Bump @remix-run/router and react-router-dom in /web

Bumps [@remix-run/router](https://github.com/remix-run/react-router/tree/HEAD/packages/router) to 1.23.3 and updates ancestor dependency [react-router-dom](https://github.com/remix-run/react-router/tree/HEAD/packages/react-router-dom). These dependencies need to be updated together.


Updates `@remix-run/router` from 1.23.2 to 1.23.3
- [Release notes](https://github.com/remix-run/react-router/releases)
- [Changelog](https://github.com/remix-run/react-router/blob/@remix-run/router@1.23.3/packages/router/CHANGELOG.md)
- [Commits](https://github.com/remix-run/react-router/commits/@remix-run/router@1.23.3/packages/router)

Updates `react-router-dom` from 6.30.3 to 6.30.4
- [Release notes](https://github.com/remix-run/react-router/releases)
- [Changelog](https://github.com/remix-run/react-router/blob/react-router-dom@6.30.4/packages/react-router-dom/CHANGELOG.md)
- [Commits](https://github.com/remix-run/react-router/commits/react-router-dom@6.30.4/packages/react-router-dom)

---
updated-dependencies:
- dependency-name: "@remix-run/router"
  dependency-version: 1.23.3
  dependency-type: indirect
- dependency-name: react-router-dom
  dependency-version: 6.30.4
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>

.cspell/frigate-dictionary.txt

@@ -8,6 +8,7 @@ amdgpu
 analyzeduration
 Annke
 apexcharts
+Aqara
 arange
 argmax
 argmin
@@ -64,6 +65,7 @@ dsize
 dtype
 ECONNRESET
 edgetpu
+Eufy
 facenet
 fastapi
 faststart

Makefile

@@ -1,7 +1,7 @@
 default_target: local
 
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.19.0
+VERSION = 0.18.0
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
 BOARDS= #Initialized empty

docker/main/requirements-wheels.txt

@@ -79,7 +79,5 @@ sherpa-onnx==1.12.*
 faster-whisper==1.1.*
 librosa==0.11.*
 soundfile==0.13.*
-# DeGirum detector
-degirum == 0.16.*
 # Memory profiling
 memray == 1.15.*

docs/data/object_detectors_models.yaml

@@ -1269,78 +1269,3 @@ axengine:
           input_dtype: int
           input_pixel_format: bgr
           labelmap_path: /labelmap/coco-80.txt
-degirumAiServer:
-  title: DeGirum AI Server
-  models:
-    - key: ai-server-inference
-      label: AI Server Inference
-      recommended: true
-      download: |-
-        Launch a DeGirum AI server as a Docker container, then point the detector at it. Add this to your `docker-compose.yml`:
-
-        ```yaml
-        degirum_detector:
-          container_name: degirum
-          image: degirum/aiserver:latest
-          privileged: true
-          ports:
-            - "8778:8778"
-        ```
-
-        Set `location` to the server's service name, container name, or `host:port`.
-      ui: |
-        Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
-
-        | Field | Value |
-        | --- | --- |
-        | **Location** | `degirum` |
-        | **Zoo** | `degirum/public` |
-        | **Token** | your AI Hub token (optional for the public zoo) |
-      yaml: |
-        degirum_detector:
-          type: degirum
-          location: degirum
-          zoo: degirum/public
-          token: dg_example_token
-degirumLocal:
-  title: DeGirum Local
-  models:
-    - key: local-inference
-      label: Local Inference
-      recommended: true
-      download: Run hardware directly inside the Frigate container with `@local`, removing the AI server hop. The matching device runtime (e.g. the Hailo runtime) must be installed in the container; confirm it with `degirum sys-info`.
-      ui: |
-        Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
-
-        | Field | Value |
-        | --- | --- |
-        | **Location** | `@local` |
-        | **Zoo** | `degirum/public` |
-        | **Token** | your AI Hub token (optional for the public zoo) |
-      yaml: |
-        degirum_detector:
-          type: degirum
-          location: @local
-          zoo: degirum/public
-          token: dg_example_token
-degirumCloud:
-  title: DeGirum AI Hub Cloud
-  models:
-    - key: ai-hub-cloud-inference
-      label: AI Hub Cloud Inference
-      recommended: true
-      download: Run inferences on DeGirum's [AI Hub](https://hub.degirum.com) cloud with `@cloud`. Sign up, create an access token, and set it as `token`. Network latency may require lowering your detection fps.
-      ui: |
-        Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
-
-        | Field | Value |
-        | --- | --- |
-        | **Location** | `@cloud` |
-        | **Zoo** | `degirum/public` |
-        | **Token** | your AI Hub token (optional for the public zoo) |
-      yaml: |
-        degirum_detector:
-          type: degirum
-          location: @cloud
-          zoo: degirum/public
-          token: dg_example_token

docs/docs/configuration/advanced/reference.md

@@ -981,7 +981,9 @@ cameras:
       # Optional: Adjust sort order of cameras in the UI. Larger numbers come later (default: shown below)
       # By default the cameras are sorted alphabetically.
       order: 0
-      # Optional: Whether or not to show the camera in the Frigate UI (default: shown below)
+      # Optional: Whether or not to show the camera on the default All Cameras live dashboard.
+      # The camera is still available everywhere else, including camera groups and settings
+      # (default: shown below)
       dashboard: True
       # Optional: Whether this camera is visible in review (the review page and its camera
       # filter, motion review, and the history view) (default: shown below)

docs/docs/configuration/advanced/system.md

@@ -293,6 +293,10 @@ networking:
 
 This setting is for advanced users. For the majority of use cases it's recommended to change the `ports` section of your Docker compose file or use the Docker `run` `--publish` option instead, e.g. `-p 443:8971`. Changing Frigate's ports may break some integrations.
 
+The internal and external ports must be different port numbers, and Frigate will refuse to start otherwise. Requests arriving on the internal port are treated as authenticated admins, so pointing both at the same port would remove authentication from the external one.
+
+Nginx binds these ports when it starts, so port changes only take effect after Frigate restarts.
+
 :::
 
 ### Customizing the Nginx configuration

docs/docs/configuration/custom_classification/object_classification.md

@@ -11,7 +11,7 @@ Object classification allows you to train a custom MobileNetV2 classification mo
 
 :::info
 
-Training a custom object classification model requires a one-time internet connection to download MobileNetV2 base weights. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+Training a custom object classification model requires an internet connection to download MobileNetV2 base weights. By default these weights are not cached in `/config/`, so they are downloaded again after the container is recreated. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
 
 :::
 

docs/docs/configuration/custom_classification/state_classification.md

@@ -11,7 +11,7 @@ State classification allows you to train a custom MobileNetV2 classification mod
 
 :::info
 
-Training a custom state classification model requires a one-time internet connection to download MobileNetV2 base weights. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+Training a custom state classification model requires an internet connection to download MobileNetV2 base weights. By default these weights are not cached in `/config/`, so they are downloaded again after the container is recreated. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
 
 :::
 

docs/docs/configuration/go2rtc.md

@@ -67,4 +67,6 @@ If your stream won't play, has no audio, uses excessive CPU, or otherwise misbeh
 
 ## Homekit Configuration
 
-To add camera streams to Homekit Frigate must be configured in docker to use `host` networking mode. Once that is done, you can use the go2rtc WebUI (accessed via port 1984, which is disabled by default) to export a camera to Homekit. Any changes made will automatically be saved to `/config/go2rtc_homekit.yml`.
+To export camera streams to HomeKit, Frigate must be configured in docker to use `host` networking mode. HomeKit settings are stored in `/config/go2rtc_homekit.yml` rather than in your Frigate config, and are edited through the go2rtc config editor at `http://<frigate_host>:1984/editor.html`. Pairings are saved back to that file automatically.
+
+See the [HomeKit integration docs](/integrations/homekit) for the full setup, including the video and audio requirements HomeKit places on the stream.

docs/docs/configuration/live.md

@@ -334,7 +334,7 @@ When your browser runs into problems playing back your camera streams, it will l
 
 - **stalled**
   - What it means: Playback has stalled because the player has fallen too far behind live (extended buffering or no data arriving).
-  - What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval: shorter intervals make playback start and recover faster. You can also try increasing the timeout value in the UI pane of Frigate's settings.
+  - What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval: shorter intervals make playback start and recover faster. You can also try increasing the timeout value in <NavPath path="Settings > UI" /> .
 
   - Possible console messages from the player code:
     - `Buffer time (10 seconds) exceeded, browser may not be playing media correctly.`

docs/docs/configuration/object_detectors.md

@@ -24,7 +24,6 @@ Frigate supports multiple different detectors that work on different types of ha
 - [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB, Mini PCIe, and m.2 formats allowing for a wide range of compatibility with devices.
 - [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
 - <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
-- <CommunityBadge /> [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com).
 
 **AMD**
 
@@ -755,87 +754,6 @@ Explanation of the parameters:
   - **example**: Specifying `output_name = "frigate-{quant}-{input_basename}-{soc}-v{tk_version}"` could result in a model called `frigate-i8-my_model-rk3588-v2.3.0.rknn`.
 - `config`: Configuration passed to `rknn-toolkit2` for model conversion. For an explanation of all available parameters have a look at section "2.2. Model configuration" of [this manual](https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.2/03_Rockchip_RKNPU_API_Reference_RKNN_Toolkit2_V2.3.2_EN.pdf).
 
-## DeGirum
-
-DeGirum is a detector that can use any type of hardware listed on [their website](https://hub.degirum.com). DeGirum can be used with local hardware through a DeGirum AI Server, or through the use of `@local`. You can also connect directly to DeGirum's AI Hub to run inferences. **Please Note:** This detector _cannot_ be used for commercial purposes.
-
-### Configuration {#configuration-degirum}
-
-#### AI Server Inference
-
-Before starting with the config file for this section, you must first launch an AI server. DeGirum has an AI server ready to use as a docker container. Add this to your `docker-compose.yml` to get started:
-
-```yaml
-degirum_detector:
-  container_name: degirum
-  image: degirum/aiserver:latest
-  privileged: true
-  ports:
-    - "8778:8778"
-```
-
-All supported hardware will automatically be found on your AI server host as long as relevant runtimes and drivers are properly installed on your machine. Refer to [DeGirum's docs site](https://docs.degirum.com/pysdk/runtimes-and-drivers) if you have any trouble.
-
-Once completed, configure the detector as follows:
-
-<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumAiServer.models} />
-
-Setting up a model in the `config.yml` is similar to setting up an AI server.
-You can set it to:
-
-- A model listed on the [AI Hub](https://hub.degirum.com), given that the correct zoo name is listed in your detector
-  - If this is what you choose to do, the correct model will be downloaded onto your machine before running.
-- A local directory acting as a zoo. See DeGirum's docs site [for more information](https://docs.degirum.com/pysdk/user-guide-pysdk/organizing-models#model-zoo-directory-structure).
-- A path to some model.json.
-
-```yaml
-model:
-  path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
-  width: 300 # width is in the model name as the first number in the "int"x"int" section
-  height: 300 # height is in the model name as the second number in the "int"x"int" section
-  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
-```
-
-#### Local Inference
-
-It is also possible to eliminate the need for an AI server and run the hardware directly. The benefit of this approach is that you eliminate any bottlenecks that occur when transferring prediction results from the AI server docker container to the frigate one. However, the method of implementing local inference is different for every device and hardware combination, so it's usually more trouble than it's worth. A general guideline to achieve this would be:
-
-1. Ensuring that the frigate docker container has the runtime you want to use. So for instance, running `@local` for Hailo means making sure the container you're using has the Hailo runtime installed.
-2. To double check the runtime is detected by the DeGirum detector, make sure the `degirum sys-info` command properly shows whatever runtimes you mean to install.
-3. Create a DeGirum detector in your configuration.
-
-<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumLocal.models} />
-
-Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
-
-```yaml
-model:
-  path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
-  width: 300 # width is in the model name as the first number in the "int"x"int" section
-  height: 300 # height is in the model name as the second number in the "int"x"int" section
-  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
-```
-
-#### AI Hub Cloud Inference
-
-If you do not possess whatever hardware you want to run, there's also the option to run cloud inferences. Do note that your detection fps might need to be lowered as network latency does significantly slow down this method of detection. For use with Frigate, we highly recommend using a local AI server as described above. To set up cloud inferences,
-
-1. Sign up at [DeGirum's AI Hub](https://hub.degirum.com).
-2. Get an access token.
-3. Create a DeGirum detector in your configuration.
-
-<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumCloud.models} />
-
-Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
-
-```yaml
-model:
-  path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
-  width: 300 # width is in the model name as the first number in the "int"x"int" section
-  height: 300 # height is in the model name as the second number in the "int"x"int" section
-  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
-```
-
 ## AXERA
 
 Hardware accelerated object detection is supported on the following SoCs:

docs/docs/configuration/profiles.md

@@ -126,7 +126,7 @@ Only the fields you explicitly set in a profile override are applied. All other
 
 ## Activating Profiles
 
-Profiles can be activated and deactivated via the Frigate UI, [MQTT](/integrations/mqtt#frigateprofileset), or the Home Assistant integration.
+Profiles can be activated and deactivated via the Frigate UI, [MQTT](/integrations/mqtt#frigateprofileset), the [HTTP API](../integrations/api/camera-set-camera-camera-name-set-feature-sub-command-put.api.mdx), or the Home Assistant integration.
 
 In the Frigate UI, open the Settings cog and select **Profiles** from the submenu to see all defined profiles. From there you can activate any profile or deactivate the current one. The active profile is indicated in the UI so you always know which profile is in effect.
 

docs/docs/frigate/network_requirements.md

@@ -34,6 +34,12 @@ The following models are downloaded automatically the first time their associate
 | [Custom classification](/configuration/custom_classification/state_classification) (training) | MobileNetV2 ImageNet base weights (via Keras)                              | Google storage       |
 | [Audio transcription](/configuration/advanced/system)                                         | Whisper or Sherpa-ONNX streaming model                                     | HuggingFace / OpenAI |
 
+:::note
+
+The MobileNetV2 base weights are the one exception to the `/config/model_cache/` rule. They are also the only entry that is not downloaded when the feature is enabled: Frigate fetches them when a training run actually starts.
+
+:::
+
 ### Hardware-Specific Detector Models
 
 If you are using one of the following hardware detectors and have not provided your own model file, a default model will be downloaded on first startup:
@@ -75,7 +81,7 @@ If your Frigate instance has restricted internet access, you can point model dow
 | `HF_ENDPOINT`                       | `https://huggingface.co`            | Semantic search, Sherpa-ONNX, AXEngine models |
 | `GITHUB_ENDPOINT`                   | `https://github.com`                | Face recognition, LPR, RKNN models            |
 | `GITHUB_RAW_ENDPOINT`               | `https://raw.githubusercontent.com` | Bird classification                           |
-| `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` | Google storage (Keras default)      | Custom classification training                |
+| `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` | Unset (Keras uses its own default)  | Custom classification training                |
 
 ## Optional Cloud Services
 
@@ -147,9 +153,23 @@ When running as a Home Assistant App, the go2rtc startup script queries the loca
 To run Frigate in an air-gapped or offline environment:
 
 1. **Pre-download models**: Start Frigate with internet access once with all desired features enabled. Models will be cached in `/config/model_cache/`.
-2. **Disable version check**: Set `telemetry.version_check: false` in your configuration.
-3. **Block outbound model requests**: Set the `HF_HUB_OFFLINE=1` and `TRANSFORMERS_OFFLINE=1` environment variables to prevent HuggingFace and Transformers from attempting any network requests.
-4. **Avoid cloud features**: Do not configure Frigate+, Generative AI providers that require internet, or cloud MQTT brokers.
-5. **Use local model mirrors**: If limited internet is available, set the `HF_ENDPOINT`, `GITHUB_ENDPOINT`, and `GITHUB_RAW_ENDPOINT` environment variables to point to local mirrors.
+2. **Pre-download the training base weights**: If you plan to train custom classification models, set `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` before training, then run one training job while online. Without this variable the base weights are cached outside `/config/` and are lost whenever the container is recreated, so a later training run will fail offline. If the machine never has internet access, copy the weights in manually as described below.
+3. **Disable version check**: Set `telemetry.version_check: false` in your configuration.
+4. **Block outbound model requests**: Set the `HF_HUB_OFFLINE=1` and `TRANSFORMERS_OFFLINE=1` environment variables to prevent HuggingFace and Transformers from attempting any network requests.
+5. **Avoid cloud features**: Do not configure Frigate+, Generative AI providers that require internet, or cloud MQTT brokers.
+6. **Use local model mirrors**: If limited internet is available, set the `HF_ENDPOINT`, `GITHUB_ENDPOINT`, `GITHUB_RAW_ENDPOINT`, and `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` environment variables to point to local mirrors.
 
 After these steps, Frigate will operate with no outbound internet connections.
+
+### Manually Copying the Training Base Weights
+
+On a machine with internet access, download the weights:
+
+```bash
+curl -L -o mobilenet_v2_weights.h5 \
+  "https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_0.35_224_no_top.h5"
+```
+
+Copy the file into your Frigate config volume as `/config/model_cache/MobileNet/mobilenet_v2_weights.h5`, keeping that exact filename, then set the environment variable `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` in your Docker compose file to the URL above and restart Frigate.
+
+The variable must be set even though the URL is never contacted. If it is unset, Frigate ignores the copied file and asks Keras to download the weights instead.

docs/docs/integrations/homekit.md

@@ -3,35 +3,100 @@ id: homekit
 title: HomeKit
 ---
 
-Frigate cameras can be integrated with Apple HomeKit through go2rtc. This allows you to view your camera streams directly in the Apple Home app on your iOS, iPadOS, macOS, and tvOS devices.
+Frigate cameras can be exported to Apple HomeKit through go2rtc. Each exported camera appears as an accessory in the Apple Home app on your iOS, iPadOS, macOS, and tvOS devices.
 
 ## Overview
 
-HomeKit integration is handled entirely through go2rtc, which is embedded in Frigate. go2rtc provides the necessary HomeKit Accessory Protocol (HAP) server to expose your cameras to HomeKit.
+Exporting cameras is handled entirely through go2rtc, which is embedded in Frigate. go2rtc provides the necessary HomeKit Accessory Protocol (HAP) server, so your camera is published to HomeKit as an accessory in its own right.
 
-## Setup
+:::note
 
-All HomeKit configuration and pairing should be done through the **go2rtc WebUI**.
+This is the opposite of importing a HomeKit camera. go2rtc can also pair with an existing HomeKit camera (Aqara, Eve, Eufy, and similar) and use it as a stream source, which is what the `add` page of the go2rtc WebUI is for. That page discovers HomeKit accessories on your network and will not list your Frigate cameras. It is not used for exporting.
 
-### Accessing the go2rtc WebUI
-
-The go2rtc WebUI is available at:
-
-```
-http://<frigate_host>:1984
-```
-
-Replace `<frigate_host>` with the IP address or hostname of your Frigate server.
-
-### Pairing Cameras
-
-1. Navigate to the go2rtc WebUI at `http://<frigate_host>:1984`
-2. Use the `add` section to add a new camera to HomeKit
-3. Follow the on-screen instructions to generate pairing codes for your cameras
+:::
 
 ## Requirements
 
-- Frigate must be accessible on your local network using host network_mode
-- Your iOS device must be on the same network as Frigate
-- Port 1984 must be accessible for the go2rtc WebUI
-- For detailed go2rtc configuration options, refer to the [go2rtc documentation](https://github.com/AlexxIT/go2rtc)
+- Frigate must be running with `network_mode: host` so that HomeKit can discover your cameras over mDNS
+- Your Apple device must be on the same network as Frigate
+- Port 1984 must be accessible so you can reach the go2rtc WebUI
+
+HomeKit also places strict limits on the stream itself. go2rtc passes your stream through without resizing or re-encoding it, so the stream you export must already meet these requirements:
+
+- **Video:** H.264 at 1920x1080, 1280x720, or 320x240
+- **Audio:** Opus, mono, 16 kHz
+
+A camera's full resolution stream usually does not qualify. See [Exporting a compatible stream](#exporting-a-compatible-stream) below.
+
+## Configuration
+
+HomeKit settings are stored in `/config/go2rtc_homekit.yml`. This is a separate file from your Frigate config, because go2rtc needs to write your pairings back to it when you pair a device.
+
+Edit it using the go2rtc config editor, which writes to that file directly:
+
+```
+http://<frigate_host>:1984/editor.html
+```
+
+Replace `<frigate_host>` with the IP address or hostname of your Frigate server. The editor will be empty until you add a HomeKit section, since this file holds only your HomeKit settings and not the rest of your go2rtc config.
+
+:::warning
+
+Do not put the `homekit:` section in the `go2rtc:` section of your Frigate config.
+
+Frigate regenerates that config on every startup, so go2rtc cannot save your pairings to it. Pairing will appear to succeed and then fail after the next restart with `PairVerify with unknown client_id`. If the section exists in both places, your saved pairings are erased on every restart.
+
+:::
+
+Add an entry for each camera you want to export. The key must match the name of a go2rtc stream, and the pin must be 8 digits. This is the number the Home app calls the setup code:
+
+```yaml
+homekit:
+  front_door:
+    name: Front Door
+    pin: "12345678"
+```
+
+If the key does not match a go2rtc stream, go2rtc logs `[homekit] missing stream:` at startup and the camera will not appear in the Home app.
+
+:::note
+
+go2rtc derives each accessory's HomeKit identity from this key, so renaming it later means the camera appears as a new accessory and has to be paired again. Settle on the name before you pair.
+
+:::
+
+Frigate keeps only the `homekit:` section of this file when it starts, so do not store streams or other go2rtc settings in it.
+
+### Exporting a compatible stream
+
+If a camera's stream does not meet the requirements listed above, define a scaled restream in your Frigate config and point HomeKit at that stream instead of the original:
+
+```yaml
+go2rtc:
+  streams:
+    front_door:
+      - rtsp://user:password@192.168.1.50:554/stream
+    front_door_homekit:
+      - "ffmpeg:front_door#video=h264#width=1280#height=720#audio=opus/16000"
+```
+
+```yaml
+# /config/go2rtc_homekit.yml
+homekit:
+  front_door_homekit:
+    name: Front Door
+    pin: "12345678"
+```
+
+Add `#hardware=cuda`, `#hardware=vaapi`, or the appropriate value for your system to transcode using your GPU. Note that NVENC cannot encode H.264 wider than 4096 pixels, so very wide streams must be scaled down as shown above rather than only re-encoded.
+
+## Pairing Cameras
+
+1. Restart Frigate after adding the `homekit:` section
+2. In the Apple Home app, choose **Add Accessory**, then **More options** to enter a code manually
+3. Select your camera and enter the pin you configured as the setup code
+4. Confirm that a `pairings:` list now appears under the camera in `/config/go2rtc_homekit.yml`
+
+Pairings are saved back to that file automatically. If step 4 shows no `pairings:` list, check the Frigate log for `[homekit] can't save`, which means the `homekit:` section is missing from `/config/go2rtc_homekit.yml`.
+
+For detailed go2rtc configuration options, refer to the [go2rtc documentation](https://github.com/AlexxIT/go2rtc).

docs/docs/troubleshooting/dummy-camera.md

@@ -39,7 +39,7 @@ The per-clip variation is typically quite low and is mostly an artifact of keyfr
 
 Debug Replay lets you re-run Frigate's detection pipeline against a section of recorded video without manually configuring a dummy camera. It automatically extracts the recording, creates a temporary camera with the same detection settings as the original, and loops the clip through the pipeline so you can observe detections in real time.
 
-The replay camera behaves like a live camera feed rather than History's video player: it loops the clip continuously as Frigate analyzes it and has no playback controls, so you cannot pause, scrub, or step through it frame by frame.
+The replay camera behaves like a live camera feed rather than History's video player: it loops the clip continuously as Frigate analyzes it and has no playback controls, so you cannot pause, scrub, or step through it frame by frame. The Debug Replay camera does not save recordings or snapshots or surface anything in Explore, but it otherwise behaves like a regular camera, including running enrichments such as Face Recognition, LPR, and custom classification.
 
 Debug Replay isn't intended to be a one-stop pane for all Frigate diagnostics or a comprehensive debugging environment for every Frigate feature. It merely makes it easier to spin up a "dummy camera" and perform some common adjustments in real time. You'll still need to use the normal tools (logs, an MQTT client, etc) to debug your feature.
 

docs/static/frigate-api.yaml

@@ -693,6 +693,43 @@ paths:
         **Access:** Admin role required.
 
         Set a camera feature state. Use camera_name='*' to target all cameras.
+
+        The value to set is sent in the request body as `{"value": "<value>"}`.
+
+        | Feature | Accepted values |
+        | --- | --- |
+        | `enabled` | `ON`, `OFF` |
+        | `detect` | `ON`, `OFF` |
+        | `motion` | `ON`, `OFF` |
+        | `recordings` | `ON`, `OFF` |
+        | `snapshots` | `ON`, `OFF` |
+        | `audio` | `ON`, `OFF` |
+        | `audio_transcription` | `ON`, `OFF` |
+        | `notifications` | `ON`, `OFF` |
+        | `review_alerts` | `ON`, `OFF` |
+        | `review_detections` | `ON`, `OFF` |
+        | `object_descriptions` | `ON`, `OFF` |
+        | `review_descriptions` | `ON`, `OFF` |
+        | `improve_contrast` | `ON`, `OFF` |
+        | `ptz_autotracker` | `ON`, `OFF` |
+        | `birdseye` | `ON`, `OFF` |
+        | `birdseye_mode` | `CONTINUOUS`, `MOTION`, `OBJECTS` |
+        | `motion_contour_area` | integer |
+        | `motion_threshold` | integer |
+        | `motion_mask` | `ON`, `OFF` |
+        | `object_mask` | `ON`, `OFF` |
+        | `zone` | `ON`, `OFF` |
+        | `profile` | a profile name, or `none` to deactivate |
+
+        `motion_mask`, `object_mask`, and `zone` require the `sub_command` path
+        parameter to be set to the name of the mask or zone. All other features
+        reject a sub-command.
+
+        `profile` applies globally rather than per camera, so it requires
+        `camera_name` to be `*`.
+
+        These features map to the equivalent MQTT topics, which document the
+        behavior of each value in more detail.
       operationId: 
         camera_set_camera__camera_name__set__feature___sub_command__put
       parameters:
@@ -746,6 +783,43 @@ paths:
         **Access:** Admin role required.
 
         Set a camera feature state. Use camera_name='*' to target all cameras.
+
+        The value to set is sent in the request body as `{"value": "<value>"}`.
+
+        | Feature | Accepted values |
+        | --- | --- |
+        | `enabled` | `ON`, `OFF` |
+        | `detect` | `ON`, `OFF` |
+        | `motion` | `ON`, `OFF` |
+        | `recordings` | `ON`, `OFF` |
+        | `snapshots` | `ON`, `OFF` |
+        | `audio` | `ON`, `OFF` |
+        | `audio_transcription` | `ON`, `OFF` |
+        | `notifications` | `ON`, `OFF` |
+        | `review_alerts` | `ON`, `OFF` |
+        | `review_detections` | `ON`, `OFF` |
+        | `object_descriptions` | `ON`, `OFF` |
+        | `review_descriptions` | `ON`, `OFF` |
+        | `improve_contrast` | `ON`, `OFF` |
+        | `ptz_autotracker` | `ON`, `OFF` |
+        | `birdseye` | `ON`, `OFF` |
+        | `birdseye_mode` | `CONTINUOUS`, `MOTION`, `OBJECTS` |
+        | `motion_contour_area` | integer |
+        | `motion_threshold` | integer |
+        | `motion_mask` | `ON`, `OFF` |
+        | `object_mask` | `ON`, `OFF` |
+        | `zone` | `ON`, `OFF` |
+        | `profile` | a profile name, or `none` to deactivate |
+
+        `motion_mask`, `object_mask`, and `zone` require the `sub_command` path
+        parameter to be set to the name of the mask or zone. All other features
+        reject a sub-command.
+
+        `profile` applies globally rather than per camera, so it requires
+        `camera_name` to be `*`.
+
+        These features map to the equivalent MQTT topics, which document the
+        behavior of each value in more detail.
       operationId: camera_set_camera__camera_name__set__feature__put
       parameters:
         - name: camera_name
@@ -2872,44 +2946,6 @@ paths:
         - frigateUserAuth: []
       x-required-role: any
       description: '**Access:** Any authenticated user.'
-  /categorized_object_names:
-    get:
-      tags:
-        - App
-      summary: Get known object names by object type
-      description: |-
-        **Access:** Any authenticated user.
-
-        Returns the sub labels and attributes this install can attach,
-            grouped by object type. Unlike /sub_labels, which reflects what has already been
-            detected, this reads the config and model files, so it covers recognized face
-            names, named license plates, custom object classification categories, and the
-            detector attributes of tracked objects.
-      operationId: categorized_object_names_categorized_object_names_get
-      parameters:
-        - name: object_type
-          in: query
-          required: false
-          schema:
-            anyOf:
-              - type: string
-              - type: 'null'
-            title: Object Type
-      responses:
-        '200':
-          description: Successful Response
-          content:
-            application/json:
-              schema: {}
-        '422':
-          description: Validation Error
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/HTTPValidationError'
-      security:
-        - frigateUserAuth: []
-      x-required-role: any
   /audio_labels:
     get:
       tags:

frigate/api/app.py

@@ -71,7 +71,6 @@ from frigate.util.config import (
     find_config_file,
     redact_credential,
 )
-from frigate.util.object_names import get_categorized_object_names
 from frigate.util.schema import get_config_schema
 from frigate.util.services import (
     get_nvidia_driver_info,
@@ -1314,28 +1313,6 @@ def get_sub_labels(
     return JSONResponse(content=sub_labels)
 
 
-@router.get(
-    "/categorized_object_names",
-    dependencies=[Depends(allow_any_authenticated())],
-    summary="Get known object names by object type",
-    description="""Returns the sub labels and attributes this install can attach,
-    grouped by object type. Unlike /sub_labels, which reflects what has already been
-    detected, this reads the config and model files, so it covers recognized face
-    names, named license plates, custom object classification categories, and the
-    detector attributes of tracked objects.""",
-)
-def categorized_object_names(
-    request: Request,
-    object_type: str | None = None,
-    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
-):
-    return JSONResponse(
-        content=get_categorized_object_names(
-            request.app.frigate_config, allowed_cameras, object_type
-        )
-    )
-
-
 @router.get("/audio_labels", dependencies=[Depends(allow_any_authenticated())])
 def get_audio_labels():
     labels = load_labels("/audio-labelmap.txt", prefill=521)

frigate/api/auth.py

@@ -31,7 +31,7 @@ from frigate.api.media_auth import (
     deny_response_for_media_uri,
     is_role_restricted,
 )
-from frigate.config import AuthConfig, NetworkingConfig, ProxyConfig
+from frigate.config import AuthConfig, ProxyConfig
 from frigate.const import CONFIG_DIR, JWT_SECRET_ENV_VAR, PASSWORD_HASH_ALGORITHM
 from frigate.models import User
 
@@ -83,7 +83,6 @@ def require_admin_by_default():
         "/nvinfo",
         "/labels",
         "/sub_labels",
-        "/categorized_object_names",
         "/plus/models",
         "/recognized_license_plates",
         "/timeline",
@@ -621,18 +620,18 @@ def resolve_role(
 def auth(request: Request):
     auth_config: AuthConfig = request.app.frigate_config.auth
     proxy_config: ProxyConfig = request.app.frigate_config.proxy
-    networking_config: NetworkingConfig = request.app.frigate_config.networking
 
     success_response = Response("", status_code=202)
 
-    # handle case where internal port is a string with ip:port
-    internal_port = networking_config.listen.internal
-    if type(internal_port) is str:
-        internal_port = int(internal_port.split(":")[-1])
-
     # dont require auth if the request is on the internal port
-    # this header is set by Frigate's nginx proxy, so it cant be spoofed
-    if int(request.headers.get("x-server-port", default=0)) == internal_port:
+    # this header is set by Frigate's nginx proxy, so it cant be spoofed.
+    # the port is the boot-time snapshot rather than the live config value:
+    # nginx's listeners are fixed at container start, so an in-memory config
+    # change must never move the port that is trusted here
+    if (
+        int(request.headers.get("x-server-port", default=0))
+        == request.app.auth_internal_port
+    ):
         success_response.headers["remote-user"] = "anonymous"
         success_response.headers["remote-role"] = "admin"
         return success_response

frigate/api/camera.py

@@ -1328,7 +1328,45 @@ def camera_set(
     body: CameraSetBody,
     sub_command: str | None = None,
 ):
-    """Set a camera feature state. Use camera_name='*' to target all cameras."""
+    """Set a camera feature state. Use camera_name='*' to target all cameras.
+
+    The value to set is sent in the request body as `{"value": "<value>"}`.
+
+    | Feature | Accepted values |
+    | --- | --- |
+    | `enabled` | `ON`, `OFF` |
+    | `detect` | `ON`, `OFF` |
+    | `motion` | `ON`, `OFF` |
+    | `recordings` | `ON`, `OFF` |
+    | `snapshots` | `ON`, `OFF` |
+    | `audio` | `ON`, `OFF` |
+    | `audio_transcription` | `ON`, `OFF` |
+    | `notifications` | `ON`, `OFF` |
+    | `review_alerts` | `ON`, `OFF` |
+    | `review_detections` | `ON`, `OFF` |
+    | `object_descriptions` | `ON`, `OFF` |
+    | `review_descriptions` | `ON`, `OFF` |
+    | `improve_contrast` | `ON`, `OFF` |
+    | `ptz_autotracker` | `ON`, `OFF` |
+    | `birdseye` | `ON`, `OFF` |
+    | `birdseye_mode` | `CONTINUOUS`, `MOTION`, `OBJECTS` |
+    | `motion_contour_area` | integer |
+    | `motion_threshold` | integer |
+    | `motion_mask` | `ON`, `OFF` |
+    | `object_mask` | `ON`, `OFF` |
+    | `zone` | `ON`, `OFF` |
+    | `profile` | a profile name, or `none` to deactivate |
+
+    `motion_mask`, `object_mask`, and `zone` require the `sub_command` path
+    parameter to be set to the name of the mask or zone. All other features
+    reject a sub-command.
+
+    `profile` applies globally rather than per camera, so it requires
+    `camera_name` to be `*`.
+
+    These features map to the equivalent MQTT topics, which document the
+    behavior of each value in more detail.
+    """
     dispatcher = request.app.dispatcher
     frigate_config: FrigateConfig = request.app.frigate_config
 

frigate/api/chat.py

@@ -50,7 +50,6 @@ from frigate.jobs.vlm_watch import (
     stop_vlm_watch_job,
 )
 from frigate.models import Event
-from frigate.util.object_names import get_categorized_object_names
 
 logger = logging.getLogger(__name__)
 
@@ -540,11 +539,6 @@ async def execute_tool(
     if tool_name == "search_objects":
         return await _execute_search_objects(request, arguments, allowed_cameras)
 
-    if tool_name == "get_categorized_object_names":
-        return JSONResponse(
-            content=_execute_get_categorized_object_names(request, allowed_cameras)
-        )
-
     if tool_name == "find_similar_objects":
         result = await _execute_find_similar_objects(
             request, arguments, allowed_cameras
@@ -723,21 +717,6 @@ async def _execute_set_camera_state(
     return {"success": True, "camera": camera, "feature": feature, "value": value}
 
 
-def _execute_get_categorized_object_names(
-    request: Request,
-    allowed_cameras: list[str],
-) -> dict[str, Any]:
-    names = get_categorized_object_names(request.app.frigate_config, allowed_cameras)
-
-    if not names:
-        return {
-            "names": {},
-            "message": "No names configured; search by label or semantic_query.",
-        }
-
-    return {"names": names}
-
-
 async def _execute_tool_internal(
     tool_name: str,
     arguments: dict[str, Any],
@@ -762,8 +741,6 @@ async def _execute_tool_internal(
         except (json.JSONDecodeError, AttributeError) as e:
             logger.warning(f"Failed to extract tool result: {e}")
             return {"error": "Failed to parse tool result"}
-    elif tool_name == "get_categorized_object_names":
-        return _execute_get_categorized_object_names(request, allowed_cameras)
     elif tool_name == "find_similar_objects":
         return await _execute_find_similar_objects(request, arguments, allowed_cameras)
     elif tool_name == "set_camera_state":
@@ -796,8 +773,8 @@ async def _execute_tool_internal(
     else:
         logger.error(
             "Tool call failed: unknown tool %r. Expected one of: search_objects, find_similar_objects, "
-            "get_categorized_object_names, get_live_context, start_camera_watch, stop_camera_watch, "
-            "get_profile_status, get_recap. Arguments received: %s",
+            "get_live_context, start_camera_watch, stop_camera_watch, get_profile_status, get_recap. "
+            "Arguments received: %s",
             tool_name,
             json.dumps(arguments),
         )

frigate/api/fastapi_app.py

@@ -152,6 +152,8 @@ def create_fastapi_app(
     app.include_router(debug_replay.router)
     # App Properties
     app.frigate_config = frigate_config
+    # snapshot the port nginx bound at startup, the live config can be swapped
+    app.auth_internal_port = frigate_config.networking.listen.internal_port
     app.genai_manager = GenAIClientManager(frigate_config)
     app.embeddings = embeddings
     app.detected_frames_processor = detected_frames_processor

frigate/api/media.py

@@ -574,11 +574,11 @@ async def vod_ts(
             Recordings.start_time,
         )
         .where(
-            Recordings.camera == camera_name,
-            Recordings.start_time >= start_ts - MAX_SEGMENT_DURATION,
-            Recordings.start_time <= end_ts,
-            Recordings.end_time >= start_ts,
+            Recordings.start_time.between(start_ts, end_ts)
+            | Recordings.end_time.between(start_ts, end_ts)
+            | ((start_ts > Recordings.start_time) & (end_ts < Recordings.end_time))
         )
+        .where(Recordings.camera == camera_name)
         .order_by(Recordings.start_time.asc())
         .iterator()
     )

frigate/api/motion_search.py

@@ -182,7 +182,7 @@ async def get_motion_search_status_endpoint(
         )
 
     job = get_motion_search_job(job_id)
-    if not job:
+    if not job or job.camera != camera_name:
         return JSONResponse(
             content={"success": False, "message": "Job not found"},
             status_code=404,
@@ -253,7 +253,7 @@ async def cancel_motion_search_endpoint(
         )
 
     job = get_motion_search_job(job_id)
-    if not job:
+    if not job or job.camera != camera_name:
         return JSONResponse(
             content={"success": False, "message": "Job not found"},
             status_code=404,

frigate/api/record.py

@@ -25,7 +25,7 @@ from frigate.api.defs.query.recordings_query_parameters import (
 )
 from frigate.api.defs.response.generic_response import GenericResponse
 from frigate.api.defs.tags import Tags
-from frigate.const import MAX_SEGMENT_DURATION, RECORD_DIR
+from frigate.const import RECORD_DIR
 from frigate.models import Event, Recordings
 from frigate.util.time import get_dst_transitions
 
@@ -243,7 +243,6 @@ async def recordings(
         )
         .where(
             Recordings.camera == camera_name,
-            Recordings.start_time >= after - MAX_SEGMENT_DURATION,
             Recordings.end_time >= after,
             Recordings.start_time <= before,
         )

frigate/app.py

@@ -122,7 +122,6 @@ class FrigateApp:
         self.ptz_metrics: dict[str, PTZMetrics] = {}
         self.processes: dict[str, int] = {}
         self.embeddings: EmbeddingsContext | None = None
-        self.profile_manager: ProfileManager | None = None
         self.config_holder = ConfigHolder(config)
 
     @property
@@ -355,25 +354,6 @@ class FrigateApp:
         )
         self.dispatcher.profile_manager = self.profile_manager
 
-    def restore_active_profile(self) -> None:
-        """Re-activate the persisted profile after subscribers are connected.
-
-        ZMQ PUB/SUB drops messages with no subscribers, so activation must
-        run after every config_updater subscriber is up.
-        """
-        if self.profile_manager is None:
-            return
-
-        persisted = ProfileManager.load_persisted_profile()
-        if persisted and any(
-            persisted in cam.profiles for cam in self.config.cameras.values()
-        ):
-            logger.info("Restoring persisted profile '%s'", persisted)
-            # runtime overrides are layered on top via restore_runtime_state()
-            self.profile_manager.activate_profile(
-                persisted, clear_runtime_overrides=False
-            )
-
     def start_detectors(self) -> None:
         for name in self.config.cameras.keys():
             try:
@@ -622,6 +602,13 @@ class FrigateApp:
         self.start_detectors()
         self.init_dispatcher()
         self.init_profile_manager()
+
+        # workers get a copy of the config and can miss the broadcast below, so
+        # apply both layers here. must stay after init_profile_manager(), which
+        # snapshots the base config that profile deactivation resets to
+        self.profile_manager.restore_persisted_profile_to_config()
+        self.dispatcher.reapply_runtime_state_to_config()
+
         self.init_embeddings_client()
         self.start_video_output_processor()
         self.start_ptz_autotracker()
@@ -636,8 +623,9 @@ class FrigateApp:
         self.start_record_cleanup()
         self.start_watchdog()
 
-        # restore persisted runtime overrides on top of config
-        self.restore_active_profile()
+        # publish for the recording/review/embeddings processes, which start
+        # before the config can be corrected, and for the retained MQTT states
+        self.profile_manager.restore_persisted_profile()
         self.dispatcher.restore_runtime_state()
 
         self.init_auth()

frigate/config/camera/ui.py

@@ -13,8 +13,8 @@ class CameraUiConfig(FrigateBaseModel):
     )
     dashboard: bool = Field(
         default=True,
-        title="Show in UI",
-        description="Toggle whether this camera is visible everywhere in the Frigate UI. Disabling this will require manually editing the config to view this camera in the UI again.",
+        title="Show on Live dashboard",
+        description="Toggle whether this camera is visible on the default All Cameras live dashboard. The camera remains available everywhere else in the UI, including camera groups and settings.",
     )
     review: bool = Field(
         default=True,

frigate/config/network.py

@@ -1,10 +1,18 @@
-from pydantic import Field
+from pydantic import Field, model_validator
 
 from .base import FrigateBaseModel
 
 __all__ = ["IPv6Config", "ListenConfig", "NetworkingConfig"]
 
 
+def parse_listen_port(value: int | str) -> int:
+    """Return the port number from a bare port or an "address:port" value."""
+    if isinstance(value, str):
+        return int(value.split(":")[-1])
+
+    return value
+
+
 class IPv6Config(FrigateBaseModel):
     enabled: bool = Field(
         default=False,
@@ -25,6 +33,21 @@ class ListenConfig(FrigateBaseModel):
         description="External listening port for Frigate (default 8971).",
     )
 
+    @property
+    def internal_port(self) -> int:
+        return parse_listen_port(self.internal)
+
+    @property
+    def external_port(self) -> int:
+        return parse_listen_port(self.external)
+
+    @model_validator(mode="after")
+    def validate_distinct_ports(self) -> "ListenConfig":
+        if self.internal_port == self.external_port:
+            raise ValueError("internal and external must listen on different ports")
+
+        return self
+
 
 class NetworkingConfig(FrigateBaseModel):
     ipv6: IPv6Config = Field(

frigate/config/profile_manager.py

@@ -169,6 +169,93 @@ class ProfileManager:
                 self.config.active_profile = None
                 self._persist_active_profile(None)
 
+    def _validate_profile_name(self, profile_name: str | None) -> str | None:
+        """Return an error message if the name is not a defined profile."""
+        if profile_name is not None and profile_name not in self.config.profiles:
+            return f"Profile '{profile_name}' is not defined in the profiles section"
+
+        return None
+
+    def _apply_to_config(
+        self, profile_name: str | None
+    ) -> tuple[dict[str, set[str]], str | None]:
+        """Reset every camera to base, then apply the named profile on top.
+
+        Returns the changed camera/section pairs, plus an error message if
+        applying the profile failed partway through.
+        """
+        changed: dict[str, set[str]] = {}
+
+        self._reset_to_base(changed)
+
+        if profile_name is not None:
+            err = self._apply_profile_overrides(profile_name, changed)
+            if err:
+                return changed, err
+
+        return changed, None
+
+    def apply_profile_to_config(self, profile_name: str | None) -> str | None:
+        """Apply a profile to the in-memory config, without publishing it.
+
+        Safe to call ahead of activate_profile: both reset to the base config
+        first, so the later call re-derives the same state and still reports
+        every section as changed.
+
+        Returns:
+            None on success, or an error message string on failure.
+        """
+        err = self._validate_profile_name(profile_name)
+
+        if err:
+            return err
+
+        return self._apply_to_config(profile_name)[1]
+
+    def _persisted_profile_to_restore(self) -> str | None:
+        """Return the persisted profile name, if it still applies to a camera."""
+        persisted = self.load_persisted_profile()
+
+        if not persisted or not any(
+            persisted in cam.profiles for cam in self.config.cameras.values()
+        ):
+            return None
+
+        return persisted
+
+    def restore_persisted_profile_to_config(self) -> None:
+        """Restore the persisted profile into the config, without publishing.
+
+        Called before worker processes start, so they are handed a config that
+        already carries the profile rather than relying on the broadcast that
+        restore_persisted_profile() sends later.
+        """
+        persisted = self._persisted_profile_to_restore()
+
+        if persisted is None:
+            return
+
+        err = self.apply_profile_to_config(persisted)
+
+        if err:
+            logger.error("Failed to apply persisted profile '%s': %s", persisted, err)
+
+    def restore_persisted_profile(self) -> None:
+        """Re-activate the persisted profile once subscribers are connected.
+
+        The config already carries the profile; this pass publishes it for the
+        processes that start before the config can be corrected, and for the
+        retained MQTT states.
+        """
+        persisted = self._persisted_profile_to_restore()
+
+        if persisted is None:
+            return
+
+        logger.info("Restoring persisted profile '%s'", persisted)
+        # runtime overrides are layered on top by the dispatcher's replay
+        self.activate_profile(persisted, clear_runtime_overrides=False)
+
     def activate_profile(
         self,
         profile_name: str | None,
@@ -187,23 +274,16 @@ class ProfileManager:
         Returns:
             None on success, or an error message string on failure.
         """
-        if profile_name is not None:
-            if profile_name not in self.config.profiles:
-                return (
-                    f"Profile '{profile_name}' is not defined in the profiles section"
-                )
+        err = self._validate_profile_name(profile_name)
+
+        if err:
+            return err
 
         # Track which camera/section pairs get changed for ZMQ publishing
-        changed: dict[str, set[str]] = {}
+        changed, err = self._apply_to_config(profile_name)
 
-        # Reset all cameras to base config
-        self._reset_to_base(changed)
-
-        # Apply new profile overrides if activating
-        if profile_name is not None:
-            err = self._apply_profile_overrides(profile_name, changed)
-            if err:
-                return err
+        if err:
+            return err
 
         # Publish ZMQ updates only for sections that actually changed
         self._publish_updates(changed)

frigate/detectors/plugins/degirum.py

@@ -1,157 +0,0 @@
-import logging
-import queue
-from typing import Literal
-
-import numpy as np
-from pydantic import ConfigDict, Field
-
-from frigate.detectors.detection_api import DetectionApi
-from frigate.detectors.detector_config import BaseDetectorConfig
-
-logger = logging.getLogger(__name__)
-DETECTOR_KEY = "degirum"
-
-
-### DETECTOR CONFIG ###
-class DGDetectorConfig(BaseDetectorConfig):
-    """DeGirum detector for running models via DeGirum cloud or local inference services."""
-
-    model_config = ConfigDict(
-        title="DeGirum",
-    )
-
-    type: Literal[DETECTOR_KEY]
-    location: str = Field(
-        default=None,
-        title="Inference Location",
-        description="Location of the DeGirim inference engine (e.g. '@cloud', '127.0.0.1').",
-    )
-    zoo: str = Field(
-        default=None,
-        title="Model Zoo",
-        description="Path or URL to the DeGirum model zoo.",
-    )
-    token: str = Field(
-        default=None,
-        title="DeGirum Cloud Token",
-        description="Token for DeGirum Cloud access.",
-    )
-
-
-### ACTUAL DETECTOR  ###
-class DGDetector(DetectionApi):
-    type_key = DETECTOR_KEY
-
-    def __init__(self, detector_config: DGDetectorConfig):
-        try:
-            import degirum as dg
-        except ModuleNotFoundError:
-            raise ImportError("Unable to import DeGirum detector.") from None
-
-        self._queue = queue.Queue()
-        self._zoo = dg.connect(
-            detector_config.location, detector_config.zoo, detector_config.token
-        )
-
-        logger.debug(f"Models in zoo: {self._zoo.list_models()}")
-
-        self.dg_model = self._zoo.load_model(
-            detector_config.model.path,
-        )
-
-        # Setting input image format to raw reduces preprocessing time
-        self.dg_model.input_image_format = "RAW"
-
-        # Prioritize the most powerful hardware available
-        self.select_best_device_type()
-        # Frigate handles pre processing as long as these are all set
-        input_shape = self.dg_model.input_shape[0]
-        self.model_height = input_shape[1]
-        self.model_width = input_shape[2]
-
-        # Passing in dummy frame so initial connection latency happens in
-        # init function and not during actual prediction
-        frame = np.zeros(
-            (detector_config.model.width, detector_config.model.height, 3),
-            dtype=np.uint8,
-        )
-        # Pass in frame to overcome first frame latency
-        self.dg_model(frame)
-        self.prediction = self.prediction_generator()
-
-    def select_best_device_type(self):
-        """
-        Helper function that selects fastest hardware available per model runtime
-        """
-        types = self.dg_model.supported_device_types
-
-        device_map = {
-            "OPENVINO": ["GPU", "NPU", "CPU"],
-            "HAILORT": ["HAILO8L", "HAILO8"],
-            "N2X": ["ORCA1", "CPU"],
-            "ONNX": ["VITIS_NPU", "CPU"],
-            "RKNN": ["RK3566", "RK3568", "RK3588"],
-            "TENSORRT": ["DLA", "GPU", "DLA_ONLY"],
-            "TFLITE": ["ARMNN", "EDGETPU", "CPU"],
-        }
-
-        runtime = types[0].split("/")[0]
-        # Just create an array of format {runtime}/{hardware} for every hardware
-        # in the value for appropriate key in device_map
-        self.dg_model.device_type = [
-            f"{runtime}/{hardware}" for hardware in device_map[runtime]
-        ]
-
-    def prediction_generator(self):
-        """
-        Generator for all incoming frames. By using this generator, we don't have to keep
-        reconnecting our websocket on every "predict" call.
-        """
-        logger.debug("Prediction generator was called")
-        with self.dg_model as model:
-            while 1:
-                logger.info(f"q size before calling get: {self._queue.qsize()}")
-                data = self._queue.get(block=True)
-                logger.info(f"q size after calling get: {self._queue.qsize()}")
-                logger.debug(
-                    f"Data we're passing into model predict: {data}, shape of data: {data.shape}"
-                )
-                result = model.predict(data)
-                logger.debug(f"Prediction result: {result}")
-                yield result
-
-    def detect_raw(self, tensor_input):
-        # Reshaping tensor to work with pysdk
-        truncated_input = tensor_input.reshape(tensor_input.shape[1:])
-        logger.debug(f"Detect raw was called for tensor input: {tensor_input}")
-
-        # add tensor_input to input queue
-        self._queue.put(truncated_input)
-        logger.debug(f"Queue size after adding truncated input: {self._queue.qsize()}")
-
-        # define empty detection result
-        detections = np.zeros((20, 6), np.float32)
-        # grab prediction
-        res = next(self.prediction)
-
-        # If we have an empty prediction, return immediately
-        if len(res.results) == 0 or len(res.results[0]) == 0:
-            return detections
-
-        i = 0
-        for result in res.results:
-            if i >= 20:
-                break
-
-            detections[i] = [
-                result["category_id"],
-                float(result["score"]),
-                result["bbox"][1] / self.model_height,
-                result["bbox"][0] / self.model_width,
-                result["bbox"][3] / self.model_height,
-                result["bbox"][2] / self.model_width,
-            ]
-            i += 1
-
-        logger.debug(f"Detections output: {detections}")
-        return detections

frigate/detectors/plugins/edgetpu_tfl.py

@@ -9,6 +9,7 @@ from pydantic import ConfigDict, Field
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
+from frigate.util.model import xyxy_to_xywh_for_nms
 
 try:
     from tflite_runtime.interpreter import Interpreter, load_delegate
@@ -297,7 +298,7 @@ class EdgeTpuTfl(DetectionApi):
             # until after filtering out redundant boxes
             # Shift the logit scores to be non-negative (required by cv2)
             indices = cv2.dnn.NMSBoxes(
-                bboxes=boxes_filtered_decoded,
+                bboxes=xyxy_to_xywh_for_nms(boxes_filtered_decoded),
                 scores=max_scores_filtered_shiftedpositive,
                 score_threshold=(
                     self.min_logit_value + self.logit_shift_to_positive_values

frigate/detectors/plugins/memryx.py

@@ -17,6 +17,7 @@ from frigate.detectors.detector_config import (
     ModelTypeEnum,
 )
 from frigate.util.file import FileLock
+from frigate.util.model import xyxy_to_xywh_for_nms
 
 logger = logging.getLogger(__name__)
 
@@ -581,7 +582,7 @@ class MemryXDetector(DetectionApi):
             # Convert coordinates to integers
             x_min, y_min, x_max, y_max = map(int, [x_min, y_min, x_max, y_max])
 
-            # Append valid detections [class_id, confidence, x, y, width, height]
+            # Append valid detections [class_id, confidence, x_min, y_min, x_max, y_max]
             detections.append([class_id, confidence, x_min, y_min, x_max, y_max])
 
         final_detections = np.zeros((20, 6), np.float32)
@@ -595,7 +596,7 @@ class MemryXDetector(DetectionApi):
         detections = np.array(detections, dtype=np.float32)
 
         # Apply Non-Maximum Suppression (NMS)
-        bboxes = detections[:, 2:6].tolist()  # (x_min, y_min, width, height)
+        bboxes = xyxy_to_xywh_for_nms(detections[:, 2:6])
         scores = detections[:, 1].tolist()  # Confidence scores
 
         indices = cv2.dnn.NMSBoxes(bboxes, scores, 0.45, 0.5)

frigate/detectors/plugins/rknn.py

@@ -12,7 +12,7 @@ from frigate.const import MODEL_CACHE_DIR, SUPPORTED_RK_SOCS
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detection_runners import RKNNModelRunner
 from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
-from frigate.util.model import post_process_yolo
+from frigate.util.model import post_process_yolo, xyxy_to_xywh_for_nms
 from frigate.util.rknn_converter import auto_convert_model
 
 logger = logging.getLogger(__name__)
@@ -285,7 +285,7 @@ class Rknn(DetectionApi):
 
         # run nms
         indices = cv2.dnn.NMSBoxes(
-            bboxes=boxes,
+            bboxes=xyxy_to_xywh_for_nms(boxes),
             scores=scores,
             score_threshold=0.4,
             nms_threshold=0.4,

frigate/genai/prompts.py

@@ -262,10 +262,6 @@ def get_tool_definitions(
     `attribute` parameter is exposed for filtering by their labels. When the
     embeddings model only understands English (JinaV1), the `semantic_query`
     description instructs the model to write the query in English.
-
-    Descriptions here stay mechanical: which tool to reach for, and how the
-    filters relate to each other, is stated once in the system prompt so the
-    guidance is not paid for twice on every request.
     """
     search_objects_properties: dict[str, Any] = {
         "camera": {
@@ -274,13 +270,26 @@ def get_tool_definitions(
         },
         "label": {
             "type": "string",
-            "description": "Tracked object class to filter by.",
+            "description": (
+                "Generic object class to filter by — one of the tracked detector "
+                "labels such as 'person', 'package', 'car', 'dog', 'bird'. Use "
+                "this for broad queries like 'show me all cars today'. Combine "
+                "with semantic_query when the user also describes appearance or "
+                "behavior (e.g. label='person', semantic_query='riding a lawn "
+                "mower')."
+            ),
         },
         "sub_label": {
             "type": "string",
             "description": (
-                "Name recognized in the detection: a person, delivery company, "
-                "animal species or breed, or license plate."
+                "Filter by a DISCRETE NAMED entity recognized in the detection. "
+                "Use this for: a known person's name ('John'), a delivery "
+                "company ('Amazon', 'UPS'), a recognized animal species or "
+                "breed ('blue jay', 'cardinal', 'golden retriever'), or a "
+                "license plate string. When filtering by a specific name, set "
+                "only sub_label and leave label unset. Do NOT use sub_label "
+                "for descriptions of appearance, clothing, or actions — those "
+                "belong in semantic_query."
             ),
         },
         "after": {
@@ -304,11 +313,20 @@ def get_tool_definitions(
     }
 
     if attribute_classifications:
+        model_outline = "; ".join(
+            f"{m['name']} (applies to {', '.join(m['objects']) or 'any object'})"
+            for m in attribute_classifications
+        )
         search_objects_properties["attribute"] = {
             "type": "string",
             "description": (
-                "Attribute label produced by a configured classification model "
-                "(case-sensitive)."
+                "Filter by a classification attribute label produced by a "
+                "configured attribute classification model. Use this INSTEAD "
+                "of semantic_query when the user's request matches one of "
+                "these classifications. Configured models: "
+                f"{model_outline}. "
+                "Set the value to the attribute label that matches the user's "
+                "phrasing (case-sensitive)."
             ),
         }
 
@@ -316,12 +334,29 @@ def get_tool_definitions(
         search_objects_properties["semantic_query"] = {
             "type": "string",
             "description": (
-                "Description of an appearance or activity, used to semantically "
-                "narrow results."
+                "Optional natural-language description of a PHYSICAL "
+                "CHARACTERISTIC, APPEARANCE, or ACTIVITY the user mentioned, "
+                "used to semantically narrow results. Only set this when the "
+                "user describes something beyond what label and sub_label can "
+                "express on their own.\n"
+                "USE for descriptive phrases like: 'riding a lawn mower', "
+                "'wearing a red jacket', 'carrying a package', 'walking a "
+                "dog', 'on a bicycle', 'holding an umbrella'.\n"
+                "DO NOT USE for:\n"
+                "- specific named people, pets, or delivery companies → use sub_label\n"
+                "- animal species or breed names like 'blue jay', 'cardinal', "
+                "'golden retriever' → use sub_label\n"
+                "- license plate strings → use sub_label\n"
+                "- generic object queries like 'all cars today' or 'every "
+                "person' → use label alone with no semantic_query\n"
+                "When set, combine with label/time/camera/zone filters as "
+                "usual (e.g. label='person', semantic_query='riding a lawn "
+                "mower', after='2024-05-01T00:00:00Z')."
                 + (
-                    " The configured embeddings model only understands English, so "
-                    "always write this in English, translating the user's "
-                    "description if they phrased it in another language."
+                    " The configured embeddings model only understands "
+                    "English, so always write semantic_query in English, "
+                    "translating the user's description if they phrased it "
+                    "in another language."
                     if embeddings_language == "english"
                     else ""
                 )
@@ -329,10 +364,26 @@ def get_tool_definitions(
         }
 
     search_objects_description = (
-        "Search the historical record of tracked detections. Use this ONLY for "
-        "questions about the PAST, e.g. 'did anyone come by today?', 'when was the "
-        "last car?'. For alerting on future events use start_camera_watch instead."
+        "Search the historical record of detected objects in Frigate. "
+        "Use this ONLY for questions about the PAST — e.g. 'did anyone come by today?', "
+        "'when was the last car?', 'show me detections from yesterday'. "
+        "Do NOT use this for monitoring or alerting requests about future events — "
+        "use start_camera_watch instead for those. "
+        "An 'object' in Frigate represents a tracked detection (e.g., a person, package, car).\n\n"
+        "Choose filters based on what the user is asking for:\n"
+        "- Generic class query ('show me all cars today'): set `label` only.\n"
+        "- Specific NAMED entity (known person, delivery company, animal "
+        "species/breed like 'blue jay' or 'golden retriever', license "
+        "plate): set `sub_label` only and leave `label` unset.\n"
     )
+    if semantic_search_enabled:
+        search_objects_description += (
+            "- Physical CHARACTERISTIC, APPEARANCE, or ACTIVITY that is not a "
+            "discrete name ('person riding a lawn mower', 'someone in a red "
+            "jacket', 'person carrying a package'): set `semantic_query` with "
+            "the descriptive phrase, optionally alongside `label` for the "
+            "object class. Do NOT put descriptive phrases in sub_label."
+        )
 
     return [
         {
@@ -347,30 +398,20 @@ def get_tool_definitions(
                 "required": [],
             },
         },
-        {
-            "type": "function",
-            "function": {
-                "name": "get_categorized_object_names",
-                "description": (
-                    "Every name that can be attached as a sub_label, grouped by object "
-                    "type: recognized faces, named license plates, classification "
-                    "categories, and delivery logos. Takes no arguments and always "
-                    "returns the complete map."
-                ),
-                "parameters": {
-                    "type": "object",
-                    "properties": {},
-                    "required": [],
-                },
-            },
-        },
         {
             "type": "function",
             "function": {
                 "name": "find_similar_objects",
                 "description": (
-                    "Find tracked objects visually and semantically similar to a "
-                    "specific past event. Requires semantic search to be enabled."
+                    "Find tracked objects that are visually and semantically similar "
+                    "to a specific past event. Use this when the user references a "
+                    "particular object they have seen and wants to find other "
+                    "sightings of the same or similar one ('that green car', 'the "
+                    "person in the red jacket', 'the package that was delivered'). "
+                    "Prefer this over search_objects whenever the user's intent is "
+                    "'find more like this specific one.' Use search_objects first "
+                    "only if you need to locate the anchor event. Requires semantic "
+                    "search to be enabled."
                 ),
                 "parameters": {
                     "type": "object",
@@ -432,8 +473,9 @@ def get_tool_definitions(
             "function": {
                 "name": "set_camera_state",
                 "description": (
-                    "Change a camera's feature state, e.g. turn detection on or off. "
-                    "Only call this when the user explicitly asks to change a setting. "
+                    "Change a camera's feature state (e.g., turn detection on/off, enable/disable recordings). "
+                    "Use camera='*' to apply to all cameras at once. "
+                    "Only call this tool when the user explicitly asks to change a camera setting. "
                     "Requires admin privileges."
                 ),
                 "parameters": {
@@ -475,7 +517,7 @@ def get_tool_definitions(
                         },
                         "value": {
                             "type": "string",
-                            "description": "The value to set, as accepted by the chosen feature.",
+                            "description": "The value to set. ON or OFF for toggles, a number for thresholds, a profile name or 'none' for profile.",
                         },
                     },
                     "required": ["camera", "feature", "value"],
@@ -487,9 +529,11 @@ def get_tool_definitions(
             "function": {
                 "name": "get_live_context",
                 "description": (
-                    "Current live image and detections (tracked objects, zones, "
-                    "timestamps) for one camera. Use this for questions about what is "
-                    "happening right now. Call it again for each additional camera."
+                    "Get the current live image and detection information for a single camera: objects being tracked, "
+                    "zones, timestamps. Use this to understand what is visible in the live view. "
+                    "Call this when answering questions about what is happening right now on a specific camera. "
+                    "Operates on one camera at a time; call the tool again for each additional camera. "
+                    "Wildcards and empty values are not accepted."
                 ),
                 "parameters": {
                     "type": "object",
@@ -497,8 +541,8 @@ def get_tool_definitions(
                         "camera": {
                             "type": "string",
                             "description": (
-                                "Exact name of a single camera. Wildcards (e.g. '*', "
-                                "'all') and empty strings are not accepted."
+                                "Exact name of a single camera to get live context for. "
+                                "Wildcards (e.g. '*', 'all') and empty strings are not accepted."
                             ),
                         },
                     },
@@ -511,9 +555,10 @@ def get_tool_definitions(
             "function": {
                 "name": "start_camera_watch",
                 "description": (
-                    "Start a continuous watch job that monitors a camera and notifies "
-                    "the user when a condition is met, e.g. 'tell me when guests "
-                    "arrive'. Only one watch job can run at a time. Returns a job ID."
+                    "Start a continuous VLM watch job that monitors a camera and sends a notification "
+                    "when a specified condition is met. Use this when the user wants to be alerted about "
+                    "a future event, e.g. 'tell me when guests arrive' or 'notify me when the package is picked up'. "
+                    "Only one watch job can run at a time. Returns a job ID."
                 ),
                 "parameters": {
                     "type": "object",
@@ -553,7 +598,10 @@ def get_tool_definitions(
             "type": "function",
             "function": {
                 "name": "stop_camera_watch",
-                "description": "Cancel the currently running watch job.",
+                "description": (
+                    "Cancel the currently running VLM watch job. Use this when the user wants to "
+                    "stop a previously started watch, e.g. 'stop watching the front door'."
+                ),
                 "parameters": {
                     "type": "object",
                     "properties": {},
@@ -566,9 +614,11 @@ def get_tool_definitions(
             "function": {
                 "name": "get_profile_status",
                 "description": (
-                    "Get the active profile and when each profile was last activated. "
-                    "Call this before get_recap to derive the time window for requests "
-                    "like 'what happened while I was away?'."
+                    "Get the current profile status including the active profile and "
+                    "timestamps of when each profile was last activated. Use this to "
+                    "determine time periods for recap requests — e.g. when the user asks "
+                    "'what happened while I was away?', call this first to find the relevant "
+                    "time window based on profile activation history."
                 ),
                 "parameters": {
                     "type": "object",
@@ -582,9 +632,11 @@ def get_tool_definitions(
             "function": {
                 "name": "get_recap",
                 "description": (
-                    "Get all activity (alerts and detections) for a time period, as a "
-                    "chronological list with camera, objects, zones, and descriptions "
-                    "when available. Summarize the results for the user."
+                    "Get a recap of all activity (alerts and detections) for a given time period. "
+                    "Use this after calling get_profile_status to retrieve what happened during "
+                    "a specific window — e.g. 'what happened while I was away?'. Returns a "
+                    "chronological list of activity with camera, objects, zones, and GenAI-generated "
+                    "descriptions when available. Summarize the results for the user."
                 ),
                 "parameters": {
                     "type": "object",
@@ -671,13 +723,14 @@ def build_chat_system_prompt(
         )
         speed_units_section = f"\n\nReport object speeds to the user in {speed_unit}."
 
-    filter_routing_section = (
-        "\n\nWhen routing a search_objects call, pick filters by the shape of the user's request:\n"
-        "- Generic class ('show me all cars today'): set `label` only.\n"
-        "- Specific named entity — a known person ('John'), delivery company ('Amazon'), animal species/breed ('blue jay', 'golden retriever'), or license plate: set `sub_label` only and leave `label` unset. Call get_categorized_object_names first and use the exact spelling it returns; a guessed spelling matches nothing. If the name is absent, say it is not configured rather than searching for it."
-    )
+    semantic_search_section = ""
     if semantic_search_enabled:
-        filter_routing_section += "\n- Physical characteristic, appearance, or activity that is NOT a discrete name ('riding a lawn mower', 'someone in a red jacket'): set `semantic_query` with the descriptive phrase, optionally combined with `label`. Never put descriptive phrases in `sub_label`."
+        semantic_search_section = (
+            "\n\nWhen routing a search_objects call, pick filters by the shape of the user's request:\n"
+            "- Generic class ('show me all cars today'): set `label` only.\n"
+            "- Specific named entity — a known person ('John'), delivery company ('Amazon'), animal species/breed ('blue jay', 'cardinal', 'golden retriever'), or license plate: set `sub_label` only and leave `label` unset.\n"
+            "- Physical characteristic, appearance, or activity that is NOT a discrete name ('find me people riding a lawn mower', 'someone in a red jacket', 'a person carrying a package'): set `semantic_query` with the descriptive phrase, optionally combined with `label` for the object class. Never put descriptive phrases in `sub_label`."
+        )
 
     attribute_classification_section = ""
     if attribute_classifications:
@@ -686,9 +739,9 @@ def build_chat_system_prompt(
             for m in attribute_classifications
         )
         attribute_classification_section = (
-            "\n\nConfigured attribute classification models:\n"
+            "\n\nAttribute classification models are configured for the following object types:\n"
             f"{model_lines}\n"
-            "When the user's request matches one of these classifications, set the search_objects `attribute` field to the matching label (case-sensitive) rather than using `semantic_query`. Reserve `semantic_query` for descriptive phrases outside the configured attribute labels."
+            "When the user's request matches one of these classifications, set the search_objects `attribute` field to the matching label rather than using `semantic_query`. Reserve `semantic_query` for descriptive phrases that fall outside the configured attribute labels."
         )
 
     return f"""You are a helpful assistant for Frigate, a security camera NVR system. You help users answer questions about their cameras, detected objects, and events.
@@ -697,6 +750,9 @@ Current server local date and time: {current_date_str} at {current_time_str}
 
 Do not start your response with phrases like "I will check...", "Let me see...", or "Let me look...". Answer directly.
 
-Always present times in the server's local timezone. When tool results include start_time_local and end_time_local, quote those strings exactly; never convert or invent timestamps, and fall back to UTC or ISO format only when a result has no local time fields. Resolve relative dates like "today" or "this week" against the current date above, and pass dates to tools in ISO 8601 (e.g. {current_date_str}T00:00:00Z for the start of today).
+Always present times to the user in the server's local timezone. When tool results include start_time_local and end_time_local, use those exact strings when listing or describing detection times—do not convert or invent timestamps. Do not use UTC or ISO format with Z for the user-facing answer unless the tool result only provides Unix timestamps without local time fields.
+When users ask about "today", "yesterday", "this week", etc., use the current date above as reference.
+When searching for objects or events, use ISO 8601 format for dates (e.g., {current_date_str}T00:00:00Z for the start of today).
+Always be accurate with time calculations based on the current date provided.
 
-When the user refers to a specific object they have seen ("that green car", "the person in the red jacket", "a package left today"), prefer find_similar_objects over search_objects, using search_objects only to locate the anchor event and passing its id along. Keep search_objects for generic queries like "show me all cars today". If a user message begins with [attached_event:<id>], treat that id as the anchor for any similarity or "tell me more" request in the same message.{filter_routing_section}{attribute_classification_section}{cameras_section}{speed_units_section}"""
+When a user refers to a specific object they have seen or describe with identifying details ("that green car", "the person in the red jacket", "a package left today"), prefer the find_similar_objects tool over search_objects. Use search_objects first only to locate the anchor event, then pass its id to find_similar_objects. For generic queries like "show me all cars today", keep using search_objects. If a user message begins with [attached_event:<id>], treat that event id as the anchor for any similarity or "tell me more" request in the same message and call find_similar_objects with that id.{semantic_search_section}{attribute_classification_section}{cameras_section}{speed_units_section}"""

frigate/ptz/onvif.py

@@ -625,14 +625,18 @@ class OnvifController:
             return
 
         self.cams[camera_name]["active"] = True
-        self.ptz_metrics[camera_name].motor_stopped.clear()
-        logger.debug(
-            f"{camera_name}: PTZ start time: {self.ptz_metrics[camera_name].frame_time.value}"
-        )
-        self.ptz_metrics[camera_name].start_time.value = self.ptz_metrics[
-            camera_name
-        ].frame_time.value
-        self.ptz_metrics[camera_name].stop_time.value = 0
+
+        # only track start_time for autotracking
+        if self.ptz_metrics[camera_name].autotracker_enabled.value:
+            self.ptz_metrics[camera_name].motor_stopped.clear()
+            logger.debug(
+                f"{camera_name}: PTZ start time: {self.ptz_metrics[camera_name].frame_time.value}"
+            )
+            self.ptz_metrics[camera_name].start_time.value = self.ptz_metrics[
+                camera_name
+            ].frame_time.value
+            self.ptz_metrics[camera_name].stop_time.value = 0
+
         move_request = self.cams[camera_name]["relative_move_request"]
 
         # function takes in -1 to 1 for pan and tilt, interpolate to the values of the camera.

frigate/test/http_api/test_http_auth_internal_port.py

@@ -0,0 +1,174 @@
+"""Tests that the internal port trusted by /auth cannot be moved at runtime."""
+
+import os
+import tempfile
+import unittest
+from unittest.mock import MagicMock, Mock, patch
+
+import ruamel.yaml
+from fastapi import Request
+
+from frigate.api.auth import get_allowed_cameras_for_filter, get_current_user
+from frigate.api.fastapi_app import create_fastapi_app
+from frigate.config import FrigateConfig
+from frigate.config.camera.updater import CameraConfigUpdatePublisher
+from frigate.const import JWT_SECRET_ENV_VAR
+from frigate.models import Event, Recordings, ReviewSegment
+from frigate.test.http_api.base_http_test import AuthTestClient, BaseTestHttp
+
+
+@patch.dict(os.environ, {JWT_SECRET_ENV_VAR: "test-secret"})
+class TestAuthInternalPort(BaseTestHttp):
+    """/auth grants anonymous admin by port, so that port must stay put.
+
+    nginx binds its listeners once at container start and never reloads them,
+    but /api/config/set can swap the live config object mid-process. If /auth
+    read the port off the live config, saving networking.listen.internal would
+    hand unauthenticated admin to whoever can reach the external port.
+    """
+
+    def setUp(self):
+        super().setUp(models=[Event, Recordings, ReviewSegment])
+        self.minimal_config = {
+            "mqtt": {"host": "mqtt"},
+            "auth": {"enabled": True},
+            "networking": {"listen": {"internal": 5000, "external": 8971}},
+            "cameras": {
+                "front_door": {
+                    "ffmpeg": {
+                        "inputs": [
+                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
+                        ]
+                    },
+                    "detect": {
+                        "height": 1080,
+                        "width": 1920,
+                        "fps": 5,
+                    },
+                }
+            },
+        }
+
+    def _create_app(self):
+        mock_publisher = Mock(spec=CameraConfigUpdatePublisher)
+        mock_publisher.publisher = MagicMock()
+
+        app = create_fastapi_app(
+            FrigateConfig(**self.minimal_config),
+            self.db,
+            None,
+            None,
+            None,
+            None,
+            None,
+            None,
+            mock_publisher,
+            None,
+            enforce_default_admin=False,
+        )
+
+        async def mock_get_current_user(request: Request):
+            return {
+                "username": request.headers.get("remote-user"),
+                "role": request.headers.get("remote-role"),
+            }
+
+        async def mock_get_allowed_cameras_for_filter(request: Request):
+            return list(self.minimal_config.get("cameras", {}).keys())
+
+        app.dependency_overrides[get_current_user] = mock_get_current_user
+        app.dependency_overrides[get_allowed_cameras_for_filter] = (
+            mock_get_allowed_cameras_for_filter
+        )
+
+        return app
+
+    def _write_config_file(self):
+        """Write the minimal config to a temp YAML file and return the path."""
+        yaml = ruamel.yaml.YAML()
+        f = tempfile.NamedTemporaryFile(mode="w", suffix=".yml", delete=False)
+        yaml.dump(self.minimal_config, f)
+        f.close()
+        return f.name
+
+    def test_internal_port_is_anonymous_admin(self):
+        app = self._create_app()
+
+        with AuthTestClient(app) as client:
+            resp = client.get("/auth", headers={"x-server-port": "5000"})
+
+            self.assertEqual(resp.status_code, 202)
+            self.assertEqual(resp.headers["remote-user"], "anonymous")
+            self.assertEqual(resp.headers["remote-role"], "admin")
+
+    def test_external_port_requires_auth(self):
+        app = self._create_app()
+
+        with AuthTestClient(app) as client:
+            resp = client.get("/auth", headers={"x-server-port": "8971"})
+
+            self.assertEqual(resp.status_code, 401)
+
+    def test_swapped_config_does_not_move_the_trusted_port(self):
+        """The live config is not what /auth trusts.
+
+        Stands in for every path that can rebind app.frigate_config while the
+        process runs, whatever restart flag the caller claimed.
+        """
+        app = self._create_app()
+
+        swapped = FrigateConfig(
+            **{
+                **self.minimal_config,
+                "networking": {"listen": {"internal": 8971, "external": 5000}},
+            }
+        )
+        app.frigate_config = swapped
+
+        with AuthTestClient(app) as client:
+            resp = client.get("/auth", headers={"x-server-port": "8971"})
+            self.assertEqual(resp.status_code, 401)
+
+            # nginx is still listening where it was told to at boot
+            resp = client.get("/auth", headers={"x-server-port": "5000"})
+            self.assertEqual(resp.status_code, 202)
+            self.assertEqual(resp.headers["remote-role"], "admin")
+
+    @patch("frigate.api.app.find_config_file")
+    def test_config_set_rejects_internal_matching_external(self, mock_find_config):
+        """Saving the internal port onto the external one is refused outright."""
+        config_path = self._write_config_file()
+        mock_find_config.return_value = config_path
+
+        try:
+            app = self._create_app()
+
+            with AuthTestClient(app) as client:
+                resp = client.put(
+                    "/config/set",
+                    json={
+                        "config_data": {"networking": {"listen": {"internal": 8971}}},
+                        "update_topic": "config/networking",
+                        "requires_restart": 1,
+                    },
+                )
+
+                self.assertEqual(resp.status_code, 400)
+                self.assertFalse(resp.json()["success"])
+
+                # the rejected save must not have reached the live config
+                self.assertEqual(
+                    app.frigate_config.networking.listen.internal_port, 5000
+                )
+
+                resp = client.get("/auth", headers={"x-server-port": "8971"})
+                self.assertEqual(resp.status_code, 401)
+
+            with open(config_path) as f:
+                self.assertNotIn("8971", f.read().split("external")[0])
+        finally:
+            os.unlink(config_path)
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)

frigate/test/http_api/test_http_media.py

@@ -1,73 +1,15 @@
 """Unit tests for recordings/media API endpoints."""
 
-from dataclasses import dataclass
 from datetime import UTC, datetime
-from unittest.mock import patch
 
 import pytz
 from fastapi import Request
 
 from frigate.api.auth import get_allowed_cameras_for_filter, get_current_user
-from frigate.const import MAX_SEGMENT_DURATION
 from frigate.models import Recordings
 from frigate.test.http_api.base_http_test import AuthTestClient, BaseTestHttp
 
 
-@dataclass(frozen=True)
-class RangeCase:
-    """Expected behavior for one segment relative to the requested range.
-
-    Offsets are seconds from REQUEST_START; the request ends at +100 seconds.
-    """
-
-    name: str
-    start_offset: float
-    end_offset: float
-    included_in_recordings: bool
-    vod_clip_from_ms: int | None = None
-    vod_duration_ms: int | None = None
-
-
-REQUEST_START = 1000
-REQUEST_END = 1100
-RANGE_CASES = (
-    RangeCase("before", -MAX_SEGMENT_DURATION + 1, -1, False),
-    RangeCase("meets_start", -10, 0, True),
-    RangeCase(
-        "overlaps_start",
-        -MAX_SEGMENT_DURATION + 0.5,
-        0.25,
-        True,
-        vod_clip_from_ms=599500,
-        vod_duration_ms=250,
-    ),
-    RangeCase("starts_at_start", 0, 10, True, vod_duration_ms=10000),
-    RangeCase("inside", 20, 80, True, vod_duration_ms=60000),
-    RangeCase("ends_at_end", 90, 100, True, vod_duration_ms=10000),
-    RangeCase("matches_range", 0, 100, True, vod_duration_ms=100000),
-    RangeCase("starts_with_range", 0, 110, True, vod_duration_ms=100000),
-    RangeCase(
-        "covers_range",
-        -20,
-        120,
-        True,
-        vod_clip_from_ms=20000,
-        vod_duration_ms=100000,
-    ),
-    RangeCase(
-        "ends_with_range",
-        -10,
-        100,
-        True,
-        vod_clip_from_ms=10000,
-        vod_duration_ms=100000,
-    ),
-    RangeCase("overlaps_end", 95, 105, True, vod_duration_ms=5000),
-    RangeCase("starts_at_end", 100, 110, True),
-    RangeCase("after", 101, 110, False),
-)
-
-
 class TestHttpMedia(BaseTestHttp):
     """Test media API endpoints, particularly recordings with DST handling."""
 
@@ -102,26 +44,6 @@ class TestHttpMedia(BaseTestHttp):
         self.app.dependency_overrides.clear()
         super().tearDown()
 
-    def _assert_vod_response(
-        self,
-        response,
-        expected_clips: list[tuple[str, int | None, int]],
-    ) -> None:
-        """Assert VOD clip metadata and its derived duration fields."""
-        assert response.status_code == 200
-        vod = response.json()
-        assert [
-            (
-                clip["path"],
-                clip.get("clipFrom"),
-                clip["keyFrameDurations"][0],
-            )
-            for clip in vod["sequences"][0]["clips"]
-        ] == expected_clips
-        expected_durations = [clip[2] for clip in expected_clips]
-        assert vod["durations"] == expected_durations
-        assert vod["segment_duration"] == max(expected_durations)
-
     def test_recordings_summary_across_dst_spring_forward(self):
         """
         Test recordings summary across spring DST transition (spring forward).
@@ -482,102 +404,6 @@ class TestHttpMedia(BaseTestHttp):
             assert "2024-03-10" in summary
             assert summary["2024-03-10"] is True
 
-    def test_recordings_handles_all_range_relations(self):
-        """Recordings return every interval relation that touches the range."""
-        with AuthTestClient(self.app) as client:
-            for case in RANGE_CASES:
-                with self.subTest(case=case.name):
-                    Recordings.delete().execute()
-                    super().insert_mock_recording(
-                        case.name,
-                        REQUEST_START + case.start_offset,
-                        REQUEST_START + case.end_offset,
-                    )
-
-                    response = client.get(
-                        "/front_door/recordings",
-                        params={"after": REQUEST_START, "before": REQUEST_END},
-                    )
-
-                    assert response.status_code == 200
-                    expected_ids = [case.name] if case.included_in_recordings else []
-                    assert [
-                        recording["id"] for recording in response.json()
-                    ] == expected_ids
-
-    def test_vod_handles_all_range_relations(self):
-        """VOD clips every interval relation with positive playback duration."""
-        with (
-            AuthTestClient(self.app) as client,
-            patch(
-                "frigate.api.media.get_keyframe_before",
-                side_effect=lambda _path, offset: offset,
-            ),
-        ):
-            for case in RANGE_CASES:
-                with self.subTest(case=case.name):
-                    Recordings.delete().execute()
-                    super().insert_mock_recording(
-                        case.name,
-                        REQUEST_START + case.start_offset,
-                        REQUEST_START + case.end_offset,
-                    )
-
-                    response = client.get(
-                        f"/vod/front_door/start/{REQUEST_START}/end/{REQUEST_END}"
-                    )
-
-                    if case.vod_duration_ms is None:
-                        assert response.status_code == 404
-                        continue
-
-                    self._assert_vod_response(
-                        response,
-                        [
-                            (
-                                case.name,
-                                case.vod_clip_from_ms,
-                                case.vod_duration_ms,
-                            )
-                        ],
-                    )
-
-    def test_vod_handles_segment_ending_at_start_with_keyframe_fallbacks(self):
-        """VOD keeps a boundary segment when keyframe lookup extends it."""
-
-        def keyframe_before(path: str, offset: int) -> int | None:
-            return offset - 1000 if path == "previous_keyframe" else None
-
-        with (
-            AuthTestClient(self.app) as client,
-            patch(
-                "frigate.api.media.get_keyframe_before",
-                side_effect=keyframe_before,
-            ),
-        ):
-            super().insert_mock_recording(
-                "previous_keyframe",
-                REQUEST_START - 10,
-                REQUEST_START,
-            )
-            super().insert_mock_recording(
-                "missing_keyframe",
-                REQUEST_START - 5,
-                REQUEST_START,
-            )
-
-            response = client.get(
-                f"/vod/front_door/start/{REQUEST_START}/end/{REQUEST_END}"
-            )
-
-            self._assert_vod_response(
-                response,
-                [
-                    ("previous_keyframe", 9000, 1000),
-                    ("missing_keyframe", None, 5000),
-                ],
-            )
-
     def test_recordings_unavailable_reports_gap_between_recordings(self):
         """A gap between two recordings is reported as an unavailable segment."""
         with AuthTestClient(self.app) as client:

frigate/test/test_dispatcher_runtime_state.py

@@ -5,6 +5,7 @@ import tempfile
 import unittest
 from unittest.mock import MagicMock, patch
 
+from frigate.app import FrigateApp
 from frigate.comms.dispatcher import Dispatcher
 from frigate.comms.runtime_state import RuntimeStatePersistence
 
@@ -363,5 +364,94 @@ class TestReapplyRuntimeStateToConfig(unittest.TestCase):
         dispatcher.reapply_runtime_state_to_config()
 
 
+class TestStartupAppliesConfigLayersBeforeWorkersStart(unittest.TestCase):
+    """Both layers must reach the config before config-carrying workers start.
+
+    A worker started before a layer is applied keeps the yaml value for the
+    rest of the session: the config_updater broadcast sent later is dropped
+    for subscribers that have not connected yet, and nothing re-sends it.
+    """
+
+    CONFIG_LAYERS = (
+        "profile_manager.restore_persisted_profile_to_config",
+        "dispatcher.reapply_runtime_state_to_config",
+    )
+
+    # started with a copy of the camera config
+    CONFIG_CARRYING_WORKERS = (
+        "start_video_output_processor",
+        "start_ptz_autotracker",
+        "start_detected_frames_processor",
+        "start_camera_processor",
+        "start_audio_processor",
+    )
+
+    def _start_call_order(self) -> list[str]:
+        """Return the names FrigateApp.start() calls, in order."""
+        app = MagicMock()
+
+        with (
+            patch("frigate.app.set_file_limit"),
+            patch("frigate.app.cleanup_replay_cameras"),
+            patch("frigate.app.reap_stale_exports"),
+            patch("frigate.app.create_fastapi_app"),
+            patch("frigate.app.uvicorn"),
+        ):
+            FrigateApp.start(app)
+
+        return [name for name, _, _ in app.mock_calls]
+
+    def test_applied_before_any_config_carrying_worker(self) -> None:
+        order = self._start_call_order()
+
+        for layer in self.CONFIG_LAYERS:
+            for worker in self.CONFIG_CARRYING_WORKERS:
+                self.assertLess(order.index(layer), order.index(worker))
+
+    def test_applied_after_the_dispatcher_exists(self) -> None:
+        order = self._start_call_order()
+
+        for layer in self.CONFIG_LAYERS:
+            self.assertLess(order.index("init_dispatcher"), order.index(layer))
+
+    def test_applied_after_the_profile_base_is_snapshotted(self) -> None:
+        # ProfileManager snapshots the config as the "no profile" base that
+        # deactivation resets to, so neither layer may be in the config yet
+        order = self._start_call_order()
+
+        for layer in self.CONFIG_LAYERS:
+            self.assertLess(order.index("init_profile_manager"), order.index(layer))
+
+    def test_layers_applied_in_order(self) -> None:
+        # a runtime toggle is the layer the user set last, so it goes on top
+        order = self._start_call_order()
+
+        self.assertLess(
+            order.index("profile_manager.restore_persisted_profile_to_config"),
+            order.index("dispatcher.reapply_runtime_state_to_config"),
+        )
+
+    def test_overrides_still_re_applied_after_the_profile_is_restored(self) -> None:
+        # activation resets the sections it owns to the base first, so the
+        # overrides have to land on top again
+        order = self._start_call_order()
+
+        self.assertLess(
+            order.index("profile_manager.restore_persisted_profile"),
+            order.index("dispatcher.restore_runtime_state"),
+        )
+
+    def test_broadcast_replay_still_runs_at_the_end(self) -> None:
+        # the broadcast is the only channel for the recording, review, and
+        # embeddings processes, which start before the config can be corrected
+        order = self._start_call_order()
+
+        for replay in (
+            "profile_manager.restore_persisted_profile",
+            "dispatcher.restore_runtime_state",
+        ):
+            self.assertLess(order.index("start_audio_processor"), order.index(replay))
+
+
 if __name__ == "__main__":
     unittest.main()

frigate/test/test_gpu_stats.py

@@ -1,12 +1,7 @@
 import unittest
-from io import StringIO
 from unittest.mock import MagicMock, patch
 
-from frigate.util.services import (
-    get_amd_gpu_stats,
-    get_intel_gpu_stats,
-    get_openvino_npu_stats,
-)
+from frigate.util.services import get_amd_gpu_stats, get_intel_gpu_stats
 
 
 class TestGpuStats(unittest.TestCase):
@@ -22,88 +17,6 @@ class TestGpuStats(unittest.TestCase):
         amd_stats = get_amd_gpu_stats()
         assert amd_stats == {"gpu": "4.17%", "mem": "60.37%"}
 
-    @patch("frigate.util.services.time.sleep")
-    @patch("frigate.util.services.time.time", side_effect=[0.0, 1.0])
-    @patch(
-        "frigate.util.services.os.readlink",
-        return_value="/sys/bus/pci/drivers/intel_vpu",
-    )
-    @patch(
-        "frigate.util.services.glob.glob",
-        return_value=["/sys/class/accel/accel0"],
-    )
-    @patch(
-        "builtins.open",
-        side_effect=[StringIO("1000"), StringIO("1250")],
-    )
-    def test_openvino_npu_stats_discovers_accel0(
-        self, open_file, glob, readlink, time, sleep
-    ):
-        assert get_openvino_npu_stats() == {"npu": "25.0", "mem": "-%"}
-
-        open_file.assert_any_call(
-            "/sys/class/accel/accel0/device/power/runtime_active_time"
-        )
-
-    @patch("frigate.util.services.time.sleep")
-    @patch("frigate.util.services.time.time", side_effect=[0.0, 1.0])
-    @patch(
-        "frigate.util.services.os.readlink",
-        side_effect=[
-            "/sys/bus/pci/drivers/other",
-            "/sys/bus/pci/drivers/intel_vpu",
-        ],
-    )
-    @patch(
-        "frigate.util.services.glob.glob",
-        return_value=[
-            "/sys/class/accel/accel0",
-            "/sys/class/accel/accel1",
-        ],
-    )
-    @patch(
-        "builtins.open",
-        side_effect=[StringIO("1000"), StringIO("1250")],
-    )
-    def test_openvino_npu_stats_skips_non_intel_accelerator(
-        self, open_file, glob, readlink, time, sleep
-    ):
-        assert get_openvino_npu_stats() == {"npu": "25.0", "mem": "-%"}
-
-        open_file.assert_any_call(
-            "/sys/class/accel/accel1/device/power/runtime_active_time"
-        )
-
-    @patch(
-        "frigate.util.services.os.readlink",
-        return_value="/sys/bus/pci/drivers/other",
-    )
-    @patch(
-        "frigate.util.services.glob.glob",
-        return_value=["/sys/class/accel/accel0"],
-    )
-    @patch("builtins.open")
-    def test_openvino_npu_stats_no_intel_accelerator(self, open_file, glob, readlink):
-        assert get_openvino_npu_stats() is None
-        open_file.assert_not_called()
-
-    @patch(
-        "frigate.util.services.os.readlink",
-        return_value="/sys/bus/pci/drivers/intel_vpu",
-    )
-    @patch(
-        "frigate.util.services.glob.glob",
-        return_value=["/sys/class/accel/accel0"],
-    )
-    @patch("builtins.open", side_effect=FileNotFoundError)
-    def test_openvino_npu_stats_runtime_counter_unavailable(
-        self, open_file, glob, readlink
-    ):
-        assert get_openvino_npu_stats() is None
-        open_file.assert_called_once_with(
-            "/sys/class/accel/accel0/device/power/runtime_active_time"
-        )
-
     @patch("frigate.stats.intel_gpu_info.intel_gpu_name_resolver.get_names")
     @patch("frigate.util.services.time.sleep")
     @patch("frigate.util.services.time.monotonic")

frigate/test/test_network_config.py

@@ -0,0 +1,41 @@
+"""Tests for networking config validation."""
+
+import unittest
+
+from pydantic import ValidationError
+
+from frigate.config.network import ListenConfig
+
+
+class TestListenConfig(unittest.TestCase):
+    def test_defaults_are_distinct(self):
+        listen = ListenConfig()
+
+        self.assertEqual(listen.internal_port, 5000)
+        self.assertEqual(listen.external_port, 8971)
+
+    def test_address_and_port_string_is_parsed(self):
+        listen = ListenConfig(internal="127.0.0.1:5000", external="0.0.0.0:8971")
+
+        self.assertEqual(listen.internal_port, 5000)
+        self.assertEqual(listen.external_port, 8971)
+
+    def test_identical_ports_rejected(self):
+        with self.assertRaises(ValidationError):
+            ListenConfig(internal=8971, external=8971)
+
+    def test_same_port_on_different_addresses_rejected(self):
+        # nginx would accept these as distinct listeners, but /auth decides on
+        # the port alone, so the external one would inherit anonymous admin
+        with self.assertRaises(ValidationError):
+            ListenConfig(internal="127.0.0.1:8971", external="0.0.0.0:8971")
+
+    def test_distinct_ports_accepted(self):
+        listen = ListenConfig(internal=5001, external="0.0.0.0:8971")
+
+        self.assertEqual(listen.internal_port, 5001)
+        self.assertEqual(listen.external_port, 8971)
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)

frigate/test/test_post_processing.py

@@ -0,0 +1,226 @@
+"""Tests for detector post-processing NMS box format handling.
+
+cv2.dnn.NMSBoxes expects boxes as [x, y, width, height]. Passing corner
+coordinates [x1, y1, x2, y2] makes OpenCV treat x2/y2 as width/height,
+inflating every box toward the bottom-right by its distance from the origin.
+Two well separated objects far from the origin then appear to overlap and the
+lower scoring one is silently suppressed.
+
+The regression geometry used throughout: two boxes with zero true overlap,
+A = (393, 499, 484, 620) and B = (527, 499, 618, 620) in a 640x640 input
+(43 px gap). Misread as [x, y, w, h] their IoU is 0.465, above the 0.4 NMS
+threshold, so the buggy format drops the lower scoring box while correct
+conversion keeps both.
+"""
+
+import math
+import unittest
+from queue import Queue
+
+import numpy as np
+
+from frigate.detectors.plugins.memryx import MemryXDetector
+from frigate.util.model import (
+    post_process_dfine,
+    post_process_rfdetr,
+    post_process_yolo,
+    post_process_yolox,
+)
+
+WIDTH = 640
+HEIGHT = 640
+
+# box A: xyxy (393, 499, 484, 620) as center format
+A_CX, A_CY, A_W, A_H = 438.5, 559.5, 91.0, 121.0
+# box B: xyxy (527, 499, 618, 620) as center format
+B_CX, B_CY, B_W, B_H = 572.5, 559.5, 91.0, 121.0
+
+# expected normalized output rows: [class_id, conf, y1, x1, y2, x2]
+A_ROW = [499 / 640, 393 / 640, 620 / 640, 484 / 640]
+B_ROW = [499 / 640, 527 / 640, 620 / 640, 618 / 640]
+
+
+def kept(detections: np.ndarray) -> np.ndarray:
+    """Rows of the padded (20, 6) output that hold real detections."""
+    return detections[detections[:, 1] > 0]
+
+
+class TestYoloNmsPostProcess(unittest.TestCase):
+    def _single_output(self, rows: list[list[float]]) -> list[np.ndarray]:
+        """Build a single-tensor YOLO output (1, attrs, anchors) from
+        [cx, cy, w, h, class scores...] rows, padded with empty anchors."""
+        anchors = np.zeros((10, len(rows[0])), dtype=np.float32)
+        anchors[: len(rows)] = np.array(rows, dtype=np.float32)
+        return [anchors.T[np.newaxis, ...]]
+
+    def test_keeps_separated_objects_far_from_origin(self):
+        output = self._single_output(
+            [
+                [A_CX, A_CY, A_W, A_H, 0.90, 0.0],
+                [B_CX, B_CY, B_W, B_H, 0.0, 0.85],
+            ]
+        )
+
+        detections = kept(post_process_yolo(output, WIDTH, HEIGHT))
+
+        self.assertEqual(len(detections), 2)
+        np.testing.assert_allclose(detections[0], [0, 0.90, *A_ROW], atol=2e-3)
+        np.testing.assert_allclose(detections[1], [1, 0.85, *B_ROW], atol=2e-3)
+
+    def test_still_suppresses_true_duplicates(self):
+        # same object twice, shifted 4 px: true IoU 0.92, must dedupe to one
+        output = self._single_output(
+            [
+                [A_CX, A_CY, A_W, A_H, 0.90, 0.0],
+                [A_CX + 4, A_CY, A_W, A_H, 0.85, 0.0],
+            ]
+        )
+
+        detections = kept(post_process_yolo(output, WIDTH, HEIGHT))
+
+        self.assertEqual(len(detections), 1)
+        np.testing.assert_allclose(detections[0], [0, 0.90, *A_ROW], atol=2e-3)
+
+
+class TestMultipartYoloPostProcess(unittest.TestCase):
+    def _multipart_output(self) -> list[np.ndarray]:
+        """Build a 3-scale anchor-based YOLO output containing boxes A and B,
+        both decoded through anchor 0 of the stride-32 scale."""
+        outputs = [
+            np.zeros((1, 255, 80, 80), dtype=np.float32),
+            np.zeros((1, 255, 40, 40), dtype=np.float32),
+            np.zeros((1, 255, 20, 20), dtype=np.float32),
+        ]
+        stride, (anchor_w, anchor_h) = 32, (142, 110)
+
+        for cx, cy, w, h, conf, class_channel in [
+            (A_CX, A_CY, A_W, A_H, 0.95, 5),  # class 0
+            (B_CX, B_CY, B_W, B_H, 0.90, 6),  # class 1
+        ]:
+            cell_x, cell_y = int(cx // stride), int(cy // stride)
+            dx = (cx / stride - cell_x + 0.5) / 2
+            dy = (cy / stride - cell_y + 0.5) / 2
+            dw = math.sqrt(w / anchor_w) / 2
+            dh = math.sqrt(h / anchor_h) / 2
+            # anchor 0 occupies channels 0-84 of the 255 channel tensor
+            outputs[2][0, 0:4, cell_y, cell_x] = [dx, dy, dw, dh]
+            outputs[2][0, 4, cell_y, cell_x] = conf
+            outputs[2][0, class_channel, cell_y, cell_x] = 1.0
+
+        return outputs
+
+    def test_keeps_separated_objects_far_from_origin(self):
+        detections = kept(post_process_yolo(self._multipart_output(), WIDTH, HEIGHT))
+
+        self.assertEqual(len(detections), 2)
+        np.testing.assert_allclose(detections[0], [0, 0.95, *A_ROW], atol=2e-3)
+        np.testing.assert_allclose(detections[1], [1, 0.90, *B_ROW], atol=2e-3)
+
+    def test_empty_output_returns_no_detections(self):
+        outputs = [
+            np.zeros((1, 255, 80, 80), dtype=np.float32),
+            np.zeros((1, 255, 40, 40), dtype=np.float32),
+            np.zeros((1, 255, 20, 20), dtype=np.float32),
+        ]
+
+        detections = kept(post_process_yolo(outputs, WIDTH, HEIGHT))
+
+        self.assertEqual(len(detections), 0)
+
+
+class TestYoloxPostProcess(unittest.TestCase):
+    def test_keeps_separated_objects_far_from_origin(self):
+        # with zero grids and unit strides the decode reduces to
+        # cx = raw cx and w = exp(raw w)
+        rows = np.zeros((10, 7), dtype=np.float32)
+        rows[0] = [A_CX, A_CY, math.log(A_W), math.log(A_H), 1.0, 0.90, 0.0]
+        rows[1] = [B_CX, B_CY, math.log(B_W), math.log(B_H), 1.0, 0.0, 0.85]
+        predictions = rows[np.newaxis, ...]
+        grids = np.zeros((1, 10, 2), dtype=np.float32)
+        expanded_strides = np.ones((1, 10, 1), dtype=np.float32)
+
+        detections = kept(
+            post_process_yolox(predictions, WIDTH, HEIGHT, grids, expanded_strides)
+        )
+
+        self.assertEqual(len(detections), 2)
+        np.testing.assert_allclose(detections[0], [0, 0.90, *A_ROW], atol=2e-3)
+        np.testing.assert_allclose(detections[1], [1, 0.85, *B_ROW], atol=2e-3)
+
+
+class TestDfinePostProcess(unittest.TestCase):
+    def test_keeps_separated_objects_far_from_origin(self):
+        # D-FINE emits absolute pixel xyxy boxes alongside labels and scores
+        labels = np.zeros((1, 10), dtype=np.int64)
+        labels[0, 1] = 1
+        boxes = np.zeros((1, 10, 4), dtype=np.float32)
+        boxes[0, 0] = [393, 499, 484, 620]
+        boxes[0, 1] = [527, 499, 618, 620]
+        scores = np.zeros((1, 10), dtype=np.float32)
+        scores[0, 0] = 0.90
+        scores[0, 1] = 0.85
+
+        detections = kept(post_process_dfine([labels, boxes, scores], WIDTH, HEIGHT))
+
+        self.assertEqual(len(detections), 2)
+        np.testing.assert_allclose(detections[0], [0, 0.90, *A_ROW], atol=2e-3)
+        np.testing.assert_allclose(detections[1], [1, 0.85, *B_ROW], atol=2e-3)
+
+
+class TestRfdetrPostProcess(unittest.TestCase):
+    def test_keeps_separated_objects_far_from_origin(self):
+        # RF-DETR emits normalized center format boxes and class logits where
+        # logit index 0 is the background class
+        boxes = np.zeros((1, 10, 4), dtype=np.float32)
+        boxes[0, 0] = [A_CX / WIDTH, A_CY / HEIGHT, A_W / WIDTH, A_H / HEIGHT]
+        boxes[0, 1] = [B_CX / WIDTH, B_CY / HEIGHT, B_W / WIDTH, B_H / HEIGHT]
+        # background heavy logits everywhere, then two confident objects
+        logits = np.tile(np.array([10.0, 0.0, 0.0], dtype=np.float32), (1, 10, 1))
+        logits[0, 0] = [0.0, 4.0, 0.0]  # class 0 after background offset
+        logits[0, 1] = [0.0, 0.0, 3.5]  # class 1 after background offset
+
+        detections = kept(post_process_rfdetr([boxes, logits]))
+
+        conf_a = math.exp(4.0) / (math.exp(4.0) + 2)
+        conf_b = math.exp(3.5) / (math.exp(3.5) + 2)
+        self.assertEqual(len(detections), 2)
+        np.testing.assert_allclose(detections[0], [0, conf_a, *A_ROW], atol=2e-3)
+        np.testing.assert_allclose(detections[1], [1, conf_b, *B_ROW], atol=2e-3)
+
+
+class TestMemryxSsdlitePostProcess(unittest.TestCase):
+    def test_keeps_separated_objects_far_from_origin(self):
+        # the NMS math runs on the host CPU, so the real method is testable
+        # without MemryX hardware; it only needs the model dimensions and
+        # the output queue
+        detector = object.__new__(MemryXDetector)
+        detector.memx_model_width = WIDTH
+        detector.memx_model_height = HEIGHT
+        detector.output_queue = Queue()
+
+        # this path uses a 0.5 NMS threshold, so use a tighter pair: zero
+        # true overlap (10 px gap), IoU 0.69 when misread as [x, y, w, h]
+        dets = np.zeros((1, 10, 5), dtype=np.float32)
+        dets[0, 0] = [480, 500, 540, 620, 0.90]
+        dets[0, 1] = [550, 500, 610, 620, 0.85]
+        labels = np.zeros((1, 10), dtype=np.float32)
+        labels[0, 1] = 1
+
+        detector.post_process_ssdlite([dets, labels])
+        detections = kept(detector.output_queue.get())
+
+        self.assertEqual(len(detections), 2)
+        np.testing.assert_allclose(
+            detections[0],
+            [0, 0.90, 500 / 640, 480 / 640, 620 / 640, 540 / 640],
+            atol=2e-3,
+        )
+        np.testing.assert_allclose(
+            detections[1],
+            [1, 0.85, 500 / 640, 550 / 640, 620 / 640, 610 / 640],
+            atol=2e-3,
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()

frigate/test/test_profiles.py

@@ -785,6 +785,98 @@ class TestProfileManager(unittest.TestCase):
         manager.activate_profile("armed", clear_runtime_overrides=False)
         dispatcher.clear_runtime_state.assert_not_called()
 
+    def test_apply_profile_to_config_mutates_the_config(self):
+        """The config-only half applies the same overrides as activation."""
+        err = self.manager.apply_profile_to_config("armed")
+        assert err is None
+
+        front = self.config.cameras["front"]
+        assert front.notifications.enabled is True
+        assert front.objects.track == ["person", "car", "package"]
+
+    def test_apply_profile_to_config_makes_no_zmq_mqtt_or_disk_writes(self):
+        """Workers are started with the values, so nothing is published yet."""
+        dispatcher = MagicMock()
+        manager = ProfileManager(self.config, self.mock_updater, dispatcher)
+
+        with patch.object(ProfileManager, "_persist_active_profile") as mock_persist:
+            manager.apply_profile_to_config("armed")
+
+        self.mock_updater.publish_update.assert_not_called()
+        dispatcher.publish.assert_not_called()
+        mock_persist.assert_not_called()
+        # bookkeeping stays with activate_profile
+        assert self.config.active_profile is None
+
+    def test_apply_profile_to_config_rejects_an_unknown_profile(self):
+        err = self.manager.apply_profile_to_config("nonexistent")
+        assert err is not None
+        assert "not defined" in err
+
+    def test_restore_persisted_profile_to_config_applies_it(self):
+        """The startup config pass restores what was persisted."""
+        with patch.object(
+            ProfileManager, "load_persisted_profile", return_value="armed"
+        ):
+            self.manager.restore_persisted_profile_to_config()
+
+        assert self.config.cameras["front"].notifications.enabled is True
+        # still the config-only half, so nothing is published or persisted
+        self.mock_updater.publish_update.assert_not_called()
+        assert self.config.active_profile is None
+
+    def test_restore_persisted_profile_to_config_no_op_when_none_persisted(self):
+        with patch.object(ProfileManager, "load_persisted_profile", return_value=None):
+            self.manager.restore_persisted_profile_to_config()
+
+        assert self.config.cameras["front"].notifications.enabled is False
+
+    def test_restore_persisted_profile_to_config_ignores_a_stale_name(self):
+        """A profile no longer offered by any camera must not be applied."""
+        with patch.object(
+            ProfileManager, "load_persisted_profile", return_value="ghost"
+        ):
+            self.manager.restore_persisted_profile_to_config()
+
+        assert self.config.cameras["front"].notifications.enabled is False
+
+    @patch.object(ProfileManager, "_persist_active_profile")
+    def test_restore_persisted_profile_activates_and_publishes(self, mock_persist):
+        """The startup publish pass runs a full activation."""
+        dispatcher = MagicMock()
+        manager = ProfileManager(self.config, self.mock_updater, dispatcher)
+
+        with patch.object(
+            ProfileManager, "load_persisted_profile", return_value="armed"
+        ):
+            manager.restore_persisted_profile()
+
+        assert self.config.active_profile == "armed"
+        self.mock_updater.publish_update.assert_called()
+        # a startup replay must not wipe the runtime overrides layered on top
+        dispatcher.clear_runtime_state.assert_not_called()
+
+    @patch.object(ProfileManager, "_persist_active_profile")
+    def test_activation_after_apply_still_publishes_every_section(self, mock_persist):
+        """Re-deriving the same state must not skip the broadcast.
+
+        The processes that started before the config was corrected have no
+        other channel.
+        """
+        self.manager.apply_profile_to_config("armed")
+        self.mock_updater.publish_update.reset_mock()
+
+        err = self.manager.activate_profile("armed", clear_runtime_overrides=False)
+        assert err is None
+
+        published = {
+            call.args[0].update_type.name
+            for call in self.mock_updater.publish_update.call_args_list
+        }
+        assert "notifications" in published
+        assert "objects" in published
+        assert self.config.active_profile == "armed"
+
     @patch.object(ProfileManager, "_persist_active_profile")
     def test_update_config_preserves_runtime_state_with_active_profile(
         self, mock_persist

frigate/test/test_ptz_onvif.py

@@ -1,4 +1,4 @@
-"""Tests for ONVIF init state that must not depend on the autotracking config.
+"""Tests for ONVIF state that must not depend on the autotracking config.
 
 Regression coverage for a camera that is initialized while autotracking is off and
 has it enabled later, which is the normal wizard flow: set the camera up first,
@@ -10,12 +10,17 @@ the tracking thread.
 
 The request objects are built from the locally parsed WSDL and cost no network, so
 they are always created and init=True now implies they exist.
+
+Also covers the inverse direction: the ptz movement timestamps must not be written
+for a camera that has autotracking off, because nothing clears them back out.
 """
 
 import unittest
 from unittest.mock import AsyncMock, MagicMock
 
+from frigate.camera import PTZMetrics
 from frigate.config import FrigateConfig
+from frigate.ptz.autotrack import ptz_moving_at_frame_time
 from frigate.ptz.onvif import OnvifController
 
 CAMERA = "ptz_cam"
@@ -97,6 +102,36 @@ def _make_controller(autotracking_enabled: bool) -> OnvifController:
     return controller
 
 
+def _make_move_controller(autotracking_enabled: bool) -> OnvifController:
+    """Build an already initialized controller for a camera that supports relative
+    FOV movement, with real metrics so the timestamp writes can be asserted on."""
+    config = _config(autotracking_enabled)
+    controller = OnvifController.__new__(OnvifController)
+    controller.config = config
+    controller.camera_configs = {CAMERA: config.cameras[CAMERA]}
+    controller.failed_cams = {}
+
+    ptz = MagicMock()
+    ptz.RelativeMove = AsyncMock()
+    controller.cams = {
+        CAMERA: {
+            "init": True,
+            "active": False,
+            "ptz": ptz,
+            "features": ["pt", "pt-r-fov"],
+            "relative_move_request": MagicMock(),
+            "relative_fov_range": {
+                "XRange": {"Min": -1.0, "Max": 1.0},
+                "YRange": {"Min": -1.0, "Max": 1.0},
+            },
+        }
+    }
+    controller.ptz_metrics = {
+        CAMERA: PTZMetrics(autotracker_enabled=autotracking_enabled)
+    }
+    return controller
+
+
 class TestOnvifInitRequests(unittest.IsolatedAsyncioTestCase):
     async def test_status_request_created_when_autotracking_disabled(self) -> None:
         # the wizard flow: onvif configured first, autotracking enabled later
@@ -143,5 +178,61 @@ class TestOnvifInitRequests(unittest.IsolatedAsyncioTestCase):
         ptz.GetStatus.assert_not_called()
 
 
+class TestManualRelativeMoveMetrics(unittest.IsolatedAsyncioTestCase):
+    """A manual move from the UI (click to move, drag to zoom) sends move_relative
+    for any camera that advertises pt-r-fov, autotracking or not."""
+
+    async def test_metrics_untouched_when_autotracking_disabled(self) -> None:
+        # only camera_maintenance polls get_camera_status, and only for autotracking
+        # cameras, so a manual move that starts the clock here is never stopped
+        controller = _make_move_controller(autotracking_enabled=False)
+        metrics = controller.ptz_metrics[CAMERA]
+        metrics.frame_time.value = 1000.0
+
+        await controller._move_relative(CAMERA, 0.25, -0.25, 0, 1)
+
+        controller.cams[CAMERA]["ptz"].RelativeMove.assert_awaited_once()
+        self.assertEqual(metrics.start_time.value, 0)
+        self.assertEqual(metrics.stop_time.value, 0)
+        self.assertTrue(metrics.motor_stopped.is_set())
+
+    async def test_detection_regions_not_suppressed_after_manual_move(self) -> None:
+        # the symptom of the bug: object detection stops entirely because motion
+        # boxes are never promoted to detection regions again
+        controller = _make_move_controller(autotracking_enabled=False)
+        metrics = controller.ptz_metrics[CAMERA]
+        metrics.frame_time.value = 1000.0
+
+        await controller._move_relative(CAMERA, 0.25, -0.25, 0, 1)
+
+        for later_frame_time in (1001.0, 1060.0, 4600.0):
+            with self.subTest(frame_time=later_frame_time):
+                self.assertFalse(
+                    ptz_moving_at_frame_time(
+                        later_frame_time,
+                        metrics.start_time.value,
+                        metrics.stop_time.value,
+                    )
+                )
+
+    async def test_metrics_written_when_autotracking_enabled(self) -> None:
+        # get_camera_status resets stop_time once the camera reports IDLE, so the
+        # autotracking path keeps its motion estimation timestamps
+        controller = _make_move_controller(autotracking_enabled=True)
+        metrics = controller.ptz_metrics[CAMERA]
+        metrics.frame_time.value = 1000.0
+
+        await controller._move_relative(CAMERA, 0.25, -0.25, 0, 1)
+
+        self.assertEqual(metrics.start_time.value, 1000.0)
+        self.assertEqual(metrics.stop_time.value, 0)
+        self.assertFalse(metrics.motor_stopped.is_set())
+        self.assertTrue(
+            ptz_moving_at_frame_time(
+                1001.0, metrics.start_time.value, metrics.stop_time.value
+            )
+        )
+
+
 if __name__ == "__main__":
     unittest.main()

frigate/util/model.py

@@ -16,6 +16,31 @@ logger = logging.getLogger(__name__)
 ### Post Processing
 
 
+def xyxy_to_xywh_for_nms(boxes: np.ndarray | list) -> np.ndarray:
+    """Convert [x1, y1, x2, y2] boxes to the [x, y, width, height] format
+    that cv2.dnn.NMSBoxes expects.
+
+    Passing corner coordinates directly makes OpenCV treat x2/y2 as the box
+    size, inflating every box toward the bottom-right by its distance from
+    the origin, which suppresses valid detections near other objects.
+
+    Args:
+        boxes: Array-like of shape (N, 4) in corner format.
+
+    Returns:
+        Float32 array of shape (N, 4) in top-left plus size format.
+    """
+    boxes = np.asarray(boxes, dtype=np.float32)
+
+    if boxes.size == 0:
+        return np.zeros((0, 4), dtype=np.float32)
+
+    xywh = boxes.copy()
+    xywh[:, 2] -= xywh[:, 0]
+    xywh[:, 3] -= xywh[:, 1]
+    return xywh
+
+
 def post_process_dfine(
     tensor_output: np.ndarray, width: int, height: int
 ) -> np.ndarray:
@@ -25,7 +50,9 @@ def post_process_dfine(
 
     input_shape = np.array([height, width, height, width])
     boxes = np.divide(boxes, input_shape, dtype=np.float32)
-    indices = cv2.dnn.NMSBoxes(boxes, scores, score_threshold=0.4, nms_threshold=0.4)
+    indices = cv2.dnn.NMSBoxes(
+        xyxy_to_xywh_for_nms(boxes), scores, score_threshold=0.4, nms_threshold=0.4
+    )
     detections = np.zeros((20, 6), np.float32)
 
     for i, (bbox, confidence, class_id) in enumerate(
@@ -78,7 +105,10 @@ def post_process_rfdetr(tensor_output: list[np.ndarray, np.ndarray]) -> np.ndarr
 
     # apply nms
     indices = cv2.dnn.NMSBoxes(
-        filtered_boxes, filtered_scores, score_threshold=0.4, nms_threshold=0.4
+        xyxy_to_xywh_for_nms(filtered_boxes),
+        filtered_scores,
+        score_threshold=0.4,
+        nms_threshold=0.4,
     )
     detections = np.zeros((20, 6), np.float32)
 
@@ -159,7 +189,7 @@ def __post_process_multipart_yolo(
                     all_class_ids.append(class_id)
 
     indices = cv2.dnn.NMSBoxes(
-        bboxes=all_boxes,
+        bboxes=xyxy_to_xywh_for_nms(all_boxes),
         scores=all_scores,
         score_threshold=0.4,
         nms_threshold=0.4,
@@ -206,7 +236,9 @@ def __post_process_nms_yolo(predictions: np.ndarray, width, height) -> np.ndarra
     boxes = boxes_xyxy
 
     # run NMS
-    indices = cv2.dnn.NMSBoxes(boxes, scores, score_threshold=0.4, nms_threshold=0.4)
+    indices = cv2.dnn.NMSBoxes(
+        xyxy_to_xywh_for_nms(boxes), scores, score_threshold=0.4, nms_threshold=0.4
+    )
     detections = np.zeros((20, 6), np.float32)
     for i, (bbox, confidence, class_id) in enumerate(
         zip(boxes[indices], scores[indices], class_ids[indices])
@@ -258,7 +290,7 @@ def post_process_yolox(
     scores = scores[np.arange(len(cls_inds)), cls_inds]
 
     indices = cv2.dnn.NMSBoxes(
-        boxes_xyxy, scores, score_threshold=0.4, nms_threshold=0.4
+        xyxy_to_xywh_for_nms(boxes_xyxy), scores, score_threshold=0.4, nms_threshold=0.4
     )
 
     detections = np.zeros((20, 6), np.float32)

frigate/util/object_names.py

@@ -1,209 +0,0 @@
-"""Aggregation of the known sub label names an object can be tagged with."""
-
-import logging
-import os
-
-from pathvalidate import sanitize_filename
-
-from frigate.config import FrigateConfig
-from frigate.config.classification import ObjectClassificationType
-from frigate.const import CLIPS_DIR, FACE_DIR, MODEL_CACHE_DIR
-from frigate.util.builtin import load_labels
-
-logger = logging.getLogger(__name__)
-
-# subdirectory of FACE_DIR holding unassigned training images, not a face name
-FACE_TRAIN_DIR = "train"
-
-# category used by classification models for "no match", never attached to an object
-CLASSIFICATION_NONE_CATEGORY = "none"
-
-
-def get_categorized_object_names(
-    config: FrigateConfig,
-    allowed_cameras: list[str],
-    object_type: str | None = None,
-) -> dict[str, list[str]]:
-    """Collect every sub label name this install can attach, by object type.
-
-    Unlike the database-backed /sub_labels endpoint, this reads the config and
-    model files, so it also covers names that are configured but have not been
-    detected yet. Names come from the detector's logo attributes (limited to
-    objects the allowed cameras actually track), LPR known plate names,
-    registered face names, and custom object classification categories.
-
-    Structural attributes such as `face` and `license_plate` are excluded: they
-    describe a part of an object rather than naming it, and are never attached
-    as a sub label.
-
-    Args:
-        config: The running Frigate config
-        allowed_cameras: Cameras the requesting user may see
-        object_type: Optional object label to restrict the result to
-
-    Returns:
-        Mapping of object label to its known sub label names, sorted and
-        deduplicated. Object types with no known names are omitted.
-    """
-    tracked_objects = _get_tracked_objects(config, allowed_cameras)
-    names: dict[str, set[str]] = {}
-    logos = set(config.model.all_attribute_logos)
-
-    # 1. detector logo attributes, only for objects that are actually tracked
-    for label, label_attributes in config.model.attributes_map.items():
-        if label not in tracked_objects:
-            continue
-
-        label_logos = logos.intersection(label_attributes)
-
-        if label_logos:
-            names.setdefault(label, set()).update(label_logos)
-
-    # 2. LPR known plate names, for objects that can carry a plate
-    if config.lpr.known_plates and _lpr_enabled(config, allowed_cameras):
-        known_plates = set(config.lpr.known_plates)
-
-        for label in _objects_with_attribute(config, tracked_objects, "license_plate"):
-            names.setdefault(label, set()).update(known_plates)
-
-    # 3. registered face names, for objects that can carry a face
-    if _face_recognition_enabled(config, allowed_cameras):
-        face_names = _get_face_names()
-
-        if face_names:
-            for label in _objects_with_attribute(config, tracked_objects, "face"):
-                names.setdefault(label, set()).update(face_names)
-
-    # 4. custom object classification categories
-    for model_key, model_config in config.classification.custom.items():
-        if not model_config.enabled or model_config.object_config is None:
-            continue
-
-        if (
-            model_config.object_config.classification_type
-            != ObjectClassificationType.sub_label
-        ):
-            continue
-
-        categories = _get_classification_categories(model_key)
-
-        if not categories:
-            continue
-
-        for label in model_config.object_config.objects:
-            names.setdefault(label, set()).update(categories)
-
-    return {
-        label: sorted(label_names)
-        for label, label_names in sorted(names.items())
-        if label_names and (object_type is None or label == object_type)
-    }
-
-
-def _get_tracked_objects(config: FrigateConfig, allowed_cameras: list[str]) -> set[str]:
-    """Get the union of objects tracked by the cameras the user can see."""
-    tracked: set[str] = set()
-
-    for camera_name in allowed_cameras:
-        camera_config = config.cameras.get(camera_name)
-
-        if camera_config is None:
-            continue
-
-        tracked.update(camera_config.objects.track)
-
-    return tracked
-
-
-def _objects_with_attribute(
-    config: FrigateConfig, tracked_objects: set[str], attribute: str
-) -> set[str]:
-    """Get the tracked objects that a given attribute can be recognized on.
-
-    The attribute may also be tracked as an object in its own right, as
-    `license_plate` is on a dedicated LPR camera, in which case the name is
-    attached to that object directly.
-    """
-    objects = {
-        label
-        for label, label_attributes in config.model.attributes_map.items()
-        if attribute in label_attributes and label in tracked_objects
-    }
-
-    if attribute in tracked_objects:
-        objects.add(attribute)
-
-    return objects
-
-
-def _lpr_enabled(config: FrigateConfig, allowed_cameras: list[str]) -> bool:
-    return any(
-        config.cameras[camera_name].lpr.enabled
-        for camera_name in allowed_cameras
-        if camera_name in config.cameras
-    )
-
-
-def _face_recognition_enabled(
-    config: FrigateConfig, allowed_cameras: list[str]
-) -> bool:
-    return any(
-        config.cameras[camera_name].face_recognition.enabled
-        for camera_name in allowed_cameras
-        if camera_name in config.cameras
-    )
-
-
-def _get_face_names() -> set[str]:
-    """Get the names of every registered face collection."""
-    if not os.path.exists(FACE_DIR):
-        return set()
-
-    try:
-        entries = os.listdir(FACE_DIR)
-    except OSError:
-        logger.debug("Failed to read face directory %s", FACE_DIR)
-        return set()
-
-    return {
-        name
-        for name in entries
-        if name != FACE_TRAIN_DIR and os.path.isdir(os.path.join(FACE_DIR, name))
-    }
-
-
-def _get_classification_categories(model_key: str) -> set[str]:
-    """Get the categories a custom classification model can output.
-
-    The trained labelmap is authoritative, but it only exists once the model
-    has been trained, so fall back to the dataset directories that will become
-    the labelmap on the next training run.
-    """
-    safe_key = sanitize_filename(model_key)
-    categories: set[str] = set()
-    labelmap_path = os.path.join(MODEL_CACHE_DIR, safe_key, "labelmap.txt")
-
-    if os.path.exists(labelmap_path):
-        try:
-            labelmap = load_labels(labelmap_path, prefill=0, indexed=False)
-        except OSError:
-            logger.debug("Failed to read labelmap %s", labelmap_path)
-            labelmap = {}
-
-        categories.update(label for label in labelmap.values() if label)
-
-    dataset_dir = os.path.join(CLIPS_DIR, safe_key, "dataset")
-
-    if os.path.exists(dataset_dir):
-        try:
-            entries = os.listdir(dataset_dir)
-        except OSError:
-            logger.debug("Failed to read dataset directory %s", dataset_dir)
-            entries = []
-
-        categories.update(
-            name for name in entries if os.path.isdir(os.path.join(dataset_dir, name))
-        )
-
-    categories.discard(CLASSIFICATION_NONE_CATEGORY)
-    return categories

frigate/util/services.py

@@ -1,7 +1,6 @@
 """Utilities for services."""
 
 import asyncio
-import glob
 import json
 import logging
 import os
@@ -671,33 +670,19 @@ def get_intel_gpu_stats(
 
 def get_openvino_npu_stats() -> dict[str, str] | None:
     """Get NPU stats using openvino."""
-    for accel_path in sorted(glob.glob("/sys/class/accel/accel*")):
-        try:
-            driver = os.path.basename(os.readlink(f"{accel_path}/device/driver"))
-        except OSError:
-            continue
-
-        if driver != "intel_vpu":
-            continue
-
-        try:
-            runtime_path = f"{accel_path}/device/power/runtime_active_time"
-            with open(runtime_path) as f:
-                initial_runtime = float(f.read().strip())
-            break
-        except (FileNotFoundError, PermissionError, ValueError):
-            continue
-    else:
-        return None
+    NPU_RUNTIME_PATH = "/sys/devices/pci0000:00/0000:00:0b.0/power/runtime_active_time"
 
     try:
+        with open(NPU_RUNTIME_PATH) as f:
+            initial_runtime = float(f.read().strip())
+
         initial_time = time.time()
 
         # Sleep for 1 second to get an accurate reading
         time.sleep(1.0)
 
         # Read runtime value again
-        with open(runtime_path) as f:
+        with open(NPU_RUNTIME_PATH) as f:
             current_runtime = float(f.read().strip())
 
         current_time = time.time()

frigate/video/detect.py

@@ -358,12 +358,17 @@ def process_frames(
             ]
 
             # only add in the motion boxes when not calibrating and a ptz is not moving via autotracking
-            # ptz_moving_at_frame_time() always returns False for non-autotracking cameras
-            if not motion_detector.is_calibrating() and not ptz_moving_at_frame_time(
-                frame_time,
-                ptz_metrics.start_time.value,
-                ptz_metrics.stop_time.value,
-            ):
+            # the ptz timestamps are only maintained while autotracking is on, so gate
+            # on the metric rather than trusting them to be reset otherwise
+            ptz_moving = ptz_metrics.autotracker_enabled.value and (
+                ptz_moving_at_frame_time(
+                    frame_time,
+                    ptz_metrics.start_time.value,
+                    ptz_metrics.stop_time.value,
+                )
+            )
+
+            if not motion_detector.is_calibrating() and not ptz_moving:
                 # find motion boxes that are not inside tracked object regions
                 standalone_motion_boxes = [
                     b for b in motion_boxes if not inside_any(b, regions)

web/e2e/specs/settings/camera-live-streams.spec.ts

@@ -0,0 +1,204 @@
+/**
+ * Camera live playback stream settings tests -- MEDIUM tier.
+ *
+ * The live streams field maps a display name to a go2rtc stream. Switching
+ * cameras from the selector keeps the form mounted and only swaps its data, so
+ * the stream name input has to follow the newly selected camera. It used to be
+ * an uncontrolled input, which left the previous camera's stream name on screen
+ * and renamed the wrong key if the stale text was ever committed.
+ *
+ * Renames are committed per keystroke so the section is marked as modified
+ * right away, except while the typed name belongs to another stream, since
+ * renaming onto an existing name merges the two entries.
+ */
+
+import { readFileSync } from "node:fs";
+import { resolve, dirname } from "node:path";
+import { fileURLToPath } from "node:url";
+import { test, expect } from "../../fixtures/frigate-test";
+import type { Page } from "@playwright/test";
+import { configFactory } from "../../fixtures/mock-data/config";
+
+const __dirname = dirname(fileURLToPath(import.meta.url));
+const CONFIG_SCHEMA = JSON.parse(
+  readFileSync(
+    resolve(__dirname, "../../fixtures/mock-data/config-schema.json"),
+    "utf-8",
+  ),
+);
+
+const GO2RTC_STREAMS = {
+  front_door_main: ["rtsp://user:pass@192.168.0.20:554/Stream1"],
+  backyard_main: ["rtsp://user:pass@192.168.0.21:554/Stream1"],
+};
+
+const CAMERA_LIVE_STREAMS = {
+  front_door: { front_door: "front_door_main" },
+  backyard: { backyard: "backyard_main" },
+};
+
+const SETTINGS_URL = "/settings?page=cameraLivePlayback&camera=front_door";
+
+async function installRoutes(
+  page: Page,
+  frontDoorStreams: Record<string, string> = CAMERA_LIVE_STREAMS.front_door,
+) {
+  const config = configFactory({
+    go2rtc: { streams: GO2RTC_STREAMS },
+    cameras: {
+      front_door: { live: { streams: frontDoorStreams } },
+      backyard: { live: { streams: CAMERA_LIVE_STREAMS.backyard } },
+    },
+  });
+
+  let lastSavedConfig: unknown = null;
+
+  await page.route("**/api/config/schema.json", (route) =>
+    route.fulfill({ json: CONFIG_SCHEMA }),
+  );
+  await page.route("**/api/config", (route) => {
+    if (route.request().method() === "GET") {
+      return route.fulfill({ json: config });
+    }
+    return route.fulfill({ json: { success: true } });
+  });
+  await page.route("**/api/config/raw_paths", (route) =>
+    route.fulfill({
+      json: {
+        go2rtc: { streams: GO2RTC_STREAMS },
+        cameras: {
+          front_door: { live: { streams: frontDoorStreams } },
+          backyard: { live: { streams: CAMERA_LIVE_STREAMS.backyard } },
+        },
+      },
+    }),
+  );
+  await page.route("**/api/config/set", async (route) => {
+    lastSavedConfig = route.request().postDataJSON();
+    await route.fulfill({ json: { success: true, require_restart: false } });
+  });
+
+  return { capturedConfig: () => lastSavedConfig };
+}
+
+async function selectCamera(page: Page, friendlyName: string) {
+  await page.getByRole("button", { name: "Select a camera" }).click();
+  await page.getByRole("switch", { name: friendlyName }).click();
+}
+
+function streamNameInputs(page: Page) {
+  return page.getByRole("textbox", { name: "Stream name" });
+}
+
+function streamNames(page: Page) {
+  return streamNameInputs(page).evaluateAll((inputs) =>
+    inputs.map((input) => (input as HTMLInputElement).value),
+  );
+}
+
+/** Rows render in config order, which is not the order they were declared in. */
+async function streamNameRow(page: Page, name: string) {
+  await expect.poll(() => streamNames(page)).toContain(name);
+  const names = await streamNames(page);
+  return streamNameInputs(page).nth(names.indexOf(name));
+}
+
+test.describe("camera live playback streams @medium", () => {
+  test("switching cameras updates the stream name field", async ({
+    frigateApp,
+  }) => {
+    await installRoutes(frigateApp.page);
+    await frigateApp.goto(SETTINGS_URL);
+
+    const streamName = frigateApp.page.getByRole("textbox", {
+      name: "Stream name",
+    });
+    await expect(streamName).toHaveValue("front_door");
+    await expect(
+      frigateApp.page.getByRole("combobox", { name: "go2rtc stream" }),
+    ).toContainText("front_door_main");
+
+    await selectCamera(frigateApp.page, "Backyard");
+
+    await expect(streamName).toHaveValue("backyard");
+    await expect(
+      frigateApp.page.getByRole("combobox", { name: "go2rtc stream" }),
+    ).toContainText("backyard_main");
+  });
+
+  test("typing a new name enables Save without leaving the field", async ({
+    frigateApp,
+  }) => {
+    await installRoutes(frigateApp.page);
+    await frigateApp.goto(SETTINGS_URL);
+
+    const save = frigateApp.page.getByRole("button", { name: "Save" });
+    await expect(save).toBeDisabled();
+
+    const streamName = await streamNameRow(frigateApp.page, "front_door");
+    await streamName.click();
+    await frigateApp.page.keyboard.press("End");
+    await frigateApp.page.keyboard.type("_hd");
+
+    // Still focused: the rename is committed per keystroke, not on blur.
+    await expect(save).toBeEnabled();
+    await expect(streamName).toBeFocused();
+    await expect(streamName).toHaveValue("front_door_hd");
+  });
+
+  test("typing through another stream's name keeps both streams", async ({
+    frigateApp,
+  }) => {
+    await installRoutes(frigateApp.page, {
+      front: "front_door_main",
+      front_door: "backyard_main",
+    });
+    await frigateApp.goto(SETTINGS_URL);
+
+    const streamName = await streamNameRow(frigateApp.page, "front_door");
+    await streamName.click();
+    await frigateApp.page.keyboard.press("End");
+    // "front_door" passes through "front", which the other row already uses.
+    await frigateApp.page.keyboard.press("Backspace");
+    await frigateApp.page.keyboard.press("Backspace");
+    await frigateApp.page.keyboard.press("Backspace");
+    await frigateApp.page.keyboard.press("Backspace");
+    await frigateApp.page.keyboard.press("Backspace");
+    await expect(streamName).toHaveValue("front");
+    await frigateApp.page.keyboard.type("yard");
+    await streamName.blur();
+
+    expect(await streamNames(frigateApp.page)).toEqual(["frontyard", "front"]);
+  });
+
+  test("renaming a stream saves the new name for the selected camera", async ({
+    frigateApp,
+  }) => {
+    const capture = await installRoutes(frigateApp.page);
+    await frigateApp.goto(SETTINGS_URL);
+
+    await selectCamera(frigateApp.page, "Backyard");
+
+    const streamName = frigateApp.page.getByRole("textbox", {
+      name: "Stream name",
+    });
+    await expect(streamName).toHaveValue("backyard");
+    await streamName.fill("Backyard HD");
+    // The rename is committed on blur, not on every keystroke.
+    await streamName.blur();
+
+    await frigateApp.page.getByRole("button", { name: "Save" }).click();
+
+    await expect
+      .poll(() => capture.capturedConfig(), { timeout: 5_000 })
+      .toMatchObject({
+        config_data: {
+          cameras: {
+            backyard: {
+              live: { streams: { "Backyard HD": "backyard_main" } },
+            },
+          },
+        },
+      });
+  });
+});

web/package-lock.json

@@ -71,7 +71,7 @@
         "react-icons": "^5.5.0",
         "react-konva": "^19.2.3",
         "react-markdown": "^9.0.1",
-        "react-router-dom": "^6.30.3",
+        "react-router-dom": "^6.30.4",
         "react-swipeable": "^7.0.2",
         "react-zoom-pan-pinch": "3.4.4",
         "remark-gfm": "^4.0.0",
@@ -4878,9 +4878,9 @@
       }
     },
     "node_modules/@remix-run/router": {
-      "version": "1.23.2",
-      "resolved": "https://registry.npmjs.org/@remix-run/router/-/router-1.23.2.tgz",
-      "integrity": "sha512-Ic6m2U/rMjTkhERIa/0ZtXJP17QUi2CbWE7cqx4J58M8aA3QTfW+2UlQ4psvTX9IO1RfNVhK3pcpdjej7L+t2w==",
+      "version": "1.23.3",
+      "resolved": "https://registry.npmjs.org/@remix-run/router/-/router-1.23.3.tgz",
+      "integrity": "sha512-4An71tdz9X8+3sI4Qqqd2LWd9vS39J7sqd9EU4Scw7TJE/qB10Flv/UuqbPVgfQV9XoK8Np6jNquZitnZq5i+Q==",
       "license": "MIT",
       "engines": {
         "node": ">=14.0.0"
@@ -12282,12 +12282,12 @@
       }
     },
     "node_modules/react-router": {
-      "version": "6.30.3",
-      "resolved": "https://registry.npmjs.org/react-router/-/react-router-6.30.3.tgz",
-      "integrity": "sha512-XRnlbKMTmktBkjCLE8/XcZFlnHvr2Ltdr1eJX4idL55/9BbORzyZEaIkBFDhFGCEWBBItsVrDxwx3gnisMitdw==",
+      "version": "6.30.4",
+      "resolved": "https://registry.npmjs.org/react-router/-/react-router-6.30.4.tgz",
+      "integrity": "sha512-SVUsDe+DybHM/WmYKIVYhZh1o5Dcuf16yM6WjG02Q9XVFMZIJyHYhwrr6bFBXZkVP6z69kNkMyBCujt8FaFLJA==",
       "license": "MIT",
       "dependencies": {
-        "@remix-run/router": "1.23.2"
+        "@remix-run/router": "1.23.3"
       },
       "engines": {
         "node": ">=14.0.0"
@@ -12297,13 +12297,13 @@
       }
     },
     "node_modules/react-router-dom": {
-      "version": "6.30.3",
-      "resolved": "https://registry.npmjs.org/react-router-dom/-/react-router-dom-6.30.3.tgz",
-      "integrity": "sha512-pxPcv1AczD4vso7G4Z3TKcvlxK7g7TNt3/FNGMhfqyntocvYKj+GCatfigGDjbLozC4baguJ0ReCigoDJXb0ag==",
+      "version": "6.30.4",
+      "resolved": "https://registry.npmjs.org/react-router-dom/-/react-router-dom-6.30.4.tgz",
+      "integrity": "sha512-q4HvNl+mmDdkS0g+MqiBZNteQJCuimWoOyHMy4T/RQLAn9Z29+E91QXRaxOujeMl2HTzRSS0KFPd7lxX3PjV0Q==",
       "license": "MIT",
       "dependencies": {
-        "@remix-run/router": "1.23.2",
-        "react-router": "6.30.3"
+        "@remix-run/router": "1.23.3",
+        "react-router": "6.30.4"
       },
       "engines": {
         "node": ">=14.0.0"

web/package.json

@@ -85,7 +85,7 @@
     "react-icons": "^5.5.0",
     "react-konva": "^19.2.3",
     "react-markdown": "^9.0.1",
-    "react-router-dom": "^6.30.3",
+    "react-router-dom": "^6.30.4",
     "react-swipeable": "^7.0.2",
     "react-zoom-pan-pinch": "3.4.4",
     "remark-gfm": "^4.0.0",

web/public/locales/en/config/cameras.json

@@ -859,8 +859,8 @@
       "description": "Numeric order used to sort the camera in the UI (default dashboard and lists); larger numbers appear later."
     },
     "dashboard": {
-      "label": "Show in UI",
-      "description": "Toggle whether this camera is visible everywhere in the Frigate UI. Disabling this will require manually editing the config to view this camera in the UI again."
+      "label": "Show on Live dashboard",
+      "description": "Toggle whether this camera is visible on the default All Cameras live dashboard. The camera remains available everywhere else in the UI, including camera groups and settings."
     },
     "review": {
       "label": "Show in review",

web/public/locales/en/config/global.json

@@ -357,22 +357,6 @@
         "description": "Optional API key for authenticated DeepStack services."
       }
     },
-    "degirum": {
-      "label": "DeGirum",
-      "description": "DeGirum detector for running models via DeGirum cloud or local inference services.",
-      "location": {
-        "label": "Inference Location",
-        "description": "Location of the DeGirim inference engine (e.g. '@cloud', '127.0.0.1')."
-      },
-      "zoo": {
-        "label": "Model Zoo",
-        "description": "Path or URL to the DeGirum model zoo."
-      },
-      "token": {
-        "label": "DeGirum Cloud Token",
-        "description": "Token for DeGirum Cloud access."
-      }
-    },
     "edgetpu": {
       "label": "EdgeTPU",
       "description": "EdgeTPU detector that runs TensorFlow Lite models compiled for Coral EdgeTPU using the EdgeTPU delegate.",
@@ -1543,8 +1527,8 @@
       "description": "Numeric order used to sort the camera in the UI (default dashboard and lists); larger numbers appear later."
     },
     "dashboard": {
-      "label": "Show in UI",
-      "description": "Toggle whether this camera is visible everywhere in the Frigate UI. Disabling this will require manually editing the config to view this camera in the UI again."
+      "label": "Show on Live dashboard",
+      "description": "Toggle whether this camera is visible on the default All Cameras live dashboard. The camera remains available everywhere else in the UI, including camera groups and settings."
     },
     "review": {
       "label": "Show in review",

web/public/locales/en/views/events.json

@@ -74,7 +74,7 @@
     "menuItem": "View motion previews",
     "title": "Motion previews: {{camera}}",
     "mobileSettingsTitle": "Motion Preview Settings",
-    "mobileSettingsDesc": "Adjust playback speed and dimming, and choose a date to review motion-only clips.",
+    "mobileSettingsDesc": "Adjust playback speed, dimming, and cropping, and choose a date to review motion-only clips.",
     "dim": "Dim",
     "dimAria": "Adjust dimming intensity",
     "dimDesc": "Increase dimming to increase motion area visibility.",
@@ -87,6 +87,9 @@
     "seekAria": "Seek {{camera}} player to {{time}}",
     "filter": "Filter",
     "filterDesc": "Select areas to only show clips with motion in those regions.",
-    "filterClear": "Clear"
+    "filterClear": "Clear",
+    "crop": "Crop to filter",
+    "cropAria": "Toggle cropping previews to the filtered areas",
+    "cropDesc": "Zoom previews into the selected filter areas instead of showing the full frame."
   }
 }

web/public/locales/en/views/settings.json

@@ -499,7 +499,7 @@
         "webuiUrlHelp": "URL to visit the camera's web UI directly from the Debug view. Leave blank to disable the link.",
         "webuiUrlInvalid": "Must be a valid URL (e.g., https://example.com).",
         "dashboardLabel": "Show on Live dashboard",
-        "dashboardHelp": "Show this camera on the Live dashboard.",
+        "dashboardHelp": "Show this camera on the default All Cameras live dashboard. It remains available everywhere else, including camera groups.",
         "reviewLabel": "Show in Review",
         "reviewHelp": "Show this camera in Review, including the camera filter, motion review, and the history view."
       }

web/src/components/config-form/theme/components/index.tsx

@@ -5,6 +5,7 @@
 import { canExpand } from "@rjsf/utils";
 import type { RJSFSchema, UiSchema } from "@rjsf/utils";
 import { Button } from "@/components/ui/button";
+import { Input } from "@/components/ui/input";
 import { LuPlus, LuChevronDown, LuChevronRight } from "react-icons/lu";
 import { useTranslation } from "react-i18next";
 import {
@@ -12,7 +13,7 @@ import {
   CollapsibleContent,
   CollapsibleTrigger,
 } from "@/components/ui/collapsible";
-import type { ReactNode } from "react";
+import { useEffect, useState, type ReactNode } from "react";
 
 interface AddPropertyButtonProps {
   /** Callback fired when the add button is clicked */
@@ -67,6 +68,72 @@ export function AddPropertyButton({
   );
 }
 
+interface MapKeyInputProps {
+  /** DOM id used for label association */
+  id: string;
+  /** The committed key as it exists in the form data */
+  value: string;
+  /** Placeholder shown when the input is empty */
+  placeholder?: string;
+  /** Whether the input is disabled */
+  disabled?: boolean;
+  /** Additional class names */
+  className?: string;
+  /** Called with the edited key when it is safe to commit */
+  onCommit: (next: string) => void;
+  /** Whether another entry already uses this key, which defers the commit */
+  isKeyTaken?: (next: string) => boolean;
+}
+
+/**
+ * Text input for the key of a map entry (e.g. a live stream name).
+ *
+ * The edit is kept in local state so that the draft can be re-synced whenever
+ * the committed key changes underneath the input, which is what happens when
+ * the selected camera changes while the field stays mounted.
+ *
+ * Each keystroke is committed so the section is marked as modified right away,
+ * except while the typed key belongs to another entry: renaming onto an
+ * existing key merges the two entries, so a name typed through a neighbor's
+ * name would silently drop it. Those keystrokes stay local until the key is
+ * free again or the input is blurred.
+ */
+export function MapKeyInput({
+  id,
+  value,
+  placeholder,
+  disabled,
+  className,
+  onCommit,
+  isKeyTaken,
+}: MapKeyInputProps) {
+  const [draft, setDraft] = useState(value);
+
+  useEffect(() => {
+    setDraft(value);
+  }, [value]);
+
+  const handleChange = (next: string) => {
+    setDraft(next);
+
+    if (!isKeyTaken?.(next)) {
+      onCommit(next);
+    }
+  };
+
+  return (
+    <Input
+      id={id}
+      value={draft}
+      placeholder={placeholder}
+      disabled={disabled}
+      className={className}
+      onChange={(e) => handleChange(e.target.value)}
+      onBlur={() => onCommit(draft)}
+    />
+  );
+}
+
 interface AdvancedCollapsibleProps {
   /** Number of advanced fields */
   count: number;

web/src/components/config-form/theme/fields/KnownPlatesField.tsx

@@ -19,6 +19,7 @@ import {
 import type { ConfigFormContext } from "@/types/configForm";
 import get from "lodash/get";
 import { isSubtreeModified } from "../utils";
+import { MapKeyInput } from "../components";
 
 type KnownPlatesData = Record<string, string[]>;
 
@@ -194,12 +195,16 @@ export function KnownPlatesField(props: FieldProps) {
                   className="space-y-2 rounded-md border p-3"
                 >
                   <div className="flex items-center gap-2">
-                    <Input
+                    <MapKeyInput
                       id={`${entryId}-key`}
-                      defaultValue={key}
+                      value={key}
                       placeholder={namePlaceholder}
                       disabled={disabled || readonly}
-                      onBlur={(e) => handleRenameKey(key, e.target.value)}
+                      onCommit={(next) => handleRenameKey(key, next)}
+                      isKeyTaken={(next) =>
+                        next !== key &&
+                        Object.prototype.hasOwnProperty.call(data, next)
+                      }
                       className="flex-1"
                     />
                     <Button

web/src/components/config-form/theme/fields/LiveStreamsField.tsx

@@ -3,7 +3,6 @@ import { useCallback, useMemo, useState } from "react";
 import { useTranslation } from "react-i18next";
 import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
 import { Button } from "@/components/ui/button";
-import { Input } from "@/components/ui/input";
 import { Label } from "@/components/ui/label";
 import {
   Command,
@@ -20,6 +19,7 @@ import {
 import { cn } from "@/lib/utils";
 import { Check, ChevronsUpDown, Plus } from "lucide-react";
 import { LuPlus, LuTrash2 } from "react-icons/lu";
+import { MapKeyInput } from "../components";
 import type { ConfigFormContext } from "@/types/configForm";
 import get from "lodash/get";
 import { isSubtreeModified } from "../utils";
@@ -288,12 +288,16 @@ export function LiveStreamsField(props: FieldProps) {
             >
               <div className="col-span-12 space-y-2 md:col-span-5">
                 <Label htmlFor={`${entryId}-key`}>{streamNameLabel}</Label>
-                <Input
+                <MapKeyInput
                   id={`${entryId}-key`}
-                  defaultValue={key}
+                  value={key}
                   placeholder={streamNamePlaceholder}
                   disabled={disabled || readonly}
-                  onBlur={(e) => handleRenameKey(key, e.target.value)}
+                  onCommit={(next) => handleRenameKey(key, next)}
+                  isKeyTaken={(next) =>
+                    next !== key &&
+                    Object.prototype.hasOwnProperty.call(data, next)
+                  }
                 />
               </div>
               <div className="col-span-10 space-y-2 md:col-span-6">

web/src/components/filter/MotionRegionFilterGrid.tsx

@@ -1,16 +1,20 @@
 import { baseUrl } from "@/api/baseUrl";
-import { useCallback, useRef } from "react";
+import { CameraConfig } from "@/types/frigateConfig";
+import { useCallback, useMemo, useRef } from "react";
 
 const GRID_SIZE = 16;
+const DEFAULT_ASPECT_RATIO = 16 / 9;
+// Cap how tall the grid can get for portrait and 4:3 cameras
+const MAX_GRID_HEIGHT = "65dvh";
 
 type MotionRegionFilterGridProps = {
-  cameraName: string;
+  camera: CameraConfig;
   selectedCells: Set<number>;
   onCellsChange: (cells: Set<number>) => void;
 };
 
 export default function MotionRegionFilterGrid({
-  cameraName,
+  camera,
   selectedCells,
   onCellsChange,
 }: MotionRegionFilterGridProps) {
@@ -21,6 +25,18 @@ export default function MotionRegionFilterGrid({
   const lastCellRef = useRef<number>(-1);
   const gridRef = useRef<HTMLDivElement>(null);
 
+  // Cells are indexed against the detect frame, so the grid has to match the
+  // frame's aspect ratio or painted cells land on the wrong part of the image
+  const aspectRatio = useMemo(() => {
+    if (!camera.detect.width || !camera.detect.height) {
+      return DEFAULT_ASPECT_RATIO;
+    }
+
+    const ratio = camera.detect.width / camera.detect.height;
+
+    return Number.isFinite(ratio) && ratio > 0 ? ratio : DEFAULT_ASPECT_RATIO;
+  }, [camera.detect.height, camera.detect.width]);
+
   const toggleCell = useCallback(
     (index: number, forceAdd?: boolean) => {
       const next = new Set(selectedCells);
@@ -109,13 +125,17 @@ export default function MotionRegionFilterGrid({
   return (
     <div className="space-y-2">
       <div
-        className="relative aspect-video w-full select-none overflow-hidden rounded-lg"
-        style={{ touchAction: "none" }}
+        className="relative mx-auto select-none overflow-hidden rounded-lg"
+        style={{
+          aspectRatio,
+          width: `min(100%, calc(${MAX_GRID_HEIGHT} * ${aspectRatio}))`,
+          touchAction: "none",
+        }}
         onPointerUp={handlePointerUp}
         onPointerLeave={handlePointerUp}
       >
         <img
-          src={`${baseUrl}api/${cameraName}/latest.jpg?h=500`}
+          src={`${baseUrl}api/${camera.name}/latest.jpg?h=500`}
           className="absolute inset-0 size-full object-contain"
           draggable={false}
           alt=""

web/src/components/settings/ZoneEditPane.tsx

@@ -78,7 +78,7 @@ export default function ZoneEditPane({
     }
 
     return Object.values(config.cameras)
-      .filter((conf) => conf.ui.dashboard && conf.enabled_in_config)
+      .filter((conf) => conf.enabled_in_config)
       .sort((aConf, bConf) => aConf.ui.order - bConf.ui.order);
   }, [config]);
 

web/src/components/timeline/ReviewTimeline.tsx

@@ -327,17 +327,20 @@ export function ReviewTimeline({
       documentInstance?.addEventListener("touchmove", handleMouseMove);
       documentInstance?.addEventListener("mouseup", handleMouseUp);
       documentInstance?.addEventListener("touchend", handleMouseUp);
+      documentInstance?.addEventListener("touchcancel", handleMouseUp);
     } else {
       documentInstance?.removeEventListener("mousemove", handleMouseMove);
       documentInstance?.removeEventListener("touchmove", handleMouseMove);
       documentInstance?.removeEventListener("mouseup", handleMouseUp);
       documentInstance?.removeEventListener("touchend", handleMouseUp);
+      documentInstance?.removeEventListener("touchcancel", handleMouseUp);
     }
     return () => {
       documentInstance?.removeEventListener("mousemove", handleMouseMove);
       documentInstance?.removeEventListener("touchmove", handleMouseMove);
       documentInstance?.removeEventListener("mouseup", handleMouseUp);
       documentInstance?.removeEventListener("touchend", handleMouseUp);
+      documentInstance?.removeEventListener("touchcancel", handleMouseUp);
     };
   }, [handleMouseMove, handleMouseUp, isDragging]);
 

web/src/components/timeline/VirtualizedEventSegments.tsx

@@ -73,7 +73,9 @@ export const VirtualizedEventSegments = forwardRef<
           Math.ceil((scrollTop + clientHeight) / SEGMENT_HEIGHT) +
             OVERSCAN_COUNT,
         );
-        setVisibleRange({ start, end });
+        setVisibleRange((prev) =>
+          prev.start === start && prev.end === end ? prev : { start, end },
+        );
       }
     }, [segments.length, timelineRef]);
 

web/src/components/timeline/VirtualizedMotionSegments.tsx

@@ -77,7 +77,9 @@ export const VirtualizedMotionSegments = forwardRef<
           Math.ceil((scrollTop + clientHeight) / SEGMENT_HEIGHT) +
             OVERSCAN_COUNT,
         );
-        setVisibleRange({ start, end });
+        setVisibleRange((prev) =>
+          prev.start === start && prev.end === end ? prev : { start, end },
+        );
       }
     }, [segments.length, timelineRef]);
 

web/src/hooks/use-draggable-element.ts

@@ -1,4 +1,4 @@
-import { useCallback, useEffect, useMemo, useState } from "react";
+import { useCallback, useEffect, useMemo, useRef, useState } from "react";
 import { useTimelineUtils } from "./use-timeline-utils";
 import { FrigateConfig } from "@/types/frigateConfig";
 import useSWR from "swr";
@@ -8,6 +8,8 @@ import { useTimeFormat } from "./use-date-utils";
 import { useTranslation } from "react-i18next";
 import useUserInteraction from "./use-user-interaction";
 
+const DRAG_STATE_COMMIT_MS = 100;
+
 type DraggableElementProps = {
   contentRef: React.RefObject<HTMLElement | null>;
   timelineRef: React.RefObject<HTMLDivElement | null>;
@@ -61,6 +63,8 @@ function useDraggableElement({
 
   const [clientYPosition, setClientYPosition] = useState<number | null>(null);
   const [initialClickAdjustment, setInitialClickAdjustment] = useState(0);
+  const lastDragTimeCommitRef = useRef(0);
+  const pendingDragTimeRef = useRef<number | null>(null);
   const [elementScrollIntoView, setElementScrollIntoView] = useState(true);
   const [scrollEdgeSize, setScrollEdgeSize] = useState<number>();
   const [fullTimelineHeight, setFullTimelineHeight] = useState<number>();
@@ -126,6 +130,7 @@ function useDraggableElement({
       }
       e.stopPropagation();
       setIsDragging(true);
+      pendingDragTimeRef.current = null;
 
       let clientY;
       if ("TouchEvent" in window && e.nativeEvent instanceof TouchEvent) {
@@ -154,9 +159,14 @@ function useDraggableElement({
       if (isDragging) {
         setIsDragging(false);
         setInitialClickAdjustment(0);
+
+        if (pendingDragTimeRef.current !== null && setDraggableElementTime) {
+          setDraggableElementTime(pendingDragTimeRef.current);
+          pendingDragTimeRef.current = null;
+        }
       }
     },
-    [isDragging, setIsDragging],
+    [isDragging, setIsDragging, setDraggableElementTime],
   );
 
   const timestampToPixels = useCallback(
@@ -346,9 +356,21 @@ function useDraggableElement({
         );
 
         if (setDraggableElementTime) {
-          setDraggableElementTime(
-            targetSegmentTime + segmentDuration * (offset / segmentHeight),
-          );
+          const newTime =
+            targetSegmentTime + segmentDuration * (offset / segmentHeight);
+          const now = performance.now();
+
+          // don't commit on every animation frame, only commit it at a
+          // set interval to avoid React's nested update limit
+          if (now - lastDragTimeCommitRef.current >= DRAG_STATE_COMMIT_MS) {
+            lastDragTimeCommitRef.current = now;
+            pendingDragTimeRef.current = null;
+            setDraggableElementTime(newTime);
+          } else {
+            // Hold the newest value; handleMouseUp flushes it so the
+            // release still lands exactly where the handle was dropped.
+            pendingDragTimeRef.current = newTime;
+          }
         }
 
         if (draggingAtTopEdge || draggingAtBottomEdge) {

web/src/hooks/use-user-interaction.ts

@@ -8,6 +8,7 @@ function useUserInteraction({ elementRef }: UseUserInteractionProps) {
   const [userInteracting, setUserInteracting] = useState(false);
   const interactionTimeout = useRef<NodeJS.Timeout>(undefined);
   const isProgrammaticScroll = useRef(false);
+  const userInteractingRef = useRef(false);
 
   const setProgrammaticScroll = useCallback(() => {
     isProgrammaticScroll.current = true;
@@ -16,13 +17,18 @@ function useUserInteraction({ elementRef }: UseUserInteractionProps) {
   useEffect(() => {
     const handleUserInteraction = () => {
       if (!isProgrammaticScroll.current) {
-        setUserInteracting(true);
+        // Only commit state on the leading edge
+        if (!userInteractingRef.current) {
+          userInteractingRef.current = true;
+          setUserInteracting(true);
+        }
 
         if (interactionTimeout.current) {
           clearTimeout(interactionTimeout.current);
         }
 
         interactionTimeout.current = setTimeout(() => {
+          userInteractingRef.current = false;
           setUserInteracting(false);
         }, 3000);
       } else {

web/src/pages/Events.tsx

@@ -198,6 +198,19 @@ export default function Events() {
     return true;
   });
 
+  const reviewCamerasParam = useMemo(() => {
+    const selected: string | undefined = reviewSearchParams["cameras"];
+
+    if (!selected) {
+      return reviewCameras.join(",");
+    }
+
+    const selectedCameras = new Set(selected.split(","));
+    return reviewCameras
+      .filter((camera) => selectedCameras.has(camera))
+      .join(",");
+  }, [reviewCameras, reviewSearchParams]);
+
   useSearchEffect("labels", (labels: string) => {
     setReviewFilter({
       ...reviewFilter,
@@ -330,8 +343,12 @@ export default function Events() {
   }, []);
 
   const getKey = useCallback(() => {
+    if (!timezone) {
+      return null;
+    }
+
     const params = {
-      cameras: reviewSearchParams["cameras"],
+      cameras: reviewCamerasParam,
       labels: reviewSearchParams["labels"],
       zones: reviewSearchParams["zones"],
       reviewed: null, // We want both reviewed and unreviewed items as we filter in the UI
@@ -339,7 +356,7 @@ export default function Events() {
       after: reviewSearchParams["after"] || last24Hours.after,
     };
     return ["review", params];
-  }, [reviewSearchParams, last24Hours]);
+  }, [reviewSearchParams, reviewCamerasParam, last24Hours, timezone]);
 
   const { data: reviews, mutate: updateSegments } = useSWR<ReviewSegment[]>(
     getKey,
@@ -361,10 +378,6 @@ export default function Events() {
     const motion: ReviewSegment[] = [];
 
     reviews?.forEach((segment) => {
-      if (config?.cameras[segment.camera]?.ui?.review === false) {
-        return;
-      }
-
       all.push(segment);
 
       switch (segment.severity) {
@@ -386,7 +399,7 @@ export default function Events() {
       detection: detections,
       significant_motion: motion,
     };
-  }, [reviews, config?.cameras]);
+  }, [reviews]);
 
   // update review items in place when a review segment ends
   const reviewUpdate = useFrigateReviews();
@@ -450,15 +463,17 @@ export default function Events() {
   // review summary
 
   const { data: reviewSummary, mutate: updateSummary } = useSWR<ReviewSummary>(
-    [
-      "review/summary",
-      {
-        timezone: timezone,
-        cameras: reviewSearchParams["cameras"] ?? null,
-        labels: reviewSearchParams["labels"] ?? null,
-        zones: reviewSearchParams["zones"] ?? null,
-      },
-    ],
+    timezone
+      ? [
+          "review/summary",
+          {
+            timezone: timezone,
+            cameras: reviewCamerasParam,
+            labels: reviewSearchParams["labels"] ?? null,
+            zones: reviewSearchParams["zones"] ?? null,
+          },
+        ]
+      : null,
     {
       revalidateOnFocus: true,
       refreshInterval: 30000,
@@ -473,13 +488,17 @@ export default function Events() {
 
   // recordings summary
 
-  const { data: recordingsSummary } = useSWR<RecordingsSummary>([
-    "recordings/summary",
-    {
-      timezone: timezone,
-      cameras: reviewSearchParams["cameras"] ?? null,
-    },
-  ]);
+  const { data: recordingsSummary } = useSWR<RecordingsSummary>(
+    timezone
+      ? [
+          "recordings/summary",
+          {
+            timezone: timezone,
+            cameras: reviewCamerasParam,
+          },
+        ]
+      : null,
+  );
 
   // preview videos
   const previewTimes = useMemo(() => {

web/src/pages/Settings.tsx

@@ -706,12 +706,7 @@ export default function Settings() {
     }
 
     return Object.values(config.cameras)
-      .filter(
-        (conf) =>
-          conf.ui.dashboard &&
-          conf.enabled_in_config &&
-          !isReplayCamera(conf.name),
-      )
+      .filter((conf) => conf.enabled_in_config && !isReplayCamera(conf.name))
       .sort((aConf, bConf) => aConf.ui.order - bConf.ui.order);
   }, [config]);
 

web/src/views/events/EventView.tsx

@@ -8,6 +8,8 @@ import EventReviewTimeline from "@/components/timeline/EventReviewTimeline";
 import ActivityIndicator from "@/components/indicators/activity-indicator";
 import { ToggleGroup, ToggleGroupItem } from "@/components/ui/toggle-group";
 import { VolumeSlider } from "@/components/ui/slider";
+import { Label } from "@/components/ui/label";
+import { Switch } from "@/components/ui/switch";
 import {
   Select,
   SelectContent,
@@ -1024,6 +1026,9 @@ function MotionReview({
       if (!allowedCameras.includes(cam.name)) {
         return false;
       }
+      if (cam.ui?.review === false) {
+        return false;
+      }
       if (selectedCams && !selectedCams.includes(cam.name)) {
         return false;
       }
@@ -1033,6 +1038,11 @@ function MotionReview({
     return cameras.sort((a, b) => a.ui.order - b.ui.order);
   }, [config, filter, allowedCameras]);
 
+  const reviewCamerasParam = useMemo(
+    () => reviewCameras.map((cam) => cam.name).join(","),
+    [reviewCameras],
+  );
+
   const videoPlayersRef = useRef<{ [camera: string]: PreviewController }>({});
 
   // motion data
@@ -1052,7 +1062,7 @@ function MotionReview({
       before: alignedBefore,
       after: alignedAfter,
       scale: segmentDuration / 2,
-      cameras: filter?.cameras?.join(",") ?? null,
+      cameras: reviewCamerasParam,
     },
   ]);
 
@@ -1061,7 +1071,7 @@ function MotionReview({
     {
       before: alignedBefore,
       after: alignedAfter,
-      cameras: filter?.cameras?.join(",") ?? null,
+      cameras: reviewCamerasParam,
     },
   ]);
 
@@ -1146,6 +1156,7 @@ function MotionReview({
     new Set(),
   );
   const [isRegionFilterOpen, setIsRegionFilterOpen] = useState(false);
+  const [cropToFilter, setCropToFilter] = useState(true);
 
   // reset filter when camera changes
   useEffect(() => {
@@ -1385,7 +1396,7 @@ function MotionReview({
                     </DialogDescription>
                   </DialogHeader>
                   <MotionRegionFilterGrid
-                    cameraName={selectedMotionPreviewCamera.name}
+                    camera={selectedMotionPreviewCamera}
                     selectedCells={pendingFilterCells}
                     onCellsChange={setPendingFilterCells}
                   />
@@ -1440,7 +1451,12 @@ function MotionReview({
 
                     <div className="space-y-3">
                       <div className="space-y-0.5">
-                        <div>{t("motionPreviews.speed")}</div>
+                        <Label
+                          className="cursor-pointer"
+                          htmlFor="motionPreviewSpeed"
+                        >
+                          {t("motionPreviews.speed")}
+                        </Label>
                         <div className="text-xs text-muted-foreground">
                           {t("motionPreviews.speedDesc")}
                         </div>
@@ -1452,6 +1468,7 @@ function MotionReview({
                         }
                       >
                         <SelectTrigger
+                          id="motionPreviewSpeed"
                           className="h-10 w-full"
                           aria-label={t("motionPreviews.speedAria")}
                         >
@@ -1469,7 +1486,7 @@ function MotionReview({
 
                     <div className="space-y-3">
                       <div className="space-y-0.5">
-                        <div>{t("motionPreviews.dim")}</div>
+                        <Label>{t("motionPreviews.dim")}</Label>
                         <div className="text-xs text-muted-foreground">
                           {t("motionPreviews.dimDesc")}
                         </div>
@@ -1494,6 +1511,26 @@ function MotionReview({
                       </div>
                     </div>
 
+                    <div className="space-y-0.5">
+                      <div className="flex items-center justify-between gap-2">
+                        <Label
+                          className="cursor-pointer"
+                          htmlFor="cropToFilter"
+                        >
+                          {t("motionPreviews.crop")}
+                        </Label>
+                        <Switch
+                          id="cropToFilter"
+                          checked={cropToFilter}
+                          onCheckedChange={setCropToFilter}
+                          aria-label={t("motionPreviews.cropAria")}
+                        />
+                      </div>
+                      <div className="text-xs text-muted-foreground">
+                        {t("motionPreviews.cropDesc")}
+                      </div>
+                    </div>
+
                     {!isDesktop && (
                       <>
                         <SelectSeparator />
@@ -1549,6 +1586,7 @@ function MotionReview({
             playbackRate={playbackRate}
             nonMotionAlpha={dimStrength / 100}
             motionFilterCells={motionFilterCells}
+            cropToFilter={cropToFilter}
             onSeek={(timestamp) => {
               onOpenRecording({
                 camera: selectedMotionPreviewCamera.name,

web/src/views/events/MotionPreviewsPane.tsx

@@ -24,6 +24,94 @@ import { FrigateConfig } from "@/types/frigateConfig";
 
 const MOTION_HEATMAP_GRID_SIZE = 16;
 const MIN_MOTION_CELL_ALPHA = 0.06;
+const DEFAULT_TILE_ASPECT_RATIO = 16 / 9;
+// Keep cropped tiles from collapsing into unusable slivers when the selection
+// is a single row or column
+const MIN_CROP_TILE_ASPECT_RATIO = 0.75;
+const MAX_CROP_TILE_ASPECT_RATIO = 4;
+
+type CropRegion = {
+  x: number;
+  y: number;
+  width: number;
+  height: number;
+};
+
+type MediaRect = {
+  x: number;
+  y: number;
+  width: number;
+  height: number;
+};
+
+function getCropRegionForCells(cells?: Set<number>): CropRegion | undefined {
+  if (!cells || cells.size === 0) {
+    return undefined;
+  }
+
+  let minRow = MOTION_HEATMAP_GRID_SIZE;
+  let maxRow = -1;
+  let minCol = MOTION_HEATMAP_GRID_SIZE;
+  let maxCol = -1;
+
+  cells.forEach((cellIndex) => {
+    if (
+      !Number.isInteger(cellIndex) ||
+      cellIndex < 0 ||
+      cellIndex >= MOTION_HEATMAP_GRID_SIZE ** 2
+    ) {
+      return;
+    }
+
+    const row = Math.floor(cellIndex / MOTION_HEATMAP_GRID_SIZE);
+    const col = cellIndex % MOTION_HEATMAP_GRID_SIZE;
+
+    minRow = Math.min(minRow, row);
+    maxRow = Math.max(maxRow, row);
+    minCol = Math.min(minCol, col);
+    maxCol = Math.max(maxCol, col);
+  });
+
+  if (maxRow < 0 || maxCol < 0) {
+    return undefined;
+  }
+
+  return {
+    x: minCol / MOTION_HEATMAP_GRID_SIZE,
+    y: minRow / MOTION_HEATMAP_GRID_SIZE,
+    width: (maxCol - minCol + 1) / MOTION_HEATMAP_GRID_SIZE,
+    height: (maxRow - minRow + 1) / MOTION_HEATMAP_GRID_SIZE,
+  };
+}
+
+// Rendered area of object-contain media inside its container, accounting for
+// letterboxing on whichever axis has slack
+function getContainedMediaRect(
+  width: number,
+  height: number,
+  mediaDimensions: { width: number; height: number } | null,
+): MediaRect {
+  if (
+    !mediaDimensions ||
+    mediaDimensions.width <= 0 ||
+    mediaDimensions.height <= 0
+  ) {
+    return { x: 0, y: 0, width, height };
+  }
+
+  const containerAspect = width / height;
+  const mediaAspect = mediaDimensions.width / mediaDimensions.height;
+
+  if (mediaAspect < containerAspect) {
+    // Portrait / tall: constrained by height, bars on left and right
+    const drawWidth = height * mediaAspect;
+    return { x: (width - drawWidth) / 2, y: 0, width: drawWidth, height };
+  }
+
+  // Wide / landscape: constrained by width, bars on top and bottom
+  const drawHeight = width / mediaAspect;
+  return { x: 0, y: (height - drawHeight) / 2, width, height: drawHeight };
+}
 
 function getPreviewForMotionRange(
   cameraPreviews: Preview[],
@@ -132,6 +220,8 @@ type MotionPreviewClipProps = {
   fallbackFrameTimes?: number[];
   motionHeatmap?: Record<string, number> | null;
   nonMotionAlpha: number;
+  cropRegion?: CropRegion;
+  aspectRatio: number;
   isVisible: boolean;
   onSeek: (timestamp: number) => void;
 };
@@ -144,6 +234,8 @@ function MotionPreviewClip({
   fallbackFrameTimes,
   motionHeatmap,
   nonMotionAlpha,
+  cropRegion,
+  aspectRatio,
   isVisible,
   onSeek,
 }: MotionPreviewClipProps) {
@@ -398,34 +490,12 @@ function MotionPreviewClip({
       return;
     }
 
-    // Calculate the actual rendered media area (object-contain letterboxing)
-    let drawX = 0;
-    let drawY = 0;
-    let drawWidth = width;
-    let drawHeight = height;
-
-    if (
-      mediaDimensions &&
-      mediaDimensions.width > 0 &&
-      mediaDimensions.height > 0
-    ) {
-      const containerAspect = width / height;
-      const mediaAspect = mediaDimensions.width / mediaDimensions.height;
-
-      if (mediaAspect < containerAspect) {
-        // Portrait / tall: constrained by height, bars on left and right
-        drawHeight = height;
-        drawWidth = height * mediaAspect;
-        drawX = (width - drawWidth) / 2;
-        drawY = 0;
-      } else {
-        // Wide / landscape: constrained by width, bars on top and bottom
-        drawWidth = width;
-        drawHeight = width / mediaAspect;
-        drawX = 0;
-        drawY = (height - drawHeight) / 2;
-      }
-    }
+    const {
+      x: drawX,
+      y: drawY,
+      width: drawWidth,
+      height: drawHeight,
+    } = getContainedMediaRect(width, height, mediaDimensions);
 
     const heatmapLevels = Object.values(motionHeatmap)
       .map((value) => Number(value))
@@ -484,17 +554,53 @@ function MotionPreviewClip({
     drawDimOverlay();
   }, [drawDimOverlay]);
 
+  // Zoom the media (and its dim overlay) so the filtered region fills the tile
+  const mediaCropStyle = useMemo(() => {
+    if (!cropRegion || overlayWidth <= 0 || overlayHeight <= 0) {
+      return undefined;
+    }
+
+    const mediaRect = getContainedMediaRect(
+      overlayWidth,
+      overlayHeight,
+      mediaDimensions,
+    );
+    const cropWidth = cropRegion.width * mediaRect.width;
+    const cropHeight = cropRegion.height * mediaRect.height;
+
+    if (cropWidth <= 0 || cropHeight <= 0) {
+      return undefined;
+    }
+
+    const cropCenterX =
+      mediaRect.x + (cropRegion.x + cropRegion.width / 2) * mediaRect.width;
+    const cropCenterY =
+      mediaRect.y + (cropRegion.y + cropRegion.height / 2) * mediaRect.height;
+    const scale = Math.min(
+      overlayWidth / cropWidth,
+      overlayHeight / cropHeight,
+    );
+    const translateX = overlayWidth / 2 - scale * cropCenterX;
+    const translateY = overlayHeight / 2 - scale * cropCenterY;
+
+    return {
+      transform: `translate(${translateX}px, ${translateY}px) scale(${scale})`,
+      transformOrigin: "0 0",
+    };
+  }, [cropRegion, mediaDimensions, overlayHeight, overlayWidth]);
+
   return (
     <div
       ref={overlayContainerRef}
-      className="relative aspect-video size-full cursor-pointer overflow-hidden rounded-lg bg-black md:rounded-2xl"
+      className="relative size-full cursor-pointer overflow-hidden rounded-lg bg-black md:rounded-2xl"
+      style={{ aspectRatio }}
       onClick={() => onSeek(range.start_time)}
     >
       {showLoadingIndicator && (
         <Skeleton className="absolute inset-0 z-10 rounded-lg md:rounded-2xl" />
       )}
       {preview && isVisible ? (
-        <>
+        <div className="absolute inset-0" style={mediaCropStyle}>
           <video
             ref={videoRef}
             className="size-full bg-black object-contain"
@@ -548,9 +654,9 @@ function MotionPreviewClip({
               aria-hidden="true"
             />
           )}
-        </>
+        </div>
       ) : fallbackFrameSrc ? (
-        <>
+        <div className="absolute inset-0" style={mediaCropStyle}>
           <img
             src={fallbackFrameSrc}
             className="size-full bg-black object-contain"
@@ -575,7 +681,7 @@ function MotionPreviewClip({
               aria-hidden="true"
             />
           )}
-        </>
+        </div>
       ) : (
         <div className="flex size-full items-center justify-center text-sm text-muted-foreground">
           {t("motionPreviews.noPreview")}
@@ -605,6 +711,7 @@ type MotionPreviewsPaneProps = {
   playbackRate: number;
   nonMotionAlpha: number;
   motionFilterCells?: Set<number>;
+  cropToFilter?: boolean;
   onSeek: (timestamp: number) => void;
 };
 
@@ -617,6 +724,7 @@ export default function MotionPreviewsPane({
   playbackRate,
   nonMotionAlpha,
   motionFilterCells,
+  cropToFilter = true,
   onSeek,
 }: MotionPreviewsPaneProps) {
   const { t } = useTranslation(["views/events"]);
@@ -916,6 +1024,35 @@ export default function MotionPreviewsPane({
     });
   }, [clipData, motionFilterCells]);
 
+  const cropRegion = useMemo(
+    () => (cropToFilter ? getCropRegionForCells(motionFilterCells) : undefined),
+    [cropToFilter, motionFilterCells],
+  );
+
+  // Every clip shares the same crop, so tiles stay uniform while matching the
+  // shape of the selected region instead of letterboxing it into 16:9
+  const tileAspectRatio = useMemo(() => {
+    if (!cropRegion) {
+      return DEFAULT_TILE_ASPECT_RATIO;
+    }
+
+    const cameraAspect =
+      camera.detect.width && camera.detect.height
+        ? camera.detect.width / camera.detect.height
+        : DEFAULT_TILE_ASPECT_RATIO;
+
+    const croppedAspect = cameraAspect * (cropRegion.width / cropRegion.height);
+
+    if (!Number.isFinite(croppedAspect) || croppedAspect <= 0) {
+      return DEFAULT_TILE_ASPECT_RATIO;
+    }
+
+    return Math.min(
+      MAX_CROP_TILE_ASPECT_RATIO,
+      Math.max(MIN_CROP_TILE_ASPECT_RATIO, croppedAspect),
+    );
+  }, [camera.detect.height, camera.detect.width, cropRegion]);
+
   const hasCurrentHourRanges = useMemo(
     () => motionRanges.some((range) => isCurrentHour(range.end_time)),
     [motionRanges],
@@ -964,6 +1101,8 @@ export default function MotionPreviewsPane({
                         fallbackFrameTimes={fallbackFrameTimes}
                         motionHeatmap={motionHeatmap}
                         nonMotionAlpha={nonMotionAlpha}
+                        cropRegion={cropRegion}
+                        aspectRatio={tileAspectRatio}
                         isVisible={
                           windowVisible &&
                           (visibleClips.includes(clipId) ||
@@ -972,7 +1111,10 @@ export default function MotionPreviewsPane({
                         onSeek={onSeek}
                       />
                     ) : (
-                      <div className="aspect-video rounded-lg bg-black md:rounded-2xl" />
+                      <div
+                        className="rounded-lg bg-black md:rounded-2xl"
+                        style={{ aspectRatio: tileAspectRatio }}
+                      />
                     )}
                   </div>
                 );

web/src/views/motion-search/MotionSearchView.tsx

@@ -51,7 +51,12 @@ import { useTimelineUtils } from "@/hooks/use-timeline-utils";
 import { useCameraPreviews } from "@/hooks/use-camera-previews";
 import { getChunkedTimeDay } from "@/utils/timelineUtil";
 
-import { MotionData, REVIEW_PADDING, ZoomLevel } from "@/types/review";
+import {
+  MotionData,
+  REVIEW_PADDING,
+  ReviewSegment,
+  ZoomLevel,
+} from "@/types/review";
 import {
   ASPECT_VERTICAL_LAYOUT,
   ASPECT_WIDE_LAYOUT,
@@ -85,6 +90,7 @@ type MotionSearchViewProps = {
 };
 
 const DEFAULT_EXPORT_WINDOW_SECONDS = 60;
+const NO_REVIEW_EVENTS: ReviewSegment[] = [];
 
 export default function MotionSearchView({
   config,
@@ -514,6 +520,12 @@ export default function MotionSearchView({
       : null,
   );
 
+  const timelineMotionEvents = useMemo(() => motionData ?? [], [motionData]);
+  const timelineNoRecordings = useMemo(
+    () => noRecordings ?? [],
+    [noRecordings],
+  );
+
   const recordingParams = useMemo(
     () => ({
       before: currentTimeRange.before,
@@ -1054,11 +1066,11 @@ export default function MotionSearchView({
           showHandlebar={true}
           handlebarTime={currentTime}
           setHandlebarTime={setCurrentTime}
-          events={[]}
-          motion_events={motionData ?? []}
-          noRecordingRanges={noRecordings ?? []}
+          events={NO_REVIEW_EVENTS}
+          motion_events={timelineMotionEvents}
+          noRecordingRanges={timelineNoRecordings}
           contentRef={contentRef}
-          onHandlebarDraggingChange={(dragging) => setScrubbing(dragging)}
+          onHandlebarDraggingChange={setScrubbing}
           showExportHandles={
             (exportMode === "timeline" || exportMode === "timeline_multi") &&
             Boolean(exportRange)
@@ -1489,7 +1501,7 @@ export default function MotionSearchView({
               isDesktop
                 ? mainCameraAspect === "tall"
                   ? "mr-2 h-full min-h-0 min-w-0 flex-1 items-center"
-                  : "mr-2 h-full min-h-0 min-w-0 flex-1"
+                  : "mx-2 h-full min-h-0 min-w-0 flex-1"
                 : mainCameraAspect === "tall"
                   ? "flex-1 portrait:h-[40dvh] portrait:max-h-[40dvh] portrait:flex-shrink-0 portrait:flex-grow-0 portrait:basis-auto portrait:items-center portrait:justify-center"
                   : "flex-1 portrait:max-h-[40dvh] portrait:flex-shrink-0 portrait:flex-grow-0 portrait:basis-auto landscape:items-center landscape:justify-center",

web/src/views/recording/RecordingView.tsx

@@ -1199,7 +1199,7 @@ function Timeline({
             motion_events={motionData ?? []}
             noRecordingRanges={noRecordings ?? []}
             contentRef={contentRef}
-            onHandlebarDraggingChange={(scrubbing) => setScrubbing(scrubbing)}
+            onHandlebarDraggingChange={setScrubbing}
             isZooming={isZooming}
             zoomDirection={zoomDirection}
             onZoomChange={handleZoomChange}

web/src/views/system/GeneralMetrics.tsx

@@ -540,6 +540,36 @@ export default function GeneralMetrics({
     return Object.keys(series).length > 0 ? Object.values(series) : undefined;
   }, [statsHistory]);
 
+  // Number of cards the hardware grid renders. Which ones appear depends on
+  // the vendor, so the column count follows the count rather than assuming a
+  // fixed set is present.
+  const hardwareCardCount = useMemo(() => {
+    if (!statsHistory[0]?.gpu_usages) {
+      return 0;
+    }
+
+    const hasNpu = statsHistory[0].npu_usages != undefined;
+
+    return (
+      1 + // gpu usage always renders alongside gpu_usages
+      (gpuMemSeries ? 1 : 0) +
+      (gpuEncSeries?.length ? 1 : 0) +
+      (gpuComputeSeries?.length ? 1 : 0) +
+      (gpuDecSeries?.length ? 1 : 0) +
+      (gpuTempSeries?.length ? 1 : 0) +
+      (hasNpu ? 1 : 0) +
+      (hasNpu && npuTempSeries?.length ? 1 : 0)
+    );
+  }, [
+    statsHistory,
+    gpuMemSeries,
+    gpuEncSeries,
+    gpuComputeSeries,
+    gpuDecSeries,
+    gpuTempSeries,
+    npuTempSeries,
+  ]);
+
   // other processes stats
 
   const hardwareType = useMemo(() => {
@@ -763,12 +793,9 @@ export default function GeneralMetrics({
             <div
               className={cn(
                 "mt-4 grid grid-cols-1 gap-2 sm:grid-cols-2",
-                gpuTempSeries?.length && "md:grid-cols-3",
-                (gpuEncSeries?.length || gpuComputeSeries?.length) &&
-                  "xl:grid-cols-4",
-                (gpuEncSeries?.length || gpuComputeSeries?.length) &&
-                  gpuTempSeries?.length &&
-                  "3xl:grid-cols-5",
+                hardwareCardCount >= 3 && "lg:grid-cols-3",
+                hardwareCardCount >= 4 && "xl:grid-cols-4",
+                hardwareCardCount >= 5 && "3xl:grid-cols-5",
               )}
             >
               {statsHistory[0]?.gpu_usages && (

web/vite.config.ts

@@ -37,6 +37,9 @@ export default defineConfig({
       },
     },
   },
+  esbuild: {
+    keepNames: true,
+  },
   build: {
     rollupOptions: {
       input: {