Fix missing space in trt model prepare script (#17661)

Fix link to 2-way audio documentation

.github/actions/setup/action.yml

@@ -33,9 +33,9 @@ runs:
       with:
         string: ${{ github.repository }}
     - name: Set up QEMU
-      uses: docker/setup-qemu-action@v2
+      uses: docker/setup-qemu-action@v3
     - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v2
+      uses: docker/setup-buildx-action@v3
     - name: Log in to the Container registry
       uses: docker/login-action@465a07811f14bebb1938fbed4728c6a1ff8901fc
       with:

.github/workflows/ci.yml

@@ -19,7 +19,7 @@ env:
 
 jobs:
   amd64_build:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
     name: AMD64 Build
     steps:
       - name: Check out code
@@ -42,7 +42,7 @@ jobs:
           tags: ${{ steps.setup.outputs.image-name }}-amd64
           cache-from: type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64
   arm64_build:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
     name: ARM Build
     steps:
       - name: Check out code
@@ -66,8 +66,9 @@ jobs:
             ${{ steps.setup.outputs.image-name }}-standard-arm64
           cache-from: type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64
       - name: Build and push RPi build
-        uses: docker/bake-action@v4
+        uses: docker/bake-action@v6
         with:
+          source: .
           push: true
           targets: rpi
           files: docker/rpi/rpi.hcl
@@ -76,7 +77,7 @@ jobs:
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64,mode=max
   jetson_jp4_build:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
     name: Jetson Jetpack 4
     steps:
       - name: Check out code
@@ -94,8 +95,9 @@ jobs:
           BASE_IMAGE: timongentzsch/l4t-ubuntu20-opencv:latest
           SLIM_BASE: timongentzsch/l4t-ubuntu20-opencv:latest
           TRT_BASE: timongentzsch/l4t-ubuntu20-opencv:latest
-        uses: docker/bake-action@v4
+        uses: docker/bake-action@v6
         with:
+          source: .
           push: true
           targets: tensorrt
           files: docker/tensorrt/trt.hcl
@@ -104,7 +106,7 @@ jobs:
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp4
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp4,mode=max
   jetson_jp5_build:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
     name: Jetson Jetpack 5
     steps:
       - name: Check out code
@@ -122,8 +124,9 @@ jobs:
           BASE_IMAGE: nvcr.io/nvidia/l4t-tensorrt:r8.5.2-runtime
           SLIM_BASE: nvcr.io/nvidia/l4t-tensorrt:r8.5.2-runtime
           TRT_BASE: nvcr.io/nvidia/l4t-tensorrt:r8.5.2-runtime
-        uses: docker/bake-action@v4
+        uses: docker/bake-action@v6
         with:
+          source: .
           push: true
           targets: tensorrt
           files: docker/tensorrt/trt.hcl
@@ -132,7 +135,7 @@ jobs:
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp5
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp5,mode=max
   amd64_extra_builds:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
     name: AMD64 Extra Build
     needs:
       - amd64_build
@@ -149,8 +152,9 @@ jobs:
       - name: Build and push TensorRT (x86 GPU)
         env:
           COMPUTE_LEVEL: "50 60 70 80 90"
-        uses: docker/bake-action@v4
+        uses: docker/bake-action@v6
         with:
+          source: .
           push: true
           targets: tensorrt
           files: docker/tensorrt/trt.hcl
@@ -159,7 +163,7 @@ jobs:
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64,mode=max
   arm64_extra_builds:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
     name: ARM Extra Build
     needs:
       - arm64_build
@@ -174,8 +178,9 @@ jobs:
         with:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       - name: Build and push Rockchip build
-        uses: docker/bake-action@v3
+        uses: docker/bake-action@v6
         with:
+          source: .
           push: true
           targets: rk
           files: docker/rockchip/rk.hcl
@@ -183,7 +188,7 @@ jobs:
             rk.tags=${{ steps.setup.outputs.image-name }}-rk
             *.cache-from=type=gha
   combined_extra_builds:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
     name: Combined Extra Builds
     needs:
       - amd64_build
@@ -199,8 +204,9 @@ jobs:
         with:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       - name: Build and push Hailo-8l build
-        uses: docker/bake-action@v4
+        uses: docker/bake-action@v6
         with:
+          source: .
           push: true
           targets: h8l
           files: docker/hailo8l/h8l.hcl
@@ -212,8 +218,9 @@ jobs:
         env:
           AMDGPU: gfx
           HSA_OVERRIDE: 0
-        uses: docker/bake-action@v3
+        uses: docker/bake-action@v6
         with:
+          source: .
           push: true
           targets: rocm
           files: docker/rocm/rocm.hcl
@@ -223,7 +230,7 @@ jobs:
   # The majority of users running arm64 are rpi users, so the rpi
   # build should be the primary arm64 image
   assemble_default_build:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
     name: Assemble and push default build
     needs:
       - amd64_build

docker/tensorrt/detector/rootfs/etc/s6-overlay/s6-rc.d/trt-model-prepare/run

@@ -20,7 +20,7 @@ FIRST_MODEL=true
 MODEL_DOWNLOAD=""
 MODEL_CONVERT=""
 
-if [ -z "$YOLO_MODELS"]; then
+if [ -z "$YOLO_MODELS" ]; then
     echo "tensorrt model preparation disabled"
     exit 0
 fi

docs/docs/configuration/advanced.md

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

docs/docs/configuration/authentication.md

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

docs/docs/configuration/camera_specific.md

@@ -65,6 +65,18 @@ ffmpeg:
 
 ## Model/vendor specific setup
 
+### Amcrest & Dahua
+
+Amcrest & Dahua cameras should be connected to via RTSP using the following format:
+
+```
+rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=0 # this is the main stream
+rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=1 # this is the sub stream, typically supporting low resolutions only
+rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=2 # higher end cameras support a third stream with a mid resolution (1280x720, 1920x1080)
+rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=3 # new higher end cameras support a fourth stream with another mid resolution (1280x720, 1920x1080)
+
+```
+
 ### Annke C800
 
 This camera is H.265 only. To be able to play clips on some devices (like MacOs or iPhone) the H.265 stream has to be repackaged and the audio stream has to be converted to aac. Unfortunately direct playback of in the browser is not working (yet), but the downloaded clip can be played locally.
@@ -77,7 +89,7 @@ cameras:
         record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v copy -tag:v hvc1 -bsf:v hevc_mp4toannexb -c:a aac
 
       inputs:
-        - path: rtsp://user:password@camera-ip:554/H264/ch1/main/av_stream # <----- Update for your camera
+        - path: rtsp://USERNAME:PASSWORD@CAMERA-IP/H264/ch1/main/av_stream # <----- Update for your camera
           roles:
             - detect
             - record
@@ -95,6 +107,29 @@ ffmpeg:
   input_args: preset-rtsp-blue-iris
 ```
 
+### Hikvision Cameras
+
+Hikvision cameras should be connected to via RTSP using the following format:
+
+```
+rtsp://USERNAME:PASSWORD@CAMERA-IP/streaming/channels/101 # this is the main stream
+rtsp://USERNAME:PASSWORD@CAMERA-IP/streaming/channels/102 # this is the sub stream, typically supporting low resolutions only
+rtsp://USERNAME:PASSWORD@CAMERA-IP/streaming/channels/103 # higher end cameras support a third stream with a mid resolution (1280x720, 1920x1080)
+```
+
+:::note
+
+[Some users have reported](https://www.reddit.com/r/frigate_nvr/comments/1hg4ze7/hikvision_security_settings) that newer Hikvision cameras require adjustments to the security settings:
+
+```
+RTSP Authentication - digest/basic
+RTSP Digest Algorithm - MD5
+WEB Authentication - digest/basic
+WEB Digest Algorithm  - MD5
+```
+
+:::
+
 ### Reolink Cameras
 
 Reolink has older cameras (ex: 410 & 520) as well as newer camera (ex: 520a & 511wa) which support different subsets of options. In both cases using the http stream is recommended.
@@ -196,3 +231,38 @@ ffmpeg:
 ### TP-Link VIGI Cameras
 
 TP-Link VIGI cameras need some adjustments to the main stream settings on the camera itself to avoid issues. The stream needs to be configured as `H264` with `Smart Coding` set to `off`. Without these settings you may have problems when trying to watch recorded footage. For example Firefox will stop playback after a few seconds and show the following error message: `The media playback was aborted due to a corruption problem or because the media used features your browser did not support.`.
+
+## USB Cameras (aka Webcams)
+
+To use a USB camera (webcam) with Frigate, the recommendation is to use go2rtc's [FFmpeg Device](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg-device) support:
+
+- Preparation outside of Frigate:
+  - Get USB camera path. Run `v4l2-ctl --list-devices` to get a listing of locally-connected cameras available. (You may need to install `v4l-utils` in a way appropriate for your Linux distribution). In the sample configuration below, we use `video=0` to correlate with a detected device path of `/dev/video0`
+  - Get USB camera formats & resolutions. Run `ffmpeg -f v4l2 -list_formats all -i /dev/video0` to get an idea of what formats and resolutions the USB Camera supports. In the sample configuration below, we use a width of 1024 and height of 576 in the stream and detection settings based on what was reported back.
+  - If using Frigate in a container (e.g. Docker on TrueNAS), ensure you have USB Passthrough support enabled, along with a specific Host Device (`/dev/video0`) + Container Device (`/dev/video0`) listed.
+
+- In your Frigate Configuration File, add the go2rtc stream and roles as appropriate:
+
+```
+go2rtc:
+  streams:
+    usb_camera:
+      - "ffmpeg:device?video=0&video_size=1024x576#video=h264" 
+
+cameras:
+  usb_camera:
+    enabled: true
+    ffmpeg:
+      inputs:
+        - path: rtsp://127.0.0.1:8554/usb_camera
+          input_args: preset-rtsp-restream
+          roles:
+            - detect
+            - record
+    detect:
+      enabled: false # <---- disable detection until you have a working camera feed
+      width: 1024
+      height: 576
+```
+
+

docs/docs/configuration/cameras.md

@@ -100,6 +100,8 @@ This list of working and non-working PTZ cameras is based on user feedback.
 | Ctronics PTZ                 |      ✅      |      ❌      |                                                                                                                                                 |
 | Dahua                        |      ✅      |      ✅      |                                                                                                                                                 |
 | Dahua DH-SD2A500HB           |      ✅      |      ❌      |                                                                                                                                                 |
+| Dahua DH-SD49825GB-HNR       |      ✅      |      ✅      |                                                                                                                                                 |
+| Dahua DH-P5AE-PV             |      ❌      |      ❌      |                                                                                                                                                 |
 | Foscam R5                    |      ✅      |      ❌      |                                                                                                                                                 |
 | Hanwha XNP-6550RH            |      ✅      |      ❌      |                                                                                                                                                 |
 | Hikvision                    |      ✅      |      ❌      | Incomplete ONVIF support (MoveStatus won't update even on latest firmware) - reported with HWP-N4215IH-DE and DS-2DE3304W-DE, but likely others |

docs/docs/configuration/genai.md

@@ -15,9 +15,9 @@ Semantic Search must be enabled to use Generative AI.
 
 ## Configuration
 
-Generative AI can be enabled for all cameras or only for specific cameras. There are currently 3 providers available to integrate with Frigate.
+Generative AI can be enabled for all cameras or only for specific cameras. There are currently 3 native providers available to integrate with Frigate. Other providers that support the OpenAI standard API can also be used. See the OpenAI section below.
 
-If the provider you choose requires an API key, you may either directly paste it in your configuration, or store it in an environment variable prefixed with `FRIGATE_`.
+To use Generative AI, you must define a single provider at the global level of your Frigate configuration. If the provider you choose requires an API key, you may either directly paste it in your configuration, or store it in an environment variable prefixed with `FRIGATE_`.
 
 ```yaml
 genai:
@@ -27,12 +27,23 @@ genai:
   model: gemini-1.5-flash
 
 cameras:
-  front_camera: ...
+  front_camera: 
+    genai:
+      enabled: True # <- enable GenAI for your front camera
+      use_snapshot: True
+      objects:
+        - person
+      required_zones:
+        - steps
   indoor_camera:
-    genai: # <- disable GenAI for your indoor camera
-      enabled: False
+    genai: 
+      enabled: False # <- disable GenAI for your indoor camera
 ```
 
+By default, descriptions will be generated for all tracked objects and all zones. But you can also optionally specify `objects` and `required_zones` to only generate descriptions for certain tracked objects or zones.
+
+Optionally, you can generate the description using a snapshot (if enabled) by setting `use_snapshot` to `True`. By default, this is set to `False`, which sends the uncompressed images from the `detect` stream collected over the object's lifetime to the model. Once the object lifecycle ends, only a single compressed and cropped thumbnail is saved with the tracked object. Using a snapshot might be useful when you want to _regenerate_ a tracked object's description as it will provide the AI with a higher-quality image (typically downscaled by the AI itself) than the cropped/compressed thumbnail. Using a snapshot otherwise has a trade-off in that only a single image is sent to your provider, which will limit the model's ability to determine object movement or direction.
+
 ## Ollama
 
 :::warning
@@ -116,6 +127,12 @@ genai:
   model: gpt-4o
 ```
 
+:::note
+
+To use a different OpenAI-compatible API endpoint, set the `OPENAI_BASE_URL` environment variable to your provider's API URL.
+
+:::
+
 ## Azure OpenAI
 
 Microsoft offers several vision models through Azure OpenAI. A subscription is required.
@@ -176,9 +193,7 @@ genai:
     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 overriden at the camera level to provide a more detailed prompt to the model about your specific camera, if you desire. By default, descriptions will be generated for all tracked objects and all zones. But you can also optionally specify `objects` and `required_zones` to only generate descriptions for certain tracked objects or zones.
-
-Optionally, you can generate the description using a snapshot (if enabled) by setting `use_snapshot` to `True`. By default, this is set to `False`, which sends the uncompressed images from the `detect` stream collected over the object's lifetime to the model. Once the object lifecycle ends, only a single compressed and cropped thumbnail is saved with the tracked object. Using a snapshot might be useful when you want to _regenerate_ a tracked object's description as it will provide the AI with a higher-quality image (typically downscaled by the AI itself) than the cropped/compressed thumbnail. Using a snapshot otherwise has a trade-off in that only a single image is sent to your provider, which will limit the model's ability to determine object movement or direction.
+Prompts can also be overriden at the camera level to provide a more detailed prompt to the model about your specific camera, if you desire. 
 
 ```yaml
 cameras:

docs/docs/configuration/live.md

@@ -145,6 +145,6 @@ For devices that support two way talk, Frigate can be configured to use the feat
 
 - Set up go2rtc with [WebRTC](#webrtc-extra-configuration).
 - Ensure you access Frigate via https (may require [opening port 8971](/frigate/installation/#ports)).
-- For the Home Assistant Frigate card, [follow the docs](https://github.com/dermotduffy/frigate-hass-card?tab=readme-ov-file#using-2-way-audio) for the correct source.
+- 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-doorbell)

docs/docs/configuration/object_detectors.md

@@ -33,6 +33,14 @@ Frigate supports multiple different detectors that work on different types of ha
 
 :::
 
+:::note
+
+Multiple detectors can not be mixed for object detection (ex: OpenVINO and Coral EdgeTPU can not be used for object detection at the same time). 
+
+This does not affect using hardware for accelerating other tasks such as [semantic search](./semantic_search.md)
+
+:::
+
 # Officially Supported Detectors
 
 Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `hailo8l`, `onnx`, `openvino`, `rknn`, `rocm`, and `tensorrt`. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
@@ -116,6 +124,30 @@ detectors:
     device: pci
 ```
 
+## Hailo-8l
+
+This detector is available for use with Hailo-8 AI Acceleration Module.
+
+See the [installation docs](../frigate/installation.md#hailo-8l) for information on configuring the hailo8.
+
+### Configuration
+
+```yaml
+detectors:
+  hailo8l:
+    type: hailo8l
+    device: PCIe
+
+model:
+  width: 300
+  height: 300
+  input_tensor: nhwc
+  input_pixel_format: bgr
+  model_type: ssd
+  path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
+```
+
+
 ## OpenVINO Detector
 
 The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel VPU hardware. To configure an OpenVINO detector, set the `"type"` attribute to `"openvino"`.
@@ -295,6 +327,7 @@ detectors:
 
 model:
   path: /config/model_cache/tensorrt/yolov7-320.trt
+  labelmap_path: /labelmap/coco-80.txt
   input_tensor: nchw
   input_pixel_format: rgb
   width: 320
@@ -624,26 +657,3 @@ $ cat /sys/kernel/debug/rknpu/load
 
 - All models are automatically downloaded and stored in the folder `config/model_cache/rknn_cache`. After upgrading Frigate, you should remove older models to free up space.
 - You can also provide your own `.rknn` model. You should not save your own models in the `rknn_cache` folder, store them directly in the `model_cache` folder or another subfolder. To convert a model to `.rknn` format see the `rknn-toolkit2` (requires a x86 machine). Note, that there is only post-processing for the supported models.
-
-## Hailo-8l
-
-This detector is available for use with Hailo-8 AI Acceleration Module.
-
-See the [installation docs](../frigate/installation.md#hailo-8l) for information on configuring the hailo8.
-
-### Configuration
-
-```yaml
-detectors:
-  hailo8l:
-    type: hailo8l
-    device: PCIe
-
-model:
-  width: 300
-  height: 300
-  input_tensor: nhwc
-  input_pixel_format: bgr
-  model_type: ssd
-  path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
-```

docs/docs/configuration/review.md

@@ -21,6 +21,21 @@ In 0.14 and later, all of that is bundled into a single review item which starts
 
 Not every segment of video captured by Frigate may be of the same level of interest to you. Video of people who enter your property may be a different priority than those walking by on the sidewalk. For this reason, Frigate 0.14 categorizes review items as _alerts_ and _detections_. By default, all person and car objects are considered alerts. You can refine categorization of your review items by configuring required zones for them.
 
+:::note
+
+Alerts and detections categorize the tracked objects in review items, but Frigate must first detect those objects with your configured object detector (Coral, OpenVINO, etc). By default, the object tracker only detects `person`. Setting `labels` for `alerts` and `detections` does not automatically enable detection of new objects. To detect more than `person`, you should add the following to your config:
+
+```yaml
+objects:
+  track:
+    - person
+    - car
+    - ...
+```
+
+See the [objects documentation](objects.md) for the list of objects that Frigate's default model tracks.
+:::
+
 ## Restricting alerts to specific labels
 
 By default a review item will only be marked as an alert if a person or car is detected. This can be configured to include any object or audio label using the following config:

docs/docs/frigate/hardware.md

@@ -13,20 +13,30 @@ Many users have reported various issues with Reolink cameras, so I do not recomm
 
 Here are some of the camera's I recommend:
 
-- <a href="https://amzn.to/3uFLtxB" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) T5442TM-AS-LED</a> (affiliate link)
-- <a href="https://amzn.to/3isJ3gU" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T5442TM-AS</a> (affiliate link)
-- <a href="https://amzn.to/2ZWNWIA" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-28MM</a> (affiliate link)
+- <a href="https://amzn.to/4fwoNWA" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T549M-ALED-S3</a> (affiliate link)
+- <a href="https://amzn.to/3YXpcMw" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T54IR-AS</a> (affiliate link)
+- <a href="https://amzn.to/3AvBHoY" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-AI-V3</a> (affiliate link)
+- <a href="https://amzn.to/4ltOpaC" target="_blank" rel="nofollow noopener sponsored">HIKVISION DS-2CD2387G2P-LSU/SL ColorVu 8MP Panoramic Turret IP Camera</a> (affiliate link)
 
 I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
 
 ## Server
 
-My current favorite is the Beelink EQ12 because of the efficient N100 CPU and dual NICs that allow you to setup a dedicated private network for your cameras where they can be blocked from accessing the internet. There are many used workstation options on eBay that work very well. Anything with an Intel CPU and capable of running Debian should work fine. As a bonus, you may want to look for devices with a M.2 or PCIe express slot that is compatible with the Google Coral. I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
+My current favorite is the Beelink EQ13 because of the efficient N100 CPU and dual NICs that allow you to setup a dedicated private network for your cameras where they can be blocked from accessing the internet. There are many used workstation options on eBay that work very well. Anything with an Intel CPU and capable of running Debian should work fine. As a bonus, you may want to look for devices with a M.2 or PCIe express slot that is compatible with the Google Coral, Hailo, or other AI accelerators.
 
-| Name                                                                                                          | Coral Inference Speed | Coral Compatibility | Notes                                                                                                                                   |
-| ------------------------------------------------------------------------------------------------------------- | --------------------- | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
-| Beelink EQ12 (<a href="https://amzn.to/3OlTMJY" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms                | USB                 | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras.                                               |
-| Intel NUC (<a href="https://amzn.to/3psFlHi" target="_blank" rel="nofollow noopener sponsored">Amazon</a>)    | 5-10ms                | USB                 | Overkill for most, but great performance. Can handle many cameras at 5fps depending on typical amounts of motion. Requires extra parts. |
+Note that many of these mini PCs come with Windows pre-installed, and you will need to install Linux according to the [getting started guide](../guides/getting_started.md).
+
+I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
+
+:::warning
+
+If the EQ13 is out of stock, the link below may take you to a suggested alternative on Amazon. The Beelink EQ14 has some known compatibility issues, so you should avoid that model for now.
+
+:::
+
+| Name                                                                                                          | Coral Inference Speed | Coral Compatibility | Notes                                                                                     |
+| ------------------------------------------------------------------------------------------------------------- | --------------------- | ------------------- | ----------------------------------------------------------------------------------------- |
+| Beelink EQ13 (<a href="https://amzn.to/4jn2qVr" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms                | USB                 | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras. |
 
 ## Detectors
 
@@ -52,24 +62,26 @@ The OpenVINO detector type is able to run on:
 
 More information is available [in the detector docs](/configuration/object_detectors#openvino-detector)
 
-Inference speeds vary greatly depending on the CPU, GPU, or VPU used, some known examples are below:
+Inference speeds vary greatly depending on the CPU or GPU used, some known examples of GPU inference times are below:
 
-| Name                 | Inference Speed | Notes                                                                 |
-| -------------------- | --------------- | --------------------------------------------------------------------- |
-| Intel NCS2 VPU       | 60 - 65 ms      | May vary based on host device                                         |
-| Intel Celeron J4105  | ~ 25 ms         | Inference speeds on CPU were 150 - 200 ms                             |
-| Intel Celeron N3060  | 130 - 150 ms    | Inference speeds on CPU were ~ 550 ms                                 |
-| Intel Celeron N3205U | ~ 120 ms        | Inference speeds on CPU were ~ 380 ms                                 |
-| Intel Celeron N4020  | 50 - 200 ms     | Inference speeds on CPU were ~ 800 ms, greatly depends on other loads |
-| Intel i3 6100T       | 15 - 35 ms      | Inference speeds on CPU were 60 - 120 ms                              |
-| Intel i3 8100        | ~ 15 ms         | Inference speeds on CPU were ~ 65 ms                                  |
-| Intel i5 4590        | ~ 20 ms         | Inference speeds on CPU were ~ 230 ms                                 |
-| Intel i5 6500        | ~ 15 ms         | Inference speeds on CPU were ~ 150 ms                                 |
-| Intel i5 7200u       | 15 - 25 ms      | Inference speeds on CPU were ~ 150 ms                                 |
-| Intel i5 7500        | ~ 15 ms         | Inference speeds on CPU were ~ 260 ms                                 |
-| Intel i5 1135G7      | 10 - 15 ms      |                                                                       |
-| Intel i5 12600K      | ~ 15 ms         | Inference speeds on CPU were ~ 35 ms                                  |
-| Intel Arc A750       | ~ 4 ms          |                                                                       |
+| Name                  | MobileNetV2 Inference Time  | YOLO-NAS Inference Time     | Notes                                   |
+| --------------------- | --------------------------- | --------------------------- | --------------------------------------- |
+| Intel Arc A750        | ~ 4 ms                      | 320: ~ 8 ms                 |                                         |
+| Intel Arc A380        | ~ 6 ms                      | 320: ~ 10 ms                |                                         |
+| Intel Ultra 5 125H    |                             | 320: ~ 10 ms 640: ~ 22 ms   |                                         |
+| Intel i5 12600K       | ~ 15 ms                     | 320: ~ 20 ms 640: ~ 46 ms   |                                         |
+| Intel i3 12000        |                             | 320: ~ 19 ms 640: ~ 54 ms   |                                         |
+| Intel i5 1135G7       | 10 - 15 ms                  |                             |                                         |
+| Intel i5 7500         | ~ 15 ms                     |                             |                                         |
+| Intel i5 7200u        | 15 - 25 ms                  |                             |                                         |
+| Intel i5 6500         | ~ 15 ms                     |                             |                                         |
+| Intel i5 4590         | ~ 20 ms                     |                             |                                         |
+| Intel i3 8100         | ~ 15 ms                     |                             |                                         |
+| Intel i3 6100T        | 15 - 35 ms                  |                             | Can only run one detector instance      |
+| Intel Celeron N4020   | 50 - 200 ms                 |                             | Inference speed depends on other loads  |
+| Intel Celeron N3205U  | ~ 120 ms                    |                             | Can only run one detector instance      |
+| Intel Celeron N3060   | 130 - 150 ms                |                             | Can only run one detector instance      |
+| Intel Celeron J4105   | ~ 25 ms                     |                             | Can only run one                        |
 
 ### TensorRT - Nvidia GPU
 
@@ -78,29 +90,35 @@ The TensortRT detector is able to run on x86 hosts that have an Nvidia GPU which
 Inference speeds will vary greatly depending on the GPU and the model used.
 `tiny` variants are faster than the equivalent non-tiny model, some known examples are below:
 
-| Name            | Inference Speed |
-| --------------- | --------------- |
-| GTX 1060 6GB    | ~ 7 ms          |
-| GTX 1070        | ~ 6 ms          |
-| GTX 1660 SUPER  | ~ 4 ms          |
-| RTX 3050        | 5 - 7 ms        |
-| RTX 3070 Mobile | ~ 5 ms          |
-| Quadro P400 2GB | 20 - 25 ms      |
-| Quadro P2000    | ~ 12 ms         |
+| Name            | YoloV7 Inference Time  | YOLO-NAS Inference Time     |
+| --------------- | ---------------------- | --------------------------- |
+| Quadro P2000    | ~ 12 ms                |                             |
+| Quadro P400 2GB | 20 - 25 ms             |                             |
+| RTX 3070 Mobile | ~ 5 ms                 |                             |
+| RTX 3050        | 5 - 7 ms               | 320: ~ 10 ms 640: ~ 16 ms   |
+| GTX 1660 SUPER  | ~ 4 ms                 |                             |
+| GTX 1070        | ~ 6 ms                 |                             |
+| GTX 1060 6GB    | ~ 7 ms                 |                             |
 
-#### AMD GPUs
+### AMD GPUs
 
-With the [rocm](../configuration/object_detectors.md#amdrocm-gpu-detector) detector Frigate can take advantage of many AMD GPUs.
+With the [rocm](../configuration/object_detectors.md#amdrocm-gpu-detector) detector Frigate can take advantage of many discrete AMD GPUs.
 
-### Community Supported:
+### Hailo-8l PCIe
 
-#### Nvidia Jetson
+Frigate supports the Hailo-8l M.2 card on any hardware but currently it is only tested on the Raspberry Pi5 PCIe hat from the AI kit.
+
+The inference time for the Hailo-8L chip at time of writing is around 17-21 ms for the SSD MobileNet Version 1 model.
+
+## Community Supported Detectors
+
+### Nvidia Jetson
 
 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#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.
 
-#### Rockchip platform
+### Rockchip platform
 
 Frigate supports hardware video processing on all Rockchip boards. However, hardware object detection is only supported on these boards:
 
@@ -112,12 +130,6 @@ Frigate supports hardware video processing on all Rockchip boards. However, hard
 
 The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms for yolo-nas s.
 
-#### Hailo-8l PCIe
-
-Frigate supports the Hailo-8l M.2 card on any hardware but currently it is only tested on the Raspberry Pi5 PCIe hat from the AI kit.
-
-The inference time for the Hailo-8L chip at time of writing is around 17-21 ms for the SSD MobileNet Version 1 model.
-
 ## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
 
 This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.

docs/docs/frigate/installation.md

@@ -111,7 +111,7 @@ For Raspberry Pi 5 users with the AI Kit, installation is straightforward. Simpl
 For other installations, follow these steps for installation:
 
 1. Install the driver from the [Hailo GitHub repository](https://github.com/hailo-ai/hailort-drivers). A convenient script for Linux is available to clone the repository, build the driver, and install it.
-2. Copy or download [this script](https://github.com/blakeblackshear/frigate/blob/41c9b13d2fffce508b32dfc971fa529b49295fbd/docker/hailo8l/user_installation.sh).
+2. Copy or download [this script](https://github.com/blakeblackshear/frigate/blob/dev/docker/hailo8l/user_installation.sh).
 3. Ensure it has execution permissions with `sudo chmod +x user_installation.sh`
 4. Run the script with `./user_installation.sh`
 

docs/docs/frigate/updating.md

@@ -0,0 +1,119 @@
+---
+id: updating
+title: Updating
+---
+
+# Updating Frigate
+
+The current stable version of Frigate is **0.15.0**. 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/v0.15.0).
+
+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.
+
+## Before You Begin
+
+- **Stop Frigate**: For most methods, you’ll need to stop the running Frigate instance before backing up and updating.
+- **Backup Your Configuration**: Always back up your `/config` directory (e.g., `config.yml` and `frigate.db`, the SQLite database) before updating. This ensures you can roll back if something goes wrong.
+- **Check Release Notes**: Carefully review the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases) for breaking changes or configuration updates that might affect your setup.
+
+## Updating with Docker
+
+If you’re running Frigate via Docker (recommended method), follow these steps:
+
+1. **Stop the Container**:
+
+   - If using Docker Compose:
+     ```bash
+     docker compose down frigate
+     ```
+   - If using `docker run`:
+     ```bash
+     docker stop frigate
+     ```
+
+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., `0.15.0` instead of `0.14.1`). For example:
+       ```yaml
+       services:
+         frigate:
+           image: ghcr.io/blakeblackshear/frigate:0.15.0
+       ```
+     - Then pull the image:
+       ```bash
+       docker pull ghcr.io/blakeblackshear/frigate:0.15.0
+       ```
+     - **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., `0.15.0`, `0.15.0-tensorrt`, or `stable`):
+       ```bash
+       docker pull ghcr.io/blakeblackshear/frigate:0.15.0
+       ```
+
+3. **Start the Container**:
+
+   - If using Docker Compose:
+     ```bash
+     docker compose up -d
+     ```
+   - If using `docker run`, re-run your original command (e.g., from the [Installation](#docker) section) with the updated image tag.
+
+4. **Verify the Update**:
+   - Check the container logs to ensure Frigate starts successfully:
+     ```bash
+     docker logs frigate
+     ```
+   - Visit the Frigate Web UI (default: `http://<your-ip>:5000`) to confirm the new version is running. The version number is displayed at the top of the System Metrics page.
+
+### Notes
+
+- If you’ve customized other settings (e.g., `shm-size`), ensure they’re still appropriate after the update.
+- Docker will automatically use the updated image when you restart the container, as long as you pulled the correct version.
+
+## Updating the Home Assistant Addon
+
+For users running Frigate as a Home Assistant Addon:
+
+1. **Check for Updates**:
+
+   - Navigate to **Settings > Add-ons** in Home Assistant.
+   - Find your installed Frigate addon (e.g., "Frigate NVR" or "Frigate NVR (Full Access)").
+   - If an update is available, you’ll see an "Update" button.
+
+2. **Update the Addon**:
+
+   - Click the "Update" button next to the Frigate addon.
+   - Wait for the process to complete. Home Assistant will handle downloading and installing the new version.
+
+3. **Restart the Addon**:
+
+   - After updating, go to the addon’s page and click "Restart" to apply the changes.
+
+4. **Verify the Update**:
+   - Check the addon logs (under the "Log" tab) to ensure Frigate starts without errors.
+   - Access the Frigate Web UI to confirm the new version is running.
+
+### Notes
+
+- Ensure your `/config/frigate.yml` is compatible with the new version by reviewing the [Release notes](https://github.com/blakeblackshear/frigate/releases).
+- If using custom hardware (e.g., Coral or GPU), verify that configurations still work, as addon updates don’t modify your hardware settings.
+
+## Rolling Back
+
+If an update causes issues:
+
+1. Stop Frigate.
+2. Restore your backed-up config file and database.
+3. Revert to the previous image version:
+   - For Docker: Specify an older tag (e.g., `ghcr.io/blakeblackshear/frigate:0.14.1`) in your `docker run` command.
+   - For Docker Compose: Edit your `docker-compose.yml`, specify the older version tag (e.g., `ghcr.io/blakeblackshear/frigate:0.14.1`), and re-run `docker compose up -d`.
+   - For Home Assistant: Reinstall the previous addon version manually via the repository if needed and restart the addon.
+4. Verify the old version is running again.
+
+## Troubleshooting
+
+- **Container Fails to Start**: Check logs (`docker logs frigate`) for errors.
+- **UI Not Loading**: Ensure ports (e.g., 5000, 8971) are still mapped correctly and the service is running.
+- **Hardware Issues**: Revisit hardware-specific setup (e.g., Coral, GPU) if detection or decoding fails post-update.
+
+Common questions are often answered in the [FAQ](https://github.com/blakeblackshear/frigate/discussions), pinned at the top of the support discussions.

docs/docs/guides/configuring_go2rtc.md

@@ -7,7 +7,7 @@ title: Configuring go2rtc
 
 Use of the bundled go2rtc is optional. You can still configure FFmpeg to connect directly to your cameras. However, adding go2rtc to your configuration is required for the following features:
 
-- WebRTC or MSE for live viewing with higher resolutions and frame rates than the jsmpeg stream which is limited to the detect stream
+- WebRTC or MSE for live viewing with audio, higher resolutions and frame rates than the jsmpeg stream which is limited to the detect stream and does not support audio
 - Live stream support for cameras in Home Assistant Integration
 - RTSP relay for use with other consumers to reduce the number of connections to your camera streams
 

docs/docs/integrations/home-assistant.md

@@ -97,13 +97,13 @@ services:
 
 If you are using HassOS with the addon, the URL should be one of the following depending on which addon version you are using. Note that if you are using the Proxy Addon, you do NOT point the integration at the proxy URL. Just enter the URL used to access Frigate directly from your network.
 
-| Addon Version                  | URL                                    |
-| ------------------------------ | -------------------------------------- |
-| Frigate NVR                    | `http://ccab4aaf-frigate:5000`         |
-| Frigate NVR (Full Access)      | `http://ccab4aaf-frigate-fa:5000`      |
-| Frigate NVR Beta               | `http://ccab4aaf-frigate-beta:5000`    |
-| Frigate NVR Beta (Full Access) | `http://ccab4aaf-frigate-fa-beta:5000` |
-| Frigate NVR HailoRT Beta       | `http://ccab4aaf-frigate-hailo-beta:5000`    |
+| Addon Version                  | URL                                       |
+| ------------------------------ | ----------------------------------------- |
+| Frigate NVR                    | `http://ccab4aaf-frigate:5000`            |
+| Frigate NVR (Full Access)      | `http://ccab4aaf-frigate-fa:5000`         |
+| Frigate NVR Beta               | `http://ccab4aaf-frigate-beta:5000`       |
+| Frigate NVR Beta (Full Access) | `http://ccab4aaf-frigate-fa-beta:5000`    |
+| Frigate NVR HailoRT Beta       | `http://ccab4aaf-frigate-hailo-beta:5000` |
 
 ### Frigate running on a separate machine
 
@@ -113,6 +113,14 @@ If you run Frigate on a separate device within your local network, Home Assistan
 
 Use `http://<frigate_device_ip>:8971` as the URL for the integration so that authentication is required.
 
+:::tip
+
+The above URL assumes you have [disabled TLS](../configuration/tls).
+By default, TLS is enabled and Frigate will be using a self-signed certificate. HomeAssistant will fail to connect HTTPS to port 8971 since it fails to verify the self-signed certificate.
+Either disable TLS and use HTTP from HomeAssistant, or configure Frigate to be acessible with a valid certificate.
+
+:::
+
 ```yaml
 services:
   frigate:
@@ -301,3 +309,7 @@ which server they are referring to.
 #### If I am detecting multiple objects, how do I assign the correct `binary_sensor` to the camera in HomeKit?
 
 The [HomeKit integration](https://www.home-assistant.io/integrations/homekit/) randomly links one of the binary sensors (motion sensor entities) grouped with the camera device in Home Assistant. You can specify a `linked_motion_sensor` in the Home Assistant [HomeKit configuration](https://www.home-assistant.io/integrations/homekit/#linked_motion_sensor) for each camera.
+
+#### I have set up automations based on the occupancy sensors. Sometimes the automation runs because the sensors are turned on, but then I look at Frigate I can't find the object that triggered the sensor. Is this a bug?
+
+No. The occupancy sensors have fewer checks in place because they are often used for things like turning the lights on where latency needs to be as low as possible. So false positives can sometimes trigger these sensors. If you want false positive filtering, you should use an mqtt sensor on the `frigate/events` or `frigate/reviews` topic.

docs/docs/integrations/mqtt.md

@@ -28,7 +28,14 @@ Message published for each changed tracked object. The first message is publishe
     "id": "1607123955.475377-mxklsc",
     "camera": "front_door",
     "frame_time": 1607123961.837752,
-    "snapshot_time": 1607123961.837752,
+    "snapshot": {
+        "frame_time": 1607123965.975463,
+        "box": [415, 489, 528, 700],
+        "area": 12728,
+        "region": [260, 446, 660, 846],
+        "score": 0.77546,
+        "attributes": [],
+    },
     "label": "person",
     "sub_label": null,
     "top_score": 0.958984375,
@@ -58,7 +65,14 @@ Message published for each changed tracked object. The first message is publishe
     "id": "1607123955.475377-mxklsc",
     "camera": "front_door",
     "frame_time": 1607123962.082975,
-    "snapshot_time": 1607123961.837752,
+    "snapshot": {
+        "frame_time": 1607123965.975463,
+        "box": [415, 489, 528, 700],
+        "area": 12728,
+        "region": [260, 446, 660, 846],
+        "score": 0.77546,
+        "attributes": [],
+    },
     "label": "person",
     "sub_label": ["John Smith", 0.79],
     "top_score": 0.958984375,

docs/docs/integrations/plus.md

@@ -29,7 +29,9 @@ You cannot use the `environment_vars` section of your Frigate configuration file
 
 ## Submit examples
 
-Once your API key is configured, you can submit examples directly from the Explore page in Frigate using the `Frigate+` button.
+Once your API key is configured, you can submit examples directly from the Explore page in Frigate. From the More Filters menu, select "Has a Snapshot - Yes" and "Submitted to Frigate+ - No", and press Apply at the bottom of the pane. Then, click on a thumbnail and select the Snapshot tab.
+
+You can use your keyboard's left and right arrow keys to quickly navigate between the tracked object snapshots.
 
 :::note
 
@@ -37,8 +39,6 @@ Snapshots must be enabled to be able to submit examples to Frigate+
 
 :::
 
-![Send To Plus](/img/plus/send-to-plus.jpg)
-
 ![Submit To Plus](/img/plus/submit-to-plus.jpg)
 
 ### Annotate and verify

docs/docs/integrations/third_party_extensions.md

@@ -13,12 +13,20 @@ Please use your own knowledge to assess and vet them before you install anything
 
 :::
 
+## [Advanced Camera Card (formerly known as Frigate Card](https://card.camera/#/README)
+
+The [Advanced Camera Card](https://card.camera/#/README) is a Home Assistant dashboard card with deep Frigate integration.
+
 ## [Double Take](https://github.com/skrashevich/double-take)
 
 [Double Take](https://github.com/skrashevich/double-take) provides an unified UI and API for processing and training images for facial recognition.
 It supports automatically setting the sub labels in Frigate for person objects that are detected and recognized.
 This is a fork (with fixed errors and new features) of [original Double Take](https://github.com/jakowenko/double-take) project which, unfortunately, isn't being maintained by author.
 
+## [Frigate Notify](https://github.com/0x2142/frigate-notify)
+
+[Frigate Notify](https://github.com/0x2142/frigate-notify) is a simple app designed to send notifications from Frigate NVR to your favorite platforms. Intended to be used with standalone Frigate installations - Home Assistant not required, MQTT is optional but recommended.
+
 ## [Frigate telegram](https://github.com/OldTyT/frigate-telegram)
 
 [Frigate telegram](https://github.com/OldTyT/frigate-telegram) makes it possible to send events from Frigate to Telegram. Events are sent as a message with a text description, video, and thumbnail.

docs/docs/plus/first_model.md

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

docs/docs/plus/improving_model.md

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

docs/docs/plus/index.md

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

docs/docs/troubleshooting/edgetpu.md

@@ -54,6 +54,17 @@ The most common reason for the PCIe Coral not being detected is that the driver
 - In most cases [the Coral docs](https://coral.ai/docs/m2/get-started/#2-install-the-pcie-driver-and-edge-tpu-runtime) show how to install the driver for the PCIe based Coral.
 - For Ubuntu 22.04+ https://github.com/jnicolson/gasket-builder can be used to build and install the latest version of the driver.
 
+### Not detected on Raspberry Pi5
+
+A kernel update to the RPi5 means an upate to config.txt is required, see [the raspberry pi forum for more info](https://forums.raspberrypi.com/viewtopic.php?t=363682&sid=cb59b026a412f0dc041595951273a9ca&start=25)
+
+Specifically, add the following to config.txt
+
+```
+dtoverlay=pciex1-compat-pi5,no-mip
+dtoverlay=pcie-32bit-dma-pi5
+```
+
 ## Only One PCIe Coral Is Detected With Coral Dual EdgeTPU
 
 Coral Dual EdgeTPU is one card with two identical TPU cores. Each core has it's own PCIe interface and motherboard needs to have two PCIe busses on the m.2 slot to make them both work.

docs/docs/troubleshooting/faqs.md

@@ -17,6 +17,10 @@ ffmpeg:
     record: preset-record-generic-audio-aac
 ```
 
+### How can I get sound in live view?
+
+Audio is only supported for live view when go2rtc is configured, see [the live docs](../configuration/live.md) for more information.
+
 ### I can't view recordings in the Web UI.
 
 Ensure your cameras send h264 encoded video, or [transcode them](/configuration/restream.md).

docs/docusaurus.config.ts

@@ -1,56 +1,89 @@
-import type * as Preset from '@docusaurus/preset-classic';
-import * as path from 'node:path';
-import type { Config, PluginConfig } from '@docusaurus/types';
-import type * as OpenApiPlugin from 'docusaurus-plugin-openapi-docs';
+import type * as Preset from "@docusaurus/preset-classic";
+import * as path from "node:path";
+import type { Config, PluginConfig } from "@docusaurus/types";
+import type * as OpenApiPlugin from "docusaurus-plugin-openapi-docs";
 
 const config: Config = {
-  title: 'Frigate',
-  tagline: 'NVR With Realtime Object Detection for IP Cameras',
-  url: 'https://docs.frigate.video',
-  baseUrl: '/',
-  onBrokenLinks: 'throw',
-  onBrokenMarkdownLinks: 'warn',
-  favicon: 'img/favicon.ico',
-  organizationName: 'blakeblackshear',
-  projectName: 'frigate',
-  themes: ['@docusaurus/theme-mermaid', 'docusaurus-theme-openapi-docs'],
+  title: "Frigate",
+  tagline: "NVR With Realtime Object Detection for IP Cameras",
+  url: "https://docs.frigate.video",
+  baseUrl: "/",
+  onBrokenLinks: "throw",
+  onBrokenMarkdownLinks: "warn",
+  favicon: "img/favicon.ico",
+  organizationName: "blakeblackshear",
+  projectName: "frigate",
+  themes: [
+    "@docusaurus/theme-mermaid",
+    "docusaurus-theme-openapi-docs",
+    "@inkeep/docusaurus/chatButton",
+    "@inkeep/docusaurus/searchBar",
+  ],
   markdown: {
     mermaid: true,
   },
   themeConfig: {
     algolia: {
-      appId: 'WIURGBNBPY',
-      apiKey: 'd02cc0a6a61178b25da550212925226b',
-      indexName: 'frigate',
+      appId: "WIURGBNBPY",
+      apiKey: "d02cc0a6a61178b25da550212925226b",
+      indexName: "frigate",
     },
     docs: {
       sidebar: {
         hideable: true,
       },
     },
+    inkeepConfig: {
+      baseSettings: {
+        apiKey: "b1a4c4d73c9b48aa5b3cdae6e4c81f0bb3d1134eeb5a7100",
+        integrationId: "cm6xmhn9h000gs601495fkkdx",
+        organizationId: "org_map2JQEOco8U1ZYY",
+        primaryBrandColor: "#010101",
+      },
+      aiChatSettings: {
+        chatSubjectName: "Frigate",
+        botAvatarSrcUrl: "https://frigate.video/images/favicon.png",
+        getHelpCallToActions: [
+          {
+            name: "GitHub",
+            url: "https://github.com/blakeblackshear/frigate",
+            icon: {
+              builtIn: "FaGithub",
+            },
+          },
+        ],
+        quickQuestions: [
+          "How to configure and setup camera settings?",
+          "How to setup notifications?",
+          "Supported builtin detectors?",
+          "How to restream video feed?",
+          "How can I get sound or audio in my recordings?",
+        ],
+      },
+    },
     prism: {
-      additionalLanguages: ['bash', 'json'],
+      additionalLanguages: ["bash", "json"],
     },
     languageTabs: [
       {
-        highlight: 'python',
-        language: 'python',
-        logoClass: 'python',
+        highlight: "python",
+        language: "python",
+        logoClass: "python",
       },
       {
-        highlight: 'javascript',
-        language: 'nodejs',
-        logoClass: 'nodejs',
+        highlight: "javascript",
+        language: "nodejs",
+        logoClass: "nodejs",
       },
       {
-        highlight: 'javascript',
-        language: 'javascript',
-        logoClass: 'javascript',
+        highlight: "javascript",
+        language: "javascript",
+        logoClass: "javascript",
       },
       {
-        highlight: 'bash',
-        language: 'curl',
-        logoClass: 'curl',
+        highlight: "bash",
+        language: "curl",
+        logoClass: "curl",
       },
       {
         highlight: "rust",
@@ -59,49 +92,49 @@ const config: Config = {
       },
     ],
     navbar: {
-      title: 'Frigate',
+      title: "Frigate",
       logo: {
-        alt: 'Frigate',
-        src: 'img/logo.svg',
-        srcDark: 'img/logo-dark.svg',
+        alt: "Frigate",
+        src: "img/logo.svg",
+        srcDark: "img/logo-dark.svg",
       },
       items: [
         {
-          to: '/',
-          activeBasePath: 'docs',
-          label: 'Docs',
-          position: 'left',
+          to: "/",
+          activeBasePath: "docs",
+          label: "Docs",
+          position: "left",
         },
         {
-          href: 'https://frigate.video',
-          label: 'Website',
-          position: 'right',
+          href: "https://frigate.video",
+          label: "Website",
+          position: "right",
         },
         {
-          href: 'http://demo.frigate.video',
-          label: 'Demo',
-          position: 'right',
+          href: "http://demo.frigate.video",
+          label: "Demo",
+          position: "right",
         },
         {
-          href: 'https://github.com/blakeblackshear/frigate',
-          label: 'GitHub',
-          position: 'right',
+          href: "https://github.com/blakeblackshear/frigate",
+          label: "GitHub",
+          position: "right",
         },
       ],
     },
     footer: {
-      style: 'dark',
+      style: "dark",
       links: [
         {
-          title: 'Community',
+          title: "Community",
           items: [
             {
-              label: 'GitHub',
-              href: 'https://github.com/blakeblackshear/frigate',
+              label: "GitHub",
+              href: "https://github.com/blakeblackshear/frigate",
             },
             {
-              label: 'Discussions',
-              href: 'https://github.com/blakeblackshear/frigate/discussions',
+              label: "Discussions",
+              href: "https://github.com/blakeblackshear/frigate/discussions",
             },
           ],
         },
@@ -110,19 +143,19 @@ const config: Config = {
     },
   },
   plugins: [
-    path.resolve(__dirname, 'plugins', 'raw-loader'),
+    path.resolve(__dirname, "plugins", "raw-loader"),
     [
-      'docusaurus-plugin-openapi-docs',
+      "docusaurus-plugin-openapi-docs",
       {
-        id: 'openapi',
-        docsPluginId: 'classic', // configured for preset-classic
+        id: "openapi",
+        docsPluginId: "classic", // configured for preset-classic
         config: {
           frigateApi: {
-            specPath: 'static/frigate-api.yaml',
-            outputDir: 'docs/integrations/api',
+            specPath: "static/frigate-api.yaml",
+            outputDir: "docs/integrations/api",
             sidebarOptions: {
-              groupPathsBy: 'tag',
-              categoryLinkSource: 'tag',
+              groupPathsBy: "tag",
+              categoryLinkSource: "tag",
               sidebarCollapsible: true,
               sidebarCollapsed: true,
             },
@@ -130,23 +163,24 @@ const config: Config = {
           } satisfies OpenApiPlugin.Options,
         },
       },
-    ]
+    ],
   ] as PluginConfig[],
   presets: [
     [
-      'classic',
+      "classic",
       {
         docs: {
-          routeBasePath: '/',
-          sidebarPath: './sidebars.ts',
+          routeBasePath: "/",
+          sidebarPath: "./sidebars.ts",
           // Please change this to your repo.
-          editUrl: 'https://github.com/blakeblackshear/frigate/edit/master/docs/',
+          editUrl:
+            "https://github.com/blakeblackshear/frigate/edit/master/docs/",
           sidebarCollapsible: false,
-          docItemComponent: '@theme/ApiItem', // Derived from docusaurus-theme-openapi
+          docItemComponent: "@theme/ApiItem", // Derived from docusaurus-theme-openapi
         },
 
         theme: {
-          customCss: './src/css/custom.css',
+          customCss: "./src/css/custom.css",
         },
       } satisfies Preset.Options,
     ],

docs/package-lock.json

@@ -12,6 +12,7 @@
         "@docusaurus/plugin-content-docs": "^3.6.3",
         "@docusaurus/preset-classic": "^3.6.3",
         "@docusaurus/theme-mermaid": "^3.6.3",
+        "@inkeep/docusaurus": "^2.0.16",
         "@mdx-js/react": "^3.1.0",
         "clsx": "^2.1.1",
         "docusaurus-plugin-openapi-docs": "^4.3.1",
@@ -4056,6 +4057,12 @@
         "react-hook-form": "^7.0.0"
       }
     },
+    "node_modules/@inkeep/docusaurus": {
+      "version": "2.0.16",
+      "resolved": "https://registry.npmjs.org/@inkeep/docusaurus/-/docusaurus-2.0.16.tgz",
+      "integrity": "sha512-dQhjlvFnl3CVr0gWeJ/V/qLnDy1XYrCfkdVSa2D3gJTxI9/vOf9639Y1aPxTxO88DiXuW9CertLrZLB6SoJ2yg==",
+      "license": "MIT"
+    },
     "node_modules/@isaacs/cliui": {
       "version": "8.0.2",
       "resolved": "https://registry.npmjs.org/@isaacs/cliui/-/cliui-8.0.2.tgz",

docs/package.json

@@ -18,9 +18,10 @@
   },
   "dependencies": {
     "@docusaurus/core": "^3.6.3",
+    "@docusaurus/plugin-content-docs": "^3.6.3",
     "@docusaurus/preset-classic": "^3.6.3",
     "@docusaurus/theme-mermaid": "^3.6.3",
-    "@docusaurus/plugin-content-docs": "^3.6.3",
+    "@inkeep/docusaurus": "^2.0.16",
     "@mdx-js/react": "^3.1.0",
     "clsx": "^2.1.1",
     "docusaurus-plugin-openapi-docs": "^4.3.1",

docs/sidebars.ts

@@ -8,6 +8,7 @@ const sidebars: SidebarsConfig = {
       'frigate/index',
       'frigate/hardware',
       'frigate/installation',
+      'frigate/updating',
       'frigate/camera_setup',
       'frigate/video_pipeline',
       'frigate/glossary',

frigate/api/app.py

@@ -153,6 +153,7 @@ def config(request: Request):
 
     config["plus"] = {"enabled": request.app.frigate_config.plus_api.is_active()}
     config["model"]["colormap"] = config_obj.model.colormap
+    config["model"]["all_attributes"] = config_obj.model.all_attributes
 
     # use merged labelamp
     for detector_config in config["detectors"].values():

frigate/api/fastapi_app.py

@@ -26,14 +26,13 @@ from frigate.storage import StorageMaintainer
 logger = logging.getLogger(__name__)
 
 
-def check_csrf(request: Request):
+def check_csrf(request: Request) -> bool:
     if request.method in ["GET", "HEAD", "OPTIONS", "TRACE"]:
-        pass
+        return True
     if "origin" in request.headers and "x-csrf-token" not in request.headers:
-        return JSONResponse(
-            content={"success": False, "message": "Missing CSRF header"},
-            status_code=401,
-        )
+        return False
+
+    return True
 
 
 # Used to retrieve the remote-user header: https://starlette-context.readthedocs.io/en/latest/plugins.html#easy-mode
@@ -71,7 +70,12 @@ def create_fastapi_app(
     @app.middleware("http")
     async def frigate_middleware(request: Request, call_next):
         # Before request
-        check_csrf(request)
+        if not check_csrf(request):
+            return JSONResponse(
+                content={"success": False, "message": "Missing CSRF header"},
+                status_code=401,
+            )
+
         if database.is_closed():
             database.connect()
 

frigate/api/review.py

@@ -490,8 +490,6 @@ def set_not_reviewed(review_id: str):
     review.save()
 
     return JSONResponse(
-        content=(
-            {"success": True, "message": "Set Review " + review_id + " as not viewed"}
-        ),
+        content=({"success": True, "message": f"Set Review {review_id} as not viewed"}),
         status_code=200,
     )

frigate/ffmpeg_presets.py

@@ -111,12 +111,12 @@ PRESETS_HW_ACCEL_ENCODE_BIRDSEYE = {
     "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}",
     "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 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} -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 high {2}",
+    "preset-jetson-h265": "{0} -hide_banner {1} -c:v h264_nvmpi -profile main {2}",
     "preset-rk-h264": "{0} -hide_banner {1} -c:v h264_rkmpp -profile:v high {2}",
-    "preset-rk-h265": "{0} -hide_banner {1} -c:v hevc_rkmpp -profile:v high {2}",
+    "preset-rk-h265": "{0} -hide_banner {1} -c:v hevc_rkmpp -profile:v main {2}",
     "default": "{0} -hide_banner {1} -c:v libx264 -g 50 -profile:v high -level:v 4.1 -preset:v superfast -tune:v zerolatency {2}",
 }
 PRESETS_HW_ACCEL_ENCODE_BIRDSEYE["preset-nvidia-h264"] = (
@@ -131,13 +131,13 @@ PRESETS_HW_ACCEL_ENCODE_TIMELAPSE = {
     "preset-rpi-64-h265": "{0} -hide_banner {1} -c:v hevc_v4l2m2m -pix_fmt yuv420p {2}",
     FFMPEG_HWACCEL_VAAPI: "{0} -hide_banner -hwaccel vaapi -hwaccel_output_format vaapi -hwaccel_device {3} {1} -c:v h264_vaapi {2}",
     "preset-intel-qsv-h264": "{0} -hide_banner {1} -c:v h264_qsv -profile:v high -level:v 4.1 -async_depth:v 1 {2}",
-    "preset-intel-qsv-h265": "{0} -hide_banner {1} -c:v hevc_qsv -profile:v high -level:v 4.1 -async_depth:v 1 {2}",
+    "preset-intel-qsv-h265": "{0} -hide_banner {1} -c:v hevc_qsv -profile:v main -level:v 4.1 -async_depth:v 1 {2}",
     FFMPEG_HWACCEL_NVIDIA: "{0} -hide_banner -hwaccel cuda -hwaccel_output_format cuda -extra_hw_frames 8 {1} -c:v h264_nvenc {2}",
     "preset-nvidia-h265": "{0} -hide_banner -hwaccel cuda -hwaccel_output_format cuda -extra_hw_frames 8 {1} -c:v hevc_nvenc {2}",
     "preset-jetson-h264": "{0} -hide_banner {1} -c:v h264_nvmpi -profile high {2}",
-    "preset-jetson-h265": "{0} -hide_banner {1} -c:v hevc_nvmpi -profile high {2}",
+    "preset-jetson-h265": "{0} -hide_banner {1} -c:v hevc_nvmpi -profile main {2}",
     "preset-rk-h264": "{0} -hide_banner {1} -c:v h264_rkmpp -profile:v high {2}",
-    "preset-rk-h265": "{0} -hide_banner {1} -c:v hevc_rkmpp -profile:v high {2}",
+    "preset-rk-h265": "{0} -hide_banner {1} -c:v hevc_rkmpp -profile:v main {2}",
     "default": "{0} -hide_banner {1} -c:v libx264 -preset:v ultrafast -tune:v zerolatency {2}",
 }
 PRESETS_HW_ACCEL_ENCODE_TIMELAPSE["preset-nvidia-h264"] = (

frigate/object_processing.py

@@ -473,7 +473,7 @@ class CameraState:
 
             if current_frame is not None:
                 self.current_frame_time = frame_time
-                self._current_frame = current_frame
+                self._current_frame = np.copy(current_frame)
 
                 if self.previous_frame_id is not None:
                     self.frame_manager.close(self.previous_frame_id)

frigate/ptz/onvif.py

@@ -411,19 +411,19 @@ class OnvifController:
         # The onvif spec says this can report as +INF and -INF, so this may need to be modified
         pan = numpy.interp(
             pan,
+            [-1, 1],
             [
                 self.cams[camera_name]["relative_fov_range"]["XRange"]["Min"],
                 self.cams[camera_name]["relative_fov_range"]["XRange"]["Max"],
             ],
-            [-1, 1],
         )
         tilt = numpy.interp(
             tilt,
+            [-1, 1],
             [
                 self.cams[camera_name]["relative_fov_range"]["YRange"]["Min"],
                 self.cams[camera_name]["relative_fov_range"]["YRange"]["Max"],
             ],
-            [-1, 1],
         )
 
         move_request.Speed = {
@@ -536,11 +536,11 @@ class OnvifController:
         # function takes in 0 to 1 for zoom, interpolate to the values of the camera.
         zoom = numpy.interp(
             zoom,
+            [0, 1],
             [
                 self.cams[camera_name]["absolute_zoom_range"]["XRange"]["Min"],
                 self.cams[camera_name]["absolute_zoom_range"]["XRange"]["Max"],
             ],
-            [0, 1],
         )
 
         move_request.Speed = {"Zoom": speed}

frigate/record/cleanup.py

@@ -121,22 +121,29 @@ class RecordingCleanup(threading.Thread):
         review_start = 0
         deleted_recordings = set()
         kept_recordings: list[tuple[float, float]] = []
+        recording: Recordings
         for recording in recordings:
             keep = False
             mode = None
             # Now look for a reason to keep this recording segment
             for idx in range(review_start, len(reviews)):
                 review: ReviewSegment = reviews[idx]
+                severity = review.severity
+                pre_capture = config.record.get_review_pre_capture(severity)
+                post_capture = config.record.get_review_post_capture(severity)
 
                 # if the review starts in the future, stop checking reviews
                 # and let this recording segment expire
-                if review.start_time > recording.end_time:
+                if review.start_time - pre_capture > recording.end_time:
                     keep = False
                     break
 
                 # if the review is in progress or ends after the recording starts, keep it
                 # and stop looking at reviews
-                if review.end_time is None or review.end_time >= recording.start_time:
+                if (
+                    review.end_time is None
+                    or review.end_time + post_capture >= recording.start_time
+                ):
                     keep = True
                     mode = (
                         config.record.alerts.retain.mode
@@ -149,7 +156,7 @@ class RecordingCleanup(threading.Thread):
                 # this review and check the next review for an overlap.
                 # since the review and recordings are sorted, we can skip review
                 # that end before the previous recording segment started on future segments
-                if review.end_time < recording.start_time:
+                if review.end_time + post_capture < recording.start_time:
                     review_start = idx
 
             # Delete recordings outside of the retention window or based on the retention mode

frigate/test/http_api/base_http_test.py

@@ -6,6 +6,7 @@ import unittest
 from peewee_migrate import Router
 from playhouse.sqlite_ext import SqliteExtDatabase
 from playhouse.sqliteq import SqliteQueueDatabase
+from pydantic import Json
 
 from frigate.api.fastapi_app import create_fastapi_app
 from frigate.config import FrigateConfig
@@ -123,7 +124,12 @@ class BaseTestHttp(unittest.TestCase):
     def insert_mock_event(
         self,
         id: str,
-        start_time: datetime.datetime = datetime.datetime.now().timestamp(),
+        start_time: float = datetime.datetime.now().timestamp(),
+        end_time: float = datetime.datetime.now().timestamp() + 20,
+        has_clip: bool = True,
+        top_score: int = 100,
+        score: int = 0,
+        data: Json = {},
     ) -> Event:
         """Inserts a basic event model with a given id."""
         return Event.insert(
@@ -131,16 +137,18 @@ class BaseTestHttp(unittest.TestCase):
             label="Mock",
             camera="front_door",
             start_time=start_time,
-            end_time=start_time + 20,
-            top_score=100,
+            end_time=end_time,
+            top_score=top_score,
+            score=score,
             false_positive=False,
             zones=list(),
             thumbnail="",
             region=[],
             box=[],
             area=0,
-            has_clip=True,
+            has_clip=has_clip,
             has_snapshot=True,
+            data=data,
         ).execute()
 
     def insert_mock_review_segment(
@@ -150,6 +158,7 @@ class BaseTestHttp(unittest.TestCase):
         end_time: float = datetime.datetime.now().timestamp() + 20,
         severity: SeverityEnum = SeverityEnum.alert,
         has_been_reviewed: bool = False,
+        data: Json = {},
     ) -> Event:
         """Inserts a review segment model with a given id."""
         return ReviewSegment.insert(
@@ -160,7 +169,7 @@ class BaseTestHttp(unittest.TestCase):
             has_been_reviewed=has_been_reviewed,
             severity=severity,
             thumb_path=False,
-            data={},
+            data=data,
         ).execute()
 
     def insert_mock_recording(
@@ -168,6 +177,7 @@ class BaseTestHttp(unittest.TestCase):
         id: str,
         start_time: float = datetime.datetime.now().timestamp(),
         end_time: float = datetime.datetime.now().timestamp() + 20,
+        motion: int = 0,
     ) -> Event:
         """Inserts a recording model with a given id."""
         return Recordings.insert(
@@ -177,4 +187,5 @@ class BaseTestHttp(unittest.TestCase):
             start_time=start_time,
             end_time=end_time,
             duration=end_time - start_time,
+            motion=motion,
         ).execute()

frigate/test/http_api/test_http_app.py

@@ -0,0 +1,26 @@
+from unittest.mock import Mock
+
+from fastapi.testclient import TestClient
+
+from frigate.models import Event, Recordings, ReviewSegment
+from frigate.stats.emitter import StatsEmitter
+from frigate.test.http_api.base_http_test import BaseTestHttp
+
+
+class TestHttpApp(BaseTestHttp):
+    def setUp(self):
+        super().setUp([Event, Recordings, ReviewSegment])
+        self.app = super().create_app()
+
+    ####################################################################################################################
+    ###################################  GET /stats Endpoint   #########################################################
+    ####################################################################################################################
+    def test_stats_endpoint(self):
+        stats = Mock(spec=StatsEmitter)
+        stats.get_latest_stats.return_value = self.test_stats
+        app = super().create_app(stats)
+
+        with TestClient(app) as client:
+            response = client.get("/stats")
+            response_json = response.json()
+            assert response_json == self.test_stats

frigate/test/http_api/test_http_event.py

@@ -0,0 +1,137 @@
+from datetime import datetime
+
+from fastapi.testclient import TestClient
+
+from frigate.models import Event, Recordings, ReviewSegment
+from frigate.test.http_api.base_http_test import BaseTestHttp
+
+
+class TestHttpApp(BaseTestHttp):
+    def setUp(self):
+        super().setUp([Event, Recordings, ReviewSegment])
+        self.app = super().create_app()
+
+    ####################################################################################################################
+    ###################################  GET /events Endpoint   #########################################################
+    ####################################################################################################################
+    def test_get_event_list_no_events(self):
+        with TestClient(self.app) as client:
+            events = client.get("/events").json()
+            assert len(events) == 0
+
+    def test_get_event_list_no_match_event_id(self):
+        id = "123456.random"
+        with TestClient(self.app) as client:
+            super().insert_mock_event(id)
+            events = client.get("/events", params={"event_id": "abc"}).json()
+            assert len(events) == 0
+
+    def test_get_event_list_match_event_id(self):
+        id = "123456.random"
+        with TestClient(self.app) as client:
+            super().insert_mock_event(id)
+            events = client.get("/events", params={"event_id": id}).json()
+            assert len(events) == 1
+            assert events[0]["id"] == id
+
+    def test_get_event_list_match_length(self):
+        now = int(datetime.now().timestamp())
+
+        id = "123456.random"
+        with TestClient(self.app) as client:
+            super().insert_mock_event(id, now, now + 1)
+            events = client.get(
+                "/events", params={"max_length": 1, "min_length": 1}
+            ).json()
+            assert len(events) == 1
+            assert events[0]["id"] == id
+
+    def test_get_event_list_no_match_max_length(self):
+        now = int(datetime.now().timestamp())
+
+        with TestClient(self.app) as client:
+            id = "123456.random"
+            super().insert_mock_event(id, now, now + 2)
+            events = client.get("/events", params={"max_length": 1}).json()
+            assert len(events) == 0
+
+    def test_get_event_list_no_match_min_length(self):
+        now = int(datetime.now().timestamp())
+
+        with TestClient(self.app) as client:
+            id = "123456.random"
+            super().insert_mock_event(id, now, now + 2)
+            events = client.get("/events", params={"min_length": 3}).json()
+            assert len(events) == 0
+
+    def test_get_event_list_limit(self):
+        id = "123456.random"
+        id2 = "54321.random"
+
+        with TestClient(self.app) as client:
+            super().insert_mock_event(id)
+            events = client.get("/events").json()
+            assert len(events) == 1
+            assert events[0]["id"] == id
+
+            super().insert_mock_event(id2)
+            events = client.get("/events").json()
+            assert len(events) == 2
+
+            events = client.get("/events", params={"limit": 1}).json()
+            assert len(events) == 1
+            assert events[0]["id"] == id
+
+            events = client.get("/events", params={"limit": 3}).json()
+            assert len(events) == 2
+
+    def test_get_event_list_no_match_has_clip(self):
+        now = int(datetime.now().timestamp())
+
+        with TestClient(self.app) as client:
+            id = "123456.random"
+            super().insert_mock_event(id, now, now + 2)
+            events = client.get("/events", params={"has_clip": 0}).json()
+            assert len(events) == 0
+
+    def test_get_event_list_has_clip(self):
+        with TestClient(self.app) as client:
+            id = "123456.random"
+            super().insert_mock_event(id, has_clip=True)
+            events = client.get("/events", params={"has_clip": 1}).json()
+            assert len(events) == 1
+            assert events[0]["id"] == id
+
+    def test_get_event_list_sort_score(self):
+        with TestClient(self.app) as client:
+            id = "123456.random"
+            id2 = "54321.random"
+            super().insert_mock_event(id, top_score=37, score=37, data={"score": 50})
+            super().insert_mock_event(id2, top_score=47, score=47, data={"score": 20})
+            events = client.get("/events", params={"sort": "score_asc"}).json()
+            assert len(events) == 2
+            assert events[0]["id"] == id2
+            assert events[1]["id"] == id
+
+            events = client.get("/events", params={"sort": "score_des"}).json()
+            assert len(events) == 2
+            assert events[0]["id"] == id
+            assert events[1]["id"] == id2
+
+    def test_get_event_list_sort_start_time(self):
+        now = int(datetime.now().timestamp())
+
+        with TestClient(self.app) as client:
+            id = "123456.random"
+            id2 = "54321.random"
+            super().insert_mock_event(id, start_time=now + 3)
+            super().insert_mock_event(id2, start_time=now)
+            events = client.get("/events", params={"sort": "date_asc"}).json()
+            assert len(events) == 2
+            assert events[0]["id"] == id2
+            assert events[1]["id"] == id
+
+            events = client.get("/events", params={"sort": "date_desc"}).json()
+            assert len(events) == 2
+            assert events[0]["id"] == id
+            assert events[1]["id"] == id2

frigate/test/http_api/test_http_review.py

@@ -569,3 +569,177 @@ class TestHttpReview(BaseTestHttp):
             recording_ids_in_db_after = self._get_recordings(ids)
             assert len(review_ids_in_db_after) == 0
             assert len(recording_ids_in_db_after) == 0
+
+    ####################################################################################################################
+    ###################################  GET /review/activity/motion Endpoint   ########################################
+    ####################################################################################################################
+    def test_review_activity_motion_no_data_for_time_range(self):
+        now = datetime.now().timestamp()
+
+        with TestClient(self.app) as client:
+            params = {
+                "after": now,
+                "before": now + 3,
+            }
+            response = client.get("/review/activity/motion", params=params)
+            assert response.status_code == 200
+            response_json = response.json()
+            assert len(response_json) == 0
+
+    def test_review_activity_motion(self):
+        now = int(datetime.now().timestamp())
+
+        with TestClient(self.app) as client:
+            one_m = int((datetime.now() + timedelta(minutes=1)).timestamp())
+            id = "123456.random"
+            id2 = "123451.random"
+            super().insert_mock_recording(id, now + 1, now + 2, motion=101)
+            super().insert_mock_recording(id2, one_m + 1, one_m + 2, motion=200)
+            params = {
+                "after": now,
+                "before": one_m + 3,
+                "scale": 1,
+            }
+            response = client.get("/review/activity/motion", params=params)
+            assert response.status_code == 200
+            response_json = response.json()
+            assert len(response_json) == 61
+            self.assertDictEqual(
+                {"motion": 50.5, "camera": "front_door", "start_time": now + 1},
+                response_json[0],
+            )
+            for item in response_json[1:-1]:
+                self.assertDictEqual(
+                    {"motion": 0.0, "camera": "", "start_time": item["start_time"]},
+                    item,
+                )
+            self.assertDictEqual(
+                {"motion": 100.0, "camera": "front_door", "start_time": one_m + 1},
+                response_json[len(response_json) - 1],
+            )
+
+    ####################################################################################################################
+    ###################################  GET /review/event/{event_id} Endpoint   #######################################
+    ####################################################################################################################
+    def test_review_event_not_found(self):
+        with TestClient(self.app) as client:
+            response = client.get("/review/event/123456.random")
+            assert response.status_code == 404
+            response_json = response.json()
+            self.assertDictEqual(
+                {"success": False, "message": "Review item not found"},
+                response_json,
+            )
+
+    def test_review_event_not_found_in_data(self):
+        now = datetime.now().timestamp()
+
+        with TestClient(self.app) as client:
+            id = "123456.random"
+            super().insert_mock_review_segment(id, now + 1, now + 2)
+            response = client.get(f"/review/event/{id}")
+            assert response.status_code == 404
+            response_json = response.json()
+            self.assertDictEqual(
+                {"success": False, "message": "Review item not found"},
+                response_json,
+            )
+
+    def test_review_get_specific_event(self):
+        now = datetime.now().timestamp()
+
+        with TestClient(self.app) as client:
+            event_id = "123456.event.random"
+            super().insert_mock_event(event_id)
+            review_id = "123456.review.random"
+            super().insert_mock_review_segment(
+                review_id, now + 1, now + 2, data={"detections": {"event_id": event_id}}
+            )
+            response = client.get(f"/review/event/{event_id}")
+            assert response.status_code == 200
+            response_json = response.json()
+            self.assertDictEqual(
+                {
+                    "id": review_id,
+                    "camera": "front_door",
+                    "start_time": now + 1,
+                    "end_time": now + 2,
+                    "has_been_reviewed": False,
+                    "severity": SeverityEnum.alert,
+                    "thumb_path": "False",
+                    "data": {"detections": {"event_id": event_id}},
+                },
+                response_json,
+            )
+
+    ####################################################################################################################
+    ###################################  GET /review/{review_id} Endpoint   #######################################
+    ####################################################################################################################
+    def test_review_not_found(self):
+        with TestClient(self.app) as client:
+            response = client.get("/review/123456.random")
+            assert response.status_code == 404
+            response_json = response.json()
+            self.assertDictEqual(
+                {"success": False, "message": "Review item not found"},
+                response_json,
+            )
+
+    def test_get_review(self):
+        now = datetime.now().timestamp()
+
+        with TestClient(self.app) as client:
+            review_id = "123456.review.random"
+            super().insert_mock_review_segment(review_id, now + 1, now + 2)
+            response = client.get(f"/review/{review_id}")
+            assert response.status_code == 200
+            response_json = response.json()
+            self.assertDictEqual(
+                {
+                    "id": review_id,
+                    "camera": "front_door",
+                    "start_time": now + 1,
+                    "end_time": now + 2,
+                    "has_been_reviewed": False,
+                    "severity": SeverityEnum.alert,
+                    "thumb_path": "False",
+                    "data": {},
+                },
+                response_json,
+            )
+
+    ####################################################################################################################
+    ###################################  DELETE /review/{review_id}/viewed Endpoint   ##################################
+    ####################################################################################################################
+    def test_delete_review_viewed_review_not_found(self):
+        with TestClient(self.app) as client:
+            review_id = "123456.random"
+            response = client.delete(f"/review/{review_id}/viewed")
+            assert response.status_code == 404
+            response_json = response.json()
+            self.assertDictEqual(
+                {"success": False, "message": f"Review {review_id} not found"},
+                response_json,
+            )
+
+    def test_delete_review_viewed(self):
+        now = datetime.now().timestamp()
+
+        with TestClient(self.app) as client:
+            review_id = "123456.review.random"
+            super().insert_mock_review_segment(
+                review_id, now + 1, now + 2, has_been_reviewed=True
+            )
+            review_before = ReviewSegment.get(ReviewSegment.id == review_id)
+            assert review_before.has_been_reviewed == True
+
+            response = client.delete(f"/review/{review_id}/viewed")
+            assert response.status_code == 200
+            response_json = response.json()
+            self.assertDictEqual(
+                {"success": True, "message": f"Set Review {review_id} as not viewed"},
+                response_json,
+            )
+
+            review_after = ReviewSegment.get(ReviewSegment.id == review_id)
+            assert review_after.has_been_reviewed == False

frigate/test/test_http.py

@@ -2,7 +2,6 @@ import datetime
 import logging
 import os
 import unittest
-from unittest.mock import Mock
 
 from fastapi.testclient import TestClient
 from peewee_migrate import Router
@@ -13,7 +12,6 @@ from playhouse.sqliteq import SqliteQueueDatabase
 from frigate.api.fastapi_app import create_fastapi_app
 from frigate.config import FrigateConfig
 from frigate.models import Event, Recordings, Timeline
-from frigate.stats.emitter import StatsEmitter
 from frigate.test.const import TEST_DB, TEST_DB_CLEANUPS
 
 
@@ -111,43 +109,6 @@ class TestHttp(unittest.TestCase):
         except OSError:
             pass
 
-    def test_get_event_list(self):
-        app = create_fastapi_app(
-            FrigateConfig(**self.minimal_config),
-            self.db,
-            None,
-            None,
-            None,
-            None,
-            None,
-            None,
-            None,
-        )
-        id = "123456.random"
-        id2 = "7890.random"
-
-        with TestClient(app) as client:
-            _insert_mock_event(id)
-            events = client.get("/events").json()
-            assert events
-            assert len(events) == 1
-            assert events[0]["id"] == id
-            _insert_mock_event(id2)
-            events = client.get("/events").json()
-            assert events
-            assert len(events) == 2
-            events = client.get(
-                "/events",
-                params={"limit": 1},
-            ).json()
-            assert events
-            assert len(events) == 1
-            events = client.get(
-                "/events",
-                params={"has_clip": 0},
-            ).json()
-            assert not events
-
     def test_get_good_event(self):
         app = create_fastapi_app(
             FrigateConfig(**self.minimal_config),
@@ -381,25 +342,6 @@ class TestHttp(unittest.TestCase):
             assert recording
             assert recording[0]["id"] == id
 
-    def test_stats(self):
-        stats = Mock(spec=StatsEmitter)
-        stats.get_latest_stats.return_value = self.test_stats
-        app = create_fastapi_app(
-            FrigateConfig(**self.minimal_config),
-            self.db,
-            None,
-            None,
-            None,
-            None,
-            None,
-            stats,
-            None,
-        )
-
-        with TestClient(app) as client:
-            full_stats = client.get("/stats").json()
-            assert full_stats == self.test_stats
-
 
 def _insert_mock_event(
     id: str,

notebooks/YOLO_NAS_Pretrained_Export.ipynb

@@ -11,6 +11,18 @@
         "! pip install -q super_gradients==3.7.1"
       ]
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.10/dist-packages/super_gradients/training/pretrained_models.py\n",
+        "! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.10/dist-packages/super_gradients/training/utils/checkpoint_utils.py"
+      ],
+      "metadata": {
+        "id": "NiRCt917KKcL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -72,4 +84,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}

web/src/components/card/SearchThumbnail.tsx

@@ -1,4 +1,4 @@
-import { useMemo } from "react";
+import { useCallback, useMemo } from "react";
 import { useApiHost } from "@/api";
 import { getIconForLabel } from "@/utils/iconUtil";
 import useSWR from "swr";
@@ -33,6 +33,16 @@ export default function SearchThumbnail({
     onClick(searchResult, true, false);
   });
 
+  const handleOnClick = useCallback(
+    (e: React.MouseEvent<HTMLDivElement>) => {
+      if (e.metaKey) {
+        e.stopPropagation();
+        onClick(searchResult, true, false);
+      }
+    },
+    [searchResult, onClick],
+  );
+
   const objectLabel = useMemo(() => {
     if (
       !config ||
@@ -57,6 +67,7 @@ export default function SearchThumbnail({
       <div className={`size-full ${imgLoaded ? "visible" : "invisible"}`}>
         <img
           ref={imgRef}
+          onClick={handleOnClick}
           className={cn(
             "size-full select-none object-cover object-center opacity-100 transition-opacity",
           )}

web/src/components/filter/SearchFilterGroup.tsx

@@ -61,7 +61,9 @@ export default function SearchFilterGroup({
       }
       const cameraConfig = config.cameras[camera];
       cameraConfig.objects.track.forEach((label) => {
-        labels.add(label);
+        if (!config.model.all_attributes.includes(label)) {
+          labels.add(label);
+        }
       });
 
       if (cameraConfig.audio.enabled_in_config) {

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

@@ -85,6 +85,7 @@ type SearchDetailDialogProps = {
   setSearch: (search: SearchResult | undefined) => void;
   setSearchPage: (page: SearchTab) => void;
   setSimilarity?: () => void;
+  setInputFocused: React.Dispatch<React.SetStateAction<boolean>>;
 };
 export default function SearchDetailDialog({
   search,
@@ -92,6 +93,7 @@ export default function SearchDetailDialog({
   setSearch,
   setSearchPage,
   setSimilarity,
+  setInputFocused,
 }: SearchDetailDialogProps) {
   const { data: config } = useSWR<FrigateConfig>("config", {
     revalidateOnFocus: false,
@@ -232,6 +234,7 @@ export default function SearchDetailDialog({
             config={config}
             setSearch={setSearch}
             setSimilarity={setSimilarity}
+            setInputFocused={setInputFocused}
           />
         )}
         {page == "snapshot" && (
@@ -266,12 +269,14 @@ type ObjectDetailsTabProps = {
   config?: FrigateConfig;
   setSearch: (search: SearchResult | undefined) => void;
   setSimilarity?: () => void;
+  setInputFocused: React.Dispatch<React.SetStateAction<boolean>>;
 };
 function ObjectDetailsTab({
   search,
   config,
   setSearch,
   setSimilarity,
+  setInputFocused,
 }: ObjectDetailsTabProps) {
   const apiHost = useApiHost();
 
@@ -283,6 +288,14 @@ function ObjectDetailsTab({
 
   const [desc, setDesc] = useState(search?.data.description);
 
+  const handleDescriptionFocus = useCallback(() => {
+    setInputFocused(true);
+  }, [setInputFocused]);
+
+  const handleDescriptionBlur = useCallback(() => {
+    setInputFocused(false);
+  }, [setInputFocused]);
+
   // we have to make sure the current selected search item stays in sync
   useEffect(() => setDesc(search?.data.description ?? ""), [search]);
 
@@ -499,6 +512,8 @@ function ObjectDetailsTab({
               placeholder="Description of the tracked object"
               value={desc}
               onChange={(e) => setDesc(e.target.value)}
+              onFocus={handleDescriptionFocus}
+              onBlur={handleDescriptionBlur}
             />
           </>
         )}

web/src/types/frigateConfig.ts

@@ -343,6 +343,7 @@ export interface FrigateConfig {
     width: number;
     colormap: { [key: string]: [number, number, number] };
     attributes_map: { [key: string]: [string] };
+    all_attributes: [string];
   };
 
   motion: Record<string, unknown> | null;

web/src/views/search/SearchView.tsx

@@ -444,6 +444,7 @@ export default function SearchView({
         setSimilarity={
           searchDetail && (() => setSimilaritySearch(searchDetail))
         }
+        setInputFocused={setInputFocused}
       />
 
       <div