { "ansi-styles": "^4.0.0", "string-width": "^4.1.0", @@ -9216,9 +9102,13 @@ } }, "node_modules/yaml": { - "version": "2.3.4", - "resolved": "https://registry.npmjs.org/yaml/-/yaml-2.3.4.tgz", - "integrity": "sha512-8aAvwVUSHpfEqTQ4w/KMlf3HcRdt50E5ODIQJBw1fQ5RL34xabzxtUlzTXVqc4rkZsPbvrXKWnABCD7kWSmocA==", + "version": "2.7.0", + "resolved": "https://registry.npmjs.org/yaml/-/yaml-2.7.0.tgz", + "integrity": "sha512-+hSoy/QHluxmC9kCIJyL/uyFmLmc+e5CFR5Wa+bpIhIj85LVb9ZH2nVnqrHoSvKogwODv0ClqZkmiSSaIH5LTA==", + "license": "ISC", + "bin": { + "yaml": "bin.mjs" + }, "engines": { "node": ">= 14" } @@ -9262,6 +9152,19 @@ "url": "https://github.com/sponsors/sindresorhus" } }, + "node_modules/yoctocolors-cjs": { + "version": "2.1.2", + "resolved": "https://registry.npmjs.org/yoctocolors-cjs/-/yoctocolors-cjs-2.1.2.tgz", + "integrity": "sha512-cYVsTjKl8b+FrnidjibDWskAv7UKOfcwaVZdp/it9n1s9fU3IkgDbhdIRKCW4JDsAlECJY0ytoVPT3sK6kideA==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=18" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, "node_modules/zod": { "version": "3.23.8", "resolved": "https://registry.npmjs.org/zod/-/zod-3.23.8.tgz", diff --git a/web/package.json b/web/package.json index d0bdd01d4..59a0a5d03 100644 --- a/web/package.json +++ b/web/package.json @@ -14,45 +14,45 @@ "coverage": "vitest run --coverage" }, "dependencies": { - "@cycjimmy/jsmpeg-player": "^6.1.1", + "@cycjimmy/jsmpeg-player": "^6.1.2", "@hookform/resolvers": "^3.9.0", "@melloware/react-logviewer": "^6.1.2", - "@radix-ui/react-alert-dialog": "^1.1.2", - "@radix-ui/react-aspect-ratio": "^1.1.0", - "@radix-ui/react-checkbox": "^1.1.2", - "@radix-ui/react-context-menu": "^2.2.2", - "@radix-ui/react-dialog": "^1.1.2", - "@radix-ui/react-dropdown-menu": "^2.1.2", - "@radix-ui/react-hover-card": "^1.1.2", - "@radix-ui/react-label": "^2.1.0", - "@radix-ui/react-popover": "^1.1.2", - "@radix-ui/react-radio-group": "^1.2.1", - "@radix-ui/react-scroll-area": "^1.2.0", - "@radix-ui/react-select": "^2.1.2", - "@radix-ui/react-separator": "^1.1.0", - "@radix-ui/react-slider": "^1.2.1", - "@radix-ui/react-slot": "^1.1.0", - "@radix-ui/react-switch": "^1.1.1", - "@radix-ui/react-tabs": "^1.1.1", - "@radix-ui/react-toggle": "^1.1.0", - "@radix-ui/react-toggle-group": "^1.1.0", - "@radix-ui/react-tooltip": "^1.1.3", + "@radix-ui/react-alert-dialog": "^1.1.6", + "@radix-ui/react-aspect-ratio": "^1.1.2", + "@radix-ui/react-checkbox": "^1.1.4", + "@radix-ui/react-context-menu": "^2.2.6", + "@radix-ui/react-dialog": "^1.1.6", + "@radix-ui/react-dropdown-menu": "^2.1.6", + "@radix-ui/react-hover-card": "^1.1.6", + "@radix-ui/react-label": "^2.1.2", + "@radix-ui/react-popover": "^1.1.6", + "@radix-ui/react-radio-group": "^1.2.3", + "@radix-ui/react-scroll-area": "^1.2.3", + "@radix-ui/react-select": "^2.1.6", + "@radix-ui/react-separator": "^1.1.2", + "@radix-ui/react-slider": "^1.2.3", + "@radix-ui/react-slot": "^1.1.2", + "@radix-ui/react-switch": "^1.1.3", + "@radix-ui/react-tabs": "^1.1.3", + "@radix-ui/react-toggle": "^1.1.2", + "@radix-ui/react-toggle-group": "^1.1.2", + "@radix-ui/react-tooltip": "^1.1.8", "apexcharts": "^3.52.0", "axios": "^1.7.7", - "class-variance-authority": "^0.7.0", + "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "cmdk": "^1.0.0", "copy-to-clipboard": "^3.3.3", "date-fns": "^3.6.0", "embla-carousel-react": "^8.2.0", "framer-motion": "^11.5.4", - "hls.js": "^1.5.17", + "hls.js": "^1.5.20", "idb-keyval": "^6.2.1", "immer": "^10.1.1", - "konva": "^9.3.16", + "konva": "^9.3.18", "lodash": "^4.17.21", - "lucide-react": "^0.407.0", - "monaco-yaml": "^5.2.2", + "lucide-react": "^0.477.0", + "monaco-yaml": "^5.3.1", "next-themes": "^0.3.0", "nosleep.js": "^0.12.0", "react": "^18.3.1", @@ -62,10 +62,10 @@ "react-dom": "^18.3.1", "react-grid-layout": "^1.5.0", "react-hook-form": "^7.52.1", - "react-icons": "^5.2.1", + "react-icons": "^5.5.0", "react-konva": "^18.2.10", "react-router-dom": "^6.26.0", - "react-swipeable": "^7.0.1", + "react-swipeable": "^7.0.2", "react-tracked": "^2.0.1", "react-transition-group": "^4.4.5", "react-use-websocket": "^4.8.1", @@ -75,7 +75,7 @@ "sonner": "^1.5.0", "sort-by": "^1.2.0", "strftime": "^0.10.3", - "swr": "^2.2.5", + "swr": "^2.3.2", "tailwind-merge": "^2.4.0", "tailwind-scrollbar": "^3.1.0", "tailwindcss-animate": "^1.0.7", @@ -97,8 +97,8 @@ "@types/strftime": "^0.9.8", "@typescript-eslint/eslint-plugin": "^7.5.0", "@typescript-eslint/parser": "^7.5.0", - "@vitejs/plugin-react-swc": "^3.7.1", - "@vitest/coverage-v8": "^2.0.5", + "@vitejs/plugin-react-swc": "^3.8.0", + "@vitest/coverage-v8": "^3.0.7", "autoprefixer": "^10.4.20", "eslint": "^8.57.0", "eslint-config-prettier": "^9.1.0", @@ -115,8 +115,8 @@ "prettier": "^3.3.3", "prettier-plugin-tailwindcss": "^0.6.5", "tailwindcss": "^3.4.9", - "typescript": "^5.5.4", - "vite": "^5.4.0", - "vitest": "^2.0.5" + "typescript": "^5.8.2", + "vite": "^6.2.0", + "vitest": "^3.0.7" } } diff --git a/web/src/App.tsx b/web/src/App.tsx index ef0a9497e..a0062549f 100644 --- a/web/src/App.tsx +++ b/web/src/App.tsx @@ -10,6 +10,8 @@ import { Suspense, lazy } from "react"; import { Redirect } from "./components/navigation/Redirect"; import { cn } from "./lib/utils"; import { isPWA } from "./utils/isPWA"; +import ProtectedRoute from "@/components/auth/ProtectedRoute"; +import { AuthProvider } from "@/context/auth-context"; const Live = lazy(() => import("@/pages/Live")); const Events = lazy(() => import("@/pages/Events")); @@ -21,45 +23,58 @@ const Settings = lazy(() => import("@/pages/Settings")); const UIPlayground = lazy(() => import("@/pages/UIPlayground")); const FaceLibrary = lazy(() => import("@/pages/FaceLibrary")); const Logs = lazy(() => import("@/pages/Logs")); +const AccessDenied = lazy(() => import("@/pages/AccessDenied")); function App() { return ( - - -
- {isDesktop && } - {isDesktop && } - {isMobile && } -
- - - } /> - } /> - } /> - } /> - } /> - } /> - } /> - } /> - } /> - } /> - } /> - } /> - - + + + +
+ {isDesktop && } + {isDesktop && } + {isMobile && } +
+ + + + } + > + } /> + } /> + } /> + } /> + } /> + + } + > + } /> + } /> + } /> + } /> + } /> + + } /> + } /> + + +
-
- - + + + ); } diff --git a/web/src/api/ws.tsx b/web/src/api/ws.tsx index a8cedf953..5eedcdbcd 100644 --- a/web/src/api/ws.tsx +++ b/web/src/api/ws.tsx @@ -46,7 +46,7 @@ function useValue(): useValueReturn { const cameraActivity: { [key: string]: object } = JSON.parse(activityValue); - if (!cameraActivity) { + if (Object.keys(cameraActivity).length === 0) { return; } @@ -56,6 +56,7 @@ function useValue(): useValueReturn { const { record, detect, + enabled, snapshots, audio, notifications, @@ -67,6 +68,7 @@ function useValue(): useValueReturn { // @ts-expect-error we know this is correct state["config"]; cameraStates[`${name}/recordings/state`] = record ? "ON" : "OFF"; + cameraStates[`${name}/enabled/state`] = enabled ? "ON" : "OFF"; cameraStates[`${name}/detect/state`] = detect ? "ON" : "OFF"; cameraStates[`${name}/snapshots/state`] = snapshots ? "ON" : "OFF"; cameraStates[`${name}/audio/state`] = audio ? "ON" : "OFF"; @@ -164,6 +166,17 @@ export function useWs(watchTopic: string, publishTopic: string) { return { value, send }; } +export function useEnabledState(camera: string): { + payload: ToggleableSetting; + send: (payload: ToggleableSetting, retain?: boolean) => void; +} { + const { + value: { payload }, + send, + } = useWs(`${camera}/enabled/state`, `${camera}/enabled/set`); + return { payload: (payload ?? "ON") as ToggleableSetting, send }; +} + export function useDetectState(camera: string): { payload: ToggleableSetting; send: (payload: ToggleableSetting, retain?: boolean) => void; diff --git a/web/src/components/auth/AuthForm.tsx b/web/src/components/auth/AuthForm.tsx index 99ce37283..617ce1693 100644 --- a/web/src/components/auth/AuthForm.tsx +++ b/web/src/components/auth/AuthForm.tsx @@ -20,24 +20,23 @@ import { import { useForm } from "react-hook-form"; import { zodResolver } from "@hookform/resolvers/zod"; import { z } from "zod"; +import { AuthContext } from "@/context/auth-context"; interface UserAuthFormProps extends React.HTMLAttributes {} export function UserAuthForm({ className, ...props }: UserAuthFormProps) { const [isLoading, setIsLoading] = React.useState(false); + const { login } = React.useContext(AuthContext); const formSchema = z.object({ - user: z.string(), - password: z.string(), + user: z.string().min(1, "Username is required"), + password: z.string().min(1, "Password is required"), }); const form = useForm>({ resolver: zodResolver(formSchema), mode: "onChange", - defaultValues: { - user: "", - password: "", - }, + defaultValues: { user: "", password: "" }, }); const onSubmit = async (values: z.infer) => { @@ -50,11 +49,14 @@ export function UserAuthForm({ className, ...props }: UserAuthFormProps) { password: values.password, }, { - headers: { - "X-CSRF-TOKEN": 1, - }, + headers: { "X-CSRF-TOKEN": 1 }, }, ); + const profileRes = await axios.get("/profile", { withCredentials: true }); + login({ + username: profileRes.data.username, + role: profileRes.data.role || "viewer", + }); window.location.href = baseUrl; } catch (error) { if (axios.isAxiosError(error)) { diff --git a/web/src/components/auth/ProtectedRoute.tsx b/web/src/components/auth/ProtectedRoute.tsx new file mode 100644 index 000000000..c35fdaebc --- /dev/null +++ b/web/src/components/auth/ProtectedRoute.tsx @@ -0,0 +1,40 @@ +import { useContext } from "react"; +import { Navigate, Outlet } from "react-router-dom"; +import { AuthContext } from "@/context/auth-context"; +import ActivityIndicator from "../indicators/activity-indicator"; + +export default function ProtectedRoute({ + requiredRoles, +}: { + requiredRoles: ("admin" | "viewer")[]; +}) { + const { auth } = useContext(AuthContext); + + if (auth.isLoading) { + return ( + + ); + } + + // Unauthenticated mode + if (!auth.isAuthenticated) { + return ; + } + + // Authenticated mode (8971): require login + if (!auth.user) { + return ; + } + + // If role is null (shouldn’t happen if isAuthenticated, but type safety), fallback + // though isAuthenticated should catch this + if (auth.user.role === null) { + return ; + } + + if (!requiredRoles.includes(auth.user.role)) { + return ; + } + + return ; +} diff --git a/web/src/components/camera/CameraImage.tsx b/web/src/components/camera/CameraImage.tsx index ba35d643e..fe6586fcc 100644 --- a/web/src/components/camera/CameraImage.tsx +++ b/web/src/components/camera/CameraImage.tsx @@ -5,6 +5,7 @@ import ActivityIndicator from "../indicators/activity-indicator"; import { useResizeObserver } from "@/hooks/resize-observer"; import { isDesktop } from "react-device-detect"; import { cn } from "@/lib/utils"; +import { useEnabledState } from "@/api/ws"; type CameraImageProps = { className?: string; @@ -26,7 +27,8 @@ export default function CameraImage({ const imgRef = useRef(null); const { name } = config ? config.cameras[camera] : ""; - const enabled = config ? config.cameras[camera].enabled : "True"; + const { payload: enabledState } = useEnabledState(camera); + const enabled = enabledState === "ON" || enabledState === undefined; const [{ width: containerWidth, height: containerHeight }] = useResizeObserver(containerRef); @@ -96,9 +98,7 @@ export default function CameraImage({ loading="lazy" /> ) : ( -
- Camera is disabled in config, no stream or snapshot available! -
+
)} {!imageLoaded && enabled ? (
diff --git a/web/src/components/camera/ResizingCameraImage.tsx b/web/src/components/camera/ResizingCameraImage.tsx index 81545c625..fbb57677b 100644 --- a/web/src/components/camera/ResizingCameraImage.tsx +++ b/web/src/components/camera/ResizingCameraImage.tsx @@ -108,9 +108,7 @@ export default function CameraImage({ width={scaledWidth} /> ) : ( -
- Camera is disabled in config, no stream or snapshot available! -
+
Camera is disabled.
)} {!hasLoaded && enabled ? (
void; + disabled?: boolean; // New prop for disabling }; export default function CameraFeatureToggle({ @@ -35,18 +40,28 @@ export default function CameraFeatureToggle({ Icon, title, onClick, + disabled = false, // Default to false }: CameraFeatureToggleProps) { const content = (
); @@ -54,7 +69,7 @@ export default function CameraFeatureToggle({ if (isDesktop) { return ( - {content} + {content}

{title}

 diff --git a/web/src/components/filter/CameraGroupSelector.tsx b/web/src/components/filter/CameraGroupSelector.tsx index 8aec2a117..cf6881056 100644 --- a/web/src/components/filter/CameraGroupSelector.tsx +++ b/web/src/components/filter/CameraGroupSelector.tsx @@ -281,10 +281,13 @@ function NewGroupDialog({ .catch((error) => { setOpen(false); setEditState("none"); - toast.error( - `Failed to save config changes: ${error.response.data.message}`, - { position: "top-center" }, - ); + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to save config changes: ${errorMessage}`, { + position: "top-center", + }); }) .finally(() => { setIsLoading(false); @@ -725,10 +728,13 @@ export function CameraGroupEdit({ } }) .catch((error) => { - toast.error( - `Failed to save config changes: ${error.response.data.message}`, - { position: "top-center" }, - ); + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to save config changes: ${errorMessage}`, { + position: "top-center", + }); }) .finally(() => { setIsLoading(false); diff --git a/web/src/components/filter/CamerasFilterButton.tsx b/web/src/components/filter/CamerasFilterButton.tsx index c584dc09d..d9deb87bd 100644 --- a/web/src/components/filter/CamerasFilterButton.tsx +++ b/web/src/components/filter/CamerasFilterButton.tsx @@ -18,6 +18,7 @@ type CameraFilterButtonProps = { groups: [string, CameraGroupConfig][]; selectedCameras: string[] | undefined; hideText?: boolean; + mainCamera?: string; updateCameraFilter: (cameras: string[] | undefined) => void; }; export function CamerasFilterButton({ @@ -25,6 +26,7 @@ export function CamerasFilterButton({ groups, selectedCameras, hideText = isMobile, + mainCamera, updateCameraFilter, }: CameraFilterButtonProps) { const [open, setOpen] = useState(false); @@ -74,6 +76,7 @@ export function CamerasFilterButton({ allCameras={allCameras} groups={groups} currentCameras={currentCameras} + mainCamera={mainCamera} setCurrentCameras={setCurrentCameras} setOpen={setOpen} updateCameraFilter={updateCameraFilter} @@ -120,6 +123,7 @@ export function CamerasFilterButton({ type CamerasFilterContentProps = { allCameras: string[]; currentCameras: string[] | undefined; + mainCamera?: string; groups: [string, CameraGroupConfig][]; setCurrentCameras: (cameras: string[] | undefined) => void; setOpen: (open: boolean) => void; @@ -128,6 +132,7 @@ type CamerasFilterContentProps = { export function CamerasFilterContent({ allCameras, currentCameras, + mainCamera, groups, setCurrentCameras, setOpen, @@ -178,12 +183,29 @@ export function CamerasFilterContent({ key={item} isChecked={currentCameras?.includes(item) ?? false} label={item.replaceAll("_", " ")} + disabled={ + mainCamera !== undefined && + currentCameras !== undefined && + item === mainCamera + } // Disable only if mainCamera exists and cameras are filtered onCheckedChange={(isChecked) => { + if ( + mainCamera !== undefined && // Only enforce if mainCamera is defined + item === mainCamera && + !isChecked && + currentCameras !== undefined + ) { + return; // Prevent deselecting mainCamera when filtered and mainCamera is defined + } if (isChecked) { const updatedCameras = currentCameras ? [...currentCameras] - : []; - updatedCameras.push(item); + : mainCamera !== undefined && item !== mainCamera // If mainCamera exists and this isn’t it + ? [mainCamera] // Start with mainCamera when transitioning from undefined + : []; // Otherwise start empty + if (!updatedCameras.includes(item)) { + updatedCameras.push(item); + } setCurrentCameras(updatedCameras); } else { const updatedCameras = currentCameras diff --git a/web/src/components/filter/ReviewFilterGroup.tsx b/web/src/components/filter/ReviewFilterGroup.tsx index dedcb06fc..09eb8092a 100644 --- a/web/src/components/filter/ReviewFilterGroup.tsx +++ b/web/src/components/filter/ReviewFilterGroup.tsx @@ -49,6 +49,7 @@ type ReviewFilterGroupProps = { motionOnly: boolean; filterList?: FilterList; showReviewed: boolean; + mainCamera?: string; setShowReviewed: (show: boolean) => void; onUpdateFilter: (filter: ReviewFilter) => void; setMotionOnly: React.Dispatch>; @@ -63,6 +64,7 @@ export default function ReviewFilterGroup({ motionOnly, filterList, showReviewed, + mainCamera, setShowReviewed, onUpdateFilter, setMotionOnly, @@ -185,6 +187,7 @@ export default function ReviewFilterGroup({ allCameras={filterValues.cameras} groups={groups} selectedCameras={filter?.cameras} + mainCamera={mainCamera} updateCameraFilter={(newCameras) => { onUpdateFilter({ ...filter, cameras: newCameras }); }} diff --git a/web/src/components/filter/SearchActionGroup.tsx b/web/src/components/filter/SearchActionGroup.tsx index aac03ad1c..32751a56f 100644 --- a/web/src/components/filter/SearchActionGroup.tsx +++ b/web/src/components/filter/SearchActionGroup.tsx @@ -44,8 +44,12 @@ export default function SearchActionGroup({ pullLatestData(); } }) - .catch(() => { - toast.error("Failed to delete tracked objects.", { + .catch((error) => { + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to delete tracked objects.: ${errorMessage}`, { position: "top-center", }); }); diff --git a/web/src/components/graph/CombinedStorageGraph.tsx b/web/src/components/graph/CombinedStorageGraph.tsx index 2a52d82b6..7f10f0ab5 100644 --- a/web/src/components/graph/CombinedStorageGraph.tsx +++ b/web/src/components/graph/CombinedStorageGraph.tsx @@ -16,7 +16,7 @@ import { PopoverTrigger, } from "@/components/ui/popover"; import { getUnitSize } from "@/utils/storageUtil"; -import { LuAlertCircle } from "react-icons/lu"; +import { CiCircleAlert } from "react-icons/ci"; type CameraStorage = { [key: string]: { @@ -199,7 +199,7 @@ export function CombinedStorageGraph({ className="focus:outline-none" aria-label="Unused Storage Information" > - diff --git a/web/src/components/indicators/activity-indicator.tsx b/web/src/components/indicators/activity-indicator.tsx index 5591c9b7d..677a815ae 100644 --- a/web/src/components/indicators/activity-indicator.tsx +++ b/web/src/components/indicators/activity-indicator.tsx @@ -1,5 +1,5 @@ import { cn } from "@/lib/utils"; -import { LuLoader2 } from "react-icons/lu"; +import { AiOutlineLoading3Quarters } from "react-icons/ai"; export default function ActivityIndicator({ className = "w-full", size = 30 }) { return ( @@ -7,7 +7,7 @@ export default function ActivityIndicator({ className = "w-full", size = 30 }) { className={cn("flex items-center justify-center", className)} aria-label="Loading…" > - +
); } diff --git a/web/src/components/menu/AccountSettings.tsx b/web/src/components/menu/AccountSettings.tsx index 0bc968061..7e948308f 100644 --- a/web/src/components/menu/AccountSettings.tsx +++ b/web/src/components/menu/AccountSettings.tsx @@ -18,22 +18,52 @@ import { } from "../ui/dropdown-menu"; import { Drawer, DrawerContent, DrawerTrigger } from "../ui/drawer"; import { DialogClose } from "../ui/dialog"; -import { LuLogOut } from "react-icons/lu"; +import { LuLogOut, LuSquarePen } from "react-icons/lu"; import useSWR from "swr"; +import { useState } from "react"; +import axios from "axios"; +import { toast } from "sonner"; +import SetPasswordDialog from "../overlay/SetPasswordDialog"; type AccountSettingsProps = { className?: string; }; + export default function AccountSettings({ className }: AccountSettingsProps) { const { data: profile } = useSWR("profile"); const { data: config } = useSWR("config"); const logoutUrl = config?.proxy?.logout_url || `${baseUrl}api/logout`; + const [passwordDialogOpen, setPasswordDialogOpen] = useState(false); + const Container = isDesktop ? DropdownMenu : Drawer; const Trigger = isDesktop ? DropdownMenuTrigger : DrawerTrigger; const Content = isDesktop ? DropdownMenuContent : DrawerContent; const MenuItem = isDesktop ? DropdownMenuItem : DialogClose; + const handlePasswordSave = async (password: string) => { + if (!profile?.username || profile.username === "anonymous") return; + axios + .put(`users/${profile.username}/password`, { password }) + .then((response) => { + if (response.status === 200) { + setPasswordDialogOpen(false); + toast.success("Password updated successfully.", { + position: "top-center", + }); + } + }) + .catch((error) => { + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Error setting password: ${errorMessage}`, { + position: "top-center", + }); + }); + }; + return ( @@ -65,9 +95,22 @@ export default function AccountSettings({ className }: AccountSettingsProps) { >
- Current User: {profile?.username || "anonymous"} + Current User: {profile?.username || "anonymous"}{" "} + {profile?.role && `(${profile.role})`} + {profile?.username && profile.username !== "anonymous" && ( + setPasswordDialogOpen(true)} + > + + Set Password + + )}
+ setPasswordDialogOpen(false)} + username={profile?.username} + /> ); } diff --git a/web/src/components/menu/GeneralSettings.tsx b/web/src/components/menu/GeneralSettings.tsx index 7473d26d7..b07ace2a3 100644 --- a/web/src/components/menu/GeneralSettings.tsx +++ b/web/src/components/menu/GeneralSettings.tsx @@ -5,7 +5,7 @@ import { LuList, LuLogOut, LuMoon, - LuPenSquare, + LuSquarePen, LuRotateCw, LuSettings, LuSun, @@ -24,7 +24,6 @@ import { DropdownMenuSubTrigger, DropdownMenuTrigger, } from "../ui/dropdown-menu"; - import { Link } from "react-router-dom"; import { CgDarkMode } from "react-icons/cg"; import { @@ -33,10 +32,8 @@ import { useTheme, } from "@/context/theme-provider"; import { IoColorPalette } from "react-icons/io5"; - import { useState } from "react"; import { useRestart } from "@/api/ws"; - import { Tooltip, TooltipContent, @@ -55,21 +52,27 @@ import { TooltipPortal } from "@radix-ui/react-tooltip"; import { cn } from "@/lib/utils"; import useSWR from "swr"; import RestartDialog from "../overlay/dialog/RestartDialog"; +import { useIsAdmin } from "@/hooks/use-is-admin"; +import SetPasswordDialog from "../overlay/SetPasswordDialog"; +import { toast } from "sonner"; +import axios from "axios"; type GeneralSettingsProps = { className?: string; }; + export default function GeneralSettings({ className }: GeneralSettingsProps) { const { data: profile } = useSWR("profile"); const { data: config } = useSWR("config"); const logoutUrl = config?.proxy?.logout_url || "/api/logout"; - // settings - const { theme, colorScheme, setTheme, setColorScheme } = useTheme(); const [restartDialogOpen, setRestartDialogOpen] = useState(false); + const [passwordDialogOpen, setPasswordDialogOpen] = useState(false); const { send: sendRestart } = useRestart(); + const isAdmin = useIsAdmin(); + const Container = isDesktop ? DropdownMenu : Drawer; const Trigger = isDesktop ? DropdownMenuTrigger : DrawerTrigger; const Content = isDesktop ? DropdownMenuContent : DrawerContent; @@ -79,6 +82,29 @@ export default function GeneralSettings({ className }: GeneralSettingsProps) { const SubItemContent = isDesktop ? DropdownMenuSubContent : DialogContent; const Portal = isDesktop ? DropdownMenuPortal : DialogPortal; + const handlePasswordSave = async (password: string) => { + if (!profile?.username || profile.username === "anonymous") return; + axios + .put(`users/${profile.username}/password`, { password }) + .then((response) => { + if (response.status === 200) { + setPasswordDialogOpen(false); + toast.success("Password updated successfully.", { + position: "top-center", + }); + } + }) + .catch((error) => { + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Error setting password: ${errorMessage}`, { + position: "top-center", + }); + }); + }; + return ( <> @@ -121,13 +147,28 @@ export default function GeneralSettings({ className }: GeneralSettingsProps) { >
{isMobile && ( - <> +
- Current User: {profile?.username || "anonymous"} + Current User: {profile?.username || "anonymous"}{" "} + {profile?.role && `(${profile.role})`} + {profile?.username && profile.username !== "anonymous" && ( + setPasswordDialogOpen(true)} + > + + Set Password + + )} Logout +
+ )} + {isAdmin && ( + <> + System + + + + + + System metrics + + + + + + System logs + + + )} - System - - - - - - System metrics - - - - - - System logs - - - - + Configuration @@ -191,143 +238,143 @@ export default function GeneralSettings({ className }: GeneralSettingsProps) { Settings - - - - Configuration editor - - - - Appearance - - - - - - Dark Mode - - - - + {isAdmin && ( + <> + setTheme("light")} + aria-label="Configuration editor" > - {theme === "light" ? ( - <> - - Light - - ) : ( - Light - )} + + Configuration editor - setTheme("dark")} - > - {theme === "dark" ? ( - <> - - Dark - - ) : ( - Dark - )} - - setTheme("system")} - > - {theme === "system" ? ( - <> - - System - - ) : ( - System - )} - - - - - - - - Theme - - - - - {colorSchemes.map((scheme) => ( - setColorScheme(scheme)} - > - {scheme === colorScheme ? ( - <> - - {friendlyColorSchemeName(scheme)} - - ) : ( - - {friendlyColorSchemeName(scheme)} - - )} - - ))} - - - + + + )} + + Appearance + + + + + + Dark Mode + + + + + setTheme("light")} + > + {theme === "light" ? ( + <> + + Light + + ) : ( + Light + )} + + setTheme("dark")} + > + {theme === "dark" ? ( + <> + + Dark + + ) : ( + Dark + )} + + setTheme("system")} + > + {theme === "system" ? ( + <> + + System + + ) : ( + System + )} + + + + + + + + Theme + + + + + {colorSchemes.map((scheme) => ( + setColorScheme(scheme)} + > + {scheme === colorScheme ? ( + <> + + {friendlyColorSchemeName(scheme)} + + ) : ( + + {friendlyColorSchemeName(scheme)} + + )} + + ))} + + + Help @@ -357,17 +404,25 @@ export default function GeneralSettings({ className }: GeneralSettingsProps) { GitHub - - setRestartDialogOpen(true)} - > - - Restart Frigate - + {isAdmin && ( + <> + + setRestartDialogOpen(true)} + > + + Restart Frigate + + + )}
@@ -376,6 +431,12 @@ export default function GeneralSettings({ className }: GeneralSettingsProps) { onClose={() => setRestartDialogOpen(false)} onRestart={() => sendRestart("restart")} /> + setPasswordDialogOpen(false)} + username={profile?.username} + /> ); } diff --git a/web/src/components/menu/LiveContextMenu.tsx b/web/src/components/menu/LiveContextMenu.tsx index 969e647a0..9c775e0ac 100644 --- a/web/src/components/menu/LiveContextMenu.tsx +++ b/web/src/components/menu/LiveContextMenu.tsx @@ -39,7 +39,11 @@ import { import { cn } from "@/lib/utils"; import { useNavigate } from "react-router-dom"; import { formatUnixTimestampToDateTime } from "@/utils/dateUtil"; -import { useNotifications, useNotificationSuspend } from "@/api/ws"; +import { + useEnabledState, + useNotifications, + useNotificationSuspend, +} from "@/api/ws"; type LiveContextMenuProps = { className?: string; @@ -83,6 +87,11 @@ export default function LiveContextMenu({ }: LiveContextMenuProps) { const [showSettings, setShowSettings] = useState(false); + // camera enabled + + const { payload: enabledState, send: sendEnabled } = useEnabledState(camera); + const isEnabled = enabledState === "ON"; + // streaming settings const { allGroupsStreamingSettings, setAllGroupsStreamingSettings } = @@ -263,7 +272,7 @@ export default function LiveContextMenu({ onClick={handleVolumeIconClick} />
sendEnabled(isEnabled ? "OFF" : "ON")} + > +
+ {isEnabled ? "Disable" : "Enable"} Camera +
+
+ + + +
Mute All Cameras
 - +
Unmute All Cameras
 - +
{statsState ? "Hide" : "Show"} Stream Stats
- +
navigate(`/settings?page=debug&camera=${camera}`)} + onClick={ + isEnabled + ? () => navigate(`/settings?page=debug&camera=${camera}`) + : undefined + } >
Debug View
@@ -315,10 +339,10 @@ export default function LiveContextMenu({ {cameraGroup && cameraGroup !== "default" && ( <> - +
setShowSettings(true)} + onClick={isEnabled ? () => setShowSettings(true) : undefined} >
Streaming Settings
@@ -328,10 +352,10 @@ export default function LiveContextMenu({ {preferredLiveMode == "jsmpeg" && isRestreamed && ( <> - +
Reset
@@ -342,7 +366,7 @@ export default function LiveContextMenu({ <> - +
Notifications
@@ -382,10 +406,15 @@ export default function LiveContextMenu({ <> { - sendNotification("ON"); - sendNotificationSuspend(0); - }} + disabled={!isEnabled} + onClick={ + isEnabled + ? () => { + sendNotification("ON"); + sendNotificationSuspend(0); + } + : undefined + } >
{notificationState === "ON" ? ( @@ -405,36 +434,71 @@ export default function LiveContextMenu({ Suspend for:

- handleSuspend("5")}> + handleSuspend("5") : undefined + } + > 5 minutes handleSuspend("10")} + disabled={!isEnabled} + onClick={ + isEnabled + ? () => handleSuspend("10") + : undefined + } > 10 minutes handleSuspend("30")} + disabled={!isEnabled} + onClick={ + isEnabled + ? () => handleSuspend("30") + : undefined + } > 30 minutes handleSuspend("60")} + disabled={!isEnabled} + onClick={ + isEnabled + ? () => handleSuspend("60") + : undefined + } > 1 hour handleSuspend("840")} + disabled={!isEnabled} + onClick={ + isEnabled + ? () => handleSuspend("840") + : undefined + } > 12 hours handleSuspend("1440")} + disabled={!isEnabled} + onClick={ + isEnabled + ? () => handleSuspend("1440") + : undefined + } > 24 hours handleSuspend("off")} + disabled={!isEnabled} + onClick={ + isEnabled + ? () => handleSuspend("off") + : undefined + } > Until restart diff --git a/web/src/components/menu/SearchResultActions.tsx b/web/src/components/menu/SearchResultActions.tsx index fee12a50f..4d1fd4966 100644 --- a/web/src/components/menu/SearchResultActions.tsx +++ b/web/src/components/menu/SearchResultActions.tsx @@ -4,7 +4,8 @@ import { FrigateConfig } from "@/types/frigateConfig"; import { baseUrl } from "@/api/baseUrl"; import { toast } from "sonner"; import axios from "axios"; -import { LuCamera, LuDownload, LuMoreVertical, LuTrash2 } from "react-icons/lu"; +import { LuCamera, LuDownload, LuTrash2 } from "react-icons/lu"; +import { FiMoreVertical } from "react-icons/fi"; import { FaArrowsRotate } from "react-icons/fa6"; import { MdImageSearch } from "react-icons/md"; import FrigatePlusIcon from "@/components/icons/FrigatePlusIcon"; @@ -73,8 +74,12 @@ export default function SearchResultActions({ refreshResults(); } }) - .catch(() => { - toast.error("Failed to delete tracked object.", { + .catch((error) => { + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to delete tracked object: ${errorMessage}`, { position: "top-center", }); }); @@ -216,7 +221,7 @@ export default function SearchResultActions({ - + {menuItems} diff --git a/web/src/components/overlay/CreateUserDialog.tsx b/web/src/components/overlay/CreateUserDialog.tsx index 7d44159dd..89403c37f 100644 --- a/web/src/components/overlay/CreateUserDialog.tsx +++ b/web/src/components/overlay/CreateUserDialog.tsx @@ -2,6 +2,7 @@ import { Button } from "../ui/button"; import { Form, FormControl, + FormDescription, FormField, FormItem, FormLabel, @@ -12,20 +13,31 @@ import { zodResolver } from "@hookform/resolvers/zod"; import { useForm } from "react-hook-form"; import { z } from "zod"; import ActivityIndicator from "../indicators/activity-indicator"; -import { useState } from "react"; +import { useEffect, useState } from "react"; import { Dialog, DialogContent, + DialogDescription, DialogFooter, DialogHeader, DialogTitle, } from "../ui/dialog"; +import { + Select, + SelectContent, + SelectItem, + SelectTrigger, + SelectValue, +} from "../ui/select"; +import { Shield, User } from "lucide-react"; +import { LuCheck, LuX } from "react-icons/lu"; type CreateUserOverlayProps = { show: boolean; - onCreate: (user: string, password: string) => void; + onCreate: (user: string, password: string, role: "admin" | "viewer") => void; onCancel: () => void; }; + export default function CreateUserDialog({ show, onCreate, @@ -33,15 +45,22 @@ export default function CreateUserDialog({ }: CreateUserOverlayProps) { const [isLoading, setIsLoading] = useState(false); - const formSchema = z.object({ - user: z - .string() - .min(1) - .regex(/^[A-Za-z0-9._]+$/, { - message: "Username may only include letters, numbers, . or _", - }), - password: z.string(), - }); + const formSchema = z + .object({ + user: z + .string() + .min(1, "Username is required") + .regex(/^[A-Za-z0-9._]+$/, { + message: "Username may only include letters, numbers, . or _", + }), + password: z.string().min(1, "Password is required"), + confirmPassword: z.string().min(1, "Please confirm your password"), + role: z.enum(["admin", "viewer"]), + }) + .refine((data) => data.password === data.confirmPassword, { + message: "Passwords don't match", + path: ["confirmPassword"], + }); const form = useForm>({ resolver: zodResolver(formSchema), @@ -49,32 +68,93 @@ export default function CreateUserDialog({ defaultValues: { user: "", password: "", + confirmPassword: "", + role: "viewer", }, }); const onSubmit = async (values: z.infer) => { setIsLoading(true); - await onCreate(values.user, values.password); + await onCreate(values.user, values.password, values.role); form.reset(); setIsLoading(false); }; + // Check if passwords match for real-time feedback + const password = form.watch("password"); + const confirmPassword = form.watch("confirmPassword"); + const passwordsMatch = password === confirmPassword; + const showMatchIndicator = password && confirmPassword; + + useEffect(() => { + if (!show) { + form.reset({ + user: "", + password: "", + role: "viewer", + }); + } + }, [show, form]); + + const handleCancel = () => { + form.reset({ + user: "", + password: "", + role: "viewer", + }); + onCancel(); + }; + return ( - + - Create User + Create New User + + Add a new user account and specify an role for access to areas of + the Frigate UI. + +
- + ( - User + + Username + + + + Only letters, numbers, periods and underscores allowed. + + + + )} + /> + + ( + + + Password + + + @@ -82,30 +162,121 @@ export default function CreateUserDialog({ )} /> + ( - Password + + Confirm Password + + {showMatchIndicator && ( +
+ {passwordsMatch ? ( + <> + + + Passwords match + + + ) : ( + <> + + + Passwords don't match + + + )} +
+ )} + )} /> - - + + ( + + Role + + + Admins have full access to all features in the Frigate UI. + Viewers are limited to viewing cameras, review items, and + historical footage in the UI. + + + + )} + /> + + +
+
+ + +
+
diff --git a/web/src/components/overlay/DeleteUserDialog.tsx b/web/src/components/overlay/DeleteUserDialog.tsx index 8638b9145..e8dfb79c1 100644 --- a/web/src/components/overlay/DeleteUserDialog.tsx +++ b/web/src/components/overlay/DeleteUserDialog.tsx @@ -6,34 +6,61 @@ import { DialogHeader, DialogTitle, } from "../ui/dialog"; +import { DialogDescription } from "@radix-ui/react-dialog"; -type SetPasswordProps = { +type DeleteUserDialogProps = { show: boolean; + username?: string; onDelete: () => void; onCancel: () => void; }; export default function DeleteUserDialog({ show, + username, onDelete, onCancel, -}: SetPasswordProps) { +}: DeleteUserDialogProps) { return ( - - - Delete User + + +
+ Delete User + + This action cannot be undone. This will permanently delete the + user account and remove all associated data. + +
-
Are you sure?
- - + +
+

+ Are you sure you want to delete{" "} + {username}? +

+
+ + +
+
+ + +
+
diff --git a/web/src/components/overlay/ExportDialog.tsx b/web/src/components/overlay/ExportDialog.tsx index 00966e06a..4f49abaf0 100644 --- a/web/src/components/overlay/ExportDialog.tsx +++ b/web/src/components/overlay/ExportDialog.tsx @@ -99,16 +99,13 @@ export default function ExportDialog({ } }) .catch((error) => { - if (error.response?.data?.message) { - toast.error( - `Failed to start export: ${error.response.data.message}`, - { position: "top-center" }, - ); - } else { - toast.error(`Failed to start export: ${error.message}`, { - position: "top-center", - }); - } + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to start export: ${errorMessage}`, { + position: "top-center", + }); }); }, [camera, name, range, setRange, setName, setMode]); diff --git a/web/src/components/overlay/MobileReviewSettingsDrawer.tsx b/web/src/components/overlay/MobileReviewSettingsDrawer.tsx index 0a316acc7..81b1eefe9 100644 --- a/web/src/components/overlay/MobileReviewSettingsDrawer.tsx +++ b/web/src/components/overlay/MobileReviewSettingsDrawer.tsx @@ -106,16 +106,13 @@ export default function MobileReviewSettingsDrawer({ } }) .catch((error) => { - if (error.response?.data?.message) { - toast.error( - `Failed to start export: ${error.response.data.message}`, - { position: "top-center" }, - ); - } else { - toast.error(`Failed to start export: ${error.message}`, { - position: "top-center", - }); - } + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to start export: ${errorMessage}`, { + position: "top-center", + }); }); }, [camera, name, range, setRange, setName, setMode]); diff --git a/web/src/components/overlay/RoleChangeDialog.tsx b/web/src/components/overlay/RoleChangeDialog.tsx new file mode 100644 index 000000000..577c748ff --- /dev/null +++ b/web/src/components/overlay/RoleChangeDialog.tsx @@ -0,0 +1,119 @@ +import { Button } from "../ui/button"; +import { + Dialog, + DialogContent, + DialogDescription, + DialogFooter, + DialogHeader, + DialogTitle, +} from "../ui/dialog"; +import { + Select, + SelectContent, + SelectItem, + SelectTrigger, + SelectValue, +} from "../ui/select"; +import { useState } from "react"; +import { LuShield, LuUser } from "react-icons/lu"; + +type RoleChangeDialogProps = { + show: boolean; + username: string; + currentRole: "admin" | "viewer"; + onSave: (role: "admin" | "viewer") => void; + onCancel: () => void; +}; + +export default function RoleChangeDialog({ + show, + username, + currentRole, + onSave, + onCancel, +}: RoleChangeDialogProps) { + const [selectedRole, setSelectedRole] = useState<"admin" | "viewer">( + currentRole, + ); + + return ( + + + + + Change User Role + + + Update permissions for{" "} + {username} + + + +
+
+

Select the appropriate role for this user:

+
    +
  • + • Admin: Full access to all + features. +
    • +
  • + • Viewer: Limited to Live + dashboards, Review, Explore, and Exports only. +
    • +
+
+ + +
+ + +
+
+ + +
+
+ + + + ); +} diff --git a/web/src/components/overlay/SetPasswordDialog.tsx b/web/src/components/overlay/SetPasswordDialog.tsx index 2f6cc4eaf..108b568d7 100644 --- a/web/src/components/overlay/SetPasswordDialog.tsx +++ b/web/src/components/overlay/SetPasswordDialog.tsx @@ -1,50 +1,202 @@ +"use client"; + import { Button } from "../ui/button"; import { Input } from "../ui/input"; -import { useState } from "react"; +import { useState, useEffect } from "react"; import { Dialog, DialogContent, + DialogDescription, DialogFooter, DialogHeader, DialogTitle, } from "../ui/dialog"; +import { Label } from "../ui/label"; +import { LuCheck, LuX } from "react-icons/lu"; type SetPasswordProps = { show: boolean; onSave: (password: string) => void; onCancel: () => void; + username?: string; }; + export default function SetPasswordDialog({ show, onSave, onCancel, + username, }: SetPasswordProps) { - const [password, setPassword] = useState(); + const [password, setPassword] = useState(""); + const [confirmPassword, setConfirmPassword] = useState(""); + const [passwordStrength, setPasswordStrength] = useState(0); + const [error, setError] = useState(null); + + // Reset state when dialog opens/closes + useEffect(() => { + if (show) { + setPassword(""); + setConfirmPassword(""); + setError(null); + } + }, [show]); + + // Simple password strength calculation + useEffect(() => { + if (!password) { + setPasswordStrength(0); + return; + } + + let strength = 0; + // Length check + if (password.length >= 8) strength += 1; + // Contains number + if (/\d/.test(password)) strength += 1; + // Contains special char + if (/[!@#$%^&*(),.?":{}|<>]/.test(password)) strength += 1; + // Contains uppercase + if (/[A-Z]/.test(password)) strength += 1; + + setPasswordStrength(strength); + }, [password]); + + const handleSave = () => { + if (!password) { + setError("Password cannot be empty"); + return; + } + + if (password !== confirmPassword) { + setError("Passwords do not match"); + return; + } + + onSave(password); + }; + + const getStrengthLabel = () => { + if (!password) return ""; + if (passwordStrength <= 1) return "Weak"; + if (passwordStrength === 2) return "Medium"; + if (passwordStrength === 3) return "Strong"; + return "Very Strong"; + }; + + const getStrengthColor = () => { + if (!password) return "bg-gray-200"; + if (passwordStrength <= 1) return "bg-red-500"; + if (passwordStrength === 2) return "bg-yellow-500"; + if (passwordStrength === 3) return "bg-green-500"; + return "bg-green-600"; + }; return ( - e.preventDefault()}> - - Set Password + + + + {username ? `Update Password for ${username}` : "Set Password"} + + + Create a strong password to secure this account. + - setPassword(event.target.value)} - /> - - + +
+
+ + { + setPassword(event.target.value); + setError(null); + }} + placeholder="Enter new password" + autoFocus + /> + + {/* Password strength indicator */} + {password && ( +
+
+
+
+

+ Password strength:{" "} + {getStrengthLabel()} +

+
+ )} +
+ +
+ + { + setConfirmPassword(event.target.value); + setError(null); + }} + placeholder="Confirm new password" + /> + + {/* Password match indicator */} + {password && confirmPassword && ( +
+ {password === confirmPassword ? ( + <> + + Passwords match + + ) : ( + <> + + Passwords don't match + + )} +
+ )} +
+ + {error && ( +
+ {error} +
+ )} +
+ + +
+
+ + +
+
diff --git a/web/src/components/overlay/detail/AnnotationSettingsPane.tsx b/web/src/components/overlay/detail/AnnotationSettingsPane.tsx index 79d078c1f..df529c0dc 100644 --- a/web/src/components/overlay/detail/AnnotationSettingsPane.tsx +++ b/web/src/components/overlay/detail/AnnotationSettingsPane.tsx @@ -87,10 +87,13 @@ export function AnnotationSettingsPane({ } }) .catch((error) => { - toast.error( - `Failed to save config changes: ${error.response.data.message}`, - { position: "top-center" }, - ); + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to save config changes: ${errorMessage}`, { + position: "top-center", + }); }) .finally(() => { setIsLoading(false); diff --git a/web/src/components/overlay/detail/ObjectLifecycle.tsx b/web/src/components/overlay/detail/ObjectLifecycle.tsx index 40ab543c3..da9bd61b0 100644 --- a/web/src/components/overlay/detail/ObjectLifecycle.tsx +++ b/web/src/components/overlay/detail/ObjectLifecycle.tsx @@ -23,7 +23,6 @@ import { LuEar, LuFolderX, LuPlay, - LuPlayCircle, LuSettings, LuTruck, } from "react-icons/lu"; @@ -54,6 +53,7 @@ import { import { useNavigate } from "react-router-dom"; import { ObjectPath } from "./ObjectPath"; import { getLifecycleItemDescription } from "@/utils/lifecycleUtil"; +import { IoPlayCircleOutline } from "react-icons/io5"; type ObjectLifecycleProps = { className?: string; @@ -733,7 +733,7 @@ export function LifecycleIcon({ case "gone": return ; case "active": - return ; + return ; case "stationary": return ; case "entered_zone": diff --git a/web/src/components/overlay/detail/ObjectPath.tsx b/web/src/components/overlay/detail/ObjectPath.tsx index d85750ee7..80f454470 100644 --- a/web/src/components/overlay/detail/ObjectPath.tsx +++ b/web/src/components/overlay/detail/ObjectPath.tsx @@ -15,6 +15,7 @@ type ObjectPathProps = { pointRadius?: number; imgRef: React.RefObject; onPointClick?: (index: number) => void; + visible?: boolean; }; const typeColorMap: Partial< @@ -37,6 +38,7 @@ export function ObjectPath({ pointRadius = 4, imgRef, onPointClick, + visible = true, }: ObjectPathProps) { const getAbsolutePositions = useCallback(() => { if (!imgRef.current || !positions) return []; @@ -69,7 +71,7 @@ export function ObjectPath({ return `rgb(${baseColor.map((c) => Math.max(0, c - 10)).join(",")})`; }; - if (!imgRef.current) return null; + if (!imgRef.current || !visible) return null; const absolutePositions = getAbsolutePositions(); const lineColor = `rgb(${color.join(",")})`; diff --git a/web/src/components/overlay/detail/ObjectPathPlotter.tsx b/web/src/components/overlay/detail/ObjectPathPlotter.tsx new file mode 100644 index 000000000..40cf1728e --- /dev/null +++ b/web/src/components/overlay/detail/ObjectPathPlotter.tsx @@ -0,0 +1,281 @@ +import { useState, useEffect, useMemo, useRef } from "react"; +import useSWR from "swr"; +import { useApiHost } from "@/api"; +import type { SearchResult } from "@/types/search"; +import { ObjectPath } from "./ObjectPath"; +import type { FrigateConfig } from "@/types/frigateConfig"; +import { + Select, + SelectContent, + SelectItem, + SelectTrigger, + SelectValue, +} from "@/components/ui/select"; +import { Card, CardContent } from "@/components/ui/card"; +import { formatUnixTimestampToDateTime } from "@/utils/dateUtil"; +import { useTimezone } from "@/hooks/use-date-utils"; +import { Button } from "@/components/ui/button"; +import { LuX } from "react-icons/lu"; +import { + Pagination, + PaginationContent, + PaginationItem, + PaginationLink, + PaginationNext, + PaginationPrevious, +} from "@/components/ui/pagination"; + +export default function ObjectPathPlotter() { + const apiHost = useApiHost(); + const [timeRange, setTimeRange] = useState("1d"); + const { data: config } = useSWR("config"); + const imgRef = useRef(null); + const timezone = useTimezone(config); + const [selectedCamera, setSelectedCamera] = useState(""); + const [selectedEvent, setSelectedEvent] = useState(null); + const [currentPage, setCurrentPage] = useState(1); + const eventsPerPage = 20; + + useEffect(() => { + if (config && !selectedCamera) { + setSelectedCamera(Object.keys(config.cameras)[0]); + } + }, [config, selectedCamera]); + + const searchQuery = useMemo(() => { + if (!selectedCamera) return null; + return [ + "events", + { + cameras: selectedCamera, + after: Math.floor(Date.now() / 1000) - getTimeRangeInSeconds(timeRange), + before: Math.floor(Date.now() / 1000), + has_clip: 1, + include_thumbnails: 0, + limit: 1000, + timezone, + }, + ]; + }, [selectedCamera, timeRange, timezone]); + + const { data: events } = useSWR(searchQuery); + + const aspectRatio = useMemo(() => { + if (!config || !selectedCamera) return 16 / 9; + return ( + config.cameras[selectedCamera].detect.width / + config.cameras[selectedCamera].detect.height + ); + }, [config, selectedCamera]); + + const pathPoints = useMemo(() => { + if (!events) return []; + return events.flatMap( + (event) => + event.data.path_data?.map( + ([coords, timestamp]: [number[], number]) => ({ + x: coords[0], + y: coords[1], + timestamp, + event, + }), + ) || [], + ); + }, [events]); + + const getRandomColor = () => { + return [ + Math.floor(Math.random() * 256), + Math.floor(Math.random() * 256), + Math.floor(Math.random() * 256), + ]; + }; + + const eventColors = useMemo(() => { + if (!events) return {}; + return events.reduce( + (acc, event) => { + acc[event.id] = getRandomColor(); + return acc; + }, + {} as Record, + ); + }, [events]); + + const [imageLoaded, setImageLoaded] = useState(false); + + useEffect(() => { + if (!selectedCamera) return; + const img = new Image(); + img.src = selectedEvent + ? `${apiHost}api/${selectedCamera}/recordings/${selectedEvent.start_time}/snapshot.jpg` + : `${apiHost}api/${selectedCamera}/latest.jpg?h=500`; + img.onload = () => { + if (imgRef.current) { + imgRef.current.src = img.src; + setImageLoaded(true); + } + }; + }, [apiHost, selectedCamera, selectedEvent]); + + const handleEventClick = (event: SearchResult) => { + setSelectedEvent(event.id === selectedEvent?.id ? null : event); + }; + + const clearSelectedEvent = () => { + setSelectedEvent(null); + }; + + const totalPages = Math.ceil((events?.length || 0) / eventsPerPage); + const paginatedEvents = events?.slice( + (currentPage - 1) * eventsPerPage, + currentPage * eventsPerPage, + ); + + return ( + + +
+

Tracked Object Paths

+
+ + +
+
+
+ {`Latest + {imgRef.current && imageLoaded && ( + + {events?.map((event) => ( + point.event.id === event.id, + )} + color={eventColors[event.id]} + width={2} + imgRef={imgRef} + visible={ + selectedEvent === null || selectedEvent.id === event.id + } + /> + ))} + + )} +
+
+
+

Legend

+ {selectedEvent && ( + + )} +
+
+ {paginatedEvents?.map((event) => ( +
handleEventClick(event)} + > +
+ + {event.label} + {formatUnixTimestampToDateTime(event.start_time, { + timezone: config?.ui.timezone, + })} + +
+ ))} +
+ + + + + setCurrentPage((prev) => Math.max(prev - 1, 1)) + } + /> + + {[...Array(totalPages)].map((_, index) => ( + + setCurrentPage(index + 1)} + isActive={currentPage === index + 1} + > + {index + 1} + + + ))} + + + setCurrentPage((prev) => Math.min(prev + 1, totalPages)) + } + /> + + + +
+ + + ); +} + +function getTimeRangeInSeconds(range: string): number { + switch (range) { + case "1h": + return 60 * 60; + case "6h": + return 6 * 60 * 60; + case "12h": + return 12 * 60 * 60; + case "1d": + return 24 * 60 * 60; + default: + return 24 * 60 * 60; + } +} diff --git a/web/src/components/overlay/detail/ReviewDetailDialog.tsx b/web/src/components/overlay/detail/ReviewDetailDialog.tsx index 76234193c..2570fd033 100644 --- a/web/src/components/overlay/detail/ReviewDetailDialog.tsx +++ b/web/src/components/overlay/detail/ReviewDetailDialog.tsx @@ -41,6 +41,7 @@ import { useOverlayState } from "@/hooks/use-overlay-state"; import { DownloadVideoButton } from "@/components/button/DownloadVideoButton"; import { TooltipPortal } from "@radix-ui/react-tooltip"; import { LuSearch } from "react-icons/lu"; +import useKeyboardListener from "@/hooks/use-keyboard-listener"; type ReviewDetailDialogProps = { review?: ReviewSegment; @@ -133,6 +134,14 @@ export default function ReviewDetailDialog({ // eslint-disable-next-line react-hooks/exhaustive-deps }, [review]); + // keyboard listener + + useKeyboardListener(["Esc"], (key, modifiers) => { + if (key == "Esc" && modifiers.down && !modifiers.repeat) { + setIsOpen(false); + } + }); + const Overlay = isDesktop ? Sheet : MobilePage; const Content = isDesktop ? SheetContent : MobilePageContent; const Header = isDesktop ? SheetHeader : MobilePageHeader; diff --git a/web/src/components/overlay/detail/SearchDetailDialog.tsx b/web/src/components/overlay/detail/SearchDetailDialog.tsx index 03054d811..c94c2cd2d 100644 --- a/web/src/components/overlay/detail/SearchDetailDialog.tsx +++ b/web/src/components/overlay/detail/SearchDetailDialog.tsx @@ -71,6 +71,8 @@ import { } from "@/components/ui/popover"; import { LuInfo } from "react-icons/lu"; import { TooltipPortal } from "@radix-ui/react-tooltip"; +import { FaPencilAlt } from "react-icons/fa"; +import TextEntryDialog from "@/components/overlay/dialog/TextEntryDialog"; const SEARCH_TABS = [ "details", @@ -288,6 +290,7 @@ function ObjectDetailsTab({ // data const [desc, setDesc] = useState(search?.data.description); + const [isSubLabelDialogOpen, setIsSubLabelDialogOpen] = useState(false); const handleDescriptionFocus = useCallback(() => { setInputFocused(true); @@ -391,8 +394,12 @@ function ObjectDetailsTab({ }, ); }) - .catch(() => { - toast.error("Failed to update the description", { + .catch((error) => { + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to update the description: ${errorMessage}`, { position: "top-center", }); setDesc(search.data.description); @@ -419,17 +426,91 @@ function ObjectDetailsTab({ } }) .catch((error) => { + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; toast.error( - `Failed to call ${capitalizeAll(config?.genai.provider.replaceAll("_", " ") ?? "Generative AI")} for a new description: ${error.response.data.message}`, - { - position: "top-center", - }, + `Failed to call ${capitalizeAll(config?.genai.provider.replaceAll("_", " ") ?? "Generative AI")} for a new description: ${errorMessage}`, + { position: "top-center" }, ); }); }, [search, config], ); + const handleSubLabelSave = useCallback( + (text: string) => { + if (!search) return; + + // set score to 1.0 if we're manually entering a sub label + const subLabelScore = + text === "" ? undefined : search.data?.sub_label_score || 1.0; + + axios + .post(`${apiHost}api/events/${search.id}/sub_label`, { + camera: search.camera, + subLabel: text, + subLabelScore: subLabelScore, + }) + .then((response) => { + if (response.status === 200) { + toast.success("Successfully updated sub label.", { + position: "top-center", + }); + + mutate( + (key) => + typeof key === "string" && + (key.includes("events") || + key.includes("events/search") || + key.includes("events/explore")), + (currentData: SearchResult[][] | SearchResult[] | undefined) => { + if (!currentData) return currentData; + return currentData.flat().map((event) => + event.id === search.id + ? { + ...event, + sub_label: text, + data: { + ...event.data, + sub_label_score: subLabelScore, + }, + } + : event, + ); + }, + { + optimisticData: true, + rollbackOnError: true, + revalidate: false, + }, + ); + + setSearch({ + ...search, + sub_label: text, + data: { + ...search.data, + sub_label_score: subLabelScore, + }, + }); + setIsSubLabelDialogOpen(false); + } + }) + .catch((error) => { + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to update sub label: ${errorMessage}`, { + position: "top-center", + }); + }); + }, + [search, apiHost, mutate, setSearch], + ); + return (
@@ -440,6 +521,21 @@ function ObjectDetailsTab({ {getIconForLabel(search.label, "size-4 text-primary")} {search.label} {search.sub_label && ` (${search.sub_label})`} + + + + { + setIsSubLabelDialogOpen(true); + }} + /> + + + + Edit sub label + +
@@ -616,6 +712,15 @@ function ObjectDetailsTab({ Save )} +
diff --git a/web/src/components/overlay/dialog/TextEntryDialog.tsx b/web/src/components/overlay/dialog/TextEntryDialog.tsx index 1b0655078..c11a84ae7 100644 --- a/web/src/components/overlay/dialog/TextEntryDialog.tsx +++ b/web/src/components/overlay/dialog/TextEntryDialog.tsx @@ -10,7 +10,7 @@ import { import { Form, FormControl, FormField, FormItem } from "@/components/ui/form"; import { Input } from "@/components/ui/input"; import { zodResolver } from "@hookform/resolvers/zod"; -import { useCallback } from "react"; +import { useCallback, useEffect } from "react"; import { useForm } from "react-hook-form"; import { z } from "zod"; @@ -20,13 +20,18 @@ type TextEntryDialogProps = { description?: string; setOpen: (open: boolean) => void; onSave: (text: string) => void; + defaultValue?: string; + allowEmpty?: boolean; }; + export default function TextEntryDialog({ open, title, description, setOpen, onSave, + defaultValue = "", + allowEmpty = false, }: TextEntryDialogProps) { const formSchema = z.object({ text: z.string(), @@ -34,6 +39,7 @@ export default function TextEntryDialog({ const form = useForm>({ resolver: zodResolver(formSchema), + defaultValues: { text: defaultValue }, }); const fileRef = form.register("text"); @@ -41,15 +47,20 @@ export default function TextEntryDialog({ const onSubmit = useCallback( (data: z.infer) => { - if (!data["text"]) { + if (!allowEmpty && !data["text"]) { return; } - onSave(data["text"]); }, - [onSave], + [onSave, allowEmpty], ); + useEffect(() => { + if (open) { + form.reset({ text: defaultValue }); + } + }, [open, defaultValue, form]); + return ( @@ -75,7 +86,9 @@ export default function TextEntryDialog({ )} /> - + diff --git a/web/src/components/player/LivePlayer.tsx b/web/src/components/player/LivePlayer.tsx index 4bd751469..ae9fd6197 100644 --- a/web/src/components/player/LivePlayer.tsx +++ b/web/src/components/player/LivePlayer.tsx @@ -22,6 +22,7 @@ import { TbExclamationCircle } from "react-icons/tb"; import { TooltipPortal } from "@radix-ui/react-tooltip"; import { baseUrl } from "@/api/baseUrl"; import { PlayerStats } from "./PlayerStats"; +import { LuVideoOff } from "react-icons/lu"; type LivePlayerProps = { cameraRef?: (ref: HTMLDivElement | null) => void; @@ -86,8 +87,13 @@ export default function LivePlayer({ // camera activity - const { activeMotion, activeTracking, objects, offline } = - useCameraActivity(cameraConfig); + const { + enabled: cameraEnabled, + activeMotion, + activeTracking, + objects, + offline, + } = useCameraActivity(cameraConfig); const cameraActive = useMemo( () => @@ -191,12 +197,40 @@ export default function LivePlayer({ setLiveReady(true); }, []); + // enabled states + + const [isReEnabling, setIsReEnabling] = useState(false); + const prevCameraEnabledRef = useRef(cameraEnabled ?? true); + + useEffect(() => { + if (cameraEnabled == undefined) { + return; + } + if (!prevCameraEnabledRef.current && cameraEnabled) { + // Camera enabled + setLiveReady(false); + setIsReEnabling(true); + setKey((prevKey) => prevKey + 1); + } else if (prevCameraEnabledRef.current && !cameraEnabled) { + // Camera disabled + setLiveReady(false); + setKey((prevKey) => prevKey + 1); + } + prevCameraEnabledRef.current = cameraEnabled; + }, [cameraEnabled]); + + useEffect(() => { + if (liveReady && isReEnabling) { + setIsReEnabling(false); + } + }, [liveReady, isReEnabling]); + if (!cameraConfig) { return ; } let player; - if (!autoLive || !streamName) { + if (!autoLive || !streamName || !cameraEnabled) { player = null; } else if (preferredLiveMode == "webrtc") { player = ( @@ -267,6 +301,22 @@ export default function LivePlayer({ player = ; } + // if (cameraConfig.name == "lpr") + // console.log( + // cameraConfig.name, + // "enabled", + // cameraEnabled, + // "prev enabled", + // prevCameraEnabledRef.current, + // "offline", + // offline, + // "show still", + // showStillWithoutActivity, + // "live ready", + // liveReady, + // player, + // ); + return (
- {((showStillWithoutActivity && !liveReady) || liveReady) && ( - <> -
-
- - )} + {cameraEnabled && + ((showStillWithoutActivity && !liveReady) || liveReady) && ( + <> +
+
+ + )} {player} - {!offline && !showStillWithoutActivity && !liveReady && ( - - )} + {cameraEnabled && + !offline && + (!showStillWithoutActivity || isReEnabling) && + !liveReady && } {((showStillWithoutActivity && !liveReady) || liveReady) && objects.length > 0 && ( @@ -344,7 +396,9 @@ export default function LivePlayer({
)} + {!cameraEnabled && ( +
+
+ +

+ Camera is disabled +

+
+
+ )} +
{autoLive && !offline && @@ -378,7 +443,7 @@ export default function LivePlayer({ ((showStillWithoutActivity && !liveReady) || liveReady) && ( )} - {offline && showStillWithoutActivity && ( + {((offline && showStillWithoutActivity) || !cameraEnabled) && ( diff --git a/web/src/components/settings/MotionMaskEditPane.tsx b/web/src/components/settings/MotionMaskEditPane.tsx index 3b73c6a23..5c83f7720 100644 --- a/web/src/components/settings/MotionMaskEditPane.tsx +++ b/web/src/components/settings/MotionMaskEditPane.tsx @@ -176,10 +176,13 @@ export default function MotionMaskEditPane({ } }) .catch((error) => { - toast.error( - `Failed to save config changes: ${error.response.data.message}`, - { position: "top-center" }, - ); + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to save config changes: ${errorMessage}`, { + position: "top-center", + }); }) .finally(() => { setIsLoading(false); diff --git a/web/src/components/settings/ObjectMaskEditPane.tsx b/web/src/components/settings/ObjectMaskEditPane.tsx index 2c63d2e63..32e878c41 100644 --- a/web/src/components/settings/ObjectMaskEditPane.tsx +++ b/web/src/components/settings/ObjectMaskEditPane.tsx @@ -208,10 +208,13 @@ export default function ObjectMaskEditPane({ } }) .catch((error) => { - toast.error( - `Failed to save config changes: ${error.response.data.message}`, - { position: "top-center" }, - ); + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to save config changes: ${errorMessage}`, { + position: "top-center", + }); }) .finally(() => { setIsLoading(false); diff --git a/web/src/components/settings/PolygonItem.tsx b/web/src/components/settings/PolygonItem.tsx index 707df7a8f..db3f173a3 100644 --- a/web/src/components/settings/PolygonItem.tsx +++ b/web/src/components/settings/PolygonItem.tsx @@ -186,10 +186,13 @@ export default function PolygonItem({ } }) .catch((error) => { - toast.error( - `Failed to save config changes: ${error.response.data.message}`, - { position: "top-center" }, - ); + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to save config changes: ${errorMessage}`, { + position: "top-center", + }); }) .finally(() => { setIsLoading(false); diff --git a/web/src/components/settings/ZoneEditPane.tsx b/web/src/components/settings/ZoneEditPane.tsx index 247ae8991..7adb3e194 100644 --- a/web/src/components/settings/ZoneEditPane.tsx +++ b/web/src/components/settings/ZoneEditPane.tsx @@ -68,7 +68,7 @@ export default function ZoneEditPane({ } return Object.values(config.cameras) - .filter((conf) => conf.ui.dashboard && conf.enabled) + .filter((conf) => conf.ui.dashboard && conf.enabled_in_config) .sort((aConf, bConf) => aConf.ui.order - bConf.ui.order); }, [config]); @@ -414,10 +414,13 @@ export default function ZoneEditPane({ } }) .catch((error) => { - toast.error( - `Failed to save config changes: ${error.response.data.message}`, - { position: "top-center" }, - ); + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to save config changes: ${errorMessage}`, { + position: "top-center", + }); }) .finally(() => { setIsLoading(false); diff --git a/web/src/components/ui/pagination.tsx b/web/src/components/ui/pagination.tsx new file mode 100644 index 000000000..ea40d196d --- /dev/null +++ b/web/src/components/ui/pagination.tsx @@ -0,0 +1,117 @@ +import * as React from "react" +import { ChevronLeft, ChevronRight, MoreHorizontal } from "lucide-react" + +import { cn } from "@/lib/utils" +import { ButtonProps, buttonVariants } from "@/components/ui/button" + +const Pagination = ({ className, ...props }: React.ComponentProps<"nav">) => ( +
)} @@ -244,17 +306,21 @@ type CameraSelectButtonProps = { allCameras: CameraConfig[]; selectedCamera: string; setSelectedCamera: React.Dispatch>; + cameraEnabledStates: Record; + currentPage: SettingsType; }; function CameraSelectButton({ allCameras, selectedCamera, setSelectedCamera, + cameraEnabledStates, + currentPage, }: CameraSelectButtonProps) { const [open, setOpen] = useState(false); if (!allCameras.length) { - return; + return null; } const trigger = ( @@ -283,19 +349,24 @@ function CameraSelectButton({ )}
- {allCameras.map((item) => ( - { - if (isChecked) { - setSelectedCamera(item.name); - setOpen(false); - } - }} - /> - ))} + {allCameras.map((item) => { + const isEnabled = cameraEnabledStates[item.name]; + const isCameraSettingsPage = currentPage === "camera settings"; + return ( + { + if (isChecked && (isEnabled || isCameraSettingsPage)) { + setSelectedCamera(item.name); + setOpen(false); + } + }} + disabled={!isEnabled && !isCameraSettingsPage} + /> + ); + })}
diff --git a/web/src/pages/System.tsx b/web/src/pages/System.tsx index 491149be2..05eed5b3e 100644 --- a/web/src/pages/System.tsx +++ b/web/src/pages/System.tsx @@ -28,7 +28,11 @@ function System() { const metrics = useMemo(() => { const metrics = [...allMetrics]; - if (!config?.semantic_search.enabled) { + if ( + !config?.semantic_search.enabled && + !config?.lpr.enabled && + !config?.face_recognition.enabled + ) { const index = metrics.indexOf("features"); metrics.splice(index, 1); } diff --git a/web/src/pages/UIPlayground.tsx b/web/src/pages/UIPlayground.tsx index 083e9c3c1..95599d594 100644 --- a/web/src/pages/UIPlayground.tsx +++ b/web/src/pages/UIPlayground.tsx @@ -30,6 +30,7 @@ import SummaryTimeline from "@/components/timeline/SummaryTimeline"; import { isMobile } from "react-device-detect"; import IconPicker, { IconElement } from "@/components/icons/IconPicker"; import { useTimelineZoom } from "@/hooks/use-timeline-zoom"; +import ObjectPathPlotter from "@/components/overlay/detail/ObjectPathPlotter"; // Color data const colors = [ @@ -224,6 +225,8 @@ function UIPlayground() {
UI Playground + + ; @@ -458,6 +464,7 @@ export interface FrigateConfig { semantic_search: { enabled: boolean; reindex: boolean; + model: SearchModel; model_size: SearchModelSize; }; diff --git a/web/src/types/search.ts b/web/src/types/search.ts index ca3c66be7..cef7f6aff 100644 --- a/web/src/types/search.ts +++ b/web/src/types/search.ts @@ -57,6 +57,7 @@ export type SearchResult = { description?: string; average_estimated_speed: number; velocity_angle: number; + path_data: [number[], number][]; }; }; diff --git a/web/src/types/user.ts b/web/src/types/user.ts index fcb37c168..c4e6fff64 100644 --- a/web/src/types/user.ts +++ b/web/src/types/user.ts @@ -1,3 +1,4 @@ export type User = { username: string; + role: string; }; diff --git a/web/src/types/ws.ts b/web/src/types/ws.ts index 397b213f6..2590d45a7 100644 --- a/web/src/types/ws.ts +++ b/web/src/types/ws.ts @@ -52,6 +52,7 @@ export type ObjectType = { }; export interface FrigateCameraState { + enabled: boolean; motion: boolean; objects: ObjectType[]; } diff --git a/web/src/utils/isIFrame.ts b/web/src/utils/isIFrame.ts new file mode 100644 index 000000000..1f78a40aa --- /dev/null +++ b/web/src/utils/isIFrame.ts @@ -0,0 +1,8 @@ +export const isInIframe = (() => { + try { + return window.self !== window.top; + } catch (e) { + // If we get a security error, we're definitely in an iframe + return true; + } +})(); diff --git a/web/src/views/events/EventView.tsx b/web/src/views/events/EventView.tsx index e8e864e32..5ffeef8af 100644 --- a/web/src/views/events/EventView.tsx +++ b/web/src/views/events/EventView.tsx @@ -204,16 +204,13 @@ export default function EventView({ } }) .catch((error) => { - if (error.response?.data?.message) { - toast.error( - `Failed to start export: ${error.response.data.message}`, - { position: "top-center" }, - ); - } else { - toast.error(`Failed to start export: ${error.message}`, { - position: "top-center", - }); - } + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to start export: ${errorMessage}`, { + position: "top-center", + }); }); }, [reviewItems], @@ -621,6 +618,16 @@ function DetectionReview({ // existing review item + useEffect(() => { + if (loading || currentItems == null || itemsToReview == undefined) { + return; + } + + if (currentItems.length == 0 && itemsToReview > 0) { + pullLatestData(); + } + }, [loading, currentItems, itemsToReview, pullLatestData]); + useEffect(() => { if (!startTime || !currentItems || currentItems.length == 0) { return; diff --git a/web/src/views/explore/ExploreView.tsx b/web/src/views/explore/ExploreView.tsx index c3ef25ad1..e754bc1bf 100644 --- a/web/src/views/explore/ExploreView.tsx +++ b/web/src/views/explore/ExploreView.tsx @@ -3,7 +3,7 @@ import { isDesktop, isIOS, isMobileOnly, isSafari } from "react-device-detect"; import useSWR from "swr"; import { useApiHost } from "@/api"; import { cn } from "@/lib/utils"; -import { LuArrowRightCircle } from "react-icons/lu"; +import { BsArrowRightCircle } from "react-icons/bs"; import { useNavigate } from "react-router-dom"; import { Tooltip, @@ -183,7 +183,7 @@ function ThumbnailRow({ > - diff --git a/web/src/views/live/DraggableGridLayout.tsx b/web/src/views/live/DraggableGridLayout.tsx index 3b85de4b3..d0da3e5ac 100644 --- a/web/src/views/live/DraggableGridLayout.tsx +++ b/web/src/views/live/DraggableGridLayout.tsx @@ -396,10 +396,12 @@ export default function DraggableGridLayout({ const initialVolumeStates: VolumeState = {}; Object.entries(allGroupsStreamingSettings).forEach(([_, groupSettings]) => { - Object.entries(groupSettings).forEach(([camera, cameraSettings]) => { - initialAudioStates[camera] = cameraSettings.playAudio ?? false; - initialVolumeStates[camera] = cameraSettings.volume ?? 1; - }); + if (groupSettings) { + Object.entries(groupSettings).forEach(([camera, cameraSettings]) => { + initialAudioStates[camera] = cameraSettings.playAudio ?? false; + initialVolumeStates[camera] = cameraSettings.volume ?? 1; + }); + } }); setAudioStates(initialAudioStates); diff --git a/web/src/views/live/LiveCameraView.tsx b/web/src/views/live/LiveCameraView.tsx index ccf06de7b..15dea59d6 100644 --- a/web/src/views/live/LiveCameraView.tsx +++ b/web/src/views/live/LiveCameraView.tsx @@ -2,6 +2,7 @@ import { useAudioState, useAutotrackingState, useDetectState, + useEnabledState, usePtzCommand, useRecordingsState, useSnapshotsState, @@ -82,6 +83,8 @@ import { LuHistory, LuInfo, LuPictureInPicture, + LuPower, + LuPowerOff, LuVideo, LuVideoOff, LuX, @@ -113,6 +116,7 @@ import { Switch } from "@/components/ui/switch"; import axios from "axios"; import { toast } from "sonner"; import { Toaster } from "@/components/ui/sonner"; +import { useIsAdmin } from "@/hooks/use-is-admin"; type LiveCameraViewProps = { config?: FrigateConfig; @@ -185,6 +189,10 @@ export default function LiveCameraView({ ); }, [cameraMetadata]); + // camera enabled state + const { payload: enabledState } = useEnabledState(camera.name); + const cameraEnabled = enabledState === "ON"; + // click overlay for ptzs const [clickOverlay, setClickOverlay] = useState(false); @@ -355,6 +363,28 @@ export default function LiveCameraView({ } }, [fullscreen, isPortrait, cameraAspectRatio, containerAspectRatio]); + // On mobile devices that support it, try to orient screen + // to best fit the camera feed in fullscreen mode + useEffect(() => { + // eslint-disable-next-line @typescript-eslint/no-explicit-any + const screenOrientation = screen.orientation as any; + if (!screenOrientation.lock || !screenOrientation.unlock) { + // Browser does not support ScreenOrientation APIs that we need + return; + } + + if (fullscreen) { + const orientationForBestFit = + cameraAspectRatio > 1 ? "landscape" : "portrait"; + + // If the current device doesn't support locking orientation, + // this promise will reject with an error that we can ignore + screenOrientation.lock(orientationForBestFit).catch(() => {}); + } + + return () => screenOrientation.unlock(); + }, [fullscreen, cameraAspectRatio]); + const handleError = useCallback( (e: LivePlayerError) => { if (e) { @@ -470,6 +500,7 @@ export default function LiveCameraView({ setPip(false); } }} + disabled={!cameraEnabled} /> )} {supports2WayTalk && ( @@ -481,11 +512,11 @@ export default function LiveCameraView({ title={`${mic ? "Disable" : "Enable"} Two Way Talk`} onClick={() => { setMic(!mic); - // Turn on audio when enabling the mic if audio is currently off if (!mic && !audio) { setAudio(true); } }} + disabled={!cameraEnabled} /> )} {supportsAudioOutput && preferredLiveMode != "jsmpeg" && ( @@ -496,6 +527,7 @@ export default function LiveCameraView({ isActive={audio ?? false} title={`${audio ? "Disable" : "Enable"} Camera Audio`} onClick={() => setAudio(!audio)} + disabled={!cameraEnabled} /> )}
@@ -913,6 +946,7 @@ type FrigateCameraFeaturesProps = { setLowBandwidth: React.Dispatch>; supportsAudioOutput: boolean; supports2WayTalk: boolean; + cameraEnabled: boolean; }; function FrigateCameraFeatures({ camera, @@ -931,10 +965,14 @@ function FrigateCameraFeatures({ setLowBandwidth, supportsAudioOutput, supports2WayTalk, + cameraEnabled, }: FrigateCameraFeaturesProps) { const { payload: detectState, send: sendDetect } = useDetectState( camera.name, ); + const { payload: enabledState, send: sendEnabled } = useEnabledState( + camera.name, + ); const { payload: recordState, send: sendRecord } = useRecordingsState( camera.name, ); @@ -945,6 +983,10 @@ function FrigateCameraFeatures({ const { payload: autotrackingState, send: sendAutotracking } = useAutotrackingState(camera.name); + // roles + + const isAdmin = useIsAdmin(); + // manual event const recordingEventIdRef = useRef(null); @@ -1043,51 +1085,71 @@ function FrigateCameraFeatures({ if (isDesktop || isTablet) { return ( <> - sendDetect(detectState == "ON" ? "OFF" : "ON")} - /> - sendRecord(recordState == "ON" ? "OFF" : "ON")} - /> - sendSnapshot(snapshotState == "ON" ? "OFF" : "ON")} - /> - {audioDetectEnabled && ( - sendAudio(audioState == "ON" ? "OFF" : "ON")} - /> - )} - {autotrackingEnabled && ( - - sendAutotracking(autotrackingState == "ON" ? "OFF" : "ON") - } - /> + {isAdmin && ( + <> + sendEnabled(enabledState == "ON" ? "OFF" : "ON")} + disabled={false} + /> + sendDetect(detectState == "ON" ? "OFF" : "ON")} + disabled={!cameraEnabled} + /> + sendRecord(recordState == "ON" ? "OFF" : "ON")} + disabled={!cameraEnabled} + /> + sendSnapshot(snapshotState == "ON" ? "OFF" : "ON")} + disabled={!cameraEnabled} + /> + {audioDetectEnabled && ( + sendAudio(audioState == "ON" ? "OFF" : "ON")} + disabled={!cameraEnabled} + /> + )} + {autotrackingEnabled && ( + + sendAutotracking(autotrackingState == "ON" ? "OFF" : "ON") + } + disabled={!cameraEnabled} + /> + )} + )} @@ -1369,48 +1432,60 @@ function FrigateCameraFeatures({
- - sendDetect(detectState == "ON" ? "OFF" : "ON") - } - /> - {recordingEnabled && ( - - sendRecord(recordState == "ON" ? "OFF" : "ON") - } - /> - )} - - sendSnapshot(snapshotState == "ON" ? "OFF" : "ON") - } - /> - {audioDetectEnabled && ( - - sendAudio(audioState == "ON" ? "OFF" : "ON") - } - /> - )} - {autotrackingEnabled && ( - - sendAutotracking(autotrackingState == "ON" ? "OFF" : "ON") - } - /> + {isAdmin && ( + <> + + sendEnabled(enabledState == "ON" ? "OFF" : "ON") + } + /> + + sendDetect(detectState == "ON" ? "OFF" : "ON") + } + /> + {recordingEnabled && ( + + sendRecord(recordState == "ON" ? "OFF" : "ON") + } + /> + )} + + sendSnapshot(snapshotState == "ON" ? "OFF" : "ON") + } + /> + {audioDetectEnabled && ( + + sendAudio(audioState == "ON" ? "OFF" : "ON") + } + /> + )} + {autotrackingEnabled && ( + + sendAutotracking(autotrackingState == "ON" ? "OFF" : "ON") + } + /> + )} + )}
+
{!isRestreamed && (
diff --git a/web/src/views/live/LiveDashboardView.tsx b/web/src/views/live/LiveDashboardView.tsx index 45d0d5302..e59fd96ca 100644 --- a/web/src/views/live/LiveDashboardView.tsx +++ b/web/src/views/live/LiveDashboardView.tsx @@ -268,10 +268,12 @@ export default function LiveDashboardView({ const initialVolumeStates: VolumeState = {}; Object.entries(allGroupsStreamingSettings).forEach(([_, groupSettings]) => { - Object.entries(groupSettings).forEach(([camera, cameraSettings]) => { - initialAudioStates[camera] = cameraSettings.playAudio ?? false; - initialVolumeStates[camera] = cameraSettings.volume ?? 1; - }); + if (groupSettings) { + Object.entries(groupSettings).forEach(([camera, cameraSettings]) => { + initialAudioStates[camera] = cameraSettings.playAudio ?? false; + initialVolumeStates[camera] = cameraSettings.volume ?? 1; + }); + } }); setAudioStates(initialAudioStates); diff --git a/web/src/views/recording/RecordingView.tsx b/web/src/views/recording/RecordingView.tsx index c5e528736..0baeca994 100644 --- a/web/src/views/recording/RecordingView.tsx +++ b/web/src/views/recording/RecordingView.tsx @@ -442,7 +442,7 @@ export function RecordingView({ )} {isDesktop && ( {}} - onUpdateFilter={updateFilter} + mainCamera={mainCamera} + onUpdateFilter={(newFilter: ReviewFilter) => { + const updatedCameras = + newFilter.cameras === undefined + ? undefined // Respect undefined as "all cameras" + : newFilter.cameras + ? Array.from( + new Set([mainCamera, ...(newFilter.cameras || [])]), + ) // Include mainCamera if specific cameras are selected + : [mainCamera]; + const adjustedFilter: ReviewFilter = { + ...newFilter, + cameras: updatedCameras, + }; + updateFilter(adjustedFilter); + }} setMotionOnly={() => {}} /> )} diff --git a/web/src/views/settings/AuthenticationView.tsx b/web/src/views/settings/AuthenticationView.tsx index 1c6df5c52..118d102d4 100644 --- a/web/src/views/settings/AuthenticationView.tsx +++ b/web/src/views/settings/AuthenticationView.tsx @@ -11,10 +11,25 @@ import axios from "axios"; import CreateUserDialog from "@/components/overlay/CreateUserDialog"; import { toast } from "sonner"; import DeleteUserDialog from "@/components/overlay/DeleteUserDialog"; -import { Card } from "@/components/ui/card"; import { HiTrash } from "react-icons/hi"; import { FaUserEdit } from "react-icons/fa"; -import { LuPlus } from "react-icons/lu"; +import { LuPlus, LuShield, LuUserCog } from "react-icons/lu"; +import { + Table, + TableBody, + TableCell, + TableHead, + TableHeader, + TableRow, +} from "@/components/ui/table"; +import { Badge } from "@/components/ui/badge"; +import { + Tooltip, + TooltipContent, + TooltipProvider, + TooltipTrigger, +} from "@/components/ui/tooltip"; +import RoleChangeDialog from "@/components/overlay/RoleChangeDialog"; export default function AuthenticationView() { const { data: config } = useSWR("config"); @@ -23,8 +38,12 @@ export default function AuthenticationView() { const [showSetPassword, setShowSetPassword] = useState(false); const [showCreate, setShowCreate] = useState(false); const [showDelete, setShowDelete] = useState(false); + const [showRoleChange, setShowRoleChange] = useState(false); const [selectedUser, setSelectedUser] = useState(); + const [selectedUserRole, setSelectedUserRole] = useState< + "admin" | "viewer" + >(); useEffect(() => { document.title = "Authentication Settings - Frigate"; @@ -32,142 +51,303 @@ export default function AuthenticationView() { const onSavePassword = useCallback((user: string, password: string) => { axios - .put(`users/${user}/password`, { - password: password, - }) + .put(`users/${user}/password`, { password }) .then((response) => { - if (response.status == 200) { + if (response.status === 200) { setShowSetPassword(false); + toast.success("Password updated successfully", { + position: "top-center", + }); } }) - .catch((_error) => { - toast.error("Error setting password", { + .catch((error) => { + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to save password: ${errorMessage}`, { position: "top-center", }); }); }, []); - const onCreate = async (user: string, password: string) => { - try { - await axios.post("users", { - username: user, - password: password, + const onCreate = ( + user: string, + password: string, + role: "admin" | "viewer", + ) => { + axios + .post("users", { username: user, password, role }) + .then((response) => { + if (response.status === 200 || response.status === 201) { + setShowCreate(false); + mutateUsers((users) => { + users?.push({ username: user, role: role }); + return users; + }, false); + toast.success(`User ${user} created successfully`, { + position: "top-center", + }); + } + }) + .catch((error) => { + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to create user: ${errorMessage}`, { + position: "top-center", + }); }); - setShowCreate(false); - mutateUsers((users) => { - users?.push({ username: user }); - return users; - }, false); - } catch (error) { - toast.error("Error creating user. Check server logs.", { - position: "top-center", - }); - } }; - const onDelete = async (user: string) => { - try { - await axios.delete(`users/${user}`); - setShowDelete(false); - mutateUsers((users) => { - return users?.filter((u) => { - return u.username !== user; + const onDelete = (user: string) => { + axios + .delete(`users/${user}`) + .then((response) => { + if (response.status === 200) { + setShowDelete(false); + mutateUsers( + (users) => users?.filter((u) => u.username !== user), + false, + ); + toast.success(`User ${user} deleted successfully`, { + position: "top-center", + }); + } + }) + .catch((error) => { + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to delete user: ${errorMessage}`, { + position: "top-center", + }); + }); + }; + + const onChangeRole = (user: string, newRole: "admin" | "viewer") => { + if (user === "admin") return; // Prevent role change for 'admin' + + axios + .put(`users/${user}/role`, { role: newRole }) + .then((response) => { + if (response.status === 200) { + setShowRoleChange(false); + mutateUsers( + (users) => + users?.map((u) => + u.username === user ? { ...u, role: newRole } : u, + ), + false, + ); + toast.success(`Role updated for ${user}`, { + position: "top-center", + }); + } + }) + .catch((error) => { + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to update role: ${errorMessage}`, { + position: "top-center", }); - }, false); - } catch (error) { - toast.error("Error deleting user. Check server logs.", { - position: "top-center", }); - } }; if (!config || !users) { - return ; + return ( +
+ +
+ ); } return (
-
- - Users - +
+
+ + User Management + +

+ Manage this Frigate instance's user accounts. +

+
-
- {users.map((u) => ( - -
-
- {u.username} -
-
- - -
-
- - ))} +
+
+
+ + + + Username + Role + Actions + + + + {users.length === 0 ? ( + + + No users found. + + + ) : ( + users.map((user) => ( + + +
+ {user.username === "admin" ? ( + + ) : ( + + )} + {user.username} +
+ + + + {user.role || "viewer"} + + + + +
+ {user.username !== "admin" && ( + + + + + +

Change user role

+ + + )} + + + + + + +

Update password

+ + + + {user.username !== "admin" && ( + + + + + +

Delete user

+ + + )} +
+ + + + )) + )} + +
+
+
+ { - setShowSetPassword(false); - }} - onSave={(password) => { - onSavePassword(selectedUser!, password); - }} + onCancel={() => setShowSetPassword(false)} + onSave={(password) => onSavePassword(selectedUser!, password)} /> { - setShowDelete(false); - }} - onDelete={() => { - onDelete(selectedUser!); - }} + username={selectedUser ?? "this user"} + onCancel={() => setShowDelete(false)} + onDelete={() => onDelete(selectedUser!)} /> { - setShowCreate(false); - }} + onCancel={() => setShowCreate(false)} /> + {selectedUser && selectedUserRole && ( + onChangeRole(selectedUser, role)} + onCancel={() => setShowRoleChange(false)} + /> + )}
); } diff --git a/web/src/views/settings/CameraSettingsView.tsx b/web/src/views/settings/CameraSettingsView.tsx index fa9d0ba58..f83bdde50 100644 --- a/web/src/views/settings/CameraSettingsView.tsx +++ b/web/src/views/settings/CameraSettingsView.tsx @@ -29,7 +29,7 @@ import { MdCircle } from "react-icons/md"; import { cn } from "@/lib/utils"; import { Switch } from "@/components/ui/switch"; import { Label } from "@/components/ui/label"; -import { useAlertsState, useDetectionsState } from "@/api/ws"; +import { useAlertsState, useDetectionsState, useEnabledState } from "@/api/ws"; type CameraSettingsViewProps = { selectedCamera: string; @@ -108,6 +108,8 @@ export default function CameraSettingsView({ const watchedAlertsZones = form.watch("alerts_zones"); const watchedDetectionsZones = form.watch("detections_zones"); + const { payload: enabledState, send: sendEnabled } = + useEnabledState(selectedCamera); const { payload: alertsState, send: sendAlerts } = useAlertsState(selectedCamera); const { payload: detectionsState, send: sendDetections } = @@ -169,10 +171,13 @@ export default function CameraSettingsView({ } }) .catch((error) => { - toast.error( - `Failed to save config changes: ${error.response.data.message}`, - { position: "top-center" }, - ); + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to save config changes: ${errorMessage}`, { + position: "top-center", + }); }) .finally(() => { setIsLoading(false); @@ -252,6 +257,31 @@ export default function CameraSettingsView({ + + Streams + + +
+ { + sendEnabled(isChecked ? "ON" : "OFF"); + }} + /> +
+ +
+
+
+ Disabling a camera completely stops Frigate's processing of this + camera's streams. Detection, recording, and debugging will be + unavailable. +
Note: This does not disable go2rtc restreams. +
+ + Review diff --git a/web/src/views/settings/NotificationsSettingsView.tsx b/web/src/views/settings/NotificationsSettingsView.tsx index edae6ba28..a7dd1c9d4 100644 --- a/web/src/views/settings/NotificationsSettingsView.tsx +++ b/web/src/views/settings/NotificationsSettingsView.tsx @@ -20,7 +20,8 @@ import { zodResolver } from "@hookform/resolvers/zod"; import axios from "axios"; import { useCallback, useContext, useEffect, useMemo, useState } from "react"; import { useForm } from "react-hook-form"; -import { LuAlertCircle, LuCheck, LuExternalLink, LuX } from "react-icons/lu"; +import { LuCheck, LuExternalLink, LuX } from "react-icons/lu"; +import { CiCircleAlert } from "react-icons/ci"; import { Link } from "react-router-dom"; import { toast } from "sonner"; import useSWR from "swr"; @@ -80,7 +81,7 @@ export default function NotificationView({ return Object.values(config.cameras) .filter( (conf) => - conf.enabled && + conf.enabled_in_config && conf.notifications && conf.notifications.enabled_in_config, ) @@ -266,10 +267,13 @@ export default function NotificationView({ } }) .catch((error) => { - toast.error( - `Failed to save config changes: ${error.response.data.message}`, - { position: "top-center" }, - ); + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to save config changes: ${errorMessage}`, { + position: "top-center", + }); }) .finally(() => { setIsLoading(false); @@ -311,7 +315,7 @@ export default function NotificationView({
- + Notifications Unavailable diff --git a/web/src/views/settings/SearchSettingsView.tsx b/web/src/views/settings/SearchSettingsView.tsx index 027f55070..b3f35bde7 100644 --- a/web/src/views/settings/SearchSettingsView.tsx +++ b/web/src/views/settings/SearchSettingsView.tsx @@ -103,10 +103,13 @@ export default function SearchSettingsView({ } }) .catch((error) => { - toast.error( - `Failed to save config changes: ${error.response.data.message}`, - { position: "top-center" }, - ); + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to save config changes: ${errorMessage}`, { + position: "top-center", + }); }) .finally(() => { setIsLoading(false); diff --git a/web/src/views/settings/UiSettingsView.tsx b/web/src/views/settings/UiSettingsView.tsx index e3b5c8c7a..03375670f 100644 --- a/web/src/views/settings/UiSettingsView.tsx +++ b/web/src/views/settings/UiSettingsView.tsx @@ -38,10 +38,13 @@ export default function UiSettingsView() { }); }) .catch((error) => { - toast.error( - `Failed to clear stored layout: ${error.response.data.message}`, - { position: "top-center" }, - ); + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to clear stored layout: ${errorMessage}`, { + position: "top-center", + }); }); }); }, [config]); @@ -58,10 +61,13 @@ export default function UiSettingsView() { }); }) .catch((error) => { - toast.error( - `Failed to clear camera groups streaming settings: ${error.response.data.message}`, - { position: "top-center" }, - ); + const errorMessage = + error.response?.data?.message || + error.response?.data?.detail || + "Unknown error"; + toast.error(`Failed to clear streaming settings: ${errorMessage}`, { + position: "top-center", + }); }); }, [config]); diff --git a/web/src/views/system/StorageMetrics.tsx b/web/src/views/system/StorageMetrics.tsx index ea2f1b7b2..4f057be29 100644 --- a/web/src/views/system/StorageMetrics.tsx +++ b/web/src/views/system/StorageMetrics.tsx @@ -8,7 +8,7 @@ import { PopoverTrigger, } from "@/components/ui/popover"; import useSWR from "swr"; -import { LuAlertCircle } from "react-icons/lu"; +import { CiCircleAlert } from "react-icons/ci"; import { FrigateConfig } from "@/types/frigateConfig"; import { useTimezone } from "@/hooks/use-date-utils"; import { RecordingsSummary } from "@/types/review"; @@ -86,7 +86,7 @@ export default function StorageMetrics({ className="focus:outline-none" aria-label="Unused Storage Information" > -