mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-12-06 13:34:13 +03:00
Compare commits
1 Commits
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| Author | SHA1 | Date | |
|---|---|---|---|
|
fa5791c06e |
@ -1,6 +0,0 @@
|
||||
---
|
||||
globs: ["**/*.ts", "**/*.tsx"]
|
||||
alwaysApply: false
|
||||
---
|
||||
|
||||
Never write strings in the frontend directly, always write to and reference the relevant translations file.
|
||||
2
.github/workflows/ci.yml
vendored
2
.github/workflows/ci.yml
vendored
@ -15,7 +15,7 @@ concurrency:
|
||||
cancel-in-progress: true
|
||||
|
||||
env:
|
||||
PYTHON_VERSION: 3.11
|
||||
PYTHON_VERSION: 3.9
|
||||
|
||||
jobs:
|
||||
amd64_build:
|
||||
|
||||
1
.gitignore
vendored
1
.gitignore
vendored
@ -15,7 +15,6 @@ frigate/version.py
|
||||
web/build
|
||||
web/node_modules
|
||||
web/coverage
|
||||
web/.env
|
||||
core
|
||||
!/web/**/*.ts
|
||||
.idea/*
|
||||
|
||||
2
LICENSE
2
LICENSE
@ -1,6 +1,6 @@
|
||||
The MIT License
|
||||
|
||||
Copyright (c) 2025 Frigate LLC (Frigate™)
|
||||
Copyright (c) 2020 Blake Blackshear
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
|
||||
1
Makefile
1
Makefile
@ -14,7 +14,6 @@ push-boards: $(BOARDS:%=push-%)
|
||||
|
||||
version:
|
||||
echo 'VERSION = "$(VERSION)-$(COMMIT_HASH)"' > frigate/version.py
|
||||
echo 'VITE_GIT_COMMIT_HASH=$(COMMIT_HASH)' > web/.env
|
||||
|
||||
local: version
|
||||
docker buildx build --target=frigate --file docker/main/Dockerfile . \
|
||||
|
||||
21
README.md
21
README.md
@ -1,10 +1,8 @@
|
||||
<p align="center">
|
||||
<img align="center" alt="logo" src="docs/static/img/branding/frigate.png">
|
||||
<img align="center" alt="logo" src="docs/static/img/frigate.png">
|
||||
</p>
|
||||
|
||||
# Frigate NVR™ - Realtime Object Detection for IP Cameras
|
||||
|
||||
[](https://opensource.org/licenses/MIT)
|
||||
# Frigate - NVR With Realtime Object Detection for IP Cameras
|
||||
|
||||
<a href="https://hosted.weblate.org/engage/frigate-nvr/">
|
||||
<img src="https://hosted.weblate.org/widget/frigate-nvr/language-badge.svg" alt="Translation status" />
|
||||
@ -14,7 +12,7 @@
|
||||
|
||||
A complete and local NVR designed for [Home Assistant](https://www.home-assistant.io) with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
|
||||
|
||||
Use of a GPU or AI accelerator is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead. See Frigate's supported [object detectors](https://docs.frigate.video/configuration/object_detectors/).
|
||||
Use of a GPU or AI accelerator such as a [Google Coral](https://coral.ai/products/) or [Hailo](https://hailo.ai/) is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead.
|
||||
|
||||
- Tight integration with Home Assistant via a [custom component](https://github.com/blakeblackshear/frigate-hass-integration)
|
||||
- Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
|
||||
@ -35,15 +33,6 @@ View the documentation at https://docs.frigate.video
|
||||
|
||||
If you would like to make a donation to support development, please use [Github Sponsors](https://github.com/sponsors/blakeblackshear).
|
||||
|
||||
## License
|
||||
|
||||
This project is licensed under the **MIT License**.
|
||||
|
||||
- **Code:** The source code, configuration files, and documentation in this repository are available under the [MIT License](LICENSE). You are free to use, modify, and distribute the code as long as you include the original copyright notice.
|
||||
- **Trademarks:** The "Frigate" name, the "Frigate NVR" brand, and the Frigate logo are **trademarks of Frigate LLC** and are **not** covered by the MIT License.
|
||||
|
||||
Please see our [Trademark Policy](TRADEMARK.md) for details on acceptable use of our brand assets.
|
||||
|
||||
## Screenshots
|
||||
|
||||
### Live dashboard
|
||||
@ -77,7 +66,3 @@ We use [Weblate](https://hosted.weblate.org/projects/frigate-nvr/) to support la
|
||||
<a href="https://hosted.weblate.org/engage/frigate-nvr/">
|
||||
<img src="https://hosted.weblate.org/widget/frigate-nvr/multi-auto.svg" alt="Translation status" />
|
||||
</a>
|
||||
|
||||
---
|
||||
|
||||
**Copyright © 2025 Frigate LLC.**
|
||||
|
||||
51
README_CN.md
51
README_CN.md
@ -1,31 +1,28 @@
|
||||
<p align="center">
|
||||
<img align="center" alt="logo" src="docs/static/img/branding/frigate.png">
|
||||
<img align="center" alt="logo" src="docs/static/img/frigate.png">
|
||||
</p>
|
||||
|
||||
# Frigate NVR™ - 一个具有实时目标检测的本地 NVR
|
||||
# Frigate - 一个具有实时目标检测的本地NVR
|
||||
|
||||
[English](https://github.com/blakeblackshear/frigate) | \[简体中文\]
|
||||
|
||||
[](https://opensource.org/licenses/MIT)
|
||||
|
||||
<a href="https://hosted.weblate.org/engage/frigate-nvr/-/zh_Hans/">
|
||||
<img src="https://hosted.weblate.org/widget/frigate-nvr/-/zh_Hans/svg-badge.svg" alt="翻译状态" />
|
||||
</a>
|
||||
|
||||
一个完整的本地网络视频录像机（NVR），专为[Home Assistant](https://www.home-assistant.io)设计，具备 AI 目标/物体检测功能。使用 OpenCV 和 TensorFlow 在本地为 IP 摄像头执行实时物体检测。
|
||||
一个完整的本地网络视频录像机（NVR），专为[Home Assistant](https://www.home-assistant.io)设计，具备AI物体检测功能。使用OpenCV和TensorFlow在本地为IP摄像头执行实时物体检测。
|
||||
|
||||
强烈推荐使用 GPU 或者 AI 加速器（例如[Google Coral 加速器](https://coral.ai/products/) 或者 [Hailo](https://hailo.ai/)等）。它们的运行效率远远高于现在的顶级 CPU，并且功耗也极低。
|
||||
|
||||
- 通过[自定义组件](https://github.com/blakeblackshear/frigate-hass-integration)与 Home Assistant 紧密集成
|
||||
- 设计上通过仅在必要时和必要地点寻找目标，最大限度地减少资源使用并最大化性能
|
||||
强烈推荐使用GPU或者AI加速器（例如[Google Coral加速器](https://coral.ai/products/) 或者 [Hailo](https://hailo.ai/)）。它们的性能甚至超过目前的顶级CPU，并且可以以极低的耗电实现更优的性能。
|
||||
- 通过[自定义组件](https://github.com/blakeblackshear/frigate-hass-integration)与Home Assistant紧密集成
|
||||
- 设计上通过仅在必要时和必要地点寻找物体，最大限度地减少资源使用并最大化性能
|
||||
- 大量利用多进程处理，强调实时性而非处理每一帧
|
||||
- 使用非常低开销的画面变动检测（也叫运动检测）来确定运行目标检测的位置
|
||||
- 使用 TensorFlow 进行目标检测，并运行在单独的进程中以达到最大 FPS
|
||||
- 通过 MQTT 进行通信，便于集成到其他系统中
|
||||
- 使用非常低开销的运动检测来确定运行物体检测的位置
|
||||
- 使用TensorFlow进行物体检测，运行在单独的进程中以达到最大FPS
|
||||
- 通过MQTT进行通信，便于集成到其他系统中
|
||||
- 根据检测到的物体设置保留时间进行视频录制
|
||||
- 24/7 全天候录制
|
||||
- 通过 RTSP 重新流传输以减少摄像头的连接数
|
||||
- 支持 WebRTC 和 MSE，实现低延迟的实时观看
|
||||
- 24/7全天候录制
|
||||
- 通过RTSP重新流传输以减少摄像头的连接数
|
||||
- 支持WebRTC和MSE，实现低延迟的实时观看
|
||||
|
||||
## 社区中文翻译文档
|
||||
|
||||
@ -35,55 +32,39 @@
|
||||
|
||||
如果您想通过捐赠支持开发，请使用 [Github Sponsors](https://github.com/sponsors/blakeblackshear)。
|
||||
|
||||
## 协议
|
||||
|
||||
本项目采用 **MIT 许可证**授权。
|
||||
**代码部分**：本代码库中的源代码、配置文件和文档均遵循 [MIT 许可证](LICENSE)。您可以自由使用、修改和分发这些代码，但必须保留原始版权声明。
|
||||
|
||||
**商标部分**：“Frigate”名称、“Frigate NVR”品牌以及 Frigate 的 Logo 为 **Frigate LLC 的商标**，**不在** MIT 许可证覆盖范围内。
|
||||
有关品牌资产的规范使用详情，请参阅我们的[《商标政策》](TRADEMARK.md)。
|
||||
|
||||
## 截图
|
||||
|
||||
### 实时监控面板
|
||||
|
||||
<div>
|
||||
<img width="800" alt="实时监控面板" src="https://github.com/blakeblackshear/frigate/assets/569905/5e713cb9-9db5-41dc-947a-6937c3bc376e">
|
||||
</div>
|
||||
|
||||
### 简单的核查工作流程
|
||||
|
||||
<div>
|
||||
<img width="800" alt="简单的审查工作流程" src="https://github.com/blakeblackshear/frigate/assets/569905/6fed96e8-3b18-40e5-9ddc-31e6f3c9f2ff">
|
||||
</div>
|
||||
|
||||
### 多摄像头可按时间轴查看
|
||||
|
||||
<div>
|
||||
<img width="800" alt="多摄像头可按时间轴查看" src="https://github.com/blakeblackshear/frigate/assets/569905/d6788a15-0eeb-4427-a8d4-80b93cae3d74">
|
||||
</div>
|
||||
|
||||
### 内置遮罩和区域编辑器
|
||||
|
||||
<div>
|
||||
<img width="800" alt="内置遮罩和区域编辑器" src="https://github.com/blakeblackshear/frigate/assets/569905/d7885fc3-bfe6-452f-b7d0-d957cb3e31f5">
|
||||
</div>
|
||||
|
||||
## 翻译
|
||||
|
||||
## 翻译
|
||||
我们使用 [Weblate](https://hosted.weblate.org/projects/frigate-nvr/) 平台提供翻译支持，欢迎参与进来一起完善。
|
||||
|
||||
## 非官方中文讨论社区
|
||||
|
||||
欢迎加入中文讨论 QQ 群：[1043861059](https://qm.qq.com/q/7vQKsTmSz)
|
||||
## 非官方中文讨论社区
|
||||
欢迎加入中文讨论QQ群：[1043861059](https://qm.qq.com/q/7vQKsTmSz)
|
||||
|
||||
Bilibili：https://space.bilibili.com/3546894915602564
|
||||
|
||||
## 中文社区赞助商
|
||||
|
||||
## 中文社区赞助商
|
||||
[](https://edgeone.ai/zh?from=github)
|
||||
本项目 CDN 加速及安全防护由 Tencent EdgeOne 赞助
|
||||
|
||||
---
|
||||
|
||||
**Copyright © 2025 Frigate LLC.**
|
||||
|
||||
58
TRADEMARK.md
58
TRADEMARK.md
@ -1,58 +0,0 @@
|
||||
# Trademark Policy
|
||||
|
||||
**Last Updated:** November 2025
|
||||
|
||||
This document outlines the policy regarding the use of the trademarks associated with the Frigate NVR project.
|
||||
|
||||
## 1. Our Trademarks
|
||||
|
||||
The following terms and visual assets are trademarks (the "Marks") of **Frigate LLC**:
|
||||
|
||||
- **Frigate™**
|
||||
- **Frigate NVR™**
|
||||
- **Frigate+™**
|
||||
- **The Frigate Logo**
|
||||
|
||||
**Note on Common Law Rights:**
|
||||
Frigate LLC asserts all common law rights in these Marks. The absence of a federal registration symbol (®) does not constitute a waiver of our intellectual property rights.
|
||||
|
||||
## 2. Interaction with the MIT License
|
||||
|
||||
The software in this repository is licensed under the [MIT License](LICENSE).
|
||||
|
||||
**Crucial Distinction:**
|
||||
|
||||
- The **Code** is free to use, modify, and distribute under the MIT terms.
|
||||
- The **Brand (Trademarks)** is **NOT** licensed under MIT.
|
||||
|
||||
You may not use the Marks in any way that is not explicitly permitted by this policy or by written agreement with Frigate LLC.
|
||||
|
||||
## 3. Acceptable Use
|
||||
|
||||
You may use the Marks without prior written permission in the following specific contexts:
|
||||
|
||||
- **Referential Use:** To truthfully refer to the software (e.g., _"I use Frigate NVR for my home security"_).
|
||||
- **Compatibility:** To indicate that your product or project works with the software (e.g., _"MyPlugin for Frigate NVR"_ or _"Compatible with Frigate"_).
|
||||
- **Commentary:** In news articles, blog posts, or tutorials discussing the software.
|
||||
|
||||
## 4. Prohibited Use
|
||||
|
||||
You may **NOT** use the Marks in the following ways:
|
||||
|
||||
- **Commercial Products:** You may not use "Frigate" in the name of a commercial product, service, or app (e.g., selling an app named _"Frigate Viewer"_ is prohibited).
|
||||
- **Implying Affiliation:** You may not use the Marks in a way that suggests your project is official, sponsored by, or endorsed by Frigate LLC.
|
||||
- **Confusing Forks:** If you fork this repository to create a derivative work, you **must** remove the Frigate logo and rename your project to avoid user confusion. You cannot distribute a modified version of the software under the name "Frigate".
|
||||
- **Domain Names:** You may not register domain names containing "Frigate" that are likely to confuse users (e.g., `frigate-official-support.com`).
|
||||
|
||||
## 5. The Logo
|
||||
|
||||
The Frigate logo (the bird icon) is a visual trademark.
|
||||
|
||||
- You generally **cannot** use the logo on your own website or product packaging without permission.
|
||||
- If you are building a dashboard or integration that interfaces with Frigate, you may use the logo only to represent the Frigate node/service, provided it does not imply you _are_ Frigate.
|
||||
|
||||
## 6. Questions & Permissions
|
||||
|
||||
If you are unsure if your intended use violates this policy, or if you wish to request a specific license to use the Marks (e.g., for a partnership), please contact us at:
|
||||
|
||||
**help@frigate.video**
|
||||
@ -5,12 +5,6 @@ set -euxo pipefail
|
||||
SQLITE3_VERSION="3.46.1"
|
||||
PYSQLITE3_VERSION="0.5.3"
|
||||
|
||||
# Install libsqlite3-dev if not present (needed for some base images like NVIDIA TensorRT)
|
||||
if ! dpkg -l | grep -q libsqlite3-dev; then
|
||||
echo "Installing libsqlite3-dev for compilation..."
|
||||
apt-get update && apt-get install -y libsqlite3-dev && rm -rf /var/lib/apt/lists/*
|
||||
fi
|
||||
|
||||
# Fetch the pre-built sqlite amalgamation instead of building from source
|
||||
if [[ ! -d "sqlite" ]]; then
|
||||
mkdir sqlite
|
||||
|
||||
@ -20,7 +20,6 @@ apt-get -qq install --no-install-recommends -y \
|
||||
libgl1 \
|
||||
libglib2.0-0 \
|
||||
libusb-1.0.0 \
|
||||
python3-h2 \
|
||||
libgomp1 # memryx detector
|
||||
|
||||
update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
|
||||
@ -96,9 +95,6 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
|
||||
|
||||
apt-get -qq install -y ocl-icd-libopencl1
|
||||
|
||||
# install libtbb12 for NPU support
|
||||
apt-get -qq install -y libtbb12
|
||||
|
||||
rm -f /usr/share/keyrings/intel-graphics.gpg
|
||||
rm -f /etc/apt/sources.list.d/intel-gpu-jammy.list
|
||||
|
||||
@ -119,11 +115,6 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
|
||||
wget https://github.com/intel/compute-runtime/releases/download/24.52.32224.5/intel-level-zero-gpu_1.6.32224.5_amd64.deb
|
||||
wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.5.6/intel-igc-opencl-2_2.5.6+18417_amd64.deb
|
||||
wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.5.6/intel-igc-core-2_2.5.6+18417_amd64.deb
|
||||
# npu packages
|
||||
wget https://github.com/oneapi-src/level-zero/releases/download/v1.21.9/level-zero_1.21.9+u22.04_amd64.deb
|
||||
wget https://github.com/intel/linux-npu-driver/releases/download/v1.17.0/intel-driver-compiler-npu_1.17.0.20250508-14912879441_ubuntu22.04_amd64.deb
|
||||
wget https://github.com/intel/linux-npu-driver/releases/download/v1.17.0/intel-fw-npu_1.17.0.20250508-14912879441_ubuntu22.04_amd64.deb
|
||||
wget https://github.com/intel/linux-npu-driver/releases/download/v1.17.0/intel-level-zero-npu_1.17.0.20250508-14912879441_ubuntu22.04_amd64.deb
|
||||
|
||||
dpkg -i *.deb
|
||||
rm *.deb
|
||||
@ -145,6 +136,6 @@ rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# Install yq, for frigate-prepare and go2rtc echo source
|
||||
curl -fsSL \
|
||||
"https://github.com/mikefarah/yq/releases/download/v4.48.2/yq_linux_$(dpkg --print-architecture)" \
|
||||
"https://github.com/mikefarah/yq/releases/download/v4.33.3/yq_linux_$(dpkg --print-architecture)" \
|
||||
--output /usr/local/bin/yq
|
||||
chmod +x /usr/local/bin/yq
|
||||
|
||||
@ -2,9 +2,9 @@
|
||||
set -e
|
||||
|
||||
# Download the MxAccl for Frigate github release
|
||||
wget https://github.com/memryx/mx_accl_frigate/archive/refs/tags/v2.1.0.zip -O /tmp/mxaccl.zip
|
||||
wget https://github.com/memryx/mx_accl_frigate/archive/refs/heads/main.zip -O /tmp/mxaccl.zip
|
||||
unzip /tmp/mxaccl.zip -d /tmp
|
||||
mv /tmp/mx_accl_frigate-2.1.0 /opt/mx_accl_frigate
|
||||
mv /tmp/mx_accl_frigate-main /opt/mx_accl_frigate
|
||||
rm /tmp/mxaccl.zip
|
||||
|
||||
# Install Python dependencies
|
||||
|
||||
@ -56,7 +56,7 @@ pywebpush == 2.0.*
|
||||
# alpr
|
||||
pyclipper == 1.3.*
|
||||
shapely == 2.0.*
|
||||
rapidfuzz==3.12.*
|
||||
Levenshtein==0.26.*
|
||||
# HailoRT Wheels
|
||||
appdirs==1.4.*
|
||||
argcomplete==2.0.*
|
||||
@ -81,5 +81,3 @@ librosa==0.11.*
|
||||
soundfile==0.13.*
|
||||
# DeGirum detector
|
||||
degirum == 0.16.*
|
||||
# Memory profiling
|
||||
memray == 1.15.*
|
||||
|
||||
@ -1 +1,2 @@
|
||||
scikit-build == 0.18.*
|
||||
nvidia-pyindex
|
||||
|
||||
@ -73,8 +73,6 @@ http {
|
||||
vod_manifest_segment_durations_mode accurate;
|
||||
vod_ignore_edit_list on;
|
||||
vod_segment_duration 10000;
|
||||
|
||||
# MPEG-TS settings (not used when fMP4 is enabled, kept for reference)
|
||||
vod_hls_mpegts_align_frames off;
|
||||
vod_hls_mpegts_interleave_frames on;
|
||||
|
||||
@ -107,10 +105,6 @@ http {
|
||||
aio threads;
|
||||
vod hls;
|
||||
|
||||
# Use fMP4 (fragmented MP4) instead of MPEG-TS for better performance
|
||||
# Smaller segments, faster generation, better browser compatibility
|
||||
vod_hls_container_format fmp4;
|
||||
|
||||
secure_token $args;
|
||||
secure_token_types application/vnd.apple.mpegurl;
|
||||
|
||||
@ -280,18 +274,6 @@ http {
|
||||
include proxy.conf;
|
||||
}
|
||||
|
||||
# Allow unauthenticated access to the first_time_login endpoint
|
||||
# so the login page can load help text before authentication.
|
||||
location /api/auth/first_time_login {
|
||||
auth_request off;
|
||||
limit_except GET {
|
||||
deny all;
|
||||
}
|
||||
rewrite ^/api(/.*)$ $1 break;
|
||||
proxy_pass http://frigate_api;
|
||||
include proxy.conf;
|
||||
}
|
||||
|
||||
location /api/stats {
|
||||
include auth_request.conf;
|
||||
access_log off;
|
||||
@ -320,12 +302,6 @@ http {
|
||||
add_header Cache-Control "public";
|
||||
}
|
||||
|
||||
location /fonts/ {
|
||||
access_log off;
|
||||
expires 1y;
|
||||
add_header Cache-Control "public";
|
||||
}
|
||||
|
||||
location /locales/ {
|
||||
access_log off;
|
||||
add_header Cache-Control "public";
|
||||
|
||||
@ -24,13 +24,10 @@ echo "Adding MemryX GPG key and repository..."
|
||||
wget -qO- https://developer.memryx.com/deb/memryx.asc | sudo tee /etc/apt/trusted.gpg.d/memryx.asc >/dev/null
|
||||
echo 'deb https://developer.memryx.com/deb stable main' | sudo tee /etc/apt/sources.list.d/memryx.list >/dev/null
|
||||
|
||||
# Update and install specific SDK 2.1 packages
|
||||
echo "Installing MemryX SDK 2.1 packages..."
|
||||
# Update and install memx-drivers
|
||||
echo "Installing memx-drivers..."
|
||||
sudo apt update
|
||||
sudo apt install -y memx-drivers=2.1.* memx-accl=2.1.* mxa-manager=2.1.*
|
||||
|
||||
# Hold packages to prevent automatic upgrades
|
||||
sudo apt-mark hold memx-drivers memx-accl mxa-manager
|
||||
sudo apt install -y memx-drivers
|
||||
|
||||
# ARM-specific board setup
|
||||
if [[ "$arch" == "aarch64" || "$arch" == "arm64" ]]; then
|
||||
@ -40,5 +37,11 @@ fi
|
||||
|
||||
echo -e "\n\n\033[1;31mYOU MUST RESTART YOUR COMPUTER NOW\033[0m\n\n"
|
||||
|
||||
echo "MemryX SDK 2.1 installation complete!"
|
||||
# Install other runtime packages
|
||||
packages=("memx-accl" "mxa-manager")
|
||||
for pkg in "${packages[@]}"; do
|
||||
echo "Installing $pkg..."
|
||||
sudo apt install -y "$pkg"
|
||||
done
|
||||
|
||||
echo "MemryX installation complete!"
|
||||
|
||||
@ -15,7 +15,7 @@ ARG AMDGPU
|
||||
|
||||
RUN apt update -qq && \
|
||||
apt install -y wget gpg && \
|
||||
wget -O rocm.deb https://repo.radeon.com/amdgpu-install/7.1.1/ubuntu/jammy/amdgpu-install_7.1.1.70101-1_all.deb && \
|
||||
wget -O rocm.deb https://repo.radeon.com/amdgpu-install/7.0.2/ubuntu/jammy/amdgpu-install_7.0.2.70002-1_all.deb && \
|
||||
apt install -y ./rocm.deb && \
|
||||
apt update && \
|
||||
apt install -qq -y rocm
|
||||
|
||||
@ -1 +1 @@
|
||||
onnxruntime-migraphx @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v7.1.0/onnxruntime_migraphx-1.23.1-cp311-cp311-linux_x86_64.whl
|
||||
onnxruntime-migraphx @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v7.0.2/onnxruntime_migraphx-1.23.1-cp311-cp311-linux_x86_64.whl
|
||||
@ -2,7 +2,7 @@ variable "AMDGPU" {
|
||||
default = "gfx900"
|
||||
}
|
||||
variable "ROCM" {
|
||||
default = "7.1.1"
|
||||
default = "7.0.2"
|
||||
}
|
||||
variable "HSA_OVERRIDE_GFX_VERSION" {
|
||||
default = ""
|
||||
|
||||
@ -21,7 +21,7 @@ FROM deps AS frigate-tensorrt
|
||||
ARG PIP_BREAK_SYSTEM_PACKAGES
|
||||
|
||||
RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
|
||||
pip3 uninstall -y onnxruntime \
|
||||
pip3 uninstall -y onnxruntime tensorflow-cpu \
|
||||
&& pip3 install -U /deps/trt-wheels/*.whl
|
||||
|
||||
COPY --from=rootfs / /
|
||||
|
||||
@ -112,7 +112,7 @@ RUN apt-get update \
|
||||
&& apt-get install -y protobuf-compiler libprotobuf-dev \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
RUN --mount=type=bind,source=docker/tensorrt/requirements-models-arm64.txt,target=/requirements-tensorrt-models.txt \
|
||||
pip3 wheel --wheel-dir=/trt-model-wheels --no-deps -r /requirements-tensorrt-models.txt
|
||||
pip3 wheel --wheel-dir=/trt-model-wheels -r /requirements-tensorrt-models.txt
|
||||
|
||||
FROM wget AS jetson-ffmpeg
|
||||
ARG DEBIAN_FRONTEND
|
||||
@ -145,8 +145,7 @@ 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 \
|
||||
pip3 uninstall -y onnxruntime \
|
||||
&& pip3 install -U /deps/trt-wheels/*.whl \
|
||||
&& pip3 install -U /deps/trt-model-wheels/*.whl \
|
||||
&& pip3 install -U /deps/trt-wheels/*.whl /deps/trt-model-wheels/*.whl \
|
||||
&& ldconfig
|
||||
|
||||
WORKDIR /opt/frigate/
|
||||
|
||||
@ -13,6 +13,7 @@ nvidia_cusolver_cu12==11.6.3.*; platform_machine == 'x86_64'
|
||||
nvidia_cusparse_cu12==12.5.1.*; platform_machine == 'x86_64'
|
||||
nvidia_nccl_cu12==2.23.4; platform_machine == 'x86_64'
|
||||
nvidia_nvjitlink_cu12==12.5.82; platform_machine == 'x86_64'
|
||||
tensorflow==2.19.*; platform_machine == 'x86_64'
|
||||
onnx==1.16.*; platform_machine == 'x86_64'
|
||||
onnxruntime-gpu==1.22.*; platform_machine == 'x86_64'
|
||||
protobuf==3.20.3; platform_machine == 'x86_64'
|
||||
|
||||
@ -1,2 +1 @@
|
||||
cuda-python == 12.6.*; platform_machine == 'aarch64'
|
||||
numpy == 1.26.*; platform_machine == 'aarch64'
|
||||
|
||||
@ -25,7 +25,7 @@ Examples of available modules are:
|
||||
|
||||
- `frigate.app`
|
||||
- `frigate.mqtt`
|
||||
- `frigate.object_detection.base`
|
||||
- `frigate.object_detection`
|
||||
- `detector.<detector_name>`
|
||||
- `watchdog.<camera_name>`
|
||||
- `ffmpeg.<camera_name>.<sorted_roles>` NOTE: All FFmpeg logs are sent as `error` level.
|
||||
@ -53,17 +53,6 @@ environment_vars:
|
||||
VARIABLE_NAME: variable_value
|
||||
```
|
||||
|
||||
#### TensorFlow Thread Configuration
|
||||
|
||||
If you encounter thread creation errors during classification model training, you can limit TensorFlow's thread usage:
|
||||
|
||||
```yaml
|
||||
environment_vars:
|
||||
TF_INTRA_OP_PARALLELISM_THREADS: "2" # Threads within operations (0 = use default)
|
||||
TF_INTER_OP_PARALLELISM_THREADS: "2" # Threads between operations (0 = use default)
|
||||
TF_DATASET_THREAD_POOL_SIZE: "2" # Data pipeline threads (0 = use default)
|
||||
```
|
||||
|
||||
### `database`
|
||||
|
||||
Tracked object and recording information is managed in a sqlite database at `/config/frigate.db`. If that database is deleted, recordings will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within Home Assistant.
|
||||
@ -258,7 +247,7 @@ 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 -o=json '.' config.yaml | curl -X POST 'http://frigate_host:5000/api/config/save?save_option=saveonly' --data-binary @-
|
||||
yq r -j config.yml | curl -X POST http://frigate_host:5000/api/config/save -d @-
|
||||
```
|
||||
|
||||
### Via Command Line
|
||||
|
||||
@ -75,13 +75,7 @@ audio:
|
||||
|
||||
### Audio Transcription
|
||||
|
||||
Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI’s open-source Whisper models via `faster-whisper`. The goal of this feature is to support Semantic Search for `speech` audio events. Frigate is not intended to act as a continuous, fully-automatic speech transcription service — automatically transcribing all speech (or queuing many audio events for transcription) requires substantial CPU (or GPU) resources and is impractical on most systems. For this reason, transcriptions for events are initiated manually from the UI or the API rather than being run continuously in the background.
|
||||
|
||||
Transcription accuracy also depends heavily on the quality of your camera's microphone and recording conditions. Many cameras use inexpensive microphones, and distance to the speaker, low audio bitrate, or background noise can significantly reduce transcription quality. If you need higher accuracy, more robust long-running queues, or large-scale automatic transcription, consider using the HTTP API in combination with an automation platform and a cloud transcription service.
|
||||
|
||||
#### Configuration
|
||||
|
||||
To enable transcription, enable it in your config. Note that audio detection must also be enabled as described above in order to use audio transcription features.
|
||||
Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI’s open-source Whisper models via `faster-whisper`. To enable transcription, enable it in your config. Note that audio detection must also be enabled as described above in order to use audio transcription features.
|
||||
|
||||
```yaml
|
||||
audio_transcription:
|
||||
@ -150,26 +144,4 @@ In order to use transcription and translation for past events, you must enable a
|
||||
|
||||
The transcribed/translated speech will appear in the description box in the Tracked Object Details pane. If Semantic Search is enabled, embeddings are generated for the transcription text and are fully searchable using the description search type.
|
||||
|
||||
:::note
|
||||
|
||||
Only one `speech` event may be transcribed at a time. Frigate does not automatically transcribe `speech` events or implement a queue for long-running transcription model inference.
|
||||
|
||||
:::
|
||||
|
||||
Recorded `speech` events will always use a `whisper` model, regardless of the `model_size` config setting. Without a supported Nvidia GPU, generating transcriptions for longer `speech` events may take a fair amount of time, so be patient.
|
||||
|
||||
#### FAQ
|
||||
|
||||
1. Why doesn't Frigate automatically transcribe all `speech` events?
|
||||
|
||||
Frigate does not implement a queue mechanism for speech transcription, and adding one is not trivial. A proper queue would need backpressure, prioritization, memory/disk buffering, retry logic, crash recovery, and safeguards to prevent unbounded growth when events outpace processing. That’s a significant amount of complexity for a feature that, in most real-world environments, would mostly just churn through low-value noise.
|
||||
|
||||
Because transcription is **serialized (one event at a time)** and speech events can be generated far faster than they can be processed, an auto-transcribe toggle would very quickly create an ever-growing backlog and degrade core functionality. For the amount of engineering and risk involved, it adds **very little practical value** for the majority of deployments, which are often on low-powered, edge hardware.
|
||||
|
||||
If you hear speech that’s actually important and worth saving/indexing for the future, **just press the transcribe button in Explore** on that specific `speech` event - that keeps things explicit, reliable, and under your control.
|
||||
|
||||
2. Why don't you save live transcription text and use that for `speech` events?
|
||||
|
||||
There’s no guarantee that a `speech` event is even created from the exact audio that went through the transcription model. Live transcription and `speech` event creation are **separate, asynchronous processes**. Even when both are correctly configured, trying to align the **precise start and end time of a speech event** with whatever audio the model happened to be processing at that moment is unreliable.
|
||||
|
||||
Automatically persisting that data would often result in **misaligned, partial, or irrelevant transcripts**, while still incurring all of the CPU, storage, and privacy costs of transcription. That’s why Frigate treats transcription as an **explicit, user-initiated action** rather than an automatic side-effect of every `speech` event.
|
||||
Recorded `speech` events will always use a `whisper` model, regardless of the `model_size` config setting. Without a GPU, generating transcriptions for longer `speech` events may take a fair amount of time, so be patient.
|
||||
|
||||
@ -164,35 +164,13 @@ According to [this discussion](https://github.com/blakeblackshear/frigate/issues
|
||||
Cameras connected via a Reolink NVR can be connected with the http stream, use `channel[0..15]` in the stream url for the additional channels.
|
||||
The setup of main stream can be also done via RTSP, but isn't always reliable on all hardware versions. The example configuration is working with the oldest HW version RLN16-410 device with multiple types of cameras.
|
||||
|
||||
<details>
|
||||
<summary>Example Config</summary>
|
||||
|
||||
:::tip
|
||||
|
||||
Reolink's latest cameras support two way audio via go2rtc and other applications. It is important that the http-flv stream is still used for stability, a secondary rtsp stream can be added that will be using for the two way audio only.
|
||||
|
||||
NOTE: The RTSP stream can not be prefixed with `ffmpeg:`, as go2rtc needs to handle the stream to support two way audio.
|
||||
|
||||
Ensure HTTP is enabled in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).
|
||||
|
||||
:::
|
||||
|
||||
```yaml
|
||||
go2rtc:
|
||||
streams:
|
||||
# example for connecting to a standard Reolink camera
|
||||
your_reolink_camera:
|
||||
- "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
|
||||
your_reolink_camera_sub:
|
||||
- "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password"
|
||||
# example for connectin to a Reolink camera that supports two way talk
|
||||
your_reolink_camera_twt:
|
||||
- "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
|
||||
- "rtsp://username:password@reolink_ip/Preview_01_sub
|
||||
your_reolink_camera_twt_sub:
|
||||
- "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password"
|
||||
- "rtsp://username:password@reolink_ip/Preview_01_sub
|
||||
# example for connecting to a Reolink NVR
|
||||
your_reolink_camera_via_nvr:
|
||||
- "ffmpeg:http://reolink_nvr_ip/flv?port=1935&app=bcs&stream=channel3_main.bcs&user=username&password=password" # channel numbers are 0-15
|
||||
- "ffmpeg:your_reolink_camera_via_nvr#audio=aac"
|
||||
@ -223,7 +201,22 @@ cameras:
|
||||
roles:
|
||||
- detect
|
||||
```
|
||||
</details>
|
||||
|
||||
#### Reolink Doorbell
|
||||
|
||||
The reolink doorbell supports two way audio via go2rtc and other applications. It is important that the http-flv stream is still used for stability, a secondary rtsp stream can be added that will be using for the two way audio only.
|
||||
|
||||
Ensure HTTP is enabled in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).
|
||||
|
||||
```yaml
|
||||
go2rtc:
|
||||
streams:
|
||||
your_reolink_doorbell:
|
||||
- "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
|
||||
- rtsp://reolink_ip/Preview_01_sub
|
||||
your_reolink_doorbell_sub:
|
||||
- "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password"
|
||||
```
|
||||
|
||||
### Unifi Protect Cameras
|
||||
|
||||
|
||||
@ -10,19 +10,9 @@ Object classification allows you to train a custom MobileNetV2 classification mo
|
||||
Object classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate.
|
||||
|
||||
Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
|
||||
When running the `-tensorrt` image, Nvidia GPUs will automatically be used to accelerate training.
|
||||
|
||||
## Classes
|
||||
|
||||
Classes are the categories your model will learn to distinguish between. Each class represents a distinct visual category that the model will predict.
|
||||
|
||||
For object classification:
|
||||
|
||||
- Define classes that represent different types or attributes of the detected object
|
||||
- Examples: For `person` objects, classes might be `delivery_person`, `resident`, `stranger`
|
||||
- Include a `none` class for objects that don't fit any specific category
|
||||
- Keep classes visually distinct to improve accuracy
|
||||
|
||||
### Classification Type
|
||||
### Sub label vs Attribute
|
||||
|
||||
- **Sub label**:
|
||||
|
||||
@ -35,15 +25,6 @@ For object classification:
|
||||
- Ideal when multiple attributes can coexist independently.
|
||||
- Example: Detecting if a `person` in a construction yard is wearing a helmet or not.
|
||||
|
||||
## Assignment Requirements
|
||||
|
||||
Sub labels and attributes are only assigned when both conditions are met:
|
||||
|
||||
1. **Threshold**: Each classification attempt must have a confidence score that meets or exceeds the configured `threshold` (default: `0.8`).
|
||||
2. **Class Consensus**: After at least 3 classification attempts, 60% of attempts must agree on the same class label. If the consensus class is `none`, no assignment is made.
|
||||
|
||||
This two-step verification prevents false positives by requiring consistent predictions across multiple frames before assigning a sub label or attribute.
|
||||
|
||||
## Example use cases
|
||||
|
||||
### Sub label
|
||||
@ -75,22 +56,18 @@ classification:
|
||||
|
||||
## Training the model
|
||||
|
||||
Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of two steps:
|
||||
Creating and training the model is done within the Frigate UI using the `Classification` page.
|
||||
|
||||
### Step 1: Name and Define
|
||||
|
||||
Enter a name for your model, select the object label to classify (e.g., `person`, `dog`, `car`), choose the classification type (sub label or attribute), and define your classes. Include a `none` class for objects that don't fit any specific category.
|
||||
|
||||
### Step 2: Assign Training Examples
|
||||
|
||||
The system will automatically generate example images from detected objects matching your selected label. You'll be guided through each class one at a time to select which images represent that class. Any images not assigned to a specific class will automatically be assigned to `none` when you complete the last class. Once all images are processed, training will begin automatically.
|
||||
### Getting Started
|
||||
|
||||
When choosing which objects to classify, start with a small number of visually distinct classes and ensure your training samples match camera viewpoints and distances typical for those objects.
|
||||
|
||||
// TODO add this section once UI is implemented. Explain process of selecting objects and curating training examples.
|
||||
|
||||
### Improving the Model
|
||||
|
||||
- **Problem framing**: Keep classes visually distinct and relevant to the chosen object types.
|
||||
- **Data collection**: Use the model’s Recent Classification tab to gather balanced examples across times of day, weather, and distances.
|
||||
- **Data collection**: Use the model’s Train tab to gather balanced examples across times of day, weather, and distances.
|
||||
- **Preprocessing**: Ensure examples reflect object crops similar to Frigate’s boxes; keep the subject centered.
|
||||
- **Labels**: Keep label names short and consistent; include a `none` class if you plan to ignore uncertain predictions for sub labels.
|
||||
- **Threshold**: Tune `threshold` per model to reduce false assignments. Start at `0.8` and adjust based on validation.
|
||||
|
||||
@ -10,17 +10,7 @@ State classification allows you to train a custom MobileNetV2 classification mod
|
||||
State classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate.
|
||||
|
||||
Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
|
||||
|
||||
## Classes
|
||||
|
||||
Classes are the different states an area on your camera can be in. Each class represents a distinct visual state that the model will learn to recognize.
|
||||
|
||||
For state classification:
|
||||
|
||||
- Define classes that represent mutually exclusive states
|
||||
- Examples: `open` and `closed` for a garage door, `on` and `off` for lights
|
||||
- Use at least 2 classes (typically binary states work best)
|
||||
- Keep class names clear and descriptive
|
||||
When running the `-tensorrt` image, Nvidia GPUs will automatically be used to accelerate training.
|
||||
|
||||
## Example use cases
|
||||
|
||||
@ -48,25 +38,15 @@ classification:
|
||||
|
||||
## Training the model
|
||||
|
||||
Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of three steps:
|
||||
Creating and training the model is done within the Frigate UI using the `Classification` page.
|
||||
|
||||
### Step 1: Name and Define
|
||||
### Getting Started
|
||||
|
||||
Enter a name for your model and define at least 2 classes (states) that represent mutually exclusive states. For example, `open` and `closed` for a door, or `on` and `off` for lights.
|
||||
When choosing a portion of the camera frame for state classification, it is important to make the crop tight around the area of interest to avoid extra signals unrelated to what is being classified.
|
||||
|
||||
### Step 2: Select the Crop Area
|
||||
|
||||
Choose one or more cameras and draw a rectangle over the area of interest for each camera. The crop should be tight around the region you want to classify to avoid extra signals unrelated to what is being classified. You can drag and resize the rectangle to adjust the crop area.
|
||||
|
||||
### Step 3: Assign Training Examples
|
||||
|
||||
The system will automatically generate example images from your camera feeds. You'll be guided through each class one at a time to select which images represent that state.
|
||||
|
||||
**Important**: All images must be assigned to a state before training can begin. This includes images that may not be optimal, such as when people temporarily block the view, sun glare is present, or other distractions occur. Assign these images to the state that is actually present (based on what you know the state to be), not based on the distraction. This training helps the model correctly identify the state even when such conditions occur during inference.
|
||||
|
||||
Once all images are assigned, training will begin automatically.
|
||||
// TODO add this section once UI is implemented. Explain process of selecting a crop.
|
||||
|
||||
### Improving the Model
|
||||
|
||||
- **Problem framing**: Keep classes visually distinct and state-focused (e.g., `open`, `closed`, `unknown`). Avoid combining object identity with state in a single model unless necessary.
|
||||
- **Data collection**: Use the model’s Recent Classifications tab to gather balanced examples across times of day and weather.
|
||||
- **Data collection**: Use the model’s Train tab to gather balanced examples across times of day and weather.
|
||||
|
||||
@ -70,7 +70,7 @@ Fine-tune face recognition with these optional parameters at the global level of
|
||||
- `min_faces`: Min face recognitions for the sub label to be applied to the person object.
|
||||
- Default: `1`
|
||||
- `save_attempts`: Number of images of recognized faces to save for training.
|
||||
- Default: `200`.
|
||||
- Default: `100`.
|
||||
- `blur_confidence_filter`: Enables a filter that calculates how blurry the face is and adjusts the confidence based on this.
|
||||
- Default: `True`.
|
||||
- `device`: Target a specific device to run the face recognition model on (multi-GPU installation).
|
||||
@ -114,9 +114,9 @@ When choosing images to include in the face training set it is recommended to al
|
||||
|
||||
:::
|
||||
|
||||
### Understanding the Recent Recognitions Tab
|
||||
### Understanding the Train Tab
|
||||
|
||||
The Recent Recognitions tab in the face library displays recent face recognition attempts. Detected face images are grouped according to the person they were identified as potentially matching.
|
||||
The Train tab in the face library displays recent face recognition attempts. Detected face images are grouped according to the person they were identified as potentially matching.
|
||||
|
||||
Each face image is labeled with a name (or `Unknown`) along with the confidence score of the recognition attempt. While each image can be used to train the system for a specific person, not all images are suitable for training.
|
||||
|
||||
@ -140,7 +140,7 @@ Once front-facing images are performing well, start choosing slightly off-angle
|
||||
|
||||
Start with the [Usage](#usage) section and re-read the [Model Requirements](#model-requirements) above.
|
||||
|
||||
1. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Recent Recognitions tab in the Frigate UI's Face Library.
|
||||
1. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Train tab in the Frigate UI's Face Library.
|
||||
|
||||
If you are using a Frigate+ or `face` detecting model:
|
||||
|
||||
@ -161,8 +161,6 @@ Start with the [Usage](#usage) section and re-read the [Model Requirements](#mod
|
||||
|
||||
Accuracy is definitely a going to be improved with higher quality cameras / streams. It is important to look at the DORI (Detection Observation Recognition Identification) range of your camera, if that specification is posted. This specification explains the distance from the camera that a person can be detected, observed, recognized, and identified. The identification range is the most relevant here, and the distance listed by the camera is the furthest that face recognition will realistically work.
|
||||
|
||||
Some users have also noted that setting the stream in camera firmware to a constant bit rate (CBR) leads to better image clarity than with a variable bit rate (VBR).
|
||||
|
||||
### Why can't I bulk upload photos?
|
||||
|
||||
It is important to methodically add photos to the library, bulk importing photos (especially from a general photo library) will lead to over-fitting in that particular scenario and hurt recognition performance.
|
||||
@ -188,7 +186,7 @@ Avoid training on images that already score highly, as this can lead to over-fit
|
||||
No, face recognition does not support negative training (i.e., explicitly telling it who someone is _not_). Instead, the best approach is to improve the training data by using a more diverse and representative set of images for each person.
|
||||
For more guidance, refer to the section above on improving recognition accuracy.
|
||||
|
||||
### I see scores above the threshold in the Recent Recognitions tab, but a sub label wasn't assigned?
|
||||
### I see scores above the threshold in the train tab, but a sub label wasn't assigned?
|
||||
|
||||
The Frigate considers the recognition scores across all recognition attempts for each person object. The scores are continually weighted based on the area of the face, and a sub label will only be assigned to person if a person is confidently recognized consistently. This avoids cases where a single high confidence recognition would throw off the results.
|
||||
|
||||
|
||||
@ -17,17 +17,18 @@ To use Generative AI, you must define a single provider at the global level of y
|
||||
genai:
|
||||
provider: gemini
|
||||
api_key: "{FRIGATE_GEMINI_API_KEY}"
|
||||
model: gemini-2.0-flash
|
||||
model: gemini-1.5-flash
|
||||
|
||||
cameras:
|
||||
front_camera:
|
||||
objects:
|
||||
genai:
|
||||
enabled: True # <- enable GenAI for your front camera
|
||||
use_snapshot: True
|
||||
objects:
|
||||
- person
|
||||
required_zones:
|
||||
- steps
|
||||
enabled: True # <- enable GenAI for your front camera
|
||||
use_snapshot: True
|
||||
objects:
|
||||
- person
|
||||
required_zones:
|
||||
- steps
|
||||
indoor_camera:
|
||||
objects:
|
||||
genai:
|
||||
@ -70,7 +71,7 @@ You should have at least 8 GB of RAM available (or VRAM if running on GPU) to ru
|
||||
genai:
|
||||
provider: ollama
|
||||
base_url: http://localhost:11434
|
||||
model: qwen3-vl:4b
|
||||
model: llava:7b
|
||||
```
|
||||
|
||||
## Google Gemini
|
||||
@ -79,7 +80,7 @@ Google Gemini has a free tier allowing [15 queries per minute](https://ai.google
|
||||
|
||||
### Supported Models
|
||||
|
||||
You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://ai.google.dev/gemini-api/docs/models/gemini).
|
||||
You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://ai.google.dev/gemini-api/docs/models/gemini). At the time of writing, this includes `gemini-1.5-pro` and `gemini-1.5-flash`.
|
||||
|
||||
### Get API Key
|
||||
|
||||
@ -96,7 +97,7 @@ To start using Gemini, you must first get an API key from [Google AI Studio](htt
|
||||
genai:
|
||||
provider: gemini
|
||||
api_key: "{FRIGATE_GEMINI_API_KEY}"
|
||||
model: gemini-2.0-flash
|
||||
model: gemini-1.5-flash
|
||||
```
|
||||
|
||||
:::note
|
||||
@ -111,7 +112,7 @@ OpenAI does not have a free tier for their API. With the release of gpt-4o, pric
|
||||
|
||||
### Supported Models
|
||||
|
||||
You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://platform.openai.com/docs/models).
|
||||
You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://platform.openai.com/docs/models). At the time of writing, this includes `gpt-4o` and `gpt-4-turbo`.
|
||||
|
||||
### Get API Key
|
||||
|
||||
@ -138,19 +139,18 @@ Microsoft offers several vision models through Azure OpenAI. A subscription is r
|
||||
|
||||
### Supported Models
|
||||
|
||||
You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models).
|
||||
You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models). At the time of writing, this includes `gpt-4o` and `gpt-4-turbo`.
|
||||
|
||||
### Create Resource and Get API Key
|
||||
|
||||
To start using Azure OpenAI, you must first [create a resource](https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource). You'll need your API key, model name, and resource URL, which must include the `api-version` parameter (see the example below).
|
||||
To start using Azure OpenAI, you must first [create a resource](https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource). You'll need your API key and resource URL, which must include the `api-version` parameter (see the example below). The model field is not required in your configuration as the model is part of the deployment name you chose when deploying the resource.
|
||||
|
||||
### Configuration
|
||||
|
||||
```yaml
|
||||
genai:
|
||||
provider: azure_openai
|
||||
base_url: https://instance.cognitiveservices.azure.com/openai/responses?api-version=2025-04-01-preview
|
||||
model: gpt-5-mini
|
||||
base_url: https://example-endpoint.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2023-03-15-preview
|
||||
api_key: "{FRIGATE_OPENAI_API_KEY}"
|
||||
```
|
||||
|
||||
@ -196,10 +196,10 @@ genai:
|
||||
model: llava
|
||||
|
||||
objects:
|
||||
prompt: "Analyze the {label} in these images from the {camera} security camera. Focus on the actions, behavior, and potential intent of the {label}, rather than just describing its appearance."
|
||||
object_prompts:
|
||||
person: "Examine the main person in these images. What are they doing and what might their actions suggest about their intent (e.g., approaching a door, leaving an area, standing still)? Do not describe the surroundings or static details."
|
||||
car: "Observe the primary vehicle in these images. Focus on its movement, direction, or purpose (e.g., parking, approaching, circling). If it's a delivery vehicle, mention the company."
|
||||
prompt: "Analyze the {label} in these images from the {camera} security camera. Focus on the actions, behavior, and potential intent of the {label}, rather than just describing its appearance."
|
||||
object_prompts:
|
||||
person: "Examine the main person in these images. What are they doing and what might their actions suggest about their intent (e.g., approaching a door, leaving an area, standing still)? Do not describe the surroundings or static details."
|
||||
car: "Observe the primary vehicle in these images. Focus on its movement, direction, or purpose (e.g., parking, approaching, circling). If it's a delivery vehicle, mention the company."
|
||||
```
|
||||
|
||||
Prompts can also be overridden at the camera level to provide a more detailed prompt to the model about your specific camera, if you desire.
|
||||
|
||||
@ -35,18 +35,18 @@ Each model is available in multiple parameter sizes (3b, 4b, 8b, etc.). Larger s
|
||||
|
||||
:::tip
|
||||
|
||||
If you are trying to use a single model for Frigate and HomeAssistant, it will need to support vision and tools calling. qwen3-VL supports vision and tools simultaneously in Ollama.
|
||||
If you are trying to use a single model for Frigate and HomeAssistant, it will need to support vision and tools calling. https://github.com/skye-harris/ollama-modelfiles contains optimized model configs for this task.
|
||||
|
||||
:::
|
||||
|
||||
The following models are recommended:
|
||||
|
||||
| Model | Notes |
|
||||
| ----------------- | -------------------------------------------------------------------- |
|
||||
| `qwen3-vl` | Strong visual and situational understanding, higher vram requirement |
|
||||
| `Intern3.5VL` | Relatively fast with good vision comprehension |
|
||||
| `gemma3` | Strong frame-to-frame understanding, slower inference times |
|
||||
| `qwen2.5-vl` | Fast but capable model with good vision comprehension |
|
||||
| Model | Notes |
|
||||
| ----------------- | ----------------------------------------------------------- |
|
||||
| `Intern3.5VL` | Relatively fast with good vision comprehension
|
||||
| `gemma3` | Strong frame-to-frame understanding, slower inference times |
|
||||
| `qwen2.5vl` | Fast but capable model with good vision comprehension |
|
||||
| `llava-phi3` | Lightweight and fast model with vision comprehension |
|
||||
|
||||
:::note
|
||||
|
||||
|
||||
@ -7,95 +7,38 @@ Generative AI can be used to automatically generate structured summaries of revi
|
||||
|
||||
Requests for a summary are requested automatically to your AI provider for alert review items when the activity has ended, they can also be optionally enabled for detections as well.
|
||||
|
||||
Generative AI review summaries can also be toggled dynamically for a [camera via MQTT](/integrations/mqtt/#frigatecamera_namereviewdescriptionsset).
|
||||
Generative AI review summaries can also be toggled dynamically for a camera via MQTT with the topic `frigate/<camera_name>/review_descriptions/set`. See the [MQTT documentation](/integrations/mqtt/#frigatecamera_namereviewdescriptionsset).
|
||||
|
||||
## Review Summary Usage and Best Practices
|
||||
|
||||
Review summaries provide structured JSON responses that are saved for each review item:
|
||||
|
||||
```
|
||||
- `title` (string): A concise, direct title that describes the purpose or overall action (e.g., "Person taking out trash", "Joe walking dog").
|
||||
- `scene` (string): A narrative description of what happens across the sequence from start to finish, including setting, detected objects, and their observable actions.
|
||||
- `confidence` (float): 0-1 confidence in the analysis. Higher confidence when objects/actions are clearly visible and context is unambiguous.
|
||||
- `scene` (string): A full description including setting, entities, actions, and any plausible supported inferences.
|
||||
- `confidence` (float): 0-1 confidence in the analysis.
|
||||
- `other_concerns` (list): List of user-defined concerns that may need additional investigation.
|
||||
- `potential_threat_level` (integer): 0, 1, or 2 as defined below.
|
||||
|
||||
Threat-level definitions:
|
||||
- 0 — Typical or expected activity for this location/time (includes residents, guests, or known animals engaged in normal activities, even if they glance around or scan surroundings).
|
||||
- 1 — Unusual or suspicious activity: At least one security-relevant behavior is present **and not explainable by a normal residential activity**.
|
||||
- 2 — Active or immediate threat: Breaking in, vandalism, aggression, weapon display.
|
||||
```
|
||||
|
||||
This will show in multiple places in the UI to give additional context about each activity, and allow viewing more details when extra attention is required. Frigate's built in notifications will also automatically show the title and description when the data is available.
|
||||
This will show in the UI as a list of concerns that each review item has along with the general description.
|
||||
|
||||
### Defining Typical Activity
|
||||
|
||||
Each installation and even camera can have different parameters for what is considered suspicious activity. Frigate allows the `activity_context_prompt` to be defined globally and at the camera level, which allows you to define more specifically what should be considered normal activity. It is important that this is not overly specific as it can sway the output of the response.
|
||||
|
||||
<details>
|
||||
<summary>Default Activity Context Prompt</summary>
|
||||
Each installation and even camera can have different parameters for what is considered suspicious activity. Frigate allows the `activity_context_prompt` to be defined globally and at the camera level, which allows you to define more specifically what should be considered normal activity. It is important that this is not overly specific as it can sway the output of the response. The default `activity_context_prompt` is below:
|
||||
|
||||
```
|
||||
### Normal Activity Indicators (Level 0)
|
||||
- Known/verified people in any zone at any time
|
||||
- People with pets in residential areas
|
||||
- Deliveries or services during daytime/evening (6 AM - 10 PM): carrying packages to doors/porches, placing items, leaving
|
||||
- Services/maintenance workers with visible tools, uniforms, or service vehicles during daytime
|
||||
- Activity confined to public areas only (sidewalks, streets) without entering property at any time
|
||||
|
||||
### Suspicious Activity Indicators (Level 1)
|
||||
- **Testing or attempting to open doors/windows/handles on vehicles or buildings** — ALWAYS Level 1 regardless of time or duration
|
||||
- **Unidentified person in private areas (driveways, near vehicles/buildings) during late night/early morning (11 PM - 5 AM)** — ALWAYS Level 1 regardless of activity or duration
|
||||
- Taking items that don't belong to them (packages, objects from porches/driveways)
|
||||
- Climbing or jumping fences/barriers to access property
|
||||
- Attempting to conceal actions or items from view
|
||||
- Prolonged loitering: remaining in same area without visible purpose throughout most of the sequence
|
||||
|
||||
### Critical Threat Indicators (Level 2)
|
||||
- Holding break-in tools (crowbars, pry bars, bolt cutters)
|
||||
- Weapons visible (guns, knives, bats used aggressively)
|
||||
- Forced entry in progress
|
||||
- Physical aggression or violence
|
||||
- Active property damage or theft in progress
|
||||
|
||||
### Assessment Guidance
|
||||
Evaluate in this order:
|
||||
|
||||
1. **If person is verified/known** → Level 0 regardless of time or activity
|
||||
2. **If person is unidentified:**
|
||||
- Check time: If late night/early morning (11 PM - 5 AM) AND in private areas (driveways, near vehicles/buildings) → Level 1
|
||||
- Check actions: If testing doors/handles, taking items, climbing → Level 1
|
||||
- Otherwise, if daytime/evening (6 AM - 10 PM) with clear legitimate purpose (delivery, service worker) → Level 0
|
||||
3. **Escalate to Level 2 if:** Weapons, break-in tools, forced entry in progress, violence, or active property damage visible (escalates from Level 0 or 1)
|
||||
|
||||
The mere presence of an unidentified person in private areas during late night hours is inherently suspicious and warrants human review, regardless of what activity they appear to be doing or how brief the sequence is.
|
||||
- **Zone context is critical**: Private enclosed spaces (back yards, back decks, fenced areas, inside garages) are resident territory where brief transient activity, routine tasks, and pet care are expected and normal. Front yards, driveways, and porches are semi-public but still resident spaces where deliveries, parking, and coming/going are routine. Consider whether the zone and activity align with normal residential use.
|
||||
- **Person + Pet = Normal Activity**: When both "Person" and "Dog" (or "Cat") are detected together in residential zones, this is routine pet care activity (walking, letting out, playing, supervising). Assign Level 0 unless there are OTHER strong suspicious behaviors present (like testing doors, taking items, etc.). A person with their pet in a residential zone is baseline normal activity.
|
||||
- Brief appearances in private zones (back yards, garages) are normal residential patterns.
|
||||
- Normal residential activity includes: residents, family members, guests, deliveries, services, maintenance workers, routine property use (parking, unloading, mail pickup, trash removal).
|
||||
- Brief movement with legitimate items (bags, packages, tools, equipment) in appropriate zones is routine.
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
### Image Source
|
||||
|
||||
By default, review summaries use preview images (cached preview frames) which have a lower resolution but use fewer tokens per image. For better image quality and more detailed analysis, you can configure Frigate to extract frames directly from recordings at a higher resolution:
|
||||
|
||||
```yaml
|
||||
review:
|
||||
genai:
|
||||
enabled: true
|
||||
image_source: recordings # Options: "preview" (default) or "recordings"
|
||||
```
|
||||
|
||||
When using `recordings`, frames are extracted at 480px height while maintaining the camera's original aspect ratio, providing better detail for the LLM while being mindful of context window size. This is particularly useful for scenarios where fine details matter, such as identifying license plates, reading text, or analyzing distant objects.
|
||||
|
||||
The number of frames sent to the LLM is dynamically calculated based on:
|
||||
|
||||
- Your LLM provider's context window size
|
||||
- The camera's resolution and aspect ratio (ultrawide cameras like 32:9 use more tokens per image)
|
||||
- The image source (recordings use more tokens than preview images)
|
||||
|
||||
Frame counts are automatically optimized to use ~98% of the available context window while capping at 20 frames maximum to ensure reasonable inference times. Note that using recordings will:
|
||||
|
||||
- Provide higher quality images to the LLM (480p vs 180p preview images)
|
||||
- Use more tokens per image due to higher resolution
|
||||
- Result in fewer frames being sent for ultrawide cameras due to larger image size
|
||||
- Require that recordings are enabled for the camera
|
||||
|
||||
If recordings are not available for a given time period, the system will automatically fall back to using preview frames.
|
||||
|
||||
### Additional Concerns
|
||||
|
||||
Along with the concern of suspicious activity or immediate threat, you may have concerns such as animals in your garden or a gate being left open. These concerns can be configured so that the review summaries will make note of them if the activity requires additional review. For example:
|
||||
|
||||
@ -5,7 +5,7 @@ title: Enrichments
|
||||
|
||||
# Enrichments
|
||||
|
||||
Some of Frigate's enrichments can use a discrete GPU or integrated GPU for accelerated processing.
|
||||
Some of Frigate's enrichments can use a discrete GPU / NPU for accelerated processing.
|
||||
|
||||
## Requirements
|
||||
|
||||
@ -18,10 +18,8 @@ Object detection and enrichments (like Semantic Search, Face Recognition, and Li
|
||||
- **Intel**
|
||||
|
||||
- OpenVINO will automatically be detected and used for enrichments in the default Frigate image.
|
||||
- **Note:** Intel NPUs have limited model support for enrichments. GPU is recommended for enrichments when available.
|
||||
|
||||
- **Nvidia**
|
||||
|
||||
- Nvidia GPUs will automatically be detected and used for enrichments in the `-tensorrt` Frigate image.
|
||||
- Jetson devices will automatically be detected and used for enrichments in the `-tensorrt-jp6` Frigate image.
|
||||
|
||||
|
||||
@ -3,18 +3,18 @@ id: license_plate_recognition
|
||||
title: License Plate Recognition (LPR)
|
||||
---
|
||||
|
||||
Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car` or `motorcycle`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
|
||||
Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a known name as a `sub_label` to tracked objects of type `car` or `motorcycle`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
|
||||
|
||||
LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. When a vehicle becomes stationary, LPR continues to run for a short time after to attempt recognition.
|
||||
|
||||
When a plate is recognized, the details are:
|
||||
|
||||
- Added as a `sub_label` (if [known](#matching)) or the `recognized_license_plate` field (if unknown) to a tracked object.
|
||||
- Viewable in the Details pane in Review/History.
|
||||
- Added as a `sub_label` (if known) or the `recognized_license_plate` field (if unknown) to a tracked object.
|
||||
- Viewable in the Review Item Details pane in Review (sub labels).
|
||||
- Viewable in the Tracked Object Details pane in Explore (sub labels and recognized license plates).
|
||||
- Filterable through the More Filters menu in Explore.
|
||||
- Published via the `frigate/events` MQTT topic as a `sub_label` ([known](#matching)) or `recognized_license_plate` (unknown) for the `car` or `motorcycle` tracked object.
|
||||
- Published via the `frigate/tracked_object_update` MQTT topic with `name` (if [known](#matching)) and `plate`.
|
||||
- Published via the `frigate/events` MQTT topic as a `sub_label` (known) or `recognized_license_plate` (unknown) for the `car` or `motorcycle` tracked object.
|
||||
- Published via the `frigate/tracked_object_update` MQTT topic with `name` (if known) and `plate`.
|
||||
|
||||
## Model Requirements
|
||||
|
||||
@ -30,7 +30,7 @@ In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle`
|
||||
|
||||
## Minimum System Requirements
|
||||
|
||||
License plate recognition works by running AI models locally on your system. The YOLOv9 plate detector model and the OCR models ([PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)) are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM is required.
|
||||
License plate recognition works by running AI models locally on your system. The models are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM is required.
|
||||
|
||||
## Configuration
|
||||
|
||||
@ -74,8 +74,8 @@ Fine-tune the LPR feature using these optional parameters at the global level of
|
||||
- Default: `small`
|
||||
- This can be `small` or `large`.
|
||||
- The `small` model is fast and identifies groups of Latin and Chinese characters.
|
||||
- The `large` model identifies Latin characters only, and uses an enhanced text detector to find characters on multi-line plates. It is significantly slower than the `small` model.
|
||||
- If your country or region does not use multi-line plates, you should use the `small` model as performance is much better for single-line plates.
|
||||
- The `large` model identifies Latin characters only, but uses an enhanced text detector and is more capable at finding characters on multi-line plates. It is significantly slower than the `small` model. Note that using the `large` model does not improve _text recognition_, but it may improve _text detection_.
|
||||
- For most users, the `small` model is recommended.
|
||||
|
||||
### Recognition
|
||||
|
||||
@ -178,7 +178,7 @@ lpr:
|
||||
|
||||
:::note
|
||||
|
||||
If a camera is configured to detect `car` or `motorcycle` but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
|
||||
If you want to detect cars on cameras but don't want to use resources to run LPR on those cars, you should disable LPR for those specific cameras.
|
||||
|
||||
```yaml
|
||||
cameras:
|
||||
@ -306,7 +306,7 @@ With this setup:
|
||||
- Review items will always be classified as a `detection`.
|
||||
- Snapshots will always be saved.
|
||||
- Zones and object masks are **not** used.
|
||||
- The `frigate/events` MQTT topic will **not** publish tracked object updates with the license plate bounding box and score, though `frigate/reviews` will publish if recordings are enabled. If a plate is recognized as a [known](#matching) plate, publishing will occur with an updated `sub_label` field. If characters are recognized, publishing will occur with an updated `recognized_license_plate` field.
|
||||
- The `frigate/events` MQTT topic will **not** publish tracked object updates with the license plate bounding box and score, though `frigate/reviews` will publish if recordings are enabled. If a plate is recognized as a known plate, publishing will occur with an updated `sub_label` field. If characters are recognized, publishing will occur with an updated `recognized_license_plate` field.
|
||||
- License plate snapshots are saved at the highest-scoring moment and appear in Explore.
|
||||
- Debug view will not show `license_plate` bounding boxes.
|
||||
|
||||
|
||||
@ -174,7 +174,7 @@ For devices that support two way talk, Frigate can be configured to use the feat
|
||||
- Ensure you access Frigate via https (may require [opening port 8971](/frigate/installation/#ports)).
|
||||
- For the Home Assistant Frigate card, [follow the docs](http://card.camera/#/usage/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-cameras)
|
||||
To use the Reolink Doorbell with two way talk, you should use the [recommended Reolink configuration](/configuration/camera_specific#reolink-doorbell)
|
||||
|
||||
As a starting point to check compatibility for your camera, view the list of cameras supported for two-way talk on the [go2rtc repository](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#two-way-audio). For cameras in the category `ONVIF Profile T`, you can use the [ONVIF Conformant Products Database](https://www.onvif.org/conformant-products/)'s FeatureList to check for the presence of `AudioOutput`. A camera that supports `ONVIF Profile T` _usually_ supports this, but due to inconsistent support, a camera that explicitly lists this feature may still not work. If no entry for your camera exists on the database, it is recommended not to buy it or to consult with the manufacturer's support on the feature availability.
|
||||
|
||||
@ -214,42 +214,6 @@ For restreamed cameras, go2rtc remains active but does not use system resources
|
||||
|
||||
Note that disabling a camera through the config file (`enabled: False`) removes all related UI elements, including historical footage access. To retain access while disabling the camera, keep it enabled in the config and use the UI or MQTT to disable it temporarily.
|
||||
|
||||
### Live player error messages
|
||||
|
||||
When your browser runs into problems playing back your camera streams, it will log short error messages to the browser console. They indicate playback, codec, or network issues on the client/browser side, not something server side with Frigate itself. Below are the common messages you may see and simple actions you can take to try to resolve them.
|
||||
|
||||
- **startup**
|
||||
|
||||
- What it means: The player failed to initialize or connect to the live stream (network or startup error).
|
||||
- What to try: Reload the Live view or click _Reset_. Verify `go2rtc` is running and the camera stream is reachable. Try switching to a different stream from the Live UI dropdown (if available) or use a different browser.
|
||||
|
||||
- Possible console messages from the player code:
|
||||
|
||||
- `Error opening MediaSource.`
|
||||
- `Browser reported a network error.`
|
||||
- `Max error count ${errorCount} exceeded.` (the numeric value will vary)
|
||||
|
||||
- **mse-decode**
|
||||
|
||||
- What it means: The browser reported a decoding error while trying to play the stream, which usually is a result of a codec incompatibility or corrupted frames.
|
||||
- What to try: Check the browser console for the supported and negotiated codecs. Ensure your camera/restream is using H.264 video and AAC audio (these are the most compatible). If your camera uses a non-standard audio codec, configure `go2rtc` to transcode the stream to AAC. Try another browser (some browsers have stricter MSE/codec support) and, for iPhone, ensure you're on iOS 17.1 or newer.
|
||||
|
||||
- Possible console messages from the player code:
|
||||
|
||||
- `Safari cannot open MediaSource.`
|
||||
- `Safari reported InvalidStateError.`
|
||||
- `Safari reported decoding errors.`
|
||||
|
||||
- **stalled**
|
||||
|
||||
- What it means: Playback has stalled because the player has fallen too far behind live (extended buffering or no data arriving).
|
||||
- What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval — shorter intervals make playback start and recover faster. You can also try increasing the timeout value in the UI pane of Frigate's settings.
|
||||
|
||||
- Possible console messages from the player code:
|
||||
|
||||
- `Buffer time (10 seconds) exceeded, browser may not be playing media correctly.`
|
||||
- `Media playback has stalled after <n> seconds due to insufficient buffering or a network interruption.` (the seconds value will vary)
|
||||
|
||||
## Live view FAQ
|
||||
|
||||
1. **Why don't I have audio in my Live view?**
|
||||
@ -313,38 +277,3 @@ When your browser runs into problems playing back your camera streams, it will l
|
||||
7. **My camera streams have lots of visual artifacts / distortion.**
|
||||
|
||||
Some cameras don't include the hardware to support multiple connections to the high resolution stream, and this can cause unexpected behavior. In this case it is recommended to [restream](./restream.md) the high resolution stream so that it can be used for live view and recordings.
|
||||
|
||||
8. **Why does my camera stream switch aspect ratios on the Live dashboard?**
|
||||
|
||||
Your camera may change aspect ratios on the dashboard because Frigate uses different streams for different purposes. With go2rtc and Smart Streaming, Frigate shows a static image from the `detect` stream when no activity is present, and switches to the live stream when motion is detected. The camera image will change size if your streams use different aspect ratios.
|
||||
|
||||
To prevent this, make the `detect` stream match the go2rtc live stream's aspect ratio (resolution does not need to match, just the aspect ratio). You can either adjust the camera's output resolution or set the `width` and `height` values in your config's `detect` section to a resolution with an aspect ratio that matches.
|
||||
|
||||
Example: Resolutions from two streams
|
||||
|
||||
- Mismatched (may cause aspect ratio switching on the dashboard):
|
||||
|
||||
- Live/go2rtc stream: 1920x1080 (16:9)
|
||||
- Detect stream: 640x352 (~1.82:1, not 16:9)
|
||||
|
||||
- Matched (prevents switching):
|
||||
- Live/go2rtc stream: 1920x1080 (16:9)
|
||||
- Detect stream: 640x360 (16:9)
|
||||
|
||||
You can update the detect settings in your camera config to match the aspect ratio of your go2rtc live stream. For example:
|
||||
|
||||
```yaml
|
||||
cameras:
|
||||
front_door:
|
||||
detect:
|
||||
width: 640
|
||||
height: 360 # set this to 360 instead of 352
|
||||
ffmpeg:
|
||||
inputs:
|
||||
- path: rtsp://127.0.0.1:8554/front_door # main stream 1920x1080
|
||||
roles:
|
||||
- record
|
||||
- path: rtsp://127.0.0.1:8554/front_door_sub # sub stream 640x352
|
||||
roles:
|
||||
- detect
|
||||
```
|
||||
|
||||
@ -3,8 +3,6 @@ id: object_detectors
|
||||
title: Object Detectors
|
||||
---
|
||||
|
||||
import CommunityBadge from '@site/src/components/CommunityBadge';
|
||||
|
||||
# Supported Hardware
|
||||
|
||||
:::info
|
||||
@ -15,8 +13,8 @@ Frigate supports multiple different detectors that work on different types of ha
|
||||
|
||||
- [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB and m.2 format allowing for a wide range of compatibility with devices.
|
||||
- [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
|
||||
- <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
|
||||
- <CommunityBadge /> [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com).
|
||||
- [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
|
||||
- [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com).
|
||||
|
||||
**AMD**
|
||||
|
||||
@ -36,16 +34,16 @@ Frigate supports multiple different detectors that work on different types of ha
|
||||
|
||||
- [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt` Frigate image when a supported ONNX model is configured.
|
||||
|
||||
**Nvidia Jetson** <CommunityBadge />
|
||||
**Nvidia Jetson**
|
||||
|
||||
- [TensortRT](#nvidia-tensorrt-detector): TensorRT can run on Jetson devices, using one of many default models.
|
||||
- [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt-jp6` Frigate image when a supported ONNX model is configured.
|
||||
|
||||
**Rockchip** <CommunityBadge />
|
||||
**Rockchip**
|
||||
|
||||
- [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs.
|
||||
|
||||
**Synaptics** <CommunityBadge />
|
||||
**Synaptics**
|
||||
|
||||
- [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs.
|
||||
|
||||
@ -255,55 +253,41 @@ Hailo8 supports all models in the Hailo Model Zoo that include HailoRT post-proc
|
||||
|
||||
## OpenVINO Detector
|
||||
|
||||
The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel NPUs. To configure an OpenVINO detector, set the `"type"` attribute to `"openvino"`.
|
||||
The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel VPU hardware. To configure an OpenVINO detector, set the `"type"` attribute to `"openvino"`.
|
||||
|
||||
The OpenVINO device to be used is specified using the `"device"` attribute according to the naming conventions in the [Device Documentation](https://docs.openvino.ai/2025/openvino-workflow/running-inference/inference-devices-and-modes.html). The most common devices are `CPU`, `GPU`, or `NPU`.
|
||||
The OpenVINO device to be used is specified using the `"device"` attribute according to the naming conventions in the [Device Documentation](https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes.html). The most common devices are `CPU` and `GPU`. Currently, there is a known issue with using `AUTO`. For backwards compatibility, Frigate will attempt to use `GPU` if `AUTO` is set in your configuration.
|
||||
|
||||
OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will also run on AMD CPUs despite having no official support for it. A supported Intel platform is required to use the `GPU` or `NPU` device with OpenVINO. For detailed system requirements, see [OpenVINO System Requirements](https://docs.openvino.ai/2025/about-openvino/release-notes-openvino/system-requirements.html)
|
||||
OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will also run on AMD CPUs despite having no official support for it. A supported Intel platform is required to use the `GPU` device with OpenVINO. For detailed system requirements, see [OpenVINO System Requirements](https://docs.openvino.ai/2024/about-openvino/release-notes-openvino/system-requirements.html)
|
||||
|
||||
:::tip
|
||||
|
||||
**NPU + GPU Systems:** If you have both NPU and GPU available (Intel Core Ultra processors), use NPU for object detection and GPU for enrichments (semantic search, face recognition, etc.) for best performance and compatibility.
|
||||
|
||||
When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
|
||||
|
||||
```yaml
|
||||
detectors:
|
||||
ov_0:
|
||||
type: openvino
|
||||
device: GPU # or NPU
|
||||
device: GPU
|
||||
ov_1:
|
||||
type: openvino
|
||||
device: GPU # or NPU
|
||||
device: GPU
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
### OpenVINO Supported Models
|
||||
|
||||
| Model | GPU | NPU | Notes |
|
||||
| ------------------------------------- | --- | --- | ------------------------------------------------------------ |
|
||||
| [YOLOv9](#yolo-v3-v4-v7-v9) | ✅ | ✅ | Recommended for GPU & NPU |
|
||||
| [RF-DETR](#rf-detr) | ✅ | ✅ | Requires XE iGPU or Arc |
|
||||
| [YOLO-NAS](#yolo-nas) | ✅ | ✅ | |
|
||||
| [MobileNet v2](#ssdlite-mobilenet-v2) | ✅ | ✅ | Fast and lightweight model, less accurate than larger models |
|
||||
| [YOLOX](#yolox) | ✅ | ? | |
|
||||
| [D-FINE](#d-fine) | ❌ | ❌ | |
|
||||
|
||||
#### SSDLite MobileNet v2
|
||||
|
||||
An OpenVINO model is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an FP16 precision IR model.
|
||||
|
||||
<details>
|
||||
<summary>MobileNet v2 Config</summary>
|
||||
|
||||
Use the model configuration shown below when using the OpenVINO detector with the default OpenVINO model:
|
||||
|
||||
```yaml
|
||||
detectors:
|
||||
ov:
|
||||
type: openvino
|
||||
device: GPU # Or NPU
|
||||
device: GPU
|
||||
|
||||
model:
|
||||
width: 300
|
||||
@ -314,8 +298,6 @@ model:
|
||||
labelmap_path: /openvino-model/coco_91cl_bkgr.txt
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
#### YOLOX
|
||||
|
||||
This detector also supports YOLOX. Frigate does not come with any YOLOX models preloaded, so you will need to supply your own models.
|
||||
@ -324,9 +306,6 @@ This detector also supports YOLOX. Frigate does not come with any YOLOX models p
|
||||
|
||||
[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.
|
||||
|
||||
<details>
|
||||
<summary>YOLO-NAS Setup & Config</summary>
|
||||
|
||||
After placing the downloaded onnx model in your config folder, you can use the following configuration:
|
||||
|
||||
```yaml
|
||||
@ -347,8 +326,6 @@ model:
|
||||
|
||||
Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
|
||||
|
||||
</details>
|
||||
|
||||
#### YOLO (v3, v4, v7, v9)
|
||||
|
||||
YOLOv3, YOLOv4, YOLOv7, and [YOLOv9](https://github.com/WongKinYiu/yolov9) models are supported, but not included by default.
|
||||
@ -359,9 +336,6 @@ The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv
|
||||
|
||||
:::
|
||||
|
||||
<details>
|
||||
<summary>YOLOv Setup & Config</summary>
|
||||
|
||||
:::warning
|
||||
|
||||
If you are using a Frigate+ YOLOv9 model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
|
||||
@ -374,7 +348,7 @@ After placing the downloaded onnx model in your config folder, you can use the f
|
||||
detectors:
|
||||
ov:
|
||||
type: openvino
|
||||
device: GPU # or NPU
|
||||
device: GPU
|
||||
|
||||
model:
|
||||
model_type: yolo-generic
|
||||
@ -388,8 +362,6 @@ model:
|
||||
|
||||
Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
|
||||
|
||||
</details>
|
||||
|
||||
#### RF-DETR
|
||||
|
||||
[RF-DETR](https://github.com/roboflow/rf-detr) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-rf-detr-model) for more informatoin on downloading the RF-DETR model for use in Frigate.
|
||||
@ -400,9 +372,6 @@ Due to the size and complexity of the RF-DETR model, it is only recommended to b
|
||||
|
||||
:::
|
||||
|
||||
<details>
|
||||
<summary>RF-DETR Setup & Config</summary>
|
||||
|
||||
After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
|
||||
|
||||
```yaml
|
||||
@ -420,8 +389,6 @@ model:
|
||||
path: /config/model_cache/rfdetr.onnx
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
#### D-FINE
|
||||
|
||||
[D-FINE](https://github.com/Peterande/D-FINE) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate.
|
||||
@ -432,9 +399,6 @@ Currently D-FINE models only run on OpenVINO in CPU mode, GPUs currently fail to
|
||||
|
||||
:::
|
||||
|
||||
<details>
|
||||
<summary>D-FINE Setup & Config</summary>
|
||||
|
||||
After placing the downloaded onnx model in your config/model_cache folder, you can use the following configuration:
|
||||
|
||||
```yaml
|
||||
@ -449,17 +413,15 @@ model:
|
||||
height: 640
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/dfine-s.onnx
|
||||
path: /config/model_cache/dfine_s_obj2coco.onnx
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
```
|
||||
|
||||
Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
|
||||
|
||||
</details>
|
||||
|
||||
## Apple Silicon detector
|
||||
|
||||
The NPU in Apple Silicon can't be accessed from within a container, so the [Apple Silicon detector client](https://github.com/frigate-nvr/apple-silicon-detector) must first be setup. It is recommended to use the Frigate docker image with `-standard-arm64` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-standard-arm64`.
|
||||
The NPU in Apple Silicon can't be accessed from within a container, so the [Apple Silicon detector client](https://github.com/frigate-nvr/apple-silicon-detector) must first be setup. It is recommended to use the Frigate docker image with `-standard-arm64` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-standard-arm64`.
|
||||
|
||||
### Setup
|
||||
|
||||
@ -647,23 +609,12 @@ detectors:
|
||||
|
||||
### ONNX Supported Models
|
||||
|
||||
| Model | Nvidia GPU | AMD GPU | Notes |
|
||||
| ----------------------------- | ---------- | ------- | --------------------------------------------------- |
|
||||
| [YOLOv9](#yolo-v3-v4-v7-v9-2) | ✅ | ✅ | Supports CUDA Graphs for optimal Nvidia performance |
|
||||
| [RF-DETR](#rf-detr) | ✅ | ❌ | Supports CUDA Graphs for optimal Nvidia performance |
|
||||
| [YOLO-NAS](#yolo-nas-1) | ⚠️ | ⚠️ | Not supported by CUDA Graphs |
|
||||
| [YOLOX](#yolox-1) | ✅ | ✅ | Supports CUDA Graphs for optimal Nvidia performance |
|
||||
| [D-FINE](#d-fine) | ⚠️ | ❌ | Not supported by CUDA Graphs |
|
||||
|
||||
There is no default model provided, the following formats are supported:
|
||||
|
||||
#### YOLO-NAS
|
||||
|
||||
[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. See [the models section](#downloading-yolo-nas-model) for more information on downloading the YOLO-NAS model for use in Frigate.
|
||||
|
||||
<details>
|
||||
<summary>YOLO-NAS Setup & Config</summary>
|
||||
|
||||
:::warning
|
||||
|
||||
If you are using a Frigate+ YOLO-NAS model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
|
||||
@ -687,8 +638,6 @@ model:
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
#### YOLO (v3, v4, v7, v9)
|
||||
|
||||
YOLOv3, YOLOv4, YOLOv7, and [YOLOv9](https://github.com/WongKinYiu/yolov9) models are supported, but not included by default.
|
||||
@ -699,9 +648,6 @@ The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv
|
||||
|
||||
:::
|
||||
|
||||
<details>
|
||||
<summary>YOLOv Setup & Config</summary>
|
||||
|
||||
:::warning
|
||||
|
||||
If you are using a Frigate+ YOLOv9 model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
|
||||
@ -725,17 +671,12 @@ model:
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
|
||||
|
||||
#### YOLOx
|
||||
|
||||
[YOLOx](https://github.com/Megvii-BaseDetection/YOLOX) models are supported, but not included by default. See [the models section](#downloading-yolo-models) for more information on downloading the YOLOx model for use in Frigate.
|
||||
|
||||
<details>
|
||||
<summary>YOLOx Setup & Config</summary>
|
||||
|
||||
After placing the downloaded onnx model in your config folder, you can use the following configuration:
|
||||
|
||||
```yaml
|
||||
@ -755,15 +696,10 @@ model:
|
||||
|
||||
Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
|
||||
|
||||
</details>
|
||||
|
||||
#### RF-DETR
|
||||
|
||||
[RF-DETR](https://github.com/roboflow/rf-detr) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-rf-detr-model) for more information on downloading the RF-DETR model for use in Frigate.
|
||||
|
||||
<details>
|
||||
<summary>RF-DETR Setup & Config</summary>
|
||||
|
||||
After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
|
||||
|
||||
```yaml
|
||||
@ -780,15 +716,10 @@ model:
|
||||
path: /config/model_cache/rfdetr.onnx
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
#### D-FINE
|
||||
|
||||
[D-FINE](https://github.com/Peterande/D-FINE) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate.
|
||||
|
||||
<details>
|
||||
<summary>D-FINE Setup & Config</summary>
|
||||
|
||||
After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
|
||||
|
||||
```yaml
|
||||
@ -806,8 +737,6 @@ model:
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
|
||||
|
||||
## CPU Detector (not recommended)
|
||||
@ -927,16 +856,16 @@ detectors:
|
||||
|
||||
model:
|
||||
model_type: yolonas
|
||||
width: 320 # (Can be set to 640 for higher resolution)
|
||||
height: 320 # (Can be set to 640 for higher resolution)
|
||||
width: 320 # (Can be set to 640 for higher resolution)
|
||||
height: 320 # (Can be set to 640 for higher resolution)
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
|
||||
# path: /config/yolonas.zip
|
||||
# The .zip file must contain:
|
||||
# ├── yolonas.dfp (a file ending with .dfp)
|
||||
# └── yolonas_post.onnx (optional; only if the model includes a cropped post-processing network)
|
||||
# The .zip file must contain:
|
||||
# ├── yolonas.dfp (a file ending with .dfp)
|
||||
# └── yolonas_post.onnx (optional; only if the model includes a cropped post-processing network)
|
||||
```
|
||||
|
||||
#### YOLOv9
|
||||
@ -955,15 +884,16 @@ detectors:
|
||||
|
||||
model:
|
||||
model_type: yolo-generic
|
||||
width: 320 # (Can be set to 640 for higher resolution)
|
||||
height: 320 # (Can be set to 640 for higher resolution)
|
||||
width: 320 # (Can be set to 640 for higher resolution)
|
||||
height: 320 # (Can be set to 640 for higher resolution)
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
|
||||
# path: /config/yolov9.zip
|
||||
# The .zip file must contain:
|
||||
# ├── yolov9.dfp (a file ending with .dfp)
|
||||
# The .zip file must contain:
|
||||
# ├── yolov9.dfp (a file ending with .dfp)
|
||||
# └── yolov9_post.onnx (optional; only if the model includes a cropped post-processing network)
|
||||
```
|
||||
|
||||
#### YOLOX
|
||||
@ -989,8 +919,8 @@ model:
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
|
||||
# path: /config/yolox.zip
|
||||
# The .zip file must contain:
|
||||
# ├── yolox.dfp (a file ending with .dfp)
|
||||
# The .zip file must contain:
|
||||
# ├── yolox.dfp (a file ending with .dfp)
|
||||
```
|
||||
|
||||
#### SSDLite MobileNet v2
|
||||
@ -1016,9 +946,9 @@ model:
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
|
||||
# path: /config/ssdlite_mobilenet.zip
|
||||
# The .zip file must contain:
|
||||
# ├── ssdlite_mobilenet.dfp (a file ending with .dfp)
|
||||
# └── ssdlite_mobilenet_post.onnx (optional; only if the model includes a cropped post-processing network)
|
||||
# The .zip file must contain:
|
||||
# ├── ssdlite_mobilenet.dfp (a file ending with .dfp)
|
||||
# └── ssdlite_mobilenet_post.onnx (optional; only if the model includes a cropped post-processing network)
|
||||
```
|
||||
|
||||
#### Using a Custom Model
|
||||
@ -1038,19 +968,18 @@ To use your own model:
|
||||
For detailed instructions on compiling models, refer to the [MemryX Compiler](https://developer.memryx.com/tools/neural_compiler.html#usage) docs and [Tutorials](https://developer.memryx.com/tutorials/tutorials.html).
|
||||
|
||||
```yaml
|
||||
# The detector automatically selects the default model if nothing is provided in the config.
|
||||
#
|
||||
# Optionally, you can specify a local model path as a .zip file to override the default.
|
||||
# If a local path is provided and the file exists, it will be used instead of downloading.
|
||||
#
|
||||
# Example:
|
||||
# path: /config/yolonas.zip
|
||||
#
|
||||
# The .zip file must contain:
|
||||
# ├── yolonas.dfp (a file ending with .dfp)
|
||||
# └── yolonas_post.onnx (optional; only if the model includes a cropped post-processing network)
|
||||
# The detector automatically selects the default model if nothing is provided in the config.
|
||||
#
|
||||
# Optionally, you can specify a local model path as a .zip file to override the default.
|
||||
# If a local path is provided and the file exists, it will be used instead of downloading.
|
||||
#
|
||||
# Example:
|
||||
# path: /config/yolonas.zip
|
||||
#
|
||||
# The .zip file must contain:
|
||||
# ├── yolonas.dfp (a file ending with .dfp)
|
||||
# └── yolonas_post.onnx (optional; only if the model includes a cropped post-processing network)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## NVidia TensorRT Detector
|
||||
@ -1158,16 +1087,16 @@ A synap model is provided in the container at /mobilenet.synap and is used by th
|
||||
Use the model configuration shown below when using the synaptics detector with the default synap model:
|
||||
|
||||
```yaml
|
||||
detectors: # required
|
||||
synap_npu: # required
|
||||
type: synaptics # required
|
||||
detectors: # required
|
||||
synap_npu: # required
|
||||
type: synaptics # required
|
||||
|
||||
model: # required
|
||||
path: /synaptics/mobilenet.synap # required
|
||||
width: 224 # required
|
||||
height: 224 # required
|
||||
tensor_format: nhwc # default value (optional. If you change the model, it is required)
|
||||
labelmap_path: /labelmap/coco-80.txt # required
|
||||
model: # required
|
||||
path: /synaptics/mobilenet.synap # required
|
||||
width: 224 # required
|
||||
height: 224 # required
|
||||
tensor_format: nhwc # default value (optional. If you change the model, it is required)
|
||||
labelmap_path: /labelmap/coco-80.txt # required
|
||||
```
|
||||
|
||||
## Rockchip platform
|
||||
@ -1341,101 +1270,97 @@ Explanation of the paramters:
|
||||
|
||||
## DeGirum
|
||||
|
||||
DeGirum is a detector that can use any type of hardware listed on [their website](https://hub.degirum.com). DeGirum can be used with local hardware through a DeGirum AI Server, or through the use of `@local`. You can also connect directly to DeGirum's AI Hub to run inferences. **Please Note:** This detector _cannot_ be used for commercial purposes.
|
||||
DeGirum is a detector that can use any type of hardware listed on [their website](https://hub.degirum.com). DeGirum can be used with local hardware through a DeGirum AI Server, or through the use of `@local`. You can also connect directly to DeGirum's AI Hub to run inferences. **Please Note:** This detector *cannot* be used for commercial purposes.
|
||||
|
||||
### Configuration
|
||||
|
||||
#### AI Server Inference
|
||||
|
||||
Before starting with the config file for this section, you must first launch an AI server. DeGirum has an AI server ready to use as a docker container. Add this to your `docker-compose.yml` to get started:
|
||||
|
||||
```yaml
|
||||
degirum_detector:
|
||||
container_name: degirum
|
||||
image: degirum/aiserver:latest
|
||||
privileged: true
|
||||
ports:
|
||||
- "8778:8778"
|
||||
container_name: degirum
|
||||
image: degirum/aiserver:latest
|
||||
privileged: true
|
||||
ports:
|
||||
- "8778:8778"
|
||||
```
|
||||
|
||||
All supported hardware will automatically be found on your AI server host as long as relevant runtimes and drivers are properly installed on your machine. Refer to [DeGirum's docs site](https://docs.degirum.com/pysdk/runtimes-and-drivers) if you have any trouble.
|
||||
|
||||
Once completed, changing the `config.yml` file is simple.
|
||||
|
||||
```yaml
|
||||
degirum_detector:
|
||||
type: degirum
|
||||
location: degirum # Set to service name (degirum_detector), container_name (degirum), or a host:port (192.168.29.4:8778)
|
||||
zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start. If you aren't pulling a model from the AI Hub, leave this and 'token' blank.
|
||||
token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the [AI Hub](https://hub.degirum.com). This can be left blank if you're pulling a model from the public zoo and running inferences on your local hardware using @local or a local DeGirum AI Server
|
||||
type: degirum
|
||||
location: degirum # Set to service name (degirum_detector), container_name (degirum), or a host:port (192.168.29.4:8778)
|
||||
zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start. If you aren't pulling a model from the AI Hub, leave this and 'token' blank.
|
||||
token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the [AI Hub](https://hub.degirum.com). This can be left blank if you're pulling a model from the public zoo and running inferences on your local hardware using @local or a local DeGirum AI Server
|
||||
```
|
||||
|
||||
Setting up a model in the `config.yml` is similar to setting up an AI server.
|
||||
You can set it to:
|
||||
|
||||
- A model listed on the [AI Hub](https://hub.degirum.com), given that the correct zoo name is listed in your detector
|
||||
- If this is what you choose to do, the correct model will be downloaded onto your machine before running.
|
||||
- If this is what you choose to do, the correct model will be downloaded onto your machine before running.
|
||||
- A local directory acting as a zoo. See DeGirum's docs site [for more information](https://docs.degirum.com/pysdk/user-guide-pysdk/organizing-models#model-zoo-directory-structure).
|
||||
- A path to some model.json.
|
||||
|
||||
```yaml
|
||||
model:
|
||||
path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
|
||||
width: 300 # width is in the model name as the first number in the "int"x"int" section
|
||||
height: 300 # height is in the model name as the second number in the "int"x"int" section
|
||||
input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
|
||||
path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
|
||||
width: 300 # width is in the model name as the first number in the "int"x"int" section
|
||||
height: 300 # height is in the model name as the second number in the "int"x"int" section
|
||||
input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
|
||||
```
|
||||
|
||||
|
||||
#### Local Inference
|
||||
|
||||
It is also possible to eliminate the need for an AI server and run the hardware directly. The benefit of this approach is that you eliminate any bottlenecks that occur when transferring prediction results from the AI server docker container to the frigate one. However, the method of implementing local inference is different for every device and hardware combination, so it's usually more trouble than it's worth. A general guideline to achieve this would be:
|
||||
|
||||
1. Ensuring that the frigate docker container has the runtime you want to use. So for instance, running `@local` for Hailo means making sure the container you're using has the Hailo runtime installed.
|
||||
2. To double check the runtime is detected by the DeGirum detector, make sure the `degirum sys-info` command properly shows whatever runtimes you mean to install.
|
||||
3. Create a DeGirum detector in your `config.yml` file.
|
||||
|
||||
```yaml
|
||||
degirum_detector:
|
||||
type: degirum
|
||||
location: "@local" # For accessing AI Hub devices and models
|
||||
zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start.
|
||||
token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the [AI Hub](https://hub.degirum.com). This can be left blank if you're pulling a model from the public zoo and running inferences on your local hardware using @local or a local DeGirum AI Server
|
||||
type: degirum
|
||||
location: "@local" # For accessing AI Hub devices and models
|
||||
zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start.
|
||||
token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the [AI Hub](https://hub.degirum.com). This can be left blank if you're pulling a model from the public zoo and running inferences on your local hardware using @local or a local DeGirum AI Server
|
||||
|
||||
```
|
||||
|
||||
Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
|
||||
|
||||
```yaml
|
||||
model:
|
||||
path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
|
||||
width: 300 # width is in the model name as the first number in the "int"x"int" section
|
||||
height: 300 # height is in the model name as the second number in the "int"x"int" section
|
||||
input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
|
||||
path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
|
||||
width: 300 # width is in the model name as the first number in the "int"x"int" section
|
||||
height: 300 # height is in the model name as the second number in the "int"x"int" section
|
||||
input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
|
||||
```
|
||||
|
||||
|
||||
#### AI Hub Cloud Inference
|
||||
|
||||
If you do not possess whatever hardware you want to run, there's also the option to run cloud inferences. Do note that your detection fps might need to be lowered as network latency does significantly slow down this method of detection. For use with Frigate, we highly recommend using a local AI server as described above. To set up cloud inferences,
|
||||
|
||||
1. Sign up at [DeGirum's AI Hub](https://hub.degirum.com).
|
||||
2. Get an access token.
|
||||
3. Create a DeGirum detector in your `config.yml` file.
|
||||
|
||||
```yaml
|
||||
degirum_detector:
|
||||
type: degirum
|
||||
location: "@cloud" # For accessing AI Hub devices and models
|
||||
zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start.
|
||||
token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the (AI Hub)[https://hub.degirum.com).
|
||||
type: degirum
|
||||
location: "@cloud" # For accessing AI Hub devices and models
|
||||
zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start.
|
||||
token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the (AI Hub)[https://hub.degirum.com).
|
||||
|
||||
```
|
||||
|
||||
Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
|
||||
|
||||
```yaml
|
||||
model:
|
||||
path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
|
||||
width: 300 # width is in the model name as the first number in the "int"x"int" section
|
||||
height: 300 # height is in the model name as the second number in the "int"x"int" section
|
||||
input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
|
||||
path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
|
||||
width: 300 # width is in the model name as the first number in the "int"x"int" section
|
||||
height: 300 # height is in the model name as the second number in the "int"x"int" section
|
||||
input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
|
||||
```
|
||||
|
||||
# Models
|
||||
@ -1458,7 +1383,7 @@ COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
|
||||
WORKDIR /dfine
|
||||
RUN git clone https://github.com/Peterande/D-FINE.git .
|
||||
RUN uv pip install --system -r requirements.txt
|
||||
RUN uv pip install --system onnx onnxruntime onnxsim onnxscript
|
||||
RUN uv pip install --system onnx onnxruntime onnxsim
|
||||
# Create output directory and download checkpoint
|
||||
RUN mkdir -p output
|
||||
ARG MODEL_SIZE
|
||||
@ -1482,9 +1407,9 @@ FROM python:3.11 AS build
|
||||
RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
|
||||
COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
|
||||
WORKDIR /rfdetr
|
||||
RUN uv pip install --system rfdetr[onnxexport] torch==2.8.0 onnxscript
|
||||
RUN uv pip install --system rfdetr onnx onnxruntime onnxsim onnx-graphsurgeon
|
||||
ARG MODEL_SIZE
|
||||
RUN python3 -c "from rfdetr import RFDETR${MODEL_SIZE}; x = RFDETR${MODEL_SIZE}(resolution=320); x.export(simplify=True)"
|
||||
RUN python3 -c "from rfdetr import RFDETR${MODEL_SIZE}; x = RFDETR${MODEL_SIZE}(resolution=320); x.export()"
|
||||
FROM scratch
|
||||
ARG MODEL_SIZE
|
||||
COPY --from=build /rfdetr/output/inference_model.onnx /rfdetr-${MODEL_SIZE}.onnx
|
||||
@ -1532,7 +1457,7 @@ COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
|
||||
WORKDIR /yolov9
|
||||
ADD https://github.com/WongKinYiu/yolov9.git .
|
||||
RUN uv pip install --system -r requirements.txt
|
||||
RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier>=0.4.1 onnxscript
|
||||
RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier>=0.4.1
|
||||
ARG MODEL_SIZE
|
||||
ARG IMG_SIZE
|
||||
ADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt
|
||||
|
||||
@ -240,13 +240,11 @@ birdseye:
|
||||
scaling_factor: 2.0
|
||||
# Optional: Maximum number of cameras to show at one time, showing the most recent (default: show all cameras)
|
||||
max_cameras: 1
|
||||
# Optional: Frames-per-second to re-send the last composed Birdseye frame when idle (no motion or active updates). (default: shown below)
|
||||
idle_heartbeat_fps: 0.0
|
||||
|
||||
# Optional: ffmpeg configuration
|
||||
# More information about presets at https://docs.frigate.video/configuration/ffmpeg_presets
|
||||
ffmpeg:
|
||||
# Optional: ffmpeg binary path (default: shown below)
|
||||
# Optional: ffmpeg binry path (default: shown below)
|
||||
# can also be set to `7.0` or `5.0` to specify one of the included versions
|
||||
# or can be set to any path that holds `bin/ffmpeg` & `bin/ffprobe`
|
||||
path: "default"
|
||||
@ -429,15 +427,6 @@ review:
|
||||
alerts: True
|
||||
# Optional: Enable GenAI review summaries for detections (default: shown below)
|
||||
detections: False
|
||||
# Optional: Activity Context Prompt to give context to the GenAI what activity is and is not suspicious.
|
||||
# It is important to be direct and detailed. See documentation for the default prompt structure.
|
||||
activity_context_prompt: """Define what is and is not suspicious
|
||||
"""
|
||||
# Optional: Image source for GenAI (default: preview)
|
||||
# Options: "preview" (uses cached preview frames at ~180p) or "recordings" (extracts frames from recordings at 480p)
|
||||
# Using "recordings" provides better image quality but uses more tokens per image.
|
||||
# Frame count is automatically calculated based on context window size, aspect ratio, and image source (capped at 20 frames).
|
||||
image_source: preview
|
||||
# Optional: Additional concerns that the GenAI should make note of (default: None)
|
||||
additional_concerns:
|
||||
- Animals in the garden
|
||||
@ -639,7 +628,7 @@ face_recognition:
|
||||
# Optional: Min face recognitions for the sub label to be applied to the person object (default: shown below)
|
||||
min_faces: 1
|
||||
# Optional: Number of images of recognized faces to save for training (default: shown below)
|
||||
save_attempts: 200
|
||||
save_attempts: 100
|
||||
# Optional: Apply a blur quality filter to adjust confidence based on the blur level of the image (default: shown below)
|
||||
blur_confidence_filter: True
|
||||
# Optional: Set the model size used face recognition. (default: shown below)
|
||||
@ -680,18 +669,20 @@ lpr:
|
||||
# Optional: List of regex replacement rules to normalize detected plates (default: shown below)
|
||||
replace_rules: {}
|
||||
|
||||
# Optional: Configuration for AI / LLM provider
|
||||
# Optional: Configuration for AI generated tracked object descriptions
|
||||
# WARNING: Depending on the provider, this will send thumbnails over the internet
|
||||
# to Google or OpenAI's LLMs to generate descriptions. GenAI features can be configured at
|
||||
# the camera level to enhance privacy for indoor cameras.
|
||||
# to Google or OpenAI's LLMs to generate descriptions. It can be overridden at
|
||||
# the camera level (enabled: False) to enhance privacy for indoor cameras.
|
||||
genai:
|
||||
# Required: Provider must be one of ollama, gemini, or openai
|
||||
# Optional: Enable AI description generation (default: shown below)
|
||||
enabled: False
|
||||
# Required if enabled: Provider must be one of ollama, gemini, or openai
|
||||
provider: ollama
|
||||
# Required if provider is ollama. May also be used for an OpenAI API compatible backend with the openai provider.
|
||||
base_url: http://localhost::11434
|
||||
# Required if gemini or openai
|
||||
api_key: "{FRIGATE_GENAI_API_KEY}"
|
||||
# Required: The model to use with the provider.
|
||||
# Required if enabled: The model to use with the provider.
|
||||
model: gemini-1.5-flash
|
||||
# Optional additional args to pass to the GenAI Provider (default: None)
|
||||
provider_options:
|
||||
@ -700,11 +691,11 @@ genai:
|
||||
# Optional: Configuration for audio transcription
|
||||
# NOTE: only the enabled option can be overridden at the camera level
|
||||
audio_transcription:
|
||||
# Optional: Enable live and speech event audio transcription (default: shown below)
|
||||
# Optional: Enable license plate recognition (default: shown below)
|
||||
enabled: False
|
||||
# Optional: The device to run the models on for live transcription. (default: shown below)
|
||||
# Optional: The device to run the models on (default: shown below)
|
||||
device: CPU
|
||||
# Optional: Set the model size used for live transcription. (default: shown below)
|
||||
# Optional: Set the model size used for transcription. (default: shown below)
|
||||
model_size: small
|
||||
# Optional: Set the language used for transcription translation. (default: shown below)
|
||||
# List of language codes: https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10
|
||||
@ -810,8 +801,6 @@ cameras:
|
||||
# NOTE: This must be different than any camera names, but can match with another zone on another
|
||||
# camera.
|
||||
front_steps:
|
||||
# Optional: A friendly name or descriptive text for the zones
|
||||
friendly_name: ""
|
||||
# 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: 0.033,0.306,0.324,0.138,0.439,0.185,0.042,0.428
|
||||
@ -931,13 +920,10 @@ cameras:
|
||||
type: thumbnail
|
||||
# Reference data for matching, either an event ID for `thumbnail` or a text string for `description`. (default: none)
|
||||
data: 1751565549.853251-b69j73
|
||||
# Similarity threshold for triggering. (default: shown below)
|
||||
threshold: 0.8
|
||||
# Similarity threshold for triggering. (default: none)
|
||||
threshold: 0.7
|
||||
# List of actions to perform when the trigger fires. (default: none)
|
||||
# Available options:
|
||||
# - `notification` (send a webpush notification)
|
||||
# - `sub_label` (add trigger friendly name as a sub label to the triggering tracked object)
|
||||
# - `attribute` (add trigger's name and similarity score as a data attribute to the triggering tracked object)
|
||||
# Available options: `notification` (send a webpush notification)
|
||||
actions:
|
||||
- notification
|
||||
|
||||
|
||||
@ -24,11 +24,6 @@ birdseye:
|
||||
restream: True
|
||||
```
|
||||
|
||||
:::tip
|
||||
|
||||
To improve connection speed when using Birdseye via restream you can enable a small idle heartbeat by setting `birdseye.idle_heartbeat_fps` to a low value (e.g. `1–2`). This makes Frigate periodically push the last frame even when no motion is detected, reducing initial connection latency.
|
||||
|
||||
:::
|
||||
### Securing Restream With Authentication
|
||||
|
||||
The go2rtc restream can be secured with RTSP based username / password authentication. Ex:
|
||||
|
||||
@ -78,7 +78,7 @@ Switching between V1 and V2 requires reindexing your embeddings. The embeddings
|
||||
|
||||
### GPU Acceleration
|
||||
|
||||
The CLIP models are downloaded in ONNX format, and the `large` model can be accelerated using GPU hardware, when available. This depends on the Docker build that is used. You can also target a specific device in a multi-GPU installation.
|
||||
The CLIP models are downloaded in ONNX format, and the `large` model can be accelerated using GPU / NPU hardware, when available. This depends on the Docker build that is used. You can also target a specific device in a multi-GPU installation.
|
||||
|
||||
```yaml
|
||||
semantic_search:
|
||||
@ -90,7 +90,7 @@ semantic_search:
|
||||
|
||||
:::info
|
||||
|
||||
If the correct build is used for your GPU / NPU and the `large` model is configured, then the GPU will be detected and used automatically.
|
||||
If the correct build is used for your GPU / NPU and the `large` model is configured, then the GPU / NPU will be detected and used automatically.
|
||||
Specify the `device` option to target a specific GPU in a multi-GPU system (see [onnxruntime's provider options](https://onnxruntime.ai/docs/execution-providers/)).
|
||||
If you do not specify a device, the first available GPU will be used.
|
||||
|
||||
@ -119,7 +119,7 @@ Semantic Search must be enabled to use Triggers.
|
||||
|
||||
### Configuration
|
||||
|
||||
Triggers are defined within the `semantic_search` configuration for each camera in your Frigate configuration file or through the UI. Each trigger consists of a `friendly_name`, a `type` (either `thumbnail` or `description`), a `data` field (the reference image event ID or text), a `threshold` for similarity matching, and a list of `actions` to perform when the trigger fires - `notification`, `sub_label`, and `attribute`.
|
||||
Triggers are defined within the `semantic_search` configuration for each camera in your Frigate configuration file or through the UI. Each trigger consists of a `friendly_name`, a `type` (either `thumbnail` or `description`), a `data` field (the reference image event ID or text), a `threshold` for similarity matching, and a list of `actions` to perform when the trigger fires.
|
||||
|
||||
Triggers are best configured through the Frigate UI.
|
||||
|
||||
@ -128,20 +128,17 @@ Triggers are best configured through the Frigate UI.
|
||||
1. Navigate to the **Settings** page and select the **Triggers** tab.
|
||||
2. Choose a camera from the dropdown menu to view or manage its triggers.
|
||||
3. Click **Add Trigger** to create a new trigger or use the pencil icon to edit an existing one.
|
||||
4. In the **Create Trigger** wizard:
|
||||
- Enter a **Name** for the trigger (e.g., "Red Car Alert").
|
||||
4. In the **Create Trigger** dialog:
|
||||
- Enter a **Name** for the trigger (e.g., "red_car_alert").
|
||||
- Enter a descriptive **Friendly Name** for the trigger (e.g., "Red car on the driveway camera").
|
||||
- Select the **Type** (`Thumbnail` or `Description`).
|
||||
- For `Thumbnail`, select an image to trigger this action when a similar thumbnail image is detected, based on the threshold.
|
||||
- For `Description`, enter text to trigger this action when a similar tracked object description is detected.
|
||||
- Set the **Threshold** for similarity matching.
|
||||
- Select **Actions** to perform when the trigger fires.
|
||||
If native webpush notifications are enabled, check the `Send Notification` box to send a notification.
|
||||
Check the `Add Sub Label` box to add the trigger's friendly name as a sub label to any triggering tracked objects.
|
||||
Check the `Add Attribute` box to add the trigger's internal ID (e.g., "red_car_alert") to a data attribute on the tracked object that can be processed via the API or MQTT.
|
||||
5. Save the trigger to update the configuration and store the embedding in the database.
|
||||
|
||||
When a trigger fires, the UI highlights the trigger with a blue dot for 3 seconds for easy identification. Additionally, the UI will show the last date/time and tracked object ID that activated your trigger. The last triggered timestamp is not saved to the database or persisted through restarts of Frigate.
|
||||
When a trigger fires, the UI highlights the trigger with a blue outline for 3 seconds for easy identification.
|
||||
|
||||
### Usage and Best Practices
|
||||
|
||||
|
||||
@ -27,7 +27,6 @@ cameras:
|
||||
- entire_yard
|
||||
zones:
|
||||
entire_yard:
|
||||
friendly_name: Entire yard # You can use characters from any language text
|
||||
coordinates: ...
|
||||
```
|
||||
|
||||
@ -45,10 +44,8 @@ cameras:
|
||||
- edge_yard
|
||||
zones:
|
||||
edge_yard:
|
||||
friendly_name: Edge yard # You can use characters from any language text
|
||||
coordinates: ...
|
||||
inner_yard:
|
||||
friendly_name: Inner yard # You can use characters from any language text
|
||||
coordinates: ...
|
||||
```
|
||||
|
||||
@ -62,7 +59,6 @@ cameras:
|
||||
- entire_yard
|
||||
zones:
|
||||
entire_yard:
|
||||
friendly_name: Entire yard
|
||||
coordinates: ...
|
||||
```
|
||||
|
||||
@ -86,7 +82,6 @@ cameras:
|
||||
|
||||
Only car objects can trigger the `front_yard_street` zone and only person can trigger the `entire_yard`. Objects will be tracked for any `person` that enter anywhere in the yard, and for cars only if they enter the street.
|
||||
|
||||
|
||||
### Zone Loitering
|
||||
|
||||
Sometimes objects are expected to be passing through a zone, but an object loitering in an area is unexpected. Zones can be configured to have a minimum loitering time after which the object will be considered in the zone.
|
||||
|
||||
@ -3,8 +3,6 @@ id: hardware
|
||||
title: Recommended hardware
|
||||
---
|
||||
|
||||
import CommunityBadge from '@site/src/components/CommunityBadge';
|
||||
|
||||
## Cameras
|
||||
|
||||
Cameras that output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. It is also helpful if your camera supports multiple substreams to allow different resolutions to be used for detection, streaming, and recordings without re-encoding.
|
||||
@ -61,7 +59,7 @@ Frigate supports multiple different detectors that work on different types of ha
|
||||
|
||||
- [Supports primarily ssdlite and mobilenet model architectures](../../configuration/object_detectors#edge-tpu-detector)
|
||||
|
||||
- <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 M.2 accelerator module is available in m.2 format allowing for a wide range of compatibility with devices.
|
||||
- [MemryX](#memryx-mx3): The MX3 M.2 accelerator module is available in m.2 format allowing for a wide range of compatibility with devices.
|
||||
- [Supports many model architectures](../../configuration/object_detectors#memryx-mx3)
|
||||
- Runs best with tiny, small, or medium-size models
|
||||
|
||||
@ -80,32 +78,38 @@ Frigate supports multiple different detectors that work on different types of ha
|
||||
|
||||
**Intel**
|
||||
|
||||
- [OpenVino](#openvino---intel): OpenVino can run on Intel Arc GPUs, Intel integrated GPUs, and Intel NPUs to provide efficient object detection.
|
||||
- [OpenVino](#openvino---intel): OpenVino can run on Intel Arc GPUs, Intel integrated GPUs, and Intel CPUs to provide efficient object detection.
|
||||
- [Supports majority of model architectures](../../configuration/object_detectors#openvino-supported-models)
|
||||
- Runs best with tiny, small, or medium models
|
||||
|
||||
**Nvidia**
|
||||
|
||||
- [TensortRT](#tensorrt---nvidia-gpu): TensorRT can run on Nvidia GPUs to provide efficient object detection.
|
||||
|
||||
- [TensortRT](#tensorrt---nvidia-gpu): TensorRT can run on Nvidia GPUs and Jetson devices.
|
||||
- [Supports majority of model architectures via ONNX](../../configuration/object_detectors#onnx-supported-models)
|
||||
- Runs well with any size models including large
|
||||
|
||||
- <CommunityBadge /> [Jetson](#nvidia-jetson): Jetson devices are supported via the TensorRT or ONNX detectors when running Jetpack 6.
|
||||
|
||||
**Rockchip** <CommunityBadge />
|
||||
**Rockchip**
|
||||
|
||||
- [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs to provide efficient object detection.
|
||||
- [Supports limited model architectures](../../configuration/object_detectors#choosing-a-model)
|
||||
- Runs best with tiny or small size models
|
||||
- Runs efficiently on low power hardware
|
||||
|
||||
**Synaptics** <CommunityBadge />
|
||||
**Synaptics**
|
||||
|
||||
- [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs to provide efficient object detection.
|
||||
|
||||
:::
|
||||
|
||||
### Synaptics
|
||||
|
||||
- **Synaptics** Default model is **mobilenet**
|
||||
|
||||
| Name | Synaptics SL1680 Inference Time |
|
||||
| ---------------- | ------------------------------- |
|
||||
| ssd mobilenet | ~ 25 ms |
|
||||
| yolov5m | ~ 118 ms |
|
||||
|
||||
### Hailo-8
|
||||
|
||||
Frigate supports both the Hailo-8 and Hailo-8L AI Acceleration Modules on compatible hardware platforms—including the Raspberry Pi 5 with the PCIe hat from the AI kit. The Hailo detector integration in Frigate automatically identifies your hardware type and selects the appropriate default model when a custom model isn’t provided.
|
||||
@ -138,7 +142,6 @@ The OpenVINO detector type is able to run on:
|
||||
|
||||
- 6th Gen Intel Platforms and newer that have an iGPU
|
||||
- x86 hosts with an Intel Arc GPU
|
||||
- Intel NPUs
|
||||
- Most modern AMD CPUs (though this is officially not supported by Intel)
|
||||
- x86 & Arm64 hosts via CPU (generally not recommended)
|
||||
|
||||
@ -159,12 +162,11 @@ Inference speeds vary greatly depending on the CPU or GPU used, some known examp
|
||||
| Intel HD 530 | 15 - 35 ms | | | | Can only run one detector instance |
|
||||
| Intel HD 620 | 15 - 25 ms | | 320: ~ 35 ms | | |
|
||||
| Intel HD 630 | ~ 15 ms | | 320: ~ 30 ms | | |
|
||||
| Intel UHD 730 | ~ 10 ms | t-320: 14ms s-320: 24ms t-640: 34ms s-640: 65ms | 320: ~ 19 ms 640: ~ 54 ms | | |
|
||||
| Intel UHD 730 | ~ 10 ms | | 320: ~ 19 ms 640: ~ 54 ms | | |
|
||||
| Intel UHD 770 | ~ 15 ms | t-320: ~ 16 ms s-320: ~ 20 ms s-640: ~ 40 ms | 320: ~ 20 ms 640: ~ 46 ms | | |
|
||||
| Intel N100 | ~ 15 ms | s-320: 30 ms | 320: ~ 25 ms | | Can only run one detector instance |
|
||||
| Intel N150 | ~ 15 ms | t-320: 16 ms s-320: 24 ms | | | |
|
||||
| Intel Iris XE | ~ 10 ms | t-320: 6 ms t-640: 14 ms s-320: 8 ms s-640: 16 ms | 320: ~ 10 ms 640: ~ 20 ms | 320-n: 33 ms | |
|
||||
| Intel NPU | ~ 6 ms | s-320: 11 ms | 320: ~ 14 ms 640: ~ 34 ms | 320-n: 40 ms | |
|
||||
| Intel Iris XE | ~ 10 ms | s-320: 12 ms s-640: 30 ms | 320: ~ 18 ms 640: ~ 50 ms | | |
|
||||
| Intel Arc A310 | ~ 5 ms | t-320: 7 ms t-640: 11 ms s-320: 8 ms s-640: 15 ms | 320: ~ 8 ms 640: ~ 14 ms | | |
|
||||
| Intel Arc A380 | ~ 6 ms | | 320: ~ 10 ms 640: ~ 22 ms | 336: 20 ms 448: 27 ms | |
|
||||
| Intel Arc A750 | ~ 4 ms | | 320: ~ 8 ms | | |
|
||||
@ -257,7 +259,7 @@ Inference speeds may vary depending on the host platform. The above data was mea
|
||||
|
||||
### Nvidia Jetson
|
||||
|
||||
Jetson devices are supported via the TensorRT or ONNX detectors when running Jetpack 6. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration_video#nvidia-jetson-orin-agx-orin-nx-orin-nano-xavier-agx-xavier-nx-tx2-tx1-nano) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets), and will make use of the Jetson's GPU and DLA for object detection when configured with the [TensorRT detector](/configuration/object_detectors#nvidia-tensorrt-detector).
|
||||
Frigate supports all Jetson boards, from the inexpensive Jetson Nano to the powerful Jetson Orin AGX. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration_video#nvidia-jetson-orin-agx-orin-nx-orin-nano-xavier-agx-xavier-nx-tx2-tx1-nano) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets), and will make use of the Jetson's GPU and DLA for object detection when configured with the [TensorRT detector](/configuration/object_detectors#nvidia-tensorrt-detector).
|
||||
|
||||
Inference speed will vary depending on the YOLO model, jetson platform and jetson nvpmodel (GPU/DLA/EMC clock speed). It is typically 20-40 ms for most models. The DLA is more efficient than the GPU, but not faster, so using the DLA will reduce power consumption but will slightly increase inference time.
|
||||
|
||||
@ -278,15 +280,6 @@ Frigate supports hardware video processing on all Rockchip boards. However, hard
|
||||
|
||||
The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms for yolo-nas s.
|
||||
|
||||
### Synaptics
|
||||
|
||||
- **Synaptics** Default model is **mobilenet**
|
||||
|
||||
| Name | Synaptics SL1680 Inference Time |
|
||||
| ------------- | ------------------------------- |
|
||||
| ssd mobilenet | ~ 25 ms |
|
||||
| yolov5m | ~ 118 ms |
|
||||
|
||||
## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
|
||||
|
||||
This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.
|
||||
|
||||
@ -56,7 +56,7 @@ services:
|
||||
volumes:
|
||||
- /path/to/your/config:/config
|
||||
- /path/to/your/storage:/media/frigate
|
||||
- type: tmpfs # Recommended: 1GB of memory
|
||||
- type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
|
||||
target: /tmp/cache
|
||||
tmpfs:
|
||||
size: 1000000000
|
||||
@ -304,13 +304,12 @@ services:
|
||||
- /dev/bus/usb:/dev/bus/usb # Passes the USB Coral, needs to be modified for other versions
|
||||
- /dev/apex_0:/dev/apex_0 # Passes a PCIe Coral, follow driver instructions here https://coral.ai/docs/m2/get-started/#2a-on-linux
|
||||
- /dev/video11:/dev/video11 # For Raspberry Pi 4B
|
||||
- /dev/dri/renderD128:/dev/dri/renderD128 # AMD / Intel GPU, needs to be updated for your hardware
|
||||
- /dev/accel:/dev/accel # Intel NPU
|
||||
- /dev/dri/renderD128:/dev/dri/renderD128 # For intel hwaccel, needs to be updated for your hardware
|
||||
volumes:
|
||||
- /etc/localtime:/etc/localtime:ro
|
||||
- /path/to/your/config:/config
|
||||
- /path/to/your/storage:/media/frigate
|
||||
- type: tmpfs # Recommended: 1GB of memory
|
||||
- type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
|
||||
target: /tmp/cache
|
||||
tmpfs:
|
||||
size: 1000000000
|
||||
|
||||
@ -5,7 +5,7 @@ title: Updating
|
||||
|
||||
# Updating Frigate
|
||||
|
||||
The current stable version of Frigate is **0.16.2**. The release notes and any breaking changes for this version can be found on the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases/tag/v0.16.2).
|
||||
The current stable version of Frigate is **0.16.1**. The release notes and any breaking changes for this version can be found on the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases/tag/v0.16.1).
|
||||
|
||||
Keeping Frigate up to date ensures you benefit from the latest features, performance improvements, and bug fixes. The update process varies slightly depending on your installation method (Docker, Home Assistant Addon, etc.). Below are instructions for the most common setups.
|
||||
|
||||
@ -33,21 +33,21 @@ If you’re running Frigate via Docker (recommended method), follow these steps:
|
||||
2. **Update and Pull the Latest Image**:
|
||||
|
||||
- If using Docker Compose:
|
||||
- Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.16.2` instead of `0.15.2`). For example:
|
||||
- Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.16.1` instead of `0.15.2`). For example:
|
||||
```yaml
|
||||
services:
|
||||
frigate:
|
||||
image: ghcr.io/blakeblackshear/frigate:0.16.2
|
||||
image: ghcr.io/blakeblackshear/frigate:0.16.1
|
||||
```
|
||||
- Then pull the image:
|
||||
```bash
|
||||
docker pull ghcr.io/blakeblackshear/frigate:0.16.2
|
||||
docker pull ghcr.io/blakeblackshear/frigate:0.16.1
|
||||
```
|
||||
- **Note for `stable` Tag Users**: If your `docker-compose.yml` uses the `stable` tag (e.g., `ghcr.io/blakeblackshear/frigate:stable`), you don’t need to update the tag manually. The `stable` tag always points to the latest stable release after pulling.
|
||||
- If using `docker run`:
|
||||
- Pull the image with the appropriate tag (e.g., `0.16.2`, `0.16.2-tensorrt`, or `stable`):
|
||||
- Pull the image with the appropriate tag (e.g., `0.16.1`, `0.16.1-tensorrt`, or `stable`):
|
||||
```bash
|
||||
docker pull ghcr.io/blakeblackshear/frigate:0.16.2
|
||||
docker pull ghcr.io/blakeblackshear/frigate:0.16.1
|
||||
```
|
||||
|
||||
3. **Start the Container**:
|
||||
|
||||
@ -159,49 +159,9 @@ Message published for updates to tracked object metadata, for example:
|
||||
}
|
||||
```
|
||||
|
||||
#### Object Classification Update
|
||||
|
||||
Message published when [object classification](/configuration/custom_classification/object_classification) reaches consensus on a classification result.
|
||||
|
||||
**Sub label type:**
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "classification",
|
||||
"id": "1607123955.475377-mxklsc",
|
||||
"camera": "front_door_cam",
|
||||
"timestamp": 1607123958.748393,
|
||||
"model": "person_classifier",
|
||||
"sub_label": "delivery_person",
|
||||
"score": 0.87
|
||||
}
|
||||
```
|
||||
|
||||
**Attribute type:**
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "classification",
|
||||
"id": "1607123955.475377-mxklsc",
|
||||
"camera": "front_door_cam",
|
||||
"timestamp": 1607123958.748393,
|
||||
"model": "helmet_detector",
|
||||
"attribute": "yes",
|
||||
"score": 0.92
|
||||
}
|
||||
```
|
||||
|
||||
### `frigate/reviews`
|
||||
|
||||
Message published for each changed review item. The first message is published when the `detection` or `alert` is initiated.
|
||||
|
||||
An `update` with the same ID will be published when:
|
||||
|
||||
- The severity changes from `detection` to `alert`
|
||||
- Additional objects are detected
|
||||
- An object is recognized via face, lpr, etc.
|
||||
|
||||
When the review activity has ended a final `end` message is published.
|
||||
Message published for each changed review item. The first message is published when the `detection` or `alert` is initiated. When additional objects are detected or when a zone change occurs, it will publish a, `update` message with the same id. When the review activity has ended a final `end` message is published.
|
||||
|
||||
```json
|
||||
{
|
||||
@ -341,11 +301,6 @@ Publishes transcribed text for audio detected on this camera.
|
||||
|
||||
**NOTE:** Requires audio detection and transcription to be enabled
|
||||
|
||||
### `frigate/<camera_name>/classification/<model_name>`
|
||||
|
||||
Publishes the current state detected by a state classification model for the camera. The topic name includes the model name as configured in your classification settings.
|
||||
The published value is the detected state class name (e.g., `open`, `closed`, `on`, `off`). The state is only published when it changes, helping to reduce unnecessary MQTT traffic.
|
||||
|
||||
### `frigate/<camera_name>/enabled/set`
|
||||
|
||||
Topic to turn Frigate's processing of a camera on and off. Expected values are `ON` and `OFF`.
|
||||
|
||||
@ -42,7 +42,6 @@ Misidentified objects should have a correct label added. For example, if a perso
|
||||
| `w` | Add box |
|
||||
| `d` | Toggle difficult |
|
||||
| `s` | Switch to the next label |
|
||||
| `Shift + s` | Switch to the previous label |
|
||||
| `tab` | Select next largest box |
|
||||
| `del` | Delete current box |
|
||||
| `esc` | Deselect/Cancel |
|
||||
|
||||
@ -1,129 +0,0 @@
|
||||
---
|
||||
id: memory
|
||||
title: Memory Troubleshooting
|
||||
---
|
||||
|
||||
Frigate includes built-in memory profiling using [memray](https://bloomberg.github.io/memray/) to help diagnose memory issues. This feature allows you to profile specific Frigate modules to identify memory leaks, excessive allocations, or other memory-related problems.
|
||||
|
||||
## Enabling Memory Profiling
|
||||
|
||||
Memory profiling is controlled via the `FRIGATE_MEMRAY_MODULES` environment variable. Set it to a comma-separated list of module names you want to profile:
|
||||
|
||||
```bash
|
||||
export FRIGATE_MEMRAY_MODULES="frigate.review_segment_manager,frigate.capture"
|
||||
```
|
||||
|
||||
### Module Names
|
||||
|
||||
Frigate processes are named using a module-based naming scheme. Common module names include:
|
||||
|
||||
- `frigate.review_segment_manager` - Review segment processing
|
||||
- `frigate.recording_manager` - Recording management
|
||||
- `frigate.capture` - Camera capture processes (all cameras with this module name)
|
||||
- `frigate.process` - Camera processing/tracking (all cameras with this module name)
|
||||
- `frigate.output` - Output processing
|
||||
- `frigate.audio_manager` - Audio processing
|
||||
- `frigate.embeddings` - Embeddings processing
|
||||
|
||||
You can also specify the full process name (including camera-specific identifiers) if you want to profile a specific camera:
|
||||
|
||||
```bash
|
||||
export FRIGATE_MEMRAY_MODULES="frigate.capture:front_door"
|
||||
```
|
||||
|
||||
When you specify a module name (e.g., `frigate.capture`), all processes with that module prefix will be profiled. For example, `frigate.capture` will profile all camera capture processes.
|
||||
|
||||
## How It Works
|
||||
|
||||
1. **Binary File Creation**: When profiling is enabled, memray creates a binary file (`.bin`) in `/config/memray_reports/` that is updated continuously in real-time as the process runs.
|
||||
|
||||
2. **Automatic HTML Generation**: On normal process exit, Frigate automatically:
|
||||
|
||||
- Stops memray tracking
|
||||
- Generates an HTML flamegraph report
|
||||
- Saves it to `/config/memray_reports/<module_name>.html`
|
||||
|
||||
3. **Crash Recovery**: If a process crashes (SIGKILL, segfault, etc.), the binary file is preserved with all data up to the crash point. You can manually generate the HTML report from the binary file.
|
||||
|
||||
## Viewing Reports
|
||||
|
||||
### Automatic Reports
|
||||
|
||||
After a process exits normally, you'll find HTML reports in `/config/memray_reports/`. Open these files in a web browser to view interactive flamegraphs showing memory usage patterns.
|
||||
|
||||
### Manual Report Generation
|
||||
|
||||
If a process crashes or you want to generate a report from an existing binary file, you can manually create the HTML report:
|
||||
|
||||
```bash
|
||||
memray flamegraph /config/memray_reports/<module_name>.bin
|
||||
```
|
||||
|
||||
This will generate an HTML file that you can open in your browser.
|
||||
|
||||
## Understanding the Reports
|
||||
|
||||
Memray flamegraphs show:
|
||||
|
||||
- **Memory allocations over time**: See where memory is being allocated in your code
|
||||
- **Call stacks**: Understand the full call chain leading to allocations
|
||||
- **Memory hotspots**: Identify functions or code paths that allocate the most memory
|
||||
- **Memory leaks**: Spot patterns where memory is allocated but not freed
|
||||
|
||||
The interactive HTML reports allow you to:
|
||||
|
||||
- Zoom into specific time ranges
|
||||
- Filter by function names
|
||||
- View detailed allocation information
|
||||
- Export data for further analysis
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Profile During Issues**: Enable profiling when you're experiencing memory issues, not all the time, as it adds some overhead.
|
||||
|
||||
2. **Profile Specific Modules**: Instead of profiling everything, focus on the modules you suspect are causing issues.
|
||||
|
||||
3. **Let Processes Run**: Allow processes to run for a meaningful duration to capture representative memory usage patterns.
|
||||
|
||||
4. **Check Binary Files**: If HTML reports aren't generated automatically (e.g., after a crash), check for `.bin` files in `/config/memray_reports/` and generate reports manually.
|
||||
|
||||
5. **Compare Reports**: Generate reports at different times to compare memory usage patterns and identify trends.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### No Reports Generated
|
||||
|
||||
- Check that the environment variable is set correctly
|
||||
- Verify the module name matches exactly (case-sensitive)
|
||||
- Check logs for memray-related errors
|
||||
- Ensure `/config/memray_reports/` directory exists and is writable
|
||||
|
||||
### Process Crashed Before Report Generation
|
||||
|
||||
- Look for `.bin` files in `/config/memray_reports/`
|
||||
- Manually generate HTML reports using: `memray flamegraph <file>.bin`
|
||||
- The binary file contains all data up to the crash point
|
||||
|
||||
### Reports Show No Data
|
||||
|
||||
- Ensure the process ran long enough to generate meaningful data
|
||||
- Check that memray is properly installed (included by default in Frigate)
|
||||
- Verify the process actually started and ran (check process logs)
|
||||
|
||||
## Example Usage
|
||||
|
||||
```bash
|
||||
# Enable profiling for review and capture modules
|
||||
export FRIGATE_MEMRAY_MODULES="frigate.review_segment_manager,frigate.capture"
|
||||
|
||||
# Start Frigate
|
||||
# ... let it run for a while ...
|
||||
|
||||
# Check for reports
|
||||
ls -lh /config/memray_reports/
|
||||
|
||||
# If a process crashed, manually generate report
|
||||
memray flamegraph /config/memray_reports/frigate_capture_front_door.bin
|
||||
```
|
||||
|
||||
For more information about memray and interpreting reports, see the [official memray documentation](https://bloomberg.github.io/memray/).
|
||||
@ -10,7 +10,7 @@ const config: Config = {
|
||||
baseUrl: "/",
|
||||
onBrokenLinks: "throw",
|
||||
onBrokenMarkdownLinks: "warn",
|
||||
favicon: "img/branding/favicon.ico",
|
||||
favicon: "img/favicon.ico",
|
||||
organizationName: "blakeblackshear",
|
||||
projectName: "frigate",
|
||||
themes: [
|
||||
@ -116,8 +116,8 @@ const config: Config = {
|
||||
title: "Frigate",
|
||||
logo: {
|
||||
alt: "Frigate",
|
||||
src: "img/branding/logo.svg",
|
||||
srcDark: "img/branding/logo-dark.svg",
|
||||
src: "img/logo.svg",
|
||||
srcDark: "img/logo-dark.svg",
|
||||
},
|
||||
items: [
|
||||
{
|
||||
@ -170,7 +170,7 @@ const config: Config = {
|
||||
],
|
||||
},
|
||||
],
|
||||
copyright: `Copyright © ${new Date().getFullYear()} Frigate LLC`,
|
||||
copyright: `Copyright © ${new Date().getFullYear()} Blake Blackshear`,
|
||||
},
|
||||
},
|
||||
plugins: [
|
||||
|
||||
@ -131,7 +131,6 @@ const sidebars: SidebarsConfig = {
|
||||
"troubleshooting/recordings",
|
||||
"troubleshooting/gpu",
|
||||
"troubleshooting/edgetpu",
|
||||
"troubleshooting/memory",
|
||||
],
|
||||
Development: [
|
||||
"development/contributing",
|
||||
|
||||
@ -1,23 +0,0 @@
|
||||
import React from "react";
|
||||
|
||||
export default function CommunityBadge() {
|
||||
return (
|
||||
<span
|
||||
title="This detector is maintained by community members who provide code, maintenance, and support. See the contributing boards documentation for more information."
|
||||
style={{
|
||||
display: "inline-block",
|
||||
backgroundColor: "#f1f3f5",
|
||||
color: "#24292f",
|
||||
fontSize: "11px",
|
||||
fontWeight: 600,
|
||||
padding: "2px 6px",
|
||||
borderRadius: "3px",
|
||||
border: "1px solid #d1d9e0",
|
||||
marginLeft: "4px",
|
||||
cursor: "help",
|
||||
}}
|
||||
>
|
||||
Community Supported
|
||||
</span>
|
||||
);
|
||||
}
|
||||
@ -1,18 +1,13 @@
|
||||
.alert {
|
||||
padding: 12px;
|
||||
background: #fff8e6;
|
||||
border-bottom: 1px solid #ffd166;
|
||||
text-align: center;
|
||||
font-size: 15px;
|
||||
}
|
||||
padding: 12px;
|
||||
background: #fff8e6;
|
||||
border-bottom: 1px solid #ffd166;
|
||||
text-align: center;
|
||||
font-size: 15px;
|
||||
}
|
||||
|
||||
[data-theme="dark"] .alert {
|
||||
background: #3b2f0b;
|
||||
border-bottom: 1px solid #665c22;
|
||||
}
|
||||
|
||||
.alert a {
|
||||
color: #1890ff;
|
||||
font-weight: 500;
|
||||
margin-left: 6px;
|
||||
}
|
||||
.alert a {
|
||||
color: #1890ff;
|
||||
font-weight: 500;
|
||||
margin-left: 6px;
|
||||
}
|
||||
30
docs/static/img/branding/LICENSE.md
vendored
30
docs/static/img/branding/LICENSE.md
vendored
@ -1,30 +0,0 @@
|
||||
# COPYRIGHT AND TRADEMARK NOTICE
|
||||
|
||||
The images, logos, and icons contained in this directory (the "Brand Assets") are
|
||||
proprietary to Frigate LLC and are NOT covered by the MIT License governing the
|
||||
rest of this repository.
|
||||
|
||||
1. TRADEMARK STATUS
|
||||
The "Frigate" name and the accompanying logo are common law trademarks™ of
|
||||
Frigate LLC. Frigate LLC reserves all rights to these marks.
|
||||
|
||||
2. LIMITED PERMISSION FOR USE
|
||||
Permission is hereby granted to display these Brand Assets strictly for the
|
||||
following purposes:
|
||||
a. To execute the software interface on a local machine.
|
||||
b. To identify the software in documentation or reviews (nominative use).
|
||||
|
||||
3. RESTRICTIONS
|
||||
You may NOT:
|
||||
a. Use these Brand Assets to represent a derivative work (fork) as an official
|
||||
product of Frigate LLC.
|
||||
b. Use these Brand Assets in a way that implies endorsement, sponsorship, or
|
||||
commercial affiliation with Frigate LLC.
|
||||
c. Modify or alter the Brand Assets.
|
||||
|
||||
If you fork this repository with the intent to distribute a modified or competing
|
||||
version of the software, you must replace these Brand Assets with your own
|
||||
original content.
|
||||
|
||||
ALL RIGHTS RESERVED.
|
||||
Copyright (c) 2025 Frigate LLC.
|
||||
|
Before Width: | Height: | Size: 15 KiB After Width: | Height: | Size: 15 KiB |
|
Before Width: | Height: | Size: 12 KiB After Width: | Height: | Size: 12 KiB |
|
Before Width: | Height: | Size: 936 B After Width: | Height: | Size: 936 B |
|
Before Width: | Height: | Size: 933 B After Width: | Height: | Size: 933 B |
@ -23,7 +23,7 @@ from markupsafe import escape
|
||||
from peewee import SQL, fn, operator
|
||||
from pydantic import ValidationError
|
||||
|
||||
from frigate.api.auth import allow_any_authenticated, allow_public, require_role
|
||||
from frigate.api.auth import require_role
|
||||
from frigate.api.defs.query.app_query_parameters import AppTimelineHourlyQueryParameters
|
||||
from frigate.api.defs.request.app_body import AppConfigSetBody
|
||||
from frigate.api.defs.tags import Tags
|
||||
@ -37,6 +37,7 @@ from frigate.stats.prometheus import get_metrics, update_metrics
|
||||
from frigate.util.builtin import (
|
||||
clean_camera_user_pass,
|
||||
flatten_config_data,
|
||||
get_tz_modifiers,
|
||||
process_config_query_string,
|
||||
update_yaml_file_bulk,
|
||||
)
|
||||
@ -47,7 +48,6 @@ from frigate.util.services import (
|
||||
restart_frigate,
|
||||
vainfo_hwaccel,
|
||||
)
|
||||
from frigate.util.time import get_tz_modifiers
|
||||
from frigate.version import VERSION
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@ -56,33 +56,29 @@ logger = logging.getLogger(__name__)
|
||||
router = APIRouter(tags=[Tags.app])
|
||||
|
||||
|
||||
@router.get(
|
||||
"/", response_class=PlainTextResponse, dependencies=[Depends(allow_public())]
|
||||
)
|
||||
@router.get("/", response_class=PlainTextResponse)
|
||||
def is_healthy():
|
||||
return "Frigate is running. Alive and healthy!"
|
||||
|
||||
|
||||
@router.get("/config/schema.json", dependencies=[Depends(allow_public())])
|
||||
@router.get("/config/schema.json")
|
||||
def config_schema(request: Request):
|
||||
return Response(
|
||||
content=request.app.frigate_config.schema_json(), media_type="application/json"
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/version", response_class=PlainTextResponse, dependencies=[Depends(allow_public())]
|
||||
)
|
||||
@router.get("/version", response_class=PlainTextResponse)
|
||||
def version():
|
||||
return VERSION
|
||||
|
||||
|
||||
@router.get("/stats", dependencies=[Depends(allow_any_authenticated())])
|
||||
@router.get("/stats")
|
||||
def stats(request: Request):
|
||||
return JSONResponse(content=request.app.stats_emitter.get_latest_stats())
|
||||
|
||||
|
||||
@router.get("/stats/history", dependencies=[Depends(allow_any_authenticated())])
|
||||
@router.get("/stats/history")
|
||||
def stats_history(request: Request, keys: str = None):
|
||||
if keys:
|
||||
keys = keys.split(",")
|
||||
@ -90,7 +86,7 @@ def stats_history(request: Request, keys: str = None):
|
||||
return JSONResponse(content=request.app.stats_emitter.get_stats_history(keys))
|
||||
|
||||
|
||||
@router.get("/metrics", dependencies=[Depends(allow_any_authenticated())])
|
||||
@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
|
||||
@ -107,7 +103,7 @@ def metrics(request: Request):
|
||||
return Response(content=content, media_type=content_type)
|
||||
|
||||
|
||||
@router.get("/config", dependencies=[Depends(allow_any_authenticated())])
|
||||
@router.get("/config")
|
||||
def config(request: Request):
|
||||
config_obj: FrigateConfig = request.app.frigate_config
|
||||
config: dict[str, dict[str, Any]] = config_obj.model_dump(
|
||||
@ -183,37 +179,7 @@ def config(request: Request):
|
||||
return JSONResponse(content=config)
|
||||
|
||||
|
||||
@router.get("/config/raw_paths", dependencies=[Depends(require_role(["admin"]))])
|
||||
def config_raw_paths(request: Request):
|
||||
"""Admin-only endpoint that returns camera paths and go2rtc streams without credential masking."""
|
||||
config_obj: FrigateConfig = request.app.frigate_config
|
||||
|
||||
raw_paths = {"cameras": {}, "go2rtc": {"streams": {}}}
|
||||
|
||||
# Extract raw camera ffmpeg input paths
|
||||
for camera_name, camera in config_obj.cameras.items():
|
||||
raw_paths["cameras"][camera_name] = {
|
||||
"ffmpeg": {
|
||||
"inputs": [
|
||||
{"path": input.path, "roles": input.roles}
|
||||
for input in camera.ffmpeg.inputs
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
# Extract raw go2rtc stream URLs
|
||||
go2rtc_config = config_obj.go2rtc.model_dump(
|
||||
mode="json", warnings="none", exclude_none=True
|
||||
)
|
||||
for stream_name, stream in go2rtc_config.get("streams", {}).items():
|
||||
if stream is None:
|
||||
continue
|
||||
raw_paths["go2rtc"]["streams"][stream_name] = stream
|
||||
|
||||
return JSONResponse(content=raw_paths)
|
||||
|
||||
|
||||
@router.get("/config/raw", dependencies=[Depends(allow_any_authenticated())])
|
||||
@router.get("/config/raw")
|
||||
def config_raw():
|
||||
config_file = find_config_file()
|
||||
|
||||
@ -421,29 +387,20 @@ def config_set(request: Request, body: AppConfigSetBody):
|
||||
old_config: FrigateConfig = request.app.frigate_config
|
||||
request.app.frigate_config = config
|
||||
|
||||
if body.update_topic:
|
||||
if body.update_topic.startswith("config/cameras/"):
|
||||
_, _, camera, field = body.update_topic.split("/")
|
||||
if body.update_topic and body.update_topic.startswith("config/cameras/"):
|
||||
_, _, camera, field = body.update_topic.split("/")
|
||||
|
||||
if field == "add":
|
||||
settings = config.cameras[camera]
|
||||
elif field == "remove":
|
||||
settings = old_config.cameras[camera]
|
||||
else:
|
||||
settings = config.get_nested_object(body.update_topic)
|
||||
|
||||
request.app.config_publisher.publish_update(
|
||||
CameraConfigUpdateTopic(CameraConfigUpdateEnum[field], camera),
|
||||
settings,
|
||||
)
|
||||
if field == "add":
|
||||
settings = config.cameras[camera]
|
||||
elif field == "remove":
|
||||
settings = old_config.cameras[camera]
|
||||
else:
|
||||
# Generic handling for global config updates
|
||||
settings = config.get_nested_object(body.update_topic)
|
||||
|
||||
# Publish None for removal, actual config for add/update
|
||||
request.app.config_publisher.publisher.publish(
|
||||
body.update_topic, settings
|
||||
)
|
||||
request.app.config_publisher.publish_update(
|
||||
CameraConfigUpdateTopic(CameraConfigUpdateEnum[field], camera),
|
||||
settings,
|
||||
)
|
||||
|
||||
return JSONResponse(
|
||||
content=(
|
||||
@ -456,7 +413,7 @@ def config_set(request: Request, body: AppConfigSetBody):
|
||||
)
|
||||
|
||||
|
||||
@router.get("/vainfo", dependencies=[Depends(allow_any_authenticated())])
|
||||
@router.get("/vainfo")
|
||||
def vainfo():
|
||||
vainfo = vainfo_hwaccel()
|
||||
return JSONResponse(
|
||||
@ -476,16 +433,12 @@ def vainfo():
|
||||
)
|
||||
|
||||
|
||||
@router.get("/nvinfo", dependencies=[Depends(allow_any_authenticated())])
|
||||
@router.get("/nvinfo")
|
||||
def nvinfo():
|
||||
return JSONResponse(content=get_nvidia_driver_info())
|
||||
|
||||
|
||||
@router.get(
|
||||
"/logs/{service}",
|
||||
tags=[Tags.logs],
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
)
|
||||
@router.get("/logs/{service}", tags=[Tags.logs])
|
||||
async def logs(
|
||||
service: str = Path(enum=["frigate", "nginx", "go2rtc"]),
|
||||
download: Optional[str] = None,
|
||||
@ -593,7 +546,7 @@ def restart():
|
||||
)
|
||||
|
||||
|
||||
@router.get("/labels", dependencies=[Depends(allow_any_authenticated())])
|
||||
@router.get("/labels")
|
||||
def get_labels(camera: str = ""):
|
||||
try:
|
||||
if camera:
|
||||
@ -611,7 +564,7 @@ def get_labels(camera: str = ""):
|
||||
return JSONResponse(content=labels)
|
||||
|
||||
|
||||
@router.get("/sub_labels", dependencies=[Depends(allow_any_authenticated())])
|
||||
@router.get("/sub_labels")
|
||||
def get_sub_labels(split_joined: Optional[int] = None):
|
||||
try:
|
||||
events = Event.select(Event.sub_label).distinct()
|
||||
@ -642,7 +595,7 @@ def get_sub_labels(split_joined: Optional[int] = None):
|
||||
return JSONResponse(content=sub_labels)
|
||||
|
||||
|
||||
@router.get("/plus/models", dependencies=[Depends(allow_any_authenticated())])
|
||||
@router.get("/plus/models")
|
||||
def plusModels(request: Request, filterByCurrentModelDetector: bool = False):
|
||||
if not request.app.frigate_config.plus_api.is_active():
|
||||
return JSONResponse(
|
||||
@ -684,9 +637,7 @@ def plusModels(request: Request, filterByCurrentModelDetector: bool = False):
|
||||
return JSONResponse(content=validModels)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/recognized_license_plates", dependencies=[Depends(allow_any_authenticated())]
|
||||
)
|
||||
@router.get("/recognized_license_plates")
|
||||
def get_recognized_license_plates(split_joined: Optional[int] = None):
|
||||
try:
|
||||
query = (
|
||||
@ -720,7 +671,7 @@ def get_recognized_license_plates(split_joined: Optional[int] = None):
|
||||
return JSONResponse(content=recognized_license_plates)
|
||||
|
||||
|
||||
@router.get("/timeline", dependencies=[Depends(allow_any_authenticated())])
|
||||
@router.get("/timeline")
|
||||
def timeline(camera: str = "all", limit: int = 100, source_id: Optional[str] = None):
|
||||
clauses = []
|
||||
|
||||
@ -737,11 +688,7 @@ def timeline(camera: str = "all", limit: int = 100, source_id: Optional[str] = N
|
||||
clauses.append((Timeline.camera == camera))
|
||||
|
||||
if source_id:
|
||||
source_ids = [sid.strip() for sid in source_id.split(",")]
|
||||
if len(source_ids) == 1:
|
||||
clauses.append((Timeline.source_id == source_ids[0]))
|
||||
else:
|
||||
clauses.append((Timeline.source_id.in_(source_ids)))
|
||||
clauses.append((Timeline.source_id == source_id))
|
||||
|
||||
if len(clauses) == 0:
|
||||
clauses.append((True))
|
||||
@ -757,7 +704,7 @@ def timeline(camera: str = "all", limit: int = 100, source_id: Optional[str] = N
|
||||
return JSONResponse(content=[t for t in timeline])
|
||||
|
||||
|
||||
@router.get("/timeline/hourly", dependencies=[Depends(allow_any_authenticated())])
|
||||
@router.get("/timeline/hourly")
|
||||
def hourly_timeline(params: AppTimelineHourlyQueryParameters = Depends()):
|
||||
"""Get hourly summary for timeline."""
|
||||
cameras = params.cameras
|
||||
|
||||
@ -32,194 +32,9 @@ from frigate.models import User
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def require_admin_by_default():
|
||||
"""
|
||||
Global admin requirement dependency for all endpoints by default.
|
||||
|
||||
This is set as the default dependency on the FastAPI app to ensure all
|
||||
endpoints require admin access unless explicitly overridden with
|
||||
allow_public(), allow_any_authenticated(), or require_role().
|
||||
|
||||
Port 5000 (internal) always has admin role set by the /auth endpoint,
|
||||
so this check passes automatically for internal requests.
|
||||
|
||||
Certain paths are exempted from the global admin check because they must
|
||||
be accessible before authentication (login, auth) or they have their own
|
||||
route-level authorization dependencies that handle access control.
|
||||
"""
|
||||
# Paths that have route-level auth dependencies and should bypass global admin check
|
||||
# These paths still have authorization - it's handled by their route-level dependencies
|
||||
EXEMPT_PATHS = {
|
||||
# Public auth endpoints (allow_public)
|
||||
"/auth",
|
||||
"/auth/first_time_login",
|
||||
"/login",
|
||||
# Authenticated user endpoints (allow_any_authenticated)
|
||||
"/logout",
|
||||
"/profile",
|
||||
# Public info endpoints (allow_public)
|
||||
"/",
|
||||
"/version",
|
||||
"/config/schema.json",
|
||||
# Authenticated user endpoints (allow_any_authenticated)
|
||||
"/metrics",
|
||||
"/stats",
|
||||
"/stats/history",
|
||||
"/config",
|
||||
"/config/raw",
|
||||
"/vainfo",
|
||||
"/nvinfo",
|
||||
"/labels",
|
||||
"/sub_labels",
|
||||
"/plus/models",
|
||||
"/recognized_license_plates",
|
||||
"/timeline",
|
||||
"/timeline/hourly",
|
||||
"/recordings/storage",
|
||||
"/recordings/summary",
|
||||
"/recordings/unavailable",
|
||||
"/go2rtc/streams",
|
||||
"/event_ids",
|
||||
"/events",
|
||||
"/exports",
|
||||
}
|
||||
|
||||
# Path prefixes that should be exempt (for paths with parameters)
|
||||
EXEMPT_PREFIXES = (
|
||||
"/logs/", # /logs/{service}
|
||||
"/review", # /review, /review/{id}, /review/summary, /review_ids, etc.
|
||||
"/reviews/", # /reviews/viewed, /reviews/delete
|
||||
"/events/", # /events/{id}/thumbnail, /events/summary, etc. (camera-scoped)
|
||||
"/export/", # /export/{camera}/start/..., /export/{id}/rename, /export/{id}
|
||||
"/go2rtc/streams/", # /go2rtc/streams/{camera}
|
||||
"/users/", # /users/{username}/password (has own auth)
|
||||
"/preview/", # /preview/{file}/thumbnail.jpg
|
||||
"/exports/", # /exports/{export_id}
|
||||
"/vod/", # /vod/{camera_name}/...
|
||||
"/notifications/", # /notifications/pubkey, /notifications/register
|
||||
)
|
||||
|
||||
async def admin_checker(request: Request):
|
||||
path = request.url.path
|
||||
|
||||
# Check exact path matches
|
||||
if path in EXEMPT_PATHS:
|
||||
return
|
||||
|
||||
# Check prefix matches for parameterized paths
|
||||
if path.startswith(EXEMPT_PREFIXES):
|
||||
return
|
||||
|
||||
# Dynamic camera path exemption:
|
||||
# Any path whose first segment matches a configured camera name should
|
||||
# bypass the global admin requirement. These endpoints enforce access
|
||||
# via route-level dependencies (e.g. require_camera_access) to ensure
|
||||
# per-camera authorization. This allows non-admin authenticated users
|
||||
# (e.g. viewer role) to access camera-specific resources without
|
||||
# needing admin privileges.
|
||||
try:
|
||||
if path.startswith("/"):
|
||||
first_segment = path.split("/", 2)[1]
|
||||
if (
|
||||
first_segment
|
||||
and first_segment in request.app.frigate_config.cameras
|
||||
):
|
||||
return
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# For all other paths, require admin role
|
||||
# Port 5000 (internal) requests have admin role set automatically
|
||||
role = request.headers.get("remote-role")
|
||||
if role == "admin":
|
||||
return
|
||||
|
||||
raise HTTPException(
|
||||
status_code=403,
|
||||
detail="Access denied. A user with the admin role is required.",
|
||||
)
|
||||
|
||||
return admin_checker
|
||||
|
||||
|
||||
def _is_authenticated(request: Request) -> bool:
|
||||
"""
|
||||
Helper to determine if a request is from an authenticated user.
|
||||
|
||||
Returns True if the request has a valid authenticated user (not anonymous).
|
||||
Port 5000 internal requests are considered anonymous despite having admin role.
|
||||
"""
|
||||
username = request.headers.get("remote-user")
|
||||
return username is not None and username != "anonymous"
|
||||
|
||||
|
||||
def allow_public():
|
||||
"""
|
||||
Override dependency to allow unauthenticated access to an endpoint.
|
||||
|
||||
Use this for endpoints that should be publicly accessible without
|
||||
authentication, such as login page, health checks, or pre-auth info.
|
||||
|
||||
Example:
|
||||
@router.get("/public-endpoint", dependencies=[Depends(allow_public())])
|
||||
"""
|
||||
|
||||
async def public_checker(request: Request):
|
||||
return # Always allow
|
||||
|
||||
return public_checker
|
||||
|
||||
|
||||
def allow_any_authenticated():
|
||||
"""
|
||||
Override dependency to allow any authenticated user (bypass admin requirement).
|
||||
|
||||
Allows:
|
||||
- Port 5000 internal requests (have admin role despite anonymous user)
|
||||
- Any authenticated user with a real username (not "anonymous")
|
||||
|
||||
Rejects:
|
||||
- Port 8971 requests with anonymous user (auth disabled, no proxy auth)
|
||||
|
||||
Example:
|
||||
@router.get("/authenticated-endpoint", dependencies=[Depends(allow_any_authenticated())])
|
||||
"""
|
||||
|
||||
async def auth_checker(request: Request):
|
||||
# Port 5000 requests have admin role and should be allowed
|
||||
role = request.headers.get("remote-role")
|
||||
if role == "admin":
|
||||
return
|
||||
|
||||
# Otherwise require a real authenticated user (not anonymous)
|
||||
if not _is_authenticated(request):
|
||||
raise HTTPException(status_code=401, detail="Authentication required")
|
||||
return
|
||||
|
||||
return auth_checker
|
||||
|
||||
|
||||
router = APIRouter(tags=[Tags.auth])
|
||||
|
||||
|
||||
@router.get("/auth/first_time_login", dependencies=[Depends(allow_public())])
|
||||
def first_time_login(request: Request):
|
||||
"""Return whether the admin first-time login help flag is set in config.
|
||||
|
||||
This endpoint is intentionally unauthenticated so the login page can
|
||||
query it before a user is authenticated.
|
||||
"""
|
||||
auth_config = request.app.frigate_config.auth
|
||||
|
||||
return JSONResponse(
|
||||
content={
|
||||
"admin_first_time_login": auth_config.admin_first_time_login
|
||||
or auth_config.reset_admin_password
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
class RateLimiter:
|
||||
_limit = ""
|
||||
|
||||
@ -520,7 +335,7 @@ def resolve_role(
|
||||
|
||||
|
||||
# Endpoints
|
||||
@router.get("/auth", dependencies=[Depends(allow_public())])
|
||||
@router.get("/auth")
|
||||
def auth(request: Request):
|
||||
auth_config: AuthConfig = request.app.frigate_config.auth
|
||||
proxy_config: ProxyConfig = request.app.frigate_config.proxy
|
||||
@ -646,7 +461,7 @@ def auth(request: Request):
|
||||
return fail_response
|
||||
|
||||
|
||||
@router.get("/profile", dependencies=[Depends(allow_any_authenticated())])
|
||||
@router.get("/profile")
|
||||
def profile(request: Request):
|
||||
username = request.headers.get("remote-user", "anonymous")
|
||||
role = request.headers.get("remote-role", "viewer")
|
||||
@ -660,7 +475,7 @@ def profile(request: Request):
|
||||
)
|
||||
|
||||
|
||||
@router.get("/logout", dependencies=[Depends(allow_any_authenticated())])
|
||||
@router.get("/logout")
|
||||
def logout(request: Request):
|
||||
auth_config: AuthConfig = request.app.frigate_config.auth
|
||||
response = RedirectResponse("/login", status_code=303)
|
||||
@ -671,7 +486,7 @@ def logout(request: Request):
|
||||
limiter = Limiter(key_func=get_remote_addr)
|
||||
|
||||
|
||||
@router.post("/login", dependencies=[Depends(allow_public())])
|
||||
@router.post("/login")
|
||||
@limiter.limit(limit_value=rateLimiter.get_limit)
|
||||
def login(request: Request, body: AppPostLoginBody):
|
||||
JWT_COOKIE_NAME = request.app.frigate_config.auth.cookie_name
|
||||
@ -700,11 +515,6 @@ def login(request: Request, body: AppPostLoginBody):
|
||||
set_jwt_cookie(
|
||||
response, JWT_COOKIE_NAME, encoded_jwt, expiration, JWT_COOKIE_SECURE
|
||||
)
|
||||
# Clear admin_first_time_login flag after successful admin login so the
|
||||
# UI stops showing the first-time login documentation link.
|
||||
if role == "admin":
|
||||
request.app.frigate_config.auth.admin_first_time_login = False
|
||||
|
||||
return response
|
||||
return JSONResponse(content={"message": "Login failed"}, status_code=401)
|
||||
|
||||
@ -746,21 +556,13 @@ def create_user(
|
||||
return JSONResponse(content={"username": body.username})
|
||||
|
||||
|
||||
@router.delete("/users/{username}", dependencies=[Depends(require_role(["admin"]))])
|
||||
def delete_user(request: Request, username: str):
|
||||
# Prevent deletion of the built-in admin user
|
||||
if username == "admin":
|
||||
return JSONResponse(
|
||||
content={"message": "Cannot delete admin user"}, status_code=403
|
||||
)
|
||||
|
||||
@router.delete("/users/{username}")
|
||||
def delete_user(username: str):
|
||||
User.delete_by_id(username)
|
||||
return JSONResponse(content={"success": True})
|
||||
|
||||
|
||||
@router.put(
|
||||
"/users/{username}/password", dependencies=[Depends(allow_any_authenticated())]
|
||||
)
|
||||
@router.put("/users/{username}/password")
|
||||
async def update_password(
|
||||
request: Request,
|
||||
username: str,
|
||||
|
||||
@ -3,23 +3,13 @@
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
from importlib.util import find_spec
|
||||
from pathlib import Path
|
||||
from urllib.parse import quote_plus
|
||||
|
||||
import httpx
|
||||
import requests
|
||||
from fastapi import APIRouter, Depends, Query, Request, Response
|
||||
from fastapi import APIRouter, Depends, Request, Response
|
||||
from fastapi.responses import JSONResponse
|
||||
from onvif import ONVIFCamera, ONVIFError
|
||||
from zeep.exceptions import Fault, TransportError
|
||||
from zeep.transports import AsyncTransport
|
||||
|
||||
from frigate.api.auth import (
|
||||
allow_any_authenticated,
|
||||
require_camera_access,
|
||||
require_role,
|
||||
)
|
||||
from frigate.api.auth import require_role
|
||||
from frigate.api.defs.tags import Tags
|
||||
from frigate.config.config import FrigateConfig
|
||||
from frigate.util.builtin import clean_camera_user_pass
|
||||
@ -54,7 +44,7 @@ def _is_valid_host(host: str) -> bool:
|
||||
return False
|
||||
|
||||
|
||||
@router.get("/go2rtc/streams", dependencies=[Depends(allow_any_authenticated())])
|
||||
@router.get("/go2rtc/streams")
|
||||
def go2rtc_streams():
|
||||
r = requests.get("http://127.0.0.1:1984/api/streams")
|
||||
if not r.ok:
|
||||
@ -70,9 +60,7 @@ def go2rtc_streams():
|
||||
return JSONResponse(content=stream_data)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/go2rtc/streams/{camera_name}", dependencies=[Depends(require_camera_access)]
|
||||
)
|
||||
@router.get("/go2rtc/streams/{camera_name}")
|
||||
def go2rtc_camera_stream(request: Request, camera_name: str):
|
||||
r = requests.get(
|
||||
f"http://127.0.0.1:1984/api/streams?src={camera_name}&video=all&audio=allµphone"
|
||||
@ -167,7 +155,7 @@ def go2rtc_delete_stream(stream_name: str):
|
||||
)
|
||||
|
||||
|
||||
@router.get("/ffprobe", dependencies=[Depends(require_role(["admin"]))])
|
||||
@router.get("/ffprobe")
|
||||
def ffprobe(request: Request, paths: str = "", detailed: bool = False):
|
||||
path_param = paths
|
||||
|
||||
@ -211,30 +199,19 @@ def ffprobe(request: Request, paths: str = "", detailed: bool = False):
|
||||
request.app.frigate_config.ffmpeg, path.strip(), detailed=detailed
|
||||
)
|
||||
|
||||
if ffprobe.returncode != 0:
|
||||
try:
|
||||
stderr_decoded = ffprobe.stderr.decode("utf-8")
|
||||
except UnicodeDecodeError:
|
||||
try:
|
||||
stderr_decoded = ffprobe.stderr.decode("unicode_escape")
|
||||
except Exception:
|
||||
stderr_decoded = str(ffprobe.stderr)
|
||||
|
||||
stderr_lines = [
|
||||
line.strip() for line in stderr_decoded.split("\n") if line.strip()
|
||||
]
|
||||
|
||||
result = {
|
||||
"return_code": ffprobe.returncode,
|
||||
"stderr": stderr_lines,
|
||||
"stdout": "",
|
||||
}
|
||||
else:
|
||||
result = {
|
||||
"return_code": ffprobe.returncode,
|
||||
"stderr": [],
|
||||
"stdout": json.loads(ffprobe.stdout.decode("unicode_escape").strip()),
|
||||
}
|
||||
result = {
|
||||
"return_code": ffprobe.returncode,
|
||||
"stderr": (
|
||||
ffprobe.stderr.decode("unicode_escape").strip()
|
||||
if ffprobe.returncode != 0
|
||||
else ""
|
||||
),
|
||||
"stdout": (
|
||||
json.loads(ffprobe.stdout.decode("unicode_escape").strip())
|
||||
if ffprobe.returncode == 0
|
||||
else ""
|
||||
),
|
||||
}
|
||||
|
||||
# Add detailed metadata if requested and probe was successful
|
||||
if detailed and ffprobe.returncode == 0 and result["stdout"]:
|
||||
@ -464,537 +441,3 @@ def _extract_fps(r_frame_rate: str) -> float | None:
|
||||
return round(float(num) / float(den), 2)
|
||||
except (ValueError, ZeroDivisionError):
|
||||
return None
|
||||
|
||||
|
||||
@router.get(
|
||||
"/onvif/probe",
|
||||
dependencies=[Depends(require_role(["admin"]))],
|
||||
summary="Probe ONVIF device",
|
||||
description=(
|
||||
"Probe an ONVIF device to determine capabilities and optionally test available stream URIs. "
|
||||
"Query params: host (required), port (default 80), username, password, test (boolean), "
|
||||
"auth_type (basic or digest, default basic)."
|
||||
),
|
||||
)
|
||||
async def onvif_probe(
|
||||
request: Request,
|
||||
host: str = Query(None),
|
||||
port: int = Query(80),
|
||||
username: str = Query(""),
|
||||
password: str = Query(""),
|
||||
test: bool = Query(False),
|
||||
auth_type: str = Query("basic"), # Add auth_type parameter
|
||||
):
|
||||
"""
|
||||
Probe a single ONVIF device to determine capabilities.
|
||||
|
||||
Connects to an ONVIF device and queries for:
|
||||
- Device information (manufacturer, model)
|
||||
- Media profiles count
|
||||
- PTZ support
|
||||
- Available presets
|
||||
- Autotracking support
|
||||
|
||||
Query Parameters:
|
||||
host: Device host/IP address (required)
|
||||
port: Device port (default 80)
|
||||
username: ONVIF username (optional)
|
||||
password: ONVIF password (optional)
|
||||
test: run ffprobe on the stream (optional)
|
||||
auth_type: Authentication type - "basic" or "digest" (default "basic")
|
||||
|
||||
Returns:
|
||||
JSON with device capabilities information
|
||||
"""
|
||||
if not host:
|
||||
return JSONResponse(
|
||||
content={"success": False, "message": "host parameter is required"},
|
||||
status_code=400,
|
||||
)
|
||||
|
||||
# Validate host format
|
||||
if not _is_valid_host(host):
|
||||
return JSONResponse(
|
||||
content={"success": False, "message": "Invalid host format"},
|
||||
status_code=400,
|
||||
)
|
||||
|
||||
# Validate auth_type
|
||||
if auth_type not in ["basic", "digest"]:
|
||||
return JSONResponse(
|
||||
content={
|
||||
"success": False,
|
||||
"message": "auth_type must be 'basic' or 'digest'",
|
||||
},
|
||||
status_code=400,
|
||||
)
|
||||
|
||||
onvif_camera = None
|
||||
|
||||
try:
|
||||
logger.debug(f"Probing ONVIF device at {host}:{port} with {auth_type} auth")
|
||||
|
||||
try:
|
||||
wsdl_base = None
|
||||
spec = find_spec("onvif")
|
||||
if spec and getattr(spec, "origin", None):
|
||||
wsdl_base = str(Path(spec.origin).parent / "wsdl")
|
||||
except Exception:
|
||||
wsdl_base = None
|
||||
|
||||
onvif_camera = ONVIFCamera(
|
||||
host, port, username or "", password or "", wsdl_dir=wsdl_base
|
||||
)
|
||||
|
||||
# Configure digest authentication if requested
|
||||
if auth_type == "digest" and username and password:
|
||||
# Create httpx client with digest auth
|
||||
auth = httpx.DigestAuth(username, password)
|
||||
client = httpx.AsyncClient(auth=auth, timeout=10.0)
|
||||
|
||||
# Replace the transport in the zeep client
|
||||
transport = AsyncTransport(client=client)
|
||||
|
||||
# Update the xaddr before setting transport
|
||||
await onvif_camera.update_xaddrs()
|
||||
|
||||
# Replace transport in all services
|
||||
if hasattr(onvif_camera, "devicemgmt"):
|
||||
onvif_camera.devicemgmt.zeep_client.transport = transport
|
||||
if hasattr(onvif_camera, "media"):
|
||||
onvif_camera.media.zeep_client.transport = transport
|
||||
if hasattr(onvif_camera, "ptz"):
|
||||
onvif_camera.ptz.zeep_client.transport = transport
|
||||
|
||||
logger.debug("Configured digest authentication")
|
||||
else:
|
||||
await onvif_camera.update_xaddrs()
|
||||
|
||||
# Get device information
|
||||
device_info = {
|
||||
"manufacturer": "Unknown",
|
||||
"model": "Unknown",
|
||||
"firmware_version": "Unknown",
|
||||
}
|
||||
try:
|
||||
device_service = await onvif_camera.create_devicemgmt_service()
|
||||
|
||||
# Update transport for device service if digest auth
|
||||
if auth_type == "digest" and username and password:
|
||||
auth = httpx.DigestAuth(username, password)
|
||||
client = httpx.AsyncClient(auth=auth, timeout=10.0)
|
||||
transport = AsyncTransport(client=client)
|
||||
device_service.zeep_client.transport = transport
|
||||
|
||||
device_info_resp = await device_service.GetDeviceInformation()
|
||||
manufacturer = getattr(device_info_resp, "Manufacturer", None) or (
|
||||
device_info_resp.get("Manufacturer")
|
||||
if isinstance(device_info_resp, dict)
|
||||
else None
|
||||
)
|
||||
model = getattr(device_info_resp, "Model", None) or (
|
||||
device_info_resp.get("Model")
|
||||
if isinstance(device_info_resp, dict)
|
||||
else None
|
||||
)
|
||||
firmware = getattr(device_info_resp, "FirmwareVersion", None) or (
|
||||
device_info_resp.get("FirmwareVersion")
|
||||
if isinstance(device_info_resp, dict)
|
||||
else None
|
||||
)
|
||||
device_info.update(
|
||||
{
|
||||
"manufacturer": manufacturer or "Unknown",
|
||||
"model": model or "Unknown",
|
||||
"firmware_version": firmware or "Unknown",
|
||||
}
|
||||
)
|
||||
except Exception as e:
|
||||
logger.debug(f"Failed to get device info: {e}")
|
||||
|
||||
# Get media profiles
|
||||
profiles = []
|
||||
profiles_count = 0
|
||||
first_profile_token = None
|
||||
ptz_config_token = None
|
||||
try:
|
||||
media_service = await onvif_camera.create_media_service()
|
||||
|
||||
# Update transport for media service if digest auth
|
||||
if auth_type == "digest" and username and password:
|
||||
auth = httpx.DigestAuth(username, password)
|
||||
client = httpx.AsyncClient(auth=auth, timeout=10.0)
|
||||
transport = AsyncTransport(client=client)
|
||||
media_service.zeep_client.transport = transport
|
||||
|
||||
profiles = await media_service.GetProfiles()
|
||||
profiles_count = len(profiles) if profiles else 0
|
||||
if profiles and len(profiles) > 0:
|
||||
p = profiles[0]
|
||||
first_profile_token = getattr(p, "token", None) or (
|
||||
p.get("token") if isinstance(p, dict) else None
|
||||
)
|
||||
# Get PTZ configuration token from the profile
|
||||
ptz_configuration = getattr(p, "PTZConfiguration", None) or (
|
||||
p.get("PTZConfiguration") if isinstance(p, dict) else None
|
||||
)
|
||||
if ptz_configuration:
|
||||
ptz_config_token = getattr(ptz_configuration, "token", None) or (
|
||||
ptz_configuration.get("token")
|
||||
if isinstance(ptz_configuration, dict)
|
||||
else None
|
||||
)
|
||||
except Exception as e:
|
||||
logger.debug(f"Failed to get media profiles: {e}")
|
||||
|
||||
# Check PTZ support and capabilities
|
||||
ptz_supported = False
|
||||
presets_count = 0
|
||||
autotrack_supported = False
|
||||
|
||||
try:
|
||||
ptz_service = await onvif_camera.create_ptz_service()
|
||||
|
||||
# Update transport for PTZ service if digest auth
|
||||
if auth_type == "digest" and username and password:
|
||||
auth = httpx.DigestAuth(username, password)
|
||||
client = httpx.AsyncClient(auth=auth, timeout=10.0)
|
||||
transport = AsyncTransport(client=client)
|
||||
ptz_service.zeep_client.transport = transport
|
||||
|
||||
# Check if PTZ service is available
|
||||
try:
|
||||
await ptz_service.GetServiceCapabilities()
|
||||
ptz_supported = True
|
||||
logger.debug("PTZ service is available")
|
||||
except Exception as e:
|
||||
logger.debug(f"PTZ service not available: {e}")
|
||||
ptz_supported = False
|
||||
|
||||
# Try to get presets if PTZ is supported and we have a profile
|
||||
if ptz_supported and first_profile_token:
|
||||
try:
|
||||
presets_resp = await ptz_service.GetPresets(
|
||||
{"ProfileToken": first_profile_token}
|
||||
)
|
||||
presets_count = len(presets_resp) if presets_resp else 0
|
||||
logger.debug(f"Found {presets_count} presets")
|
||||
except Exception as e:
|
||||
logger.debug(f"Failed to get presets: {e}")
|
||||
presets_count = 0
|
||||
|
||||
# Check for autotracking support - requires both FOV relative movement and MoveStatus
|
||||
if ptz_supported and first_profile_token and ptz_config_token:
|
||||
# First check for FOV relative movement support
|
||||
pt_r_fov_supported = False
|
||||
try:
|
||||
config_request = ptz_service.create_type("GetConfigurationOptions")
|
||||
config_request.ConfigurationToken = ptz_config_token
|
||||
ptz_config = await ptz_service.GetConfigurationOptions(
|
||||
config_request
|
||||
)
|
||||
|
||||
if ptz_config:
|
||||
# Check for pt-r-fov support
|
||||
spaces = getattr(ptz_config, "Spaces", None) or (
|
||||
ptz_config.get("Spaces")
|
||||
if isinstance(ptz_config, dict)
|
||||
else None
|
||||
)
|
||||
|
||||
if spaces:
|
||||
rel_pan_tilt_space = getattr(
|
||||
spaces, "RelativePanTiltTranslationSpace", None
|
||||
) or (
|
||||
spaces.get("RelativePanTiltTranslationSpace")
|
||||
if isinstance(spaces, dict)
|
||||
else None
|
||||
)
|
||||
|
||||
if rel_pan_tilt_space:
|
||||
# Look for FOV space
|
||||
for i, space in enumerate(rel_pan_tilt_space):
|
||||
uri = None
|
||||
if isinstance(space, dict):
|
||||
uri = space.get("URI")
|
||||
else:
|
||||
uri = getattr(space, "URI", None)
|
||||
|
||||
if uri and "TranslationSpaceFov" in uri:
|
||||
pt_r_fov_supported = True
|
||||
logger.debug(
|
||||
"FOV relative movement (pt-r-fov) supported"
|
||||
)
|
||||
break
|
||||
|
||||
logger.debug(f"PTZ config spaces: {ptz_config}")
|
||||
except Exception as e:
|
||||
logger.debug(f"Failed to check FOV relative movement: {e}")
|
||||
pt_r_fov_supported = False
|
||||
|
||||
# Now check for MoveStatus support via GetServiceCapabilities
|
||||
if pt_r_fov_supported:
|
||||
try:
|
||||
service_capabilities_request = ptz_service.create_type(
|
||||
"GetServiceCapabilities"
|
||||
)
|
||||
service_capabilities = await ptz_service.GetServiceCapabilities(
|
||||
service_capabilities_request
|
||||
)
|
||||
|
||||
# Look for MoveStatus in the capabilities
|
||||
move_status_capable = False
|
||||
if service_capabilities:
|
||||
# Try to find MoveStatus key recursively
|
||||
def find_move_status(obj, key="MoveStatus"):
|
||||
if isinstance(obj, dict):
|
||||
if key in obj:
|
||||
return obj[key]
|
||||
for v in obj.values():
|
||||
result = find_move_status(v, key)
|
||||
if result is not None:
|
||||
return result
|
||||
elif hasattr(obj, key):
|
||||
return getattr(obj, key)
|
||||
elif hasattr(obj, "__dict__"):
|
||||
for v in vars(obj).values():
|
||||
result = find_move_status(v, key)
|
||||
if result is not None:
|
||||
return result
|
||||
return None
|
||||
|
||||
move_status_value = find_move_status(service_capabilities)
|
||||
|
||||
# MoveStatus should return "true" if supported
|
||||
if isinstance(move_status_value, bool):
|
||||
move_status_capable = move_status_value
|
||||
elif isinstance(move_status_value, str):
|
||||
move_status_capable = (
|
||||
move_status_value.lower() == "true"
|
||||
)
|
||||
|
||||
logger.debug(f"MoveStatus capability: {move_status_value}")
|
||||
|
||||
# Autotracking is supported if both conditions are met
|
||||
autotrack_supported = pt_r_fov_supported and move_status_capable
|
||||
|
||||
if autotrack_supported:
|
||||
logger.debug(
|
||||
"Autotracking fully supported (pt-r-fov + MoveStatus)"
|
||||
)
|
||||
else:
|
||||
logger.debug(
|
||||
f"Autotracking not fully supported - pt-r-fov: {pt_r_fov_supported}, MoveStatus: {move_status_capable}"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.debug(f"Failed to check MoveStatus support: {e}")
|
||||
autotrack_supported = False
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Failed to probe PTZ service: {e}")
|
||||
|
||||
result = {
|
||||
"success": True,
|
||||
"host": host,
|
||||
"port": port,
|
||||
"manufacturer": device_info["manufacturer"],
|
||||
"model": device_info["model"],
|
||||
"firmware_version": device_info["firmware_version"],
|
||||
"profiles_count": profiles_count,
|
||||
"ptz_supported": ptz_supported,
|
||||
"presets_count": presets_count,
|
||||
"autotrack_supported": autotrack_supported,
|
||||
}
|
||||
|
||||
# Gather RTSP candidates
|
||||
rtsp_candidates: list[dict] = []
|
||||
try:
|
||||
media_service = await onvif_camera.create_media_service()
|
||||
|
||||
# Update transport for media service if digest auth
|
||||
if auth_type == "digest" and username and password:
|
||||
auth = httpx.DigestAuth(username, password)
|
||||
client = httpx.AsyncClient(auth=auth, timeout=10.0)
|
||||
transport = AsyncTransport(client=client)
|
||||
media_service.zeep_client.transport = transport
|
||||
|
||||
if profiles_count and media_service:
|
||||
for p in profiles or []:
|
||||
token = getattr(p, "token", None) or (
|
||||
p.get("token") if isinstance(p, dict) else None
|
||||
)
|
||||
if not token:
|
||||
continue
|
||||
try:
|
||||
stream_setup = {
|
||||
"Stream": "RTP-Unicast",
|
||||
"Transport": {"Protocol": "RTSP"},
|
||||
}
|
||||
stream_req = {
|
||||
"ProfileToken": token,
|
||||
"StreamSetup": stream_setup,
|
||||
}
|
||||
stream_uri_resp = await media_service.GetStreamUri(stream_req)
|
||||
uri = (
|
||||
stream_uri_resp.get("Uri")
|
||||
if isinstance(stream_uri_resp, dict)
|
||||
else getattr(stream_uri_resp, "Uri", None)
|
||||
)
|
||||
if uri:
|
||||
logger.debug(
|
||||
f"GetStreamUri returned for token {token}: {uri}"
|
||||
)
|
||||
# If credentials were provided, do NOT add the unauthenticated URI.
|
||||
try:
|
||||
if isinstance(uri, str) and uri.startswith("rtsp://"):
|
||||
if username and password and "@" not in uri:
|
||||
# Inject URL-encoded credentials and add only the
|
||||
# authenticated version.
|
||||
cred = f"{quote_plus(username)}:{quote_plus(password)}@"
|
||||
injected = uri.replace(
|
||||
"rtsp://", f"rtsp://{cred}", 1
|
||||
)
|
||||
rtsp_candidates.append(
|
||||
{
|
||||
"source": "GetStreamUri",
|
||||
"profile_token": token,
|
||||
"uri": injected,
|
||||
}
|
||||
)
|
||||
else:
|
||||
# No credentials provided or URI already contains
|
||||
# credentials — add the URI as returned.
|
||||
rtsp_candidates.append(
|
||||
{
|
||||
"source": "GetStreamUri",
|
||||
"profile_token": token,
|
||||
"uri": uri,
|
||||
}
|
||||
)
|
||||
else:
|
||||
# Non-RTSP URIs (e.g., http-flv) — add as returned.
|
||||
rtsp_candidates.append(
|
||||
{
|
||||
"source": "GetStreamUri",
|
||||
"profile_token": token,
|
||||
"uri": uri,
|
||||
}
|
||||
)
|
||||
except Exception as e:
|
||||
logger.debug(
|
||||
f"Skipping stream URI for token {token} due to processing error: {e}"
|
||||
)
|
||||
continue
|
||||
except Exception:
|
||||
logger.debug(
|
||||
f"GetStreamUri failed for token {token}", exc_info=True
|
||||
)
|
||||
continue
|
||||
|
||||
# Add common RTSP patterns as fallback
|
||||
if not rtsp_candidates:
|
||||
common_paths = [
|
||||
"/h264",
|
||||
"/live.sdp",
|
||||
"/media.amp",
|
||||
"/Streaming/Channels/101",
|
||||
"/Streaming/Channels/1",
|
||||
"/stream1",
|
||||
"/cam/realmonitor?channel=1&subtype=0",
|
||||
"/11",
|
||||
]
|
||||
# Use URL-encoded credentials for pattern fallback URIs when provided
|
||||
auth_str = (
|
||||
f"{quote_plus(username)}:{quote_plus(password)}@"
|
||||
if username and password
|
||||
else ""
|
||||
)
|
||||
rtsp_port = 554
|
||||
for path in common_paths:
|
||||
uri = f"rtsp://{auth_str}{host}:{rtsp_port}{path}"
|
||||
rtsp_candidates.append({"source": "pattern", "uri": uri})
|
||||
except Exception:
|
||||
logger.debug("Failed to collect RTSP candidates")
|
||||
|
||||
# Optionally test RTSP candidates using ffprobe_stream
|
||||
tested_candidates = []
|
||||
if test and rtsp_candidates:
|
||||
for c in rtsp_candidates:
|
||||
uri = c["uri"]
|
||||
to_test = [uri]
|
||||
try:
|
||||
if (
|
||||
username
|
||||
and password
|
||||
and isinstance(uri, str)
|
||||
and uri.startswith("rtsp://")
|
||||
and "@" not in uri
|
||||
):
|
||||
cred = f"{quote_plus(username)}:{quote_plus(password)}@"
|
||||
cred_uri = uri.replace("rtsp://", f"rtsp://{cred}", 1)
|
||||
if cred_uri not in to_test:
|
||||
to_test.append(cred_uri)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
for test_uri in to_test:
|
||||
try:
|
||||
probe = ffprobe_stream(
|
||||
request.app.frigate_config.ffmpeg, test_uri, detailed=False
|
||||
)
|
||||
print(probe)
|
||||
ok = probe is not None and getattr(probe, "returncode", 1) == 0
|
||||
tested_candidates.append(
|
||||
{
|
||||
"uri": test_uri,
|
||||
"source": c.get("source"),
|
||||
"ok": ok,
|
||||
"profile_token": c.get("profile_token"),
|
||||
}
|
||||
)
|
||||
except Exception as e:
|
||||
logger.debug(f"Unable to probe stream: {e}")
|
||||
tested_candidates.append(
|
||||
{
|
||||
"uri": test_uri,
|
||||
"source": c.get("source"),
|
||||
"ok": False,
|
||||
"profile_token": c.get("profile_token"),
|
||||
}
|
||||
)
|
||||
|
||||
result["rtsp_candidates"] = rtsp_candidates
|
||||
if test:
|
||||
result["rtsp_tested"] = tested_candidates
|
||||
|
||||
logger.debug(f"ONVIF probe successful: {result}")
|
||||
return JSONResponse(content=result)
|
||||
|
||||
except ONVIFError as e:
|
||||
logger.warning(f"ONVIF error probing {host}:{port}: {e}")
|
||||
return JSONResponse(
|
||||
content={"success": False, "message": "ONVIF error"},
|
||||
status_code=400,
|
||||
)
|
||||
except (Fault, TransportError) as e:
|
||||
logger.warning(f"Connection error probing {host}:{port}: {e}")
|
||||
return JSONResponse(
|
||||
content={"success": False, "message": "Connection error"},
|
||||
status_code=503,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(f"Error probing ONVIF device at {host}:{port}, {e}")
|
||||
return JSONResponse(
|
||||
content={"success": False, "message": "Probe failed"},
|
||||
status_code=500,
|
||||
)
|
||||
|
||||
finally:
|
||||
# Best-effort cleanup of ONVIF camera client session
|
||||
if onvif_camera is not None:
|
||||
try:
|
||||
# Check if the camera has a close method and call it
|
||||
if hasattr(onvif_camera, "close"):
|
||||
await onvif_camera.close()
|
||||
except Exception as e:
|
||||
logger.debug(f"Error closing ONVIF camera session: {e}")
|
||||
|
||||
@ -3,9 +3,7 @@
|
||||
import datetime
|
||||
import logging
|
||||
import os
|
||||
import random
|
||||
import shutil
|
||||
import string
|
||||
from typing import Any
|
||||
|
||||
import cv2
|
||||
@ -19,8 +17,6 @@ from frigate.api.auth import require_role
|
||||
from frigate.api.defs.request.classification_body import (
|
||||
AudioTranscriptionBody,
|
||||
DeleteFaceImagesBody,
|
||||
GenerateObjectExamplesBody,
|
||||
GenerateStateExamplesBody,
|
||||
RenameFaceBody,
|
||||
)
|
||||
from frigate.api.defs.response.classification_response import (
|
||||
@ -31,16 +27,10 @@ from frigate.api.defs.response.generic_response import GenericResponse
|
||||
from frigate.api.defs.tags import Tags
|
||||
from frigate.config import FrigateConfig
|
||||
from frigate.config.camera import DetectConfig
|
||||
from frigate.const import CLIPS_DIR, FACE_DIR, MODEL_CACHE_DIR
|
||||
from frigate.const import CLIPS_DIR, FACE_DIR
|
||||
from frigate.embeddings import EmbeddingsContext
|
||||
from frigate.models import Event
|
||||
from frigate.util.classification import (
|
||||
collect_object_classification_examples,
|
||||
collect_state_classification_examples,
|
||||
get_dataset_image_count,
|
||||
read_training_metadata,
|
||||
)
|
||||
from frigate.util.file import get_event_snapshot
|
||||
from frigate.util.path import get_event_snapshot
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@ -114,18 +104,9 @@ def reclassify_face(request: Request, body: dict = None):
|
||||
context: EmbeddingsContext = request.app.embeddings
|
||||
response = context.reprocess_face(training_file)
|
||||
|
||||
if not isinstance(response, dict):
|
||||
return JSONResponse(
|
||||
status_code=500,
|
||||
content={
|
||||
"success": False,
|
||||
"message": "Could not process request.",
|
||||
},
|
||||
)
|
||||
|
||||
return JSONResponse(
|
||||
status_code=200 if response.get("success", True) else 400,
|
||||
content=response,
|
||||
status_code=200,
|
||||
)
|
||||
|
||||
|
||||
@ -178,7 +159,8 @@ def train_face(request: Request, name: str, body: dict = None):
|
||||
new_name = f"{sanitized_name}-{datetime.datetime.now().timestamp()}.webp"
|
||||
new_file_folder = os.path.join(FACE_DIR, f"{sanitized_name}")
|
||||
|
||||
os.makedirs(new_file_folder, exist_ok=True)
|
||||
if not os.path.exists(new_file_folder):
|
||||
os.mkdir(new_file_folder)
|
||||
|
||||
if training_file_name:
|
||||
shutil.move(training_file, os.path.join(new_file_folder, new_name))
|
||||
@ -542,7 +524,6 @@ def transcribe_audio(request: Request, body: AudioTranscriptionBody):
|
||||
status_code=409, # 409 Conflict
|
||||
)
|
||||
else:
|
||||
logger.debug(f"Failed to transcribe audio, response: {response}")
|
||||
return JSONResponse(
|
||||
content={
|
||||
"success": False,
|
||||
@ -567,59 +548,23 @@ def get_classification_dataset(name: str):
|
||||
dataset_dir = os.path.join(CLIPS_DIR, sanitize_filename(name), "dataset")
|
||||
|
||||
if not os.path.exists(dataset_dir):
|
||||
return JSONResponse(
|
||||
status_code=200, content={"categories": {}, "training_metadata": None}
|
||||
)
|
||||
return JSONResponse(status_code=200, content={})
|
||||
|
||||
for category_name in os.listdir(dataset_dir):
|
||||
category_dir = os.path.join(dataset_dir, category_name)
|
||||
for name in os.listdir(dataset_dir):
|
||||
category_dir = os.path.join(dataset_dir, name)
|
||||
|
||||
if not os.path.isdir(category_dir):
|
||||
continue
|
||||
|
||||
dataset_dict[category_name] = []
|
||||
dataset_dict[name] = []
|
||||
|
||||
for file in filter(
|
||||
lambda f: (f.lower().endswith((".webp", ".png", ".jpg", ".jpeg"))),
|
||||
os.listdir(category_dir),
|
||||
):
|
||||
dataset_dict[category_name].append(file)
|
||||
dataset_dict[name].append(file)
|
||||
|
||||
# Get training metadata
|
||||
metadata = read_training_metadata(sanitize_filename(name))
|
||||
current_image_count = get_dataset_image_count(sanitize_filename(name))
|
||||
|
||||
if metadata is None:
|
||||
training_metadata = {
|
||||
"has_trained": False,
|
||||
"last_training_date": None,
|
||||
"last_training_image_count": 0,
|
||||
"current_image_count": current_image_count,
|
||||
"new_images_count": current_image_count,
|
||||
"dataset_changed": current_image_count > 0,
|
||||
}
|
||||
else:
|
||||
last_training_count = metadata.get("last_training_image_count", 0)
|
||||
# Dataset has changed if count is different (either added or deleted images)
|
||||
dataset_changed = current_image_count != last_training_count
|
||||
# Only show positive count for new images (ignore deletions in the count display)
|
||||
new_images_count = max(0, current_image_count - last_training_count)
|
||||
training_metadata = {
|
||||
"has_trained": True,
|
||||
"last_training_date": metadata.get("last_training_date"),
|
||||
"last_training_image_count": last_training_count,
|
||||
"current_image_count": current_image_count,
|
||||
"new_images_count": new_images_count,
|
||||
"dataset_changed": dataset_changed,
|
||||
}
|
||||
|
||||
return JSONResponse(
|
||||
status_code=200,
|
||||
content={
|
||||
"categories": dataset_dict,
|
||||
"training_metadata": training_metadata,
|
||||
},
|
||||
)
|
||||
return JSONResponse(status_code=200, content=dataset_dict)
|
||||
|
||||
|
||||
@router.get(
|
||||
@ -710,106 +655,12 @@ def delete_classification_dataset_images(
|
||||
if os.path.isfile(file_path):
|
||||
os.unlink(file_path)
|
||||
|
||||
if os.path.exists(folder) and not os.listdir(folder):
|
||||
os.rmdir(folder)
|
||||
|
||||
return JSONResponse(
|
||||
content=({"success": True, "message": "Successfully deleted images."}),
|
||||
content=({"success": True, "message": "Successfully deleted faces."}),
|
||||
status_code=200,
|
||||
)
|
||||
|
||||
|
||||
@router.put(
|
||||
"/classification/{name}/dataset/{old_category}/rename",
|
||||
response_model=GenericResponse,
|
||||
dependencies=[Depends(require_role(["admin"]))],
|
||||
summary="Rename a classification category",
|
||||
description="""Renames a classification category for a given classification model.
|
||||
The old category must exist and the new name must be valid. Returns a success message or an error if the name is invalid.""",
|
||||
)
|
||||
def rename_classification_category(
|
||||
request: Request, name: str, old_category: str, body: dict = None
|
||||
):
|
||||
config: FrigateConfig = request.app.frigate_config
|
||||
|
||||
if name not in config.classification.custom:
|
||||
return JSONResponse(
|
||||
content=(
|
||||
{
|
||||
"success": False,
|
||||
"message": f"{name} is not a known classification model.",
|
||||
}
|
||||
),
|
||||
status_code=404,
|
||||
)
|
||||
|
||||
json: dict[str, Any] = body or {}
|
||||
new_category = sanitize_filename(json.get("new_category", ""))
|
||||
|
||||
if not new_category:
|
||||
return JSONResponse(
|
||||
content=(
|
||||
{
|
||||
"success": False,
|
||||
"message": "New category name is required.",
|
||||
}
|
||||
),
|
||||
status_code=400,
|
||||
)
|
||||
|
||||
old_folder = os.path.join(
|
||||
CLIPS_DIR, sanitize_filename(name), "dataset", sanitize_filename(old_category)
|
||||
)
|
||||
new_folder = os.path.join(
|
||||
CLIPS_DIR, sanitize_filename(name), "dataset", new_category
|
||||
)
|
||||
|
||||
if not os.path.exists(old_folder):
|
||||
return JSONResponse(
|
||||
content=(
|
||||
{
|
||||
"success": False,
|
||||
"message": f"Category {old_category} does not exist.",
|
||||
}
|
||||
),
|
||||
status_code=404,
|
||||
)
|
||||
|
||||
if os.path.exists(new_folder):
|
||||
return JSONResponse(
|
||||
content=(
|
||||
{
|
||||
"success": False,
|
||||
"message": f"Category {new_category} already exists.",
|
||||
}
|
||||
),
|
||||
status_code=400,
|
||||
)
|
||||
|
||||
try:
|
||||
os.rename(old_folder, new_folder)
|
||||
return JSONResponse(
|
||||
content=(
|
||||
{
|
||||
"success": True,
|
||||
"message": f"Successfully renamed category to {new_category}.",
|
||||
}
|
||||
),
|
||||
status_code=200,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"Error renaming category: {e}")
|
||||
return JSONResponse(
|
||||
content=(
|
||||
{
|
||||
"success": False,
|
||||
"message": "Failed to rename category",
|
||||
}
|
||||
),
|
||||
status_code=500,
|
||||
)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/classification/{name}/dataset/categorize",
|
||||
response_model=GenericResponse,
|
||||
@ -850,14 +701,13 @@ def categorize_classification_image(request: Request, name: str, body: dict = No
|
||||
status_code=404,
|
||||
)
|
||||
|
||||
random_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
|
||||
timestamp = datetime.datetime.now().timestamp()
|
||||
new_name = f"{category}-{timestamp}-{random_id}.png"
|
||||
new_name = f"{category}-{datetime.datetime.now().timestamp()}.png"
|
||||
new_file_folder = os.path.join(
|
||||
CLIPS_DIR, sanitize_filename(name), "dataset", category
|
||||
)
|
||||
|
||||
os.makedirs(new_file_folder, exist_ok=True)
|
||||
if not os.path.exists(new_file_folder):
|
||||
os.mkdir(new_file_folder)
|
||||
|
||||
# use opencv because webp images can not be used to train
|
||||
img = cv2.imread(training_file)
|
||||
@ -865,47 +715,7 @@ def categorize_classification_image(request: Request, name: str, body: dict = No
|
||||
os.unlink(training_file)
|
||||
|
||||
return JSONResponse(
|
||||
content=({"success": True, "message": "Successfully categorized image."}),
|
||||
status_code=200,
|
||||
)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/classification/{name}/dataset/{category}/create",
|
||||
response_model=GenericResponse,
|
||||
dependencies=[Depends(require_role(["admin"]))],
|
||||
summary="Create an empty classification category folder",
|
||||
description="""Creates an empty folder for a classification category.
|
||||
This is used to create folders for categories that don't have images yet.
|
||||
Returns a success message or an error if the name is invalid.""",
|
||||
)
|
||||
def create_classification_category(request: Request, name: str, category: str):
|
||||
config: FrigateConfig = request.app.frigate_config
|
||||
|
||||
if name not in config.classification.custom:
|
||||
return JSONResponse(
|
||||
content=(
|
||||
{
|
||||
"success": False,
|
||||
"message": f"{name} is not a known classification model.",
|
||||
}
|
||||
),
|
||||
status_code=404,
|
||||
)
|
||||
|
||||
category_folder = os.path.join(
|
||||
CLIPS_DIR, sanitize_filename(name), "dataset", sanitize_filename(category)
|
||||
)
|
||||
|
||||
os.makedirs(category_folder, exist_ok=True)
|
||||
|
||||
return JSONResponse(
|
||||
content=(
|
||||
{
|
||||
"success": True,
|
||||
"message": f"Successfully created category folder: {category}",
|
||||
}
|
||||
),
|
||||
content=({"success": True, "message": "Successfully deleted faces."}),
|
||||
status_code=200,
|
||||
)
|
||||
|
||||
@ -943,87 +753,6 @@ def delete_classification_train_images(request: Request, name: str, body: dict =
|
||||
os.unlink(file_path)
|
||||
|
||||
return JSONResponse(
|
||||
content=({"success": True, "message": "Successfully deleted images."}),
|
||||
status_code=200,
|
||||
)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/classification/generate_examples/state",
|
||||
response_model=GenericResponse,
|
||||
dependencies=[Depends(require_role(["admin"]))],
|
||||
summary="Generate state classification examples",
|
||||
)
|
||||
async def generate_state_examples(request: Request, body: GenerateStateExamplesBody):
|
||||
"""Generate examples for state classification."""
|
||||
model_name = sanitize_filename(body.model_name)
|
||||
cameras_normalized = {
|
||||
camera_name: tuple(crop)
|
||||
for camera_name, crop in body.cameras.items()
|
||||
if camera_name in request.app.frigate_config.cameras
|
||||
}
|
||||
|
||||
collect_state_classification_examples(model_name, cameras_normalized)
|
||||
|
||||
return JSONResponse(
|
||||
content={"success": True, "message": "Example generation completed"},
|
||||
status_code=200,
|
||||
)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/classification/generate_examples/object",
|
||||
response_model=GenericResponse,
|
||||
dependencies=[Depends(require_role(["admin"]))],
|
||||
summary="Generate object classification examples",
|
||||
)
|
||||
async def generate_object_examples(request: Request, body: GenerateObjectExamplesBody):
|
||||
"""Generate examples for object classification."""
|
||||
model_name = sanitize_filename(body.model_name)
|
||||
collect_object_classification_examples(model_name, body.label)
|
||||
|
||||
return JSONResponse(
|
||||
content={"success": True, "message": "Example generation completed"},
|
||||
status_code=200,
|
||||
)
|
||||
|
||||
|
||||
@router.delete(
|
||||
"/classification/{name}",
|
||||
response_model=GenericResponse,
|
||||
dependencies=[Depends(require_role(["admin"]))],
|
||||
summary="Delete a classification model",
|
||||
description="""Deletes a specific classification model and all its associated data.
|
||||
Works even if the model is not in the config (e.g., partially created during wizard).
|
||||
Returns a success message.""",
|
||||
)
|
||||
def delete_classification_model(request: Request, name: str):
|
||||
sanitized_name = sanitize_filename(name)
|
||||
|
||||
# Delete the classification model's data directory in clips
|
||||
data_dir = os.path.join(CLIPS_DIR, sanitized_name)
|
||||
if os.path.exists(data_dir):
|
||||
try:
|
||||
shutil.rmtree(data_dir)
|
||||
logger.info(f"Deleted classification data directory for {name}")
|
||||
except Exception as e:
|
||||
logger.debug(f"Failed to delete data directory for {name}: {e}")
|
||||
|
||||
# Delete the classification model's files in model_cache
|
||||
model_dir = os.path.join(MODEL_CACHE_DIR, sanitized_name)
|
||||
if os.path.exists(model_dir):
|
||||
try:
|
||||
shutil.rmtree(model_dir)
|
||||
logger.info(f"Deleted classification model directory for {name}")
|
||||
except Exception as e:
|
||||
logger.debug(f"Failed to delete model directory for {name}: {e}")
|
||||
|
||||
return JSONResponse(
|
||||
content=(
|
||||
{
|
||||
"success": True,
|
||||
"message": f"Successfully deleted classification model {name}.",
|
||||
}
|
||||
),
|
||||
content=({"success": True, "message": "Successfully deleted faces."}),
|
||||
status_code=200,
|
||||
)
|
||||
|
||||
@ -1,31 +1,17 @@
|
||||
from typing import Dict, List, Tuple
|
||||
from typing import List
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class RenameFaceBody(BaseModel):
|
||||
new_name: str = Field(description="New name for the face")
|
||||
new_name: str
|
||||
|
||||
|
||||
class AudioTranscriptionBody(BaseModel):
|
||||
event_id: str = Field(description="ID of the event to transcribe audio for")
|
||||
event_id: str
|
||||
|
||||
|
||||
class DeleteFaceImagesBody(BaseModel):
|
||||
ids: List[str] = Field(
|
||||
description="List of image filenames to delete from the face folder"
|
||||
)
|
||||
|
||||
|
||||
class GenerateStateExamplesBody(BaseModel):
|
||||
model_name: str = Field(description="Name of the classification model")
|
||||
cameras: Dict[str, Tuple[float, float, float, float]] = Field(
|
||||
description="Dictionary mapping camera names to normalized crop coordinates in [x1, y1, x2, y2] format (values 0-1)"
|
||||
)
|
||||
|
||||
|
||||
class GenerateObjectExamplesBody(BaseModel):
|
||||
model_name: str = Field(description="Name of the classification model")
|
||||
label: str = Field(
|
||||
description="Object label to collect examples for (e.g., 'person', 'car')"
|
||||
)
|
||||
|
||||
@ -2,7 +2,6 @@
|
||||
|
||||
import base64
|
||||
import datetime
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import random
|
||||
@ -22,7 +21,6 @@ from peewee import JOIN, DoesNotExist, fn, operator
|
||||
from playhouse.shortcuts import model_to_dict
|
||||
|
||||
from frigate.api.auth import (
|
||||
allow_any_authenticated,
|
||||
get_allowed_cameras_for_filter,
|
||||
require_camera_access,
|
||||
require_role,
|
||||
@ -59,8 +57,8 @@ from frigate.const import CLIPS_DIR, TRIGGER_DIR
|
||||
from frigate.embeddings import EmbeddingsContext
|
||||
from frigate.models import Event, ReviewSegment, Timeline, Trigger
|
||||
from frigate.track.object_processing import TrackedObject
|
||||
from frigate.util.file import get_event_thumbnail_bytes
|
||||
from frigate.util.time import get_dst_transitions, get_tz_modifiers
|
||||
from frigate.util.builtin import get_tz_modifiers
|
||||
from frigate.util.path import get_event_thumbnail_bytes
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@ -70,7 +68,6 @@ router = APIRouter(tags=[Tags.events])
|
||||
@router.get(
|
||||
"/events",
|
||||
response_model=list[EventResponse],
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
summary="Get events",
|
||||
description="Returns a list of events.",
|
||||
)
|
||||
@ -345,7 +342,6 @@ def events(
|
||||
@router.get(
|
||||
"/events/explore",
|
||||
response_model=list[EventResponse],
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
summary="Get summary of objects.",
|
||||
description="""Gets a summary of objects from the database.
|
||||
Returns a list of objects with a max of `limit` objects for each label.
|
||||
@ -438,7 +434,6 @@ def events_explore(
|
||||
@router.get(
|
||||
"/event_ids",
|
||||
response_model=list[EventResponse],
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
summary="Get events by ids.",
|
||||
description="""Gets events by a list of ids.
|
||||
Returns a list of events.
|
||||
@ -472,7 +467,6 @@ async def event_ids(ids: str, request: Request):
|
||||
|
||||
@router.get(
|
||||
"/events/search",
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
summary="Search events.",
|
||||
description="""Searches for events in the database.
|
||||
Returns a list of events.
|
||||
@ -813,12 +807,13 @@ def events_search(
|
||||
return JSONResponse(content=processed_events)
|
||||
|
||||
|
||||
@router.get("/events/summary", dependencies=[Depends(allow_any_authenticated())])
|
||||
@router.get("/events/summary")
|
||||
def events_summary(
|
||||
params: EventsSummaryQueryParams = Depends(),
|
||||
allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
|
||||
):
|
||||
tz_name = params.timezone
|
||||
hour_modifier, minute_modifier, seconds_offset = get_tz_modifiers(tz_name)
|
||||
has_clip = params.has_clip
|
||||
has_snapshot = params.has_snapshot
|
||||
|
||||
@ -833,97 +828,38 @@ def events_summary(
|
||||
if len(clauses) == 0:
|
||||
clauses.append((True))
|
||||
|
||||
time_range_query = (
|
||||
groups = (
|
||||
Event.select(
|
||||
fn.MIN(Event.start_time).alias("min_time"),
|
||||
fn.MAX(Event.start_time).alias("max_time"),
|
||||
Event.camera,
|
||||
Event.label,
|
||||
Event.sub_label,
|
||||
Event.data,
|
||||
fn.strftime(
|
||||
"%Y-%m-%d",
|
||||
fn.datetime(
|
||||
Event.start_time, "unixepoch", hour_modifier, minute_modifier
|
||||
),
|
||||
).alias("day"),
|
||||
Event.zones,
|
||||
fn.COUNT(Event.id).alias("count"),
|
||||
)
|
||||
.where(reduce(operator.and_, clauses) & (Event.camera << allowed_cameras))
|
||||
.dicts()
|
||||
.get()
|
||||
.group_by(
|
||||
Event.camera,
|
||||
Event.label,
|
||||
Event.sub_label,
|
||||
Event.data,
|
||||
(Event.start_time + seconds_offset).cast("int") / (3600 * 24),
|
||||
Event.zones,
|
||||
)
|
||||
)
|
||||
|
||||
min_time = time_range_query.get("min_time")
|
||||
max_time = time_range_query.get("max_time")
|
||||
|
||||
if min_time is None or max_time is None:
|
||||
return JSONResponse(content=[])
|
||||
|
||||
dst_periods = get_dst_transitions(tz_name, min_time, max_time)
|
||||
|
||||
grouped: dict[tuple, dict] = {}
|
||||
|
||||
for period_start, period_end, period_offset in dst_periods:
|
||||
hours_offset = int(period_offset / 60 / 60)
|
||||
minutes_offset = int(period_offset / 60 - hours_offset * 60)
|
||||
period_hour_modifier = f"{hours_offset} hour"
|
||||
period_minute_modifier = f"{minutes_offset} minute"
|
||||
|
||||
period_groups = (
|
||||
Event.select(
|
||||
Event.camera,
|
||||
Event.label,
|
||||
Event.sub_label,
|
||||
Event.data,
|
||||
fn.strftime(
|
||||
"%Y-%m-%d",
|
||||
fn.datetime(
|
||||
Event.start_time,
|
||||
"unixepoch",
|
||||
period_hour_modifier,
|
||||
period_minute_modifier,
|
||||
),
|
||||
).alias("day"),
|
||||
Event.zones,
|
||||
fn.COUNT(Event.id).alias("count"),
|
||||
)
|
||||
.where(
|
||||
reduce(operator.and_, clauses)
|
||||
& (Event.camera << allowed_cameras)
|
||||
& (Event.start_time >= period_start)
|
||||
& (Event.start_time <= period_end)
|
||||
)
|
||||
.group_by(
|
||||
Event.camera,
|
||||
Event.label,
|
||||
Event.sub_label,
|
||||
Event.data,
|
||||
(Event.start_time + period_offset).cast("int") / (3600 * 24),
|
||||
Event.zones,
|
||||
)
|
||||
.namedtuples()
|
||||
)
|
||||
|
||||
for g in period_groups:
|
||||
key = (
|
||||
g.camera,
|
||||
g.label,
|
||||
g.sub_label,
|
||||
json.dumps(g.data, sort_keys=True) if g.data is not None else None,
|
||||
g.day,
|
||||
json.dumps(g.zones, sort_keys=True) if g.zones is not None else None,
|
||||
)
|
||||
|
||||
if key in grouped:
|
||||
grouped[key]["count"] += int(g.count or 0)
|
||||
else:
|
||||
grouped[key] = {
|
||||
"camera": g.camera,
|
||||
"label": g.label,
|
||||
"sub_label": g.sub_label,
|
||||
"data": g.data,
|
||||
"day": g.day,
|
||||
"zones": g.zones,
|
||||
"count": int(g.count or 0),
|
||||
}
|
||||
|
||||
return JSONResponse(content=sorted(grouped.values(), key=lambda x: x["day"]))
|
||||
return JSONResponse(content=[e for e in groups.dicts()])
|
||||
|
||||
|
||||
@router.get(
|
||||
"/events/{event_id}",
|
||||
response_model=EventResponse,
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
summary="Get event by id.",
|
||||
description="Gets an event by its id.",
|
||||
)
|
||||
@ -967,7 +903,6 @@ def set_retain(event_id: str):
|
||||
@router.post(
|
||||
"/events/{event_id}/plus",
|
||||
response_model=EventUploadPlusResponse,
|
||||
dependencies=[Depends(require_role(["admin"]))],
|
||||
summary="Send event to Frigate+.",
|
||||
description="""Sends an event to Frigate+.
|
||||
Returns a success message or an error if the event is not found.
|
||||
@ -1108,7 +1043,6 @@ async def send_to_plus(request: Request, event_id: str, body: SubmitPlusBody = N
|
||||
@router.put(
|
||||
"/events/{event_id}/false_positive",
|
||||
response_model=EventUploadPlusResponse,
|
||||
dependencies=[Depends(require_role(["admin"]))],
|
||||
summary="Submit false positive to Frigate+",
|
||||
description="""Submit an event as a false positive to Frigate+.
|
||||
This endpoint is the same as the standard Frigate+ submission endpoint,
|
||||
@ -1761,7 +1695,7 @@ def create_trigger_embedding(
|
||||
body.data, (base64.b64encode(thumbnail).decode("ASCII"))
|
||||
)
|
||||
|
||||
if not embedding:
|
||||
if embedding is None:
|
||||
return JSONResponse(
|
||||
content={
|
||||
"success": False,
|
||||
@ -1789,8 +1723,9 @@ def create_trigger_embedding(
|
||||
logger.debug(
|
||||
f"Writing thumbnail for trigger with data {body.data} in {camera_name}."
|
||||
)
|
||||
except Exception:
|
||||
logger.exception(
|
||||
except Exception as e:
|
||||
logger.error(e.with_traceback())
|
||||
logger.error(
|
||||
f"Failed to write thumbnail for trigger with data {body.data} in {camera_name}"
|
||||
)
|
||||
|
||||
@ -1814,8 +1749,8 @@ def create_trigger_embedding(
|
||||
status_code=200,
|
||||
)
|
||||
|
||||
except Exception:
|
||||
logger.exception("Error creating trigger embedding")
|
||||
except Exception as e:
|
||||
logger.error(e.with_traceback())
|
||||
return JSONResponse(
|
||||
content={
|
||||
"success": False,
|
||||
@ -1896,7 +1831,7 @@ def update_trigger_embedding(
|
||||
body.data, (base64.b64encode(thumbnail).decode("ASCII"))
|
||||
)
|
||||
|
||||
if not embedding:
|
||||
if embedding is None:
|
||||
return JSONResponse(
|
||||
content={
|
||||
"success": False,
|
||||
@ -1924,8 +1859,9 @@ def update_trigger_embedding(
|
||||
logger.debug(
|
||||
f"Deleted thumbnail for trigger with data {trigger.data} in {camera_name}."
|
||||
)
|
||||
except Exception:
|
||||
logger.exception(
|
||||
except Exception as e:
|
||||
logger.error(e.with_traceback())
|
||||
logger.error(
|
||||
f"Failed to delete thumbnail for trigger with data {trigger.data} in {camera_name}"
|
||||
)
|
||||
|
||||
@ -1964,8 +1900,9 @@ def update_trigger_embedding(
|
||||
logger.debug(
|
||||
f"Writing thumbnail for trigger with data {body.data} in {camera_name}."
|
||||
)
|
||||
except Exception:
|
||||
logger.exception(
|
||||
except Exception as e:
|
||||
logger.error(e.with_traceback())
|
||||
logger.error(
|
||||
f"Failed to write thumbnail for trigger with data {body.data} in {camera_name}"
|
||||
)
|
||||
|
||||
@ -1977,8 +1914,8 @@ def update_trigger_embedding(
|
||||
status_code=200,
|
||||
)
|
||||
|
||||
except Exception:
|
||||
logger.exception("Error updating trigger embedding")
|
||||
except Exception as e:
|
||||
logger.error(e.with_traceback())
|
||||
return JSONResponse(
|
||||
content={
|
||||
"success": False,
|
||||
@ -2038,8 +1975,9 @@ def delete_trigger_embedding(
|
||||
logger.debug(
|
||||
f"Deleted thumbnail for trigger with data {trigger.data} in {camera_name}."
|
||||
)
|
||||
except Exception:
|
||||
logger.exception(
|
||||
except Exception as e:
|
||||
logger.error(e.with_traceback())
|
||||
logger.error(
|
||||
f"Failed to delete thumbnail for trigger with data {trigger.data} in {camera_name}"
|
||||
)
|
||||
|
||||
@ -2051,8 +1989,8 @@ def delete_trigger_embedding(
|
||||
status_code=200,
|
||||
)
|
||||
|
||||
except Exception:
|
||||
logger.exception("Error deleting trigger embedding")
|
||||
except Exception as e:
|
||||
logger.error(e.with_traceback())
|
||||
return JSONResponse(
|
||||
content={
|
||||
"success": False,
|
||||
|
||||
@ -9,12 +9,10 @@ from typing import List
|
||||
import psutil
|
||||
from fastapi import APIRouter, Depends, Request
|
||||
from fastapi.responses import JSONResponse
|
||||
from pathvalidate import sanitize_filepath
|
||||
from peewee import DoesNotExist
|
||||
from playhouse.shortcuts import model_to_dict
|
||||
|
||||
from frigate.api.auth import (
|
||||
allow_any_authenticated,
|
||||
get_allowed_cameras_for_filter,
|
||||
require_camera_access,
|
||||
require_role,
|
||||
@ -28,14 +26,14 @@ from frigate.api.defs.response.export_response import (
|
||||
)
|
||||
from frigate.api.defs.response.generic_response import GenericResponse
|
||||
from frigate.api.defs.tags import Tags
|
||||
from frigate.const import CLIPS_DIR, EXPORT_DIR
|
||||
from frigate.const import EXPORT_DIR
|
||||
from frigate.models import Export, Previews, Recordings
|
||||
from frigate.record.export import (
|
||||
PlaybackFactorEnum,
|
||||
PlaybackSourceEnum,
|
||||
RecordingExporter,
|
||||
)
|
||||
from frigate.util.time import is_current_hour
|
||||
from frigate.util.builtin import is_current_hour
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@ -45,7 +43,6 @@ router = APIRouter(tags=[Tags.export])
|
||||
@router.get(
|
||||
"/exports",
|
||||
response_model=ExportsResponse,
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
summary="Get exports",
|
||||
description="""Gets all exports from the database for cameras the user has access to.
|
||||
Returns a list of exports ordered by date (most recent first).""",
|
||||
@ -91,14 +88,7 @@ def export_recording(
|
||||
playback_factor = body.playback
|
||||
playback_source = body.source
|
||||
friendly_name = body.name
|
||||
existing_image = sanitize_filepath(body.image_path) if body.image_path else None
|
||||
|
||||
# Ensure that existing_image is a valid path
|
||||
if existing_image and not existing_image.startswith(CLIPS_DIR):
|
||||
return JSONResponse(
|
||||
content=({"success": False, "message": "Invalid image path"}),
|
||||
status_code=400,
|
||||
)
|
||||
existing_image = body.image_path
|
||||
|
||||
if playback_source == "recordings":
|
||||
recordings_count = (
|
||||
@ -274,7 +264,6 @@ async def export_delete(event_id: str, request: Request):
|
||||
@router.get(
|
||||
"/exports/{export_id}",
|
||||
response_model=ExportModel,
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
summary="Get a single export",
|
||||
description="""Gets a specific export by ID. The user must have access to the camera
|
||||
associated with the export.""",
|
||||
|
||||
@ -2,7 +2,7 @@ import logging
|
||||
import re
|
||||
from typing import Optional
|
||||
|
||||
from fastapi import Depends, FastAPI, Request
|
||||
from fastapi import FastAPI, Request
|
||||
from fastapi.responses import JSONResponse
|
||||
from joserfc.jwk import OctKey
|
||||
from playhouse.sqliteq import SqliteQueueDatabase
|
||||
@ -24,7 +24,7 @@ from frigate.api import (
|
||||
preview,
|
||||
review,
|
||||
)
|
||||
from frigate.api.auth import get_jwt_secret, limiter, require_admin_by_default
|
||||
from frigate.api.auth import get_jwt_secret, limiter
|
||||
from frigate.comms.event_metadata_updater import (
|
||||
EventMetadataPublisher,
|
||||
)
|
||||
@ -62,15 +62,11 @@ def create_fastapi_app(
|
||||
stats_emitter: StatsEmitter,
|
||||
event_metadata_updater: EventMetadataPublisher,
|
||||
config_publisher: CameraConfigUpdatePublisher,
|
||||
enforce_default_admin: bool = True,
|
||||
):
|
||||
logger.info("Starting FastAPI app")
|
||||
app = FastAPI(
|
||||
debug=False,
|
||||
swagger_ui_parameters={"apisSorter": "alpha", "operationsSorter": "alpha"},
|
||||
dependencies=[Depends(require_admin_by_default())]
|
||||
if enforce_default_admin
|
||||
else [],
|
||||
)
|
||||
|
||||
# update the request_address with the x-forwarded-for header from nginx
|
||||
|
||||
@ -22,11 +22,7 @@ from pathvalidate import sanitize_filename
|
||||
from peewee import DoesNotExist, fn, operator
|
||||
from tzlocal import get_localzone_name
|
||||
|
||||
from frigate.api.auth import (
|
||||
allow_any_authenticated,
|
||||
get_allowed_cameras_for_filter,
|
||||
require_camera_access,
|
||||
)
|
||||
from frigate.api.auth import get_allowed_cameras_for_filter, require_camera_access
|
||||
from frigate.api.defs.query.media_query_parameters import (
|
||||
Extension,
|
||||
MediaEventsSnapshotQueryParams,
|
||||
@ -48,9 +44,9 @@ from frigate.const import (
|
||||
)
|
||||
from frigate.models import Event, Previews, Recordings, Regions, ReviewSegment
|
||||
from frigate.track.object_processing import TrackedObjectProcessor
|
||||
from frigate.util.file import get_event_thumbnail_bytes
|
||||
from frigate.util.builtin import get_tz_modifiers
|
||||
from frigate.util.image import get_image_from_recording
|
||||
from frigate.util.time import get_dst_transitions
|
||||
from frigate.util.path import get_event_thumbnail_bytes
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@ -397,7 +393,7 @@ async def submit_recording_snapshot_to_plus(
|
||||
)
|
||||
|
||||
|
||||
@router.get("/recordings/storage", dependencies=[Depends(allow_any_authenticated())])
|
||||
@router.get("/recordings/storage")
|
||||
def get_recordings_storage_usage(request: Request):
|
||||
recording_stats = request.app.stats_emitter.get_latest_stats()["service"][
|
||||
"storage"
|
||||
@ -421,13 +417,14 @@ def get_recordings_storage_usage(request: Request):
|
||||
return JSONResponse(content=camera_usages)
|
||||
|
||||
|
||||
@router.get("/recordings/summary", dependencies=[Depends(allow_any_authenticated())])
|
||||
@router.get("/recordings/summary")
|
||||
def all_recordings_summary(
|
||||
request: Request,
|
||||
params: MediaRecordingsSummaryQueryParams = Depends(),
|
||||
allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
|
||||
):
|
||||
"""Returns true/false by day indicating if recordings exist"""
|
||||
hour_modifier, minute_modifier, seconds_offset = get_tz_modifiers(params.timezone)
|
||||
|
||||
cameras = params.cameras
|
||||
if cameras != "all":
|
||||
@ -435,72 +432,43 @@ def all_recordings_summary(
|
||||
filtered = requested.intersection(allowed_cameras)
|
||||
if not filtered:
|
||||
return JSONResponse(content={})
|
||||
camera_list = list(filtered)
|
||||
cameras = ",".join(filtered)
|
||||
else:
|
||||
camera_list = allowed_cameras
|
||||
cameras = allowed_cameras
|
||||
|
||||
time_range_query = (
|
||||
query = (
|
||||
Recordings.select(
|
||||
fn.MIN(Recordings.start_time).alias("min_time"),
|
||||
fn.MAX(Recordings.start_time).alias("max_time"),
|
||||
fn.strftime(
|
||||
"%Y-%m-%d",
|
||||
fn.datetime(
|
||||
Recordings.start_time + seconds_offset,
|
||||
"unixepoch",
|
||||
hour_modifier,
|
||||
minute_modifier,
|
||||
),
|
||||
).alias("day")
|
||||
)
|
||||
.where(Recordings.camera << camera_list)
|
||||
.dicts()
|
||||
.get()
|
||||
.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())
|
||||
)
|
||||
|
||||
min_time = time_range_query.get("min_time")
|
||||
max_time = time_range_query.get("max_time")
|
||||
if params.cameras != "all":
|
||||
query = query.where(Recordings.camera << cameras.split(","))
|
||||
|
||||
if min_time is None or max_time is None:
|
||||
return JSONResponse(content={})
|
||||
recording_days = query.namedtuples()
|
||||
days = {day.day: True for day in recording_days}
|
||||
|
||||
dst_periods = get_dst_transitions(params.timezone, min_time, max_time)
|
||||
|
||||
days: dict[str, bool] = {}
|
||||
|
||||
for period_start, period_end, period_offset in dst_periods:
|
||||
hours_offset = int(period_offset / 60 / 60)
|
||||
minutes_offset = int(period_offset / 60 - hours_offset * 60)
|
||||
period_hour_modifier = f"{hours_offset} hour"
|
||||
period_minute_modifier = f"{minutes_offset} minute"
|
||||
|
||||
period_query = (
|
||||
Recordings.select(
|
||||
fn.strftime(
|
||||
"%Y-%m-%d",
|
||||
fn.datetime(
|
||||
Recordings.start_time,
|
||||
"unixepoch",
|
||||
period_hour_modifier,
|
||||
period_minute_modifier,
|
||||
),
|
||||
).alias("day")
|
||||
)
|
||||
.where(
|
||||
(Recordings.camera << camera_list)
|
||||
& (Recordings.end_time >= period_start)
|
||||
& (Recordings.start_time <= period_end)
|
||||
)
|
||||
.group_by(
|
||||
fn.strftime(
|
||||
"%Y-%m-%d",
|
||||
fn.datetime(
|
||||
Recordings.start_time,
|
||||
"unixepoch",
|
||||
period_hour_modifier,
|
||||
period_minute_modifier,
|
||||
),
|
||||
)
|
||||
)
|
||||
.order_by(Recordings.start_time.desc())
|
||||
.namedtuples()
|
||||
)
|
||||
|
||||
for g in period_query:
|
||||
days[g.day] = True
|
||||
|
||||
return JSONResponse(content=dict(sorted(days.items())))
|
||||
return JSONResponse(content=days)
|
||||
|
||||
|
||||
@router.get(
|
||||
@ -508,103 +476,61 @@ def all_recordings_summary(
|
||||
)
|
||||
async def recordings_summary(camera_name: str, timezone: str = "utc"):
|
||||
"""Returns hourly summary for recordings of given camera"""
|
||||
|
||||
time_range_query = (
|
||||
hour_modifier, minute_modifier, seconds_offset = get_tz_modifiers(timezone)
|
||||
recording_groups = (
|
||||
Recordings.select(
|
||||
fn.MIN(Recordings.start_time).alias("min_time"),
|
||||
fn.MAX(Recordings.start_time).alias("max_time"),
|
||||
fn.strftime(
|
||||
"%Y-%m-%d %H",
|
||||
fn.datetime(
|
||||
Recordings.start_time, "unixepoch", hour_modifier, minute_modifier
|
||||
),
|
||||
).alias("hour"),
|
||||
fn.SUM(Recordings.duration).alias("duration"),
|
||||
fn.SUM(Recordings.motion).alias("motion"),
|
||||
fn.SUM(Recordings.objects).alias("objects"),
|
||||
)
|
||||
.where(Recordings.camera == camera_name)
|
||||
.dicts()
|
||||
.get()
|
||||
.group_by((Recordings.start_time + seconds_offset).cast("int") / 3600)
|
||||
.order_by(Recordings.start_time.desc())
|
||||
.namedtuples()
|
||||
)
|
||||
|
||||
min_time = time_range_query.get("min_time")
|
||||
max_time = time_range_query.get("max_time")
|
||||
|
||||
days: dict[str, dict] = {}
|
||||
|
||||
if min_time is None or max_time is None:
|
||||
return JSONResponse(content=list(days.values()))
|
||||
|
||||
dst_periods = get_dst_transitions(timezone, min_time, max_time)
|
||||
|
||||
for period_start, period_end, period_offset in dst_periods:
|
||||
hours_offset = int(period_offset / 60 / 60)
|
||||
minutes_offset = int(period_offset / 60 - hours_offset * 60)
|
||||
period_hour_modifier = f"{hours_offset} hour"
|
||||
period_minute_modifier = f"{minutes_offset} minute"
|
||||
|
||||
recording_groups = (
|
||||
Recordings.select(
|
||||
fn.strftime(
|
||||
"%Y-%m-%d %H",
|
||||
fn.datetime(
|
||||
Recordings.start_time,
|
||||
"unixepoch",
|
||||
period_hour_modifier,
|
||||
period_minute_modifier,
|
||||
),
|
||||
).alias("hour"),
|
||||
fn.SUM(Recordings.duration).alias("duration"),
|
||||
fn.SUM(Recordings.motion).alias("motion"),
|
||||
fn.SUM(Recordings.objects).alias("objects"),
|
||||
)
|
||||
.where(
|
||||
(Recordings.camera == camera_name)
|
||||
& (Recordings.end_time >= period_start)
|
||||
& (Recordings.start_time <= period_end)
|
||||
)
|
||||
.group_by((Recordings.start_time + period_offset).cast("int") / 3600)
|
||||
.order_by(Recordings.start_time.desc())
|
||||
.namedtuples()
|
||||
event_groups = (
|
||||
Event.select(
|
||||
fn.strftime(
|
||||
"%Y-%m-%d %H",
|
||||
fn.datetime(
|
||||
Event.start_time, "unixepoch", hour_modifier, minute_modifier
|
||||
),
|
||||
).alias("hour"),
|
||||
fn.COUNT(Event.id).alias("count"),
|
||||
)
|
||||
.where(Event.camera == camera_name, Event.has_clip)
|
||||
.group_by((Event.start_time + seconds_offset).cast("int") / 3600)
|
||||
.namedtuples()
|
||||
)
|
||||
|
||||
event_groups = (
|
||||
Event.select(
|
||||
fn.strftime(
|
||||
"%Y-%m-%d %H",
|
||||
fn.datetime(
|
||||
Event.start_time,
|
||||
"unixepoch",
|
||||
period_hour_modifier,
|
||||
period_minute_modifier,
|
||||
),
|
||||
).alias("hour"),
|
||||
fn.COUNT(Event.id).alias("count"),
|
||||
)
|
||||
.where(Event.camera == camera_name, Event.has_clip)
|
||||
.where(
|
||||
(Event.start_time >= period_start) & (Event.start_time <= period_end)
|
||||
)
|
||||
.group_by((Event.start_time + period_offset).cast("int") / 3600)
|
||||
.namedtuples()
|
||||
)
|
||||
event_map = {g.hour: g.count for g in event_groups}
|
||||
|
||||
event_map = {g.hour: g.count for g in event_groups}
|
||||
days = {}
|
||||
|
||||
for recording_group in recording_groups:
|
||||
parts = recording_group.hour.split()
|
||||
hour = parts[1]
|
||||
day = parts[0]
|
||||
events_count = event_map.get(recording_group.hour, 0)
|
||||
hour_data = {
|
||||
"hour": hour,
|
||||
"events": events_count,
|
||||
"motion": recording_group.motion,
|
||||
"objects": recording_group.objects,
|
||||
"duration": round(recording_group.duration),
|
||||
}
|
||||
if day in days:
|
||||
# merge counts if already present (edge-case at DST boundary)
|
||||
days[day]["events"] += events_count or 0
|
||||
days[day]["hours"].append(hour_data)
|
||||
else:
|
||||
days[day] = {
|
||||
"events": events_count or 0,
|
||||
"hours": [hour_data],
|
||||
"day": day,
|
||||
}
|
||||
for recording_group in recording_groups:
|
||||
parts = recording_group.hour.split()
|
||||
hour = parts[1]
|
||||
day = parts[0]
|
||||
events_count = event_map.get(recording_group.hour, 0)
|
||||
hour_data = {
|
||||
"hour": hour,
|
||||
"events": events_count,
|
||||
"motion": recording_group.motion,
|
||||
"objects": recording_group.objects,
|
||||
"duration": round(recording_group.duration),
|
||||
}
|
||||
if day not in days:
|
||||
days[day] = {"events": events_count, "hours": [hour_data], "day": day}
|
||||
else:
|
||||
days[day]["events"] += events_count
|
||||
days[day]["hours"].append(hour_data)
|
||||
|
||||
return JSONResponse(content=list(days.values()))
|
||||
|
||||
@ -639,11 +565,7 @@ async def recordings(
|
||||
return JSONResponse(content=list(recordings))
|
||||
|
||||
|
||||
@router.get(
|
||||
"/recordings/unavailable",
|
||||
response_model=list[dict],
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
)
|
||||
@router.get("/recordings/unavailable", response_model=list[dict])
|
||||
async def no_recordings(
|
||||
request: Request,
|
||||
params: MediaRecordingsAvailabilityQueryParams = Depends(),
|
||||
@ -667,7 +589,7 @@ async def no_recordings(
|
||||
)
|
||||
scale = params.scale
|
||||
|
||||
clauses = [(Recordings.end_time >= after) & (Recordings.start_time <= before)]
|
||||
clauses = [(Recordings.start_time > after) & (Recordings.end_time < before)]
|
||||
if cameras != "all":
|
||||
camera_list = cameras.split(",")
|
||||
clauses.append((Recordings.camera << camera_list))
|
||||
@ -686,39 +608,33 @@ async def no_recordings(
|
||||
# Convert recordings to list of (start, end) tuples
|
||||
recordings = [(r["start_time"], r["end_time"]) for r in data]
|
||||
|
||||
# Iterate through time segments and check if each has any recording
|
||||
no_recording_segments = []
|
||||
# Generate all time segments
|
||||
current = after
|
||||
current_gap_start = None
|
||||
no_recording_segments = []
|
||||
current_start = None
|
||||
|
||||
while current < before:
|
||||
segment_end = min(current + scale, before)
|
||||
|
||||
# Check if this segment overlaps with any recording
|
||||
segment_end = current + scale
|
||||
# Check if segment overlaps with any recording
|
||||
has_recording = any(
|
||||
rec_start < segment_end and rec_end > current
|
||||
for rec_start, rec_end in recordings
|
||||
start <= segment_end and end >= current for start, end in recordings
|
||||
)
|
||||
|
||||
if not has_recording:
|
||||
# This segment has no recordings
|
||||
if current_gap_start is None:
|
||||
current_gap_start = current # Start a new gap
|
||||
if current_start is None:
|
||||
current_start = current # Start a new gap
|
||||
else:
|
||||
# This segment has recordings
|
||||
if current_gap_start is not None:
|
||||
if current_start is not None:
|
||||
# End the current gap and append it
|
||||
no_recording_segments.append(
|
||||
{"start_time": int(current_gap_start), "end_time": int(current)}
|
||||
{"start_time": int(current_start), "end_time": int(current)}
|
||||
)
|
||||
current_gap_start = None
|
||||
|
||||
current_start = None
|
||||
current = segment_end
|
||||
|
||||
# Append the last gap if it exists
|
||||
if current_gap_start is not None:
|
||||
if current_start is not None:
|
||||
no_recording_segments.append(
|
||||
{"start_time": int(current_gap_start), "end_time": int(before)}
|
||||
{"start_time": int(current_start), "end_time": int(before)}
|
||||
)
|
||||
|
||||
return JSONResponse(content=no_recording_segments)
|
||||
@ -770,15 +686,6 @@ async def recording_clip(
|
||||
.order_by(Recordings.start_time.asc())
|
||||
)
|
||||
|
||||
if recordings.count() == 0:
|
||||
return JSONResponse(
|
||||
content={
|
||||
"success": False,
|
||||
"message": "No recordings found for the specified time range",
|
||||
},
|
||||
status_code=400,
|
||||
)
|
||||
|
||||
file_name = sanitize_filename(f"playlist_{camera_name}_{start_ts}-{end_ts}.txt")
|
||||
file_path = os.path.join(CACHE_DIR, file_name)
|
||||
with open(file_path, "w") as file:
|
||||
@ -857,7 +764,6 @@ async def vod_ts(camera_name: str, start_ts: float, end_ts: float):
|
||||
|
||||
clips = []
|
||||
durations = []
|
||||
min_duration_ms = 100 # Minimum 100ms to ensure at least one video frame
|
||||
max_duration_ms = MAX_SEGMENT_DURATION * 1000
|
||||
|
||||
recording: Recordings
|
||||
@ -875,11 +781,11 @@ async def vod_ts(camera_name: str, start_ts: float, end_ts: float):
|
||||
if recording.end_time > end_ts:
|
||||
duration -= int((recording.end_time - end_ts) * 1000)
|
||||
|
||||
if duration < min_duration_ms:
|
||||
# skip if the clip has no valid duration (too short to contain frames)
|
||||
if duration <= 0:
|
||||
# skip if the clip has no valid duration
|
||||
continue
|
||||
|
||||
if min_duration_ms <= duration < max_duration_ms:
|
||||
if 0 < duration < max_duration_ms:
|
||||
clip["keyFrameDurations"] = [duration]
|
||||
clips.append(clip)
|
||||
durations.append(duration)
|
||||
@ -945,7 +851,6 @@ async def vod_hour(
|
||||
|
||||
@router.get(
|
||||
"/vod/event/{event_id}",
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
description="Returns an HLS playlist for the specified object. Append /master.m3u8 or /index.m3u8 for HLS playback.",
|
||||
)
|
||||
async def vod_event(
|
||||
@ -1062,10 +967,7 @@ async def event_snapshot(
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/events/{event_id}/thumbnail.{extension}",
|
||||
dependencies=[Depends(require_camera_access)],
|
||||
)
|
||||
@router.get("/events/{event_id}/thumbnail.{extension}")
|
||||
async def event_thumbnail(
|
||||
request: Request,
|
||||
event_id: str,
|
||||
@ -1263,10 +1165,7 @@ def grid_snapshot(
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/events/{event_id}/snapshot-clean.webp",
|
||||
dependencies=[Depends(require_camera_access)],
|
||||
)
|
||||
@router.get("/events/{event_id}/snapshot-clean.webp")
|
||||
def event_snapshot_clean(request: Request, event_id: str, download: bool = False):
|
||||
webp_bytes = None
|
||||
try:
|
||||
@ -1390,9 +1289,7 @@ def event_snapshot_clean(request: Request, event_id: str, download: bool = False
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/events/{event_id}/clip.mp4", dependencies=[Depends(require_camera_access)]
|
||||
)
|
||||
@router.get("/events/{event_id}/clip.mp4")
|
||||
async def event_clip(
|
||||
request: Request,
|
||||
event_id: str,
|
||||
@ -1420,9 +1317,7 @@ async def event_clip(
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/events/{event_id}/preview.gif", dependencies=[Depends(require_camera_access)]
|
||||
)
|
||||
@router.get("/events/{event_id}/preview.gif")
|
||||
def event_preview(request: Request, event_id: str):
|
||||
try:
|
||||
event: Event = Event.get(Event.id == event_id)
|
||||
@ -1775,7 +1670,7 @@ def preview_mp4(
|
||||
)
|
||||
|
||||
|
||||
@router.get("/review/{event_id}/preview", dependencies=[Depends(require_camera_access)])
|
||||
@router.get("/review/{event_id}/preview")
|
||||
def review_preview(
|
||||
request: Request,
|
||||
event_id: str,
|
||||
@ -1801,12 +1696,8 @@ def review_preview(
|
||||
return preview_mp4(request, review.camera, start_ts, end_ts)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/preview/{file_name}/thumbnail.jpg", dependencies=[Depends(require_camera_access)]
|
||||
)
|
||||
@router.get(
|
||||
"/preview/{file_name}/thumbnail.webp", dependencies=[Depends(require_camera_access)]
|
||||
)
|
||||
@router.get("/preview/{file_name}/thumbnail.jpg")
|
||||
@router.get("/preview/{file_name}/thumbnail.webp")
|
||||
def preview_thumbnail(file_name: str):
|
||||
"""Get a thumbnail from the cached preview frames."""
|
||||
if len(file_name) > 1000:
|
||||
|
||||
@ -5,12 +5,11 @@ import os
|
||||
from typing import Any
|
||||
|
||||
from cryptography.hazmat.primitives import serialization
|
||||
from fastapi import APIRouter, Depends, Request
|
||||
from fastapi import APIRouter, Request
|
||||
from fastapi.responses import JSONResponse
|
||||
from peewee import DoesNotExist
|
||||
from py_vapid import Vapid01, utils
|
||||
|
||||
from frigate.api.auth import allow_any_authenticated
|
||||
from frigate.api.defs.tags import Tags
|
||||
from frigate.const import CONFIG_DIR
|
||||
from frigate.models import User
|
||||
@ -22,7 +21,6 @@ router = APIRouter(tags=[Tags.notifications])
|
||||
|
||||
@router.get(
|
||||
"/notifications/pubkey",
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
summary="Get VAPID public key",
|
||||
description="""Gets the VAPID public key for the notifications.
|
||||
Returns the public key or an error if notifications are not enabled.
|
||||
@ -49,7 +47,6 @@ def get_vapid_pub_key(request: Request):
|
||||
|
||||
@router.post(
|
||||
"/notifications/register",
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
summary="Register notifications",
|
||||
description="""Registers a notifications subscription.
|
||||
Returns a success message or an error if the subscription is not provided.
|
||||
|
||||
@ -14,7 +14,6 @@ from peewee import Case, DoesNotExist, IntegrityError, fn, operator
|
||||
from playhouse.shortcuts import model_to_dict
|
||||
|
||||
from frigate.api.auth import (
|
||||
allow_any_authenticated,
|
||||
get_allowed_cameras_for_filter,
|
||||
get_current_user,
|
||||
require_camera_access,
|
||||
@ -37,18 +36,14 @@ from frigate.config import FrigateConfig
|
||||
from frigate.embeddings import EmbeddingsContext
|
||||
from frigate.models import Recordings, ReviewSegment, UserReviewStatus
|
||||
from frigate.review.types import SeverityEnum
|
||||
from frigate.util.time import get_dst_transitions
|
||||
from frigate.util.builtin import get_tz_modifiers
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter(tags=[Tags.review])
|
||||
|
||||
|
||||
@router.get(
|
||||
"/review",
|
||||
response_model=list[ReviewSegmentResponse],
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
)
|
||||
@router.get("/review", response_model=list[ReviewSegmentResponse])
|
||||
async def review(
|
||||
params: ReviewQueryParams = Depends(),
|
||||
current_user: dict = Depends(get_current_user),
|
||||
@ -157,11 +152,7 @@ async def review(
|
||||
return JSONResponse(content=[r for r in review_query])
|
||||
|
||||
|
||||
@router.get(
|
||||
"/review_ids",
|
||||
response_model=list[ReviewSegmentResponse],
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
)
|
||||
@router.get("/review_ids", response_model=list[ReviewSegmentResponse])
|
||||
async def review_ids(request: Request, ids: str):
|
||||
ids = ids.split(",")
|
||||
|
||||
@ -195,11 +186,7 @@ async def review_ids(request: Request, ids: str):
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/review/summary",
|
||||
response_model=ReviewSummaryResponse,
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
)
|
||||
@router.get("/review/summary", response_model=ReviewSummaryResponse)
|
||||
async def review_summary(
|
||||
params: ReviewSummaryQueryParams = Depends(),
|
||||
current_user: dict = Depends(get_current_user),
|
||||
@ -210,6 +197,7 @@ async def review_summary(
|
||||
|
||||
user_id = current_user["username"]
|
||||
|
||||
hour_modifier, minute_modifier, seconds_offset = get_tz_modifiers(params.timezone)
|
||||
day_ago = (datetime.datetime.now() - datetime.timedelta(hours=24)).timestamp()
|
||||
|
||||
cameras = params.cameras
|
||||
@ -341,144 +329,94 @@ async def review_summary(
|
||||
)
|
||||
clauses.append(reduce(operator.or_, label_clauses))
|
||||
|
||||
# Find the time range of available data
|
||||
time_range_query = (
|
||||
day_in_seconds = 60 * 60 * 24
|
||||
last_month_query = (
|
||||
ReviewSegment.select(
|
||||
fn.MIN(ReviewSegment.start_time).alias("min_time"),
|
||||
fn.MAX(ReviewSegment.start_time).alias("max_time"),
|
||||
fn.strftime(
|
||||
"%Y-%m-%d",
|
||||
fn.datetime(
|
||||
ReviewSegment.start_time,
|
||||
"unixepoch",
|
||||
hour_modifier,
|
||||
minute_modifier,
|
||||
),
|
||||
).alias("day"),
|
||||
fn.SUM(
|
||||
Case(
|
||||
None,
|
||||
[
|
||||
(
|
||||
(ReviewSegment.severity == SeverityEnum.alert)
|
||||
& (UserReviewStatus.has_been_reviewed == True),
|
||||
1,
|
||||
)
|
||||
],
|
||||
0,
|
||||
)
|
||||
).alias("reviewed_alert"),
|
||||
fn.SUM(
|
||||
Case(
|
||||
None,
|
||||
[
|
||||
(
|
||||
(ReviewSegment.severity == SeverityEnum.detection)
|
||||
& (UserReviewStatus.has_been_reviewed == True),
|
||||
1,
|
||||
)
|
||||
],
|
||||
0,
|
||||
)
|
||||
).alias("reviewed_detection"),
|
||||
fn.SUM(
|
||||
Case(
|
||||
None,
|
||||
[
|
||||
(
|
||||
(ReviewSegment.severity == SeverityEnum.alert),
|
||||
1,
|
||||
)
|
||||
],
|
||||
0,
|
||||
)
|
||||
).alias("total_alert"),
|
||||
fn.SUM(
|
||||
Case(
|
||||
None,
|
||||
[
|
||||
(
|
||||
(ReviewSegment.severity == SeverityEnum.detection),
|
||||
1,
|
||||
)
|
||||
],
|
||||
0,
|
||||
)
|
||||
).alias("total_detection"),
|
||||
)
|
||||
.left_outer_join(
|
||||
UserReviewStatus,
|
||||
on=(
|
||||
(ReviewSegment.id == UserReviewStatus.review_segment)
|
||||
& (UserReviewStatus.user_id == user_id)
|
||||
),
|
||||
)
|
||||
.where(reduce(operator.and_, clauses) if clauses else True)
|
||||
.dicts()
|
||||
.get()
|
||||
.group_by(
|
||||
(ReviewSegment.start_time + seconds_offset).cast("int") / day_in_seconds
|
||||
)
|
||||
.order_by(ReviewSegment.start_time.desc())
|
||||
)
|
||||
|
||||
min_time = time_range_query.get("min_time")
|
||||
max_time = time_range_query.get("max_time")
|
||||
|
||||
data = {
|
||||
"last24Hours": last_24_query,
|
||||
}
|
||||
|
||||
# If no data, return early
|
||||
if min_time is None or max_time is None:
|
||||
return JSONResponse(content=data)
|
||||
|
||||
# Get DST transition periods
|
||||
dst_periods = get_dst_transitions(params.timezone, min_time, max_time)
|
||||
|
||||
day_in_seconds = 60 * 60 * 24
|
||||
|
||||
# Query each DST period separately with the correct offset
|
||||
for period_start, period_end, period_offset in dst_periods:
|
||||
# Calculate hour/minute modifiers for this period
|
||||
hours_offset = int(period_offset / 60 / 60)
|
||||
minutes_offset = int(period_offset / 60 - hours_offset * 60)
|
||||
period_hour_modifier = f"{hours_offset} hour"
|
||||
period_minute_modifier = f"{minutes_offset} minute"
|
||||
|
||||
# Build clauses including time range for this period
|
||||
period_clauses = clauses.copy()
|
||||
period_clauses.append(
|
||||
(ReviewSegment.start_time >= period_start)
|
||||
& (ReviewSegment.start_time <= period_end)
|
||||
)
|
||||
|
||||
period_query = (
|
||||
ReviewSegment.select(
|
||||
fn.strftime(
|
||||
"%Y-%m-%d",
|
||||
fn.datetime(
|
||||
ReviewSegment.start_time,
|
||||
"unixepoch",
|
||||
period_hour_modifier,
|
||||
period_minute_modifier,
|
||||
),
|
||||
).alias("day"),
|
||||
fn.SUM(
|
||||
Case(
|
||||
None,
|
||||
[
|
||||
(
|
||||
(ReviewSegment.severity == SeverityEnum.alert)
|
||||
& (UserReviewStatus.has_been_reviewed == True),
|
||||
1,
|
||||
)
|
||||
],
|
||||
0,
|
||||
)
|
||||
).alias("reviewed_alert"),
|
||||
fn.SUM(
|
||||
Case(
|
||||
None,
|
||||
[
|
||||
(
|
||||
(ReviewSegment.severity == SeverityEnum.detection)
|
||||
& (UserReviewStatus.has_been_reviewed == True),
|
||||
1,
|
||||
)
|
||||
],
|
||||
0,
|
||||
)
|
||||
).alias("reviewed_detection"),
|
||||
fn.SUM(
|
||||
Case(
|
||||
None,
|
||||
[
|
||||
(
|
||||
(ReviewSegment.severity == SeverityEnum.alert),
|
||||
1,
|
||||
)
|
||||
],
|
||||
0,
|
||||
)
|
||||
).alias("total_alert"),
|
||||
fn.SUM(
|
||||
Case(
|
||||
None,
|
||||
[
|
||||
(
|
||||
(ReviewSegment.severity == SeverityEnum.detection),
|
||||
1,
|
||||
)
|
||||
],
|
||||
0,
|
||||
)
|
||||
).alias("total_detection"),
|
||||
)
|
||||
.left_outer_join(
|
||||
UserReviewStatus,
|
||||
on=(
|
||||
(ReviewSegment.id == UserReviewStatus.review_segment)
|
||||
& (UserReviewStatus.user_id == user_id)
|
||||
),
|
||||
)
|
||||
.where(reduce(operator.and_, period_clauses))
|
||||
.group_by(
|
||||
(ReviewSegment.start_time + period_offset).cast("int") / day_in_seconds
|
||||
)
|
||||
.order_by(ReviewSegment.start_time.desc())
|
||||
)
|
||||
|
||||
# Merge results from this period
|
||||
for e in period_query.dicts().iterator():
|
||||
day_key = e["day"]
|
||||
if day_key in data:
|
||||
# Merge counts if day already exists (edge case at DST boundary)
|
||||
data[day_key]["reviewed_alert"] += e["reviewed_alert"] or 0
|
||||
data[day_key]["reviewed_detection"] += e["reviewed_detection"] or 0
|
||||
data[day_key]["total_alert"] += e["total_alert"] or 0
|
||||
data[day_key]["total_detection"] += e["total_detection"] or 0
|
||||
else:
|
||||
data[day_key] = e
|
||||
for e in last_month_query.dicts().iterator():
|
||||
data[e["day"]] = e
|
||||
|
||||
return JSONResponse(content=data)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/reviews/viewed",
|
||||
response_model=GenericResponse,
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
)
|
||||
@router.post("/reviews/viewed", response_model=GenericResponse)
|
||||
async def set_multiple_reviewed(
|
||||
request: Request,
|
||||
body: ReviewModifyMultipleBody,
|
||||
@ -577,9 +515,7 @@ def delete_reviews(body: ReviewModifyMultipleBody):
|
||||
|
||||
|
||||
@router.get(
|
||||
"/review/activity/motion",
|
||||
response_model=list[ReviewActivityMotionResponse],
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
"/review/activity/motion", response_model=list[ReviewActivityMotionResponse]
|
||||
)
|
||||
def motion_activity(
|
||||
params: ReviewActivityMotionQueryParams = Depends(),
|
||||
@ -663,11 +599,7 @@ def motion_activity(
|
||||
return JSONResponse(content=normalized)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/review/event/{event_id}",
|
||||
response_model=ReviewSegmentResponse,
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
)
|
||||
@router.get("/review/event/{event_id}", response_model=ReviewSegmentResponse)
|
||||
async def get_review_from_event(request: Request, event_id: str):
|
||||
try:
|
||||
review = ReviewSegment.get(
|
||||
@ -682,11 +614,7 @@ async def get_review_from_event(request: Request, event_id: str):
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/review/{review_id}",
|
||||
response_model=ReviewSegmentResponse,
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
)
|
||||
@router.get("/review/{review_id}", response_model=ReviewSegmentResponse)
|
||||
async def get_review(request: Request, review_id: str):
|
||||
try:
|
||||
review = ReviewSegment.get(ReviewSegment.id == review_id)
|
||||
@ -699,11 +627,7 @@ async def get_review(request: Request, review_id: str):
|
||||
)
|
||||
|
||||
|
||||
@router.delete(
|
||||
"/review/{review_id}/viewed",
|
||||
response_model=GenericResponse,
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
)
|
||||
@router.delete("/review/{review_id}/viewed", response_model=GenericResponse)
|
||||
async def set_not_reviewed(
|
||||
review_id: str,
|
||||
current_user: dict = Depends(get_current_user),
|
||||
@ -741,7 +665,6 @@ async def set_not_reviewed(
|
||||
|
||||
@router.post(
|
||||
"/review/summarize/start/{start_ts}/end/{end_ts}",
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
description="Use GenAI to summarize review items over a period of time.",
|
||||
)
|
||||
def generate_review_summary(request: Request, start_ts: float, end_ts: float):
|
||||
|
||||
@ -488,8 +488,6 @@ class FrigateApp:
|
||||
}
|
||||
).execute()
|
||||
|
||||
self.config.auth.admin_first_time_login = True
|
||||
|
||||
logger.info("********************************************************")
|
||||
logger.info("********************************************************")
|
||||
logger.info("*** Auth is enabled, but no users exist. ***")
|
||||
|
||||
@ -136,7 +136,6 @@ class CameraMaintainer(threading.Thread):
|
||||
self.ptz_metrics[name],
|
||||
self.region_grids[name],
|
||||
self.stop_event,
|
||||
self.config.logger,
|
||||
)
|
||||
self.camera_processes[config.name] = camera_process
|
||||
camera_process.start()
|
||||
@ -157,11 +156,7 @@ class CameraMaintainer(threading.Thread):
|
||||
self.frame_manager.create(f"{config.name}_frame{i}", frame_size)
|
||||
|
||||
capture_process = CameraCapture(
|
||||
config,
|
||||
count,
|
||||
self.camera_metrics[name],
|
||||
self.stop_event,
|
||||
self.config.logger,
|
||||
config, count, self.camera_metrics[name], self.stop_event
|
||||
)
|
||||
capture_process.daemon = True
|
||||
self.capture_processes[name] = capture_process
|
||||
|
||||
@ -23,7 +23,6 @@ from frigate.const import (
|
||||
NOTIFICATION_TEST,
|
||||
REQUEST_REGION_GRID,
|
||||
UPDATE_AUDIO_ACTIVITY,
|
||||
UPDATE_AUDIO_TRANSCRIPTION_STATE,
|
||||
UPDATE_BIRDSEYE_LAYOUT,
|
||||
UPDATE_CAMERA_ACTIVITY,
|
||||
UPDATE_EMBEDDINGS_REINDEX_PROGRESS,
|
||||
@ -62,7 +61,6 @@ class Dispatcher:
|
||||
self.model_state: dict[str, ModelStatusTypesEnum] = {}
|
||||
self.embeddings_reindex: dict[str, Any] = {}
|
||||
self.birdseye_layout: dict[str, Any] = {}
|
||||
self.audio_transcription_state: str = "idle"
|
||||
self._camera_settings_handlers: dict[str, Callable] = {
|
||||
"audio": self._on_audio_command,
|
||||
"audio_transcription": self._on_audio_transcription_command,
|
||||
@ -180,19 +178,6 @@ class Dispatcher:
|
||||
def handle_model_state() -> None:
|
||||
self.publish("model_state", json.dumps(self.model_state.copy()))
|
||||
|
||||
def handle_update_audio_transcription_state() -> None:
|
||||
if payload:
|
||||
self.audio_transcription_state = payload
|
||||
self.publish(
|
||||
"audio_transcription_state",
|
||||
json.dumps(self.audio_transcription_state),
|
||||
)
|
||||
|
||||
def handle_audio_transcription_state() -> None:
|
||||
self.publish(
|
||||
"audio_transcription_state", json.dumps(self.audio_transcription_state)
|
||||
)
|
||||
|
||||
def handle_update_embeddings_reindex_progress() -> None:
|
||||
self.embeddings_reindex = payload
|
||||
self.publish(
|
||||
@ -279,12 +264,10 @@ class Dispatcher:
|
||||
UPDATE_MODEL_STATE: handle_update_model_state,
|
||||
UPDATE_EMBEDDINGS_REINDEX_PROGRESS: handle_update_embeddings_reindex_progress,
|
||||
UPDATE_BIRDSEYE_LAYOUT: handle_update_birdseye_layout,
|
||||
UPDATE_AUDIO_TRANSCRIPTION_STATE: handle_update_audio_transcription_state,
|
||||
NOTIFICATION_TEST: handle_notification_test,
|
||||
"restart": handle_restart,
|
||||
"embeddingsReindexProgress": handle_embeddings_reindex_progress,
|
||||
"modelState": handle_model_state,
|
||||
"audioTranscriptionState": handle_audio_transcription_state,
|
||||
"birdseyeLayout": handle_birdseye_layout,
|
||||
"onConnect": handle_on_connect,
|
||||
}
|
||||
|
||||
@ -375,19 +375,7 @@ class WebPushClient(Communicator):
|
||||
ended = state == "end" or state == "genai"
|
||||
|
||||
if state == "genai" and payload["after"]["data"]["metadata"]:
|
||||
base_title = payload["after"]["data"]["metadata"]["title"]
|
||||
threat_level = payload["after"]["data"]["metadata"].get(
|
||||
"potential_threat_level", 0
|
||||
)
|
||||
|
||||
# Add prefix for threat levels 1 and 2
|
||||
if threat_level == 1:
|
||||
title = f"Needs Review: {base_title}"
|
||||
elif threat_level == 2:
|
||||
title = f"Security Concern: {base_title}"
|
||||
else:
|
||||
title = base_title
|
||||
|
||||
title = payload["after"]["data"]["metadata"]["title"]
|
||||
message = payload["after"]["data"]["metadata"]["scene"]
|
||||
else:
|
||||
title = f"{titlecase(', '.join(sorted_objects).replace('_', ' '))}{' was' if state == 'end' else ''} detected in {titlecase(', '.join(payload['after']['data']['zones']).replace('_', ' '))}"
|
||||
|
||||
@ -38,13 +38,6 @@ class AuthConfig(FrigateBaseModel):
|
||||
default_factory=dict,
|
||||
title="Role to camera mappings. Empty list grants access to all cameras.",
|
||||
)
|
||||
admin_first_time_login: Optional[bool] = Field(
|
||||
default=False,
|
||||
title="Internal field to expose first-time admin login flag to the UI",
|
||||
description=(
|
||||
"When true the UI may show a help link on the login page informing users how to sign in after an admin password reset. "
|
||||
),
|
||||
)
|
||||
|
||||
@field_validator("roles")
|
||||
@classmethod
|
||||
|
||||
@ -55,12 +55,6 @@ class BirdseyeConfig(FrigateBaseModel):
|
||||
layout: BirdseyeLayoutConfig = Field(
|
||||
default_factory=BirdseyeLayoutConfig, title="Birdseye Layout Config"
|
||||
)
|
||||
idle_heartbeat_fps: float = Field(
|
||||
default=0.0,
|
||||
ge=0.0,
|
||||
le=10.0,
|
||||
title="Idle heartbeat FPS (0 disables, max 10)",
|
||||
)
|
||||
|
||||
|
||||
# uses BaseModel because some global attributes are not available at the camera level
|
||||
|
||||
@ -177,12 +177,6 @@ class CameraConfig(FrigateBaseModel):
|
||||
def ffmpeg_cmds(self) -> list[dict[str, list[str]]]:
|
||||
return self._ffmpeg_cmds
|
||||
|
||||
def get_formatted_name(self) -> str:
|
||||
"""Return the friendly name if set, otherwise return a formatted version of the camera name."""
|
||||
if self.friendly_name:
|
||||
return self.friendly_name
|
||||
return self.name.replace("_", " ").title() if self.name else ""
|
||||
|
||||
def create_ffmpeg_cmds(self):
|
||||
if "_ffmpeg_cmds" in self:
|
||||
return
|
||||
|
||||
@ -1,18 +1,10 @@
|
||||
from enum import Enum
|
||||
from typing import Optional, Union
|
||||
|
||||
from pydantic import Field, field_validator
|
||||
|
||||
from ..base import FrigateBaseModel
|
||||
|
||||
__all__ = ["ReviewConfig", "DetectionsConfig", "AlertsConfig", "ImageSourceEnum"]
|
||||
|
||||
|
||||
class ImageSourceEnum(str, Enum):
|
||||
"""Image source options for GenAI Review."""
|
||||
|
||||
preview = "preview"
|
||||
recordings = "recordings"
|
||||
__all__ = ["ReviewConfig", "DetectionsConfig", "AlertsConfig"]
|
||||
|
||||
|
||||
DEFAULT_ALERT_OBJECTS = ["person", "car"]
|
||||
@ -85,10 +77,6 @@ class GenAIReviewConfig(FrigateBaseModel):
|
||||
)
|
||||
alerts: bool = Field(default=True, title="Enable GenAI for alerts.")
|
||||
detections: bool = Field(default=False, title="Enable GenAI for detections.")
|
||||
image_source: ImageSourceEnum = Field(
|
||||
default=ImageSourceEnum.preview,
|
||||
title="Image source for review descriptions.",
|
||||
)
|
||||
additional_concerns: list[str] = Field(
|
||||
default=[],
|
||||
title="Additional concerns that GenAI should make note of on this camera.",
|
||||
@ -105,40 +93,13 @@ class GenAIReviewConfig(FrigateBaseModel):
|
||||
default=None,
|
||||
)
|
||||
activity_context_prompt: str = Field(
|
||||
default="""### Normal Activity Indicators (Level 0)
|
||||
- Known/verified people in any zone at any time
|
||||
- People with pets in residential areas
|
||||
- Deliveries or services during daytime/evening (6 AM - 10 PM): carrying packages to doors/porches, placing items, leaving
|
||||
- Services/maintenance workers with visible tools, uniforms, or service vehicles during daytime
|
||||
- Activity confined to public areas only (sidewalks, streets) without entering property at any time
|
||||
|
||||
### Suspicious Activity Indicators (Level 1)
|
||||
- **Testing or attempting to open doors/windows/handles on vehicles or buildings** — ALWAYS Level 1 regardless of time or duration
|
||||
- **Unidentified person in private areas (driveways, near vehicles/buildings) during late night/early morning (11 PM - 5 AM)** — ALWAYS Level 1 regardless of activity or duration
|
||||
- Taking items that don't belong to them (packages, objects from porches/driveways)
|
||||
- Climbing or jumping fences/barriers to access property
|
||||
- Attempting to conceal actions or items from view
|
||||
- Prolonged loitering: remaining in same area without visible purpose throughout most of the sequence
|
||||
|
||||
### Critical Threat Indicators (Level 2)
|
||||
- Holding break-in tools (crowbars, pry bars, bolt cutters)
|
||||
- Weapons visible (guns, knives, bats used aggressively)
|
||||
- Forced entry in progress
|
||||
- Physical aggression or violence
|
||||
- Active property damage or theft in progress
|
||||
|
||||
### Assessment Guidance
|
||||
Evaluate in this order:
|
||||
|
||||
1. **If person is verified/known** → Level 0 regardless of time or activity
|
||||
2. **If person is unidentified:**
|
||||
- Check time: If late night/early morning (11 PM - 5 AM) AND in private areas (driveways, near vehicles/buildings) → Level 1
|
||||
- Check actions: If testing doors/handles, taking items, climbing → Level 1
|
||||
- Otherwise, if daytime/evening (6 AM - 10 PM) with clear legitimate purpose (delivery, service worker) → Level 0
|
||||
3. **Escalate to Level 2 if:** Weapons, break-in tools, forced entry in progress, violence, or active property damage visible (escalates from Level 0 or 1)
|
||||
|
||||
The mere presence of an unidentified person in private areas during late night hours is inherently suspicious and warrants human review, regardless of what activity they appear to be doing or how brief the sequence is.""",
|
||||
title="Custom activity context prompt defining normal and suspicious activity patterns for this property.",
|
||||
default="""- **Zone context is critical**: Private enclosed spaces (back yards, back decks, fenced areas, inside garages) are resident territory where brief transient activity, routine tasks, and pet care are expected and normal. Front yards, driveways, and porches are semi-public but still resident spaces where deliveries, parking, and coming/going are routine. Consider whether the zone and activity align with normal residential use.
|
||||
- **Person + Pet = Normal Activity**: When both "Person" and "Dog" (or "Cat") are detected together in residential zones, this is routine pet care activity (walking, letting out, playing, supervising). Assign Level 0 unless there are OTHER strong suspicious behaviors present (like testing doors, taking items, etc.). A person with their pet in a residential zone is baseline normal activity.
|
||||
- Brief appearances in private zones (back yards, garages) are normal residential patterns.
|
||||
- Normal residential activity includes: residents, family members, guests, deliveries, services, maintenance workers, routine property use (parking, unloading, mail pickup, trash removal).
|
||||
- Brief movement with legitimate items (bags, packages, tools, equipment) in appropriate zones is routine.
|
||||
""",
|
||||
title="Custom activity context prompt defining normal activity patterns for this property.",
|
||||
)
|
||||
|
||||
|
||||
|
||||
@ -13,9 +13,6 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ZoneConfig(BaseModel):
|
||||
friendly_name: Optional[str] = Field(
|
||||
None, title="Zone friendly name used in the Frigate UI."
|
||||
)
|
||||
filters: dict[str, FilterConfig] = Field(
|
||||
default_factory=dict, title="Zone filters."
|
||||
)
|
||||
@ -56,12 +53,6 @@ class ZoneConfig(BaseModel):
|
||||
def contour(self) -> np.ndarray:
|
||||
return self._contour
|
||||
|
||||
def get_formatted_name(self, zone_name: str) -> str:
|
||||
"""Return the friendly name if set, otherwise return a formatted version of the zone name."""
|
||||
if self.friendly_name:
|
||||
return self.friendly_name
|
||||
return zone_name.replace("_", " ").title()
|
||||
|
||||
@field_validator("objects", mode="before")
|
||||
@classmethod
|
||||
def validate_objects(cls, v):
|
||||
|
||||
@ -33,8 +33,6 @@ class TriggerType(str, Enum):
|
||||
|
||||
class TriggerAction(str, Enum):
|
||||
NOTIFICATION = "notification"
|
||||
SUB_LABEL = "sub_label"
|
||||
ATTRIBUTE = "attribute"
|
||||
|
||||
|
||||
class ObjectClassificationType(str, Enum):
|
||||
@ -71,7 +69,7 @@ class BirdClassificationConfig(FrigateBaseModel):
|
||||
|
||||
|
||||
class CustomClassificationStateCameraConfig(FrigateBaseModel):
|
||||
crop: list[float, float, float, float] = Field(
|
||||
crop: list[int, int, int, int] = Field(
|
||||
title="Crop of image frame on this camera to run classification on."
|
||||
)
|
||||
|
||||
@ -199,9 +197,7 @@ class FaceRecognitionConfig(FrigateBaseModel):
|
||||
title="Min face recognitions for the sub label to be applied to the person object.",
|
||||
)
|
||||
save_attempts: int = Field(
|
||||
default=200,
|
||||
ge=0,
|
||||
title="Number of face attempts to save in the recent recognitions tab.",
|
||||
default=100, ge=0, title="Number of face attempts to save in the train tab."
|
||||
)
|
||||
blur_confidence_filter: bool = Field(
|
||||
default=True, title="Apply blur quality filter to face confidence."
|
||||
|
||||
@ -792,10 +792,6 @@ class FrigateConfig(FrigateBaseModel):
|
||||
# copy over auth and proxy config in case auth needs to be enforced
|
||||
safe_config["auth"] = config.get("auth", {})
|
||||
safe_config["proxy"] = config.get("proxy", {})
|
||||
|
||||
# copy over database config for auth and so a new db is not created
|
||||
safe_config["database"] = config.get("database", {})
|
||||
|
||||
return cls.parse_object(safe_config, **context)
|
||||
|
||||
# Validate and return the config dict.
|
||||
|
||||
@ -113,7 +113,6 @@ CLEAR_ONGOING_REVIEW_SEGMENTS = "clear_ongoing_review_segments"
|
||||
UPDATE_CAMERA_ACTIVITY = "update_camera_activity"
|
||||
UPDATE_AUDIO_ACTIVITY = "update_audio_activity"
|
||||
EXPIRE_AUDIO_ACTIVITY = "expire_audio_activity"
|
||||
UPDATE_AUDIO_TRANSCRIPTION_STATE = "update_audio_transcription_state"
|
||||
UPDATE_EVENT_DESCRIPTION = "update_event_description"
|
||||
UPDATE_REVIEW_DESCRIPTION = "update_review_description"
|
||||
UPDATE_MODEL_STATE = "update_model_state"
|
||||
|
||||
@ -4,6 +4,7 @@ import logging
|
||||
import os
|
||||
|
||||
import sherpa_onnx
|
||||
from faster_whisper.utils import download_model
|
||||
|
||||
from frigate.comms.inter_process import InterProcessRequestor
|
||||
from frigate.const import MODEL_CACHE_DIR
|
||||
@ -24,9 +25,6 @@ class AudioTranscriptionModelRunner:
|
||||
|
||||
if model_size == "large":
|
||||
# use the Whisper download function instead of our own
|
||||
# Import dynamically to avoid crashes on systems without AVX support
|
||||
from faster_whisper.utils import download_model
|
||||
|
||||
logger.debug("Downloading Whisper audio transcription model")
|
||||
download_model(
|
||||
size_or_id="small" if device == "cuda" else "tiny",
|
||||
|
||||
@ -14,8 +14,8 @@ from typing import Any, List, Optional, Tuple
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
from Levenshtein import distance, jaro_winkler
|
||||
from pyclipper import ET_CLOSEDPOLYGON, JT_ROUND, PyclipperOffset
|
||||
from rapidfuzz.distance import JaroWinkler, Levenshtein
|
||||
from shapely.geometry import Polygon
|
||||
|
||||
from frigate.comms.event_metadata_updater import (
|
||||
@ -1123,9 +1123,7 @@ class LicensePlateProcessingMixin:
|
||||
for i, plate in enumerate(plates):
|
||||
merged = False
|
||||
for j, cluster in enumerate(clusters):
|
||||
sims = [
|
||||
JaroWinkler.similarity(plate["plate"], v["plate"]) for v in cluster
|
||||
]
|
||||
sims = [jaro_winkler(plate["plate"], v["plate"]) for v in cluster]
|
||||
if len(sims) > 0:
|
||||
avg_sim = sum(sims) / len(sims)
|
||||
if avg_sim >= self.cluster_threshold:
|
||||
@ -1502,7 +1500,7 @@ class LicensePlateProcessingMixin:
|
||||
and current_time - data["last_seen"]
|
||||
<= self.config.cameras[camera].lpr.expire_time
|
||||
):
|
||||
similarity = JaroWinkler.similarity(data["plate"], top_plate)
|
||||
similarity = jaro_winkler(data["plate"], top_plate)
|
||||
if similarity >= self.similarity_threshold:
|
||||
plate_id = existing_id
|
||||
logger.debug(
|
||||
@ -1582,8 +1580,7 @@ class LicensePlateProcessingMixin:
|
||||
for label, plates_list in self.lpr_config.known_plates.items()
|
||||
if any(
|
||||
re.match(f"^{plate}$", rep_plate)
|
||||
or Levenshtein.distance(plate, rep_plate)
|
||||
<= self.lpr_config.match_distance
|
||||
or distance(plate, rep_plate) <= self.lpr_config.match_distance
|
||||
for plate in plates_list
|
||||
)
|
||||
),
|
||||
|
||||
@ -6,14 +6,15 @@ import threading
|
||||
import time
|
||||
from typing import Optional
|
||||
|
||||
from faster_whisper import WhisperModel
|
||||
from peewee import DoesNotExist
|
||||
|
||||
from frigate.comms.embeddings_updater import EmbeddingsRequestEnum
|
||||
from frigate.comms.inter_process import InterProcessRequestor
|
||||
from frigate.config import FrigateConfig
|
||||
from frigate.const import (
|
||||
CACHE_DIR,
|
||||
MODEL_CACHE_DIR,
|
||||
UPDATE_AUDIO_TRANSCRIPTION_STATE,
|
||||
UPDATE_EVENT_DESCRIPTION,
|
||||
)
|
||||
from frigate.data_processing.types import PostProcessDataEnum
|
||||
@ -31,13 +32,11 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
|
||||
self,
|
||||
config: FrigateConfig,
|
||||
requestor: InterProcessRequestor,
|
||||
embeddings,
|
||||
metrics: DataProcessorMetrics,
|
||||
):
|
||||
super().__init__(config, metrics, None)
|
||||
self.config = config
|
||||
self.requestor = requestor
|
||||
self.embeddings = embeddings
|
||||
self.recognizer = None
|
||||
self.transcription_lock = threading.Lock()
|
||||
self.transcription_thread = None
|
||||
@ -51,9 +50,6 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
|
||||
|
||||
def __build_recognizer(self) -> None:
|
||||
try:
|
||||
# Import dynamically to avoid crashes on systems without AVX support
|
||||
from faster_whisper import WhisperModel
|
||||
|
||||
self.recognizer = WhisperModel(
|
||||
model_size_or_path="small",
|
||||
device="cuda"
|
||||
@ -132,7 +128,10 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
|
||||
)
|
||||
|
||||
# Embed the description
|
||||
self.embeddings.embed_description(event_id, transcription)
|
||||
self.requestor.send_data(
|
||||
EmbeddingsRequestEnum.embed_description.value,
|
||||
{"id": event_id, "description": transcription},
|
||||
)
|
||||
|
||||
except DoesNotExist:
|
||||
logger.debug("No recording found for audio transcription post-processing")
|
||||
@ -191,8 +190,6 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
|
||||
self.transcription_running = False
|
||||
self.transcription_thread = None
|
||||
|
||||
self.requestor.send_data(UPDATE_AUDIO_TRANSCRIPTION_STATE, "idle")
|
||||
|
||||
def handle_request(self, topic: str, request_data: dict[str, any]) -> str | None:
|
||||
if topic == "transcribe_audio":
|
||||
event = request_data["event"]
|
||||
@ -206,8 +203,6 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
|
||||
|
||||
# Mark as running and start the thread
|
||||
self.transcription_running = True
|
||||
self.requestor.send_data(UPDATE_AUDIO_TRANSCRIPTION_STATE, "processing")
|
||||
|
||||
self.transcription_thread = threading.Thread(
|
||||
target=self._transcription_wrapper, args=(event,), daemon=True
|
||||
)
|
||||
|
||||
@ -20,8 +20,8 @@ from frigate.genai import GenAIClient
|
||||
from frigate.models import Event
|
||||
from frigate.types import TrackedObjectUpdateTypesEnum
|
||||
from frigate.util.builtin import EventsPerSecond, InferenceSpeed
|
||||
from frigate.util.file import get_event_thumbnail_bytes
|
||||
from frigate.util.image import create_thumbnail, ensure_jpeg_bytes
|
||||
from frigate.util.path import get_event_thumbnail_bytes
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from frigate.embeddings import Embeddings
|
||||
|
||||
@ -3,7 +3,6 @@
|
||||
import copy
|
||||
import datetime
|
||||
import logging
|
||||
import math
|
||||
import os
|
||||
import shutil
|
||||
import threading
|
||||
@ -11,29 +10,22 @@ from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import cv2
|
||||
from peewee import DoesNotExist
|
||||
from titlecase import titlecase
|
||||
|
||||
from frigate.comms.embeddings_updater import EmbeddingsRequestEnum
|
||||
from frigate.comms.inter_process import InterProcessRequestor
|
||||
from frigate.config import FrigateConfig
|
||||
from frigate.config.camera import CameraConfig
|
||||
from frigate.config.camera.review import GenAIReviewConfig, ImageSourceEnum
|
||||
from frigate.config.camera.review import GenAIReviewConfig
|
||||
from frigate.const import CACHE_DIR, CLIPS_DIR, UPDATE_REVIEW_DESCRIPTION
|
||||
from frigate.data_processing.types import PostProcessDataEnum
|
||||
from frigate.genai import GenAIClient
|
||||
from frigate.models import Recordings, ReviewSegment
|
||||
from frigate.models import ReviewSegment
|
||||
from frigate.util.builtin import EventsPerSecond, InferenceSpeed
|
||||
from frigate.util.image import get_image_from_recording
|
||||
|
||||
from ..post.api import PostProcessorApi
|
||||
from ..types import DataProcessorMetrics
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
RECORDING_BUFFER_EXTENSION_PERCENT = 0.10
|
||||
MIN_RECORDING_DURATION = 10
|
||||
|
||||
|
||||
class ReviewDescriptionProcessor(PostProcessorApi):
|
||||
def __init__(
|
||||
@ -51,53 +43,20 @@ class ReviewDescriptionProcessor(PostProcessorApi):
|
||||
self.review_descs_dps = EventsPerSecond()
|
||||
self.review_descs_dps.start()
|
||||
|
||||
def calculate_frame_count(
|
||||
self,
|
||||
camera: str,
|
||||
image_source: ImageSourceEnum = ImageSourceEnum.preview,
|
||||
height: int = 480,
|
||||
) -> int:
|
||||
"""Calculate optimal number of frames based on context size, image source, and resolution.
|
||||
|
||||
Token usage varies by resolution: larger images (ultrawide aspect ratios) use more tokens.
|
||||
Estimates ~1 token per 1250 pixels. Targets 98% context utilization with safety margin.
|
||||
Capped at 20 frames.
|
||||
"""
|
||||
def calculate_frame_count(self) -> int:
|
||||
"""Calculate optimal number of frames based on context size."""
|
||||
# With our preview images (height of 180px) each image should be ~100 tokens per image
|
||||
# We want to be conservative to not have too long of query times with too many images
|
||||
context_size = self.genai_client.get_context_size()
|
||||
camera_config = self.config.cameras[camera]
|
||||
|
||||
detect_width = camera_config.detect.width
|
||||
detect_height = camera_config.detect.height
|
||||
aspect_ratio = detect_width / detect_height
|
||||
|
||||
if image_source == ImageSourceEnum.recordings:
|
||||
if aspect_ratio >= 1:
|
||||
# Landscape or square: constrain height
|
||||
width = int(height * aspect_ratio)
|
||||
else:
|
||||
# Portrait: constrain width
|
||||
width = height
|
||||
height = int(width / aspect_ratio)
|
||||
if context_size > 10000:
|
||||
return 20
|
||||
elif context_size > 6000:
|
||||
return 16
|
||||
elif context_size > 4000:
|
||||
return 12
|
||||
else:
|
||||
if aspect_ratio >= 1:
|
||||
# Landscape or square: constrain height
|
||||
target_height = 180
|
||||
width = int(target_height * aspect_ratio)
|
||||
height = target_height
|
||||
else:
|
||||
# Portrait: constrain width
|
||||
target_width = 180
|
||||
width = target_width
|
||||
height = int(target_width / aspect_ratio)
|
||||
|
||||
pixels_per_image = width * height
|
||||
tokens_per_image = pixels_per_image / 1250
|
||||
prompt_tokens = 3500
|
||||
response_tokens = 300
|
||||
available_tokens = context_size - prompt_tokens - response_tokens
|
||||
max_frames = int(available_tokens / tokens_per_image)
|
||||
|
||||
return min(max(max_frames, 3), 20)
|
||||
return 8
|
||||
|
||||
def process_data(self, data, data_type):
|
||||
self.metrics.review_desc_dps.value = self.review_descs_dps.eps()
|
||||
@ -129,61 +88,36 @@ class ReviewDescriptionProcessor(PostProcessorApi):
|
||||
):
|
||||
return
|
||||
|
||||
image_source = camera_config.review.genai.image_source
|
||||
frames = self.get_cache_frames(
|
||||
camera, final_data["start_time"], final_data["end_time"]
|
||||
)
|
||||
|
||||
if image_source == ImageSourceEnum.recordings:
|
||||
duration = final_data["end_time"] - final_data["start_time"]
|
||||
buffer_extension = min(5, duration * RECORDING_BUFFER_EXTENSION_PERCENT)
|
||||
if not frames:
|
||||
frames = [final_data["thumb_path"]]
|
||||
|
||||
# Ensure minimum total duration for short review items
|
||||
# This provides better context for brief events
|
||||
total_duration = duration + (2 * buffer_extension)
|
||||
if total_duration < MIN_RECORDING_DURATION:
|
||||
# Expand buffer to reach minimum duration, still respecting max of 5s per side
|
||||
additional_buffer_per_side = (MIN_RECORDING_DURATION - duration) / 2
|
||||
buffer_extension = min(5, additional_buffer_per_side)
|
||||
thumbs = []
|
||||
|
||||
thumbs = self.get_recording_frames(
|
||||
camera,
|
||||
final_data["start_time"] - buffer_extension,
|
||||
final_data["end_time"] + buffer_extension,
|
||||
height=480, # Use 480p for good balance between quality and token usage
|
||||
for idx, thumb_path in enumerate(frames):
|
||||
thumb_data = cv2.imread(thumb_path)
|
||||
ret, jpg = cv2.imencode(
|
||||
".jpg", thumb_data, [int(cv2.IMWRITE_JPEG_QUALITY), 100]
|
||||
)
|
||||
|
||||
if not thumbs:
|
||||
# Fallback to preview frames if no recordings available
|
||||
logger.warning(
|
||||
f"No recording frames found for {camera}, falling back to preview frames"
|
||||
)
|
||||
thumbs = self.get_preview_frames_as_bytes(
|
||||
camera,
|
||||
final_data["start_time"],
|
||||
final_data["end_time"],
|
||||
final_data["thumb_path"],
|
||||
id,
|
||||
camera_config.review.genai.debug_save_thumbnails,
|
||||
)
|
||||
elif camera_config.review.genai.debug_save_thumbnails:
|
||||
# Save debug thumbnails for recordings
|
||||
Path(os.path.join(CLIPS_DIR, "genai-requests", id)).mkdir(
|
||||
if ret:
|
||||
thumbs.append(jpg.tobytes())
|
||||
|
||||
if camera_config.review.genai.debug_save_thumbnails:
|
||||
id = data["after"]["id"]
|
||||
Path(os.path.join(CLIPS_DIR, "genai-requests", f"{id}")).mkdir(
|
||||
parents=True, exist_ok=True
|
||||
)
|
||||
for idx, frame_bytes in enumerate(thumbs):
|
||||
with open(
|
||||
os.path.join(CLIPS_DIR, f"genai-requests/{id}/{idx}.jpg"),
|
||||
"wb",
|
||||
) as f:
|
||||
f.write(frame_bytes)
|
||||
else:
|
||||
# Use preview frames
|
||||
thumbs = self.get_preview_frames_as_bytes(
|
||||
camera,
|
||||
final_data["start_time"],
|
||||
final_data["end_time"],
|
||||
final_data["thumb_path"],
|
||||
id,
|
||||
camera_config.review.genai.debug_save_thumbnails,
|
||||
)
|
||||
shutil.copy(
|
||||
thumb_path,
|
||||
os.path.join(
|
||||
CLIPS_DIR,
|
||||
f"genai-requests/{id}/{idx}.webp",
|
||||
),
|
||||
)
|
||||
|
||||
# kickoff analysis
|
||||
self.review_descs_dps.update()
|
||||
@ -193,12 +127,11 @@ class ReviewDescriptionProcessor(PostProcessorApi):
|
||||
self.requestor,
|
||||
self.genai_client,
|
||||
self.review_desc_speed,
|
||||
camera_config,
|
||||
camera,
|
||||
final_data,
|
||||
thumbs,
|
||||
camera_config.review.genai,
|
||||
list(self.config.model.merged_labelmap.values()),
|
||||
self.config.model.all_attributes,
|
||||
),
|
||||
).start()
|
||||
|
||||
@ -284,7 +217,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
|
||||
all_frames.append(os.path.join(preview_dir, file))
|
||||
|
||||
frame_count = len(all_frames)
|
||||
desired_frame_count = self.calculate_frame_count(camera)
|
||||
desired_frame_count = self.calculate_frame_count()
|
||||
|
||||
if frame_count <= desired_frame_count:
|
||||
return all_frames
|
||||
@ -298,179 +231,48 @@ class ReviewDescriptionProcessor(PostProcessorApi):
|
||||
|
||||
return selected_frames
|
||||
|
||||
def get_recording_frames(
|
||||
self,
|
||||
camera: str,
|
||||
start_time: float,
|
||||
end_time: float,
|
||||
height: int = 480,
|
||||
) -> list[bytes]:
|
||||
"""Get frames from recordings at specified timestamps."""
|
||||
duration = end_time - start_time
|
||||
desired_frame_count = self.calculate_frame_count(
|
||||
camera, ImageSourceEnum.recordings, height
|
||||
)
|
||||
|
||||
# Calculate evenly spaced timestamps throughout the duration
|
||||
if desired_frame_count == 1:
|
||||
timestamps = [start_time + duration / 2]
|
||||
else:
|
||||
step = duration / (desired_frame_count - 1)
|
||||
timestamps = [start_time + (i * step) for i in range(desired_frame_count)]
|
||||
|
||||
def extract_frame_from_recording(ts: float) -> bytes | None:
|
||||
"""Extract a single frame from recording at given timestamp."""
|
||||
try:
|
||||
recording = (
|
||||
Recordings.select(
|
||||
Recordings.path,
|
||||
Recordings.start_time,
|
||||
)
|
||||
.where((ts >= Recordings.start_time) & (ts <= Recordings.end_time))
|
||||
.where(Recordings.camera == camera)
|
||||
.order_by(Recordings.start_time.desc())
|
||||
.limit(1)
|
||||
.get()
|
||||
)
|
||||
|
||||
time_in_segment = ts - recording.start_time
|
||||
return get_image_from_recording(
|
||||
self.config.ffmpeg,
|
||||
recording.path,
|
||||
time_in_segment,
|
||||
"mjpeg",
|
||||
height=height,
|
||||
)
|
||||
except DoesNotExist:
|
||||
return None
|
||||
|
||||
frames = []
|
||||
|
||||
for timestamp in timestamps:
|
||||
try:
|
||||
# Try to extract frame at exact timestamp
|
||||
image_data = extract_frame_from_recording(timestamp)
|
||||
|
||||
if not image_data:
|
||||
# Try with rounded timestamp as fallback
|
||||
rounded_timestamp = math.ceil(timestamp)
|
||||
image_data = extract_frame_from_recording(rounded_timestamp)
|
||||
|
||||
if image_data:
|
||||
frames.append(image_data)
|
||||
else:
|
||||
logger.warning(
|
||||
f"No recording found for {camera} at timestamp {timestamp}"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Error extracting frame from recording for {camera} at {timestamp}: {e}"
|
||||
)
|
||||
continue
|
||||
|
||||
return frames
|
||||
|
||||
def get_preview_frames_as_bytes(
|
||||
self,
|
||||
camera: str,
|
||||
start_time: float,
|
||||
end_time: float,
|
||||
thumb_path_fallback: str,
|
||||
review_id: str,
|
||||
save_debug: bool,
|
||||
) -> list[bytes]:
|
||||
"""Get preview frames and convert them to JPEG bytes.
|
||||
|
||||
Args:
|
||||
camera: Camera name
|
||||
start_time: Start timestamp
|
||||
end_time: End timestamp
|
||||
thumb_path_fallback: Fallback thumbnail path if no preview frames found
|
||||
review_id: Review item ID for debug saving
|
||||
save_debug: Whether to save debug thumbnails
|
||||
|
||||
Returns:
|
||||
List of JPEG image bytes
|
||||
"""
|
||||
frame_paths = self.get_cache_frames(camera, start_time, end_time)
|
||||
if not frame_paths:
|
||||
frame_paths = [thumb_path_fallback]
|
||||
|
||||
thumbs = []
|
||||
for idx, thumb_path in enumerate(frame_paths):
|
||||
thumb_data = cv2.imread(thumb_path)
|
||||
ret, jpg = cv2.imencode(
|
||||
".jpg", thumb_data, [int(cv2.IMWRITE_JPEG_QUALITY), 100]
|
||||
)
|
||||
if ret:
|
||||
thumbs.append(jpg.tobytes())
|
||||
|
||||
if save_debug:
|
||||
Path(os.path.join(CLIPS_DIR, "genai-requests", review_id)).mkdir(
|
||||
parents=True, exist_ok=True
|
||||
)
|
||||
shutil.copy(
|
||||
thumb_path,
|
||||
os.path.join(CLIPS_DIR, f"genai-requests/{review_id}/{idx}.webp"),
|
||||
)
|
||||
|
||||
return thumbs
|
||||
|
||||
|
||||
@staticmethod
|
||||
def run_analysis(
|
||||
requestor: InterProcessRequestor,
|
||||
genai_client: GenAIClient,
|
||||
review_inference_speed: InferenceSpeed,
|
||||
camera_config: CameraConfig,
|
||||
camera: str,
|
||||
final_data: dict[str, str],
|
||||
thumbs: list[bytes],
|
||||
genai_config: GenAIReviewConfig,
|
||||
labelmap_objects: list[str],
|
||||
attribute_labels: list[str],
|
||||
) -> None:
|
||||
start = datetime.datetime.now().timestamp()
|
||||
|
||||
# Format zone names using zone config friendly names if available
|
||||
formatted_zones = []
|
||||
for zone_name in final_data["data"]["zones"]:
|
||||
if zone_name in camera_config.zones:
|
||||
formatted_zones.append(
|
||||
camera_config.zones[zone_name].get_formatted_name(zone_name)
|
||||
)
|
||||
|
||||
analytics_data = {
|
||||
"id": final_data["id"],
|
||||
"camera": camera_config.get_formatted_name(),
|
||||
"zones": formatted_zones,
|
||||
"camera": camera,
|
||||
"zones": final_data["data"]["zones"],
|
||||
"start": datetime.datetime.fromtimestamp(final_data["start_time"]).strftime(
|
||||
"%A, %I:%M %p"
|
||||
),
|
||||
"duration": round(final_data["end_time"] - final_data["start_time"]),
|
||||
}
|
||||
|
||||
unified_objects = []
|
||||
objects = []
|
||||
named_objects = []
|
||||
|
||||
objects_list = final_data["data"]["objects"]
|
||||
sub_labels_list = final_data["data"]["sub_labels"]
|
||||
|
||||
for i, verified_label in enumerate(final_data["data"]["verified_objects"]):
|
||||
object_type = verified_label.replace("-verified", "").replace("_", " ")
|
||||
name = titlecase(sub_labels_list[i].replace("_", " "))
|
||||
unified_objects.append(f"{name} ({object_type})")
|
||||
|
||||
for label in objects_list:
|
||||
if "-verified" in label:
|
||||
continue
|
||||
elif label in labelmap_objects:
|
||||
object_type = titlecase(label.replace("_", " "))
|
||||
objects.append(label.replace("_", " ").title())
|
||||
|
||||
if label in attribute_labels:
|
||||
unified_objects.append(f"{object_type} (delivery/service)")
|
||||
else:
|
||||
unified_objects.append(object_type)
|
||||
for i, verified_label in enumerate(final_data["data"]["verified_objects"]):
|
||||
named_objects.append(
|
||||
f"{sub_labels_list[i].replace('_', ' ').title()} ({verified_label.replace('-verified', '')})"
|
||||
)
|
||||
|
||||
analytics_data["unified_objects"] = unified_objects
|
||||
analytics_data["objects"] = objects
|
||||
analytics_data["recognized_objects"] = named_objects
|
||||
|
||||
metadata = genai_client.generate_review_description(
|
||||
analytics_data,
|
||||
|
||||
@ -10,10 +10,6 @@ import cv2
|
||||
import numpy as np
|
||||
from peewee import DoesNotExist
|
||||
|
||||
from frigate.comms.event_metadata_updater import (
|
||||
EventMetadataPublisher,
|
||||
EventMetadataTypeEnum,
|
||||
)
|
||||
from frigate.comms.inter_process import InterProcessRequestor
|
||||
from frigate.config import FrigateConfig
|
||||
from frigate.const import CONFIG_DIR
|
||||
@ -22,7 +18,7 @@ from frigate.db.sqlitevecq import SqliteVecQueueDatabase
|
||||
from frigate.embeddings.util import ZScoreNormalization
|
||||
from frigate.models import Event, Trigger
|
||||
from frigate.util.builtin import cosine_distance
|
||||
from frigate.util.file import get_event_thumbnail_bytes
|
||||
from frigate.util.path import get_event_thumbnail_bytes
|
||||
|
||||
from ..post.api import PostProcessorApi
|
||||
from ..types import DataProcessorMetrics
|
||||
@ -38,7 +34,6 @@ class SemanticTriggerProcessor(PostProcessorApi):
|
||||
db: SqliteVecQueueDatabase,
|
||||
config: FrigateConfig,
|
||||
requestor: InterProcessRequestor,
|
||||
sub_label_publisher: EventMetadataPublisher,
|
||||
metrics: DataProcessorMetrics,
|
||||
embeddings,
|
||||
):
|
||||
@ -46,7 +41,6 @@ class SemanticTriggerProcessor(PostProcessorApi):
|
||||
self.db = db
|
||||
self.embeddings = embeddings
|
||||
self.requestor = requestor
|
||||
self.sub_label_publisher = sub_label_publisher
|
||||
self.trigger_embeddings: list[np.ndarray] = []
|
||||
|
||||
self.thumb_stats = ZScoreNormalization()
|
||||
@ -190,44 +184,14 @@ class SemanticTriggerProcessor(PostProcessorApi):
|
||||
),
|
||||
)
|
||||
|
||||
friendly_name = (
|
||||
self.config.cameras[camera]
|
||||
.semantic_search.triggers[trigger["name"]]
|
||||
.friendly_name
|
||||
)
|
||||
|
||||
if (
|
||||
self.config.cameras[camera]
|
||||
.semantic_search.triggers[trigger["name"]]
|
||||
.actions
|
||||
):
|
||||
# handle actions for the trigger
|
||||
# TODO: handle actions for the trigger
|
||||
# notifications already handled by webpush
|
||||
if (
|
||||
"sub_label"
|
||||
in self.config.cameras[camera]
|
||||
.semantic_search.triggers[trigger["name"]]
|
||||
.actions
|
||||
):
|
||||
self.sub_label_publisher.publish(
|
||||
(event_id, friendly_name, similarity),
|
||||
EventMetadataTypeEnum.sub_label,
|
||||
)
|
||||
if (
|
||||
"attribute"
|
||||
in self.config.cameras[camera]
|
||||
.semantic_search.triggers[trigger["name"]]
|
||||
.actions
|
||||
):
|
||||
self.sub_label_publisher.publish(
|
||||
(
|
||||
event_id,
|
||||
trigger["name"],
|
||||
trigger["type"],
|
||||
similarity,
|
||||
),
|
||||
EventMetadataTypeEnum.attribute.value,
|
||||
)
|
||||
pass
|
||||
|
||||
if WRITE_DEBUG_IMAGES:
|
||||
try:
|
||||
|
||||
@ -1,7 +1,6 @@
|
||||
"""Real time processor that works with classification tflite models."""
|
||||
|
||||
import datetime
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
from typing import Any
|
||||
@ -22,7 +21,6 @@ from frigate.config.classification import (
|
||||
)
|
||||
from frigate.const import CLIPS_DIR, MODEL_CACHE_DIR
|
||||
from frigate.log import redirect_output_to_logger
|
||||
from frigate.types import TrackedObjectUpdateTypesEnum
|
||||
from frigate.util.builtin import EventsPerSecond, InferenceSpeed, load_labels
|
||||
from frigate.util.object import box_overlaps, calculate_region
|
||||
|
||||
@ -36,8 +34,6 @@ except ModuleNotFoundError:
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
MAX_OBJECT_CLASSIFICATIONS = 16
|
||||
|
||||
|
||||
class CustomStateClassificationProcessor(RealTimeProcessorApi):
|
||||
def __init__(
|
||||
@ -57,18 +53,9 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
|
||||
self.tensor_output_details: dict[str, Any] | None = None
|
||||
self.labelmap: dict[int, str] = {}
|
||||
self.classifications_per_second = EventsPerSecond()
|
||||
self.state_history: dict[str, dict[str, Any]] = {}
|
||||
|
||||
if (
|
||||
self.metrics
|
||||
and self.model_config.name in self.metrics.classification_speeds
|
||||
):
|
||||
self.inference_speed = InferenceSpeed(
|
||||
self.metrics.classification_speeds[self.model_config.name]
|
||||
)
|
||||
else:
|
||||
self.inference_speed = None
|
||||
|
||||
self.inference_speed = InferenceSpeed(
|
||||
self.metrics.classification_speeds[self.model_config.name]
|
||||
)
|
||||
self.last_run = datetime.datetime.now().timestamp()
|
||||
self.__build_detector()
|
||||
|
||||
@ -96,86 +83,12 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
|
||||
|
||||
def __update_metrics(self, duration: float) -> None:
|
||||
self.classifications_per_second.update()
|
||||
if self.inference_speed:
|
||||
self.inference_speed.update(duration)
|
||||
|
||||
def _should_save_image(
|
||||
self, camera: str, detected_state: str, score: float = 1.0
|
||||
) -> bool:
|
||||
"""
|
||||
Determine if we should save the image for training.
|
||||
Save when:
|
||||
- State is changing or being verified (regardless of score)
|
||||
- Score is less than 100% (even if state matches, useful for training)
|
||||
Don't save when:
|
||||
- State is stable (matches current_state) AND score is 100%
|
||||
"""
|
||||
if camera not in self.state_history:
|
||||
# First detection for this camera, save it
|
||||
return True
|
||||
|
||||
verification = self.state_history[camera]
|
||||
current_state = verification.get("current_state")
|
||||
pending_state = verification.get("pending_state")
|
||||
|
||||
# Save if there's a pending state change being verified
|
||||
if pending_state is not None:
|
||||
return True
|
||||
|
||||
# Save if the detected state differs from the current verified state
|
||||
# (state is changing)
|
||||
if current_state is not None and detected_state != current_state:
|
||||
return True
|
||||
|
||||
# If score is less than 100%, save even if state matches
|
||||
# (useful for training to improve confidence)
|
||||
if score < 1.0:
|
||||
return True
|
||||
|
||||
# Don't save if state is stable (detected_state == current_state) AND score is 100%
|
||||
return False
|
||||
|
||||
def verify_state_change(self, camera: str, detected_state: str) -> str | None:
|
||||
"""
|
||||
Verify state change requires 3 consecutive identical states before publishing.
|
||||
Returns state to publish or None if verification not complete.
|
||||
"""
|
||||
if camera not in self.state_history:
|
||||
self.state_history[camera] = {
|
||||
"current_state": None,
|
||||
"pending_state": None,
|
||||
"consecutive_count": 0,
|
||||
}
|
||||
|
||||
verification = self.state_history[camera]
|
||||
|
||||
if detected_state == verification["current_state"]:
|
||||
verification["pending_state"] = None
|
||||
verification["consecutive_count"] = 0
|
||||
return None
|
||||
|
||||
if detected_state == verification["pending_state"]:
|
||||
verification["consecutive_count"] += 1
|
||||
|
||||
if verification["consecutive_count"] >= 3:
|
||||
verification["current_state"] = detected_state
|
||||
verification["pending_state"] = None
|
||||
verification["consecutive_count"] = 0
|
||||
return detected_state
|
||||
else:
|
||||
verification["pending_state"] = detected_state
|
||||
verification["consecutive_count"] = 1
|
||||
logger.debug(
|
||||
f"New state '{detected_state}' detected for {camera}, need {3 - verification['consecutive_count']} more consecutive detections"
|
||||
)
|
||||
|
||||
return None
|
||||
self.inference_speed.update(duration)
|
||||
|
||||
def process_frame(self, frame_data: dict[str, Any], frame: np.ndarray):
|
||||
if self.metrics and self.model_config.name in self.metrics.classification_cps:
|
||||
self.metrics.classification_cps[
|
||||
self.model_config.name
|
||||
].value = self.classifications_per_second.eps()
|
||||
self.metrics.classification_cps[
|
||||
self.model_config.name
|
||||
].value = self.classifications_per_second.eps()
|
||||
camera = frame_data.get("camera")
|
||||
|
||||
if camera not in self.model_config.state_config.cameras:
|
||||
@ -183,10 +96,10 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
|
||||
|
||||
camera_config = self.model_config.state_config.cameras[camera]
|
||||
crop = [
|
||||
camera_config.crop[0] * self.config.cameras[camera].detect.width,
|
||||
camera_config.crop[1] * self.config.cameras[camera].detect.height,
|
||||
camera_config.crop[2] * self.config.cameras[camera].detect.width,
|
||||
camera_config.crop[3] * self.config.cameras[camera].detect.height,
|
||||
camera_config.crop[0],
|
||||
camera_config.crop[1],
|
||||
camera_config.crop[2],
|
||||
camera_config.crop[3],
|
||||
]
|
||||
should_run = False
|
||||
|
||||
@ -208,19 +121,6 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
|
||||
self.last_run = now
|
||||
should_run = True
|
||||
|
||||
# Shortcut: always run if we have a pending state verification to complete
|
||||
if (
|
||||
not should_run
|
||||
and camera in self.state_history
|
||||
and self.state_history[camera]["pending_state"] is not None
|
||||
and now > self.last_run + 0.5
|
||||
):
|
||||
self.last_run = now
|
||||
should_run = True
|
||||
logger.debug(
|
||||
f"Running verification check for pending state: {self.state_history[camera]['pending_state']} ({self.state_history[camera]['consecutive_count']}/3)"
|
||||
)
|
||||
|
||||
if not should_run:
|
||||
return
|
||||
|
||||
@ -248,16 +148,14 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
|
||||
return
|
||||
|
||||
if self.interpreter is None:
|
||||
# When interpreter is None, always save (score is 0.0, which is < 1.0)
|
||||
if self._should_save_image(camera, "unknown", 0.0):
|
||||
write_classification_attempt(
|
||||
self.train_dir,
|
||||
cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
|
||||
"none-none",
|
||||
now,
|
||||
"unknown",
|
||||
0.0,
|
||||
)
|
||||
write_classification_attempt(
|
||||
self.train_dir,
|
||||
cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
|
||||
"none-none",
|
||||
now,
|
||||
"unknown",
|
||||
0.0,
|
||||
)
|
||||
return
|
||||
|
||||
input = np.expand_dims(resized_frame, axis=0)
|
||||
@ -267,37 +165,23 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
|
||||
self.tensor_output_details[0]["index"]
|
||||
)[0]
|
||||
probs = res / res.sum(axis=0)
|
||||
logger.debug(
|
||||
f"{self.model_config.name} Ran state classification with probabilities: {probs}"
|
||||
)
|
||||
best_id = np.argmax(probs)
|
||||
score = round(probs[best_id], 2)
|
||||
self.__update_metrics(datetime.datetime.now().timestamp() - now)
|
||||
|
||||
detected_state = self.labelmap[best_id]
|
||||
write_classification_attempt(
|
||||
self.train_dir,
|
||||
cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
|
||||
"none-none",
|
||||
now,
|
||||
self.labelmap[best_id],
|
||||
score,
|
||||
)
|
||||
|
||||
if self._should_save_image(camera, detected_state, score):
|
||||
write_classification_attempt(
|
||||
self.train_dir,
|
||||
cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
|
||||
"none-none",
|
||||
now,
|
||||
detected_state,
|
||||
score,
|
||||
)
|
||||
|
||||
if score < self.model_config.threshold:
|
||||
logger.debug(
|
||||
f"Score {score} below threshold {self.model_config.threshold}, skipping verification"
|
||||
)
|
||||
return
|
||||
|
||||
verified_state = self.verify_state_change(camera, detected_state)
|
||||
|
||||
if verified_state is not None:
|
||||
if score >= self.model_config.threshold:
|
||||
self.requestor.send_data(
|
||||
f"{camera}/classification/{self.model_config.name}",
|
||||
verified_state,
|
||||
self.labelmap[best_id],
|
||||
)
|
||||
|
||||
def handle_request(self, topic, request_data):
|
||||
@ -326,7 +210,6 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
|
||||
config: FrigateConfig,
|
||||
model_config: CustomClassificationConfig,
|
||||
sub_label_publisher: EventMetadataPublisher,
|
||||
requestor: InterProcessRequestor,
|
||||
metrics: DataProcessorMetrics,
|
||||
):
|
||||
super().__init__(config, metrics)
|
||||
@ -335,23 +218,14 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
|
||||
self.train_dir = os.path.join(CLIPS_DIR, self.model_config.name, "train")
|
||||
self.interpreter: Interpreter | None = None
|
||||
self.sub_label_publisher = sub_label_publisher
|
||||
self.requestor = requestor
|
||||
self.tensor_input_details: dict[str, Any] | None = None
|
||||
self.tensor_output_details: dict[str, Any] | None = None
|
||||
self.classification_history: dict[str, list[tuple[str, float, float]]] = {}
|
||||
self.detected_objects: dict[str, float] = {}
|
||||
self.labelmap: dict[int, str] = {}
|
||||
self.classifications_per_second = EventsPerSecond()
|
||||
|
||||
if (
|
||||
self.metrics
|
||||
and self.model_config.name in self.metrics.classification_speeds
|
||||
):
|
||||
self.inference_speed = InferenceSpeed(
|
||||
self.metrics.classification_speeds[self.model_config.name]
|
||||
)
|
||||
else:
|
||||
self.inference_speed = None
|
||||
|
||||
self.inference_speed = InferenceSpeed(
|
||||
self.metrics.classification_speeds[self.model_config.name]
|
||||
)
|
||||
self.__build_detector()
|
||||
|
||||
@redirect_output_to_logger(logger, logging.DEBUG)
|
||||
@ -377,64 +251,12 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
|
||||
|
||||
def __update_metrics(self, duration: float) -> None:
|
||||
self.classifications_per_second.update()
|
||||
if self.inference_speed:
|
||||
self.inference_speed.update(duration)
|
||||
|
||||
def get_weighted_score(
|
||||
self,
|
||||
object_id: str,
|
||||
current_label: str,
|
||||
current_score: float,
|
||||
current_time: float,
|
||||
) -> tuple[str | None, float]:
|
||||
"""
|
||||
Determine weighted score based on history to prevent false positives/negatives.
|
||||
Requires 60% of attempts to agree on a label before publishing.
|
||||
Returns (weighted_label, weighted_score) or (None, 0.0) if no weighted score.
|
||||
"""
|
||||
if object_id not in self.classification_history:
|
||||
self.classification_history[object_id] = []
|
||||
|
||||
self.classification_history[object_id].append(
|
||||
(current_label, current_score, current_time)
|
||||
)
|
||||
|
||||
history = self.classification_history[object_id]
|
||||
|
||||
if len(history) < 3:
|
||||
return None, 0.0
|
||||
|
||||
label_counts = {}
|
||||
label_scores = {}
|
||||
total_attempts = len(history)
|
||||
|
||||
for label, score, timestamp in history:
|
||||
if label not in label_counts:
|
||||
label_counts[label] = 0
|
||||
label_scores[label] = []
|
||||
|
||||
label_counts[label] += 1
|
||||
label_scores[label].append(score)
|
||||
|
||||
best_label = max(label_counts, key=label_counts.get)
|
||||
best_count = label_counts[best_label]
|
||||
|
||||
consensus_threshold = total_attempts * 0.6
|
||||
if best_count < consensus_threshold:
|
||||
return None, 0.0
|
||||
|
||||
avg_score = sum(label_scores[best_label]) / len(label_scores[best_label])
|
||||
|
||||
if best_label == "none":
|
||||
return None, 0.0
|
||||
|
||||
return best_label, avg_score
|
||||
self.inference_speed.update(duration)
|
||||
|
||||
def process_frame(self, obj_data, frame):
|
||||
if self.metrics and self.model_config.name in self.metrics.classification_cps:
|
||||
self.metrics.classification_cps[
|
||||
self.model_config.name
|
||||
].value = self.classifications_per_second.eps()
|
||||
self.metrics.classification_cps[
|
||||
self.model_config.name
|
||||
].value = self.classifications_per_second.eps()
|
||||
|
||||
if obj_data["false_positive"]:
|
||||
return
|
||||
@ -442,18 +264,6 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
|
||||
if obj_data["label"] not in self.model_config.object_config.objects:
|
||||
return
|
||||
|
||||
if obj_data.get("end_time") is not None:
|
||||
return
|
||||
|
||||
object_id = obj_data["id"]
|
||||
|
||||
if (
|
||||
object_id in self.classification_history
|
||||
and len(self.classification_history[object_id])
|
||||
>= MAX_OBJECT_CLASSIFICATIONS
|
||||
):
|
||||
return
|
||||
|
||||
now = datetime.datetime.now().timestamp()
|
||||
x, y, x2, y2 = calculate_region(
|
||||
frame.shape,
|
||||
@ -462,8 +272,8 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
|
||||
obj_data["box"][2],
|
||||
obj_data["box"][3],
|
||||
max(
|
||||
obj_data["box"][2] - obj_data["box"][0],
|
||||
obj_data["box"][3] - obj_data["box"][1],
|
||||
obj_data["box"][1] - obj_data["box"][0],
|
||||
obj_data["box"][3] - obj_data["box"][2],
|
||||
),
|
||||
1.0,
|
||||
)
|
||||
@ -485,7 +295,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
|
||||
write_classification_attempt(
|
||||
self.train_dir,
|
||||
cv2.cvtColor(crop, cv2.COLOR_RGB2BGR),
|
||||
object_id,
|
||||
obj_data["id"],
|
||||
now,
|
||||
"unknown",
|
||||
0.0,
|
||||
@ -499,85 +309,48 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
|
||||
self.tensor_output_details[0]["index"]
|
||||
)[0]
|
||||
probs = res / res.sum(axis=0)
|
||||
logger.debug(
|
||||
f"{self.model_config.name} Ran object classification with probabilities: {probs}"
|
||||
)
|
||||
best_id = np.argmax(probs)
|
||||
score = round(probs[best_id], 2)
|
||||
previous_score = self.detected_objects.get(obj_data["id"], 0.0)
|
||||
self.__update_metrics(datetime.datetime.now().timestamp() - now)
|
||||
|
||||
write_classification_attempt(
|
||||
self.train_dir,
|
||||
cv2.cvtColor(crop, cv2.COLOR_RGB2BGR),
|
||||
object_id,
|
||||
obj_data["id"],
|
||||
now,
|
||||
self.labelmap[best_id],
|
||||
score,
|
||||
max_files=200,
|
||||
)
|
||||
|
||||
if score < self.model_config.threshold:
|
||||
logger.debug(f"Score {score} is less than threshold.")
|
||||
return
|
||||
|
||||
if score <= previous_score:
|
||||
logger.debug(f"Score {score} is worse than previous score {previous_score}")
|
||||
return
|
||||
|
||||
sub_label = self.labelmap[best_id]
|
||||
self.detected_objects[obj_data["id"]] = score
|
||||
|
||||
consensus_label, consensus_score = self.get_weighted_score(
|
||||
object_id, sub_label, score, now
|
||||
)
|
||||
|
||||
if consensus_label is not None:
|
||||
camera = obj_data["camera"]
|
||||
|
||||
if (
|
||||
self.model_config.object_config.classification_type
|
||||
== ObjectClassificationType.sub_label
|
||||
):
|
||||
if (
|
||||
self.model_config.object_config.classification_type
|
||||
== ObjectClassificationType.sub_label
|
||||
):
|
||||
if sub_label != "none":
|
||||
self.sub_label_publisher.publish(
|
||||
(object_id, consensus_label, consensus_score),
|
||||
(obj_data["id"], sub_label, score),
|
||||
EventMetadataTypeEnum.sub_label,
|
||||
)
|
||||
self.requestor.send_data(
|
||||
"tracked_object_update",
|
||||
json.dumps(
|
||||
{
|
||||
"type": TrackedObjectUpdateTypesEnum.classification,
|
||||
"id": object_id,
|
||||
"camera": camera,
|
||||
"timestamp": now,
|
||||
"model": self.model_config.name,
|
||||
"sub_label": consensus_label,
|
||||
"score": consensus_score,
|
||||
}
|
||||
),
|
||||
)
|
||||
elif (
|
||||
self.model_config.object_config.classification_type
|
||||
== ObjectClassificationType.attribute
|
||||
):
|
||||
self.sub_label_publisher.publish(
|
||||
(
|
||||
object_id,
|
||||
self.model_config.name,
|
||||
consensus_label,
|
||||
consensus_score,
|
||||
),
|
||||
EventMetadataTypeEnum.attribute.value,
|
||||
)
|
||||
self.requestor.send_data(
|
||||
"tracked_object_update",
|
||||
json.dumps(
|
||||
{
|
||||
"type": TrackedObjectUpdateTypesEnum.classification,
|
||||
"id": object_id,
|
||||
"camera": camera,
|
||||
"timestamp": now,
|
||||
"model": self.model_config.name,
|
||||
"attribute": consensus_label,
|
||||
"score": consensus_score,
|
||||
}
|
||||
),
|
||||
)
|
||||
elif (
|
||||
self.model_config.object_config.classification_type
|
||||
== ObjectClassificationType.attribute
|
||||
):
|
||||
self.sub_label_publisher.publish(
|
||||
(obj_data["id"], self.model_config.name, sub_label, score),
|
||||
EventMetadataTypeEnum.attribute.value,
|
||||
)
|
||||
|
||||
def handle_request(self, topic, request_data):
|
||||
if topic == EmbeddingsRequestEnum.reload_classification_model.value:
|
||||
@ -595,8 +368,8 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
|
||||
return None
|
||||
|
||||
def expire_object(self, object_id, camera):
|
||||
if object_id in self.classification_history:
|
||||
self.classification_history.pop(object_id)
|
||||
if object_id in self.detected_objects:
|
||||
self.detected_objects.pop(object_id)
|
||||
|
||||
|
||||
@staticmethod
|
||||
@ -607,7 +380,6 @@ def write_classification_attempt(
|
||||
timestamp: float,
|
||||
label: str,
|
||||
score: float,
|
||||
max_files: int = 100,
|
||||
) -> None:
|
||||
if "-" in label:
|
||||
label = label.replace("-", "_")
|
||||
@ -623,8 +395,5 @@ def write_classification_attempt(
|
||||
)
|
||||
|
||||
# delete oldest face image if maximum is reached
|
||||
try:
|
||||
if len(files) > max_files:
|
||||
os.unlink(os.path.join(folder, files[-1]))
|
||||
except FileNotFoundError:
|
||||
pass
|
||||
if len(files) > 100:
|
||||
os.unlink(os.path.join(folder, files[-1]))
|
||||
|
||||
@ -166,7 +166,6 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
|
||||
camera = obj_data["camera"]
|
||||
|
||||
if not self.config.cameras[camera].face_recognition.enabled:
|
||||
logger.debug(f"Face recognition disabled for camera {camera}, skipping")
|
||||
return
|
||||
|
||||
start = datetime.datetime.now().timestamp()
|
||||
@ -209,7 +208,6 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
|
||||
person_box = obj_data.get("box")
|
||||
|
||||
if not person_box:
|
||||
logger.debug(f"No person box available for {id}")
|
||||
return
|
||||
|
||||
rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
|
||||
@ -235,8 +233,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
|
||||
|
||||
try:
|
||||
face_frame = cv2.cvtColor(face_frame, cv2.COLOR_RGB2BGR)
|
||||
except Exception as e:
|
||||
logger.debug(f"Failed to convert face frame color for {id}: {e}")
|
||||
except Exception:
|
||||
return
|
||||
else:
|
||||
# don't run for object without attributes
|
||||
@ -254,7 +251,6 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
|
||||
|
||||
# no faces detected in this frame
|
||||
if not face:
|
||||
logger.debug(f"No face attributes found for {id}")
|
||||
return
|
||||
|
||||
face_box = face.get("box")
|
||||
@ -278,7 +274,6 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
|
||||
res = self.recognizer.classify(face_frame)
|
||||
|
||||
if not res:
|
||||
logger.debug(f"Face recognizer returned no result for {id}")
|
||||
self.__update_metrics(datetime.datetime.now().timestamp() - start)
|
||||
return
|
||||
|
||||
@ -335,7 +330,6 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
|
||||
def handle_request(self, topic, request_data) -> dict[str, Any] | None:
|
||||
if topic == EmbeddingsRequestEnum.clear_face_classifier.value:
|
||||
self.recognizer.clear()
|
||||
return {"success": True, "message": "Face classifier cleared."}
|
||||
elif topic == EmbeddingsRequestEnum.recognize_face.value:
|
||||
img = cv2.imdecode(
|
||||
np.frombuffer(base64.b64decode(request_data["image"]), dtype=np.uint8),
|
||||
@ -423,10 +417,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
|
||||
res = self.recognizer.classify(img)
|
||||
|
||||
if not res:
|
||||
return {
|
||||
"message": "Model is still training, please try again in a few moments.",
|
||||
"success": False,
|
||||
}
|
||||
return
|
||||
|
||||
sub_label, score = res
|
||||
|
||||
@ -445,13 +436,6 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
|
||||
)
|
||||
shutil.move(current_file, new_file)
|
||||
|
||||
return {
|
||||
"message": f"Successfully reprocessed face. Result: {sub_label} (score: {score:.2f})",
|
||||
"success": True,
|
||||
"face_name": sub_label,
|
||||
"score": score,
|
||||
}
|
||||
|
||||
def expire_object(self, object_id: str, camera: str):
|
||||
if object_id in self.person_face_history:
|
||||
self.person_face_history.pop(object_id)
|
||||
|
||||
@ -3,7 +3,6 @@
|
||||
import logging
|
||||
import os
|
||||
import platform
|
||||
import threading
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any
|
||||
|
||||
@ -22,25 +21,21 @@ def is_arm64_platform() -> bool:
|
||||
return machine in ("aarch64", "arm64", "armv8", "armv7l")
|
||||
|
||||
|
||||
def get_ort_session_options(
|
||||
is_complex_model: bool = False,
|
||||
) -> ort.SessionOptions | None:
|
||||
def get_ort_session_options() -> ort.SessionOptions | None:
|
||||
"""Get ONNX Runtime session options with appropriate settings.
|
||||
|
||||
Args:
|
||||
is_complex_model: Whether the model needs basic optimization to avoid graph fusion issues.
|
||||
|
||||
Returns:
|
||||
SessionOptions with appropriate optimization level, or None for default settings.
|
||||
On ARM/RKNN platforms, use basic optimizations to avoid graph fusion issues
|
||||
that can break certain models. On amd64, use default optimizations for better performance.
|
||||
"""
|
||||
if is_complex_model:
|
||||
sess_options = None
|
||||
|
||||
if is_arm64_platform():
|
||||
sess_options = ort.SessionOptions()
|
||||
sess_options.graph_optimization_level = (
|
||||
ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
|
||||
)
|
||||
return sess_options
|
||||
|
||||
return None
|
||||
return sess_options
|
||||
|
||||
|
||||
# Import OpenVINO only when needed to avoid circular dependencies
|
||||
@ -108,21 +103,6 @@ class BaseModelRunner(ABC):
|
||||
class ONNXModelRunner(BaseModelRunner):
|
||||
"""Run ONNX models using ONNX Runtime."""
|
||||
|
||||
@staticmethod
|
||||
def is_cpu_complex_model(model_type: str) -> bool:
|
||||
"""Check if model needs basic optimization level to avoid graph fusion issues.
|
||||
|
||||
Some models (like Jina-CLIP) have issues with aggressive optimizations like
|
||||
SimplifiedLayerNormFusion that create or expect nodes that don't exist.
|
||||
"""
|
||||
# Import here to avoid circular imports
|
||||
from frigate.embeddings.types import EnrichmentModelTypeEnum
|
||||
|
||||
return model_type in [
|
||||
EnrichmentModelTypeEnum.jina_v1.value,
|
||||
EnrichmentModelTypeEnum.jina_v2.value,
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def is_migraphx_complex_model(model_type: str) -> bool:
|
||||
# Import here to avoid circular imports
|
||||
@ -162,12 +142,12 @@ class CudaGraphRunner(BaseModelRunner):
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def is_model_supported(model_type: str) -> bool:
|
||||
def is_complex_model(model_type: str) -> bool:
|
||||
# Import here to avoid circular imports
|
||||
from frigate.detectors.detector_config import ModelTypeEnum
|
||||
from frigate.embeddings.types import EnrichmentModelTypeEnum
|
||||
|
||||
return model_type not in [
|
||||
return model_type in [
|
||||
ModelTypeEnum.yolonas.value,
|
||||
EnrichmentModelTypeEnum.paddleocr.value,
|
||||
EnrichmentModelTypeEnum.jina_v1.value,
|
||||
@ -235,36 +215,11 @@ class OpenVINOModelRunner(BaseModelRunner):
|
||||
# Import here to avoid circular imports
|
||||
from frigate.embeddings.types import EnrichmentModelTypeEnum
|
||||
|
||||
return model_type in [
|
||||
EnrichmentModelTypeEnum.paddleocr.value,
|
||||
EnrichmentModelTypeEnum.jina_v2.value,
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def is_model_npu_supported(model_type: str) -> bool:
|
||||
# Import here to avoid circular imports
|
||||
from frigate.embeddings.types import EnrichmentModelTypeEnum
|
||||
|
||||
return model_type not in [
|
||||
EnrichmentModelTypeEnum.paddleocr.value,
|
||||
EnrichmentModelTypeEnum.jina_v1.value,
|
||||
EnrichmentModelTypeEnum.jina_v2.value,
|
||||
EnrichmentModelTypeEnum.arcface.value,
|
||||
]
|
||||
return model_type in [EnrichmentModelTypeEnum.paddleocr.value]
|
||||
|
||||
def __init__(self, model_path: str, device: str, model_type: str, **kwargs):
|
||||
self.model_path = model_path
|
||||
self.device = device
|
||||
self.model_type = model_type
|
||||
|
||||
if device == "NPU" and not OpenVINOModelRunner.is_model_npu_supported(
|
||||
model_type
|
||||
):
|
||||
logger.warning(
|
||||
f"OpenVINO model {model_type} is not supported on NPU, using GPU instead"
|
||||
)
|
||||
device = "GPU"
|
||||
|
||||
self.complex_model = OpenVINOModelRunner.is_complex_model(model_type)
|
||||
|
||||
if not os.path.isfile(model_path):
|
||||
@ -292,10 +247,6 @@ class OpenVINOModelRunner(BaseModelRunner):
|
||||
self.infer_request = self.compiled_model.create_infer_request()
|
||||
self.input_tensor: ov.Tensor | None = None
|
||||
|
||||
# Thread lock to prevent concurrent inference (needed for JinaV2 which shares
|
||||
# one runner between text and vision embeddings called from different threads)
|
||||
self._inference_lock = threading.Lock()
|
||||
|
||||
if not self.complex_model:
|
||||
try:
|
||||
input_shape = self.compiled_model.inputs[0].get_shape()
|
||||
@ -339,81 +290,55 @@ class OpenVINOModelRunner(BaseModelRunner):
|
||||
Returns:
|
||||
List of output tensors
|
||||
"""
|
||||
# Lock prevents concurrent access to infer_request
|
||||
# Needed for JinaV2: genai thread (text) + embeddings thread (vision)
|
||||
with self._inference_lock:
|
||||
from frigate.embeddings.types import EnrichmentModelTypeEnum
|
||||
|
||||
if self.model_type in [EnrichmentModelTypeEnum.arcface.value]:
|
||||
# For face recognition models, create a fresh infer_request
|
||||
# for each inference to avoid state pollution that causes incorrect results.
|
||||
self.infer_request = self.compiled_model.create_infer_request()
|
||||
|
||||
# Handle single input case for backward compatibility
|
||||
if (
|
||||
len(inputs) == 1
|
||||
and len(self.compiled_model.inputs) == 1
|
||||
and self.input_tensor is not None
|
||||
):
|
||||
# Single input case - use the pre-allocated tensor for efficiency
|
||||
input_data = list(inputs.values())[0]
|
||||
np.copyto(self.input_tensor.data, input_data)
|
||||
self.infer_request.infer(self.input_tensor)
|
||||
else:
|
||||
if self.complex_model:
|
||||
try:
|
||||
# This ensures the model starts with a clean state for each sequence
|
||||
# Important for RNN models like PaddleOCR recognition
|
||||
self.infer_request.reset_state()
|
||||
except Exception:
|
||||
# this will raise an exception for models with AUTO set as the device
|
||||
pass
|
||||
|
||||
# Multiple inputs case - set each input by name
|
||||
for input_name, input_data in inputs.items():
|
||||
# Find the input by name and its index
|
||||
input_port = None
|
||||
input_index = None
|
||||
for idx, port in enumerate(self.compiled_model.inputs):
|
||||
if port.get_any_name() == input_name:
|
||||
input_port = port
|
||||
input_index = idx
|
||||
break
|
||||
|
||||
if input_port is None:
|
||||
raise ValueError(f"Input '{input_name}' not found in model")
|
||||
|
||||
# Create tensor with the correct element type
|
||||
input_element_type = input_port.get_element_type()
|
||||
|
||||
# Ensure input data matches the expected dtype to prevent type mismatches
|
||||
# that can occur with models like Jina-CLIP v2 running on OpenVINO
|
||||
expected_dtype = input_element_type.to_dtype()
|
||||
if input_data.dtype != expected_dtype:
|
||||
logger.debug(
|
||||
f"Converting input '{input_name}' from {input_data.dtype} to {expected_dtype}"
|
||||
)
|
||||
input_data = input_data.astype(expected_dtype)
|
||||
|
||||
input_tensor = ov.Tensor(input_element_type, input_data.shape)
|
||||
np.copyto(input_tensor.data, input_data)
|
||||
|
||||
# Set the input tensor for the specific port index
|
||||
self.infer_request.set_input_tensor(input_index, input_tensor)
|
||||
|
||||
# Run inference
|
||||
# Handle single input case for backward compatibility
|
||||
if (
|
||||
len(inputs) == 1
|
||||
and len(self.compiled_model.inputs) == 1
|
||||
and self.input_tensor is not None
|
||||
):
|
||||
# Single input case - use the pre-allocated tensor for efficiency
|
||||
input_data = list(inputs.values())[0]
|
||||
np.copyto(self.input_tensor.data, input_data)
|
||||
self.infer_request.infer(self.input_tensor)
|
||||
else:
|
||||
if self.complex_model:
|
||||
try:
|
||||
self.infer_request.infer()
|
||||
except Exception as e:
|
||||
logger.error(f"Error during OpenVINO inference: {e}")
|
||||
return []
|
||||
# This ensures the model starts with a clean state for each sequence
|
||||
# Important for RNN models like PaddleOCR recognition
|
||||
self.infer_request.reset_state()
|
||||
except Exception:
|
||||
# this will raise an exception for models with AUTO set as the device
|
||||
pass
|
||||
|
||||
# Get all output tensors
|
||||
outputs = []
|
||||
for i in range(len(self.compiled_model.outputs)):
|
||||
outputs.append(self.infer_request.get_output_tensor(i).data)
|
||||
# Multiple inputs case - set each input by name
|
||||
for input_name, input_data in inputs.items():
|
||||
# Find the input by name
|
||||
input_port = None
|
||||
for port in self.compiled_model.inputs:
|
||||
if port.get_any_name() == input_name:
|
||||
input_port = port
|
||||
break
|
||||
|
||||
return outputs
|
||||
if input_port is None:
|
||||
raise ValueError(f"Input '{input_name}' not found in model")
|
||||
|
||||
# Create tensor with the correct element type
|
||||
input_element_type = input_port.get_element_type()
|
||||
input_tensor = ov.Tensor(input_element_type, input_data.shape)
|
||||
np.copyto(input_tensor.data, input_data)
|
||||
|
||||
# Set the input tensor
|
||||
self.infer_request.set_input_tensor(input_tensor)
|
||||
|
||||
# Run inference
|
||||
self.infer_request.infer()
|
||||
|
||||
# Get all output tensors
|
||||
outputs = []
|
||||
for i in range(len(self.compiled_model.outputs)):
|
||||
outputs.append(self.infer_request.get_output_tensor(i).data)
|
||||
|
||||
return outputs
|
||||
|
||||
|
||||
class RKNNModelRunner(BaseModelRunner):
|
||||
@ -541,7 +466,7 @@ def get_optimized_runner(
|
||||
return OpenVINOModelRunner(model_path, device, model_type, **kwargs)
|
||||
|
||||
if (
|
||||
CudaGraphRunner.is_model_supported(model_type)
|
||||
not CudaGraphRunner.is_complex_model(model_type)
|
||||
and providers[0] == "CUDAExecutionProvider"
|
||||
):
|
||||
options[0] = {
|
||||
@ -569,9 +494,7 @@ def get_optimized_runner(
|
||||
return ONNXModelRunner(
|
||||
ort.InferenceSession(
|
||||
model_path,
|
||||
sess_options=get_ort_session_options(
|
||||
ONNXModelRunner.is_cpu_complex_model(model_type)
|
||||
),
|
||||
sess_options=get_ort_session_options(),
|
||||
providers=providers,
|
||||
provider_options=options,
|
||||
)
|
||||
|
||||
@ -2,6 +2,7 @@ import glob
|
||||
import logging
|
||||
import os
|
||||
import shutil
|
||||
import time
|
||||
import urllib.request
|
||||
import zipfile
|
||||
from queue import Queue
|
||||
@ -16,7 +17,7 @@ from frigate.detectors.detector_config import (
|
||||
BaseDetectorConfig,
|
||||
ModelTypeEnum,
|
||||
)
|
||||
from frigate.util.file import FileLock
|
||||
from frigate.util.model import post_process_yolo
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@ -54,9 +55,6 @@ class MemryXDetector(DetectionApi):
|
||||
)
|
||||
return
|
||||
|
||||
# Initialize stop_event as None, will be set later by set_stop_event()
|
||||
self.stop_event = None
|
||||
|
||||
model_cfg = getattr(detector_config, "model", None)
|
||||
|
||||
# Check if model_type was explicitly set by the user
|
||||
@ -179,14 +177,44 @@ class MemryXDetector(DetectionApi):
|
||||
logger.error(f"Failed to initialize MemryX model: {e}")
|
||||
raise
|
||||
|
||||
def _acquire_file_lock(self, lock_path: str, timeout: int = 60, poll: float = 0.2):
|
||||
"""
|
||||
Create an exclusive lock file. Blocks (with polling) until it can acquire,
|
||||
or raises TimeoutError. Uses only stdlib (os.O_EXCL).
|
||||
"""
|
||||
start = time.time()
|
||||
while True:
|
||||
try:
|
||||
fd = os.open(lock_path, os.O_CREAT | os.O_EXCL | os.O_RDWR)
|
||||
os.close(fd)
|
||||
return
|
||||
except FileExistsError:
|
||||
if time.time() - start > timeout:
|
||||
raise TimeoutError(f"Timeout waiting for lock: {lock_path}")
|
||||
time.sleep(poll)
|
||||
|
||||
def _release_file_lock(self, lock_path: str):
|
||||
"""Best-effort removal of the lock file."""
|
||||
try:
|
||||
os.remove(lock_path)
|
||||
except FileNotFoundError:
|
||||
pass
|
||||
|
||||
def load_yolo_constants(self):
|
||||
base = f"{self.cache_dir}/{self.model_folder}"
|
||||
# constants for yolov9 post-processing
|
||||
self.const_A = np.load(f"{base}/_model_22_Constant_9_output_0.npy")
|
||||
self.const_B = np.load(f"{base}/_model_22_Constant_10_output_0.npy")
|
||||
self.const_C = np.load(f"{base}/_model_22_Constant_12_output_0.npy")
|
||||
|
||||
def check_and_prepare_model(self):
|
||||
if not os.path.exists(self.cache_dir):
|
||||
os.makedirs(self.cache_dir, exist_ok=True)
|
||||
|
||||
lock_path = os.path.join(self.cache_dir, f".{self.model_folder}.lock")
|
||||
lock = FileLock(lock_path, timeout=60)
|
||||
self._acquire_file_lock(lock_path)
|
||||
|
||||
with lock:
|
||||
try:
|
||||
# ---------- CASE 1: user provided a custom model path ----------
|
||||
if self.memx_model_path:
|
||||
if not self.memx_model_path.endswith(".zip"):
|
||||
@ -230,6 +258,7 @@ class MemryXDetector(DetectionApi):
|
||||
|
||||
# Handle post model requirements by model type
|
||||
if self.memx_model_type in [
|
||||
ModelTypeEnum.yologeneric,
|
||||
ModelTypeEnum.yolonas,
|
||||
ModelTypeEnum.ssd,
|
||||
]:
|
||||
@ -238,10 +267,7 @@ class MemryXDetector(DetectionApi):
|
||||
f"No *_post.onnx file found in custom model zip for {self.memx_model_type.name}."
|
||||
)
|
||||
self.memx_post_model = post_candidates[0]
|
||||
elif self.memx_model_type in [
|
||||
ModelTypeEnum.yolox,
|
||||
ModelTypeEnum.yologeneric,
|
||||
]:
|
||||
elif self.memx_model_type == ModelTypeEnum.yolox:
|
||||
# Explicitly ignore any post model even if present
|
||||
self.memx_post_model = None
|
||||
else:
|
||||
@ -269,6 +295,8 @@ class MemryXDetector(DetectionApi):
|
||||
logger.info("Using cached models.")
|
||||
self.memx_model_path = dfp_path
|
||||
self.memx_post_model = post_path
|
||||
if self.memx_model_type == ModelTypeEnum.yologeneric:
|
||||
self.load_yolo_constants()
|
||||
return
|
||||
|
||||
# ---------- CASE 3: download MemryX model (no cache) ----------
|
||||
@ -297,6 +325,9 @@ class MemryXDetector(DetectionApi):
|
||||
else None
|
||||
)
|
||||
|
||||
if self.memx_model_type == ModelTypeEnum.yologeneric:
|
||||
self.load_yolo_constants()
|
||||
|
||||
finally:
|
||||
if os.path.exists(zip_path):
|
||||
try:
|
||||
@ -307,6 +338,9 @@ class MemryXDetector(DetectionApi):
|
||||
f"Failed to remove downloaded zip {zip_path}: {e}"
|
||||
)
|
||||
|
||||
finally:
|
||||
self._release_file_lock(lock_path)
|
||||
|
||||
def send_input(self, connection_id, tensor_input: np.ndarray):
|
||||
"""Pre-process (if needed) and send frame to MemryX input queue"""
|
||||
if tensor_input is None:
|
||||
@ -365,43 +399,26 @@ class MemryXDetector(DetectionApi):
|
||||
def process_input(self):
|
||||
"""Input callback function: wait for frames in the input queue, preprocess, and send to MX3 (return)"""
|
||||
while True:
|
||||
# Check if shutdown is requested
|
||||
if self.stop_event and self.stop_event.is_set():
|
||||
logger.debug("[process_input] Stop event detected, returning None")
|
||||
return None
|
||||
try:
|
||||
# Wait for a frame from the queue with timeout to check stop_event periodically
|
||||
frame = self.capture_queue.get(block=True, timeout=0.5)
|
||||
# Wait for a frame from the queue (blocking call)
|
||||
frame = self.capture_queue.get(
|
||||
block=True
|
||||
) # Blocks until data is available
|
||||
|
||||
return frame
|
||||
|
||||
except Exception as e:
|
||||
# Silently handle queue.Empty timeouts (expected during normal operation)
|
||||
# Log any other unexpected exceptions
|
||||
if "Empty" not in str(type(e).__name__):
|
||||
logger.warning(f"[process_input] Unexpected error: {e}")
|
||||
# Loop continues and will check stop_event at the top
|
||||
logger.info(f"[process_input] Error processing input: {e}")
|
||||
time.sleep(0.1) # Prevent busy waiting in case of error
|
||||
|
||||
def receive_output(self):
|
||||
"""Retrieve processed results from MemryX output queue + a copy of the original frame"""
|
||||
try:
|
||||
# Get connection ID with timeout
|
||||
connection_id = self.capture_id_queue.get(
|
||||
block=True, timeout=1.0
|
||||
) # Get the corresponding connection ID
|
||||
detections = self.output_queue.get() # Get detections from MemryX
|
||||
connection_id = (
|
||||
self.capture_id_queue.get()
|
||||
) # Get the corresponding connection ID
|
||||
detections = self.output_queue.get() # Get detections from MemryX
|
||||
|
||||
return connection_id, detections
|
||||
|
||||
except Exception as e:
|
||||
# On timeout or stop event, return None
|
||||
if self.stop_event and self.stop_event.is_set():
|
||||
logger.debug("[receive_output] Stop event detected, exiting")
|
||||
# Silently handle queue.Empty timeouts, they're expected during normal operation
|
||||
elif "Empty" not in str(type(e).__name__):
|
||||
logger.warning(f"[receive_output] Error receiving output: {e}")
|
||||
|
||||
return None, None
|
||||
return connection_id, detections
|
||||
|
||||
def post_process_yolonas(self, output):
|
||||
predictions = output[0]
|
||||
@ -608,232 +625,127 @@ class MemryXDetector(DetectionApi):
|
||||
|
||||
self.output_queue.put(final_detections)
|
||||
|
||||
def _generate_anchors(self, sizes=[80, 40, 20]):
|
||||
"""Generate anchor points for YOLOv9 style processing"""
|
||||
yscales = []
|
||||
xscales = []
|
||||
for s in sizes:
|
||||
r = np.arange(s) + 0.5
|
||||
yscales.append(np.repeat(r, s))
|
||||
xscales.append(np.repeat(r[None, ...], s, axis=0).flatten())
|
||||
|
||||
yscales = np.concatenate(yscales)
|
||||
xscales = np.concatenate(xscales)
|
||||
anchors = np.stack([xscales, yscales], axis=1)
|
||||
return anchors
|
||||
|
||||
def _generate_scales(self, sizes=[80, 40, 20]):
|
||||
"""Generate scaling factors for each detection level"""
|
||||
factors = [8, 16, 32]
|
||||
s = np.concatenate([np.ones([int(s * s)]) * f for s, f in zip(sizes, factors)])
|
||||
return s[:, None]
|
||||
|
||||
@staticmethod
|
||||
def _softmax(x: np.ndarray, axis: int) -> np.ndarray:
|
||||
"""Efficient softmax implementation"""
|
||||
x = x - np.max(x, axis=axis, keepdims=True)
|
||||
np.exp(x, out=x)
|
||||
x /= np.sum(x, axis=axis, keepdims=True)
|
||||
return x
|
||||
|
||||
def dfl(self, x: np.ndarray) -> np.ndarray:
|
||||
"""Distribution Focal Loss decoding - YOLOv9 style"""
|
||||
x = x.reshape(-1, 4, 16)
|
||||
weights = np.arange(16, dtype=np.float32)
|
||||
p = self._softmax(x, axis=2)
|
||||
p = p * weights[None, None, :]
|
||||
out = np.sum(p, axis=2, keepdims=False)
|
||||
return out
|
||||
|
||||
def dist2bbox(
|
||||
self, x: np.ndarray, anchors: np.ndarray, scales: np.ndarray
|
||||
def onnx_reshape_with_allowzero(
|
||||
self, data: np.ndarray, shape: np.ndarray, allowzero: int = 0
|
||||
) -> np.ndarray:
|
||||
"""Convert distances to bounding boxes - YOLOv9 style"""
|
||||
lt = x[:, :2]
|
||||
rb = x[:, 2:]
|
||||
shape = shape.astype(int)
|
||||
input_shape = data.shape
|
||||
output_shape = []
|
||||
|
||||
x1y1 = anchors - lt
|
||||
x2y2 = anchors + rb
|
||||
for i, dim in enumerate(shape):
|
||||
if dim == 0 and allowzero == 0:
|
||||
output_shape.append(input_shape[i]) # Copy dimension from input
|
||||
else:
|
||||
output_shape.append(dim)
|
||||
|
||||
wh = x2y2 - x1y1
|
||||
c_xy = (x1y1 + x2y2) / 2
|
||||
# Now let NumPy infer any -1 if needed
|
||||
reshaped = np.reshape(data, output_shape)
|
||||
|
||||
out = np.concatenate([c_xy, wh], axis=1)
|
||||
out = out * scales
|
||||
return out
|
||||
|
||||
def post_process_yolo_optimized(self, outputs):
|
||||
"""
|
||||
Custom YOLOv9 post-processing optimized for MemryX ONNX outputs.
|
||||
Implements DFL decoding, confidence filtering, and NMS in pure NumPy.
|
||||
"""
|
||||
# YOLOv9 outputs: 6 outputs (lbox, lcls, mbox, mcls, sbox, scls)
|
||||
conv_out1, conv_out2, conv_out3, conv_out4, conv_out5, conv_out6 = outputs
|
||||
|
||||
# Determine grid sizes based on input resolution
|
||||
# YOLOv9 uses 3 detection heads with strides [8, 16, 32]
|
||||
# Grid sizes = input_size / stride
|
||||
sizes = [
|
||||
self.memx_model_height
|
||||
// 8, # Large objects (e.g., 80 for 640x640, 40 for 320x320)
|
||||
self.memx_model_height
|
||||
// 16, # Medium objects (e.g., 40 for 640x640, 20 for 320x320)
|
||||
self.memx_model_height
|
||||
// 32, # Small objects (e.g., 20 for 640x640, 10 for 320x320)
|
||||
]
|
||||
|
||||
# Generate anchors and scales if not already done
|
||||
if not hasattr(self, "anchors"):
|
||||
self.anchors = self._generate_anchors(sizes)
|
||||
self.scales = self._generate_scales(sizes)
|
||||
|
||||
# Process outputs in YOLOv9 format: reshape and moveaxis for ONNX format
|
||||
lbox = np.moveaxis(conv_out1, 1, -1) # Large boxes
|
||||
lcls = np.moveaxis(conv_out2, 1, -1) # Large classes
|
||||
mbox = np.moveaxis(conv_out3, 1, -1) # Medium boxes
|
||||
mcls = np.moveaxis(conv_out4, 1, -1) # Medium classes
|
||||
sbox = np.moveaxis(conv_out5, 1, -1) # Small boxes
|
||||
scls = np.moveaxis(conv_out6, 1, -1) # Small classes
|
||||
|
||||
# Determine number of classes dynamically from the class output shape
|
||||
# lcls shape should be (batch, height, width, num_classes)
|
||||
num_classes = lcls.shape[-1]
|
||||
|
||||
# Validate that all class outputs have the same number of classes
|
||||
if not (mcls.shape[-1] == num_classes and scls.shape[-1] == num_classes):
|
||||
raise ValueError(
|
||||
f"Class output shapes mismatch: lcls={lcls.shape}, mcls={mcls.shape}, scls={scls.shape}"
|
||||
)
|
||||
|
||||
# Concatenate boxes and classes
|
||||
boxes = np.concatenate(
|
||||
[
|
||||
lbox.reshape(-1, 64), # 64 is for 4 bbox coords * 16 DFL bins
|
||||
mbox.reshape(-1, 64),
|
||||
sbox.reshape(-1, 64),
|
||||
],
|
||||
axis=0,
|
||||
)
|
||||
|
||||
classes = np.concatenate(
|
||||
[
|
||||
lcls.reshape(-1, num_classes),
|
||||
mcls.reshape(-1, num_classes),
|
||||
scls.reshape(-1, num_classes),
|
||||
],
|
||||
axis=0,
|
||||
)
|
||||
|
||||
# Apply sigmoid to classes
|
||||
classes = self.sigmoid(classes)
|
||||
|
||||
# Apply DFL to box predictions
|
||||
boxes = self.dfl(boxes)
|
||||
|
||||
# YOLOv9 postprocessing with confidence filtering and NMS
|
||||
confidence_thres = 0.4
|
||||
iou_thres = 0.6
|
||||
|
||||
# Find the class with the highest score for each detection
|
||||
max_scores = np.max(classes, axis=1) # Maximum class score for each detection
|
||||
class_ids = np.argmax(classes, axis=1) # Index of the best class
|
||||
|
||||
# Filter out detections with scores below the confidence threshold
|
||||
valid_indices = np.where(max_scores >= confidence_thres)[0]
|
||||
if len(valid_indices) == 0:
|
||||
# Return empty detections array
|
||||
final_detections = np.zeros((20, 6), np.float32)
|
||||
return final_detections
|
||||
|
||||
# Select only valid detections
|
||||
valid_boxes = boxes[valid_indices]
|
||||
valid_class_ids = class_ids[valid_indices]
|
||||
valid_scores = max_scores[valid_indices]
|
||||
|
||||
# Convert distances to actual bounding boxes using anchors and scales
|
||||
valid_boxes = self.dist2bbox(
|
||||
valid_boxes, self.anchors[valid_indices], self.scales[valid_indices]
|
||||
)
|
||||
|
||||
# Convert bounding box coordinates from (x_center, y_center, w, h) to (x_min, y_min, x_max, y_max)
|
||||
x_center, y_center, width, height = (
|
||||
valid_boxes[:, 0],
|
||||
valid_boxes[:, 1],
|
||||
valid_boxes[:, 2],
|
||||
valid_boxes[:, 3],
|
||||
)
|
||||
x_min = x_center - width / 2
|
||||
y_min = y_center - height / 2
|
||||
x_max = x_center + width / 2
|
||||
y_max = y_center + height / 2
|
||||
|
||||
# Convert to format expected by cv2.dnn.NMSBoxes: [x, y, width, height]
|
||||
boxes_for_nms = []
|
||||
scores_for_nms = []
|
||||
|
||||
for i in range(len(valid_indices)):
|
||||
# Ensure coordinates are within bounds and positive
|
||||
x_min_clipped = max(0, x_min[i])
|
||||
y_min_clipped = max(0, y_min[i])
|
||||
x_max_clipped = min(self.memx_model_width, x_max[i])
|
||||
y_max_clipped = min(self.memx_model_height, y_max[i])
|
||||
|
||||
width_clipped = x_max_clipped - x_min_clipped
|
||||
height_clipped = y_max_clipped - y_min_clipped
|
||||
|
||||
if width_clipped > 0 and height_clipped > 0:
|
||||
boxes_for_nms.append(
|
||||
[x_min_clipped, y_min_clipped, width_clipped, height_clipped]
|
||||
)
|
||||
scores_for_nms.append(float(valid_scores[i]))
|
||||
|
||||
final_detections = np.zeros((20, 6), np.float32)
|
||||
|
||||
if len(boxes_for_nms) == 0:
|
||||
return final_detections
|
||||
|
||||
# Apply NMS using OpenCV
|
||||
indices = cv2.dnn.NMSBoxes(
|
||||
boxes_for_nms, scores_for_nms, confidence_thres, iou_thres
|
||||
)
|
||||
|
||||
if len(indices) > 0:
|
||||
# Flatten indices if they are returned as a list of arrays
|
||||
if isinstance(indices[0], list) or isinstance(indices[0], np.ndarray):
|
||||
indices = [i[0] for i in indices]
|
||||
|
||||
# Limit to top 20 detections
|
||||
indices = indices[:20]
|
||||
|
||||
# Convert to Frigate format: [class_id, confidence, y_min, x_min, y_max, x_max] (normalized)
|
||||
for i, idx in enumerate(indices):
|
||||
class_id = valid_class_ids[idx]
|
||||
confidence = valid_scores[idx]
|
||||
|
||||
# Get the box coordinates
|
||||
box = boxes_for_nms[idx]
|
||||
x_min_norm = box[0] / self.memx_model_width
|
||||
y_min_norm = box[1] / self.memx_model_height
|
||||
x_max_norm = (box[0] + box[2]) / self.memx_model_width
|
||||
y_max_norm = (box[1] + box[3]) / self.memx_model_height
|
||||
|
||||
final_detections[i] = [
|
||||
class_id,
|
||||
confidence,
|
||||
y_min_norm, # Frigate expects y_min first
|
||||
x_min_norm,
|
||||
y_max_norm,
|
||||
x_max_norm,
|
||||
]
|
||||
|
||||
return final_detections
|
||||
return reshaped
|
||||
|
||||
def process_output(self, *outputs):
|
||||
"""Output callback function -- receives frames from the MX3 and triggers post-processing"""
|
||||
if self.memx_model_type == ModelTypeEnum.yologeneric:
|
||||
# Use complete YOLOv9-style postprocessing (includes NMS)
|
||||
final_detections = self.post_process_yolo_optimized(outputs)
|
||||
if not self.memx_post_model:
|
||||
conv_out1 = outputs[0]
|
||||
conv_out2 = outputs[1]
|
||||
conv_out3 = outputs[2]
|
||||
conv_out4 = outputs[3]
|
||||
conv_out5 = outputs[4]
|
||||
conv_out6 = outputs[5]
|
||||
|
||||
concat_1 = self.onnx_concat([conv_out1, conv_out2], axis=1)
|
||||
concat_2 = self.onnx_concat([conv_out3, conv_out4], axis=1)
|
||||
concat_3 = self.onnx_concat([conv_out5, conv_out6], axis=1)
|
||||
|
||||
shape = np.array([1, 144, -1], dtype=np.int64)
|
||||
|
||||
reshaped_1 = self.onnx_reshape_with_allowzero(
|
||||
concat_1, shape, allowzero=0
|
||||
)
|
||||
reshaped_2 = self.onnx_reshape_with_allowzero(
|
||||
concat_2, shape, allowzero=0
|
||||
)
|
||||
reshaped_3 = self.onnx_reshape_with_allowzero(
|
||||
concat_3, shape, allowzero=0
|
||||
)
|
||||
|
||||
concat_4 = self.onnx_concat([reshaped_1, reshaped_2, reshaped_3], 2)
|
||||
|
||||
axis = 1
|
||||
split_sizes = [64, 80]
|
||||
|
||||
# Calculate indices at which to split
|
||||
indices = np.cumsum(split_sizes)[
|
||||
:-1
|
||||
] # [64] — split before the second chunk
|
||||
|
||||
# Perform split along axis 1
|
||||
split_0, split_1 = np.split(concat_4, indices, axis=axis)
|
||||
|
||||
num_boxes = 2100 if self.memx_model_height == 320 else 8400
|
||||
shape1 = np.array([1, 4, 16, num_boxes])
|
||||
reshape_4 = self.onnx_reshape_with_allowzero(
|
||||
split_0, shape1, allowzero=0
|
||||
)
|
||||
|
||||
transpose_1 = reshape_4.transpose(0, 2, 1, 3)
|
||||
|
||||
axis = 1 # As per ONNX softmax node
|
||||
|
||||
# Subtract max for numerical stability
|
||||
x_max = np.max(transpose_1, axis=axis, keepdims=True)
|
||||
x_exp = np.exp(transpose_1 - x_max)
|
||||
x_sum = np.sum(x_exp, axis=axis, keepdims=True)
|
||||
softmax_output = x_exp / x_sum
|
||||
|
||||
# Weight W from the ONNX initializer (1, 16, 1, 1) with values 0 to 15
|
||||
W = np.arange(16, dtype=np.float32).reshape(
|
||||
1, 16, 1, 1
|
||||
) # (1, 16, 1, 1)
|
||||
|
||||
# Apply 1x1 convolution: this is a weighted sum over channels
|
||||
conv_output = np.sum(
|
||||
softmax_output * W, axis=1, keepdims=True
|
||||
) # shape: (1, 1, 4, 8400)
|
||||
|
||||
shape2 = np.array([1, 4, num_boxes])
|
||||
reshape_5 = self.onnx_reshape_with_allowzero(
|
||||
conv_output, shape2, allowzero=0
|
||||
)
|
||||
|
||||
# ONNX Slice — get first 2 channels: [0:2] along axis 1
|
||||
slice_output1 = reshape_5[:, 0:2, :] # Result: (1, 2, 8400)
|
||||
|
||||
# Slice channels 2 to 4 → axis = 1
|
||||
slice_output2 = reshape_5[:, 2:4, :]
|
||||
|
||||
# Perform Subtraction
|
||||
sub_output = self.const_A - slice_output1 # Equivalent to ONNX Sub
|
||||
|
||||
# Perform the ONNX-style Add
|
||||
add_output = self.const_B + slice_output2
|
||||
|
||||
sub1 = add_output - sub_output
|
||||
|
||||
add1 = sub_output + add_output
|
||||
|
||||
div_output = add1 / 2.0
|
||||
|
||||
concat_5 = self.onnx_concat([div_output, sub1], axis=1)
|
||||
|
||||
# Expand B to (1, 1, 8400) so it can broadcast across axis=1 (4 channels)
|
||||
const_C_expanded = self.const_C[:, np.newaxis, :] # Shape: (1, 1, 8400)
|
||||
|
||||
# Perform ONNX-style element-wise multiplication
|
||||
mul_output = concat_5 * const_C_expanded # Result: (1, 4, 8400)
|
||||
|
||||
sigmoid_output = self.sigmoid(split_1)
|
||||
outputs = self.onnx_concat([mul_output, sigmoid_output], axis=1)
|
||||
|
||||
final_detections = post_process_yolo(
|
||||
outputs, self.memx_model_width, self.memx_model_height
|
||||
)
|
||||
self.output_queue.put(final_detections)
|
||||
|
||||
elif self.memx_model_type == ModelTypeEnum.yolonas:
|
||||
@ -850,19 +762,6 @@ class MemryXDetector(DetectionApi):
|
||||
f"{self.memx_model_type} is currently not supported for memryx. See the docs for more info on supported models."
|
||||
)
|
||||
|
||||
def set_stop_event(self, stop_event):
|
||||
"""Set the stop event for graceful shutdown."""
|
||||
self.stop_event = stop_event
|
||||
|
||||
def shutdown(self):
|
||||
"""Gracefully shutdown the MemryX accelerator"""
|
||||
try:
|
||||
if hasattr(self, "accl") and self.accl is not None:
|
||||
self.accl.shutdown()
|
||||
logger.info("MemryX accelerator shutdown complete")
|
||||
except Exception as e:
|
||||
logger.error(f"Error during MemryX shutdown: {e}")
|
||||
|
||||
def detect_raw(self, tensor_input: np.ndarray):
|
||||
"""Removed synchronous detect_raw() function so that we only use async"""
|
||||
return 0
|
||||
|
||||
@ -29,7 +29,7 @@ from frigate.db.sqlitevecq import SqliteVecQueueDatabase
|
||||
from frigate.models import Event, Trigger
|
||||
from frigate.types import ModelStatusTypesEnum
|
||||
from frigate.util.builtin import EventsPerSecond, InferenceSpeed, serialize
|
||||
from frigate.util.file import get_event_thumbnail_bytes
|
||||
from frigate.util.path import get_event_thumbnail_bytes
|
||||
|
||||
from .onnx.jina_v1_embedding import JinaV1ImageEmbedding, JinaV1TextEmbedding
|
||||
from .onnx.jina_v2_embedding import JinaV2Embedding
|
||||
@ -472,7 +472,7 @@ class Embeddings:
|
||||
)
|
||||
thumbnail_missing = True
|
||||
except DoesNotExist:
|
||||
logger.debug(
|
||||
logger.warning(
|
||||
f"Event ID {trigger.data} for trigger {trigger_name} does not exist."
|
||||
)
|
||||
continue
|
||||
|
||||
@ -9,7 +9,6 @@ from typing import Any
|
||||
|
||||
from peewee import DoesNotExist
|
||||
|
||||
from frigate.comms.config_updater import ConfigSubscriber
|
||||
from frigate.comms.detections_updater import DetectionSubscriber, DetectionTypeEnum
|
||||
from frigate.comms.embeddings_updater import (
|
||||
EmbeddingsRequestEnum,
|
||||
@ -62,8 +61,8 @@ from frigate.events.types import EventTypeEnum, RegenerateDescriptionEnum
|
||||
from frigate.genai import get_genai_client
|
||||
from frigate.models import Event, Recordings, ReviewSegment, Trigger
|
||||
from frigate.util.builtin import serialize
|
||||
from frigate.util.file import get_event_thumbnail_bytes
|
||||
from frigate.util.image import SharedMemoryFrameManager
|
||||
from frigate.util.path import get_event_thumbnail_bytes
|
||||
|
||||
from .embeddings import Embeddings
|
||||
|
||||
@ -96,9 +95,6 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
CameraConfigUpdateEnum.semantic_search,
|
||||
],
|
||||
)
|
||||
self.classification_config_subscriber = ConfigSubscriber(
|
||||
"config/classification/custom/"
|
||||
)
|
||||
|
||||
# Configure Frigate DB
|
||||
db = SqliteVecQueueDatabase(
|
||||
@ -158,13 +154,11 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
self.realtime_processors: list[RealTimeProcessorApi] = []
|
||||
|
||||
if self.config.face_recognition.enabled:
|
||||
logger.debug("Face recognition enabled, initializing FaceRealTimeProcessor")
|
||||
self.realtime_processors.append(
|
||||
FaceRealTimeProcessor(
|
||||
self.config, self.requestor, self.event_metadata_publisher, metrics
|
||||
)
|
||||
)
|
||||
logger.debug("FaceRealTimeProcessor initialized successfully")
|
||||
|
||||
if self.config.classification.bird.enabled:
|
||||
self.realtime_processors.append(
|
||||
@ -195,7 +189,6 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
self.config,
|
||||
model_config,
|
||||
self.event_metadata_publisher,
|
||||
self.requestor,
|
||||
self.metrics,
|
||||
)
|
||||
)
|
||||
@ -227,9 +220,7 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
for c in self.config.cameras.values()
|
||||
):
|
||||
self.post_processors.append(
|
||||
AudioTranscriptionPostProcessor(
|
||||
self.config, self.requestor, self.embeddings, metrics
|
||||
)
|
||||
AudioTranscriptionPostProcessor(self.config, self.requestor, metrics)
|
||||
)
|
||||
|
||||
semantic_trigger_processor: SemanticTriggerProcessor | None = None
|
||||
@ -238,7 +229,6 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
db,
|
||||
self.config,
|
||||
self.requestor,
|
||||
self.event_metadata_publisher,
|
||||
metrics,
|
||||
self.embeddings,
|
||||
)
|
||||
@ -265,7 +255,6 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
"""Maintain a SQLite-vec database for semantic search."""
|
||||
while not self.stop_event.is_set():
|
||||
self.config_updater.check_for_updates()
|
||||
self._check_classification_config_updates()
|
||||
self._process_requests()
|
||||
self._process_updates()
|
||||
self._process_recordings_updates()
|
||||
@ -276,7 +265,6 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
self._process_event_metadata()
|
||||
|
||||
self.config_updater.stop()
|
||||
self.classification_config_subscriber.stop()
|
||||
self.event_subscriber.stop()
|
||||
self.event_end_subscriber.stop()
|
||||
self.recordings_subscriber.stop()
|
||||
@ -287,68 +275,6 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
self.requestor.stop()
|
||||
logger.info("Exiting embeddings maintenance...")
|
||||
|
||||
def _check_classification_config_updates(self) -> None:
|
||||
"""Check for classification config updates and add/remove processors."""
|
||||
topic, model_config = self.classification_config_subscriber.check_for_update()
|
||||
|
||||
if topic:
|
||||
model_name = topic.split("/")[-1]
|
||||
|
||||
if model_config is None:
|
||||
self.realtime_processors = [
|
||||
processor
|
||||
for processor in self.realtime_processors
|
||||
if not (
|
||||
isinstance(
|
||||
processor,
|
||||
(
|
||||
CustomStateClassificationProcessor,
|
||||
CustomObjectClassificationProcessor,
|
||||
),
|
||||
)
|
||||
and processor.model_config.name == model_name
|
||||
)
|
||||
]
|
||||
|
||||
logger.info(
|
||||
f"Successfully removed classification processor for model: {model_name}"
|
||||
)
|
||||
else:
|
||||
self.config.classification.custom[model_name] = model_config
|
||||
|
||||
# Check if processor already exists
|
||||
for processor in self.realtime_processors:
|
||||
if isinstance(
|
||||
processor,
|
||||
(
|
||||
CustomStateClassificationProcessor,
|
||||
CustomObjectClassificationProcessor,
|
||||
),
|
||||
):
|
||||
if processor.model_config.name == model_name:
|
||||
logger.debug(
|
||||
f"Classification processor for model {model_name} already exists, skipping"
|
||||
)
|
||||
return
|
||||
|
||||
if model_config.state_config is not None:
|
||||
processor = CustomStateClassificationProcessor(
|
||||
self.config, model_config, self.requestor, self.metrics
|
||||
)
|
||||
else:
|
||||
processor = CustomObjectClassificationProcessor(
|
||||
self.config,
|
||||
model_config,
|
||||
self.event_metadata_publisher,
|
||||
self.requestor,
|
||||
self.metrics,
|
||||
)
|
||||
|
||||
self.realtime_processors.append(processor)
|
||||
logger.info(
|
||||
f"Added classification processor for model: {model_name} (type: {type(processor).__name__})"
|
||||
)
|
||||
|
||||
def _process_requests(self) -> None:
|
||||
"""Process embeddings requests"""
|
||||
|
||||
@ -401,14 +327,7 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
|
||||
source_type, _, camera, frame_name, data = update
|
||||
|
||||
logger.debug(
|
||||
f"Received update - source_type: {source_type}, camera: {camera}, data label: {data.get('label') if data else 'None'}"
|
||||
)
|
||||
|
||||
if not camera or source_type != EventTypeEnum.tracked_object:
|
||||
logger.debug(
|
||||
f"Skipping update - camera: {camera}, source_type: {source_type}"
|
||||
)
|
||||
return
|
||||
|
||||
if self.config.semantic_search.enabled:
|
||||
@ -418,9 +337,6 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
|
||||
# no need to process updated objects if no processors are active
|
||||
if len(self.realtime_processors) == 0 and len(self.post_processors) == 0:
|
||||
logger.debug(
|
||||
f"No processors active - realtime: {len(self.realtime_processors)}, post: {len(self.post_processors)}"
|
||||
)
|
||||
return
|
||||
|
||||
# Create our own thumbnail based on the bounding box and the frame time
|
||||
@ -429,7 +345,6 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
frame_name, camera_config.frame_shape_yuv
|
||||
)
|
||||
except FileNotFoundError:
|
||||
logger.debug(f"Frame {frame_name} not found for camera {camera}")
|
||||
pass
|
||||
|
||||
if yuv_frame is None:
|
||||
@ -438,11 +353,7 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
)
|
||||
return
|
||||
|
||||
logger.debug(
|
||||
f"Processing {len(self.realtime_processors)} realtime processors for object {data.get('id')} (label: {data.get('label')})"
|
||||
)
|
||||
for processor in self.realtime_processors:
|
||||
logger.debug(f"Calling process_frame on {processor.__class__.__name__}")
|
||||
processor.process_frame(data, yuv_frame)
|
||||
|
||||
for processor in self.post_processors:
|
||||
|
||||
@ -12,7 +12,7 @@ from frigate.config import FrigateConfig
|
||||
from frigate.const import CLIPS_DIR
|
||||
from frigate.db.sqlitevecq import SqliteVecQueueDatabase
|
||||
from frigate.models import Event, Timeline
|
||||
from frigate.util.file import delete_event_snapshot, delete_event_thumbnail
|
||||
from frigate.util.path import delete_event_snapshot, delete_event_thumbnail
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@ -150,10 +150,10 @@ PRESETS_HW_ACCEL_SCALE["preset-rk-h265"] = PRESETS_HW_ACCEL_SCALE[FFMPEG_HWACCEL
|
||||
PRESETS_HW_ACCEL_ENCODE_BIRDSEYE = {
|
||||
"preset-rpi-64-h264": "{0} -hide_banner {1} -c:v h264_v4l2m2m {2}",
|
||||
"preset-rpi-64-h265": "{0} -hide_banner {1} -c:v hevc_v4l2m2m {2}",
|
||||
FFMPEG_HWACCEL_VAAPI: "{0} -hide_banner -hwaccel vaapi -hwaccel_output_format vaapi -hwaccel_device {3} {1} -c:v h264_vaapi -g 50 -bf 0 -profile:v high -level:v 4.1 -sei:v 0 -an -vf format=vaapi|nv12,hwupload {2}",
|
||||
FFMPEG_HWACCEL_VAAPI: "{0} -hide_banner -hwaccel vaapi -hwaccel_output_format vaapi {3} {1} -c:v h264_vaapi -g 50 -bf 0 -profile:v high -level:v 4.1 -sei:v 0 -an -vf format=vaapi|nv12,hwupload {2}",
|
||||
"preset-intel-qsv-h264": "{0} -hide_banner {1} -c:v h264_qsv -g 50 -bf 0 -profile:v high -level:v 4.1 -async_depth:v 1 {2}",
|
||||
"preset-intel-qsv-h265": "{0} -hide_banner {1} -c:v h264_qsv -g 50 -bf 0 -profile:v main -level:v 4.1 -async_depth:v 1 {2}",
|
||||
FFMPEG_HWACCEL_NVIDIA: "{0} -hide_banner {1} -hwaccel device {3} -c:v h264_nvenc -g 50 -profile:v high -level:v auto -preset:v p2 -tune:v ll {2}",
|
||||
FFMPEG_HWACCEL_NVIDIA: "{0} -hide_banner {1} {3} -c:v h264_nvenc -g 50 -profile:v high -level:v auto -preset:v p2 -tune:v ll {2}",
|
||||
"preset-jetson-h264": "{0} -hide_banner {1} -c:v h264_nvmpi -profile high {2}",
|
||||
"preset-jetson-h265": "{0} -hide_banner {1} -c:v h264_nvmpi -profile main {2}",
|
||||
FFMPEG_HWACCEL_RKMPP: "{0} -hide_banner {1} -c:v h264_rkmpp -profile:v high {2}",
|
||||
@ -246,7 +246,7 @@ def parse_preset_hardware_acceleration_scale(
|
||||
",hwdownload,format=nv12,eq=gamma=1.4:gamma_weight=0.5" in scale
|
||||
and os.environ.get("FFMPEG_DISABLE_GAMMA_EQUALIZER") is not None
|
||||
):
|
||||
scale = scale.replace(
|
||||
scale.replace(
|
||||
",hwdownload,format=nv12,eq=gamma=1.4:gamma_weight=0.5",
|
||||
":format=nv12,hwdownload,format=nv12,format=yuv420p",
|
||||
)
|
||||
|
||||
@ -51,7 +51,8 @@ class GenAIClient:
|
||||
def get_concern_prompt() -> str:
|
||||
if concerns:
|
||||
concern_list = "\n - ".join(concerns)
|
||||
return f"""- `other_concerns` (list of strings): Include a list of any of the following concerns that are occurring:
|
||||
return f"""
|
||||
- `other_concerns` (list of strings): Include a list of any of the following concerns that are occurring:
|
||||
- {concern_list}"""
|
||||
else:
|
||||
return ""
|
||||
@ -62,68 +63,58 @@ class GenAIClient:
|
||||
else:
|
||||
return ""
|
||||
|
||||
def get_objects_list() -> str:
|
||||
if review_data["unified_objects"]:
|
||||
return "\n- " + "\n- ".join(review_data["unified_objects"])
|
||||
def get_verified_objects() -> str:
|
||||
if review_data["recognized_objects"]:
|
||||
return " - " + "\n - ".join(review_data["recognized_objects"])
|
||||
else:
|
||||
return "\n- (No objects detected)"
|
||||
return " None"
|
||||
|
||||
context_prompt = f"""
|
||||
Your task is to analyze the sequence of images ({len(thumbnails)} total) taken in chronological order from the perspective of the {review_data["camera"]} security camera.
|
||||
|
||||
## Normal Activity Patterns for This Property
|
||||
Please analyze the sequence of images ({len(thumbnails)} total) taken in chronological order from the perspective of the {review_data["camera"].replace("_", " ")} security camera.
|
||||
|
||||
**Normal activity patterns for this property:**
|
||||
{activity_context_prompt}
|
||||
|
||||
## Task Instructions
|
||||
|
||||
Your task is to provide a clear, accurate description of the scene that:
|
||||
1. States exactly what is happening based on observable actions and movements.
|
||||
2. Evaluates the activity against the Normal and Suspicious Activity Indicators above.
|
||||
3. Assigns a potential_threat_level (0, 1, or 2) based on the threat level indicators defined above, applying them consistently.
|
||||
2. Evaluates whether the observable evidence suggests normal activity for this property or genuine security concerns.
|
||||
3. Assigns a potential_threat_level based on the definitions below, applying them consistently.
|
||||
|
||||
**Use the activity patterns above as guidance to calibrate your assessment. Match the activity against both normal and suspicious indicators, then use your judgment based on the complete context.**
|
||||
|
||||
## Analysis Guidelines
|
||||
**IMPORTANT: Start by checking if the activity matches the normal patterns above. If it does, assign Level 0. Only consider higher threat levels if the activity clearly deviates from normal patterns or shows genuine security concerns.**
|
||||
|
||||
When forming your description:
|
||||
- **CRITICAL: Only describe objects explicitly listed in "Objects in Scene" below.** Do not infer or mention additional people, vehicles, or objects not present in this list, even if visual patterns suggest them. If only a car is listed, do not describe a person interacting with it unless "person" is also in the objects list.
|
||||
- **CRITICAL: Only describe objects explicitly listed in "Detected objects" below.** Do not infer or mention additional people, vehicles, or objects not present in the detected objects list, even if visual patterns suggest them. If only a car is detected, do not describe a person interacting with it unless "person" is also in the detected objects list.
|
||||
- **Only describe actions actually visible in the frames.** Do not assume or infer actions that you don't observe happening. If someone walks toward furniture but you never see them sit, do not say they sat. Stick to what you can see across the sequence.
|
||||
- Describe what you observe: actions, movements, interactions with objects and the environment. Include any observable environmental changes (e.g., lighting changes triggered by activity).
|
||||
- Note visible details such as clothing, items being carried or placed, tools or equipment present, and how they interact with the property or objects.
|
||||
- Consider the full sequence chronologically: what happens from start to finish, how duration and actions relate to the location and objects involved.
|
||||
- **Use the actual timestamp provided in "Activity started at"** below for time of day context—do not infer time from image brightness or darkness. Unusual hours (late night/early morning) should increase suspicion when the observable behavior itself appears questionable. However, recognize that some legitimate activities can occur at any hour.
|
||||
- **Consider duration as a primary factor**: Apply the duration thresholds defined in the activity patterns above. Brief sequences during normal hours with apparent purpose typically indicate normal activity unless explicit suspicious actions are visible.
|
||||
- **Weigh all evidence holistically**: Match the activity against the normal and suspicious patterns defined above, then evaluate based on the complete context (zone, objects, time, actions, duration). Apply the threat level indicators consistently. Use your judgment for edge cases.
|
||||
|
||||
## Response Format
|
||||
- Identify patterns that suggest genuine security concerns: testing doors/windows on vehicles or buildings, accessing unauthorized areas, attempting to conceal actions, extended loitering without apparent purpose, taking items, behavior that clearly doesn't align with the zone context and detected objects.
|
||||
- **Weigh all evidence holistically**: Start by checking if the activity matches the normal patterns above. If it does, assign Level 0. Only consider Level 1 if the activity clearly deviates from normal patterns or shows genuine security concerns that warrant attention.
|
||||
|
||||
Your response MUST be a flat JSON object with:
|
||||
- `title` (string): A concise, direct title that describes the primary action or event in the sequence, not just what you literally see. Use spatial context when available to make titles more meaningful. When multiple objects/actions are present, prioritize whichever is most prominent or occurs first. Use names from "Objects in Scene" based on what you visually observe. If you see both a name and an unidentified object of the same type but visually observe only one person/object, use ONLY the name. Examples: "Joe walking dog", "Person taking out trash", "Vehicle arriving in driveway", "Joe accessing vehicle", "Person leaving porch for driveway".
|
||||
- `scene` (string): A narrative description of what happens across the sequence from start to finish, in chronological order. Start by describing how the sequence begins, then describe the progression of events. **Describe all significant movements and actions in the order they occur.** For example, if a vehicle arrives and then a person exits, describe both actions sequentially. **Only describe actions you can actually observe happening in the frames provided.** Do not infer or assume actions that aren't visible (e.g., if you see someone walking but never see them sit, don't say they sat down). Include setting, detected objects, and their observable actions. Avoid speculation or filling in assumed behaviors. Your description should align with and support the threat level you assign.
|
||||
- `title` (string): A concise, one-sentence title that captures the main activity. Include any verified recognized objects (from the "Verified recognized objects" list below) and key detected objects. Examples: "Joe walking dog in backyard", "Unknown person testing car doors at night".
|
||||
- `scene` (string): A narrative description of what happens across the sequence from start to finish. **Only describe actions you can actually observe happening in the frames provided.** Do not infer or assume actions that aren't visible (e.g., if you see someone walking but never see them sit, don't say they sat down). Include setting, detected objects, and their observable actions. Avoid speculation or filling in assumed behaviors. Your description should align with and support the threat level you assign.
|
||||
- `confidence` (float): 0-1 confidence in your analysis. Higher confidence when objects/actions are clearly visible and context is unambiguous. Lower confidence when the sequence is unclear, objects are partially obscured, or context is ambiguous.
|
||||
- `potential_threat_level` (integer): 0, 1, or 2 as defined in "Normal Activity Patterns for This Property" above. Your threat level must be consistent with your scene description and the guidance above.
|
||||
- `potential_threat_level` (integer): 0, 1, or 2 as defined below. Your threat level must be consistent with your scene description and the guidance above.
|
||||
{get_concern_prompt()}
|
||||
|
||||
## Sequence Details
|
||||
Threat-level definitions:
|
||||
- 0 — **Normal activity (DEFAULT)**: What you observe matches the normal activity patterns above or is consistent with expected activity for this property type. The observable evidence—considering zone context, detected objects, and timing together—supports a benign explanation. **Use this level for routine activities even if minor ambiguous elements exist.**
|
||||
- 1 — **Potentially suspicious**: Observable behavior raises genuine security concerns that warrant human review. The evidence doesn't support a routine explanation and clearly deviates from the normal patterns above. Examples: testing doors/windows on vehicles or structures, accessing areas that don't align with the activity, taking items that likely don't belong to them, behavior clearly inconsistent with the zone and context, or activity that lacks any visible legitimate indicators. **Only use this level when the activity clearly doesn't match normal patterns.**
|
||||
- 2 — **Immediate threat**: Clear evidence of forced entry, break-in, vandalism, aggression, weapons, theft in progress, or active property damage.
|
||||
|
||||
Sequence details:
|
||||
- Frame 1 = earliest, Frame {len(thumbnails)} = latest
|
||||
- Activity started at {review_data["start"]} and lasted {review_data["duration"]} seconds
|
||||
- Zones involved: {", ".join(review_data["zones"]) if review_data["zones"] else "None"}
|
||||
- Detected objects: {", ".join(review_data["objects"])}
|
||||
- Verified recognized objects (use these names when describing these objects):
|
||||
{get_verified_objects()}
|
||||
- Zones involved: {", ".join(z.replace("_", " ").title() for z in review_data["zones"]) or "None"}
|
||||
|
||||
## Objects in Scene
|
||||
|
||||
Each line represents a detection state, not necessarily unique individuals. Parentheses indicate object type or category, use only the name/label in your response, not the parentheses.
|
||||
|
||||
**CRITICAL: When you see both recognized and unrecognized entries of the same type (e.g., "Joe (person)" and "Person"), visually count how many distinct people/objects you actually see based on appearance and clothing. If you observe only ONE person throughout the sequence, use ONLY the recognized name (e.g., "Joe"). The same person may be recognized in some frames but not others. Only describe both if you visually see MULTIPLE distinct people with clearly different appearances.**
|
||||
|
||||
**Note: Unidentified objects (without names) are NOT indicators of suspicious activity—they simply mean the system hasn't identified that object.**
|
||||
{get_objects_list()}
|
||||
|
||||
## Important Notes
|
||||
**IMPORTANT:**
|
||||
- Values must be plain strings, floats, or integers — no nested objects, no extra commentary.
|
||||
- Only describe objects from the "Objects in Scene" list above. Do not hallucinate additional objects.
|
||||
- When describing people or vehicles, use the exact names provided.
|
||||
- Only describe objects from the "Detected objects" list above. Do not hallucinate additional objects.
|
||||
{get_language_prompt()}
|
||||
"""
|
||||
logger.debug(
|
||||
@ -158,8 +149,7 @@ Each line represents a detection state, not necessarily unique individuals. Pare
|
||||
try:
|
||||
metadata = ReviewMetadata.model_validate_json(clean_json)
|
||||
|
||||
# If any verified objects (contain parentheses with name), set to 0
|
||||
if any("(" in obj for obj in review_data["unified_objects"]):
|
||||
if review_data["recognized_objects"]:
|
||||
metadata.potential_threat_level = 0
|
||||
|
||||
metadata.time = review_data["start"]
|
||||
@ -205,20 +195,14 @@ Rules for the report:
|
||||
- Group bullets under subheadings when multiple events fall into the same category (e.g., Vehicle Activity, Porch Activity, Unusual Behavior).
|
||||
|
||||
- Threat levels
|
||||
- Always show the threat level for each event using these labels:
|
||||
- Threat level 0: "Normal"
|
||||
- Threat level 1: "Needs review"
|
||||
- Threat level 2: "Security concern"
|
||||
- Format as (threat level: Normal), (threat level: Needs review), or (threat level: Security concern).
|
||||
- Always show (threat level: X) for each event.
|
||||
- If multiple events at the same time share the same threat level, only state it once.
|
||||
|
||||
- Final assessment
|
||||
- End with a Final Assessment section.
|
||||
- If all events are threat level 0:
|
||||
- If all events are threat level 1 with no escalation:
|
||||
Final assessment: Only normal residential activity observed during this period.
|
||||
- If threat level 1 events are present:
|
||||
Final assessment: Some activity requires review but no security concerns identified.
|
||||
- If threat level 2 events are present, clearly summarize them as Security concerns requiring immediate attention.
|
||||
- If threat level 2+ events are present, clearly summarize them as Potential concerns requiring review.
|
||||
|
||||
- Conciseness
|
||||
- Do not repeat benign clothing/appearance details unless they distinguish individuals.
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
"""Ollama Provider for Frigate AI."""
|
||||
|
||||
import logging
|
||||
from typing import Any, Optional
|
||||
from typing import Optional
|
||||
|
||||
from httpx import TimeoutException
|
||||
from ollama import Client as ApiClient
|
||||
@ -17,24 +17,10 @@ logger = logging.getLogger(__name__)
|
||||
class OllamaClient(GenAIClient):
|
||||
"""Generative AI client for Frigate using Ollama."""
|
||||
|
||||
LOCAL_OPTIMIZED_OPTIONS = {
|
||||
"options": {
|
||||
"temperature": 0.5,
|
||||
"repeat_penalty": 1.05,
|
||||
"presence_penalty": 0.3,
|
||||
},
|
||||
}
|
||||
|
||||
provider: ApiClient
|
||||
provider_options: dict[str, Any]
|
||||
|
||||
def _init_provider(self):
|
||||
"""Initialize the client."""
|
||||
self.provider_options = {
|
||||
**self.LOCAL_OPTIMIZED_OPTIONS,
|
||||
**self.genai_config.provider_options,
|
||||
}
|
||||
|
||||
try:
|
||||
client = ApiClient(host=self.genai_config.base_url, timeout=self.timeout)
|
||||
# ensure the model is available locally
|
||||
@ -62,13 +48,10 @@ class OllamaClient(GenAIClient):
|
||||
self.genai_config.model,
|
||||
prompt,
|
||||
images=images if images else None,
|
||||
**self.provider_options,
|
||||
)
|
||||
logger.debug(
|
||||
f"Ollama tokens used: eval_count={result.get('eval_count')}, prompt_eval_count={result.get('prompt_eval_count')}"
|
||||
**self.genai_config.provider_options,
|
||||
)
|
||||
return result["response"].strip()
|
||||
except (TimeoutException, ResponseError, ConnectionError) as e:
|
||||
except (TimeoutException, ResponseError) as e:
|
||||
logger.warning("Ollama returned an error: %s", str(e))
|
||||
return None
|
||||
|
||||
|
||||
@ -18,7 +18,6 @@ class OpenAIClient(GenAIClient):
|
||||
"""Generative AI client for Frigate using OpenAI."""
|
||||
|
||||
provider: OpenAI
|
||||
context_size: Optional[int] = None
|
||||
|
||||
def _init_provider(self):
|
||||
"""Initialize the client."""
|
||||
@ -70,33 +69,5 @@ class OpenAIClient(GenAIClient):
|
||||
|
||||
def get_context_size(self) -> int:
|
||||
"""Get the context window size for OpenAI."""
|
||||
if self.context_size is not None:
|
||||
return self.context_size
|
||||
|
||||
try:
|
||||
models = self.provider.models.list()
|
||||
for model in models.data:
|
||||
if model.id == self.genai_config.model:
|
||||
if hasattr(model, "max_model_len") and model.max_model_len:
|
||||
self.context_size = model.max_model_len
|
||||
logger.debug(
|
||||
f"Retrieved context size {self.context_size} for model {self.genai_config.model}"
|
||||
)
|
||||
return self.context_size
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(
|
||||
f"Failed to fetch model context size from API: {e}, using default"
|
||||
)
|
||||
|
||||
# Default to 128K for ChatGPT models, 8K for others
|
||||
model_name = self.genai_config.model.lower()
|
||||
if "gpt" in model_name:
|
||||
self.context_size = 128000
|
||||
else:
|
||||
self.context_size = 8192
|
||||
|
||||
logger.debug(
|
||||
f"Using default context size {self.context_size} for model {self.genai_config.model}"
|
||||
)
|
||||
return self.context_size
|
||||
# OpenAI GPT-4 Vision models have 128K token context window
|
||||
return 128000
|
||||
|
||||
@ -9,7 +9,6 @@ from multiprocessing import Queue, Value
|
||||
from multiprocessing.synchronize import Event as MpEvent
|
||||
|
||||
import numpy as np
|
||||
import zmq
|
||||
|
||||
from frigate.comms.object_detector_signaler import (
|
||||
ObjectDetectorPublisher,
|
||||
@ -43,7 +42,6 @@ class BaseLocalDetector(ObjectDetector):
|
||||
self,
|
||||
detector_config: BaseDetectorConfig = None,
|
||||
labels: str = None,
|
||||
stop_event: MpEvent = None,
|
||||
):
|
||||
self.fps = EventsPerSecond()
|
||||
if labels is None:
|
||||
@ -61,10 +59,6 @@ class BaseLocalDetector(ObjectDetector):
|
||||
|
||||
self.detect_api = create_detector(detector_config)
|
||||
|
||||
# If the detector supports stop_event, pass it
|
||||
if hasattr(self.detect_api, "set_stop_event") and stop_event:
|
||||
self.detect_api.set_stop_event(stop_event)
|
||||
|
||||
def _transform_input(self, tensor_input: np.ndarray) -> np.ndarray:
|
||||
if self.input_transform:
|
||||
tensor_input = np.transpose(tensor_input, self.input_transform)
|
||||
@ -245,10 +239,6 @@ class AsyncDetectorRunner(FrigateProcess):
|
||||
while not self.stop_event.is_set():
|
||||
connection_id, detections = self._detector.async_receive_output()
|
||||
|
||||
# Handle timeout case (queue.Empty) - just continue
|
||||
if connection_id is None:
|
||||
continue
|
||||
|
||||
if not self.send_times:
|
||||
# guard; shouldn't happen if send/recv are balanced
|
||||
continue
|
||||
@ -275,38 +265,21 @@ class AsyncDetectorRunner(FrigateProcess):
|
||||
|
||||
self._frame_manager = SharedMemoryFrameManager()
|
||||
self._publisher = ObjectDetectorPublisher()
|
||||
self._detector = AsyncLocalObjectDetector(
|
||||
detector_config=self.detector_config, stop_event=self.stop_event
|
||||
)
|
||||
self._detector = AsyncLocalObjectDetector(detector_config=self.detector_config)
|
||||
|
||||
for name in self.cameras:
|
||||
self.create_output_shm(name)
|
||||
|
||||
t_detect = threading.Thread(target=self._detect_worker, daemon=False)
|
||||
t_result = threading.Thread(target=self._result_worker, daemon=False)
|
||||
t_detect = threading.Thread(target=self._detect_worker, daemon=True)
|
||||
t_result = threading.Thread(target=self._result_worker, daemon=True)
|
||||
t_detect.start()
|
||||
t_result.start()
|
||||
|
||||
try:
|
||||
while not self.stop_event.is_set():
|
||||
time.sleep(0.5)
|
||||
while not self.stop_event.is_set():
|
||||
time.sleep(0.5)
|
||||
|
||||
logger.info(
|
||||
"Stop event detected, waiting for detector threads to finish..."
|
||||
)
|
||||
|
||||
# Wait for threads to finish processing
|
||||
t_detect.join(timeout=5)
|
||||
t_result.join(timeout=5)
|
||||
|
||||
# Shutdown the AsyncDetector
|
||||
self._detector.detect_api.shutdown()
|
||||
|
||||
self._publisher.stop()
|
||||
except Exception as e:
|
||||
logger.error(f"Error during async detector shutdown: {e}")
|
||||
finally:
|
||||
logger.info("Exited Async detection process...")
|
||||
self._publisher.stop()
|
||||
logger.info("Exited async detection process...")
|
||||
|
||||
|
||||
class ObjectDetectProcess:
|
||||
@ -334,7 +307,7 @@ class ObjectDetectProcess:
|
||||
# if the process has already exited on its own, just return
|
||||
if self.detect_process and self.detect_process.exitcode:
|
||||
return
|
||||
|
||||
self.detect_process.terminate()
|
||||
logging.info("Waiting for detection process to exit gracefully...")
|
||||
self.detect_process.join(timeout=30)
|
||||
if self.detect_process.exitcode is None:
|
||||
@ -404,15 +377,6 @@ class RemoteObjectDetector:
|
||||
if self.stop_event.is_set():
|
||||
return detections
|
||||
|
||||
# Drain any stale detection results from the ZMQ buffer before making a new request
|
||||
# This prevents reading detection results from a previous request
|
||||
# NOTE: This should never happen, but can in some rare cases
|
||||
while True:
|
||||
try:
|
||||
self.detector_subscriber.socket.recv_string(flags=zmq.NOBLOCK)
|
||||
except zmq.Again:
|
||||
break
|
||||
|
||||
# copy input to shared memory
|
||||
self.np_shm[:] = tensor_input[:]
|
||||
self.detection_queue.put(self.name)
|
||||
|
||||
@ -9,7 +9,6 @@ import os
|
||||
import queue
|
||||
import subprocess as sp
|
||||
import threading
|
||||
import time
|
||||
import traceback
|
||||
from typing import Any, Optional
|
||||
|
||||
@ -792,10 +791,6 @@ class Birdseye:
|
||||
self.frame_manager = SharedMemoryFrameManager()
|
||||
self.stop_event = stop_event
|
||||
self.requestor = InterProcessRequestor()
|
||||
self.idle_fps: float = self.config.birdseye.idle_heartbeat_fps
|
||||
self._idle_interval: Optional[float] = (
|
||||
(1.0 / self.idle_fps) if self.idle_fps > 0 else None
|
||||
)
|
||||
|
||||
if config.birdseye.restream:
|
||||
self.birdseye_buffer = self.frame_manager.create(
|
||||
@ -853,15 +848,6 @@ class Birdseye:
|
||||
if frame_layout_changed:
|
||||
coordinates = self.birdseye_manager.get_camera_coordinates()
|
||||
self.requestor.send_data(UPDATE_BIRDSEYE_LAYOUT, coordinates)
|
||||
if self._idle_interval:
|
||||
now = time.monotonic()
|
||||
is_idle = len(self.birdseye_manager.camera_layout) == 0
|
||||
if (
|
||||
is_idle
|
||||
and (now - self.birdseye_manager.last_output_time)
|
||||
>= self._idle_interval
|
||||
):
|
||||
self.__send_new_frame()
|
||||
|
||||
def stop(self) -> None:
|
||||
self.converter.join()
|
||||
|
||||
@ -190,11 +190,7 @@ class OnvifController:
|
||||
ptz: ONVIFService = await onvif.create_ptz_service()
|
||||
self.cams[camera_name]["ptz"] = ptz
|
||||
|
||||
try:
|
||||
imaging: ONVIFService = await onvif.create_imaging_service()
|
||||
except (Fault, ONVIFError, TransportError, Exception) as e:
|
||||
logger.debug(f"Imaging service not supported for {camera_name}: {e}")
|
||||
imaging = None
|
||||
imaging: ONVIFService = await onvif.create_imaging_service()
|
||||
self.cams[camera_name]["imaging"] = imaging
|
||||
try:
|
||||
video_sources = await media.GetVideoSources()
|
||||
@ -385,10 +381,7 @@ class OnvifController:
|
||||
f"Disabling autotracking zooming for {camera_name}: Absolute zoom not supported. Exception: {e}"
|
||||
)
|
||||
|
||||
if (
|
||||
self.cams[camera_name]["video_source_token"] is not None
|
||||
and imaging is not None
|
||||
):
|
||||
if self.cams[camera_name]["video_source_token"] is not None:
|
||||
try:
|
||||
imaging_capabilities = await imaging.GetImagingSettings(
|
||||
{"VideoSourceToken": self.cams[camera_name]["video_source_token"]}
|
||||
@ -428,7 +421,6 @@ class OnvifController:
|
||||
if (
|
||||
"focus" in self.cams[camera_name]["features"]
|
||||
and self.cams[camera_name]["video_source_token"]
|
||||
and self.cams[camera_name]["imaging"] is not None
|
||||
):
|
||||
try:
|
||||
stop_request = self.cams[camera_name]["imaging"].create_type("Stop")
|
||||
@ -656,7 +648,6 @@ class OnvifController:
|
||||
if (
|
||||
"focus" not in self.cams[camera_name]["features"]
|
||||
or not self.cams[camera_name]["video_source_token"]
|
||||
or self.cams[camera_name]["imaging"] is None
|
||||
):
|
||||
logger.error(f"{camera_name} does not support ONVIF continuous focus.")
|
||||
return
|
||||
|
||||
@ -14,8 +14,7 @@ from frigate.config import CameraConfig, FrigateConfig, RetainModeEnum
|
||||
from frigate.const import CACHE_DIR, CLIPS_DIR, MAX_WAL_SIZE, RECORD_DIR
|
||||
from frigate.models import Previews, Recordings, ReviewSegment, UserReviewStatus
|
||||
from frigate.record.util import remove_empty_directories, sync_recordings
|
||||
from frigate.util.builtin import clear_and_unlink
|
||||
from frigate.util.time import get_tomorrow_at_time
|
||||
from frigate.util.builtin import clear_and_unlink, get_tomorrow_at_time
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@ -28,7 +28,7 @@ from frigate.ffmpeg_presets import (
|
||||
parse_preset_hardware_acceleration_encode,
|
||||
)
|
||||
from frigate.models import Export, Previews, Recordings
|
||||
from frigate.util.time import is_current_hour
|
||||
from frigate.util.builtin import is_current_hour
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@ -407,19 +407,6 @@ class ReviewSegmentMaintainer(threading.Thread):
|
||||
segment.last_detection_time = frame_time
|
||||
|
||||
for object in activity.get_all_objects():
|
||||
# Alert-level objects should always be added (they extend/upgrade the segment)
|
||||
# Detection-level objects should only be added if:
|
||||
# - The segment is a detection segment (matching severity), OR
|
||||
# - The segment is an alert AND the object started before the alert ended
|
||||
# (objects starting after will be in the new detection segment)
|
||||
is_alert_object = object in activity.categorized_objects["alerts"]
|
||||
|
||||
if not is_alert_object and segment.severity == SeverityEnum.alert:
|
||||
# This is a detection-level object
|
||||
# Only add if it started during the alert's active period
|
||||
if object["start_time"] > segment.last_alert_time:
|
||||
continue
|
||||
|
||||
if not object["sub_label"]:
|
||||
segment.detections[object["id"]] = object["label"]
|
||||
elif object["sub_label"][0] in self.config.model.all_attributes:
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue
Block a user