Merge branch 'fastapi-poc' into fastapi-poc-media-endpoints

.cspell/frigate-dictionary.txt

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

.github/workflows/ci.yml

@@ -179,57 +179,18 @@ jobs:
             h8l.tags=${{ steps.setup.outputs.image-name }}-h8l
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-h8l
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-h8l,mode=max
-      #- name: AMD/ROCm general build
-      #  env:
-      #    AMDGPU: gfx
-      #    HSA_OVERRIDE: 0
-      #  uses: docker/bake-action@v3
-      #  with:
-      #    push: true
-      #    targets: rocm
-      #    files: docker/rocm/rocm.hcl
-      #    set: |
-      #      rocm.tags=${{ steps.setup.outputs.image-name }}-rocm
-      #      *.cache-from=type=gha
-      #- name: AMD/ROCm gfx900
-      #  env:
-      #    AMDGPU: gfx900
-      #    HSA_OVERRIDE: 1
-      #    HSA_OVERRIDE_GFX_VERSION: 9.0.0
-      #  uses: docker/bake-action@v3
-      #  with:
-      #    push: true
-      #    targets: rocm
-      #    files: docker/rocm/rocm.hcl
-      #    set: |
-      #      rocm.tags=${{ steps.setup.outputs.image-name }}-rocm-gfx900
-      #      *.cache-from=type=gha
-      #- name: AMD/ROCm gfx1030
-      #  env:
-      #    AMDGPU: gfx1030
-      #    HSA_OVERRIDE: 1
-      #    HSA_OVERRIDE_GFX_VERSION: 10.3.0
-      #  uses: docker/bake-action@v3
-      #  with:
-      #    push: true
-      #    targets: rocm
-      #    files: docker/rocm/rocm.hcl
-      #    set: |
-      #      rocm.tags=${{ steps.setup.outputs.image-name }}-rocm-gfx1030
-      #      *.cache-from=type=gha
-      #- name: AMD/ROCm gfx1100
-      #  env:
-      #    AMDGPU: gfx1100
-      #    HSA_OVERRIDE: 1
-      #    HSA_OVERRIDE_GFX_VERSION: 11.0.0
-      #  uses: docker/bake-action@v3
-      #  with:
-      #    push: true
-      #    targets: rocm
-      #    files: docker/rocm/rocm.hcl
-      #    set: |
-      #      rocm.tags=${{ steps.setup.outputs.image-name }}-rocm-gfx1100
-      #      *.cache-from=type=gha
+      - name: AMD/ROCm general build
+        env:
+          AMDGPU: gfx
+          HSA_OVERRIDE: 0
+        uses: docker/bake-action@v3
+        with:
+          push: true
+          targets: rocm
+          files: docker/rocm/rocm.hcl
+          set: |
+            rocm.tags=${{ steps.setup.outputs.image-name }}-rocm
+            *.cache-from=type=gha
   # The majority of users running arm64 are rpi users, so the rpi
   # build should be the primary arm64 image
   assemble_default_build:

.vscode/launch.json

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

Makefile

@@ -4,8 +4,6 @@ COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
 VERSION = 0.15.0
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
-CURRENT_UID := $(shell id -u)
-CURRENT_GID := $(shell id -g)
 BOARDS= #Initialized empty
 
 include docker/*/*.mk
@@ -18,25 +16,38 @@ version:
 	echo 'VERSION = "$(VERSION)-$(COMMIT_HASH)"' > frigate/version.py
 
 local: version
-	docker buildx build --target=frigate --tag frigate:latest --load --file docker/main/Dockerfile .
+	docker buildx build --target=frigate --file docker/main/Dockerfile . \
+		--tag frigate:latest \
+		--load
 
 amd64:
-	docker buildx build --platform linux/amd64 --target=frigate --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) --file docker/main/Dockerfile .
+	docker buildx build --target=frigate --file docker/main/Dockerfile . \
+		--tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) \
+		--platform linux/amd64
 
 arm64:
-	docker buildx build --platform linux/arm64 --target=frigate --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) --file docker/main/Dockerfile .
+	docker buildx build --target=frigate --file docker/main/Dockerfile . \
+		--tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) \
+		--platform linux/arm64
 
 build: version amd64 arm64
-	docker buildx build --platform linux/arm64/v8,linux/amd64 --target=frigate --tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) --file docker/main/Dockerfile .
+	docker buildx build --target=frigate --file docker/main/Dockerfile . \
+		--tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) \
+		--platform linux/arm64/v8,linux/amd64
 
 push: push-boards
-	docker buildx build --push --platform linux/arm64/v8,linux/amd64 --target=frigate --tag $(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH) --file docker/main/Dockerfile .
+	docker buildx build --target=frigate --file docker/main/Dockerfile . \
+		--tag $(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH) \
+		--platform linux/arm64/v8,linux/amd64 \
+		--push
 
 run: local
 	docker run --rm --publish=5000:5000 --volume=${PWD}/config:/config frigate:latest
 
 run_tests: local
-	docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest python3 -u -m unittest
-	docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest python3 -u -m mypy --config-file frigate/mypy.ini frigate
+	docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest \
+		python3 -u -m unittest
+	docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest \
+		python3 -u -m mypy --config-file frigate/mypy.ini frigate
 
 .PHONY: run_tests

cspell.json

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

docker/hailo8l/h8l.mk

@@ -1,10 +1,15 @@
 BOARDS += h8l
 
 local-h8l: version
-	docker buildx bake --load --file=docker/hailo8l/h8l.hcl --set h8l.tags=frigate:latest-h8l h8l
+	docker buildx bake --file=docker/hailo8l/h8l.hcl h8l \
+		--set h8l.tags=frigate:latest-h8l \
+		--load
 
 build-h8l: version
-	docker buildx bake --file=docker/hailo8l/h8l.hcl --set h8l.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-h8l h8l
+	docker buildx bake --file=docker/hailo8l/h8l.hcl h8l \
+		--set h8l.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-h8l
 
 push-h8l: build-h8l
-	docker buildx bake --push --file=docker/hailo8l/h8l.hcl --set h8l.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-h8l h8l
+	docker buildx bake --file=docker/hailo8l/h8l.hcl h8l \
+	 	--set h8l.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-h8l \
+		--push

docker/main/Dockerfile

@@ -170,6 +170,9 @@ RUN /build_pysqlite3.sh
 COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
 RUN pip3 wheel --wheel-dir=/wheels -r /requirements-wheels.txt
 
+COPY docker/main/requirements-wheels-post.txt /requirements-wheels-post.txt
+RUN pip3 wheel --no-deps --wheel-dir=/wheels-post -r /requirements-wheels-post.txt
+
 
 # Collect deps in a single layer
 FROM scratch AS deps-rootfs
@@ -212,6 +215,14 @@ RUN --mount=type=bind,from=wheels,source=/wheels,target=/deps/wheels \
     python3 -m pip install --upgrade pip && \
     pip3 install -U /deps/wheels/*.whl
 
+# We have to uninstall this dependency specifically
+# as it will break onnxruntime-openvino
+RUN pip3 uninstall -y onnxruntime
+
+RUN --mount=type=bind,from=wheels,source=/wheels-post,target=/deps/wheels \
+    python3 -m pip install --upgrade pip && \
+    pip3 install -U /deps/wheels/*.whl
+
 COPY --from=deps-rootfs / /
 
 RUN ldconfig

docker/main/install_deps.sh

@@ -44,7 +44,7 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
     wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linux64-gpl-5.1.tar.xz"
     tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1
     rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/5.0/doc /usr/lib/ffmpeg/5.0/bin/ffplay
-    wget -qO btbn-ffmpeg.tar.xz "https://github.com/BtbN/FFmpeg-Builds/releases/download/autobuild-2024-09-13-12-57/ffmpeg-n7.0.2-17-gf705bc5b73-linux64-gpl-7.0.tar.xz"
+    wget -qO btbn-ffmpeg.tar.xz "https://github.com/BtbN/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linux64-gpl-7.0.tar.xz"
     tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1
     rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/7.0/doc /usr/lib/ffmpeg/7.0/bin/ffplay
 fi
@@ -56,19 +56,18 @@ if [[ "${TARGETARCH}" == "arm64" ]]; then
     wget -qO btbn-ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2022-07-31-12-37/ffmpeg-n5.1-2-g915ef932a3-linuxarm64-gpl-5.1.tar.xz"
     tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1
     rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/5.0/doc /usr/lib/ffmpeg/5.0/bin/ffplay
-    wget -qO btbn-ffmpeg.tar.xz "https://github.com/BtbN/FFmpeg-Builds/releases/download/autobuild-2024-09-13-12-57/ffmpeg-n7.0.2-17-gf705bc5b73-linuxarm64-gpl-7.0.tar.xz"
+    wget -qO btbn-ffmpeg.tar.xz "https://github.com/BtbN/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linuxarm64-gpl-7.0.tar.xz"
     tar -xf btbn-ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1
     rm -rf btbn-ffmpeg.tar.xz /usr/lib/ffmpeg/7.0/doc /usr/lib/ffmpeg/7.0/bin/ffplay
 fi
 
 # arch specific packages
 if [[ "${TARGETARCH}" == "amd64" ]]; then
-    # use debian bookworm for hwaccel packages
+    # use debian bookworm for amd / intel-i965 driver packages
     echo 'deb https://deb.debian.org/debian bookworm main contrib non-free' >/etc/apt/sources.list.d/debian-bookworm.list
     apt-get -qq update
     apt-get -qq install --no-install-recommends --no-install-suggests -y \
-        intel-opencl-icd intel-media-va-driver-non-free i965-va-driver \
-        libmfx-gen1.2 libmfx1 onevpl-tools intel-gpu-tools \
+        i965-va-driver intel-gpu-tools onevpl-tools \
         libva-drm2 \
         mesa-va-drivers radeontop
 
@@ -77,6 +76,17 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
         i965-va-driver-shaders
 
     rm -f /etc/apt/sources.list.d/debian-bookworm.list
+
+    # use intel apt intel packages
+    wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg
+    echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | tee /etc/apt/sources.list.d/intel-gpu-jammy.list
+    apt-get -qq update
+    apt-get -qq install --no-install-recommends --no-install-suggests -y \
+        intel-opencl-icd intel-level-zero-gpu intel-media-va-driver-non-free \
+        libmfx1 libmfxgen1 libvpl2
+
+    rm -f /usr/share/keyrings/intel-graphics.gpg
+    rm -f /etc/apt/sources.list.d/intel-gpu-jammy.list
 fi
 
 if [[ "${TARGETARCH}" == "arm64" ]]; then

docker/main/requirements-wheels-post.txt

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

docker/main/requirements-wheels.txt

@@ -28,13 +28,11 @@ norfair == 2.2.*
 setproctitle == 1.3.*
 ws4py == 0.5.*
 unidecode == 1.3.*
-# OpenVino & ONNX
-openvino == 2024.1.*
-onnxruntime-openvino == 1.18.* ; platform_machine == 'x86_64'
-onnxruntime == 1.18.* ; platform_machine == 'aarch64'
+# OpenVino (ONNX installed in wheels-post)
+openvino == 2024.3.*
 # Embeddings
-onnx_clip == 4.0.*
 chromadb == 0.5.0
+onnx_clip == 4.0.*
 # Generative AI
 google-generativeai == 0.6.*
 ollama == 0.2.*

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

@@ -2,16 +2,19 @@
 
 import json
 import os
+import shutil
 import sys
 from pathlib import Path
 
 import yaml
 
 sys.path.insert(0, "/opt/frigate")
-from frigate.const import BIRDSEYE_PIPE  # noqa: E402
-from frigate.ffmpeg_presets import (  # noqa: E402
-    parse_preset_hardware_acceleration_encode,
+from frigate.const import (
+    BIRDSEYE_PIPE,
+    DEFAULT_FFMPEG_VERSION,
+    INCLUDED_FFMPEG_VERSIONS,
 )
+from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_encode
 
 sys.path.remove("/opt/frigate")
 
@@ -108,14 +111,12 @@ else:
 # ensure ffmpeg path is set correctly
 path = config.get("ffmpeg", {}).get("path", "default")
 if path == "default":
-    if int(os.getenv("", "59") or "59") >= 59:
-        ffmpeg_path = "/usr/lib/ffmpeg/7.0/bin/ffmpeg"
+    if shutil.which("ffmpeg") is None:
+        ffmpeg_path = f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg"
     else:
         ffmpeg_path = "ffmpeg"
-elif path == "7.0":
-    ffmpeg_path = "/usr/lib/ffmpeg/7.0/bin/ffmpeg"
-elif path == "5.0":
-    ffmpeg_path = "/usr/lib/ffmpeg/5.0/bin/ffmpeg"
+elif path in INCLUDED_FFMPEG_VERSIONS:
+    ffmpeg_path = f"/usr/lib/ffmpeg/{path}/bin/ffmpeg"
 else:
     ffmpeg_path = f"{path}/bin/ffmpeg"
 

docker/rockchip/Dockerfile

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

docker/rockchip/rk.mk

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

docker/rocm/Dockerfile

@@ -23,11 +23,11 @@ COPY docker/rocm/rocm-pin-600 /etc/apt/preferences.d/
 
 RUN apt-get update
 
-RUN apt-get -y install --no-install-recommends migraphx
+RUN apt-get -y install --no-install-recommends migraphx hipfft roctracer
 RUN apt-get -y install --no-install-recommends migraphx-dev
 
 RUN mkdir -p /opt/rocm-dist/opt/rocm-$ROCM/lib
-RUN cd /opt/rocm-$ROCM/lib && cp -dpr libMIOpen*.so* libamd*.so* libhip*.so* libhsa*.so* libmigraphx*.so* librocm*.so* librocblas*.so* /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-$ROCM/lib/
 RUN cd /opt/rocm-dist/opt/ && ln -s rocm-$ROCM rocm
 
 RUN mkdir -p /opt/rocm-dist/etc/ld.so.conf.d/
@@ -69,7 +69,11 @@ RUN apt-get -y install libnuma1
 
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
-COPY docker/rocm/rootfs/ /
+
+COPY docker/rocm/requirements-wheels-rocm.txt /requirements.txt
+RUN python3 -m pip install --upgrade pip \
+    && pip3 uninstall -y onnxruntime-openvino \
+    && pip3 install -r /requirements.txt
 
 #######################################################################
 FROM scratch AS rocm-dist
@@ -101,6 +105,3 @@ ENV HSA_OVERRIDE_GFX_VERSION=$HSA_OVERRIDE_GFX_VERSION
 #######################################################################
 FROM rocm-prelim-hsa-override$HSA_OVERRIDE as rocm-deps
 
-# Request yolov8 download at startup
-ENV DOWNLOAD_YOLOV8=1
-

docker/rocm/requirements-wheels-rocm.txt

@@ -0,0 +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

docker/rocm/rocm.mk

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

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

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

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

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

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

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

docker/rpi/rpi.mk

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

docker/tensorrt/Dockerfile.amd64

@@ -12,12 +12,28 @@ ARG TARGETARCH
 COPY docker/tensorrt/requirements-amd64.txt /requirements-tensorrt.txt
 RUN mkdir -p /trt-wheels && pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt
 
+# Build CuDNN
+FROM wget AS cudnn-deps
+
+ARG COMPUTE_LEVEL
+
+RUN apt-get update \
+    && apt-get install -y git build-essential
+
+RUN wget https://developer.download.nvidia.com/compute/cuda/repos/debian11/x86_64/cuda-keyring_1.1-1_all.deb \
+&& dpkg -i cuda-keyring_1.1-1_all.deb \
+&& apt-get update \
+&& apt-get -y install cuda-toolkit \
+&& rm -rf /var/lib/apt/lists/*
+
 FROM tensorrt-base AS frigate-tensorrt
 ENV TRT_VER=8.5.3
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
     pip3 install -U /deps/trt-wheels/*.whl && \
     ldconfig
+COPY --from=cudnn-deps /usr/local/cuda-12.6 /usr/local/cuda
 
+ENV LD_LIBRARY_PATH=/usr/local/lib/python3.9/dist-packages/tensorrt:/usr/local/cuda/lib64:/usr/local/lib/python3.9/dist-packages/nvidia/cufft/lib
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
 
@@ -26,6 +42,7 @@ FROM devcontainer AS devcontainer-trt
 
 COPY --from=trt-deps /usr/local/lib/libyolo_layer.so /usr/local/lib/libyolo_layer.so
 COPY --from=trt-deps /usr/local/src/tensorrt_demos /usr/local/src/tensorrt_demos
+COPY --from=cudnn-deps /usr/local/cuda-12.6 /usr/local/cuda
 COPY docker/tensorrt/detector/rootfs/ /
 COPY --from=trt-deps /usr/local/lib/libyolo_layer.so /usr/local/lib/libyolo_layer.so
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \

docker/tensorrt/requirements-amd64.txt

@@ -8,5 +8,7 @@ nvidia-cuda-runtime-cu12 == 12.1.*; platform_machine == 'x86_64'
 nvidia-cuda-runtime-cu11 == 11.8.*; platform_machine == 'x86_64'
 nvidia-cublas-cu11 == 11.11.3.6; platform_machine == 'x86_64'
 nvidia-cudnn-cu11 == 8.6.0.*; platform_machine == 'x86_64'
+nvidia-cufft-cu11==10.*; platform_machine == 'x86_64'
 onnx==1.14.0; platform_machine == 'x86_64'
-protobuf==3.20.3; platform_machine == 'x86_64'
+onnxruntime-gpu==1.17.*; platform_machine == 'x86_64'
+protobuf==3.20.3; platform_machine == 'x86_64'

docker/tensorrt/trt.mk

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

docs/docs/configuration/genai.md

@@ -124,13 +124,25 @@ genai:
   model: llava
   prompt: "Describe the {label} in these images from the {camera} security camera."
   object_prompts:
-    person: "Describe the main person in these images (gender, age, clothing, activity, etc). Do not include where the activity is occurring (sidewalk, concrete, driveway, etc). If delivering a package, include the company the package is from."
+    person: "Describe the main person in these images (gender, age, clothing, activity, etc). Do not include where the activity is occurring (sidewalk, concrete, driveway, etc)."
     car: "Label the primary vehicle in these images with just the name of the company if it is a delivery vehicle, or the color make and model."
 ```
 
+Prompts can also be overriden at the camera level to provide a more detailed prompt to the model about your specific camera, if you desire.
+
+```yaml
+cameras:
+  front_door:
+    genai:
+      prompt: "Describe the {label} in these images from the {camera} security camera at the front door of a house, aimed outward toward the street."
+      object_prompts:
+        person: "Describe the main person in these images (gender, age, clothing, activity, etc). Do not include where the activity is occurring (sidewalk, concrete, driveway, etc). If delivering a package, include the company the package is from."
+        cat: "Describe the cat in these images (color, size, tail). Indicate whether or not the cat is by the flower pots. If the cat is chasing a mouse, make up a name for the mouse."
+```
+
 ### Experiment with prompts
 
-Providers also has a public facing chat interface for their models. Download a couple different thumbnails or snapshots from Frigate and try new things in the playground to get descriptions to your liking before updating the prompt in Frigate.
+Many providers also have a public facing chat interface for their models. Download a couple of different thumbnails or snapshots from Frigate and try new things in the playground to get descriptions to your liking before updating the prompt in Frigate.
 
 - OpenAI - [ChatGPT](https://chatgpt.com)
 - Gemini - [Google AI Studio](https://aistudio.google.com)

docs/docs/configuration/hardware_acceleration.md

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

docs/docs/configuration/object_detectors.md

@@ -3,6 +3,29 @@ id: object_detectors
 title: Object Detectors
 ---
 
+# Supported Hardware
+
+Frigate supports multiple different detectors that work on different types of hardware:
+
+**Most Hardware**
+- [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB and m.2 format allowing for a wide range of compatibility with devices.
+- [Hailo](#hailo-8l): The Hailo8 AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
+
+**AMD**
+- [ROCm](#amdrocm-gpu-detector): ROCm can run on AMD Discrete GPUs to provide efficient object detection.
+- [ONNX](#onnx): ROCm will automatically be detected and used as a detector in the `-rocm` Frigate image when a supported ONNX model is configured.
+
+**Intel**
+- [OpenVino](#openvino-detector): OpenVino can run on Intel Arc GPUs, Intel integrated GPUs, and Intel CPUs to provide efficient object detection.
+- [ONNX](#onnx): OpenVINO will automatically be detected and used as a detector in the default Frigate image when a supported ONNX model is configured.
+
+**Nvidia**
+- [TensortRT](#nvidia-tensorrt-detector): TensorRT can run on Nvidia GPUs, using one of many default models.
+- [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt` Frigate image when a supported ONNX model is configured.
+
+**Rockchip**
+- [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs.
+
 # Officially Supported Detectors
 
 Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `openvino`, `tensorrt`, `rknn`, and `hailo8l`. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
@@ -122,6 +145,22 @@ The OpenVINO device to be used is specified using the `"device"` attribute accor
 
 OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will also run on AMD CPUs despite having no official support for it. A supported Intel platform is required to use the `GPU` device with OpenVINO. For detailed system requirements, see [OpenVINO System Requirements](https://docs.openvino.ai/2024/about-openvino/release-notes-openvino/system-requirements.html)
 
+:::tip
+
+When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
+
+```yaml
+detectors:
+  ov_0:
+    type: openvino
+    device: GPU
+  ov_1:
+    type: openvino
+    device: GPU
+```
+
+:::
+
 ### Supported Models
 
 #### SSDLite MobileNet v2
@@ -278,6 +317,173 @@ model:
   height: 320
 ```
 
+## AMD/ROCm GPU detector
+
+### Setup
+
+The `rocm` detector supports running YOLO-NAS models on AMD GPUs. Use a frigate docker image with `-rocm` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-rocm`.
+
+### Docker settings for GPU access
+
+ROCm needs access to the `/dev/kfd` and `/dev/dri` devices. When docker or frigate is not run under root then also `video` (and possibly `render` and `ssl/_ssl`) groups should be added.
+
+When running docker directly the following flags should be added for device access:
+
+```bash
+$ docker run --device=/dev/kfd --device=/dev/dri  \
+    ...
+```
+
+When using docker compose:
+
+```yaml
+services:
+  frigate:
+---
+devices:
+  - /dev/dri
+  - /dev/kfd
+```
+
+For reference on recommended settings see [running ROCm/pytorch in Docker](https://rocm.docs.amd.com/projects/install-on-linux/en/develop/how-to/3rd-party/pytorch-install.html#using-docker-with-pytorch-pre-installed).
+
+### Docker settings for overriding the GPU chipset
+
+Your GPU might work just fine without any special configuration but in many cases they need manual settings. AMD/ROCm software stack comes with a limited set of GPU drivers and for newer or missing models you will have to override the chipset version to an older/generic version to get things working.
+
+Also AMD/ROCm does not "officially" support integrated GPUs. It still does work with most of them just fine but requires special settings. One has to configure the `HSA_OVERRIDE_GFX_VERSION` environment variable. See the [ROCm bug report](https://github.com/ROCm/ROCm/issues/1743) for context and examples.
+
+For the rocm frigate build there is some automatic detection:
+
+- gfx90c -> 9.0.0
+- gfx1031 -> 10.3.0
+- gfx1103 -> 11.0.0
+
+If you have something else you might need to override the `HSA_OVERRIDE_GFX_VERSION` at Docker launch. Suppose the version you want is `9.0.0`, then you should configure it from command line as:
+
+```bash
+$ docker run -e HSA_OVERRIDE_GFX_VERSION=9.0.0 \
+    ...
+```
+
+When using docker compose:
+
+```yaml
+services:
+  frigate:
+...
+environment:
+  HSA_OVERRIDE_GFX_VERSION: "9.0.0"
+```
+
+Figuring out what version you need can be complicated as you can't tell the chipset name and driver from the AMD brand name.
+
+- first make sure that rocm environment is running properly by running `/opt/rocm/bin/rocminfo` in the frigate container -- it should list both the CPU and the GPU with their properties
+- find the chipset version you have (gfxNNN) from the output of the `rocminfo` (see below)
+- use a search engine to query what `HSA_OVERRIDE_GFX_VERSION` you need for the given gfx name ("gfxNNN ROCm HSA_OVERRIDE_GFX_VERSION")
+- override the `HSA_OVERRIDE_GFX_VERSION` with relevant value
+- if things are not working check the frigate docker logs
+
+#### Figuring out if AMD/ROCm is working and found your GPU
+
+```bash
+$ docker exec -it frigate /opt/rocm/bin/rocminfo
+```
+
+#### Figuring out your AMD GPU chipset version:
+
+We unset the `HSA_OVERRIDE_GFX_VERSION` to prevent an existing override from messing up the result:
+
+```bash
+$ docker exec -it frigate /bin/bash -c '(unset HSA_OVERRIDE_GFX_VERSION && /opt/rocm/bin/rocminfo |grep gfx)'
+```
+
+### Supported Models
+
+There is no default model provided, the following formats are supported:
+
+#### YOLO-NAS
+
+[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
+
+:::warning
+
+The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
+
+:::
+
+The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.
+
+After placing the downloaded onnx model in your config folder, you can use the following configuration:
+
+```yaml
+detectors:
+  onnx:
+    type: rocm
+
+model:
+  model_type: yolonas
+  width: 320 # <--- should match whatever was set in notebook
+  height: 320 # <--- should match whatever was set in notebook
+  input_pixel_format: bgr
+  path: /config/yolo_nas_s.onnx
+  labelmap_path: /labelmap/coco-80.txt
+```
+
+Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
+
+## ONNX
+
+ONNX is an open format for building machine learning models, Frigate supports running ONNX models on CPU, OpenVINO, and TensorRT. On startup Frigate will automatically try to use a GPU if one is available.
+
+:::tip
+
+When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
+
+```yaml
+detectors:
+  onnx_0:
+    type: onnx
+  onnx_1:
+    type: onnx
+```
+
+:::
+
+### Supported Models
+
+There is no default model provided, the following formats are supported:
+
+#### YOLO-NAS
+
+[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
+
+:::warning
+
+The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
+
+:::
+
+The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.
+
+After placing the downloaded onnx model in your config folder, you can use the following configuration:
+
+```yaml
+detectors:
+  onnx:
+    type: onnx
+
+model:
+  model_type: yolonas
+  width: 320 # <--- should match whatever was set in notebook
+  height: 320 # <--- should match whatever was set in notebook
+  input_pixel_format: bgr
+  path: /config/yolo_nas_s.onnx
+  labelmap_path: /labelmap/coco-80.txt
+```
+
+Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
+
 ## Deepstack / CodeProject.AI Server Detector
 
 The Deepstack / CodeProject.AI Server detector for Frigate allows you to integrate Deepstack and CodeProject.AI object detection capabilities into Frigate. CodeProject.AI and DeepStack are open-source AI platforms that can be run on various devices such as the Raspberry Pi, Nvidia Jetson, and other compatible hardware. It is important to note that the integration is performed over the network, so the inference times may not be as fast as native Frigate detectors, but it still provides an efficient and reliable solution for object detection and tracking.
@@ -389,7 +595,7 @@ $ cat /sys/kernel/debug/rknpu/load
 
 ## Hailo-8l
 
-This detector is available if you are using the Raspberry Pi 5 with Hailo-8L AI Kit. This has not been tested using the Hailo-8L with other hardware.
+This detector is available for use with Hailo-8 AI Acceleration Module.
 
 ### Configuration
 

docs/docs/configuration/reference.md

@@ -504,7 +504,7 @@ semantic_search:
 # to Google or OpenAI's LLMs to generate descriptions. It can be overridden at
 # the camera level (enabled: False) to enhance privacy for indoor cameras.
 genai:
-  # Optional: Enable Google Gemini description generation (default: shown below)
+  # Optional: Enable AI description generation (default: shown below)
   enabled: False
   # Required if enabled: Provider must be one of ollama, gemini, or openai
   provider: ollama
@@ -712,6 +712,18 @@ cameras:
       # By default the cameras are sorted alphabetically.
       order: 0
 
+    # Optional: Configuration for AI generated tracked object descriptions
+    genai:
+      # Optional: Enable AI description generation (default: shown below)
+      enabled: False
+      # Optional: The default prompt for generating descriptions. Can use replacement
+      # variables like "label", "sub_label", "camera" to make more dynamic. (default: shown below)
+      prompt: "Describe the {label} in the sequence of images with as much detail as possible. Do not describe the background."
+      # Optional: Object specific prompts to customize description results
+      # Format: {label}: {prompt}
+      object_prompts:
+        person: "My special person prompt."
+
 # Optional
 ui:
   # Optional: Set a timezone to use in the UI (default: use browser local time)

docs/docs/frigate/hardware.md

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

docs/package.json

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

frigate/__main__.py

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

frigate/api/event.py

@@ -353,7 +353,10 @@ def events_search():
     after = request.args.get("after", type=float)
     before = request.args.get("before", type=float)
 
-    if not query:
+    # for similarity search
+    event_id = request.args.get("event_id", type=str)
+
+    if not query and not event_id:
         return make_response(
             jsonify(
                 {
@@ -432,7 +435,7 @@ def events_search():
     if search_type == "similarity":
         # Grab the ids of events that match the thumbnail image embeddings
         try:
-            search_event: Event = Event.get(Event.id == query)
+            search_event: Event = Event.get(Event.id == event_id)
         except DoesNotExist:
             return make_response(
                 jsonify(

frigate/api/export.py

@@ -149,9 +149,9 @@ def export_delete(id: str):
         try:
             if process.name() != "ffmpeg":
                 continue
-            flist = process.open_files()
-            if flist:
-                for nt in flist:
+            file_list = process.open_files()
+            if file_list:
+                for nt in file_list:
                     if nt.path.startswith(EXPORT_DIR):
                         files_in_use.append(nt.path.split("/")[-1])
         except psutil.Error:

frigate/app.py

@@ -46,7 +46,7 @@ from frigate.events.audio import listen_to_audio
 from frigate.events.cleanup import EventCleanup
 from frigate.events.external import ExternalEventProcessor
 from frigate.events.maintainer import EventProcessor
-from frigate.log import log_process, root_configurer
+from frigate.log import log_thread
 from frigate.models import (
     Event,
     Export,
@@ -116,15 +116,6 @@ class FrigateApp:
             else:
                 logger.debug(f"Skipping directory: {d}")
 
-    def init_logger(self) -> None:
-        self.log_process = mp.Process(
-            target=log_process, args=(self.log_queue,), name="log_process"
-        )
-        self.log_process.daemon = True
-        self.log_process.start()
-        self.processes["logger"] = self.log_process.pid or 0
-        root_configurer(self.log_queue)
-
     def init_config(self) -> None:
         config_file = os.environ.get("CONFIG_FILE", "/config/config.yml")
 
@@ -679,6 +670,7 @@ class FrigateApp:
                 logger.info("********************************************************")
                 logger.info("********************************************************")
 
+    @log_thread()
     def start(self) -> None:
         parser = argparse.ArgumentParser(
             prog="Frigate",
@@ -687,7 +679,6 @@ class FrigateApp:
         parser.add_argument("--validate-config", action="store_true")
         args = parser.parse_args()
 
-        self.init_logger()
         logger.info(f"Starting Frigate ({VERSION})")
 
         try:
@@ -714,13 +705,11 @@ class FrigateApp:
                 print("*************************************************************")
                 print("***    End Config Validation Errors                       ***")
                 print("*************************************************************")
-                self.log_process.terminate()
                 sys.exit(1)
             if args.validate_config:
                 print("*************************************************************")
                 print("*** Your config file is valid.                            ***")
                 print("*************************************************************")
-                self.log_process.terminate()
                 sys.exit(0)
             self.set_environment_vars()
             self.set_log_levels()
@@ -737,7 +726,6 @@ class FrigateApp:
             self.init_dispatcher()
         except Exception as e:
             print(e)
-            self.log_process.terminate()
             sys.exit(1)
         self.start_detectors()
         self.start_video_output_processor()
@@ -866,7 +854,4 @@ class FrigateApp:
             shm.close()
             shm.unlink()
 
-        self.log_process.terminate()
-        self.log_process.join()
-
         os._exit(os.EX_OK)

frigate/comms/webpush.py

@@ -106,10 +106,10 @@ class WebPushClient(Communicator):  # type: ignore[misc]
     def publish(self, topic: str, payload: Any, retain: bool = False) -> None:
         """Wrapper for publishing when client is in valid state."""
         # check for updated notification config
-        _, updated_notif_config = self.config_subscriber.check_for_update()
+        _, updated_notification_config = self.config_subscriber.check_for_update()
 
-        if updated_notif_config:
-            self.config.notifications = updated_notif_config
+        if updated_notification_config:
+            self.config.notifications = updated_notification_config
 
         if not self.config.notifications.enabled:
             return

frigate/config.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 import json
 import logging
 import os
+import shutil
 from enum import Enum
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple, Union
@@ -25,7 +26,9 @@ from frigate.const import (
     CACHE_DIR,
     CACHE_SEGMENT_FORMAT,
     DEFAULT_DB_PATH,
+    DEFAULT_FFMPEG_VERSION,
     FREQUENCY_STATS_POINTS,
+    INCLUDED_FFMPEG_VERSIONS,
     MAX_PRE_CAPTURE,
     REGEX_CAMERA_NAME,
     YAML_EXT,
@@ -762,8 +765,14 @@ class GenAIConfig(FrigateBaseModel):
     object_prompts: Dict[str, str] = Field(default={}, title="Object specific prompts.")
 
 
-class GenAICameraConfig(FrigateBaseModel):
+# 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.")
+    prompt: str = Field(
+        default="Describe the {label} in the sequence of images with as much detail as possible. Do not describe the background.",
+        title="Default caption prompt.",
+    )
+    object_prompts: Dict[str, str] = Field(default={}, title="Object specific prompts.")
 
 
 class AudioConfig(FrigateBaseModel):
@@ -888,28 +897,24 @@ class FfmpegConfig(FrigateBaseModel):
     @property
     def ffmpeg_path(self) -> str:
         if self.path == "default":
-            if int(os.getenv("LIBAVFORMAT_VERSION_MAJOR", "59")) >= 59:
-                return "/usr/lib/ffmpeg/7.0/bin/ffmpeg"
+            if shutil.which("ffmpeg") is None:
+                return f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg"
             else:
                 return "ffmpeg"
-        elif self.path == "7.0":
-            return "/usr/lib/ffmpeg/7.0/bin/ffmpeg"
-        elif self.path == "5.0":
-            return "/usr/lib/ffmpeg/5.0/bin/ffmpeg"
+        elif self.path in INCLUDED_FFMPEG_VERSIONS:
+            return f"/usr/lib/ffmpeg/{self.path}/bin/ffmpeg"
         else:
             return f"{self.path}/bin/ffmpeg"
 
     @property
     def ffprobe_path(self) -> str:
         if self.path == "default":
-            if int(os.getenv("LIBAVFORMAT_VERSION_MAJOR", "59")) >= 59:
-                return "/usr/lib/ffmpeg/7.0/bin/ffprobe"
+            if shutil.which("ffprobe") is None:
+                return f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffprobe"
             else:
                 return "ffprobe"
-        elif self.path == "7.0":
-            return "/usr/lib/ffmpeg/7.0/bin/ffprobe"
-        elif self.path == "5.0":
-            return "/usr/lib/ffmpeg/5.0/bin/ffprobe"
+        elif self.path in INCLUDED_FFMPEG_VERSIONS:
+            return f"/usr/lib/ffmpeg/{self.path}/bin/ffprobe"
         else:
             return f"{self.path}/bin/ffprobe"
 
@@ -1187,7 +1192,7 @@ class CameraConfig(FrigateBaseModel):
                 + ffmpeg_output_args
             )
 
-        # if there arent any outputs enabled for this input
+        # if there aren't any outputs enabled for this input
         if len(ffmpeg_output_args) == 0:
             return None
 
@@ -1519,7 +1524,7 @@ class FrigateConfig(FrigateBaseModel):
                 "live": ...,
                 "objects": ...,
                 "review": ...,
-                "genai": {"enabled"},
+                "genai": ...,
                 "motion": ...,
                 "detect": ...,
                 "ffmpeg": ...,

frigate/const.py

@@ -12,7 +12,7 @@ FRIGATE_LOCALHOST = "http://127.0.0.1:5000"
 PLUS_ENV_VAR = "PLUS_API_KEY"
 PLUS_API_HOST = "https://api.frigate.video"
 
-# Attribute & Object Consts
+# Attribute & Object constants
 
 ATTRIBUTE_LABEL_MAP = {
     "person": ["face", "amazon"],
@@ -31,7 +31,7 @@ LABEL_NMS_MAP = {
 }
 LABEL_NMS_DEFAULT = 0.4
 
-# Audio Consts
+# Audio constants
 
 AUDIO_DURATION = 0.975
 AUDIO_FORMAT = "s16le"
@@ -39,17 +39,19 @@ AUDIO_MAX_BIT_RANGE = 32768.0
 AUDIO_SAMPLE_RATE = 16000
 AUDIO_MIN_CONFIDENCE = 0.5
 
-# DB Consts
+# DB constants
 
 MAX_WAL_SIZE = 10  # MB
 
-# Ffmpeg Presets
+# Ffmpeg constants
 
+DEFAULT_FFMPEG_VERSION = "7.0"
+INCLUDED_FFMPEG_VERSIONS = ["7.0", "5.0"]
 FFMPEG_HWACCEL_NVIDIA = "preset-nvidia"
 FFMPEG_HWACCEL_VAAPI = "preset-vaapi"
 FFMPEG_HWACCEL_VULKAN = "preset-vulkan"
 
-# Regex Consts
+# Regex constants
 
 REGEX_CAMERA_NAME = r"^[a-zA-Z0-9_-]+$"
 REGEX_RTSP_CAMERA_USER_PASS = r":\/\/[a-zA-Z0-9_-]+:[\S]+@"

frigate/detectors/plugins/hailo8l.py

@@ -24,7 +24,6 @@ from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig
-from frigate.detectors.util import preprocess  # Assuming this function is available
 
 # Set up logging
 logger = logging.getLogger(__name__)
@@ -83,11 +82,11 @@ class HailoDetector(DetectionApi):
             self.network_group_params = self.network_group.create_params()
 
             # Create input and output virtual stream parameters
-            self.input_vstreams_params = InputVStreamParams.make(
+            self.input_vstream_params = InputVStreamParams.make(
                 self.network_group,
                 format_type=self.hef.get_input_vstream_infos()[0].format.type,
             )
-            self.output_vstreams_params = OutputVStreamParams.make(
+            self.output_vstream_params = OutputVStreamParams.make(
                 self.network_group, format_type=getattr(FormatType, output_type)
             )
 
@@ -146,24 +145,16 @@ class HailoDetector(DetectionApi):
                 f"[detect_raw] Converted tensor_input to numpy array: shape {tensor_input.shape}"
             )
 
-        # Preprocess the tensor input using Frigate's preprocess function
-        processed_tensor = preprocess(
-            tensor_input, (1, self.h8l_model_height, self.h8l_model_width, 3), np.uint8
-        )
+        input_data = tensor_input
         logger.debug(
-            f"[detect_raw] Tensor data and shape after preprocessing: {processed_tensor} {processed_tensor.shape}"
-        )
-
-        input_data = processed_tensor
-        logger.debug(
-            f"[detect_raw] Input data for inference shape: {processed_tensor.shape}, dtype: {processed_tensor.dtype}"
+            f"[detect_raw] Input data for inference shape: {tensor_input.shape}, dtype: {tensor_input.dtype}"
         )
 
         try:
             with InferVStreams(
                 self.network_group,
-                self.input_vstreams_params,
-                self.output_vstreams_params,
+                self.input_vstream_params,
+                self.output_vstream_params,
             ) as infer_pipeline:
                 input_dict = {}
                 if isinstance(input_data, dict):

frigate/detectors/plugins/onnx.py

@@ -1,11 +1,15 @@
 import logging
+import os
 
 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
-from frigate.detectors.util import preprocess
+from frigate.detectors.detector_config import (
+    BaseDetectorConfig,
+    ModelTypeEnum,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -14,6 +18,7 @@ DETECTOR_KEY = "onnx"
 
 class ONNXDetectorConfig(BaseDetectorConfig):
     type: Literal[DETECTOR_KEY]
+    device: str = Field(default="AUTO", title="Device Type")
 
 
 class ONNXDetector(DetectionApi):
@@ -21,7 +26,7 @@ class ONNXDetector(DetectionApi):
 
     def __init__(self, detector_config: ONNXDetectorConfig):
         try:
-            import onnxruntime
+            import onnxruntime as ort
 
             logger.info("ONNX: loaded onnxruntime module")
         except ModuleNotFoundError:
@@ -32,16 +37,79 @@ class ONNXDetector(DetectionApi):
 
         path = detector_config.model.path
         logger.info(f"ONNX: loading {detector_config.model.path}")
-        self.model = onnxruntime.InferenceSession(path)
+
+        providers = (
+            ["CPUExecutionProvider"]
+            if detector_config.device == "CPU"
+            else ort.get_available_providers()
+        )
+        options = []
+
+        for provider in providers:
+            if provider == "TensorrtExecutionProvider":
+                os.makedirs(
+                    "/config/model_cache/tensorrt/ort/trt-engines", exist_ok=True
+                )
+                options.append(
+                    {
+                        "trt_timing_cache_enable": True,
+                        "trt_timing_cache_path": "/config/model_cache/tensorrt/ort",
+                        "trt_engine_cache_enable": True,
+                        "trt_dump_ep_context_model": True,
+                        "trt_engine_cache_path": "/config/model_cache/tensorrt/ort/trt-engines",
+                        "trt_ep_context_file_path": "/config/model_cache/tensorrt/ort",
+                    }
+                )
+            elif provider == "OpenVINOExecutionProvider":
+                os.makedirs("/config/model_cache/openvino/ort", exist_ok=True)
+                options.append(
+                    {
+                        "cache_dir": "/config/model_cache/openvino/ort",
+                        "device_type": detector_config.device,
+                    }
+                )
+            else:
+                options.append({})
+
+        self.model = ort.InferenceSession(
+            path, providers=providers, provider_options=options
+        )
+
+        self.h = detector_config.model.height
+        self.w = detector_config.model.width
+        self.onnx_model_type = detector_config.model.model_type
+        self.onnx_model_px = detector_config.model.input_pixel_format
+        self.onnx_model_shape = detector_config.model.input_tensor
+        path = detector_config.model.path
+
         logger.info(f"ONNX: {path} loaded")
 
     def detect_raw(self, tensor_input):
         model_input_name = self.model.get_inputs()[0].name
-        model_input_shape = self.model.get_inputs()[0].shape
-        tensor_input = preprocess(tensor_input, model_input_shape, np.float32)
-        # ruff: noqa: F841
-        tensor_output = self.model.run(None, {model_input_name: tensor_input})[0]
+        tensor_output = self.model.run(None, {model_input_name: tensor_input})
 
-        raise Exception(
-            "No models are currently supported via onnx. See the docs for more info."
-        )
+        if self.onnx_model_type == ModelTypeEnum.yolonas:
+            predictions = tensor_output[0]
+
+            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.onnx_model_type} is currently not supported for rocm. See the docs for more info on supported models."
+            )

frigate/detectors/plugins/openvino.py

@@ -30,12 +30,6 @@ class OvDetector(DetectionApi):
         self.h = detector_config.model.height
         self.w = detector_config.model.width
 
-        if detector_config.device == "AUTO":
-            logger.warning(
-                "OpenVINO AUTO device type is not currently supported. Attempting to use GPU instead."
-            )
-            detector_config.device = "GPU"
-
         if not os.path.isfile(detector_config.model.path):
             logger.error(f"OpenVino model file {detector_config.model.path} not found.")
             raise FileNotFoundError
@@ -129,10 +123,10 @@ class OvDetector(DetectionApi):
 
         strides = [8, 16, 32]
 
-        hsizes = [self.h // stride for stride in strides]
-        wsizes = [self.w // stride for stride in strides]
+        hsize_list = [self.h // stride for stride in strides]
+        wsize_list = [self.w // stride for stride in strides]
 
-        for hsize, wsize, stride in zip(hsizes, wsizes, strides):
+        for hsize, wsize, stride in zip(hsize_list, wsize_list, strides):
             xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
             grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
             grids.append(grid)
@@ -216,10 +210,12 @@ class OvDetector(DetectionApi):
 
             conf_mask = (image_pred[:, 4] * class_conf.squeeze() >= 0.3).squeeze()
             # Detections ordered as (x1, y1, x2, y2, obj_conf, class_conf, class_pred)
-            dets = np.concatenate((image_pred[:, :5], class_conf, class_pred), axis=1)
-            dets = dets[conf_mask]
+            detections = np.concatenate(
+                (image_pred[:, :5], class_conf, class_pred), axis=1
+            )
+            detections = detections[conf_mask]
 
-            ordered = dets[dets[:, 5].argsort()[::-1]][:20]
+            ordered = detections[detections[:, 5].argsort()[::-1]][:20]
 
             for i, object_detected in enumerate(ordered):
                 detections[i] = self.process_yolo(

frigate/detectors/plugins/rknn.py

@@ -17,7 +17,7 @@ supported_socs = ["rk3562", "rk3566", "rk3568", "rk3576", "rk3588"]
 
 supported_models = {ModelTypeEnum.yolonas: "^deci-fp16-yolonas_[sml]$"}
 
-model_chache_dir = "/config/model_cache/rknn_cache/"
+model_cache_dir = "/config/model_cache/rknn_cache/"
 
 
 class RknnDetectorConfig(BaseDetectorConfig):
@@ -110,7 +110,7 @@ class Rknn(DetectionApi):
             if model_matched:
                 model_props["filename"] = model_path + f"-{soc}-v2.0.0-1.rknn"
 
-                model_props["path"] = model_chache_dir + model_props["filename"]
+                model_props["path"] = model_cache_dir + model_props["filename"]
 
                 if not os.path.isfile(model_props["path"]):
                     self.download_model(model_props["filename"])
@@ -125,12 +125,12 @@ class Rknn(DetectionApi):
         return model_props
 
     def download_model(self, filename):
-        if not os.path.isdir(model_chache_dir):
-            os.mkdir(model_chache_dir)
+        if not os.path.isdir(model_cache_dir):
+            os.mkdir(model_cache_dir)
 
         urllib.request.urlretrieve(
             f"https://github.com/MarcA711/rknn-models/releases/download/v2.0.0/{filename}",
-            model_chache_dir + filename,
+            model_cache_dir + filename,
         )
 
     def check_config(self, config):

frigate/detectors/plugins/rocm.py

@@ -9,8 +9,10 @@ from pydantic import Field
 from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
-from frigate.detectors.detector_config import BaseDetectorConfig
-from frigate.detectors.util import preprocess
+from frigate.detectors.detector_config import (
+    BaseDetectorConfig,
+    ModelTypeEnum,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -74,7 +76,16 @@ class ROCmDetector(DetectionApi):
             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}")
@@ -84,6 +95,7 @@ class ROCmDetector(DetectionApi):
             self.model = migraphx.load(mxr_path)
         else:
             logger.info(f"AMD/ROCm: loading model from {path}")
+
             if path.endswith(".onnx"):
                 self.model = migraphx.parse_onnx(path)
             elif (
@@ -95,30 +107,51 @@ class ROCmDetector(DetectionApi):
                 self.model = migraphx.parse_tf(path)
             else:
                 raise Exception(f"AMD/ROCm: unknown model format {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 = preprocess(tensor_input, model_input_shape, np.float32)
-
         detector_result = self.model.run({model_input_name: tensor_input})[0]
-
         addr = ctypes.cast(detector_result.data_ptr(), ctypes.POINTER(ctypes.c_float))
-        # ruff: noqa: F841
+
         tensor_output = np.ctypeslib.as_array(
             addr, shape=detector_result.get_shape().lens()
         )
 
-        raise Exception(
-            "No models are currently supported for rocm. See the docs for more info."
-        )
+        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."
+            )

frigate/detectors/plugins/tensorrt.py

@@ -285,14 +285,14 @@ class TensorRtDetector(DetectionApi):
             boxes, scores, classes
         """
         # filter low-conf detections and concatenate results of all yolo layers
-        detections = []
+        detection_list = []
         for o in trt_outputs:
-            dets = o.reshape((-1, 7))
-            dets = dets[dets[:, 4] * dets[:, 6] >= conf_th]
-            detections.append(dets)
-        detections = np.concatenate(detections, axis=0)
+            detections = o.reshape((-1, 7))
+            detections = detections[detections[:, 4] * detections[:, 6] >= conf_th]
+            detection_list.append(detections)
+        detection_list = np.concatenate(detection_list, axis=0)
 
-        return detections
+        return detection_list
 
     def detect_raw(self, tensor_input):
         # Input tensor has the shape of the [height, width, 3]

frigate/detectors/util.py

@@ -1,36 +0,0 @@
-import logging
-
-import cv2
-import numpy as np
-
-logger = logging.getLogger(__name__)
-
-
-def preprocess(tensor_input, model_input_shape, model_input_element_type):
-    model_input_shape = tuple(model_input_shape)
-    assert tensor_input.dtype == np.uint8, f"tensor_input.dtype: {tensor_input.dtype}"
-    if len(tensor_input.shape) == 3:
-        tensor_input = tensor_input[np.newaxis, :]
-    if model_input_element_type == np.uint8:
-        # nothing to do for uint8 model input
-        assert (
-            model_input_shape == tensor_input.shape
-        ), f"model_input_shape: {model_input_shape}, tensor_input.shape: {tensor_input.shape}"
-        return tensor_input
-    assert (
-        model_input_element_type == np.float32
-    ), f"model_input_element_type: {model_input_element_type}"
-    # tensor_input must be nhwc
-    assert tensor_input.shape[3] == 3, f"tensor_input.shape: {tensor_input.shape}"
-    if tensor_input.shape[1:3] != model_input_shape[2:4]:
-        logger.warn(
-            f"preprocess: tensor_input.shape {tensor_input.shape} and model_input_shape {model_input_shape} do not match!"
-        )
-    # cv2.dnn.blobFromImage is faster than numpying it
-    return cv2.dnn.blobFromImage(
-        tensor_input[0],
-        1.0 / 255,
-        (model_input_shape[3], model_input_shape[2]),
-        None,
-        swapRB=False,
-    )

frigate/embeddings/embeddings.py

@@ -15,7 +15,7 @@ from frigate.models import Event
 # Squelch posthog logging
 logging.getLogger("chromadb.telemetry.product.posthog").setLevel(logging.CRITICAL)
 
-# Hotsawp the sqlite3 module for Chroma compatibility
+# Hot-swap the sqlite3 module for Chroma compatibility
 try:
     from chromadb import Collection
     from chromadb import HttpClient as ChromaClient
@@ -85,7 +85,10 @@ class Embeddings:
     @property
     def description(self) -> Collection:
         return self.client.get_or_create_collection(
-            name="event_description", embedding_function=MiniLMEmbedding()
+            name="event_description",
+            embedding_function=MiniLMEmbedding(
+                preferred_providers=["CPUExecutionProvider"]
+            ),
         )
 
     def reindex(self) -> None:

frigate/embeddings/functions/clip.py

@@ -1,9 +1,13 @@
 """CLIP Embeddings for Frigate."""
 
+import errno
+import logging
 import os
+from pathlib import Path
 from typing import Tuple, Union
 
 import onnxruntime as ort
+import requests
 from chromadb import EmbeddingFunction, Embeddings
 from chromadb.api.types import (
     Documents,
@@ -39,10 +43,49 @@ class Clip(OnnxClip):
         models = []
         for model_file in [IMAGE_MODEL_FILE, TEXT_MODEL_FILE]:
             path = os.path.join(MODEL_CACHE_DIR, "clip", model_file)
-            models.append(OnnxClip._load_model(path, silent))
+            models.append(Clip._load_model(path, silent))
 
         return models[0], models[1]
 
+    @staticmethod
+    def _load_model(path: str, silent: bool):
+        providers = ["CPUExecutionProvider"]
+
+        try:
+            if os.path.exists(path):
+                return ort.InferenceSession(path, providers=providers)
+            else:
+                raise FileNotFoundError(
+                    errno.ENOENT,
+                    os.strerror(errno.ENOENT),
+                    path,
+                )
+        except Exception:
+            s3_url = f"https://lakera-clip.s3.eu-west-1.amazonaws.com/{os.path.basename(path)}"
+            if not silent:
+                logging.info(
+                    f"The model file ({path}) doesn't exist "
+                    f"or it is invalid. Downloading it from the public S3 "
+                    f"bucket: {s3_url}."  # noqa: E501
+                )
+
+            # Download from S3
+            # Saving to a temporary file first to avoid corrupting the file
+            temporary_filename = Path(path).with_name(os.path.basename(path) + ".part")
+
+            # Create any missing directories in the path
+            temporary_filename.parent.mkdir(parents=True, exist_ok=True)
+
+            with requests.get(s3_url, stream=True) as r:
+                r.raise_for_status()
+                with open(temporary_filename, "wb") as f:
+                    for chunk in r.iter_content(chunk_size=8192):
+                        f.write(chunk)
+                    f.flush()
+            # Finally move the temporary file to the correct location
+            temporary_filename.rename(path)
+            return ort.InferenceSession(path, providers=providers)
+
 
 class ClipEmbedding(EmbeddingFunction):
     """Embedding function for CLIP model used in Chroma."""

frigate/embeddings/maintainer.py

@@ -171,8 +171,11 @@ class EmbeddingMaintainer(threading.Thread):
         self, event: Event, thumbnails: list[bytes], metadata: dict
     ) -> None:
         """Embed the description for an event."""
+        camera_config = self.config.cameras[event.camera]
 
-        description = self.genai_client.generate_description(thumbnails, metadata)
+        description = self.genai_client.generate_description(
+            camera_config, thumbnails, metadata
+        )
 
         if description is None:
             logger.debug("Failed to generate description for %s", event.id)

frigate/ffmpeg_presets.py

@@ -91,10 +91,10 @@ PRESETS_HW_ACCEL_DECODE["preset-nvidia-mjpeg"] = PRESETS_HW_ACCEL_DECODE[
 PRESETS_HW_ACCEL_SCALE = {
     "preset-rpi-64-h264": "-r {0} -vf fps={0},scale={1}:{2}",
     "preset-rpi-64-h265": "-r {0} -vf fps={0},scale={1}:{2}",
-    FFMPEG_HWACCEL_VAAPI: "-r {0} -vf fps={0},scale_vaapi=w={1}:h={2},hwdownload,eq=gamma=1.05",
+    FFMPEG_HWACCEL_VAAPI: "-r {0} -vf fps={0},scale_vaapi=w={1}:h={2},hwdownload,format=nv12,eq=gamma=1.05",
     "preset-intel-qsv-h264": "-r {0} -vf vpp_qsv=framerate={0}:w={1}:h={2}:format=nv12,hwdownload,format=nv12,format=yuv420p",
     "preset-intel-qsv-h265": "-r {0} -vf vpp_qsv=framerate={0}:w={1}:h={2}:format=nv12,hwdownload,format=nv12,format=yuv420p",
-    FFMPEG_HWACCEL_NVIDIA: "-r {0} -vf fps={0},scale_cuda=w={1}:h={2}:format=nv12,hwdownload,format=nv12,format=yuv420p",
+    FFMPEG_HWACCEL_NVIDIA: "-r {0} -vf fps={0},scale_cuda=w={1}:h={2},hwdownload,format=nv12,eq=gamma=1.05",
     "preset-jetson-h264": "-r {0}",  # scaled in decoder
     "preset-jetson-h265": "-r {0}",  # scaled in decoder
     "preset-rk-h264": "-r {0} -vf scale_rkrga=w={1}:h={2}:format=yuv420p:force_original_aspect_ratio=0,hwmap=mode=read,format=yuv420p",
@@ -185,6 +185,15 @@ def parse_preset_hardware_acceleration_scale(
     else:
         scale = PRESETS_HW_ACCEL_SCALE.get(arg, PRESETS_HW_ACCEL_SCALE["default"])
 
+    if (
+        ",hwdownload,format=nv12,eq=gamma=1.05" in scale
+        and os.environ.get("FFMPEG_DISABLE_GAMMA_EQUALIZER") is not None
+    ):
+        scale.replace(
+            ",hwdownload,format=nv12,eq=gamma=1.05",
+            ":format=nv12,hwdownload,format=nv12,format=yuv420p",
+        )
+
     scale = scale.format(fps, width, height).split(" ")
     scale.extend(detect_args)
     return scale

frigate/genai/__init__.py

@@ -4,7 +4,7 @@ import importlib
 import os
 from typing import Optional
 
-from frigate.config import GenAIConfig, GenAIProviderEnum
+from frigate.config import CameraConfig, GenAIConfig, GenAIProviderEnum
 
 PROVIDERS = {}
 
@@ -28,11 +28,14 @@ class GenAIClient:
         self.provider = self._init_provider()
 
     def generate_description(
-        self, thumbnails: list[bytes], metadata: dict[str, any]
+        self,
+        camera_config: CameraConfig,
+        thumbnails: list[bytes],
+        metadata: dict[str, any],
     ) -> Optional[str]:
         """Generate a description for the frame."""
-        prompt = self.genai_config.object_prompts.get(
-            metadata["label"], self.genai_config.prompt
+        prompt = camera_config.genai.object_prompts.get(
+            metadata["label"], camera_config.genai.prompt
         ).format(**metadata)
         return self._send(prompt, thumbnails)
 

frigate/log.py

@@ -1,71 +1,71 @@
-# adapted from https://medium.com/@jonathonbao/python3-logging-with-multiprocessing-f51f460b8778
+import atexit
 import logging
 import multiprocessing as mp
 import os
-import queue
-import signal
 import threading
 from collections import deque
-from logging import handlers
-from multiprocessing import Queue
-from types import FrameType
+from contextlib import AbstractContextManager, ContextDecorator
+from logging.handlers import QueueHandler, QueueListener
+from types import TracebackType
 from typing import Deque, Optional
 
-from setproctitle import setproctitle
+from typing_extensions import Self
 
 from frigate.util.builtin import clean_camera_user_pass
 
-
-def listener_configurer() -> None:
-    root = logging.getLogger()
-
-    if root.hasHandlers():
-        root.handlers.clear()
-
-    console_handler = logging.StreamHandler()
-    formatter = logging.Formatter(
-        "[%(asctime)s] %(name)-30s %(levelname)-8s: %(message)s", "%Y-%m-%d %H:%M:%S"
+LOG_HANDLER = logging.StreamHandler()
+LOG_HANDLER.setFormatter(
+    logging.Formatter(
+        "[%(asctime)s] %(name)-30s %(levelname)-8s: %(message)s",
+        "%Y-%m-%d %H:%M:%S",
     )
-    console_handler.setFormatter(formatter)
-    root.addHandler(console_handler)
-    root.setLevel(logging.INFO)
+)
+
+LOG_HANDLER.addFilter(
+    lambda record: not record.getMessage().startswith(
+        "You are using a scalar distance function"
+    )
+)
 
 
-def root_configurer(queue: Queue) -> None:
-    h = handlers.QueueHandler(queue)
-    root = logging.getLogger()
+class log_thread(AbstractContextManager, ContextDecorator):
+    def __init__(self, *, handler: logging.Handler = LOG_HANDLER):
+        super().__init__()
 
-    if root.hasHandlers():
-        root.handlers.clear()
+        self._handler = handler
 
-    root.addHandler(h)
-    root.setLevel(logging.INFO)
+        log_queue: mp.Queue = mp.Queue()
+        self._queue_handler = QueueHandler(log_queue)
 
+        self._log_listener = QueueListener(
+            log_queue, self._handler, respect_handler_level=True
+        )
 
-def log_process(log_queue: Queue) -> None:
-    threading.current_thread().name = "logger"
-    setproctitle("frigate.logger")
-    listener_configurer()
+    @property
+    def handler(self) -> logging.Handler:
+        return self._handler
 
-    stop_event = mp.Event()
+    def _stop_thread(self) -> None:
+        self._log_listener.stop()
 
-    def receiveSignal(signalNumber: int, frame: Optional[FrameType]) -> None:
-        stop_event.set()
+    def __enter__(self) -> Self:
+        logging.getLogger().addHandler(self._queue_handler)
 
-    signal.signal(signal.SIGTERM, receiveSignal)
-    signal.signal(signal.SIGINT, receiveSignal)
+        atexit.register(self._stop_thread)
+        self._log_listener.start()
 
-    while True:
-        try:
-            record = log_queue.get(block=True, timeout=1.0)
-        except queue.Empty:
-            if stop_event.is_set():
-                break
-            continue
-        if record.msg.startswith("You are using a scalar distance function"):
-            continue
-        logger = logging.getLogger(record.name)
-        logger.handle(record)
+        return self
+
+    def __exit__(
+        self,
+        exc_type: Optional[type[BaseException]],
+        exc_info: Optional[BaseException],
+        exc_tb: Optional[TracebackType],
+    ) -> None:
+        logging.getLogger().removeHandler(self._queue_handler)
+
+        atexit.unregister(self._stop_thread)
+        self._stop_thread()
 
 
 # based on https://codereview.stackexchange.com/a/17959

frigate/motion/frigate_motion.py

@@ -55,13 +55,13 @@ class FrigateMotionDetector(MotionDetector):
 
         # Improve contrast
         if self.improve_contrast.value:
-            minval = np.percentile(resized_frame, 4)
-            maxval = np.percentile(resized_frame, 96)
+            min_value = np.percentile(resized_frame, 4)
+            max_value = np.percentile(resized_frame, 96)
             # don't adjust if the image is a single color
-            if minval < maxval:
-                resized_frame = np.clip(resized_frame, minval, maxval)
+            if min_value < max_value:
+                resized_frame = np.clip(resized_frame, min_value, max_value)
                 resized_frame = (
-                    ((resized_frame - minval) / (maxval - minval)) * 255
+                    ((resized_frame - min_value) / (max_value - min_value)) * 255
                 ).astype(np.uint8)
 
         # mask frame
@@ -100,13 +100,13 @@ class FrigateMotionDetector(MotionDetector):
             # dilate the thresholded image to fill in holes, then find contours
             # on thresholded image
             thresh_dilated = cv2.dilate(thresh, None, iterations=2)
-            cnts = cv2.findContours(
+            contours = cv2.findContours(
                 thresh_dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
             )
-            cnts = imutils.grab_contours(cnts)
+            contours = imutils.grab_contours(contours)
 
             # loop over the contours
-            for c in cnts:
+            for c in contours:
                 # if the contour is big enough, count it as motion
                 contour_area = cv2.contourArea(c)
                 if contour_area > self.contour_area.value:
@@ -124,7 +124,7 @@ class FrigateMotionDetector(MotionDetector):
                 thresh_dilated = cv2.cvtColor(thresh_dilated, cv2.COLOR_GRAY2BGR)
                 # print("--------")
                 # print(self.frame_counter)
-                for c in cnts:
+                for c in contours:
                     contour_area = cv2.contourArea(c)
                     if contour_area > self.contour_area.value:
                         x, y, w, h = cv2.boundingRect(c)

frigate/motion/improved_motion.py

@@ -79,12 +79,15 @@ class ImprovedMotionDetector(MotionDetector):
         # Improve contrast
         if self.config.improve_contrast:
             # TODO tracking moving average of min/max to avoid sudden contrast changes
-            minval = np.percentile(resized_frame, 4).astype(np.uint8)
-            maxval = np.percentile(resized_frame, 96).astype(np.uint8)
+            min_value = np.percentile(resized_frame, 4).astype(np.uint8)
+            max_value = np.percentile(resized_frame, 96).astype(np.uint8)
             # skip contrast calcs if the image is a single color
-            if minval < maxval:
+            if min_value < max_value:
                 # keep track of the last 50 contrast values
-                self.contrast_values[self.contrast_values_index] = [minval, maxval]
+                self.contrast_values[self.contrast_values_index] = [
+                    min_value,
+                    max_value,
+                ]
                 self.contrast_values_index += 1
                 if self.contrast_values_index == len(self.contrast_values):
                     self.contrast_values_index = 0
@@ -122,14 +125,14 @@ class ImprovedMotionDetector(MotionDetector):
         # dilate the thresholded image to fill in holes, then find contours
         # on thresholded image
         thresh_dilated = cv2.dilate(thresh, None, iterations=1)
-        cnts = cv2.findContours(
+        contours = cv2.findContours(
             thresh_dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
         )
-        cnts = imutils.grab_contours(cnts)
+        contours = imutils.grab_contours(contours)
 
         # loop over the contours
         total_contour_area = 0
-        for c in cnts:
+        for c in contours:
             # if the contour is big enough, count it as motion
             contour_area = cv2.contourArea(c)
             total_contour_area += contour_area

frigate/object_detection.py

@@ -125,6 +125,7 @@ def run_detector(
         start.value = datetime.datetime.now().timestamp()
         detections = object_detector.detect_raw(input_frame)
         duration = datetime.datetime.now().timestamp() - start.value
+        frame_manager.close(connection_id)
         outputs[connection_id]["np"][:] = detections[:]
         out_events[connection_id].set()
         start.value = 0.0

frigate/object_processing.py

@@ -1138,12 +1138,14 @@ class TrackedObjectProcessor(threading.Thread):
                 )
             )
             or (
-                not review_config.detections.labels
-                or obj.obj_data["label"] in review_config.detections.labels
-            )
-            and (
-                not review_config.detections.required_zones
-                or set(obj.entered_zones) & set(review_config.alerts.required_zones)
+                (
+                    not review_config.detections.labels
+                    or obj.obj_data["label"] in review_config.detections.labels
+                )
+                and (
+                    not review_config.detections.required_zones
+                    or set(obj.entered_zones) & set(review_config.alerts.required_zones)
+                )
             )
         ):
             logger.debug(

frigate/ptz/autotrack.py

@@ -268,9 +268,9 @@ class PtzAutoTracker:
                 self.ptz_metrics[camera]["ptz_autotracker_enabled"].value = False
                 return
 
-            movestatus_supported = self.onvif.get_service_capabilities(camera)
+            move_status_supported = self.onvif.get_service_capabilities(camera)
 
-            if movestatus_supported is None or movestatus_supported.lower() != "true":
+            if move_status_supported is None or move_status_supported.lower() != "true":
                 logger.warning(
                     f"Disabling autotracking for {camera}: ONVIF MoveStatus not supported"
                 )
@@ -807,8 +807,8 @@ class PtzAutoTracker:
         invalid_delta = np.any(delta > delta_thresh)
 
         # Check variance
-        stdevs = np.std(velocities, axis=0)
-        high_variances = np.any(stdevs > var_thresh)
+        stdev_list = np.std(velocities, axis=0)
+        high_variances = np.any(stdev_list > var_thresh)
 
         # Check direction difference
         velocities = np.round(velocities)

frigate/ptz/onvif.py

@@ -335,6 +335,10 @@ class OnvifController:
             )
             self._stop(camera_name)
 
+        if "pt" not in self.cams[camera_name]["features"]:
+            logger.error(f"{camera_name} does not support ONVIF pan/tilt movement.")
+            return
+
         self.cams[camera_name]["active"] = True
         onvif: ONVIFCamera = self.cams[camera_name]["onvif"]
         move_request = self.cams[camera_name]["move_request"]
@@ -476,6 +480,10 @@ class OnvifController:
             )
             self._stop(camera_name)
 
+        if "zoom" not in self.cams[camera_name]["features"]:
+            logger.error(f"{camera_name} does not support ONVIF zooming.")
+            return
+
         self.cams[camera_name]["active"] = True
         onvif: ONVIFCamera = self.cams[camera_name]["onvif"]
         move_request = self.cams[camera_name]["move_request"]

frigate/record/maintainer.py

@@ -90,9 +90,9 @@ class RecordingMaintainer(threading.Thread):
             try:
                 if process.name() != "ffmpeg":
                     continue
-                flist = process.open_files()
-                if flist:
-                    for nt in flist:
+                file_list = process.open_files()
+                if file_list:
+                    for nt in file_list:
                         if nt.path.startswith(CACHE_DIR):
                             files_in_use.append(nt.path.split("/")[-1])
             except psutil.Error:

frigate/stats/util.py

@@ -250,7 +250,7 @@ def stats_snapshot(
         ffmpeg_pid = (
             camera_stats["ffmpeg_pid"].value if camera_stats["ffmpeg_pid"] else None
         )
-        cpid = (
+        capture_pid = (
             camera_stats["capture_process"].pid
             if camera_stats["capture_process"]
             else None
@@ -262,7 +262,7 @@ def stats_snapshot(
             "detection_fps": round(camera_stats["detection_fps"].value, 2),
             "detection_enabled": config.cameras[name].detect.enabled,
             "pid": pid,
-            "capture_pid": cpid,
+            "capture_pid": capture_pid,
             "ffmpeg_pid": ffmpeg_pid,
             "audio_rms": round(camera_stats["audio_rms"].value, 4),
             "audio_dBFS": round(camera_stats["audio_dBFS"].value, 4),

frigate/test/const.py

@@ -1,4 +1,4 @@
-"""Consts for testing."""
+"""Constants for testing."""
 
 TEST_DB = "test.db"
 TEST_DB_CLEANUPS = ["test.db", "test.db-shm", "test.db-wal"]

frigate/test/test_ffmpeg_presets.py

@@ -78,7 +78,7 @@ class TestFfmpegPresets(unittest.TestCase):
             " ".join(frigate_config.cameras["back"].ffmpeg_cmds[0]["cmd"])
         )
         assert (
-            "fps=10,scale_cuda=w=2560:h=1920:format=nv12,hwdownload,format=nv12,format=yuv420p"
+            "fps=10,scale_cuda=w=2560:h=1920,hwdownload,format=nv12,eq=gamma=1.05"
             in (" ".join(frigate_config.cameras["back"].ffmpeg_cmds[0]["cmd"]))
         )
 

frigate/timeline.py

@@ -44,9 +44,10 @@ class TimelineProcessor(threading.Thread):
                 continue
 
             if input_type == EventTypeEnum.tracked_object:
-                self.handle_object_detection(
-                    camera, event_type, prev_event_data, event_data
-                )
+                if prev_event_data is not None and event_data is not None:
+                    self.handle_object_detection(
+                        camera, event_type, prev_event_data, event_data
+                    )
             elif input_type == EventTypeEnum.api:
                 self.handle_api_entry(camera, event_type, event_data)
 

frigate/util/object.py

@@ -511,12 +511,12 @@ def reduce_detections(
             # due to min score requirement of NMSBoxes
             confidences = [0.6 if clipped(o, frame_shape) else o[1] for o in group]
 
-            idxs = cv2.dnn.NMSBoxes(
+            indices = cv2.dnn.NMSBoxes(
                 boxes, confidences, 0.5, LABEL_NMS_MAP.get(label, LABEL_NMS_DEFAULT)
             )
 
             # add objects
-            for index in idxs:
+            for index in indices:
                 index = index if isinstance(index, np.int32) else index[0]
                 obj = group[index]
                 selected_objects.append(obj)

frigate/util/services.py

@@ -122,7 +122,7 @@ def get_cpu_stats() -> dict[str, dict]:
                 stats = f.readline().split()
             utime = int(stats[13])
             stime = int(stats[14])
-            starttime = int(stats[21])
+            start_time = int(stats[21])
 
             with open("/proc/uptime") as f:
                 system_uptime_sec = int(float(f.read().split()[0]))
@@ -131,9 +131,9 @@ def get_cpu_stats() -> dict[str, dict]:
 
             process_utime_sec = utime // clk_tck
             process_stime_sec = stime // clk_tck
-            process_starttime_sec = starttime // clk_tck
+            process_start_time_sec = start_time // clk_tck
 
-            process_elapsed_sec = system_uptime_sec - process_starttime_sec
+            process_elapsed_sec = system_uptime_sec - process_start_time_sec
             process_usage_sec = process_utime_sec + process_stime_sec
             cpu_average_usage = process_usage_sec * 100 // process_elapsed_sec
 

process_clip.py

@@ -28,8 +28,7 @@ from frigate.video import (  # noqa: E402
     start_or_restart_ffmpeg,
 )
 
-logging.basicConfig()
-logging.root.setLevel(logging.DEBUG)
+logging.basicConfig(level=logging.DEBUG)
 
 logger = logging.getLogger(__name__)
 

web/package-lock.json

@@ -34,6 +34,7 @@
         "axios": "^1.7.3",
         "class-variance-authority": "^0.7.0",
         "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",
@@ -3722,6 +3723,384 @@
         "node": ">=6"
       }
     },
+    "node_modules/cmdk": {
+      "version": "1.0.0",
+      "resolved": "https://registry.npmjs.org/cmdk/-/cmdk-1.0.0.tgz",
+      "integrity": "sha512-gDzVf0a09TvoJ5jnuPvygTB77+XdOSwEmJ88L6XPFPlv7T3RxbP9jgenfylrAMD0+Le1aO0nVjQUzl2g+vjz5Q==",
+      "license": "MIT",
+      "dependencies": {
+        "@radix-ui/react-dialog": "1.0.5",
+        "@radix-ui/react-primitive": "1.0.3"
+      },
+      "peerDependencies": {
+        "react": "^18.0.0",
+        "react-dom": "^18.0.0"
+      }
+    },
+    "node_modules/cmdk/node_modules/@radix-ui/primitive": {
+      "version": "1.0.1",
+      "resolved": "https://registry.npmjs.org/@radix-ui/primitive/-/primitive-1.0.1.tgz",
+      "integrity": "sha512-yQ8oGX2GVsEYMWGxcovu1uGWPCxV5BFfeeYxqPmuAzUyLT9qmaMXSAhXpb0WrspIeqYzdJpkh2vHModJPgRIaw==",
+      "license": "MIT",
+      "dependencies": {
+        "@babel/runtime": "^7.13.10"
+      }
+    },
+    "node_modules/cmdk/node_modules/@radix-ui/react-compose-refs": {
+      "version": "1.0.1",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-compose-refs/-/react-compose-refs-1.0.1.tgz",
+      "integrity": "sha512-fDSBgd44FKHa1FRMU59qBMPFcl2PZE+2nmqunj+BWFyYYjnhIDWL2ItDs3rrbJDQOtzt5nIebLCQc4QRfz6LJw==",
+      "license": "MIT",
+      "dependencies": {
+        "@babel/runtime": "^7.13.10"
+      },
+      "peerDependencies": {
+        "@types/react": "*",
+        "react": "^16.8 || ^17.0 || ^18.0"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/cmdk/node_modules/@radix-ui/react-context": {
+      "version": "1.0.1",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-context/-/react-context-1.0.1.tgz",
+      "integrity": "sha512-ebbrdFoYTcuZ0v4wG5tedGnp9tzcV8awzsxYph7gXUyvnNLuTIcCk1q17JEbnVhXAKG9oX3KtchwiMIAYp9NLg==",
+      "license": "MIT",
+      "dependencies": {
+        "@babel/runtime": "^7.13.10"
+      },
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+        "@types/react": "*",
+        "react": "^16.8 || ^17.0 || ^18.0"
+      },
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+        "@types/react": {
+          "optional": true
+        }
+      }
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+    "node_modules/cmdk/node_modules/@radix-ui/react-dialog": {
+      "version": "1.0.5",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-dialog/-/react-dialog-1.0.5.tgz",
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+      "license": "MIT",
+      "dependencies": {
+        "@babel/runtime": "^7.13.10",
+        "@radix-ui/primitive": "1.0.1",
+        "@radix-ui/react-compose-refs": "1.0.1",
+        "@radix-ui/react-context": "1.0.1",
+        "@radix-ui/react-dismissable-layer": "1.0.5",
+        "@radix-ui/react-focus-guards": "1.0.1",
+        "@radix-ui/react-focus-scope": "1.0.4",
+        "@radix-ui/react-id": "1.0.1",
+        "@radix-ui/react-portal": "1.0.4",
+        "@radix-ui/react-presence": "1.0.1",
+        "@radix-ui/react-primitive": "1.0.3",
+        "@radix-ui/react-slot": "1.0.2",
+        "@radix-ui/react-use-controllable-state": "1.0.1",
+        "aria-hidden": "^1.1.1",
+        "react-remove-scroll": "2.5.5"
+      },
+      "peerDependencies": {
+        "@types/react": "*",
+        "@types/react-dom": "*",
+        "react": "^16.8 || ^17.0 || ^18.0",
+        "react-dom": "^16.8 || ^17.0 || ^18.0"
+      },
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+        "@types/react": {
+          "optional": true
+        },
+        "@types/react-dom": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/cmdk/node_modules/@radix-ui/react-dismissable-layer": {
+      "version": "1.0.5",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-dismissable-layer/-/react-dismissable-layer-1.0.5.tgz",
+      "integrity": "sha512-aJeDjQhywg9LBu2t/At58hCvr7pEm0o2Ke1x33B+MhjNmmZ17sy4KImo0KPLgsnc/zN7GPdce8Cnn0SWvwZO7g==",
+      "license": "MIT",
+      "dependencies": {
+        "@babel/runtime": "^7.13.10",
+        "@radix-ui/primitive": "1.0.1",
+        "@radix-ui/react-compose-refs": "1.0.1",
+        "@radix-ui/react-primitive": "1.0.3",
+        "@radix-ui/react-use-callback-ref": "1.0.1",
+        "@radix-ui/react-use-escape-keydown": "1.0.3"
+      },
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+        "@types/react": "*",
+        "@types/react-dom": "*",
+        "react": "^16.8 || ^17.0 || ^18.0",
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+      "license": "MIT",
+      "dependencies": {
+        "@babel/runtime": "^7.13.10"
+      },
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+        "@types/react": "*",
+        "react": "^16.8 || ^17.0 || ^18.0"
+      },
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+        "@types/react": {
+          "optional": true
+        }
+      }
+    },
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+      "version": "1.0.4",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-focus-scope/-/react-focus-scope-1.0.4.tgz",
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+      "license": "MIT",
+      "dependencies": {
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+        "@radix-ui/react-compose-refs": "1.0.1",
+        "@radix-ui/react-primitive": "1.0.3",
+        "@radix-ui/react-use-callback-ref": "1.0.1"
+      },
+      "peerDependencies": {
+        "@types/react": "*",
+        "@types/react-dom": "*",
+        "react": "^16.8 || ^17.0 || ^18.0",
+        "react-dom": "^16.8 || ^17.0 || ^18.0"
+      },
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+        "@types/react": {
+          "optional": true
+        },
+        "@types/react-dom": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/cmdk/node_modules/@radix-ui/react-id": {
+      "version": "1.0.1",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-id/-/react-id-1.0.1.tgz",
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+      "license": "MIT",
+      "dependencies": {
+        "@babel/runtime": "^7.13.10",
+        "@radix-ui/react-use-layout-effect": "1.0.1"
+      },
+      "peerDependencies": {
+        "@types/react": "*",
+        "react": "^16.8 || ^17.0 || ^18.0"
+      },
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+        "@types/react": {
+          "optional": true
+        }
+      }
+    },
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+      "version": "1.0.4",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-portal/-/react-portal-1.0.4.tgz",
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+      "license": "MIT",
+      "dependencies": {
+        "@babel/runtime": "^7.13.10",
+        "@radix-ui/react-primitive": "1.0.3"
+      },
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+        "@types/react": "*",
+        "@types/react-dom": "*",
+        "react": "^16.8 || ^17.0 || ^18.0",
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+        "@types/react": {
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+        },
+        "@types/react-dom": {
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+        }
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+      "dependencies": {
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+        "@radix-ui/react-compose-refs": "1.0.1",
+        "@radix-ui/react-use-layout-effect": "1.0.1"
+      },
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+        "@types/react": "*",
+        "@types/react-dom": "*",
+        "react": "^16.8 || ^17.0 || ^18.0",
+        "react-dom": "^16.8 || ^17.0 || ^18.0"
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+        "@types/react": {
+          "optional": true
+        },
+        "@types/react-dom": {
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+        }
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+      "resolved": "https://registry.npmjs.org/@radix-ui/react-primitive/-/react-primitive-1.0.3.tgz",
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+      "license": "MIT",
+      "dependencies": {
+        "@babel/runtime": "^7.13.10",
+        "@radix-ui/react-slot": "1.0.2"
+      },
+      "peerDependencies": {
+        "@types/react": "*",
+        "@types/react-dom": "*",
+        "react": "^16.8 || ^17.0 || ^18.0",
+        "react-dom": "^16.8 || ^17.0 || ^18.0"
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+        "@types/react": {
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+        }
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+    },
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+      "version": "1.0.2",
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+      "license": "MIT",
+      "dependencies": {
+        "@babel/runtime": "^7.13.10",
+        "@radix-ui/react-compose-refs": "1.0.1"
+      },
+      "peerDependencies": {
+        "@types/react": "*",
+        "react": "^16.8 || ^17.0 || ^18.0"
+      },
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+        "@types/react": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/cmdk/node_modules/@radix-ui/react-use-callback-ref": {
+      "version": "1.0.1",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-use-callback-ref/-/react-use-callback-ref-1.0.1.tgz",
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+      "license": "MIT",
+      "dependencies": {
+        "@babel/runtime": "^7.13.10"
+      },
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+        "@types/react": "*",
+        "react": "^16.8 || ^17.0 || ^18.0"
+      },
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+        "@types/react": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/cmdk/node_modules/@radix-ui/react-use-controllable-state": {
+      "version": "1.0.1",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-use-controllable-state/-/react-use-controllable-state-1.0.1.tgz",
+      "integrity": "sha512-Svl5GY5FQeN758fWKrjM6Qb7asvXeiZltlT4U2gVfl8Gx5UAv2sMR0LWo8yhsIZh2oQ0eFdZ59aoOOMV7b47VA==",
+      "license": "MIT",
+      "dependencies": {
+        "@babel/runtime": "^7.13.10",
+        "@radix-ui/react-use-callback-ref": "1.0.1"
+      },
+      "peerDependencies": {
+        "@types/react": "*",
+        "react": "^16.8 || ^17.0 || ^18.0"
+      },
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+        "@types/react": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/cmdk/node_modules/@radix-ui/react-use-escape-keydown": {
+      "version": "1.0.3",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-use-escape-keydown/-/react-use-escape-keydown-1.0.3.tgz",
+      "integrity": "sha512-vyL82j40hcFicA+M4Ex7hVkB9vHgSse1ZWomAqV2Je3RleKGO5iM8KMOEtfoSB0PnIelMd2lATjTGMYqN5ylTg==",
+      "license": "MIT",
+      "dependencies": {
+        "@babel/runtime": "^7.13.10",
+        "@radix-ui/react-use-callback-ref": "1.0.1"
+      },
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+        "@types/react": "*",
+        "react": "^16.8 || ^17.0 || ^18.0"
+      },
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+        "@types/react": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/cmdk/node_modules/@radix-ui/react-use-layout-effect": {
+      "version": "1.0.1",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-use-layout-effect/-/react-use-layout-effect-1.0.1.tgz",
+      "integrity": "sha512-v/5RegiJWYdoCvMnITBkNNx6bCj20fiaJnWtRkU18yITptraXjffz5Qbn05uOiQnOvi+dbkznkoaMltz1GnszQ==",
+      "license": "MIT",
+      "dependencies": {
+        "@babel/runtime": "^7.13.10"
+      },
+      "peerDependencies": {
+        "@types/react": "*",
+        "react": "^16.8 || ^17.0 || ^18.0"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/cmdk/node_modules/react-remove-scroll": {
+      "version": "2.5.5",
+      "resolved": "https://registry.npmjs.org/react-remove-scroll/-/react-remove-scroll-2.5.5.tgz",
+      "integrity": "sha512-ImKhrzJJsyXJfBZ4bzu8Bwpka14c/fQt0k+cyFp/PBhTfyDnU5hjOtM4AG/0AMyy8oKzOTR0lDgJIM7pYXI0kw==",
+      "license": "MIT",
+      "dependencies": {
+        "react-remove-scroll-bar": "^2.3.3",
+        "react-style-singleton": "^2.2.1",
+        "tslib": "^2.1.0",
+        "use-callback-ref": "^1.3.0",
+        "use-sidecar": "^1.1.2"
+      },
+      "engines": {
+        "node": ">=10"
+      },
+      "peerDependencies": {
+        "@types/react": "^16.8.0 || ^17.0.0 || ^18.0.0",
+        "react": "^16.8.0 || ^17.0.0 || ^18.0.0"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        }
+      }
+    },
     "node_modules/color-convert": {
       "version": "2.0.1",
       "resolved": "https://registry.npmjs.org/color-convert/-/color-convert-2.0.1.tgz",

web/package.json

@@ -40,6 +40,7 @@
     "axios": "^1.7.3",
     "class-variance-authority": "^0.7.0",
     "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",

web/public/vite.svg

@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>

web/src/components/filter/SearchFilterGroup.tsx

@@ -43,7 +43,6 @@ type SearchFilterGroupProps = {
   className: string;
   filters?: SearchFilters[];
   filter?: SearchFilter;
-  searchTerm: string;
   filterList?: FilterList;
   onUpdateFilter: (filter: SearchFilter) => void;
 };
@@ -51,7 +50,6 @@ export default function SearchFilterGroup({
   className,
   filters = DEFAULT_REVIEW_FILTERS,
   filter,
-  searchTerm,
   filterList,
   onUpdateFilter,
 }: SearchFilterGroupProps) {
@@ -109,11 +107,8 @@ export default function SearchFilterGroup({
         return;
       }
       const cameraConfig = config.cameras[camera];
-      cameraConfig.review.alerts.required_zones.forEach((zone) => {
-        zones.add(zone);
-      });
-      cameraConfig.review.detections.required_zones.forEach((zone) => {
-        zones.add(zone);
+      Object.entries(cameraConfig.zones).map(([name, _]) => {
+        zones.add(name);
       });
     });
 
@@ -216,7 +211,7 @@ export default function SearchFilterGroup({
       )}
       {config?.semantic_search?.enabled &&
         filters.includes("source") &&
-        !searchTerm.includes("similarity:") && (
+        !filter?.search_type?.includes("similarity") && (
           <SearchTypeButton
             selectedSearchSources={
               filter?.search_type ?? ["thumbnail", "description"]
@@ -917,7 +912,7 @@ export function SearchTypeContent({
         <div className="my-2.5 flex flex-col gap-2.5">
           <FilterSwitch
             label="Thumbnail Image"
-            isChecked={selectedSearchSources?.includes("thumbnail") ?? false}
+            isChecked={currentSearchSources?.includes("thumbnail") ?? false}
             onCheckedChange={(isChecked) => {
               const updatedSources = currentSearchSources
                 ? [...currentSearchSources]

web/src/components/graph/SystemGraph.tsx

@@ -32,6 +32,16 @@ export function ThresholdBarGraph({
     [data],
   );
 
+  const yMax = useMemo(() => {
+    if (unit != "%") {
+      return undefined;
+    }
+
+    // @ts-expect-error y is valid
+    const yValues: number[] = data[0].data.map((point) => point?.y);
+    return Math.max(threshold.warning, ...yValues);
+  }, [data, threshold, unit]);
+
   const { theme, systemTheme } = useTheme();
 
   const formatTime = useCallback(
@@ -130,9 +140,10 @@ export function ThresholdBarGraph({
           formatter: (val: number) => Math.ceil(val).toString(),
         },
         min: 0,
+        max: yMax,
       },
     } as ApexCharts.ApexOptions;
-  }, [graphId, threshold, unit, systemTheme, theme, formatTime]);
+  }, [graphId, threshold, unit, yMax, systemTheme, theme, formatTime]);
 
   useEffect(() => {
     ApexCharts.exec(graphId, "updateOptions", options, true, true);

web/src/components/input/DeleteSearchDialog.tsx

@@ -0,0 +1,46 @@
+import {
+  AlertDialog,
+  AlertDialogAction,
+  AlertDialogCancel,
+  AlertDialogContent,
+  AlertDialogDescription,
+  AlertDialogFooter,
+  AlertDialogHeader,
+  AlertDialogTitle,
+} from "@/components/ui/alert-dialog";
+
+type DeleteSearchDialogProps = {
+  isOpen: boolean;
+  onClose: () => void;
+  onConfirm: () => void;
+  searchName: string;
+};
+
+export function DeleteSearchDialog({
+  isOpen,
+  onClose,
+  onConfirm,
+  searchName,
+}: DeleteSearchDialogProps) {
+  return (
+    <AlertDialog open={isOpen} onOpenChange={onClose}>
+      <AlertDialogContent>
+        <AlertDialogHeader>
+          <AlertDialogTitle>Are you sure?</AlertDialogTitle>
+          <AlertDialogDescription>
+            This will permanently delete the saved search "{searchName}".
+          </AlertDialogDescription>
+        </AlertDialogHeader>
+        <AlertDialogFooter>
+          <AlertDialogCancel onClick={onClose}>Cancel</AlertDialogCancel>
+          <AlertDialogAction
+            onClick={onConfirm}
+            className="bg-destructive text-white"
+          >
+            Delete
+          </AlertDialogAction>
+        </AlertDialogFooter>
+      </AlertDialogContent>
+    </AlertDialog>
+  );
+}

web/src/components/input/InputWithTags.tsx

@@ -0,0 +1,701 @@
+import { useState, useRef, useEffect, useCallback } from "react";
+import {
+  LuX,
+  LuFilter,
+  LuImage,
+  LuChevronDown,
+  LuChevronUp,
+  LuTrash2,
+  LuStar,
+} from "react-icons/lu";
+import {
+  FilterType,
+  SavedSearchQuery,
+  SearchFilter,
+  SearchSource,
+} from "@/types/search";
+import useSuggestions from "@/hooks/use-suggestions";
+import {
+  Command,
+  CommandInput,
+  CommandList,
+  CommandGroup,
+  CommandItem,
+} from "@/components/ui/command";
+import {
+  Popover,
+  PopoverContent,
+  PopoverTrigger,
+} from "@/components/ui/popover";
+import { cn } from "@/lib/utils";
+import { Tooltip, TooltipContent, TooltipTrigger } from "../ui/tooltip";
+import { TooltipPortal } from "@radix-ui/react-tooltip";
+import { usePersistence } from "@/hooks/use-persistence";
+import { SaveSearchDialog } from "./SaveSearchDialog";
+import { DeleteSearchDialog } from "./DeleteSearchDialog";
+import {
+  convertLocalDateToTimestamp,
+  getIntlDateFormat,
+} from "@/utils/dateUtil";
+import { toast } from "sonner";
+
+type InputWithTagsProps = {
+  filters: SearchFilter;
+  setFilters: (filter: SearchFilter) => void;
+  search: string;
+  setSearch: (search: string) => void;
+  allSuggestions: {
+    [K in keyof SearchFilter]: string[];
+  };
+};
+
+export default function InputWithTags({
+  filters,
+  setFilters,
+  search,
+  setSearch,
+  allSuggestions,
+}: InputWithTagsProps) {
+  const [inputValue, setInputValue] = useState(search || "");
+  const [currentFilterType, setCurrentFilterType] = useState<FilterType | null>(
+    null,
+  );
+  const [inputFocused, setInputFocused] = useState(false);
+  const [isSimilaritySearch, setIsSimilaritySearch] = useState(false);
+  const inputRef = useRef<HTMLInputElement>(null);
+  const commandRef = useRef<HTMLDivElement>(null);
+
+  // TODO: search history from browser storage
+
+  const [searchHistory, setSearchHistory, searchHistoryLoaded] = usePersistence<
+    SavedSearchQuery[]
+  >("frigate-search-history");
+
+  const [isSaveDialogOpen, setIsSaveDialogOpen] = useState(false);
+  const [isDeleteDialogOpen, setIsDeleteDialogOpen] = useState(false);
+  const [searchToDelete, setSearchToDelete] = useState<string | null>(null);
+
+  const handleSetSearchHistory = useCallback(() => {
+    setIsSaveDialogOpen(true);
+  }, []);
+
+  const handleSaveSearch = useCallback(
+    (name: string) => {
+      if (searchHistoryLoaded) {
+        setSearchHistory([
+          ...(searchHistory ?? []),
+          {
+            name: name,
+            search: search,
+            filter: filters,
+          },
+        ]);
+      }
+    },
+    [search, filters, searchHistory, setSearchHistory, searchHistoryLoaded],
+  );
+
+  const handleLoadSavedSearch = useCallback(
+    (name: string) => {
+      if (searchHistoryLoaded) {
+        const savedSearchEntry = searchHistory?.find(
+          (entry) => entry.name === name,
+        );
+        if (savedSearchEntry) {
+          setFilters(savedSearchEntry.filter!);
+          setSearch(savedSearchEntry.search);
+        }
+      }
+    },
+    [searchHistory, searchHistoryLoaded, setFilters, setSearch],
+  );
+
+  const handleDeleteSearch = useCallback((name: string) => {
+    setSearchToDelete(name);
+    setIsDeleteDialogOpen(true);
+  }, []);
+
+  const confirmDeleteSearch = useCallback(() => {
+    if (searchToDelete && searchHistory) {
+      setSearchHistory(
+        searchHistory.filter((item) => item.name !== searchToDelete) ?? [],
+      );
+      setSearchToDelete(null);
+      setIsDeleteDialogOpen(false);
+    }
+  }, [searchToDelete, searchHistory, setSearchHistory]);
+
+  // suggestions
+
+  const { suggestions, updateSuggestions } = useSuggestions(
+    filters,
+    allSuggestions,
+    searchHistory,
+  );
+
+  const resetSuggestions = useCallback(
+    (value: string) => {
+      setCurrentFilterType(null);
+      updateSuggestions(value, null);
+    },
+    [updateSuggestions],
+  );
+
+  const filterSuggestions = useCallback(
+    (current_suggestions: string[]) => {
+      if (!inputValue || currentFilterType) return suggestions;
+      const words = inputValue.split(/[\s,]+/);
+      const lastNonEmptyWordIndex = words
+        .map((word) => word.trim())
+        .lastIndexOf(words.filter((word) => word.trim() !== "").pop() || "");
+      const currentWord = words[lastNonEmptyWordIndex];
+      return current_suggestions.filter((suggestion) =>
+        suggestion.toLowerCase().includes(currentWord.toLowerCase()),
+      );
+    },
+    [inputValue, suggestions, currentFilterType],
+  );
+
+  const removeFilter = useCallback(
+    (filterType: FilterType, filterValue: string | number) => {
+      const newFilters = { ...filters };
+      if (Array.isArray(newFilters[filterType])) {
+        (newFilters[filterType] as string[]) = (
+          newFilters[filterType] as string[]
+        ).filter((v) => v !== filterValue);
+        if ((newFilters[filterType] as string[]).length === 0) {
+          delete newFilters[filterType];
+        }
+      } else if (filterType === "before" || filterType === "after") {
+        if (newFilters[filterType] === filterValue) {
+          delete newFilters[filterType];
+        }
+      } else {
+        delete newFilters[filterType];
+      }
+      setFilters(newFilters as SearchFilter);
+    },
+    [filters, setFilters],
+  );
+
+  const createFilter = useCallback(
+    (type: FilterType, value: string) => {
+      if (allSuggestions[type as FilterType]?.includes(value)) {
+        const newFilters = { ...filters };
+        let timestamp = 0;
+
+        switch (type) {
+          case "before":
+          case "after":
+            timestamp = convertLocalDateToTimestamp(value);
+            if (timestamp > 0) {
+              // Check for conflicts with existing before/after filters
+              if (
+                type === "before" &&
+                filters.after &&
+                timestamp <= filters.after * 1000
+              ) {
+                toast.error(
+                  "The 'before' date must be later than the 'after' date.",
+                  {
+                    position: "top-center",
+                  },
+                );
+                return;
+              }
+              if (
+                type === "after" &&
+                filters.before &&
+                timestamp >= filters.before * 1000
+              ) {
+                toast.error(
+                  "The 'after' date must be earlier than the 'before' date.",
+                  {
+                    position: "top-center",
+                  },
+                );
+                return;
+              }
+              if (type === "before") {
+                timestamp -= 1;
+              }
+              newFilters[type] = timestamp / 1000;
+            }
+            break;
+          case "search_type":
+            if (!newFilters.search_type) newFilters.search_type = [];
+            if (
+              !(newFilters.search_type as SearchSource[]).includes(
+                value as SearchSource,
+              )
+            ) {
+              (newFilters.search_type as SearchSource[]).push(
+                value as SearchSource,
+              );
+            }
+            break;
+          case "event_id":
+            newFilters.event_id = value;
+            break;
+          default:
+            // Handle array types (cameras, labels, subLabels, zones)
+            if (!newFilters[type]) newFilters[type] = [];
+            if (Array.isArray(newFilters[type])) {
+              if (!(newFilters[type] as string[]).includes(value)) {
+                (newFilters[type] as string[]).push(value);
+              }
+            }
+            break;
+        }
+
+        setFilters(newFilters);
+        setInputValue((prev) => prev.replace(`${type}:${value}`, "").trim());
+        setCurrentFilterType(null);
+      }
+    },
+    [filters, setFilters, allSuggestions],
+  );
+
+  // handlers
+
+  const handleFilterCreation = useCallback(
+    (filterType: FilterType, filterValue: string) => {
+      const trimmedValue = filterValue.trim();
+      if (
+        allSuggestions[filterType]?.includes(trimmedValue) ||
+        ((filterType === "before" || filterType === "after") &&
+          trimmedValue.match(/^\d{8}$/))
+      ) {
+        createFilter(filterType, trimmedValue);
+        setInputValue((prev) => {
+          const regex = new RegExp(
+            `${filterType}:${filterValue.trim()}[,\\s]*`,
+          );
+          const newValue = prev.replace(regex, "").trim();
+          return newValue.endsWith(",")
+            ? newValue.slice(0, -1).trim()
+            : newValue;
+        });
+        setCurrentFilterType(null);
+      }
+    },
+    [allSuggestions, createFilter],
+  );
+
+  const handleInputChange = useCallback(
+    (value: string) => {
+      setInputValue(value);
+
+      const words = value.split(/[\s,]+/);
+      const lastNonEmptyWordIndex = words
+        .map((word) => word.trim())
+        .lastIndexOf(words.filter((word) => word.trim() !== "").pop() || "");
+      const currentWord = words[lastNonEmptyWordIndex];
+      const isLastCharSpaceOrComma = value.endsWith(" ") || value.endsWith(",");
+
+      // Check if the current word is a filter type
+      const filterTypeMatch = currentWord.match(/^(\w+):(.*)$/);
+      if (filterTypeMatch) {
+        const [_, filterType, filterValue] = filterTypeMatch as [
+          string,
+          FilterType,
+          string,
+        ];
+
+        // Check if filter type is valid
+        if (
+          filterType in allSuggestions ||
+          filterType === "before" ||
+          filterType === "after"
+        ) {
+          setCurrentFilterType(filterType);
+
+          if (filterType === "before" || filterType === "after") {
+            // For before and after, we don't need to update suggestions
+            if (filterValue.match(/^\d{8}$/)) {
+              handleFilterCreation(filterType, filterValue);
+            }
+          } else {
+            updateSuggestions(filterValue, filterType);
+
+            // Check if the last character is a space or comma
+            if (isLastCharSpaceOrComma) {
+              handleFilterCreation(filterType, filterValue);
+            }
+          }
+        } else {
+          resetSuggestions(value);
+        }
+      } else {
+        resetSuggestions(value);
+      }
+    },
+    [updateSuggestions, resetSuggestions, allSuggestions, handleFilterCreation],
+  );
+
+  const handleInputFocus = useCallback(() => {
+    setInputFocused(true);
+  }, []);
+
+  const handleClearInput = useCallback(() => {
+    setInputFocused(false);
+    setInputValue("");
+    resetSuggestions("");
+    setSearch("");
+    inputRef?.current?.blur();
+    setFilters({});
+    setCurrentFilterType(null);
+    setIsSimilaritySearch(false);
+  }, [setFilters, resetSuggestions, setSearch]);
+
+  const handleInputBlur = useCallback((e: React.FocusEvent) => {
+    if (
+      commandRef.current &&
+      !commandRef.current.contains(e.relatedTarget as Node)
+    ) {
+      setInputFocused(false);
+    }
+  }, []);
+
+  const handleSuggestionClick = useCallback(
+    (suggestion: string) => {
+      if (currentFilterType) {
+        // Apply the selected suggestion to the current filter type
+        createFilter(currentFilterType, suggestion);
+        setInputValue((prev) => {
+          const regex = new RegExp(`${currentFilterType}:[^\\s,]*`, "g");
+          return prev.replace(regex, "").trim();
+        });
+      } else if (suggestion in allSuggestions) {
+        // Set the suggestion as a new filter type
+        setCurrentFilterType(suggestion as FilterType);
+        setInputValue((prev) => {
+          // Remove any partial match of the filter type, including incomplete matches
+          const words = prev.split(/\s+/);
+          const lastWord = words[words.length - 1];
+          if (lastWord && suggestion.startsWith(lastWord.toLowerCase())) {
+            words[words.length - 1] = suggestion + ":";
+          } else {
+            words.push(suggestion + ":");
+          }
+          return words.join(" ").trim();
+        });
+      } else {
+        // Add the suggestion as a standalone word
+        setInputValue((prev) => `${prev}${suggestion} `);
+      }
+
+      inputRef.current?.focus();
+    },
+    [createFilter, currentFilterType, allSuggestions],
+  );
+
+  const handleSearch = useCallback(
+    (value: string) => {
+      setSearch(value);
+      setInputFocused(false);
+      inputRef?.current?.blur();
+    },
+    [setSearch],
+  );
+
+  const handleInputKeyDown = useCallback(
+    (e: React.KeyboardEvent<HTMLInputElement>) => {
+      if (
+        e.key === "Enter" &&
+        inputValue.trim() !== "" &&
+        filterSuggestions(suggestions).length == 0
+      ) {
+        e.preventDefault();
+
+        handleSearch(inputValue);
+      }
+    },
+    [inputValue, handleSearch, filterSuggestions, suggestions],
+  );
+
+  // effects
+
+  useEffect(() => {
+    updateSuggestions(inputValue, currentFilterType);
+  }, [currentFilterType, inputValue, updateSuggestions]);
+
+  useEffect(() => {
+    if (filters?.search_type && filters?.search_type.includes("similarity")) {
+      setIsSimilaritySearch(true);
+      setInputValue("");
+    } else {
+      setIsSimilaritySearch(false);
+      setInputValue(search || "");
+    }
+  }, [filters, search]);
+
+  return (
+    <>
+      <Command
+        shouldFilter={false}
+        ref={commandRef}
+        className="rounded-md border"
+      >
+        <div className="relative">
+          <CommandInput
+            ref={inputRef}
+            value={inputValue}
+            onValueChange={handleInputChange}
+            onFocus={handleInputFocus}
+            onBlur={handleInputBlur}
+            onKeyDown={handleInputKeyDown}
+            className="text-md h-10 pr-24"
+            placeholder="Search..."
+          />
+          <div className="absolute right-3 top-0 flex h-full flex-row items-center justify-center gap-5">
+            {(search || Object.keys(filters).length > 0) && (
+              <Tooltip>
+                <TooltipTrigger>
+                  <LuX
+                    className="size-4 cursor-pointer text-secondary-foreground"
+                    onClick={handleClearInput}
+                  />
+                </TooltipTrigger>
+                <TooltipPortal>
+                  <TooltipContent>Clear search</TooltipContent>
+                </TooltipPortal>
+              </Tooltip>
+            )}
+
+            {(search || Object.keys(filters).length > 0) && (
+              <Tooltip>
+                <TooltipTrigger>
+                  <LuStar
+                    className="size-4 cursor-pointer text-secondary-foreground"
+                    onClick={handleSetSearchHistory}
+                  />
+                </TooltipTrigger>
+                <TooltipPortal>
+                  <TooltipContent>Save search</TooltipContent>
+                </TooltipPortal>
+              </Tooltip>
+            )}
+
+            {isSimilaritySearch && (
+              <Tooltip>
+                <TooltipTrigger className="cursor-default">
+                  <LuImage
+                    aria-label="Similarity search active"
+                    className="size-4 text-selected"
+                  />
+                </TooltipTrigger>
+                <TooltipPortal>
+                  <TooltipContent>Similarity search active</TooltipContent>
+                </TooltipPortal>
+              </Tooltip>
+            )}
+
+            <Popover>
+              <PopoverTrigger asChild>
+                <button
+                  className="focus:outline-none"
+                  aria-label="Filter information"
+                >
+                  <LuFilter
+                    aria-label="Filters active"
+                    className={cn(
+                      "size-4",
+                      Object.keys(filters).length > 0
+                        ? "text-selected"
+                        : "text-secondary-foreground",
+                    )}
+                  />
+                </button>
+              </PopoverTrigger>
+              <PopoverContent className="w-80">
+                <div className="space-y-2">
+                  <h3 className="font-medium">How to use text filters</h3>
+                  <p className="text-sm text-muted-foreground">
+                    Filters help you narrow down your search results. Here's how
+                    to use them:
+                  </p>
+                  <ul className="list-disc pl-5 text-sm text-primary-variant">
+                    <li>
+                      Type a filter name followed by a colon (e.g., "cameras:").
+                    </li>
+                    <li>
+                      Select a value from the suggestions or type your own.
+                    </li>
+                    <li>
+                      Use multiple filters by adding them one after another.
+                    </li>
+                    <li>
+                      Date filters (before: and after:) use{" "}
+                      {getIntlDateFormat()} format.
+                    </li>
+                    <li>Remove filters by clicking the 'x' next to them.</li>
+                  </ul>
+                  <p className="text-sm text-muted-foreground">
+                    Example:{" "}
+                    <code className="text-primary">
+                      cameras:front_door label:person before:01012024
+                    </code>
+                  </p>
+                </div>
+              </PopoverContent>
+            </Popover>
+
+            {inputFocused ? (
+              <LuChevronUp
+                onClick={() => setInputFocused(false)}
+                className="size-4 cursor-pointer text-secondary-foreground"
+              />
+            ) : (
+              <LuChevronDown
+                onClick={() => setInputFocused(true)}
+                className="size-4 cursor-pointer text-secondary-foreground"
+              />
+            )}
+          </div>
+        </div>
+
+        <CommandList
+          className={cn(
+            "scrollbar-container border-t duration-200 animate-in fade-in",
+            inputFocused ? "visible" : "hidden",
+          )}
+        >
+          {(Object.keys(filters).length > 0 || isSimilaritySearch) && (
+            <CommandGroup heading="Active Filters">
+              <div className="my-2 flex flex-wrap gap-2 px-2">
+                {isSimilaritySearch && (
+                  <span className="inline-flex items-center whitespace-nowrap rounded-full bg-blue-100 px-2 py-0.5 text-sm text-blue-800">
+                    Similarity Search
+                    <button
+                      onClick={handleClearInput}
+                      className="ml-1 focus:outline-none"
+                      aria-label="Clear similarity search"
+                    >
+                      <LuX className="h-3 w-3" />
+                    </button>
+                  </span>
+                )}
+                {Object.entries(filters).map(([filterType, filterValues]) =>
+                  Array.isArray(filterValues)
+                    ? filterValues
+                        .filter(() => filterType !== "query")
+                        .filter(() => !filterValues.includes("similarity"))
+                        .map((value, index) => (
+                          <span
+                            key={`${filterType}-${index}`}
+                            className="inline-flex items-center whitespace-nowrap rounded-full bg-green-100 px-2 py-0.5 text-sm capitalize text-green-800"
+                          >
+                            {filterType.replaceAll("_", " ")}:{" "}
+                            {value.replaceAll("_", " ")}
+                            <button
+                              onClick={() =>
+                                removeFilter(filterType as FilterType, value)
+                              }
+                              className="ml-1 focus:outline-none"
+                              aria-label={`Remove ${filterType}:${value.replaceAll("_", " ")} filter`}
+                            >
+                              <LuX className="h-3 w-3" />
+                            </button>
+                          </span>
+                        ))
+                    : filterType !== "event_id" && (
+                        <span
+                          key={filterType}
+                          className="inline-flex items-center whitespace-nowrap rounded-full bg-green-100 px-2 py-0.5 text-sm capitalize text-green-800"
+                        >
+                          {filterType}:
+                          {filterType === "before" || filterType === "after"
+                            ? new Date(
+                                (filterType === "before"
+                                  ? (filterValues as number) + 1
+                                  : (filterValues as number)) * 1000,
+                              ).toLocaleDateString(
+                                window.navigator?.language || "en-US",
+                              )
+                            : filterValues}
+                          <button
+                            onClick={() =>
+                              removeFilter(
+                                filterType as FilterType,
+                                filterValues as string | number,
+                              )
+                            }
+                            className="ml-1 focus:outline-none"
+                            aria-label={`Remove ${filterType}:${filterValues} filter`}
+                          >
+                            <LuX className="h-3 w-3" />
+                          </button>
+                        </span>
+                      ),
+                )}
+              </div>
+            </CommandGroup>
+          )}
+
+          {!currentFilterType &&
+            !inputValue &&
+            searchHistoryLoaded &&
+            (searchHistory?.length ?? 0) > 0 && (
+              <CommandGroup heading="Saved Searches">
+                {searchHistory?.map((suggestion, index) => (
+                  <CommandItem
+                    key={index}
+                    className="flex cursor-pointer items-center justify-between"
+                    onSelect={() => handleLoadSavedSearch(suggestion.name)}
+                  >
+                    <span>{suggestion.name}</span>
+                    <Tooltip>
+                      <TooltipTrigger asChild>
+                        <button
+                          onClick={(e) => {
+                            e.stopPropagation();
+                            handleDeleteSearch(suggestion.name);
+                          }}
+                          className="focus:outline-none"
+                        >
+                          <LuTrash2 className="h-4 w-4 text-secondary-foreground" />
+                        </button>
+                      </TooltipTrigger>
+                      <TooltipPortal>
+                        <TooltipContent>Delete saved search</TooltipContent>
+                      </TooltipPortal>
+                    </Tooltip>
+                  </CommandItem>
+                ))}
+              </CommandGroup>
+            )}
+          <CommandGroup
+            heading={currentFilterType ? "Filter Values" : "Filters"}
+          >
+            {filterSuggestions(suggestions)
+              .filter(
+                (item) =>
+                  !searchHistory?.some((history) => history.name === item),
+              )
+              .map((suggestion, index) => (
+                <CommandItem
+                  key={index + (searchHistory?.length ?? 0)}
+                  className="cursor-pointer"
+                  onSelect={() => handleSuggestionClick(suggestion)}
+                >
+                  {suggestion}
+                </CommandItem>
+              ))}
+          </CommandGroup>
+        </CommandList>
+      </Command>
+      <SaveSearchDialog
+        isOpen={isSaveDialogOpen}
+        onClose={() => setIsSaveDialogOpen(false)}
+        onSave={handleSaveSearch}
+      />
+      <DeleteSearchDialog
+        isOpen={isDeleteDialogOpen}
+        onClose={() => setIsDeleteDialogOpen(false)}
+        onConfirm={confirmDeleteSearch}
+        searchName={searchToDelete || ""}
+      />
+    </>
+  );
+}

web/src/components/input/SaveSearchDialog.tsx

@@ -0,0 +1,74 @@
+import {
+  Dialog,
+  DialogContent,
+  DialogHeader,
+  DialogTitle,
+  DialogFooter,
+  DialogDescription,
+} from "@/components/ui/dialog";
+
+import { Button } from "@/components/ui/button";
+import { Input } from "@/components/ui/input";
+import { useState } from "react";
+import { isMobile } from "react-device-detect";
+import { toast } from "sonner";
+
+type SaveSearchDialogProps = {
+  isOpen: boolean;
+  onClose: () => void;
+  onSave: (name: string) => void;
+};
+
+export function SaveSearchDialog({
+  isOpen,
+  onClose,
+  onSave,
+}: SaveSearchDialogProps) {
+  const [searchName, setSearchName] = useState("");
+
+  const handleSave = () => {
+    if (searchName.trim()) {
+      onSave(searchName.trim());
+      setSearchName("");
+      toast.success(`Search (${searchName.trim()}) has been saved.`, {
+        position: "top-center",
+      });
+      onClose();
+    }
+  };
+
+  return (
+    <Dialog open={isOpen} onOpenChange={onClose}>
+      <DialogContent
+        onOpenAutoFocus={(e) => {
+          if (isMobile) {
+            e.preventDefault();
+          }
+        }}
+      >
+        <DialogHeader>
+          <DialogTitle>Save Search</DialogTitle>
+          <DialogDescription className="sr-only">
+            Provide a name for this saved search.
+          </DialogDescription>
+        </DialogHeader>
+        <Input
+          value={searchName}
+          className="text-md"
+          onChange={(e) => setSearchName(e.target.value)}
+          placeholder="Enter a name for your search"
+        />
+        <DialogFooter>
+          <Button onClick={onClose}>Cancel</Button>
+          <Button
+            onClick={handleSave}
+            variant="select"
+            className="mb-2 md:mb-0"
+          >
+            Save
+          </Button>
+        </DialogFooter>
+      </DialogContent>
+    </Dialog>
+  );
+}

web/src/components/overlay/detail/AnnotationSettingsPane.tsx

@@ -121,7 +121,7 @@ export function AnnotationSettingsPane({
   }
 
   return (
-    <div className="space-y-3 rounded-lg border border-secondary-foreground bg-background_alt p-2">
+    <div className="mb-3 space-y-3 rounded-lg border border-secondary-foreground bg-background_alt p-2">
       <Heading as="h4" className="my-2">
         Annotation Settings
       </Heading>
@@ -152,8 +152,8 @@ export function AnnotationSettingsPane({
             render={({ field }) => (
               <FormItem>
                 <FormLabel>Annotation Offset</FormLabel>
-                <div className="flex flex-col gap-8 md:flex-row-reverse">
-                  <div className="my-5 flex flex-row items-center gap-3 rounded-lg bg-destructive/50 p-3 text-sm text-primary-variant md:my-0">
+                <div className="flex flex-col gap-3 md:flex-row-reverse md:gap-8">
+                  <div className="flex flex-row items-center gap-3 rounded-lg bg-destructive/50 p-3 text-sm text-primary-variant md:my-0 md:my-5">
                     <PiWarningCircle className="size-24" />
                     <div>
                       This data comes from your camera's detect feed but is
@@ -161,7 +161,7 @@ export function AnnotationSettingsPane({
                       unlikely that the two streams are perfectly in sync. As a
                       result, the bounding box and the footage will not line up
                       perfectly. However, the <code>annotation_offset</code>{" "}
-                      field in your config can be used to adjust this.
+                      field can be used to adjust this.
                       <div className="mt-2 flex items-center text-primary">
                         <Link
                           to="https://docs.frigate.video/configuration/reference"

web/src/components/overlay/detail/ObjectLifecycle.tsx

@@ -357,10 +357,7 @@ export default function ObjectLifecycle({
       )}
 
       <div className="relative flex flex-col items-center justify-center">
-        <Carousel
-          className={cn("m-0 w-full", fullscreen && isDesktop && "w-[75%]")}
-          setApi={setMainApi}
-        >
+        <Carousel className="m-0 w-full" setApi={setMainApi}>
           <CarouselContent>
             {eventSequence.map((item, index) => (
               <CarouselItem key={index}>

web/src/components/overlay/detail/ReviewDetailDialog.tsx

@@ -234,7 +234,7 @@ export default function ReviewDetailDialog({
           )}
 
           {pane == "details" && selectedEvent && (
-            <div className="scrollbar-container overflow-x-none mt-0 flex size-full flex-col gap-2 overflow-y-auto overflow-x-hidden">
+            <div className="mt-0 flex size-full flex-col gap-2">
               <ObjectLifecycle event={selectedEvent} setPane={setPane} />
             </div>
           )}
@@ -370,7 +370,9 @@ function EventItem({
                     <Chip
                       className="cursor-pointer rounded-md bg-gray-500 bg-gradient-to-br from-gray-400 to-gray-500"
                       onClick={() => {
-                        navigate(`/explore?similarity_search_id=${event.id}`);
+                        navigate(
+                          `/explore?search_type=similarity&event_id=${event.id}`,
+                        );
                       }}
                     >
                       <FaImages className="size-4 text-white" />

web/src/components/ui/command.tsx

@@ -0,0 +1,153 @@
+import * as React from "react";
+import { type DialogProps } from "@radix-ui/react-dialog";
+import { Command as CommandPrimitive } from "cmdk";
+import { Search } from "lucide-react";
+
+import { cn } from "@/lib/utils";
+import { Dialog, DialogContent } from "@/components/ui/dialog";
+
+const Command = React.forwardRef<
+  React.ElementRef<typeof CommandPrimitive>,
+  React.ComponentPropsWithoutRef<typeof CommandPrimitive>
+>(({ className, ...props }, ref) => (
+  <CommandPrimitive
+    ref={ref}
+    className={cn(
+      "flex h-full w-full flex-col overflow-hidden rounded-md bg-popover text-popover-foreground",
+      className,
+    )}
+    {...props}
+  />
+));
+Command.displayName = CommandPrimitive.displayName;
+
+interface CommandDialogProps extends DialogProps {}
+
+const CommandDialog = ({ children, ...props }: CommandDialogProps) => {
+  return (
+    <Dialog {...props}>
+      <DialogContent className="overflow-hidden p-0 shadow-lg">
+        <Command className="[&_[cmdk-group-heading]]:px-2 [&_[cmdk-group-heading]]:font-medium [&_[cmdk-group-heading]]:text-muted-foreground [&_[cmdk-group]:not([hidden])_~[cmdk-group]]:pt-0 [&_[cmdk-group]]:px-2 [&_[cmdk-input-wrapper]_svg]:h-5 [&_[cmdk-input-wrapper]_svg]:w-5 [&_[cmdk-input]]:h-12 [&_[cmdk-item]]:px-2 [&_[cmdk-item]]:py-3 [&_[cmdk-item]_svg]:h-5 [&_[cmdk-item]_svg]:w-5">
+          {children}
+        </Command>
+      </DialogContent>
+    </Dialog>
+  );
+};
+
+const CommandInput = React.forwardRef<
+  React.ElementRef<typeof CommandPrimitive.Input>,
+  React.ComponentPropsWithoutRef<typeof CommandPrimitive.Input>
+>(({ className, ...props }, ref) => (
+  <div className="flex items-center px-3" cmdk-input-wrapper="">
+    <Search className="mr-2 h-4 w-4 shrink-0 opacity-50" />
+    <CommandPrimitive.Input
+      ref={ref}
+      className={cn(
+        "flex h-11 w-full rounded-md bg-transparent py-3 text-sm outline-none placeholder:text-muted-foreground disabled:cursor-not-allowed disabled:opacity-50",
+        className,
+      )}
+      {...props}
+    />
+  </div>
+));
+
+CommandInput.displayName = CommandPrimitive.Input.displayName;
+
+const CommandList = React.forwardRef<
+  React.ElementRef<typeof CommandPrimitive.List>,
+  React.ComponentPropsWithoutRef<typeof CommandPrimitive.List>
+>(({ className, ...props }, ref) => (
+  <CommandPrimitive.List
+    ref={ref}
+    className={cn("max-h-[300px] overflow-y-auto overflow-x-hidden", className)}
+    {...props}
+  />
+));
+
+CommandList.displayName = CommandPrimitive.List.displayName;
+
+const CommandEmpty = React.forwardRef<
+  React.ElementRef<typeof CommandPrimitive.Empty>,
+  React.ComponentPropsWithoutRef<typeof CommandPrimitive.Empty>
+>((props, ref) => (
+  <CommandPrimitive.Empty
+    ref={ref}
+    className="py-6 text-center text-sm"
+    {...props}
+  />
+));
+
+CommandEmpty.displayName = CommandPrimitive.Empty.displayName;
+
+const CommandGroup = React.forwardRef<
+  React.ElementRef<typeof CommandPrimitive.Group>,
+  React.ComponentPropsWithoutRef<typeof CommandPrimitive.Group>
+>(({ className, ...props }, ref) => (
+  <CommandPrimitive.Group
+    ref={ref}
+    className={cn(
+      "overflow-hidden p-1 text-foreground [&_[cmdk-group-heading]]:px-2 [&_[cmdk-group-heading]]:py-1.5 [&_[cmdk-group-heading]]:text-xs [&_[cmdk-group-heading]]:font-medium [&_[cmdk-group-heading]]:text-muted-foreground",
+      className,
+    )}
+    {...props}
+  />
+));
+
+CommandGroup.displayName = CommandPrimitive.Group.displayName;
+
+const CommandSeparator = React.forwardRef<
+  React.ElementRef<typeof CommandPrimitive.Separator>,
+  React.ComponentPropsWithoutRef<typeof CommandPrimitive.Separator>
+>(({ className, ...props }, ref) => (
+  <CommandPrimitive.Separator
+    ref={ref}
+    className={cn("-mx-1 h-px bg-border", className)}
+    {...props}
+  />
+));
+CommandSeparator.displayName = CommandPrimitive.Separator.displayName;
+
+const CommandItem = React.forwardRef<
+  React.ElementRef<typeof CommandPrimitive.Item>,
+  React.ComponentPropsWithoutRef<typeof CommandPrimitive.Item>
+>(({ className, ...props }, ref) => (
+  <CommandPrimitive.Item
+    ref={ref}
+    className={cn(
+      "relative flex cursor-default select-none items-center rounded-sm px-2 py-1.5 text-sm outline-none data-[disabled=true]:pointer-events-none data-[selected='true']:bg-accent data-[selected=true]:text-accent-foreground data-[disabled=true]:opacity-50",
+      className,
+    )}
+    {...props}
+  />
+));
+
+CommandItem.displayName = CommandPrimitive.Item.displayName;
+
+const CommandShortcut = ({
+  className,
+  ...props
+}: React.HTMLAttributes<HTMLSpanElement>) => {
+  return (
+    <span
+      className={cn(
+        "ml-auto text-xs tracking-widest text-muted-foreground",
+        className,
+      )}
+      {...props}
+    />
+  );
+};
+CommandShortcut.displayName = "CommandShortcut";
+
+export {
+  Command,
+  CommandDialog,
+  CommandInput,
+  CommandList,
+  CommandEmpty,
+  CommandGroup,
+  CommandItem,
+  CommandShortcut,
+  CommandSeparator,
+};

web/src/hooks/use-overlay-state.tsx

@@ -125,7 +125,7 @@ export function useSearchEffect(
     const remove = callback(param[1]);
 
     if (remove) {
-      setSearchParams();
+      setSearchParams(undefined, { replace: true });
     }
   }, [param, callback, setSearchParams]);
 }

web/src/hooks/use-suggestions.ts

@@ -0,0 +1,63 @@
+import { FilterType, SavedSearchQuery, SearchFilter } from "@/types/search";
+import { useCallback, useState } from "react";
+
+// Custom hook for managing suggestions
+export type UseSuggestionsType = (
+  filters: SearchFilter,
+  allSuggestions: { [K in keyof SearchFilter]: string[] },
+  searchHistory: SavedSearchQuery[],
+) => ReturnType<typeof useSuggestions>;
+
+// Define and export the useSuggestions hook
+export default function useSuggestions(
+  filters: SearchFilter,
+  allSuggestions: { [K in keyof SearchFilter]: string[] },
+  searchHistory?: SavedSearchQuery[],
+) {
+  const [suggestions, setSuggestions] = useState<string[]>([]);
+
+  const updateSuggestions = useCallback(
+    (value: string, currentFilterType: FilterType | null) => {
+      if (currentFilterType && currentFilterType in allSuggestions) {
+        const filterValue = value.split(":").pop() || "";
+        const currentFilterValues = filters[currentFilterType] || [];
+        setSuggestions(
+          allSuggestions[currentFilterType]?.filter(
+            (item) =>
+              item.toLowerCase().startsWith(filterValue.toLowerCase()) &&
+              !(currentFilterValues as (string | number)[]).includes(item),
+          ) ?? [],
+        );
+      } else {
+        const availableFilters = Object.keys(allSuggestions).filter(
+          (filter) => {
+            const filterKey = filter as FilterType;
+            const filterValues = filters[filterKey];
+            const suggestionValues = allSuggestions[filterKey];
+
+            if (!filterValues) return true;
+            if (
+              Array.isArray(filterValues) &&
+              Array.isArray(suggestionValues)
+            ) {
+              return filterValues.length < suggestionValues.length;
+            }
+            return false;
+          },
+        );
+        setSuggestions([
+          ...(searchHistory?.map((search) => search.name) ?? []),
+          ...availableFilters,
+          "before",
+          "after",
+        ]);
+      }
+    },
+    [filters, allSuggestions, searchHistory],
+  );
+
+  return {
+    suggestions,
+    updateSuggestions,
+  };
+}

web/src/pages/ConfigEditor.tsx

@@ -31,6 +31,7 @@ function ConfigEditor() {
   const editorRef = useRef<monaco.editor.IStandaloneCodeEditor | null>(null);
   const modelRef = useRef<monaco.editor.ITextModel | null>(null);
   const configRef = useRef<HTMLDivElement | null>(null);
+  const schemaConfiguredRef = useRef(false);
 
   const onHandleSaveConfig = useCallback(
     async (save_option: SaveOptions) => {
@@ -79,50 +80,59 @@ function ConfigEditor() {
       return;
     }
 
-    if (modelRef.current != null) {
-      // we don't need to recreate the editor if it already exists
-      editorRef.current?.layout();
-      return;
+    const modelUri = monaco.Uri.parse(
+      `a://b/api/config/schema_${Date.now()}.json`,
+    );
+
+    // Configure Monaco YAML schema only once
+    if (!schemaConfiguredRef.current) {
+      configureMonacoYaml(monaco, {
+        enableSchemaRequest: true,
+        hover: true,
+        completion: true,
+        validate: true,
+        format: true,
+        schemas: [
+          {
+            uri: `${apiHost}api/config/schema.json`,
+            fileMatch: [String(modelUri)],
+          },
+        ],
+      });
+      schemaConfiguredRef.current = true;
     }
 
-    const modelUri = monaco.Uri.parse("a://b/api/config/schema.json");
-
-    if (monaco.editor.getModels().length > 0) {
-      modelRef.current = monaco.editor.getModel(modelUri);
-    } else {
+    if (!modelRef.current) {
       modelRef.current = monaco.editor.createModel(config, "yaml", modelUri);
+    } else {
+      modelRef.current.setValue(config);
     }
 
-    configureMonacoYaml(monaco, {
-      enableSchemaRequest: true,
-      hover: true,
-      completion: true,
-      validate: true,
-      format: true,
-      schemas: [
-        {
-          uri: `${apiHost}api/config/schema.json`,
-          fileMatch: [String(modelUri)],
-        },
-      ],
-    });
-
     const container = configRef.current;
 
-    if (container != null) {
+    if (container && !editorRef.current) {
       editorRef.current = monaco.editor.create(container, {
         language: "yaml",
         model: modelRef.current,
         scrollBeyondLastLine: false,
         theme: (systemTheme || theme) == "dark" ? "vs-dark" : "vs-light",
       });
+    } else if (editorRef.current) {
+      editorRef.current.setModel(modelRef.current);
     }
 
     return () => {
-      configRef.current = null;
-      modelRef.current = null;
+      if (editorRef.current) {
+        editorRef.current.dispose();
+        editorRef.current = null;
+      }
+      if (modelRef.current) {
+        modelRef.current.dispose();
+        modelRef.current = null;
+      }
+      schemaConfiguredRef.current = false;
     };
-  });
+  }, [config, apiHost, systemTheme, theme]);
 
   // monitoring state
 

web/src/pages/Events.tsx

@@ -14,6 +14,10 @@ import {
   ReviewSummary,
   SegmentedReviewData,
 } from "@/types/review";
+import {
+  getBeginningOfDayTimestamp,
+  getEndOfDayTimestamp,
+} from "@/utils/dateUtil";
 import EventView from "@/views/events/EventView";
 import { RecordingView } from "@/views/recording/RecordingView";
 import axios from "axios";
@@ -43,10 +47,17 @@ export default function Events() {
       .get(`review/${reviewId}`)
       .then((resp) => {
         if (resp.status == 200 && resp.data) {
+          const startTime = resp.data.start_time - REVIEW_PADDING;
+          const date = new Date(startTime * 1000);
+
+          setReviewFilter({
+            after: getBeginningOfDayTimestamp(date),
+            before: getEndOfDayTimestamp(date),
+          });
           setRecording(
             {
               camera: resp.data.camera,
-              startTime: resp.data.start_time - REVIEW_PADDING,
+              startTime,
               severity: resp.data.severity,
             },
             true,

web/src/pages/Explore.tsx

@@ -1,105 +1,60 @@
 import { useApiFilterArgs } from "@/hooks/use-api-filter";
-import { useCameraPreviews } from "@/hooks/use-camera-previews";
-import { useOverlayState, useSearchEffect } from "@/hooks/use-overlay-state";
-import { FrigateConfig } from "@/types/frigateConfig";
-import { RecordingStartingPoint } from "@/types/record";
 import { SearchFilter, SearchQuery, SearchResult } from "@/types/search";
-import { TimeRange } from "@/types/timeline";
-import { RecordingView } from "@/views/recording/RecordingView";
 import SearchView from "@/views/search/SearchView";
 import { useCallback, useEffect, useMemo, useState } from "react";
-import useSWR from "swr";
+import { TbExclamationCircle } from "react-icons/tb";
 import useSWRInfinite from "swr/infinite";
 
 const API_LIMIT = 25;
 
 export default function Explore() {
-  const { data: config } = useSWR<FrigateConfig>("config", {
-    revalidateOnFocus: false,
-  });
-
   // search field handler
 
-  const [searchTimeout, setSearchTimeout] = useState<NodeJS.Timeout>();
   const [search, setSearch] = useState("");
-  const [searchTerm, setSearchTerm] = useState("");
-
-  const [recording, setRecording] =
-    useOverlayState<RecordingStartingPoint>("recording");
-
-  // search filter
-
-  const similaritySearch = useMemo(() => {
-    if (!searchTerm.includes("similarity:")) {
-      return undefined;
-    }
-
-    return searchTerm.split(":")[1];
-  }, [searchTerm]);
 
   const [searchFilter, setSearchFilter, searchSearchParams] =
     useApiFilterArgs<SearchFilter>();
 
-  // search api
+  const searchTerm = useMemo(
+    () => searchSearchParams?.["query"] || "",
+    [searchSearchParams],
+  );
 
-  useSearchEffect("similarity_search_id", (similarityId) => {
-    setSearch(`similarity:${similarityId}`);
-    // @ts-expect-error we want to clear this
-    setSearchFilter({ ...searchFilter, similarity_search_id: undefined });
-    return false;
-  });
+  const similaritySearch = useMemo(
+    () => searchSearchParams["search_type"] == "similarity",
+    [searchSearchParams],
+  );
 
   useEffect(() => {
-    if (searchTimeout) {
-      clearTimeout(searchTimeout);
+    if (!searchTerm && !search) {
+      return;
     }
 
-    setSearchTimeout(
-      setTimeout(() => {
-        setSearchTimeout(undefined);
-        setSearchTerm(search);
-      }, 750),
-    );
-    // we only want to update the searchTerm when search changes
+    // switch back to normal search when query is entered
+    setSearchFilter({
+      ...searchFilter,
+      search_type:
+        similaritySearch && search ? undefined : searchFilter?.search_type,
+      event_id: similaritySearch && search ? undefined : searchFilter?.event_id,
+      query: search.length > 0 ? search : undefined,
+    });
+    // only update when search is updated
     // eslint-disable-next-line react-hooks/exhaustive-deps
   }, [search]);
 
   const searchQuery: SearchQuery = useMemo(() => {
-    if (similaritySearch) {
-      return [
-        "events/search",
-        {
-          query: similaritySearch,
-          cameras: searchSearchParams["cameras"],
-          labels: searchSearchParams["labels"],
-          sub_labels: searchSearchParams["subLabels"],
-          zones: searchSearchParams["zones"],
-          before: searchSearchParams["before"],
-          after: searchSearchParams["after"],
-          include_thumbnails: 0,
-          search_type: "similarity",
-        },
-      ];
+    // no search parameters
+    if (searchSearchParams && Object.keys(searchSearchParams).length === 0) {
+      return null;
     }
 
-    if (searchTerm) {
-      return [
-        "events/search",
-        {
-          query: searchTerm,
-          cameras: searchSearchParams["cameras"],
-          labels: searchSearchParams["labels"],
-          sub_labels: searchSearchParams["subLabels"],
-          zones: searchSearchParams["zones"],
-          before: searchSearchParams["before"],
-          after: searchSearchParams["after"],
-          search_type: searchSearchParams["search_type"],
-          include_thumbnails: 0,
-        },
-      ];
-    }
-
-    if (searchSearchParams && Object.keys(searchSearchParams).length !== 0) {
+    // parameters, but no search term and not similarity
+    if (
+      searchSearchParams &&
+      Object.keys(searchSearchParams).length !== 0 &&
+      !searchTerm &&
+      !similaritySearch
+    ) {
       return [
         "events",
         {
@@ -118,15 +73,38 @@ export default function Explore() {
       ];
     }
 
-    return null;
+    // parameters and search term
+    if (!similaritySearch) {
+      setSearch(searchTerm);
+    }
+
+    return [
+      "events/search",
+      {
+        query: similaritySearch ? undefined : searchTerm,
+        cameras: searchSearchParams["cameras"],
+        labels: searchSearchParams["labels"],
+        sub_labels: searchSearchParams["subLabels"],
+        zones: searchSearchParams["zones"],
+        before: searchSearchParams["before"],
+        after: searchSearchParams["after"],
+        search_type: searchSearchParams["search_type"],
+        event_id: searchSearchParams["event_id"],
+        include_thumbnails: 0,
+      },
+    ];
   }, [searchTerm, searchSearchParams, similaritySearch]);
 
   // paging
 
+  // usually slow only on first run while downloading models
+  const [isSlowLoading, setIsSlowLoading] = useState(false);
+
   const getKey = (
     pageIndex: number,
     previousPageData: SearchResult[] | null,
   ): SearchQuery => {
+    if (isSlowLoading && !similaritySearch) return null;
     if (previousPageData && !previousPageData.length) return null; // reached the end
     if (!searchQuery) return null;
 
@@ -148,8 +126,14 @@ export default function Explore() {
   const { data, size, setSize, isValidating } = useSWRInfinite<SearchResult[]>(
     getKey,
     {
-      revalidateFirstPage: false,
+      revalidateFirstPage: true,
       revalidateAll: false,
+      onLoadingSlow: () => {
+        if (!similaritySearch) {
+          setIsSlowLoading(true);
+        }
+      },
+      loadingTimeout: 10000,
     },
   );
 
@@ -180,108 +164,40 @@ export default function Explore() {
     }
   }, [isReachingEnd, isLoadingMore, setSize, size, searchResults, searchQuery]);
 
-  // previews
-
-  const previewTimeRange = useMemo<TimeRange>(() => {
-    if (!searchResults) {
-      return { after: 0, before: 0 };
-    }
-
-    return {
-      after: Math.min(...searchResults.map((res) => res.start_time)),
-      before: Math.max(
-        ...searchResults.map((res) => res.end_time ?? Date.now() / 1000),
-      ),
-    };
-  }, [searchResults]);
-
-  const allPreviews = useCameraPreviews(previewTimeRange, {
-    autoRefresh: false,
-    fetchPreviews: searchResults != undefined,
-  });
-
-  // selection
-
-  const onOpenSearch = useCallback(
-    (item: SearchResult) => {
-      setRecording({
-        camera: item.camera,
-        startTime: item.start_time,
-        severity: "alert",
-      });
-    },
-    [setRecording],
-  );
-
-  const selectedReviewData = useMemo(() => {
-    if (!recording) {
-      return undefined;
-    }
-
-    if (!config) {
-      return undefined;
-    }
-
-    if (!searchResults) {
-      return undefined;
-    }
-
-    const allCameras = searchFilter?.cameras ?? Object.keys(config.cameras);
-
-    return {
-      camera: recording.camera,
-      start_time: recording.startTime,
-      allCameras: allCameras,
-    };
-
-    // previews will not update after item is selected
-    // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, [recording, searchResults]);
-
-  const selectedTimeRange = useMemo(() => {
-    if (!recording) {
-      return undefined;
-    }
-
-    const time = new Date(recording.startTime * 1000);
-    time.setUTCMinutes(0, 0, 0);
-    const start = time.getTime() / 1000;
-    time.setHours(time.getHours() + 2);
-    const end = time.getTime() / 1000;
-    return {
-      after: start,
-      before: end,
-    };
-  }, [recording]);
-
-  if (recording) {
-    if (selectedReviewData && selectedTimeRange) {
-      return (
-        <RecordingView
-          startCamera={selectedReviewData.camera}
-          startTime={selectedReviewData.start_time}
-          allCameras={selectedReviewData.allCameras}
-          allPreviews={allPreviews}
-          timeRange={selectedTimeRange}
-          updateFilter={setSearchFilter}
+  return (
+    <>
+      {isSlowLoading && !similaritySearch ? (
+        <div className="absolute inset-0 left-1/2 top-1/2 flex h-96 w-96 -translate-x-1/2 -translate-y-1/2">
+          <div className="flex flex-col items-center justify-center rounded-lg bg-background/50 p-5">
+            <p className="my-5 text-lg">Search Unavailable</p>
+            <TbExclamationCircle className="mb-3 size-10" />
+            <p className="max-w-96 text-center">
+              If this is your first time using Search, be patient while Frigate
+              downloads the necessary embeddings models. Check Frigate logs.
+            </p>
+          </div>
+        </div>
+      ) : (
+        <SearchView
+          search={search}
+          searchTerm={searchTerm}
+          searchFilter={searchFilter}
+          searchResults={searchResults}
+          isLoading={(isLoadingInitialData || isLoadingMore) ?? true}
+          setSearch={setSearch}
+          setSimilaritySearch={(search) => {
+            setSearchFilter({
+              ...searchFilter,
+              search_type: ["similarity"],
+              event_id: search.id,
+            });
+          }}
+          setSearchFilter={setSearchFilter}
+          onUpdateFilter={setSearchFilter}
+          loadMore={loadMore}
+          hasMore={!isReachingEnd}
         />
-      );
-    }
-  } else {
-    return (
-      <SearchView
-        search={search}
-        searchTerm={searchTerm}
-        searchFilter={searchFilter}
-        searchResults={searchResults}
-        isLoading={(isLoadingInitialData || isLoadingMore) ?? true}
-        setSearch={setSearch}
-        setSimilaritySearch={(search) => setSearch(`similarity:${search.id}`)}
-        onUpdateFilter={setSearchFilter}
-        onOpenSearch={onOpenSearch}
-        loadMore={loadMore}
-        hasMore={!isReachingEnd}
-      />
-    );
-  }
+      )}
+    </>
+  );
 }

web/src/types/search.ts

@@ -29,6 +29,7 @@ export type SearchResult = {
 };
 
 export type SearchFilter = {
+  query?: string;
   cameras?: string[];
   labels?: string[];
   subLabels?: string[];
@@ -54,4 +55,11 @@ export type SearchQueryParams = {
   page?: number;
 };
 
-export type SearchQuery = [string, SearchQueryParams] | null;
+export type SearchQuery = [string, SearchQueryParams] | null;
+export type FilterType = Exclude<keyof SearchFilter, "query">;
+
+export type SavedSearchQuery = {
+  name: string;
+  search: string;
+  filter: SearchFilter | undefined;
+};

web/src/utils/dateUtil.ts

@@ -285,6 +285,11 @@ export function endOfHourOrCurrentTime(timestamp: number) {
   return Math.min(timestamp, now.getTime() / 1000);
 }
 
+export function getBeginningOfDayTimestamp(date: Date) {
+  date.setHours(0, 0, 0, 0);
+  return date.getTime() / 1000;
+}
+
 export function getEndOfDayTimestamp(date: Date) {
   date.setHours(23, 59, 59, 999);
   return date.getTime() / 1000;
@@ -296,3 +301,75 @@ export function isCurrentHour(timestamp: number) {
 
   return timestamp > now.getTime() / 1000;
 }
+
+export const convertLocalDateToTimestamp = (dateString: string): number => {
+  // Ensure the date string is in the correct format (8 digits)
+  if (!/^\d{8}$/.test(dateString)) {
+    return 0;
+  }
+
+  // Determine the local date format
+  const format = new Intl.DateTimeFormat()
+    .formatToParts(new Date())
+    .reduce((acc, part) => {
+      if (part.type === "day") acc.push("D");
+      if (part.type === "month") acc.push("M");
+      if (part.type === "year") acc.push("Y");
+      return acc;
+    }, [] as string[])
+    .join("");
+
+  let day: string, month: string, year: string;
+
+  // Parse the date string according to the detected format
+  switch (format) {
+    case "DMY":
+      [day, month, year] = [
+        dateString.slice(0, 2),
+        dateString.slice(2, 4),
+        dateString.slice(4),
+      ];
+      break;
+    case "MDY":
+      [month, day, year] = [
+        dateString.slice(0, 2),
+        dateString.slice(2, 4),
+        dateString.slice(4),
+      ];
+      break;
+    case "YMD":
+      [year, month, day] = [
+        dateString.slice(0, 2),
+        dateString.slice(2, 4),
+        dateString.slice(4),
+      ];
+      break;
+    default:
+      return 0;
+  }
+
+  // Create a Date object based on the local timezone
+  const localDate = new Date(`${year}-${month}-${day}T00:00:00`);
+
+  // Check if the date is valid
+  if (isNaN(localDate.getTime())) {
+    return 0;
+  }
+
+  // Convert local date to UTC timestamp
+  const timestamp = localDate.getTime();
+
+  return timestamp;
+};
+
+export function getIntlDateFormat() {
+  return new Intl.DateTimeFormat()
+    .formatToParts(new Date())
+    .reduce((acc, part) => {
+      if (part.type === "day") acc.push("DD");
+      if (part.type === "month") acc.push("MM");
+      if (part.type === "year") acc.push("YYYY");
+      return acc;
+    }, [] as string[])
+    .join("");
+}

web/src/views/search/SearchView.tsx

@@ -3,7 +3,6 @@ import SearchFilterGroup from "@/components/filter/SearchFilterGroup";
 import ActivityIndicator from "@/components/indicators/activity-indicator";
 import Chip from "@/components/indicators/Chip";
 import SearchDetailDialog from "@/components/overlay/detail/SearchDetailDialog";
-import { Input } from "@/components/ui/input";
 import { Toaster } from "@/components/ui/sonner";
 import {
   Tooltip,
@@ -12,16 +11,17 @@ import {
 } from "@/components/ui/tooltip";
 import { cn } from "@/lib/utils";
 import { FrigateConfig } from "@/types/frigateConfig";
-import { SearchFilter, SearchResult } from "@/types/search";
+import { SearchFilter, SearchResult, SearchSource } from "@/types/search";
 import { useCallback, useEffect, useMemo, useRef, useState } from "react";
 import { isMobileOnly } from "react-device-detect";
-import { LuImage, LuSearchX, LuText, LuXCircle } from "react-icons/lu";
+import { LuImage, LuSearchX, LuText } from "react-icons/lu";
 import useSWR from "swr";
 import ExploreView from "../explore/ExploreView";
 import useKeyboardListener, {
   KeyModifiers,
 } from "@/hooks/use-keyboard-listener";
 import scrollIntoView from "scroll-into-view-if-needed";
+import InputWithTags from "@/components/input/InputWithTags";
 
 type SearchViewProps = {
   search: string;
@@ -31,8 +31,8 @@ type SearchViewProps = {
   isLoading: boolean;
   setSearch: (search: string) => void;
   setSimilaritySearch: (search: SearchResult) => void;
+  setSearchFilter: (filter: SearchFilter) => void;
   onUpdateFilter: (filter: SearchFilter) => void;
-  onOpenSearch: (item: SearchResult) => void;
   loadMore: () => void;
   hasMore: boolean;
 };
@@ -44,6 +44,7 @@ export default function SearchView({
   isLoading,
   setSearch,
   setSimilaritySearch,
+  setSearchFilter,
   onUpdateFilter,
   loadMore,
   hasMore,
@@ -52,6 +53,69 @@ export default function SearchView({
     revalidateOnFocus: false,
   });
 
+  // suggestions values
+
+  const allLabels = useMemo<string[]>(() => {
+    if (!config) {
+      return [];
+    }
+
+    const labels = new Set<string>();
+    const cameras = searchFilter?.cameras || Object.keys(config.cameras);
+
+    cameras.forEach((camera) => {
+      if (camera == "birdseye") {
+        return;
+      }
+      const cameraConfig = config.cameras[camera];
+      cameraConfig.objects.track.forEach((label) => {
+        labels.add(label);
+      });
+
+      if (cameraConfig.audio.enabled_in_config) {
+        cameraConfig.audio.listen.forEach((label) => {
+          labels.add(label);
+        });
+      }
+    });
+
+    return [...labels].sort();
+  }, [config, searchFilter]);
+
+  const { data: allSubLabels } = useSWR("sub_labels");
+
+  const allZones = useMemo<string[]>(() => {
+    if (!config) {
+      return [];
+    }
+
+    const zones = new Set<string>();
+    const cameras = searchFilter?.cameras || Object.keys(config.cameras);
+
+    cameras.forEach((camera) => {
+      if (camera == "birdseye") {
+        return;
+      }
+      const cameraConfig = config.cameras[camera];
+      Object.entries(cameraConfig.zones).map(([name, _]) => {
+        zones.add(name);
+      });
+    });
+
+    return [...zones].sort();
+  }, [config, searchFilter]);
+
+  const suggestionsValues = useMemo(
+    () => ({
+      cameras: Object.keys(config?.cameras || {}),
+      labels: Object.values(allLabels || {}),
+      zones: Object.values(allZones || {}),
+      sub_labels: allSubLabels,
+      search_type: ["thumbnail", "description"] as SearchSource[],
+    }),
+    [config, allLabels, allZones, allSubLabels],
+  );
+
   // remove duplicate event ids
 
   const uniqueResults = useMemo(() => {
@@ -192,7 +256,7 @@ export default function SearchView({
 
       <div
         className={cn(
-          "flex flex-col items-start space-y-2 pl-2 pr-2 md:mb-2 md:pl-3 lg:h-10 lg:flex-row lg:items-center lg:space-y-0",
+          "flex flex-col items-start space-y-2 pl-2 pr-2 md:mb-2 md:pl-3 lg:relative lg:h-10 lg:flex-row lg:items-center lg:space-y-0",
           config?.semantic_search?.enabled
             ? "justify-between"
             : "justify-center",
@@ -200,24 +264,14 @@ export default function SearchView({
         )}
       >
         {config?.semantic_search?.enabled && (
-          <div
-            className={cn(
-              "relative w-full",
-              hasExistingSearch ? "lg:mr-3 lg:w-1/3" : "lg:ml-[25%] lg:w-1/2",
-            )}
-          >
-            <Input
-              className="text-md w-full bg-muted pr-10"
-              placeholder={"Search for a tracked object..."}
-              value={search}
-              onChange={(e) => setSearch(e.target.value)}
+          <div className={cn("z-[41] w-full lg:absolute lg:top-0 lg:w-1/3")}>
+            <InputWithTags
+              filters={searchFilter ?? {}}
+              setFilters={setSearchFilter}
+              search={search}
+              setSearch={setSearch}
+              allSuggestions={suggestionsValues}
             />
-            {search && (
-              <LuXCircle
-                className="absolute right-2 top-1/2 h-5 w-5 -translate-y-1/2 cursor-pointer text-muted-foreground hover:text-primary"
-                onClick={() => setSearch("")}
-              />
-            )}
           </div>
         )}
 
@@ -227,7 +281,6 @@ export default function SearchView({
               "w-full justify-between md:justify-start lg:justify-end",
             )}
             filter={searchFilter}
-            searchTerm={searchTerm}
             onUpdateFilter={onUpdateFilter}
           />
         )}