nik/frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2026-04-03 06:40:22 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'dev' of https://github.com/blakeblackshear/frigate into addon_config

Browse Source
This commit is contained in:
Felipe Santos 2025-02-18 19:47:42 -03:00
commit 5b99d7d9e7
270 changed files with 14706 additions and 2767 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
.cspell/frigate-dictionary.txt
View File

@ -2,6 +2,7 @@ aarch
absdiff
airockchip
Alloc
alpr
Amcrest
amdgpu
analyzeduration
@ -43,6 +44,7 @@ codeproject
colormap
colorspace
comms
cooldown
coro
ctypeslib
CUDA
@ -61,6 +63,7 @@ dsize
dtype
ECONNRESET
edgetpu
facenet
fastapi
faststart
fflags
@ -114,6 +117,8 @@ itemsize
Jellyfin
jetson
jetsons
jina
jinaai
joserfc
jsmpeg
jsonify
@ -187,6 +192,7 @@ openai
opencv
openvino
OWASP
paddleocr
paho
passwordless
popleft
@ -308,4 +314,4 @@ yolo
yolonas
yolox
zeep
zerolatency
zerolatency

6
.github/pull_request_template.md vendored
View File

@ -1,5 +1,11 @@
## Proposed change
<!--
Thank you!
If you're introducing a new feature or significantly refactoring existing functionality,
we encourage you to start a discussion first. This helps ensure your idea aligns with
Frigate's development goals.
Describe what this pull request does and how it will benefit users of Frigate.
Please describe in detail any considerations, breaking changes, etc. that are
made in this pull request.

112
.github/workflows/ci.yml vendored
View File

@ -76,36 +76,8 @@ jobs:
rpi.tags=${{ steps.setup.outputs.image-name }}-rpi
*.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64
*.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64,mode=max
jetson_jp4_build:
runs-on: ubuntu-22.04
name: Jetson Jetpack 4
steps:
- name: Check out code
uses: actions/checkout@v4
with:
persist-credentials: false
- name: Set up QEMU and Buildx
id: setup
uses: ./.github/actions/setup
with:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- name: Build and push TensorRT (Jetson, Jetpack 4)
env:
ARCH: arm64
BASE_IMAGE: timongentzsch/l4t-ubuntu20-opencv:latest
SLIM_BASE: timongentzsch/l4t-ubuntu20-opencv:latest
TRT_BASE: timongentzsch/l4t-ubuntu20-opencv:latest
uses: docker/bake-action@v6
with:
source: .
push: true
targets: tensorrt
files: docker/tensorrt/trt.hcl
set: |
tensorrt.tags=${{ steps.setup.outputs.image-name }}-tensorrt-jp4
*.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp4
*.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp4,mode=max
jetson_jp5_build:
if: false
runs-on: ubuntu-22.04
name: Jetson Jetpack 5
steps:
@ -134,6 +106,35 @@ jobs:
tensorrt.tags=${{ steps.setup.outputs.image-name }}-tensorrt-jp5
*.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp5
*.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp5,mode=max
jetson_jp6_build:
runs-on: ubuntu-22.04
name: Jetson Jetpack 6
steps:
- name: Check out code
uses: actions/checkout@v4
with:
persist-credentials: false
- name: Set up QEMU and Buildx
id: setup
uses: ./.github/actions/setup
with:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- name: Build and push TensorRT (Jetson, Jetpack 6)
env:
ARCH: arm64
BASE_IMAGE: nvcr.io/nvidia/tensorrt:23.12-py3-igpu
SLIM_BASE: nvcr.io/nvidia/tensorrt:23.12-py3-igpu
TRT_BASE: nvcr.io/nvidia/tensorrt:23.12-py3-igpu
uses: docker/bake-action@v6
with:
source: .
push: true
targets: tensorrt
files: docker/tensorrt/trt.hcl
set: |
tensorrt.tags=${{ steps.setup.outputs.image-name }}-tensorrt-jp6
*.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp6
*.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp6,mode=max
amd64_extra_builds:
runs-on: ubuntu-22.04
name: AMD64 Extra Build
@ -162,6 +163,19 @@ jobs:
tensorrt.tags=${{ steps.setup.outputs.image-name }}-tensorrt
*.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64
*.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64,mode=max
- name: AMD/ROCm general build
env:
AMDGPU: gfx
HSA_OVERRIDE: 0
uses: docker/bake-action@v6
with:
source: .
push: true
targets: rocm
files: docker/rocm/rocm.hcl
set: |
rocm.tags=${{ steps.setup.outputs.image-name }}-rocm
*.cache-from=type=gha
arm64_extra_builds:
runs-on: ubuntu-22.04
name: ARM Extra Build
@ -187,46 +201,6 @@ jobs:
set: |
rk.tags=${{ steps.setup.outputs.image-name }}-rk
*.cache-from=type=gha
combined_extra_builds:
runs-on: ubuntu-22.04
name: Combined Extra Builds
needs:
- amd64_build
- arm64_build
steps:
- name: Check out code
uses: actions/checkout@v4
with:
persist-credentials: false
- name: Set up QEMU and Buildx
id: setup
uses: ./.github/actions/setup
with:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- name: Build and push Hailo-8l build
uses: docker/bake-action@v6
with:
source: .
push: true
targets: h8l
files: docker/hailo8l/h8l.hcl
set: |
h8l.tags=${{ steps.setup.outputs.image-name }}-h8l
*.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-h8l
*.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-h8l,mode=max
- name: AMD/ROCm general build
env:
AMDGPU: gfx
HSA_OVERRIDE: 0
uses: docker/bake-action@v6
with:
source: .
push: true
targets: rocm
files: docker/rocm/rocm.hcl
set: |
rocm.tags=${{ steps.setup.outputs.image-name }}-rocm
*.cache-from=type=gha
# The majority of users running arm64 are rpi users, so the rpi
# build should be the primary arm64 image
assemble_default_build:

3
.github/workflows/pull_request.yml vendored
View File

@ -4,9 +4,10 @@ on:
pull_request:
paths-ignore:
- "docs/**"
- ".github/**"
env:
DEFAULT_PYTHON: 3.9
DEFAULT_PYTHON: 3.11
jobs:
build_devcontainer:

4
.github/workflows/release.yml vendored
View File

@ -39,14 +39,14 @@ jobs:
STABLE_TAG=${BASE}:stable
PULL_TAG=${BASE}:${BUILD_TAG}
docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG} docker://${VERSION_TAG}
for variant in standard-arm64 tensorrt tensorrt-jp4 tensorrt-jp5 rk h8l rocm; do
for variant in standard-arm64 tensorrt tensorrt-jp5 tensorrt-jp6 rk h8l rocm; do
docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG}-${variant} docker://${VERSION_TAG}-${variant}
done
# stable tag
if [[ "${BUILD_TYPE}" == "stable" ]]; then
docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG} docker://${STABLE_TAG}
for variant in standard-arm64 tensorrt tensorrt-jp4 tensorrt-jp5 rk h8l rocm; do
for variant in standard-arm64 tensorrt tensorrt-jp5 tensorrt-jp6 rk h8l rocm; do
docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG}-${variant} docker://${STABLE_TAG}-${variant}
done
fi

2
Makefile
View File

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

2
docker-compose.yml
View File

@ -38,4 +38,4 @@ services:
container_name: mqtt
image: eclipse-mosquitto:1.6
ports:
- "1883:1883"
- "1883:1883"

40
docker/hailo8l/Dockerfile
View File

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

34
docker/hailo8l/h8l.hcl
View File

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

15
docker/hailo8l/h8l.mk
View File

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

19
docker/hailo8l/install_hailort.sh
View File

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

12
docker/hailo8l/requirements-wheels-h8l.txt
View File

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

3
docker/hailo8l/user_installation.sh
View File

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

41
docker/main/Dockerfile
View File

@ -3,14 +3,27 @@
# https://askubuntu.com/questions/972516/debian-frontend-environment-variable
ARG DEBIAN_FRONTEND=noninteractive
ARG BASE_IMAGE=debian:11
ARG SLIM_BASE=debian:11-slim
# Globally set pip break-system-packages option to avoid having to specify it every time
ARG PIP_BREAK_SYSTEM_PACKAGES=1
ARG BASE_IMAGE=debian:12
ARG SLIM_BASE=debian:12-slim
# A hook that allows us to inject commands right after the base images
ARG BASE_HOOK=
FROM ${BASE_IMAGE} AS base
ARG PIP_BREAK_SYSTEM_PACKAGES
FROM --platform=${BUILDPLATFORM} debian:11 AS base_host
RUN ${BASE_HOOK}
FROM --platform=${BUILDPLATFORM} debian:12 AS base_host
ARG PIP_BREAK_SYSTEM_PACKAGES
FROM ${SLIM_BASE} AS slim-base
ARG PIP_BREAK_SYSTEM_PACKAGES
RUN ${BASE_HOOK}
FROM slim-base AS wget
ARG DEBIAN_FRONTEND
@ -139,24 +152,17 @@ ARG TARGETARCH
# Use a separate container to build wheels to prevent build dependencies in final image
RUN apt-get -qq update \
&& apt-get -qq install -y \
apt-transport-https \
gnupg \
wget \
# the key fingerprint can be obtained from https://ftp-master.debian.org/keys.html
&& wget -qO- "https://keyserver.ubuntu.com/pks/lookup?op=get&search=0xA4285295FC7B1A81600062A9605C66F00D6C9793" | \
gpg --dearmor > /usr/share/keyrings/debian-archive-bullseye-stable.gpg \
&& echo "deb [signed-by=/usr/share/keyrings/debian-archive-bullseye-stable.gpg] http://deb.debian.org/debian bullseye main contrib non-free" | \
tee /etc/apt/sources.list.d/debian-bullseye-nonfree.list \
apt-transport-https wget \
&& apt-get -qq update \
&& apt-get -qq install -y \
python3.9 \
python3.9-dev \
python3.11 \
python3.11-dev \
# opencv dependencies
build-essential cmake git pkg-config libgtk-3-dev \
libavcodec-dev libavformat-dev libswscale-dev libv4l-dev \
libxvidcore-dev libx264-dev libjpeg-dev libpng-dev libtiff-dev \
gfortran openexr libatlas-base-dev libssl-dev\
libtbb2 libtbb-dev libdc1394-22-dev libopenexr-dev \
libtbbmalloc2 libtbb-dev libdc1394-dev libopenexr-dev \
libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev \
# sqlite3 dependencies
tclsh \
@ -164,8 +170,7 @@ RUN apt-get -qq update \
gcc gfortran libopenblas-dev liblapack-dev && \
rm -rf /var/lib/apt/lists/*
# Ensure python3 defaults to python3.9
RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1
RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
&& python3 get-pip.py "pip"
@ -180,6 +185,9 @@ RUN /build_pysqlite3.sh
COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
RUN pip3 wheel --wheel-dir=/wheels -r /requirements-wheels.txt
# Install HailoRT & Wheels
RUN --mount=type=bind,source=docker/main/install_hailort.sh,target=/deps/install_hailort.sh \
/deps/install_hailort.sh
# Collect deps in a single layer
FROM scratch AS deps-rootfs
@ -190,6 +198,7 @@ COPY --from=libusb-build /usr/local/lib /usr/local/lib
COPY --from=tempio /rootfs/ /
COPY --from=s6-overlay /rootfs/ /
COPY --from=models /rootfs/ /
COPY --from=wheels /rootfs/ /
COPY docker/main/rootfs/ /

12
docker/main/build_nginx.sh
View File

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

2
docker/main/build_ov_model.py
View File

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

11
docker/main/build_sqlite_vec.sh
View File

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

40
docker/main/install_deps.sh
View File

@ -11,33 +11,25 @@ apt-get -qq install --no-install-recommends -y \
lbzip2 \
procps vainfo \
unzip locales tzdata libxml2 xz-utils \
python3.9 \
python3.11 \
python3-pip \
curl \
lsof \
jq \
nethogs
nethogs \
libgl1 \
libglib2.0-0 \
libusb-1.0.0
# ensure python3 defaults to python3.9
update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1
update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
mkdir -p -m 600 /root/.gnupg
# add coral repo
curl -fsSLo - https://packages.cloud.google.com/apt/doc/apt-key.gpg | \
gpg --dearmor -o /etc/apt/trusted.gpg.d/google-cloud-packages-archive-keyring.gpg
echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | tee /etc/apt/sources.list.d/coral-edgetpu.list
echo "libedgetpu1-max libedgetpu/accepted-eula select true" | debconf-set-selections
# enable non-free repo in Debian
if grep -q "Debian" /etc/issue; then
sed -i -e's/ main/ main contrib non-free/g' /etc/apt/sources.list
fi
# coral drivers
apt-get -qq update
apt-get -qq install --no-install-recommends --no-install-suggests -y \
libedgetpu1-max python3-tflite-runtime python3-pycoral
# install coral runtime
wget -q -O /tmp/libedgetpu1-max.deb "https://github.com/feranick/libedgetpu/releases/download/16.0TF2.17.1-1/libedgetpu1-max_16.0tf2.17.1-1.bookworm_${TARGETARCH}.deb"
unset DEBIAN_FRONTEND
yes | dpkg -i /tmp/libedgetpu1-max.deb && export DEBIAN_FRONTEND=noninteractive
rm /tmp/libedgetpu1-max.deb
# btbn-ffmpeg -> amd64
if [[ "${TARGETARCH}" == "amd64" ]]; then
@ -65,23 +57,15 @@ fi
# arch specific packages
if [[ "${TARGETARCH}" == "amd64" ]]; then
# use debian bookworm for amd / intel-i965 driver packages
echo 'deb https://deb.debian.org/debian bookworm main contrib non-free' >/etc/apt/sources.list.d/debian-bookworm.list
apt-get -qq update
# install amd / intel-i965 driver packages
apt-get -qq install --no-install-recommends --no-install-suggests -y \
i965-va-driver intel-gpu-tools onevpl-tools \
libva-drm2 \
mesa-va-drivers radeontop
# something about this dependency requires it to be installed in a separate call rather than in the line above
apt-get -qq install --no-install-recommends --no-install-suggests -y \
i965-va-driver-shaders
# intel packages use zst compression so we need to update dpkg
apt-get install -y dpkg
rm -f /etc/apt/sources.list.d/debian-bookworm.list
# use intel apt intel packages
wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg
echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | tee /etc/apt/sources.list.d/intel-gpu-jammy.list

14
docker/main/install_hailort.sh Executable file
View File

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

41
docker/main/requirements-wheels.txt
View File

@ -1,3 +1,4 @@
aiofiles == 24.1.*
click == 8.1.*
# FastAPI
aiohttp == 3.11.2
@ -10,10 +11,10 @@ imutils == 0.5.*
joserfc == 1.0.*
pathvalidate == 3.2.*
markupsafe == 2.1.*
python-multipart == 0.0.12
# General
mypy == 1.6.1
numpy == 1.26.*
onvif_zeep == 0.2.12
opencv-python-headless == 4.9.0.*
onvif-zeep-async == 3.1.*
paho-mqtt == 2.1.*
pandas == 2.2.*
peewee == 3.17.*
@ -27,15 +28,19 @@ ruamel.yaml == 0.18.*
tzlocal == 5.2
requests == 2.32.*
types-requests == 2.32.*
scipy == 1.13.*
norfair == 2.2.*
setproctitle == 1.3.*
ws4py == 0.5.*
unidecode == 1.3.*
# Image Manipulation
numpy == 1.26.*
opencv-python-headless == 4.10.0.*
opencv-contrib-python == 4.9.0.*
scipy == 1.14.*
# OpenVino & ONNX
openvino == 2024.3.*
onnxruntime-openvino == 1.19.* ; platform_machine == 'x86_64'
onnxruntime == 1.19.* ; platform_machine == 'aarch64'
openvino == 2024.4.*
onnxruntime-openvino == 1.20.* ; platform_machine == 'x86_64'
onnxruntime == 1.20.* ; platform_machine == 'aarch64'
# Embeddings
transformers == 4.45.*
# Generative AI
@ -45,3 +50,25 @@ openai == 1.51.*
# push notifications
py-vapid == 1.9.*
pywebpush == 2.0.*
# alpr
pyclipper == 1.3.*
shapely == 2.0.*
Levenshtein==0.26.*
prometheus-client == 0.21.*
# HailoRT Wheels
appdirs==1.4.*
argcomplete==2.0.*
contextlib2==0.6.*
distlib==0.3.*
filelock==3.8.*
future==0.18.*
importlib-metadata==5.1.*
importlib-resources==5.1.*
netaddr==0.8.*
netifaces==0.10.*
verboselogs==1.7.*
virtualenv==20.17.*
prometheus-client == 0.21.*
# TFLite
tflite_runtime @ https://github.com/feranick/TFlite-builds/releases/download/v2.17.1/tflite_runtime-2.17.1-cp311-cp311-linux_x86_64.whl; platform_machine == 'x86_64'
tflite_runtime @ https://github.com/feranick/TFlite-builds/releases/download/v2.17.1/tflite_runtime-2.17.1-cp311-cp311-linux_aarch64.whl; platform_machine == 'aarch64'

2
docker/main/requirements.txt
View File

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

25
docker/main/rootfs/usr/local/go2rtc/create_config.py
View File

@ -66,29 +66,32 @@ elif go2rtc_config["log"].get("format") is None:
go2rtc_config["log"]["format"] = "text"
# ensure there is a default webrtc config
if not go2rtc_config.get("webrtc"):
if go2rtc_config.get("webrtc") is None:
go2rtc_config["webrtc"] = {}
# go2rtc should listen on 8555 tcp & udp by default
if not go2rtc_config["webrtc"].get("listen"):
if go2rtc_config["webrtc"].get("listen") is None:
go2rtc_config["webrtc"]["listen"] = ":8555"
if not go2rtc_config["webrtc"].get("candidates", []):
if go2rtc_config["webrtc"].get("candidates") is None:
default_candidates = []
# use internal candidate if it was discovered when running through the add-on
internal_candidate = os.environ.get(
"FRIGATE_GO2RTC_WEBRTC_CANDIDATE_INTERNAL", None
)
internal_candidate = os.environ.get("FRIGATE_GO2RTC_WEBRTC_CANDIDATE_INTERNAL")
if internal_candidate is not None:
default_candidates.append(internal_candidate)
# should set default stun server so webrtc can work
default_candidates.append("stun:8555")
go2rtc_config["webrtc"] = {"candidates": default_candidates}
else:
print(
"[INFO] Not injecting WebRTC candidates into go2rtc config as it has been set manually",
)
go2rtc_config["webrtc"]["candidates"] = default_candidates
# This prevents WebRTC from attempting to establish a connection to the internal
# docker IPs which are not accessible from outside the container itself and just
# wastes time during negotiation. Note that this is only necessary because
# Frigate container doesn't run in host network mode.
if go2rtc_config["webrtc"].get("filter") is None:
go2rtc_config["webrtc"]["filter"] = {"candidates": []}
elif go2rtc_config["webrtc"]["filter"].get("candidates") is None:
go2rtc_config["webrtc"]["filter"]["candidates"] = []
# sets default RTSP response to be equivalent to ?video=h264,h265&audio=aac
# this means user does not need to specify audio codec when using restream

11
docker/main/rootfs/usr/local/nginx/conf/nginx.conf
View File

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

20
docker/rockchip/COCO/coco_subset_20.txt Normal file
View File

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

BIN
docker/rockchip/COCO/subset/000000005001.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 207 KiB

BIN
docker/rockchip/COCO/subset/000000038829.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 209 KiB

BIN
docker/rockchip/COCO/subset/000000052891.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 150 KiB

BIN
docker/rockchip/COCO/subset/000000075612.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 102 KiB

BIN
docker/rockchip/COCO/subset/000000098261.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 14 KiB

BIN
docker/rockchip/COCO/subset/000000181542.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 201 KiB

BIN
docker/rockchip/COCO/subset/000000215245.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 233 KiB

BIN
docker/rockchip/COCO/subset/000000277005.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 242 KiB

BIN
docker/rockchip/COCO/subset/000000288685.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 230 KiB

BIN
docker/rockchip/COCO/subset/000000301421.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 80 KiB

BIN
docker/rockchip/COCO/subset/000000334371.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 136 KiB

BIN
docker/rockchip/COCO/subset/000000348481.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 113 KiB

BIN
docker/rockchip/COCO/subset/000000373353.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 281 KiB

BIN
docker/rockchip/COCO/subset/000000397681.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 272 KiB

BIN
docker/rockchip/COCO/subset/000000414673.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 152 KiB

BIN
docker/rockchip/COCO/subset/000000419312.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 166 KiB

BIN
docker/rockchip/COCO/subset/000000465822.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 109 KiB

BIN
docker/rockchip/COCO/subset/000000475732.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 103 KiB

BIN
docker/rockchip/COCO/subset/000000559707.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 203 KiB

BIN
docker/rockchip/COCO/subset/000000574315.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 110 KiB

8
docker/rockchip/Dockerfile
View File

@ -7,18 +7,22 @@ FROM wheels as rk-wheels
COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
COPY docker/rockchip/requirements-wheels-rk.txt /requirements-wheels-rk.txt
RUN sed -i "/https:\/\//d" /requirements-wheels.txt
RUN sed -i "/onnxruntime/d" /requirements-wheels.txt
RUN pip3 wheel --wheel-dir=/rk-wheels -c /requirements-wheels.txt -r /requirements-wheels-rk.txt
RUN rm -rf /rk-wheels/opencv_python-*
FROM deps AS rk-frigate
ARG TARGETARCH
RUN --mount=type=bind,from=rk-wheels,source=/rk-wheels,target=/deps/rk-wheels \
pip3 install -U /deps/rk-wheels/*.whl
pip3 install --no-deps -U /deps/rk-wheels/*.whl
WORKDIR /opt/frigate/
COPY --from=rootfs / /
COPY docker/rockchip/COCO /COCO
COPY docker/rockchip/conv2rknn.py /opt/conv2rknn.py
ADD https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.0.0/librknnrt.so /usr/lib/
ADD https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.0/librknnrt.so /usr/lib/
RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffmpeg
RUN rm -rf /usr/lib/btbn-ffmpeg/bin/ffprobe

82
docker/rockchip/conv2rknn.py Normal file
View File

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

3
docker/rockchip/requirements-wheels-rk.txt
View File

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

19
docker/rocm/Dockerfile
View File

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

5
docker/rpi/install_deps.sh
View File

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

5
docker/tensorrt/Dockerfile.amd64
View File

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

29
docker/tensorrt/Dockerfile.arm64
View File

@ -7,20 +7,25 @@ ARG BASE_IMAGE
FROM ${BASE_IMAGE} AS build-wheels
ARG DEBIAN_FRONTEND
# Add deadsnakes PPA for python3.11
RUN apt-get -qq update && \
apt-get -qq install -y --no-install-recommends \
software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa
# Use a separate container to build wheels to prevent build dependencies in final image
RUN apt-get -qq update \
&& apt-get -qq install -y --no-install-recommends \
python3.9 python3.9-dev \
python3.11 python3.11-dev \
wget build-essential cmake git \
&& rm -rf /var/lib/apt/lists/*
# Ensure python3 defaults to python3.9
RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1
# Ensure python3 defaults to python3.11
RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
&& python3 get-pip.py "pip"
FROM build-wheels AS trt-wheels
ARG DEBIAN_FRONTEND
ARG TARGETARCH
@ -41,11 +46,12 @@ RUN --mount=type=bind,source=docker/tensorrt/detector/build_python_tensorrt.sh,t
&& TENSORRT_VER=$(cat /etc/TENSORRT_VER) /deps/build_python_tensorrt.sh
COPY docker/tensorrt/requirements-arm64.txt /requirements-tensorrt.txt
ADD https://nvidia.box.com/shared/static/9aemm4grzbbkfaesg5l7fplgjtmswhj8.whl /tmp/onnxruntime_gpu-1.15.1-cp39-cp39-linux_aarch64.whl
# See https://elinux.org/Jetson_Zoo#ONNX_Runtime
ADD https://nvidia.box.com/shared/static/9yvw05k6u343qfnkhdv2x6xhygze0aq1.whl /tmp/onnxruntime_gpu-1.19.0-cp311-cp311-linux_aarch64.whl
RUN pip3 uninstall -y onnxruntime-openvino \
&& pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt \
&& pip3 install --no-deps /tmp/onnxruntime_gpu-1.15.1-cp39-cp39-linux_aarch64.whl
&& pip3 install --no-deps /tmp/onnxruntime_gpu-1.19.0-cp311-cp311-linux_aarch64.whl
FROM build-wheels AS trt-model-wheels
ARG DEBIAN_FRONTEND
@ -67,12 +73,18 @@ RUN --mount=type=bind,source=docker/tensorrt/build_jetson_ffmpeg.sh,target=/deps
# Frigate w/ TensorRT for NVIDIA Jetson platforms
FROM tensorrt-base AS frigate-tensorrt
RUN apt-get update \
&& apt-get install -y python-is-python3 libprotobuf17 \
&& apt-get install -y python-is-python3 libprotobuf23 \
&& rm -rf /var/lib/apt/lists/*
RUN rm -rf /usr/lib/btbn-ffmpeg/
COPY --from=jetson-ffmpeg /rootfs /
# ffmpeg runtime dependencies
RUN apt-get -qq update \
&& apt-get -qq install -y --no-install-recommends \
libx264-163 libx265-199 libegl1 \
&& rm -rf /var/lib/apt/lists/*
COPY --from=trt-wheels /etc/TENSORRT_VER /etc/TENSORRT_VER
RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
--mount=type=bind,from=trt-model-wheels,source=/trt-model-wheels,target=/deps/trt-model-wheels \
@ -81,3 +93,6 @@ RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels
WORKDIR /opt/frigate/
COPY --from=rootfs / /
# Fixes "Error importing detector runtime: /usr/lib/aarch64-linux-gnu/libstdc++.so.6: cannot allocate memory in static TLS block"
ENV LD_PRELOAD /usr/lib/aarch64-linux-gnu/libstdc++.so.6

2
docker/tensorrt/Dockerfile.base
View File

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

17
docker/tensorrt/build_jetson_ffmpeg.sh
View File

@ -14,14 +14,27 @@ apt-get -qq install -y --no-install-recommends libx264-dev libx265-dev
pushd /tmp
# Install libnvmpi to enable nvmpi decoders (h264_nvmpi, hevc_nvmpi)
if [ -e /usr/local/cuda-10.2 ]; then
if [ -e /usr/local/cuda-12 ]; then
# assume Jetpack 6.2
apt-key adv --fetch-key https://repo.download.nvidia.com/jetson/jetson-ota-public.asc
echo "deb https://repo.download.nvidia.com/jetson/common r36.4 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list
echo "deb https://repo.download.nvidia.com/jetson/t234 r36.4 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list
echo "deb https://repo.download.nvidia.com/jetson/ffmpeg r36.4 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list
mkdir -p /opt/nvidia/l4t-packages/
touch /opt/nvidia/l4t-packages/.nv-l4t-disable-boot-fw-update-in-preinstall
apt-get update
apt-get -qq install -y --no-install-recommends -o Dpkg::Options::="--force-confold" nvidia-l4t-jetson-multimedia-api
elif [ -e /usr/local/cuda-10.2 ]; then
# assume Jetpack 4.X
wget -q https://developer.nvidia.com/embedded/L4T/r32_Release_v5.0/T186/Jetson_Multimedia_API_R32.5.0_aarch64.tbz2 -O jetson_multimedia_api.tbz2
tar xaf jetson_multimedia_api.tbz2 -C / && rm jetson_multimedia_api.tbz2
else
# assume Jetpack 5.X
wget -q https://developer.nvidia.com/downloads/embedded/l4t/r35_release_v3.1/release/jetson_multimedia_api_r35.3.1_aarch64.tbz2 -O jetson_multimedia_api.tbz2
tar xaf jetson_multimedia_api.tbz2 -C / && rm jetson_multimedia_api.tbz2
fi
tar xaf jetson_multimedia_api.tbz2 -C / && rm jetson_multimedia_api.tbz2
wget -q https://github.com/AndBobsYourUncle/jetson-ffmpeg/archive/9c17b09.zip -O jetson-ffmpeg.zip
unzip jetson-ffmpeg.zip && rm jetson-ffmpeg.zip && mv jetson-ffmpeg-* jetson-ffmpeg && cd jetson-ffmpeg

14
docker/tensorrt/detector/build_python_tensorrt.sh
View File

@ -6,23 +6,23 @@ mkdir -p /trt-wheels
if [[ "${TARGETARCH}" == "arm64" ]]; then
# NVIDIA supplies python-tensorrt for python3.8, but frigate uses python3.9,
# NVIDIA supplies python-tensorrt for python3.10, but frigate uses python3.11,
# so we must build python-tensorrt ourselves.
# Get python-tensorrt source
mkdir /workspace
mkdir -p /workspace
cd /workspace
git clone -b ${TENSORRT_VER} https://github.com/NVIDIA/TensorRT.git --depth=1
git clone -b release/8.6 https://github.com/NVIDIA/TensorRT.git --depth=1
# Collect dependencies
EXT_PATH=/workspace/external && mkdir -p $EXT_PATH
pip3 install pybind11 && ln -s /usr/local/lib/python3.9/dist-packages/pybind11 $EXT_PATH/pybind11
ln -s /usr/include/python3.9 $EXT_PATH/python3.9
pip3 install pybind11 && ln -s /usr/local/lib/python3.11/dist-packages/pybind11 $EXT_PATH/pybind11
ln -s /usr/include/python3.11 $EXT_PATH/python3.11
ln -s /usr/include/aarch64-linux-gnu/NvOnnxParser.h /workspace/TensorRT/parsers/onnx/
# Build wheel
cd /workspace/TensorRT/python
EXT_PATH=$EXT_PATH PYTHON_MAJOR_VERSION=3 PYTHON_MINOR_VERSION=9 TARGET_ARCHITECTURE=aarch64 /bin/bash ./build.sh
mv build/dist/*.whl /trt-wheels/
EXT_PATH=$EXT_PATH PYTHON_MAJOR_VERSION=3 PYTHON_MINOR_VERSION=11 TARGET_ARCHITECTURE=aarch64 TENSORRT_MODULE=tensorrt /bin/bash ./build.sh
mv build/bindings_wheel/dist/*.whl /trt-wheels/
fi

12
docker/tensorrt/detector/rootfs/etc/ld.so.conf.d/cuda_tensorrt.conf
View File

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

10
docker/tensorrt/requirements-amd64.txt
View File

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

2
docker/tensorrt/requirements-arm64.txt
View File

@ -1 +1 @@
cuda-python == 11.7; platform_machine == 'aarch64'
cuda-python == 12.6.*; platform_machine == 'aarch64'

13
docker/tensorrt/trt.hcl
View File

@ -13,13 +13,24 @@ variable "TRT_BASE" {
variable "COMPUTE_LEVEL" {
default = ""
}
variable "BASE_HOOK" {
# Ensure an up-to-date python 3.11 is available in tensorrt/jetson image
default = <<EOT
if grep -iq \"ubuntu\" /etc/os-release; then
apt-get update &&
apt-get install -y software-properties-common &&
add-apt-repository ppa:deadsnakes/ppa;
fi
EOT
}
target "_build_args" {
args = {
BASE_IMAGE = BASE_IMAGE,
SLIM_BASE = SLIM_BASE,
TRT_BASE = TRT_BASE,
COMPUTE_LEVEL = COMPUTE_LEVEL
COMPUTE_LEVEL = COMPUTE_LEVEL,
BASE_HOOK = BASE_HOOK
}
platforms = ["linux/${ARCH}"]
}

24
docker/tensorrt/trt.mk
View File

@ -1,41 +1,41 @@
BOARDS += trt
JETPACK4_BASE ?= timongentzsch/l4t-ubuntu20-opencv:latest # L4T 32.7.1 JetPack 4.6.1
JETPACK5_BASE ?= nvcr.io/nvidia/l4t-tensorrt:r8.5.2-runtime # L4T 35.3.1 JetPack 5.1.1
JETPACK6_BASE ?= nvcr.io/nvidia/tensorrt:23.12-py3-igpu
X86_DGPU_ARGS := ARCH=amd64 COMPUTE_LEVEL="50 60 70 80 90"
JETPACK4_ARGS := ARCH=arm64 BASE_IMAGE=$(JETPACK4_BASE) SLIM_BASE=$(JETPACK4_BASE) TRT_BASE=$(JETPACK4_BASE)
JETPACK5_ARGS := ARCH=arm64 BASE_IMAGE=$(JETPACK5_BASE) SLIM_BASE=$(JETPACK5_BASE) TRT_BASE=$(JETPACK5_BASE)
JETPACK6_ARGS := ARCH=arm64 BASE_IMAGE=$(JETPACK6_BASE) SLIM_BASE=$(JETPACK6_BASE) TRT_BASE=$(JETPACK6_BASE)
local-trt: version
$(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 --file=docker/tensorrt/trt.hcl tensorrt \
--set tensorrt.tags=frigate:latest-tensorrt-jp4 \
--load
local-trt-jp5: version
$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
--set tensorrt.tags=frigate:latest-tensorrt-jp5 \
--load
local-trt-jp6: version
$(JETPACK6_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
--set tensorrt.tags=frigate:latest-tensorrt-jp6 \
--load
build-trt:
$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt
$(JETPACK4_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp4
$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp5
$(JETPACK6_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp6
push-trt: build-trt
$(X86_DGPU_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt \
--push
$(JETPACK4_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp4 \
--push
$(JETPACK5_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp5 \
--push
$(JETPACK6_ARGS) docker buildx bake --file=docker/tensorrt/trt.hcl tensorrt \
--set tensorrt.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-tensorrt-jp6 \
--push

22
docs/docs/configuration/advanced.md
View File

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

5
docs/docs/configuration/authentication.md
View File

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

4
docs/docs/configuration/autotracking.md
View File

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

44
docs/docs/configuration/camera_specific.md
View File

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

55
docs/docs/configuration/face_recognition.md Normal file
View File

@ -0,0 +1,55 @@
---
id: face_recognition
title: Face Recognition
---
Face recognition allows people to be assigned names and when their face is recognized Frigate will assign the person's name as a sub label. This information is included in the UI, filters, as well as in notifications.
Frigate has support for CV2 Local Binary Pattern Face Recognizer to recognize faces, which runs locally. A lightweight face landmark detection model is also used to align faces before running them through the face recognizer.
## Configuration
Face recognition is disabled by default and requires semantic search to be enabled, face recognition must be enabled in your config file before it can be used. Semantic Search and face recognition are global configuration settings.
```yaml
face_recognition:
enabled: true
```
## Dataset
The number of images needed for a sufficient training set for face recognition varies depending on several factors:
- Diversity of the dataset: A dataset with diverse images, including variations in lighting, pose, and facial expressions, will require fewer images per person than a less diverse dataset.
- Desired accuracy: The higher the desired accuracy, the more images are typically needed.
However, here are some general guidelines:
- Minimum: For basic face recognition tasks, a minimum of 10-20 images per person is often recommended.
- Recommended: For more robust and accurate systems, 30-50 images per person is a good starting point.
- Ideal: For optimal performance, especially in challenging conditions, 100 or more images per person can be beneficial.
## Creating a Robust Training Set
The accuracy of face recognition is heavily dependent on the quality of data given to it for training. It is recommended to build the face training library in phases.
:::tip
When choosing images to include in the face training set it is recommended to always follow these recommendations:
- If it is difficult to make out details in a persons face it will not be helpful in training.
- Avoid images with under/over-exposure.
- Avoid blurry / pixelated images.
- Be careful when uploading images of people when they are wearing clothing that covers a lot of their face as this may confuse the training.
- Do not upload too many images at the same time, it is recommended to train 4-6 images for each person each day so it is easier to know if the previously added images helped or hurt performance.
:::
### Step 1 - Building a Strong Foundation
When first enabling face recognition it is important to build a foundation of strong images. It is recommended to start by uploading 1-2 photos taken by a smartphone for each person. It is important that the person's face in the photo is straight-on and not turned which will ensure a good starting point.
Then it is recommended to use the `Face Library` tab in Frigate to select and train images for each person as they are detected. When building a strong foundation it is strongly recommended to only train on images that are straight-on. Ignore images from cameras that recognize faces from an angle. Once a person starts to be consistently recognized correctly on images that are straight-on, it is time to move on to the next step.
### Step 2 - Expanding The Dataset
Once straight-on images are performing well, start choosing slightly off-angle images to include for training. It is important to still choose images where enough face detail is visible to recognize someone.

16
docs/docs/configuration/hardware_acceleration.md
View File

@ -175,6 +175,16 @@ For more information on the various values across different distributions, see h
Depending on your OS and kernel configuration, you may need to change the `/proc/sys/kernel/perf_event_paranoid` kernel tunable. You can test the change by running `sudo sh -c 'echo 2 >/proc/sys/kernel/perf_event_paranoid'` which will persist until a reboot. Make it permanent by running `sudo sh -c 'echo kernel.perf_event_paranoid=2 >> /etc/sysctl.d/local.conf'`
#### Stats for SR-IOV devices
When using virtualized GPUs via SR-IOV, additional args are needed for GPU stats to function. This can be enabled with the following config:
```yaml
telemetry:
stats:
sriov: True
```
## AMD/ATI GPUs (Radeon HD 2000 and newer GPUs) via libva-mesa-driver
VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams.
@ -285,10 +295,8 @@ These instructions were originally based on the [Jellyfin documentation](https:/
## NVIDIA Jetson (Orin AGX, Orin NX, Orin Nano\*, Xavier AGX, Xavier NX, TX2, TX1, Nano)
A separate set of docker images is available that is based on Jetpack/L4T. They come with an `ffmpeg` build
with codecs that use the Jetson's dedicated media engine. If your Jetson host is running Jetpack 4.6, use the
`stable-tensorrt-jp4` tagged image, or if your Jetson host is running Jetpack 5.0+, use the `stable-tensorrt-jp5`
tagged image. Note that the Orin Nano has no video encoder, so frigate will use software encoding on this platform,
but the image will still allow hardware decoding and tensorrt object detection.
with codecs that use the Jetson's dedicated media engine. If your Jetson host is running Jetpack 5.0+ use the `stable-tensorrt-jp5`
tagged image, or if your Jetson host is running Jetpack 6.0+ use the `stable-tensorrt-jp6` tagged image. Note that the Orin Nano has no video encoder, so frigate will use software encoding on this platform, but the image will still allow hardware decoding and tensorrt object detection.
You will need to use the image with the nvidia container runtime:

88
docs/docs/configuration/license_plate_recognition.md Normal file
View File

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

80
docs/docs/configuration/live.md
View File

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

99
docs/docs/configuration/metrics.md Normal file
View File

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

29
docs/docs/configuration/notifications.md
View File

@ -11,14 +11,37 @@ Frigate offers native notifications using the [WebPush Protocol](https://web.dev
In order to use notifications the following requirements must be met:
- Frigate must be accessed via a secure https connection
- Frigate must be accessed via a secure `https` connection ([see the authorization docs](/configuration/authentication)).
- A supported browser must be used. Currently Chrome, Firefox, and Safari are known to be supported.
- In order for notifications to be usable externally, Frigate must be accessible externally
- In order for notifications to be usable externally, Frigate must be accessible externally.
### Configuration
To configure notifications, go to the Frigate WebUI -> Settings -> Notifications and enable, then fill out the fields and save.
Optionally, you can change the default cooldown period for notifications through the `cooldown` parameter in your config file. This parameter can also be overridden at the camera level.
Notifications will be prevented if either:
- The global cooldown period hasn't elapsed since any camera's last notification
- The camera-specific cooldown period hasn't elapsed for the specific camera
```yaml
notifications:
enabled: True
email: "johndoe@gmail.com"
cooldown: 10 # wait 10 seconds before sending another notification from any camera
```
```yaml
cameras:
doorbell:
...
notifications:
enabled: True
cooldown: 30 # wait 30 seconds before sending another notification from the doorbell camera
```
### Registration
Once notifications are enabled, press the `Register for Notifications` button on all devices that you would like to receive notifications on. This will register the background worker. After this Frigate must be restarted and then notifications will begin to be sent.
@ -39,4 +62,4 @@ Different platforms handle notifications differently, some settings changes may
### Android
Most Android phones have battery optimization settings. To get reliable Notification delivery the browser (Chrome, Firefox) should have battery optimizations disabled. If Frigate is running as a PWA then the Frigate app should have battery optimizations disabled as well.
Most Android phones have battery optimization settings. To get reliable Notification delivery the browser (Chrome, Firefox) should have battery optimizations disabled. If Frigate is running as a PWA then the Frigate app should have battery optimizations disabled as well.

152
docs/docs/configuration/object_detectors.md
View File

@ -35,7 +35,7 @@ Frigate supports multiple different detectors that work on different types of ha
:::note
Multiple detectors can not be mixed for object detection (ex: OpenVINO and Coral EdgeTPU can not be used for object detection at the same time).
Multiple detectors can not be mixed for object detection (ex: OpenVINO and Coral EdgeTPU can not be used for object detection at the same time).
This does not affect using hardware for accelerating other tasks such as [semantic search](./semantic_search.md)
@ -201,15 +201,7 @@ This detector also supports YOLOX. Frigate does not come with any YOLOX models p
#### YOLO-NAS
[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
:::warning
The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
:::
The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.
[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. See [the models section](#downloading-yolo-nas-model) for more information on downloading the YOLO-NAS model for use in Frigate.
After placing the downloaded onnx model in your config folder, you can use the following configuration:
@ -231,13 +223,43 @@ model:
Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
#### YOLOv9
[YOLOv9](https://github.com/MultimediaTechLab/YOLO) models are supported, but not included by default.
:::tip
The YOLOv9 detector has been designed to support YOLOv9 models, but may support other YOLO model architectures as well.
:::
After placing the downloaded onnx model in your config folder, you can use the following configuration:
```yaml
detectors:
ov:
type: openvino
device: GPU
model:
model_type: yolov9
width: 640 # <--- should match the imgsize set during model export
height: 640 # <--- should match the imgsize set during model export
input_tensor: nchw
input_dtype: float
path: /config/model_cache/yolov9-t.onnx
labelmap_path: /labelmap/coco-80.txt
```
Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
## NVidia TensorRT Detector
Nvidia GPUs may be used for object detection using the TensorRT libraries. Due to the size of the additional libraries, this detector is only provided in images with the `-tensorrt` tag suffix, e.g. `ghcr.io/blakeblackshear/frigate:stable-tensorrt`. This detector is designed to work with Yolo models for object detection.
### Minimum Hardware Support
The TensorRT detector uses the 12.x series of CUDA libraries which have minor version compatibility. The minimum driver version on the host system must be `>=530`. Also the GPU must support a Compute Capability of `5.0` or greater. This generally correlates to a Maxwell-era GPU or newer, check the NVIDIA GPU Compute Capability table linked below.
The TensorRT detector uses the 12.x series of CUDA libraries which have minor version compatibility. The minimum driver version on the host system must be `>=545`. Also the GPU must support a Compute Capability of `5.0` or greater. This generally correlates to a Maxwell-era GPU or newer, check the NVIDIA GPU Compute Capability table linked below.
To use the TensorRT detector, make sure your host system has the [nvidia-container-runtime](https://docs.docker.com/config/containers/resource_constraints/#access-an-nvidia-gpu) installed to pass through the GPU to the container and the host system has a compatible driver installed for your GPU.
@ -265,6 +287,8 @@ If your GPU does not support FP16 operations, you can pass the environment varia
Specific models can be selected by passing an environment variable to the `docker run` command or in your `docker-compose.yml` file. Use the form `-e YOLO_MODELS=yolov4-416,yolov4-tiny-416` to select one or more model names. The models available are shown below.
<details>
<summary>Available Models</summary>
```
yolov3-288
yolov3-416
@ -293,6 +317,7 @@ yolov7-320
yolov7x-640
yolov7x-320
```
</details>
An example `docker-compose.yml` fragment that converts the `yolov4-608` and `yolov7x-640` models for a Pascal card would look something like this:
@ -420,15 +445,7 @@ There is no default model provided, the following formats are supported:
#### YOLO-NAS
[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
:::warning
The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
:::
The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.
[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. See [the models section](#downloading-yolo-nas-model) for more information on downloading the YOLO-NAS model for use in Frigate.
After placing the downloaded onnx model in your config folder, you can use the following configuration:
@ -450,7 +467,7 @@ Note that the labelmap uses a subset of the complete COCO label set that has onl
## ONNX
ONNX is an open format for building machine learning models, Frigate supports running ONNX models on CPU, OpenVINO, and TensorRT. On startup Frigate will automatically try to use a GPU if one is available.
ONNX is an open format for building machine learning models, Frigate supports running ONNX models on CPU, OpenVINO, ROCm, and TensorRT. On startup Frigate will automatically try to use a GPU if one is available.
:::info
@ -490,15 +507,7 @@ There is no default model provided, the following formats are supported:
#### YOLO-NAS
[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
:::warning
The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
:::
The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.
[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. See [the models section](#downloading-yolo-nas-model) for more information on downloading the YOLO-NAS model for use in Frigate.
After placing the downloaded onnx model in your config folder, you can use the following configuration:
@ -517,6 +526,33 @@ model:
labelmap_path: /labelmap/coco-80.txt
```
#### YOLOv9
[YOLOv9](https://github.com/MultimediaTechLab/YOLO) models are supported, but not included by default.
:::tip
The YOLOv9 detector has been designed to support YOLOv9 models, but may support other YOLO model architectures as well.
:::
After placing the downloaded onnx model in your config folder, you can use the following configuration:
```yaml
detectors:
onnx:
type: onnx
model:
model_type: yolov9
width: 640 # <--- should match the imgsize set during model export
height: 640 # <--- should match the imgsize set during model export
input_tensor: nchw
input_dtype: float
path: /config/model_cache/yolov9-t.onnx
labelmap_path: /labelmap/coco-80.txt
```
Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
## CPU Detector (not recommended)
@ -582,7 +618,7 @@ Hardware accelerated object detection is supported on the following SoCs:
- RK3576
- RK3588
This implementation uses the [Rockchip's RKNN-Toolkit2](https://github.com/airockchip/rknn-toolkit2/), version v2.0.0.beta0. Currently, only [Yolo-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) is supported as object detection model.
This implementation uses the [Rockchip's RKNN-Toolkit2](https://github.com/airockchip/rknn-toolkit2/), version v2.3.0. Currently, only [Yolo-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) is supported as object detection model.
### Prerequisites
@ -656,3 +692,57 @@ $ cat /sys/kernel/debug/rknpu/load
- All models are automatically downloaded and stored in the folder `config/model_cache/rknn_cache`. After upgrading Frigate, you should remove older models to free up space.
- You can also provide your own `.rknn` model. You should not save your own models in the `rknn_cache` folder, store them directly in the `model_cache` folder or another subfolder. To convert a model to `.rknn` format see the `rknn-toolkit2` (requires a x86 machine). Note, that there is only post-processing for the supported models.
### Converting your own onnx model to rknn format
To convert a onnx model to the rknn format using the [rknn-toolkit2](https://github.com/airockchip/rknn-toolkit2/) you have to:
- Place one ore more models in onnx format in the directory `config/model_cache/rknn_cache/onnx` on your docker host (this might require `sudo` privileges).
- Save the configuration file under `config/conv2rknn.yaml` (see below for details).
- Run `docker exec <frigate_container_id> python3 /opt/conv2rknn.py`. If the conversion was successful, the rknn models will be placed in `config/model_cache/rknn_cache`.
This is an example configuration file that you need to adjust to your specific onnx model:
```yaml
soc: ["rk3562","rk3566", "rk3568", "rk3576", "rk3588"]
quantization: false
output_name: "{input_basename}"
config:
mean_values: [[0, 0, 0]]
std_values: [[255, 255, 255]]
quant_img_rgb2bgr: true
```
Explanation of the paramters:
- `soc`: A list of all SoCs you want to build the rknn model for. If you don't specify this parameter, the script tries to find out your SoC and builds the rknn model for this one.
- `quantization`: true: 8 bit integer (i8) quantization, false: 16 bit float (fp16). Default: false.
- `output_name`: The output name of the model. The following variables are available:
- `quant`: "i8" or "fp16" depending on the config
- `input_basename`: the basename of the input model (e.g. "my_model" if the input model is calles "my_model.onnx")
- `soc`: the SoC this model was build for (e.g. "rk3588")
- `tk_version`: Version of `rknn-toolkit2` (e.g. "2.3.0")
- **example**: Specifying `output_name = "frigate-{quant}-{input_basename}-{soc}-v{tk_version}"` could result in a model called `frigate-i8-my_model-rk3588-v2.3.0.rknn`.
- `config`: Configuration passed to `rknn-toolkit2` for model conversion. For an explanation of all available parameters have a look at section "2.2. Model configuration" of [this manual](https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.0/03_Rockchip_RKNPU_API_Reference_RKNN_Toolkit2_V2.3.0_EN.pdf).
# Models
Some model types are not included in Frigate by default.
## Downloading Models
Here are some tips for getting different model types
### Downloading YOLO-NAS Model
You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
:::warning
The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
:::
The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.

2
docs/docs/configuration/object_filters.md
View File

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

71
docs/docs/configuration/reference.md
View File

@ -46,6 +46,11 @@ mqtt:
tls_insecure: false
# Optional: interval in seconds for publishing stats (default: shown below)
stats_interval: 60
# Optional: QoS level for subscriptions and publishing (default: shown below)
# 0 = at most once
# 1 = at least once
# 2 = exactly once
qos: 0
# Optional: Detectors configuration. Defaults to a single CPU detector
detectors:
@ -244,6 +249,8 @@ ffmpeg:
# If set too high, then if a ffmpeg crash or camera stream timeout occurs, you could potentially lose up to a maximum of retry_interval second(s) of footage
# NOTE: this can be a useful setting for Wireless / Battery cameras to reduce how much footage is potentially lost during a connection timeout.
retry_interval: 10
# Optional: Set tag on HEVC (H.265) recording stream to improve compatibility with Apple players. (default: shown below)
apple_compatibility: false
# Optional: Detect configuration
# NOTE: Can be overridden at the camera level
@ -310,9 +317,11 @@ objects:
# Optional: filters to reduce false positives for specific object types
filters:
person:
# Optional: minimum width*height of the bounding box for the detected object (default: 0)
# Optional: minimum size of the bounding box for the detected object (default: 0).
# Can be specified as an integer for width*height in pixels or as a decimal representing the percentage of the frame (0.000001 to 0.99).
min_area: 5000
# Optional: maximum width*height of the bounding box for the detected object (default: 24000000)
# Optional: maximum size of the bounding box for the detected object (default: 24000000).
# Can be specified as an integer for width*height in pixels or as a decimal representing the percentage of the frame (0.000001 to 0.99).
max_area: 100000
# Optional: minimum width/height of the bounding box for the detected object (default: 0)
min_ratio: 0.5
@ -331,6 +340,8 @@ objects:
review:
# Optional: alerts configuration
alerts:
# Optional: enables alerts for the camera (default: shown below)
enabled: True
# Optional: labels that qualify as an alert (default: shown below)
labels:
- car
@ -343,6 +354,8 @@ review:
- driveway
# Optional: detections configuration
detections:
# Optional: enables detections for the camera (default: shown below)
enabled: True
# Optional: labels that qualify as a detection (default: all labels that are tracked / listened to)
labels:
- car
@ -400,12 +413,15 @@ motion:
mqtt_off_delay: 30
# Optional: Notification Configuration
# NOTE: Can be overridden at the camera level (except email)
notifications:
# Optional: Enable notification service (default: shown below)
enabled: False
# Optional: Email for push service to reach out to
# NOTE: This is required to use notifications
email: "admin@example.com"
# Optional: Cooldown time for notifications in seconds (default: shown below)
cooldown: 0
# Optional: Record configuration
# NOTE: Can be overridden at the camera level
@ -524,6 +540,33 @@ semantic_search:
# NOTE: small model runs on CPU and large model runs on GPU
model_size: "small"
# Optional: Configuration for face recognition capability
face_recognition:
# Optional: Enable semantic search (default: shown below)
enabled: False
# Optional: Set the model size used for embeddings. (default: shown below)
# NOTE: small model runs on CPU and large model runs on GPU
model_size: "small"
# Optional: Configuration for license plate recognition capability
lpr:
# Optional: Enable license plate recognition (default: shown below)
enabled: False
# Optional: License plate object confidence score required to begin running recognition (default: shown below)
detection_threshold: 0.7
# Optional: Minimum area of license plate to begin running recognition (default: shown below)
min_area: 1000
# Optional: Recognition confidence score required to add the plate to the object as a sub label (default: shown below)
recognition_threshold: 0.9
# Optional: Minimum number of characters a license plate must have to be added to the object as a sub label (default: shown below)
min_plate_length: 4
# Optional: Regular expression for the expected format of a license plate (default: shown below)
format: None
# Optional: Allow this number of missing/incorrect characters to still cause a detected plate to match a known plate
match_distance: 1
# Optional: Known plates to track (strings or regular expressions) (default: shown below)
known_plates: {}
# Optional: Configuration for AI generated tracked object descriptions
# NOTE: Semantic Search must be enabled for this to do anything.
# WARNING: Depending on the provider, this will send thumbnails over the internet
@ -549,16 +592,18 @@ genai:
# Optional: Restream configuration
# Uses https://github.com/AlexxIT/go2rtc (v1.9.2)
# NOTE: The default go2rtc API port (1984) must be used,
# changing this port for the integrated go2rtc instance is not supported.
# changing this port for the integrated go2rtc instance is not supported.
go2rtc:
# Optional: Live stream configuration for WebUI.
# NOTE: Can be overridden at the camera level
live:
# Optional: Set the name of the stream configured in go2rtc
# Optional: Set the streams configured in go2rtc
# that should be used for live view in frigate WebUI. (default: name of camera)
# NOTE: In most cases this should be set at the camera level only.
stream_name: camera_name
streams:
main_stream: main_stream_name
sub_stream: sub_stream_name
# Optional: Set the height of the jsmpeg stream. (default: 720)
# This must be less than or equal to the height of the detect stream. Lower resolutions
# reduce bandwidth required for viewing the jsmpeg stream. Width is computed to match known aspect ratio.
@ -643,7 +688,10 @@ cameras:
front_steps:
# Required: List of x,y coordinates to define the polygon of the zone.
# NOTE: Presence in a zone is evaluated only based on the bottom center of the objects bounding box.
coordinates: 0.284,0.997,0.389,0.869,0.410,0.745
coordinates: 0.033,0.306,0.324,0.138,0.439,0.185,0.042,0.428
# Optional: The real-world distances of a 4-sided zone used for zones with speed estimation enabled (default: none)
# List distances in order of the zone points coordinates and use the unit system defined in the ui config
distances: 10,15,12,11
# Optional: Number of consecutive frames required for object to be considered present in the zone (default: shown below).
inertia: 3
# Optional: Number of seconds that an object must loiter to be considered in the zone (default: shown below)
@ -794,6 +842,9 @@ ui:
# https://www.gnu.org/software/libc/manual/html_node/Formatting-Calendar-Time.html
# possible values are shown above (default: not set)
strftime_fmt: "%Y/%m/%d %H:%M"
# Optional: Set the unit system to either "imperial" or "metric" (default: metric)
# Used in the UI and in MQTT topics
unit_system: metric
# Optional: Telemetry configuration
telemetry:
@ -807,11 +858,13 @@ telemetry:
- lo
# Optional: Configure system stats
stats:
# Enable AMD GPU stats (default: shown below)
# Optional: Enable AMD GPU stats (default: shown below)
amd_gpu_stats: True
# Enable Intel GPU stats (default: shown below)
# Optional: Enable Intel GPU stats (default: shown below)
intel_gpu_stats: True
# Enable network bandwidth stats monitoring for camera ffmpeg processes, go2rtc, and object detectors. (default: shown below)
# Optional: Treat GPU as SR-IOV to fix GPU stats (default: shown below)
sriov: False
# Optional: Enable network bandwidth stats monitoring for camera ffmpeg processes, go2rtc, and object detectors. (default: shown below)
# NOTE: The container must either be privileged or have cap_net_admin, cap_net_raw capabilities enabled.
network_bandwidth: False
# Optional: Enable the latest version outbound check (default: shown below)

2
docs/docs/configuration/semantic_search.md
View File

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

55
docs/docs/configuration/zones.md
View File

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

2
docs/docs/development/contributing.md
View File

@ -34,7 +34,7 @@ Fork [blakeblackshear/frigate-hass-integration](https://github.com/blakeblackshe
### Prerequisites
- GNU make
- Docker
- Docker (including buildx plugin)
- An extra detector (Coral, OpenVINO, etc.) is optional but recommended to simulate real world performance.
:::note

68
docs/docs/frigate/hardware.md
View File

@ -13,20 +13,19 @@ Many users have reported various issues with Reolink cameras, so I do not recomm
Here are some of the camera's I recommend:
- <a href="https://amzn.to/3uFLtxB" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) T5442TM-AS-LED</a> (affiliate link)
- <a href="https://amzn.to/3isJ3gU" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T5442TM-AS</a> (affiliate link)
- <a href="https://amzn.to/2ZWNWIA" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-28MM</a> (affiliate link)
- <a href="https://amzn.to/4fwoNWA" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T549M-ALED-S3</a> (affiliate link)
- <a href="https://amzn.to/3YXpcMw" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T54IR-AS</a> (affiliate link)
- <a href="https://amzn.to/3AvBHoY" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-AI-V3</a> (affiliate link)
I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
## Server
My current favorite is the Beelink EQ12 because of the efficient N100 CPU and dual NICs that allow you to setup a dedicated private network for your cameras where they can be blocked from accessing the internet. There are many used workstation options on eBay that work very well. Anything with an Intel CPU and capable of running Debian should work fine. As a bonus, you may want to look for devices with a M.2 or PCIe express slot that is compatible with the Google Coral. I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
My current favorite is the Beelink EQ13 because of the efficient N100 CPU and dual NICs that allow you to setup a dedicated private network for your cameras where they can be blocked from accessing the internet. There are many used workstation options on eBay that work very well. Anything with an Intel CPU and capable of running Debian should work fine. As a bonus, you may want to look for devices with a M.2 or PCIe express slot that is compatible with the Google Coral. I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
| Name | Coral Inference Speed | Coral Compatibility | Notes |
| ------------------------------------------------------------------------------------------------------------- | --------------------- | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
| Beelink EQ12 (<a href="https://amzn.to/3OlTMJY" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms | USB | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras. |
| Intel NUC (<a href="https://amzn.to/3psFlHi" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms | USB | Overkill for most, but great performance. Can handle many cameras at 5fps depending on typical amounts of motion. Requires extra parts. |
| Name | Coral Inference Speed | Coral Compatibility | Notes |
| ------------------------------------------------------------------------------------------------------------- | --------------------- | ------------------- | ----------------------------------------------------------------------------------------- |
| Beelink EQ13 (<a href="https://amzn.to/4iQaBKu" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms | USB | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras. |
## Detectors
@ -52,24 +51,25 @@ The OpenVINO detector type is able to run on:
More information is available [in the detector docs](/configuration/object_detectors#openvino-detector)
Inference speeds vary greatly depending on the CPU, GPU, or VPU used, some known examples are below:
Inference speeds vary greatly depending on the CPU or GPU used, some known examples of GPU inference times are below:
| Name | MobileNetV2 Inference Speed | YOLO-NAS Inference Speed | Notes |
| -------------------- | --------------------------- | ------------------------- | -------------------------------------- |
| Intel Celeron J4105 | ~ 25 ms | | Can only run one detector instance |
| Intel Celeron N3060 | 130 - 150 ms | | Can only run one detector instance |
| Intel Celeron N3205U | ~ 120 ms | | Can only run one detector instance |
| Intel Celeron N4020 | 50 - 200 ms | | Inference speed depends on other loads |
| Intel i3 6100T | 15 - 35 ms | | Can only run one detector instance |
| Intel i3 8100 | ~ 15 ms | | |
| Intel i5 4590 | ~ 20 ms | | |
| Intel i5 6500 | ~ 15 ms | | |
| Intel i5 7200u | 15 - 25 ms | | |
| Intel i5 7500 | ~ 15 ms | | |
| Intel i5 1135G7 | 10 - 15 ms | | |
| Intel i5 12600K | ~ 15 ms | 320: ~ 20 ms 640: ~ 46 ms | |
| Intel Arc A380 | ~ 6 ms | 320: ~ 10 ms | |
| Intel Arc A750 | ~ 4 ms | 320: ~ 8 ms | |
| Name | MobileNetV2 Inference Time | YOLO-NAS Inference Time | Notes |
| -------------------- | -------------------------- | ------------------------- | -------------------------------------- |
| Intel Celeron J4105 | ~ 25 ms | | Can only run one detector instance |
| Intel Celeron N3060 | 130 - 150 ms | | Can only run one detector instance |
| Intel Celeron N3205U | ~ 120 ms | | Can only run one detector instance |
| Intel Celeron N4020 | 50 - 200 ms | | Inference speed depends on other loads |
| Intel i3 6100T | 15 - 35 ms | | Can only run one detector instance |
| Intel i3 8100 | ~ 15 ms | | |
| Intel i5 4590 | ~ 20 ms | | |
| Intel i5 6500 | ~ 15 ms | | |
| Intel i5 7200u | 15 - 25 ms | | |
| Intel i5 7500 | ~ 15 ms | | |
| Intel i5 1135G7 | 10 - 15 ms | | |
| Intel i3 12000 | | 320: ~ 19 ms 640: ~ 54 ms | |
| Intel i5 12600K | ~ 15 ms | 320: ~ 20 ms 640: ~ 46 ms | |
| Intel Arc A380 | ~ 6 ms | 320: ~ 10 ms | |
| Intel Arc A750 | ~ 4 ms | 320: ~ 8 ms | |
### TensorRT - Nvidia GPU
@ -78,15 +78,15 @@ The TensortRT detector is able to run on x86 hosts that have an Nvidia GPU which
Inference speeds will vary greatly depending on the GPU and the model used.
`tiny` variants are faster than the equivalent non-tiny model, some known examples are below:
| Name | YoloV7 Inference Speed | YOLO-NAS Inference Speed |
| --------------- | ---------------------- | ------------------------- |
| GTX 1060 6GB | ~ 7 ms | |
| GTX 1070 | ~ 6 ms | |
| GTX 1660 SUPER | ~ 4 ms | |
| RTX 3050 | 5 - 7 ms | 320: ~ 10 ms 640: ~ 16 ms |
| RTX 3070 Mobile | ~ 5 ms | |
| Quadro P400 2GB | 20 - 25 ms | |
| Quadro P2000 | ~ 12 ms | |
| Name | YoloV7 Inference Time | YOLO-NAS Inference Time |
| --------------- | --------------------- | ------------------------- |
| GTX 1060 6GB | ~ 7 ms | |
| GTX 1070 | ~ 6 ms | |
| GTX 1660 SUPER | ~ 4 ms | |
| RTX 3050 | 5 - 7 ms | 320: ~ 10 ms 640: ~ 16 ms |
| RTX 3070 Mobile | ~ 5 ms | |
| Quadro P400 2GB | 20 - 25 ms | |
| Quadro P2000 | ~ 12 ms | |
### AMD GPUs

6
docs/docs/frigate/installation.md
View File

@ -111,13 +111,13 @@ For Raspberry Pi 5 users with the AI Kit, installation is straightforward. Simpl
For other installations, follow these steps for installation:
1. Install the driver from the [Hailo GitHub repository](https://github.com/hailo-ai/hailort-drivers). A convenient script for Linux is available to clone the repository, build the driver, and install it.
2. Copy or download [this script](https://github.com/blakeblackshear/frigate/blob/41c9b13d2fffce508b32dfc971fa529b49295fbd/docker/hailo8l/user_installation.sh).
2. Copy or download [this script](https://github.com/blakeblackshear/frigate/blob/dev/docker/hailo8l/user_installation.sh).
3. Ensure it has execution permissions with `sudo chmod +x user_installation.sh`
4. Run the script with `./user_installation.sh`
#### Setup
To set up Frigate, follow the default installation instructions, but use a Docker image with the `-h8l` suffix, for example: `ghcr.io/blakeblackshear/frigate:stable-h8l`
To set up Frigate, follow the default installation instructions, for example: `ghcr.io/blakeblackshear/frigate:stable`
Next, grant Docker permissions to access your hardware by adding the following lines to your `docker-compose.yml` file:
@ -250,7 +250,7 @@ The official docker image tags for the current stable version are:
The community supported docker image tags for the current stable version are:
- `stable-tensorrt-jp5` - Frigate build optimized for nvidia Jetson devices running Jetpack 5
- `stable-tensorrt-jp4` - Frigate build optimized for nvidia Jetson devices running Jetpack 4.6
- `stable-tensorrt-jp6` - Frigate build optimized for nvidia Jetson devices running Jetpack 6
- `stable-rk` - Frigate build for SBCs with Rockchip SoC
- `stable-rocm` - Frigate build for [AMD GPUs](../configuration/object_detectors.md#amdrocm-gpu-detector)
- `stable-h8l` - Frigate build for the Hailo-8L M.2 PICe Raspberry Pi 5 hat

40
docs/docs/integrations/mqtt.md
View File

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

4
docs/docs/integrations/third_party_extensions.md
View File

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

2
docs/docs/plus/first_model.md
View File

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

4
docs/docs/plus/improving_model.md
View File

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

18
docs/docs/plus/index.md
View File

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

15
docs/docs/troubleshooting/edgetpu.md
View File

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

5
docs/sidebars.ts
View File

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

BIN
docs/static/img/ground-plane.jpg vendored Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 231 KiB

50
frigate/__main__.py
View File

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

145
frigate/api/app.py
View File

@ -1,5 +1,6 @@
"""Main api runner."""
import asyncio
import copy
import json
import logging
@ -7,21 +8,26 @@ import os
import traceback
from datetime import datetime, timedelta
from functools import reduce
from io import StringIO
from typing import Any, Optional
import aiofiles
import requests
import ruamel.yaml
from fastapi import APIRouter, Body, Path, Request, Response
from fastapi.encoders import jsonable_encoder
from fastapi.params import Depends
from fastapi.responses import JSONResponse, PlainTextResponse
from fastapi.responses import JSONResponse, PlainTextResponse, StreamingResponse
from markupsafe import escape
from peewee import operator
from pydantic import ValidationError
from frigate.api.defs.query.app_query_parameters import AppTimelineHourlyQueryParameters
from frigate.api.defs.request.app_body import AppConfigSetBody
from frigate.api.defs.tags import Tags
from frigate.config import FrigateConfig
from frigate.models import Event, Timeline
from frigate.stats.prometheus import get_metrics, update_metrics
from frigate.util.builtin import (
clean_camera_user_pass,
get_tz_modifiers,
@ -31,6 +37,7 @@ from frigate.util.config import find_config_file
from frigate.util.services import (
ffprobe_stream,
get_nvidia_driver_info,
process_logs,
restart_frigate,
vainfo_hwaccel,
)
@ -105,6 +112,16 @@ def stats_history(request: Request, keys: str = None):
return JSONResponse(content=request.app.stats_emitter.get_stats_history(keys))
@router.get("/metrics")
def metrics(request: Request):
"""Expose Prometheus metrics endpoint and update metrics with latest stats"""
# Retrieve the latest statistics and update the Prometheus metrics
stats = request.app.stats_emitter.get_latest_stats()
update_metrics(stats)
content, content_type = get_metrics()
return Response(content=content, media_type=content_type)
@router.get("/config")
def config(request: Request):
config_obj: FrigateConfig = request.app.frigate_config
@ -154,6 +171,7 @@ def config(request: Request):
config["plus"] = {"enabled": request.app.frigate_config.plus_api.is_active()}
config["model"]["colormap"] = config_obj.model.colormap
config["model"]["all_attributes"] = config_obj.model.all_attributes
config["model"]["non_logo_attributes"] = config_obj.model.non_logo_attributes
# use merged labelamp
for detector_config in config["detectors"].values():
@ -186,7 +204,6 @@ def config_raw():
@router.post("/config/save")
def config_save(save_option: str, body: Any = Body(media_type="text/plain")):
new_config = body.decode()
if not new_config:
return JSONResponse(
content=(
@ -197,13 +214,64 @@ def config_save(save_option: str, body: Any = Body(media_type="text/plain")):
# Validate the config schema
try:
# Use ruamel to parse and preserve line numbers
yaml_config = ruamel.yaml.YAML()
yaml_config.preserve_quotes = True
full_config = yaml_config.load(StringIO(new_config))
FrigateConfig.parse_yaml(new_config)
except ValidationError as e:
error_message = []
for error in e.errors():
error_path = error["loc"]
current = full_config
line_number = "Unknown"
last_line_number = "Unknown"
try:
for i, part in enumerate(error_path):
key = int(part) if part.isdigit() else part
if isinstance(current, ruamel.yaml.comments.CommentedMap):
current = current[key]
elif isinstance(current, list):
current = current[key]
if hasattr(current, "lc"):
last_line_number = current.lc.line
if i == len(error_path) - 1:
if hasattr(current, "lc"):
line_number = current.lc.line
else:
line_number = last_line_number
except Exception:
line_number = "Unable to determine"
error_message.append(
f"Line {line_number}: {' -> '.join(map(str, error_path))} - {error.get('msg', error.get('type', 'Unknown'))}"
)
return JSONResponse(
content=(
{
"success": False,
"message": "Your configuration is invalid.\nSee the official documentation at docs.frigate.video.\n\n"
+ "\n".join(error_message),
}
),
status_code=400,
)
except Exception:
return JSONResponse(
content=(
{
"success": False,
"message": f"\nConfig Error:\n\n{escape(str(traceback.format_exc()))}",
"message": f"\nYour configuration is invalid.\nSee the official documentation at docs.frigate.video.\n\n{escape(str(traceback.format_exc()))}",
}
),
status_code=400,
@ -394,9 +462,10 @@ def nvinfo():
@router.get("/logs/{service}", tags=[Tags.logs])
def logs(
async def logs(
service: str = Path(enum=["frigate", "nginx", "go2rtc"]),
download: Optional[str] = None,
stream: Optional[bool] = False,
start: Optional[int] = 0,
end: Optional[int] = None,
):
@ -415,6 +484,27 @@ def logs(
status_code=500,
)
async def stream_logs(file_path: str):
"""Asynchronously stream log lines."""
buffer = ""
try:
async with aiofiles.open(file_path, "r") as file:
await file.seek(0, 2)
while True:
line = await file.readline()
if line:
buffer += line
# Process logs only when there are enough lines in the buffer
if "\n" in buffer:
_, processed_lines = process_logs(buffer, service)
buffer = ""
for processed_line in processed_lines:
yield f"{processed_line}\n"
else:
await asyncio.sleep(0.1)
except FileNotFoundError:
yield "Log file not found.\n"
log_locations = {
"frigate": "/dev/shm/logs/frigate/current",
"go2rtc": "/dev/shm/logs/go2rtc/current",
@ -431,48 +521,17 @@ def logs(
if download:
return download_logs(service_location)
if stream:
return StreamingResponse(stream_logs(service_location), media_type="text/plain")
# For full logs initially
try:
file = open(service_location, "r")
contents = file.read()
file.close()
# use the start timestamp to group logs together``
logLines = []
keyLength = 0
dateEnd = 0
currentKey = ""
currentLine = ""
for rawLine in contents.splitlines():
cleanLine = rawLine.strip()
if len(cleanLine) < 10:
continue
# handle cases where S6 does not include date in log line
if " " not in cleanLine:
cleanLine = f"{datetime.now()} {cleanLine}"
if dateEnd == 0:
dateEnd = cleanLine.index(" ")
keyLength = dateEnd - (6 if service_location == "frigate" else 0)
newKey = cleanLine[0:keyLength]
if newKey == currentKey:
currentLine += f"\n{cleanLine[dateEnd:].strip()}"
continue
else:
if len(currentLine) > 0:
logLines.append(currentLine)
currentKey = newKey
currentLine = cleanLine
logLines.append(currentLine)
async with aiofiles.open(service_location, "r") as file:
contents = await file.read()
total_lines, log_lines = process_logs(contents, service, start, end)
return JSONResponse(
content={"totalLines": len(logLines), "lines": logLines[start:end]},
content={"totalLines": total_lines, "lines": log_lines},
status_code=200,
)
except FileNotFoundError as e:

178
frigate/api/classification.py Normal file
View File

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

4
frigate/api/defs/query/events_query_parameters.py
View File

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

6
frigate/api/defs/query/media_query_parameters.py
View File

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

3
frigate/api/defs/request/events_body.py
View File

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

5
frigate/api/defs/request/export_rename_body.py Normal file
View File

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

1
frigate/api/defs/tags.py
View File

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

106
frigate/api/event.py
View File

@ -92,6 +92,8 @@ def events(params: EventsQueryParams = Depends()):
favorites = params.favorites
min_score = params.min_score
max_score = params.max_score
min_speed = params.min_speed
max_speed = params.max_speed
is_submitted = params.is_submitted
min_length = params.min_length
max_length = params.max_length
@ -226,6 +228,12 @@ def events(params: EventsQueryParams = Depends()):
if min_score is not None:
clauses.append((Event.data["score"] >= min_score))
if max_speed is not None:
clauses.append((Event.data["average_estimated_speed"] <= max_speed))
if min_speed is not None:
clauses.append((Event.data["average_estimated_speed"] >= min_speed))
if min_length is not None:
clauses.append(((Event.end_time - Event.start_time) >= min_length))
@ -249,6 +257,10 @@ def events(params: EventsQueryParams = Depends()):
order_by = Event.data["score"].asc()
elif sort == "score_desc":
order_by = Event.data["score"].desc()
elif sort == "speed_asc":
order_by = Event.data["average_estimated_speed"].asc()
elif sort == "speed_desc":
order_by = Event.data["average_estimated_speed"].desc()
elif sort == "date_asc":
order_by = Event.start_time.asc()
elif sort == "date_desc":
@ -316,7 +328,16 @@ def events_explore(limit: int = 10):
k: v
for k, v in event.data.items()
if k
in ["type", "score", "top_score", "description", "sub_label_score"]
in [
"type",
"score",
"top_score",
"description",
"sub_label_score",
"average_estimated_speed",
"velocity_angle",
"path_data",
]
},
"event_count": label_counts[event.label],
}
@ -367,6 +388,8 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
before = params.before
min_score = params.min_score
max_score = params.max_score
min_speed = params.min_speed
max_speed = params.max_speed
time_range = params.time_range
has_clip = params.has_clip
has_snapshot = params.has_snapshot
@ -466,6 +489,16 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
if max_score is not None:
event_filters.append((Event.data["score"] <= max_score))
if min_speed is not None and max_speed is not None:
event_filters.append(
(Event.data["average_estimated_speed"].between(min_speed, max_speed))
)
else:
if min_speed is not None:
event_filters.append((Event.data["average_estimated_speed"] >= min_speed))
if max_speed is not None:
event_filters.append((Event.data["average_estimated_speed"] <= max_speed))
if time_range != DEFAULT_TIME_RANGE:
tz_name = params.timezone
hour_modifier, minute_modifier, _ = get_tz_modifiers(tz_name)
@ -581,7 +614,17 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
processed_event["data"] = {
k: v
for k, v in event["data"].items()
if k in ["type", "score", "top_score", "description"]
if k
in [
"type",
"score",
"top_score",
"description",
"sub_label_score",
"average_estimated_speed",
"velocity_angle",
"path_data",
]
}
if event["id"] in search_results:
@ -596,6 +639,10 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
processed_events.sort(key=lambda x: x["score"])
elif min_score is not None and max_score is not None and sort == "score_desc":
processed_events.sort(key=lambda x: x["score"], reverse=True)
elif min_speed is not None and max_speed is not None and sort == "speed_asc":
processed_events.sort(key=lambda x: x["average_estimated_speed"])
elif min_speed is not None and max_speed is not None and sort == "speed_desc":
processed_events.sort(key=lambda x: x["average_estimated_speed"], reverse=True)
elif sort == "date_asc":
processed_events.sort(key=lambda x: x["start_time"])
else:
@ -909,38 +956,59 @@ def set_sub_label(
try:
event: Event = Event.get(Event.id == event_id)
except DoesNotExist:
if not body.camera:
return JSONResponse(
content=(
{
"success": False,
"message": "Event "
+ event_id
+ " not found and camera is not provided.",
}
),
status_code=404,
)
event = None
if request.app.detected_frames_processor:
tracked_obj: TrackedObject = (
request.app.detected_frames_processor.camera_states[
event.camera if event else body.camera
].tracked_objects.get(event_id)
)
else:
tracked_obj = None
if not event and not tracked_obj:
return JSONResponse(
content=({"success": False, "message": "Event " + event_id + " not found"}),
content=(
{"success": False, "message": "Event " + event_id + " not found."}
),
status_code=404,
)
new_sub_label = body.subLabel
new_score = body.subLabelScore
if not event.end_time:
# update tracked object
tracked_obj: TrackedObject = (
request.app.detected_frames_processor.camera_states[
event.camera
].tracked_objects.get(event.id)
)
if tracked_obj:
tracked_obj.obj_data["sub_label"] = (new_sub_label, new_score)
if tracked_obj:
tracked_obj.obj_data["sub_label"] = (new_sub_label, new_score)
# update timeline items
Timeline.update(
data=Timeline.data.update({"sub_label": (new_sub_label, new_score)})
).where(Timeline.source_id == event_id).execute()
event.sub_label = new_sub_label
if event:
event.sub_label = new_sub_label
if new_score:
data = event.data
data["sub_label_score"] = new_score
event.data = data
if new_score:
data = event.data
data["sub_label_score"] = new_score
event.data = data
event.save()
event.save()
return JSONResponse(
content=(
{

7
frigate/api/export.py
View File

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

12
frigate/api/fastapi_app.py
View File

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

122
frigate/api/media.py
View File

@ -1,6 +1,5 @@
"""Image and video apis."""
import base64
import glob
import logging
import os
@ -25,6 +24,7 @@ from frigate.api.defs.query.media_query_parameters import (
MediaEventsSnapshotQueryParams,
MediaLatestFrameQueryParams,
MediaMjpegFeedQueryParams,
MediaRecordingsSummaryQueryParams,
)
from frigate.api.defs.tags import Tags
from frigate.config import FrigateConfig
@ -39,6 +39,7 @@ from frigate.models import Event, Previews, Recordings, Regions, ReviewSegment
from frigate.object_processing import TrackedObjectProcessor
from frigate.util.builtin import get_tz_modifiers
from frigate.util.image import get_image_from_recording
from frigate.util.path import get_event_thumbnail_bytes
logger = logging.getLogger(__name__)
@ -182,11 +183,16 @@ def latest_frame(
frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
ret, img = cv2.imencode(f".{extension}", frame, quality_params)
_, img = cv2.imencode(f".{extension}", frame, quality_params)
return Response(
content=img.tobytes(),
media_type=f"image/{mime_type}",
headers={"Content-Type": f"image/{mime_type}", "Cache-Control": "no-store"},
headers={
"Content-Type": f"image/{mime_type}",
"Cache-Control": "no-store"
if not params.store
else "private, max-age=60",
},
)
elif camera_name == "birdseye" and request.app.frigate_config.birdseye.restream:
frame = cv2.cvtColor(
@ -199,11 +205,16 @@ def latest_frame(
frame = cv2.resize(frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
ret, img = cv2.imencode(f".{extension}", frame, quality_params)
_, img = cv2.imencode(f".{extension}", frame, quality_params)
return Response(
content=img.tobytes(),
media_type=f"image/{mime_type}",
headers={"Content-Type": f"image/{mime_type}", "Cache-Control": "no-store"},
headers={
"Content-Type": f"image/{mime_type}",
"Cache-Control": "no-store"
if not params.store
else "private, max-age=60",
},
)
else:
return JSONResponse(
@ -362,6 +373,48 @@ def get_recordings_storage_usage(request: Request):
return JSONResponse(content=camera_usages)
@router.get("/recordings/summary")
def all_recordings_summary(params: MediaRecordingsSummaryQueryParams = Depends()):
"""Returns true/false by day indicating if recordings exist"""
hour_modifier, minute_modifier, seconds_offset = get_tz_modifiers(params.timezone)
cameras = params.cameras
query = (
Recordings.select(
fn.strftime(
"%Y-%m-%d",
fn.datetime(
Recordings.start_time + seconds_offset,
"unixepoch",
hour_modifier,
minute_modifier,
),
).alias("day")
)
.group_by(
fn.strftime(
"%Y-%m-%d",
fn.datetime(
Recordings.start_time + seconds_offset,
"unixepoch",
hour_modifier,
minute_modifier,
),
)
)
.order_by(Recordings.start_time.desc())
)
if cameras != "all":
query = query.where(Recordings.camera << cameras.split(","))
recording_days = query.namedtuples()
days = {day.day: True for day in recording_days}
return JSONResponse(content=days)
@router.get("/{camera_name}/recordings/summary")
def recordings_summary(camera_name: str, timezone: str = "utc"):
"""Returns hourly summary for recordings of given camera"""
@ -751,10 +804,11 @@ def event_snapshot(
)
@router.get("/events/{event_id}/thumbnail.jpg")
@router.get("/events/{event_id}/thumbnail.{extension}")
def event_thumbnail(
request: Request,
event_id: str,
extension: str,
max_cache_age: int = Query(
2592000, description="Max cache age in seconds. Default 30 days in seconds."
),
@ -763,11 +817,15 @@ def event_thumbnail(
thumbnail_bytes = None
event_complete = False
try:
event = Event.get(Event.id == event_id)
event: Event = Event.get(Event.id == event_id)
if event.end_time is not None:
event_complete = True
thumbnail_bytes = base64.b64decode(event.thumbnail)
thumbnail_bytes = get_event_thumbnail_bytes(event)
except DoesNotExist:
thumbnail_bytes = None
if thumbnail_bytes is None:
# see if the object is currently being tracked
try:
camera_states = request.app.detected_frames_processor.camera_states.values()
@ -775,7 +833,7 @@ def event_thumbnail(
if event_id in camera_state.tracked_objects:
tracked_obj = camera_state.tracked_objects.get(event_id)
if tracked_obj is not None:
thumbnail_bytes = tracked_obj.get_thumbnail()
thumbnail_bytes = tracked_obj.get_thumbnail(extension)
except Exception:
return JSONResponse(
content={"success": False, "message": "Event not found"},
@ -790,8 +848,8 @@ def event_thumbnail(
# android notifications prefer a 2:1 ratio
if format == "android":
jpg_as_np = np.frombuffer(thumbnail_bytes, dtype=np.uint8)
img = cv2.imdecode(jpg_as_np, flags=1)
img_as_np = np.frombuffer(thumbnail_bytes, dtype=np.uint8)
img = cv2.imdecode(img_as_np, flags=1)
thumbnail = cv2.copyMakeBorder(
img,
0,
@ -801,17 +859,25 @@ def event_thumbnail(
cv2.BORDER_CONSTANT,
(0, 0, 0),
)
ret, jpg = cv2.imencode(".jpg", thumbnail, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
thumbnail_bytes = jpg.tobytes()
quality_params = None
if extension == "jpg" or extension == "jpeg":
quality_params = [int(cv2.IMWRITE_JPEG_QUALITY), 70]
elif extension == "webp":
quality_params = [int(cv2.IMWRITE_WEBP_QUALITY), 60]
_, img = cv2.imencode(f".{img}", thumbnail, quality_params)
thumbnail_bytes = img.tobytes()
return Response(
thumbnail_bytes,
media_type="image/jpeg",
media_type=f"image/{extension}",
headers={
"Cache-Control": f"private, max-age={max_cache_age}"
if event_complete
else "no-store",
"Content-Type": "image/jpeg",
"Content-Type": f"image/{extension}",
},
)
@ -1035,30 +1101,8 @@ def event_clip(request: Request, event_id: str):
content={"success": False, "message": "Clip not available"}, status_code=404
)
file_name = f"{event.camera}-{event.id}.mp4"
clip_path = os.path.join(CLIPS_DIR, file_name)
if not os.path.isfile(clip_path):
end_ts = (
datetime.now().timestamp() if event.end_time is None else event.end_time
)
return recording_clip(request, event.camera, event.start_time, end_ts)
headers = {
"Content-Description": "File Transfer",
"Cache-Control": "no-cache",
"Content-Type": "video/mp4",
"Content-Length": str(os.path.getsize(clip_path)),
# nginx: https://nginx.org/en/docs/http/ngx_http_proxy_module.html#proxy_ignore_headers
"X-Accel-Redirect": f"/clips/{file_name}",
}
return FileResponse(
clip_path,
media_type="video/mp4",
filename=file_name,
headers=headers,
)
end_ts = datetime.now().timestamp() if event.end_time is None else event.end_time
return recording_clip(request, event.camera, event.start_time, end_ts)
@router.get("/events/{event_id}/preview.gif")

22
frigate/api/review.py
View File

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

40
frigate/app.py
View File

@ -17,8 +17,9 @@ import frigate.util as util
from frigate.api.auth import hash_password
from frigate.api.fastapi_app import create_fastapi_app
from frigate.camera import CameraMetrics, PTZMetrics
from frigate.comms.base_communicator import Communicator
from frigate.comms.config_updater import ConfigPublisher
from frigate.comms.dispatcher import Communicator, Dispatcher
from frigate.comms.dispatcher import Dispatcher
from frigate.comms.event_metadata_updater import (
EventMetadataPublisher,
EventMetadataTypeEnum,
@ -34,10 +35,13 @@ from frigate.const import (
CLIPS_DIR,
CONFIG_DIR,
EXPORT_DIR,
FACE_DIR,
MODEL_CACHE_DIR,
RECORD_DIR,
SHM_FRAMES_VAR,
THUMB_DIR,
)
from frigate.data_processing.types import DataProcessorMetrics
from frigate.db.sqlitevecq import SqliteVecQueueDatabase
from frigate.embeddings import EmbeddingsContext, manage_embeddings
from frigate.events.audio import AudioProcessor
@ -88,6 +92,9 @@ class FrigateApp:
self.detection_shms: list[mp.shared_memory.SharedMemory] = []
self.log_queue: Queue = mp.Queue()
self.camera_metrics: dict[str, CameraMetrics] = {}
self.embeddings_metrics: DataProcessorMetrics | None = (
DataProcessorMetrics() if config.semantic_search.enabled else None
)
self.ptz_metrics: dict[str, PTZMetrics] = {}
self.processes: dict[str, int] = {}
self.embeddings: Optional[EmbeddingsContext] = None
@ -96,14 +103,20 @@ class FrigateApp:
self.config = config
def ensure_dirs(self) -> None:
for d in [
dirs = [
CONFIG_DIR,
RECORD_DIR,
THUMB_DIR,
f"{CLIPS_DIR}/cache",
CACHE_DIR,
MODEL_CACHE_DIR,
EXPORT_DIR,
]:
]
if self.config.face_recognition.enabled:
dirs.append(FACE_DIR)
for d in dirs:
if not os.path.exists(d) and not os.path.islink(d):
logger.info(f"Creating directory: {d}")
os.makedirs(d)
@ -229,7 +242,10 @@ class FrigateApp:
embedding_process = util.Process(
target=manage_embeddings,
name="embeddings_manager",
args=(self.config,),
args=(
self.config,
self.embeddings_metrics,
),
)
embedding_process.daemon = True
self.embedding_process = embedding_process
@ -301,8 +317,14 @@ class FrigateApp:
if self.config.mqtt.enabled:
comms.append(MqttClient(self.config))
if self.config.notifications.enabled_in_config:
comms.append(WebPushClient(self.config))
notification_cameras = [
c
for c in self.config.cameras.values()
if c.enabled and c.notifications.enabled_in_config
]
if notification_cameras:
comms.append(WebPushClient(self.config, self.stop_event))
comms.append(WebSocketClient(self.config))
comms.append(self.inter_process_communicator)
@ -491,7 +513,11 @@ class FrigateApp:
self.stats_emitter = StatsEmitter(
self.config,
stats_init(
self.config, self.camera_metrics, self.detectors, self.processes
self.config,
self.camera_metrics,
self.embeddings_metrics,
self.detectors,
self.processes,
),
self.stop_event,
)

130
frigate/camera/activity_manager.py Normal file
View File

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

Some files were not shown because too many files have changed in this diff Show More