fix tests

* fix record status never returning online after a record ffmpeg restart

The record ffmpeg restart paths sent `offline` directly, so the cached record status still read `online` and the recovery was never published. Detect and record also shared one resend timestamp, and detect's resend always ran first, so record's periodic resend never fired either. Record's offline now goes through the cache and each status has its own timestamp.

* don't publish record online when the record process has exited

.github/actions/setup/action.yml

@@ -17,9 +17,14 @@ runs:
       shell: bash
     # This creates a virtual volume at /var/lib/docker to maximize the size
     # As of 2/14/2024, this results in 97G for docker images
+    # Runners no longer have a separate /mnt disk, so the temp PV is also carved
+    # from root and temp-reserve-mb is what actually stays free on root. Keep 4G
+    # there for setup-qemu/buildx caches in ~/.docker and the tool cache
     - name: Maximize build space
       uses: easimon/maximize-build-space@master
       with:
+        root-reserve-mb: 8192
+        temp-reserve-mb: 4096
         remove-dotnet: 'true'
         remove-android: 'true'
         remove-haskell: 'true'

.github/workflows/pull_request.yml

@@ -124,7 +124,5 @@ jobs:
         run: devcontainer exec --workspace-folder . bash -lc "python3 -u -m mypy --config-file frigate/mypy.ini frigate"
       - name: Check API spec is up to date
         run: devcontainer exec --workspace-folder . bash -lc "python3 generate_api_auth_spec.py --check"
-      - name: Check analytics schema is up to date
-        run: devcontainer exec --workspace-folder . bash -lc "python3 generate_analytics_schema.py --check"
       - name: Run unit tests in devcontainer
         run: devcontainer exec --workspace-folder . bash -lc "python3 -u -m unittest"

docker/main/Dockerfile

@@ -381,7 +381,6 @@ FROM deps AS frigate
 
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
-ENV FRIGATE_IMAGE_VARIANT=standard
 
 # Pre-compile bytecode so a read-only rootfs doesn't force re-parsing the
 # source tree on every boot (pip-installed packages are already compiled)

docker/main/requirements-wheels.txt

@@ -27,7 +27,7 @@ pydantic == 2.10.*
 git+https://github.com/fbcotter/py3nvml#egg=py3nvml
 pytz == 2025.*
 pyzmq == 27.1.*
-ruamel.yaml == 0.18.*
+ruamel.yaml == 0.19.*
 tzlocal == 5.2
 requests == 2.33.*
 types-requests == 2.32.*
@@ -43,7 +43,7 @@ opencv-contrib-python == 4.11.0.*
 scipy == 1.16.*
 # OpenVino & ONNX
 openvino == 2025.4.*
-onnxruntime == 1.22.*
+onnxruntime == 1.30.*
 # Embeddings
 transformers == 4.45.*
 # Generative AI
@@ -58,7 +58,7 @@ pyclipper == 1.4.*
 shapely == 2.0.*
 rapidfuzz==3.12.*
 # HailoRT
-argcomplete==2.0.*
+argcomplete==3.7.*
 contextlib2==0.6.*
 future==0.18.*
 netaddr==1.3.*

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

@@ -15,6 +15,7 @@ from frigate.const import (
 )
 from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_encode
 from frigate.util.config import find_config_file, resolve_ffmpeg_path
+from frigate.util.live_streams import raw_transcode_streams
 from frigate.util.services import (
     is_go2rtc_arbitrary_exec_allowed,
     is_restricted_go2rtc_source,
@@ -174,6 +175,17 @@ for name in list(go2rtc_config.get("streams", {})):
             del go2rtc_config["streams"][name]
             continue
 
+# add transcoded live streams; a user stream with the same name wins here and
+# fails Frigate's config validation
+transcoded_streams = raw_transcode_streams(config)
+
+if transcoded_streams:
+    if go2rtc_config.get("streams") is None:
+        go2rtc_config["streams"] = {}
+
+    for name, source in transcoded_streams.items():
+        go2rtc_config["streams"].setdefault(name, source)
+
 # add birdseye restream stream if enabled
 if config.get("birdseye", {}).get("restream", False):
     birdseye: dict[str, Any] = config.get("birdseye")

docker/rockchip/Dockerfile

@@ -25,7 +25,6 @@ RUN --mount=type=bind,from=rk-wheels,source=/rk-wheels,target=/deps/rk-wheels \
 
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
-ENV FRIGATE_IMAGE_VARIANT=rk
 COPY docker/rockchip/COCO /COCO
 COPY docker/rockchip/conv2rknn.py /opt/conv2rknn.py
 

docker/rocm/Dockerfile

@@ -44,7 +44,6 @@ RUN echo "deb http://deb.debian.org/debian trixie main" > /etc/apt/sources.list.
 
 WORKDIR /opt/frigate
 COPY --from=rootfs / /
-ENV FRIGATE_IMAGE_VARIANT=rocm
 
 RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
     && sed -i 's/args.append("setuptools")/args.append("setuptools==77.0.3")/' get-pip.py \

docker/rpi/Dockerfile

@@ -15,4 +15,3 @@ ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:${INCLUDED_FFMPEG_VERSIO
 
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
-ENV FRIGATE_IMAGE_VARIANT=rpi

docker/synaptics/Dockerfile

@@ -22,7 +22,6 @@ pip3 install --no-deps -U /deps/synap-wheels/*.whl
 
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
-ENV FRIGATE_IMAGE_VARIANT=synaptics
 
 COPY --from=synap1680-wheels /rootfs/usr/local/lib/*.so /usr/lib
 

docker/tensorrt/Dockerfile.amd64

@@ -25,7 +25,6 @@ RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels
     && pip3 install -U /deps/trt-wheels/*.whl
 
 COPY --from=rootfs / /
-ENV FRIGATE_IMAGE_VARIANT=tensorrt
 COPY docker/tensorrt/detector/rootfs/etc/ld.so.conf.d /etc/ld.so.conf.d
 RUN ldconfig
 

docker/tensorrt/Dockerfile.arm64

@@ -151,7 +151,6 @@ RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels
 
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
-ENV FRIGATE_IMAGE_VARIANT=tensorrt-jp6
 
 # Fixes "Error importing detector runtime: /usr/lib/aarch64-linux-gnu/libstdc++.so.6: cannot allocate memory in static TLS block"
 ENV LD_PRELOAD /usr/lib/aarch64-linux-gnu/libstdc++.so.6

docs/data/object_detectors_models.yaml

@@ -824,6 +824,38 @@ cpu:
         models:
           - devices:
               - cpu:3
+xdna2:
+  title: AMD XDNA2
+  models:
+    - key: yolov9
+      label: YOLOv9
+      recommended: true
+      download: |-
+        Prepare the model using the frigate-xdna setup instructions linked above. For local YOLO models, Frigate must have access to the same ONNX file bytes as the sidecar. The example below uses YOLOv9-C at 320x320. Frigate+ models may instead use the same `plus://MODEL_ID` in Frigate and the sidecar.
+      ui: |-
+        Navigate to **Settings > System > Detection models** and add a model. The ZMQ endpoint is not reported by the hardware probe, so set `devices` to `zmq:tcp://xdna:5555` in YAML. Then, on the same model, open the **Custom Model** tab and configure:
+
+        | Field                                    | Value                                      |
+        | ---------------------------------------- | ------------------------------------------ |
+        | **Custom object detector model path**    | `/config/models/yolov9-c-320.onnx`       |
+        | **Label map for custom object detector** | `/labelmap/coco-80.txt`                   |
+        | **Object detection model input width**   | `320`                                    |
+        | **Object detection model input height**  | `320`                                    |
+        | **Model Input Pixel Color Format**       | `rgb` (Frigate's default value)          |
+        | **Model Input Tensor Shape**             | `nchw`                                   |
+        | **Model Input D Type**                   | `float`                                  |
+        | **Object Detection Model Type**          | `yolo-generic`                           |
+      yaml: |-
+        models:
+          - devices:
+              - zmq:tcp://xdna:5555
+            model_type: yolo-generic
+            width: 320
+            height: 320
+            input_tensor: nchw
+            input_dtype: float
+            path: /config/models/yolov9-c-320.onnx
+            labelmap_path: /labelmap/coco-80.txt
 memryx:
   title: MemryX
   models:

docs/docs/configuration/advanced/analytics.md

@@ -1,41 +0,0 @@
----
-id: analytics
-title: Anonymous Analytics
----
-
-import AnalyticsFields from "@site/src/components/AnalyticsFields";
-import NavPath from "@site/src/components/NavPath";
-
-Frigate can send one anonymous usage report a day. The reports show the maintainers which hardware to support, which features people use, and how releases perform. Sharing is off until you turn it on.
-
-## Turning it on
-
-Enable **Share anonymous analytics** at <NavPath path="Settings > System > Telemetry" />, or set it in your config:
-
-```yaml
-telemetry:
-  analytics: true
-```
-
-The same page has a **Preview the report** button that shows exactly what the next report contains.
-
-## How it's sent
-
-- Once a day, as a JSON POST to `https://analytics.frigate.video/report`
-- The server looks up your country and region from your IP address and never stores the address
-- Raw reports are kept for 60 days; only aggregate totals are published
-- A random install ID, stored in `/config/.analytics.json`, keeps your install from being counted twice. Turning sharing off deletes it
-
-## What's never sent
-
-- Camera, zone, group, profile, or user names
-- Object labels, face names, or license plate text
-- IP addresses, hostnames, URLs, or stream paths
-- Credentials or API keys
-- Events, recordings, or anything from them
-
-## Every field
-
-Fields marked public appear in the published totals. The machine-readable schema is [frigate-analytics-schema.json](pathname:///frigate-analytics-schema.json).
-
-<AnalyticsFields />

docs/docs/configuration/advanced/reference.md

@@ -152,9 +152,10 @@ auth:
 models:
     # Optional: the camera environment this model is for (default: shown below)
     # Cameras select a model by setting detect -> scene to a matching value, and
-    # a model with a scene of all is used by any camera that does not set one.
-    # Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
-  - scene: all
+    # the model with a scene of default is used by any camera that does not set one.
+    # Any name made up of letters, numbers, _ and - is valid, such as thermal.
+    # Models that use the same model file are combined into one model.
+  - scene: default
     # Required: hardware this model runs on, as <detector> or <detector>:<device>
     # See https://docs.frigate.video/configuration/object_detectors for the
     # detectors available and the devices each one accepts. All of a model's
@@ -293,9 +294,9 @@ ffmpeg:
     # Optional: output args for detect streams (default: shown below)
     detect: -threads 2 -f rawvideo -pix_fmt yuv420p
     # Optional: output args for record streams (default: shown below)
-    record: preset-record-generic
+    record: preset-record-generic-audio-aac
     # Optional: output args for sub stream record streams (default: the record output args above)
-    # record_sub: preset-record-generic
+    # record_sub: preset-record-generic-audio-aac
   # Optional: Time in seconds to wait before ffmpeg retries connecting to the camera. (default: shown below)
   # If set too low, frigate will retry a connection to the camera's stream too frequently, using up the limited streams some cameras can allow at once
   # If set too high, then if a ffmpeg crash or camera stream timeout occurs, you could potentially lose up to a maximum of retry_interval second(s) of footage
@@ -316,9 +317,9 @@ detect:
   # Optional: height of the frame for the input with the detect role (default: use native stream resolution)
   height: 720
   # Optional: the environment this camera looks at, which picks the model it runs on
-  # (default: the model with a scene of all)
-  # Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
-  scene: outdoor
+  # (default: the model with a scene of default)
+  # Must match the scene of a configured model
+  scene: thermal
   # Optional: desired fps for your camera for the input with the detect role (default: shown below)
   # NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
   fps: 5
@@ -900,6 +901,21 @@ live:
   streams:
     main_stream: main_stream_name
     sub_stream: sub_stream_name
+  # Optional: Lower-quality live streams transcoded by go2rtc while someone is watching.
+  # NOTE: Set at the camera level only.
+  transcode:
+    # Optional: Enable transcoded streams (default: shown below)
+    enabled: False
+    # Optional: go2rtc stream to transcode (default: the first live stream)
+    source: main_stream_name
+    # Optional: One transcoded stream per quality (default: shown below)
+    qualities:
+      - height: 720
+        bitrate: 1200
+      - height: 480
+        bitrate: 500
+      - height: 360
+        bitrate: 250
   # Optional: Set the height of the jsmpeg stream. (default: 720)
   # This must be less than or equal to the height of the detect stream. Lower resolutions
   # reduce bandwidth required for viewing the jsmpeg stream. Width is computed to match known aspect ratio.
@@ -1194,9 +1210,6 @@ ui:
 
 # Optional: Telemetry configuration
 telemetry:
-  # Optional: Share one anonymous usage report a day (default: shown below)
-  # NOTE: See https://docs.frigate.video/configuration/advanced/analytics for what is sent
-  analytics: False
   # Optional: Enabled network interfaces for bandwidth stats monitoring (default: empty list, let nethogs search all)
   network_interfaces:
     - eth

docs/docs/configuration/custom_classification/state_classification.md

@@ -85,6 +85,14 @@ An optional config, `save_attempts`, can be set as a key under the model name. T
 </TabItem>
 </ConfigTabs>
 
+## Review items
+
+When a model's state changes while its camera has an active review item, the change is recorded on that review item. This includes changes in the few seconds before the item starts, such as a garage door opening just before the car is detected. State changes never create or extend review items on their own, and the first state reported after Frigate starts is not recorded as a change.
+
+Recorded changes appear in the review item's data as `classification_state_changes` (see the [`frigate/reviews`](/integrations/mqtt#frigatereviews) MQTT topic) and are passed to [GenAI review summaries](/configuration/genai/genai_review) as facts, so a description can note that a gate was opened during the activity.
+
+Change times are most accurate with `motion: true`. A model that only runs on an `interval` notices a change at its next run, so the change may be recorded late or attached to a later review item.
+
 ## Training the model
 
 Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of three steps:

docs/docs/configuration/ffmpeg_presets.md

@@ -18,9 +18,11 @@ Hardware acceleration arguments tell FFmpeg to decode your camera's video stream
 See [the hardware acceleration docs](/configuration/hardware_acceleration_video.md) for details on setting up hardware acceleration for your GPU / iGPU, then select the preset that matches your hardware.
 
 | Preset (YAML config)      | UI Label                | Usage                                         | Notes                                                           |
-| --------------------- | ----------------------- | --------------------------------- | --------------------------------------------------------------- |
+| ------------------------- | ----------------------- | --------------------------------------------- | --------------------------------------------------------------- |
 | preset-rpi-64-h264        | Raspberry Pi (H.264)    | 64-bit Raspberry Pi, H.264 stream             |                                                                 |
 | preset-rpi-64-h265        | Raspberry Pi (H.265)    | 64-bit Raspberry Pi, H.265 stream             |                                                                 |
+| preset-apple-silicon-h264 | Apple Silicon (H.264)   | Apple Silicon Mac under lighter, H.264 stream | Needs the `lighter.sh/video` device                             |
+| preset-apple-silicon-h265 | Apple Silicon (H.265)   | Apple Silicon Mac under lighter, H.265 stream | Needs the `lighter.sh/video` device                             |
 | preset-vaapi              | VAAPI (Intel/AMD GPU)   | Intel or AMD GPU via VAAPI                    | Check the hwaccel docs to ensure the correct driver is selected |
 | preset-intel-qsv-h264     | Intel QuickSync (H.264) | Intel QuickSync, H.264 stream                 | If you have issues, use the VAAPI preset instead                |
 | preset-intel-qsv-h265     | Intel QuickSync (H.265) | Intel QuickSync, H.265 stream                 | If you have issues, use the VAAPI preset instead                |

docs/docs/configuration/genai/review_summaries.md

@@ -201,6 +201,8 @@ Review items are sent to the model as a sequence of still frames. Some models fo
 
 The notes come from tracking data rather than from the images, so they describe activity the model may not have picked up on its own. In testing with a person carrying three waste bins to the curb one at a time, `gemma4` described a single trip on every attempt with `frames`, and consistently described multiple trips with `annotated_frames`. Models that already handle these sequences well, such as the `qwen3-vl` family, gain little and should stay on `frames`.
 
+Changes reported by [state classification](/configuration/custom_classification/state_classification#review-items) models during the review item are listed in the prompt in both modes. `annotated_frames` also notes each change before the frame where it happened.
+
 Annotated mode also caps the number of frames, since the notes already establish the order of events and extra near-duplicate frames tend to crowd out the middle of a clip. Longer review items are sampled more sparsely as a result, and typically use fewer tokens than `frames` mode for the same item.
 
 :::note

docs/docs/configuration/hardware_acceleration_video.md

@@ -43,6 +43,10 @@ Frigate supports presets for optimal hardware accelerated video decoding:
 
 - [RKNN](#rockchip-platform): Frigate can utilize the media engine in RockChip SOCs to accelerate video decoding.
 
+**Apple Silicon Mac** <CommunityBadge />
+
+- [lighter](#apple-silicon-mac-lighter): Frigate can utilize the media engine in Apple Silicon Macs to accelerate video decoding, when running under the lighter container runtime.
+
 **Other Hardware**
 
 Depending on your system, these presets may not be compatible, and you may need to use manual hwaccel args to take advantage of your hardware. More information on hardware accelerated decoding for ffmpeg can be found here: https://trac.ffmpeg.org/wiki/HWAccelIntro
@@ -533,3 +537,35 @@ output_args:
 Make sure that your SoC supports hardware acceleration for your input stream and your input stream is h264 encoding. For example, if your camera streams with h264 encoding, your SoC must be able to de- and encode with it. If you are unsure whether your SoC meets the requirements, take a look at the datasheet.
 
 :::
+
+## Apple Silicon Mac (lighter)
+
+[lighter](https://github.com/fieldwork-ai/lighter) is an open-source container runtime for macOS. It gives a container the Mac's media engine as a standard V4L2 decoder, backed by VideoToolbox, so Frigate decodes H.264 and H.265 streams in hardware with the ffmpeg it already ships. It works on M1 and newer Macs with lighter 0.9.2 or newer.
+
+Give the container the video device. With Docker Compose:
+
+```yaml {4-5}
+services:
+  frigate:
+    ...
+    devices:
+      - lighter.sh/video=all
+```
+
+Or with `docker run`, add `--device lighter.sh/video=all`.
+
+Then set the preset for the codec your cameras stream. The decoder is specific to the codec, so if your cameras mix H.264 and H.265, set the preset for the most common codec globally and override it on the other cameras:
+
+```yaml
+ffmpeg:
+  hwaccel_args: preset-apple-silicon-h264
+
+cameras:
+  garage: # an H.265 camera
+    ffmpeg:
+      hwaccel_args: preset-apple-silicon-h265
+```
+
+The presets decode on the media engine and encode the Birdseye restream and timelapses there too. Scaling to the detect resolution runs on the CPU, as ffmpeg's V4L2 decoders cannot scale.
+
+lighter can also run object detection on the Mac's Neural Engine; see [Apple Neural Engine (lighter)](object_detectors.md#apple-neural-engine-lighter).

docs/docs/configuration/license_plate_recognition.md

@@ -379,9 +379,9 @@ Navigate to <NavPath path="Settings > Camera configuration > Object detection" /
 Navigate to <NavPath path="Settings > Camera configuration > Objects" />.
 
 | Field                                                     | Description         |
-| ---------------------------------------------- | ------------------- |
+| --------------------------------------------------------- | ------------------- |
 | **Objects to track**                                      | Add `license_plate` |
-| **Object filters > License Plate > Threshold** | Set to `0.7`        |
+| **Object filters > License Plate > Confidence threshold** | Set to `0.7`        |
 
 Navigate to <NavPath path="Settings > Camera configuration > Motion detection" />.
 

docs/docs/configuration/live.md

@@ -92,7 +92,7 @@ go2rtc:
 
 ### Setting Streams For Live UI
 
-You can configure Frigate to allow manual selection of the stream you want to view in the Live UI. For example, you may want to view your camera's substream on mobile devices, but the full resolution stream on desktop devices. Setting the streams list will populate a dropdown in the UI's Live view that allows you to choose between the streams. This stream setting is _per device_ and is saved in your browser's local storage.
+You can configure Frigate to allow manual selection of the stream you want to view in the Live UI. For example, you may want to view your camera's substream on mobile devices, but the full resolution stream on desktop devices. Setting the streams list will populate a dropdown in the UI's Live view that allows you to choose between the streams. This stream setting is _per device_ and is saved in your browser's local storage. When a camera has more than one stream, the dropdown also offers **Auto**, which is used until you pick a specific stream. Auto starts on the first stream, steps down the list when your connection can't keep up, and steps back up when it recovers. To retry the top stream right away, select **Try highest quality** under the stream picker. List streams from highest to lowest quality, and avoid names that are plain numbers (such as `720`), which the browser sorts ahead of the others. In the UI, drag streams to reorder them, or use **Auto order** to sort them by measured bitrate.
 
 Additionally, when creating and editing camera groups in the UI, you can choose the stream you want to use for your camera group's Live dashboard.
 
@@ -158,6 +158,26 @@ cameras:
 </TabItem>
 </ConfigTabs>
 
+### Transcoded streams
+
+When a camera has no suitable sub stream, Frigate can add lower-quality streams that go2rtc transcodes to H.264 while someone is watching. They appear in the stream list like any other stream, so Auto mode can step down to them. Enable them under <NavPath path="Settings > Camera configuration > Live playback" />, or in YAML:
+
+```yaml
+cameras:
+  test_cam:
+    live:
+      transcode:
+        enabled: true
+        source: test_cam # optional, defaults to the first live stream
+        qualities:
+          - height: 720
+            bitrate: 1200 # kbps
+          - height: 480
+            bitrate: 500
+```
+
+Each quality becomes a go2rtc stream named `<camera>_transcode_<height>p`. go2rtc picks a hardware encoder automatically and falls back to the CPU, which costs CPU for each transcode while it is being watched. Check go2rtc's `api/ffmpeg/hardware` page to see which encoder it found. Using a sub stream as the `source` lowers the cost.
+
 ### WebRTC extra configuration:
 
 WebRTC works by creating a TCP or UDP connection on port `8555`. However, it requires additional configuration:
@@ -363,6 +383,13 @@ When your browser runs into problems playing back your camera streams, it will l
     - `Safari reported InvalidStateError.`
     - `Safari reported decoding errors.`
 
+- **mse-codec**
+  - What it means: go2rtc has no codec for this stream that the browser can play.
+  - What to try: Pick a stream with a codec the browser supports (H.264 is the most compatible), or use a browser that supports the stream's codec. In Auto, Frigate skips this stream for the rest of the session.
+
+  - Possible console messages from the player code:
+    - `mse: streams: codecs not matched: ...`
+
 - **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 <NavPath path="Settings > UI" /> .

docs/docs/configuration/object_detectors.md

@@ -30,10 +30,12 @@ Frigate supports multiple different detectors that work on different types of ha
 
 - [ROCm](#amdrocm-gpu-detector): ROCm can run on AMD Discrete GPUs to provide efficient object detection.
 - [ONNX](#onnx): ROCm will automatically be detected and used as a detector in the `-rocm` Frigate image when a supported ONNX model is configured.
+- <CommunityBadge /> [XDNA2](#amd-xdna2): AMD Ryzen AI / XDNA2 NPUs can run object detection through the community-maintained `frigate-xdna` ZMQ sidecar.
 
 **Apple Silicon**
 
 - [Apple Silicon](#apple-silicon-detector): Apple Silicon can run on M1 and newer Apple Silicon devices.
+- <CommunityBadge /> [ONNX](#apple-neural-engine-lighter): the ONNX detector runs on the Neural Engine of M1 and newer Macs when Frigate runs under the lighter container runtime.
 
 **Intel**
 
@@ -102,32 +104,36 @@ Coral EdgeTPU and MemryX accelerators can only be opened by one process, so thos
 
 ### Running more than one model
 
-Cameras can be split across models by scene, which is useful when indoor and outdoor cameras benefit from differently trained models. Each model declares the `scene` it is for, and each camera picks one with `detect -> scene`:
+Cameras can be split across models by scene, which is useful when some cameras benefit from a differently trained model, such as thermal cameras. Each model declares the `scene` it is for, and each camera picks one with `detect -> scene`:
 
 ```yaml
 models:
-  - scene: outdoor
-    path: plus://your-outdoor-model
+  - scene: default
+    path: plus://your-model
     devices:
       - edgetpu:pci:0
-  - scene: indoor
-    path: /config/model_cache/indoor.onnx
+  - scene: thermal
+    path: /config/model_cache/thermal.onnx
     model_type: yolo-generic
     devices:
       - openvino:GPU
 
 cameras:
   driveway:
-    detect:
-      scene: outdoor
     ...
-  hallway:
+  backyard_thermal:
     detect:
-      scene: indoor
+      scene: thermal
     ...
 ```
 
-Available scenes are `all`, `indoor`, `outdoor`, `indoor_thermal`, and `outdoor_thermal`. A model with a scene of `all` is used by every camera that does not set one, and `all` is the default when a model does not declare a scene. Changing a camera's scene requires a restart.
+A scene is any name made up of letters, numbers, `_`, and `-`. The model with a scene of `default` is used by every camera that does not set one (or sets a scene that no model is configured for), and `default` is used when a model does not declare a scene. Changing a camera's scene requires a restart.
+
+:::warning
+
+Scenes are for running **different** models. Do not configure the same model under several scenes to dedicate a detector to specific cameras: every detector of a model already serves every camera using it, and splitting them only leaves some detectors idle while others fall behind. Frigate detects models that use the same model file, even under a different path or file name, combines them into one model with all of their hardware, and logs a warning.
+
+:::
 
 ### Choosing a model size
 
@@ -484,7 +490,7 @@ See [ONNX supported models](#onnx) for supported models, there are some caveats:
 
 ## ONNX
 
-ONNX is an open format for building machine learning models, Frigate supports running ONNX models on CPU, OpenVINO, ROCm, and TensorRT. On startup Frigate will automatically try to use a GPU if one is available.
+ONNX is an open format for building machine learning models, Frigate supports running ONNX models on CPU, OpenVINO, ROCm, TensorRT, and a Mac's Neural Engine. On startup Frigate will automatically try to use a GPU if one is available.
 
 :::info
 
@@ -500,6 +506,9 @@ If the correct build is used for your GPU then the GPU will be detected and used
   - Nvidia GPUs will automatically be detected and used with the ONNX detector in the `-tensorrt` Frigate image.
   - Jetson devices will automatically be detected and used with the ONNX detector in the `-tensorrt-jp6` Frigate image.
 
+- **Apple Silicon Mac** <CommunityBadge />
+  - The Neural Engine will automatically be detected and used with the ONNX detector when Frigate runs under lighter with its Neural Engine device. See [Apple Neural Engine (lighter)](#apple-neural-engine-lighter).
+
 :::
 
 :::tip
@@ -515,6 +524,22 @@ models:
 
 :::
 
+### Apple Neural Engine (lighter) {#apple-neural-engine-lighter}
+
+[lighter](https://github.com/fieldwork-ai/lighter) is an open-source container runtime for macOS. A container started with its `lighter.sh/ane` device gets an ONNX Runtime execution provider that runs models on the Mac's Neural Engine, and the ONNX detector uses it automatically, with the same models and configuration as on any other hardware. It works on M1 and newer Macs with lighter 0.9.2 or newer.
+
+Give the Frigate container the Neural Engine device. With Docker Compose:
+
+```yaml
+services:
+  frigate:
+    image: ghcr.io/blakeblackshear/frigate:stable-standard-arm64
+    devices:
+      - lighter.sh/ane=all
+```
+
+Or with `docker run`, add `--device lighter.sh/ane=all`. Frigate then reports the Neural Engine under **Settings > System > Detection models**, and the ONNX detector's model loads on it. lighter can also decode camera streams on the Mac's media engine; see [Video Decoding](hardware_acceleration_video.md#apple-silicon-mac-lighter).
+
 ### Configuration {#configuration-onnx}
 
 <ModelConfigDropdown detectorTitle="ONNX" models={objectDetectorsModels.onnx.models} />
@@ -543,6 +568,28 @@ When using CPU detectors, you can add one CPU detector per camera. Adding more d
 
 # Community Supported Detectors
 
+## AMD XDNA2
+
+AMD Ryzen AI / XDNA2 NPUs can be used through the community-maintained
+[frigate-xdna](https://github.com/mitchins/frigate-xdna) detector sidecar.
+The sidecar runs separately from Frigate and connects using Frigate's ZMQ
+detector interface.
+
+Currently qualified on **Ryzen AI Max 300 / Strix Halo**. Other XDNA2 devices
+are not yet qualified; XDNA1 is unsupported.
+
+Follow the frigate-xdna setup instructions to prepare and start the sidecar
+before starting Frigate.
+
+### Configuration {#configuration-xdna2}
+
+Using the detector config below will connect Frigate to the sidecar:
+
+<ModelConfigDropdown detectorTitle="AMD XDNA2" models={objectDetectorsModels.xdna2.models} />
+
+The example assumes Frigate and the sidecar share a Docker network where the
+sidecar is named `xdna`.
+
 ## MemryX MX3
 
 This detector is available for use with the MemryX MX3 accelerator M.2 module. Frigate supports the MX3 on compatible hardware platforms, providing efficient and high-performance object detection.

docs/docs/configuration/object_filters.md

@@ -46,9 +46,9 @@ Any detection below `min_score` will be immediately thrown out and never tracked
 Navigate to <NavPath path="Settings > Global configuration > Objects" /> to set score filters globally.
 
 | Field                                              | Description                                                      |
-| --------------------------------------- | ---------------------------------------------------------------- |
-| **Object filters > Person > Min Score** | Minimum score for a single detection to initiate tracking        |
-| **Object filters > Person > Threshold** | Minimum computed (median) score to be considered a true positive |
+| -------------------------------------------------- | ---------------------------------------------------------------- |
+| **Object filters > Person > Minimum confidence**   | Minimum score for a single detection to initiate tracking        |
+| **Object filters > Person > Confidence threshold** | Minimum computed (median) score to be considered a true positive |
 
 To override score filters for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Objects" /> and select the camera.
 
@@ -104,11 +104,11 @@ Conceptually, a ratio of 1 is a square, 0.5 is a "tall skinny" box, and 2 is a "
 Navigate to <NavPath path="Settings > Global configuration > Objects" /> to set shape filters globally.
 
 | Field                                              | Description                                                              |
-| --------------------------------------- | ------------------------------------------------------------------------ |
-| **Object filters > Person > Min Area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Max Area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Min Ratio** | Minimum width/height ratio of the bounding box                           |
-| **Object filters > Person > Max Ratio** | Maximum width/height ratio of the bounding box                           |
+| -------------------------------------------------- | ------------------------------------------------------------------------ |
+| **Object filters > Person > Minimum object area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Maximum object area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Minimum aspect ratio** | Minimum width/height ratio of the bounding box                           |
+| **Object filters > Person > Maximum aspect ratio** | Maximum width/height ratio of the bounding box                           |
 
 To override shape filters for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Objects" /> and select the camera.
 

docs/docs/configuration/objects.md

@@ -71,13 +71,13 @@ Object filters help reduce false positives by constraining the size, shape, and
 Navigate to <NavPath path="Settings > Global configuration > Objects" />.
 
 | Field                                              | Description                                                              |
-| --------------------------------------- | ------------------------------------------------------------------------ |
-| **Object filters > Person > Min Area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Max Area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Min Ratio** | Minimum width/height ratio of the bounding box                           |
-| **Object filters > Person > Max Ratio** | Maximum width/height ratio of the bounding box                           |
-| **Object filters > Person > Min Score** | Minimum score for the object to initiate tracking                        |
-| **Object filters > Person > Threshold** | Minimum computed score to be considered a true positive                  |
+| -------------------------------------------------- | ------------------------------------------------------------------------ |
+| **Object filters > Person > Minimum object area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Maximum object area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Minimum aspect ratio** | Minimum width/height ratio of the bounding box                           |
+| **Object filters > Person > Maximum aspect ratio** | Maximum width/height ratio of the bounding box                           |
+| **Object filters > Person > Minimum confidence**   | Minimum score for the object to initiate tracking                        |
+| **Object filters > Person > Confidence threshold** | Minimum computed score to be considered a true positive                  |
 
 To override filters for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Objects" />.
 

docs/docs/configuration/profiles.md

@@ -191,14 +191,12 @@ cameras:
     detect:
       enabled: false
     record:
-      enabled: false
+      enabled: true
     profiles:
       away:
         enabled: true
         detect:
           enabled: true
-        record:
-          enabled: true
       home:
         enabled: false
 ```
@@ -251,6 +249,12 @@ Leaving the `objects` section empty (or omitting `track`) does not clear the lis
 
 Fields that require a Frigate restart to take effect cannot be overridden by profiles, since profiles are applied at runtime without restarting. Those fields are hidden when editing a profile override and can only be changed on the base configuration.
 
+### Why can't a profile enable recording when it's disabled in the base config?
+
+Frigate only sets up a camera's recording stream at startup when recording is enabled in the base config, so enabling it later from a profile has no effect. The same applies to turning recording on from the UI or MQTT.
+
+To keep recording off by default, leave `record.enabled: true` in the base config and create a profile that sets `record.enabled: false`. Activate that profile and it will be restored automatically when Frigate starts.
+
 ### Can I schedule profiles to be enabled or disabled at certain times?
 
 Not within Frigate itself. Frigate is an NVR, not an automation platform, so it intentionally does not include a scheduler for activating profiles. Instead, activate profiles from an automation platform that already handles time- and event-based triggers well, such as [Home Assistant](https://www.home-assistant.io/) or [Node-RED](https://nodered.org/). These integrate with Frigate and give you far more robust and flexible scheduling than a built-in scheduler could.

docs/docs/configuration/semantic_search.md

@@ -245,8 +245,8 @@ Triggers are best configured through the Frigate UI.
 1. Navigate to <NavPath path="Settings > Enrichments > Triggers" /> and select a camera from the dropdown menu.
 2. Click **Add Trigger** to create a new trigger or use the pencil icon to edit an existing one.
 3. In the **Create Trigger** wizard:
-   - Enter a **Name** for the trigger (e.g., "Red Car Alert").
-   - Enter a descriptive **Friendly Name** for the trigger (e.g., "Red car on the driveway camera").
+   - Enter a **Name** for the trigger (e.g., "Red Car Alert"). Frigate derives the trigger's
+     internal **ID** from this name, which can be revealed and edited with the show/hide toggle.
    - Select the **Type** (`Thumbnail` or `Description`).
    - For `Thumbnail`, select an image to trigger this action when a similar thumbnail image is detected, based on the threshold.
    - For `Description`, enter text to trigger this action when a similar tracked object description is detected.

docs/docs/configuration/zones.md

@@ -28,7 +28,7 @@ During testing, enable the Zones option for the [Debug view](/usage/live#the-sin
 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Under the **Zones** section, click the plus icon to add a new zone.
 3. Click on the camera's latest image to create the points for the zone boundary. Click the first point again to close the polygon.
-4. Configure zone options such as **Friendly name**, **Objects**, **Loitering time**, and **Inertia** in the zone editor.
+4. Configure zone options such as **Name**, **Objects**, **Loitering Time**, and **Inertia** in the zone editor.
 5. Press **Save** when finished.
 
 </TabItem>
@@ -200,7 +200,7 @@ When using loitering zones, a review item will behave in the following way:
 
 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Edit or create the zone (e.g., `sidewalk`).
-   - Set **Loitering time** to the desired number of seconds (e.g., `4`)
+   - Set **Loitering Time** to the desired number of seconds (e.g., `4`)
    - Under **Objects**, add the relevant object types (e.g., `person`)
 
 </TabItem>
@@ -291,7 +291,7 @@ Accurate real-world distance measurements are required to estimate speeds. These
 
 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Create or edit a zone with exactly 4 points aligned to the ground plane.
-3. In the zone editor, enter the real-world **Distances** between each pair of consecutive points.
+3. In the zone editor, enable **Speed Estimation** and enter the real-world **Line A distance**, **Line B distance**, **Line C distance**, and **Line D distance** between each pair of consecutive points.
    - For example, if the distance between the first and second points is 10 meters, between the second and third is 12 meters, etc.
 4. Distances are measured in meters (metric) or feet (imperial), depending on the **Unit system** setting.
 
@@ -358,7 +358,7 @@ Zones can be configured with a minimum speed requirement, meaning an object must
 
 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Edit or create the zone with distances configured.
-   - Set **Speed threshold** to the desired minimum speed (e.g., `20`)
+   - Set **Speed Threshold** to the desired minimum speed (e.g., `20`)
    - The unit is kph or mph, depending on the **Unit system** setting
 
 </TabItem>

docs/docs/frigate/glossary.md

@@ -54,7 +54,7 @@ An object filter mask drops any [bounding box](#bounding-box) whose bottom cente
 
 ## Min Score
 
-The lowest score a detected object can have to be kept during tracking. Anything scoring below the minimum is assumed to be a [false positive](#false-positive) and discarded.
+The lowest score a detected object can have to be kept during tracking. Anything scoring below the minimum is assumed to be a [false positive](#false-positive) and discarded. Set with `min_score` in the config, shown as **Minimum confidence** in the settings UI.
 
 ## Model
 
@@ -86,7 +86,7 @@ A more specific identity assigned to a [tracked object](#tracked-object-event-in
 
 ## Threshold
 
-The median score an object must reach to be considered a true positive.
+The median score an object must reach to be considered a true positive. Set with `threshold` in the config, shown as **Confidence threshold** in the settings UI.
 
 ## Top Score
 

docs/docs/frigate/hardware.md

@@ -75,9 +75,16 @@ Frigate supports multiple different detectors that work on different types of ha
 - [ROCm](#rocm---amd-gpu): ROCm can run on AMD Discrete GPUs to provide efficient object detection
   - [Supports limited model architectures](../../configuration/object_detectors#amdrocm-gpu-detector)
   - Runs best on discrete AMD GPUs
+- <CommunityBadge /> [XDNA2 (Ryzen AI)](#amd-xdna2): AMD XDNA2 NPU (sub-watt power AI/ML processor separate to the GPU) inside Strix and other "AI" branded AMD platforms
+  - Has only been tested with YOLOv9, in theory other graphs may be compiled too.
+  - Runs via ZMQ proxy which adds some latency, only recommended for local connection
 
 **Apple Silicon**
 
+- [ONNX via lighter](#apple-silicon): The ONNX detector runs on the Neural Engine of M1 and newer Macs when Frigate runs in the lighter container runtime
+  - [Supports the same model architectures as the ONNX detector](../../configuration/object_detectors#apple-neural-engine-lighter)
+  - Runs inside the Frigate container, with no separate detector process to set up
+  - The recommended way to run Frigate on a Mac
 - [Apple Silicon](#apple-silicon): Apple Silicon is usable on all M1 and newer Apple Silicon devices to provide efficient and fast object detection
   - [Supports primarily ssdlite and mobilenet model architectures](../../configuration/object_detectors#apple-silicon-detector)
   - Runs well with any size models including large
@@ -211,7 +218,13 @@ Inference is done with the `onnx` detector type. Speeds will vary greatly depend
 
 ### Apple Silicon
 
-With the [Apple Silicon](../configuration/object_detectors.md#apple-silicon-detector) detector Frigate can take advantage of the NPU in M1 and newer Apple Silicon.
+Frigate on a Mac is best run in the [lighter](https://github.com/fieldwork-ai/lighter) container runtime, where the [ONNX detector](../configuration/object_detectors.md#apple-neural-engine-lighter) runs on the Neural Engine of M1 and newer Macs from inside the Frigate container. There is no separate detector process to install or keep running, and the same container can decode video on the Mac's media engine.
+
+| Name | YOLOv9 Inference Time                  | YOLO-NAS Inference Time | RF-DETR Inference Time |
+| ---- | -------------------------------------- | ----------------------- | ---------------------- |
+| M1   | t-320: 3.3 ms s-320: 7 ms s-640: 13 ms | 320: 6.6 ms             | Nano-320: 38 ms        |
+
+Alternatively, with the [Apple Silicon](../configuration/object_detectors.md#apple-silicon-detector) detector Frigate can take advantage of the NPU in M1 and newer Apple Silicon.
 
 :::warning
 
@@ -328,6 +341,32 @@ The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms fo
 | ---------------- | ----------------------------------- |
 | yolov9-tiny      | ~ 4 ms                              |
 
+### AMD Ryzen AI / XDNA2
+
+Frigate supports AMD XDNA2 NPUs through the community-maintained
+frigate-xdna ZMQ sidecar. It works with stock Frigate and supports
+Frigate+ models or compatible local YOLO ONNX models. Models are compiled
+once on the target system and cached for subsequent use.
+
+Currently qualified on **Ryzen AI Max 300 / Strix Halo**. Other XDNA2
+devices are not yet qualified; XDNA1 is unsupported.
+
+Measured YOLOv9 detector latency on Strix Halo:
+
+| Model | 320 | 640 |
+| ----- | ---: | ---: |
+| YOLOv9-T | ~7.4 ms | unsupported |
+| YOLOv9-S | ~9.0 ms | ~20.0 ms |
+| YOLOv9-M | ~13.1 ms | ~34.4 ms |
+| YOLOv9-C | ~14.1 ms | ~35.2 ms |
+| YOLOv9-E | ~69.4 ms | ~224.8 ms |
+
+**YOLOv9-C at 320 is the recommended quality/performance balance.**
+C at 640 is also usable where the lower throughput is acceptable.
+
+Setup, model preparation, and compatibility details are available
+[in the frigate-xdna documentation](https://github.com/mitchins/frigate-xdna).
+
 ## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
 
 This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.

docs/docs/frigate/network_requirements.md

@@ -134,15 +134,6 @@ telemetry:
   version_check: false
 ```
 
-### Anonymous Analytics
-
-If [anonymous analytics](/configuration/advanced/analytics) sharing is turned on, Frigate sends one report a day to `https://analytics.frigate.video`. It's off by default, so no outbound connection happens unless you enable it:
-
-```yaml
-telemetry:
-  analytics: true
-```
-
 ### Push Notifications
 
 When [notifications](/configuration/notifications) are enabled and users have registered for push notifications in the web UI, Frigate sends push messages through the browser vendor's push service (e.g., Google FCM, Mozilla autopush). This requires internet access from the Frigate server to these push endpoints.
@@ -179,7 +170,7 @@ To run Frigate in an air-gapped or offline environment:
 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, and leave anonymous analytics off (its default).
+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.

docs/docs/integrations/mqtt.md

@@ -11,6 +11,12 @@ MQTT requires a network connection to your broker. This is typically local, but
 
 :::
 
+:::note
+
+Wherever a topic below includes a camera, mask, or zone name, use its `ID` from the config, not its `friendly_name`. For example, a camera with `friendly_name: "Back Yard"` and ID `back_yard` publishes to `frigate/back_yard/...`, not `frigate/Back Yard/...`.
+
+:::
+
 ## General Frigate Topics
 
 ### `frigate/available`
@@ -212,6 +218,7 @@ An `update` with the same ID will be published when:
 - The severity changes from `detection` to `alert`
 - Additional objects are detected
 - An object is recognized via face, lpr, etc.
+- A [state classification](/configuration/custom_classification/state_classification#review-items) model changes state
 
 When the review activity has ended a final `end` message is published.
 
@@ -235,7 +242,8 @@ When the review activity has ended a final `end` message is published.
       "objects": ["person", "car"],
       "sub_labels": [],
       "zones": [],
-      "audio": []
+      "audio": [],
+      "classification_state_changes": []
     }
   },
   "after": {
@@ -254,7 +262,16 @@ When the review activity has ended a final `end` message is published.
       "objects": ["person", "car"],
       "sub_labels": ["Bob"],
       "zones": ["front_yard"],
-      "audio": []
+      "audio": [],
+      "classification_state_changes": [
+        // verified changes of state classification models on this camera
+        {
+          "model": "front_gate",
+          "from": "closed",
+          "to": "open",
+          "timestamp": 1718987131.52
+        }
+      ]
     }
   }
 }

docs/docs/integrations/third_party_extensions.md

@@ -27,6 +27,10 @@ The [Advanced Camera Card](https://card.camera/#/README) is a Home Assistant das
 It supports automatically setting the sub labels in Frigate for person objects that are detected and recognized.
 This is a fork (with fixed errors and new features) of [original Double Take](https://github.com/jakowenko/double-take) project which, unfortunately, isn't being maintained by author.
 
+## [frigate-abr](https://github.com/007hacky007/frigate-abr)
+
+[frigate-abr](https://github.com/007hacky007/frigate-abr) is a drop-in Docker image of Frigate that adds adaptive bitrate (ABR) playback for recordings: a sidecar transcodes footage to lower quality tiers on demand, for reviewing over slow remote connections. Segments are transcoded when played and cached, so no additional stream is recorded. Frigate itself is not modified.
+
 ## [Frigate Notify](https://github.com/0x2142/frigate-notify)
 
 [Frigate Notify](https://github.com/0x2142/frigate-notify) is a simple app designed to send notifications from Frigate to your favorite platforms. Intended to be used with standalone Frigate installations - Home Assistant not required, MQTT is optional but recommended.

docs/docs/plus/faq.md

@@ -21,7 +21,13 @@ Yes. Models and metadata are stored in the `model_cache` directory within the co
 
 ### Can I keep using my Frigate+ models even if I do not renew my subscription?
 
-Yes. Subscriptions to Frigate+ provide access to the infrastructure used to train the models. Models trained with your subscription are yours to keep and use forever. However, do note that the terms and conditions prohibit you from sharing, reselling, or creating derivative products from the models.
+Yes. Subscriptions to Frigate+ provide access to the infrastructure used to train the models. Models you train during an active subscription remain licensed for your continued use even after your subscription ends — models already in your model cache will keep working indefinitely. An active subscription is required to train new models and download new versions.
+
+### Can I use Frigate+ models commercially?
+
+A standard subscription covers use on camera systems you own or operate, including for your business. A shop, restaurant, warehouse, or office running Frigate+ at its own locations (including multiple locations) is exactly the kind of use the subscription is for.
+What the standard subscription does not cover is using Frigate+ models to provide a product or service to others. If you're deploying models at your customers' sites, bundling them with hardware you sell, or running them as part of a hosted or managed service, even if your customers never receive the model files themselves, you'll need a commercial license.
+Note that professional installers are fine under standard subscriptions when each customer holds their own Frigate+ subscription. The commercial license is for cases where your license powers your customers' sites.
 
 ### Why can't I submit images to Frigate+?
 

docs/docs/plus/first_model.md

@@ -63,10 +63,10 @@ Frigate+ models generally have much higher scores than the default model provide
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > Global configuration > Objects" />. Under **Object filters**, set **Min Score** and **Threshold** for each object type, then click **Save**.
+Navigate to <NavPath path="Settings > Global configuration > Objects" />. Under **Object filters**, set **Minimum confidence** and **Confidence threshold** for each object type, then click **Save**.
 
-| Object            | Min Score | Threshold |
-| ----------------- | --------- | --------- |
+| Object            | Minimum confidence | Confidence threshold |
+| ----------------- | ------------------ | -------------------- |
 | **dog**           | .7                 | .9                   |
 | **cat**           | .65                | .8                   |
 | **face**          | .7                 |                      |

docs/docs/plus/index.md

@@ -65,11 +65,11 @@ Some users may find that Frigate+ models result in more false positives initiall
 
 Frigate+ models support a more relevant set of objects for security cameras. The labels for annotation in Frigate+ are configurable by editing the camera in the Cameras section of Frigate+. Currently, the following objects are supported:
 
-- **People**: `person`, `face`
-- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `school_bus`, `license_plate`
+- **People**: `person`, `face`, `baby`
+- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `school_bus`, `garbage truck`, `license_plate`
 - **Delivery Logos**: `amazon`, `usps`, `ups`, `fedex`, `dhl`, `an_post`, `purolator`, `postnl`, `nzpost`, `postnord`, `gls`, `dpd`, `canada_post`, `royal_mail`
-- **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`, `skunk`, `kangaroo`
-- **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`
+- **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`, `skunk`, `kangaroo`, `possum`, `rodent`
+- **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`, `baby_stroller`
 
 Other object types available in the default Frigate model are not available. Additional object types will be added in future releases.
 
@@ -77,9 +77,12 @@ Other object types available in the default Frigate model are not available. Add
 
 Candidate labels are also available for annotation. These labels don't have enough data to be included in the model yet, but using them will help add support sooner. You can enable these labels by editing the camera settings.
 
-Where possible, these labels are mapped to existing labels during training. For example, any `baby` labels are mapped to `person` until support for new labels is added.
+Where possible, these labels are mapped to existing labels during training. For example, any `duck` labels are mapped to `bird` until support for new labels is added.
 
-The candidate labels are: `baby`, `bpost`, `badger`, `possum`, `rodent`, `chicken`, `groundhog`, `boar`, `hedgehog`, `tractor`, `golf cart`, `garbage truck`, `bus`, `sports ball`, `la_poste`, `lawnmower`, `heron`, `rickshaw`, `wombat`, `auspost`, `aramex`, `bobcat`, `mustelid`, `transoflex`, `airplane`, `drone`, `mountain_lion`, `crocodile`, `turkey`, `baby_stroller`, `monkey`, `coyote`, `porcupine`, `parcelforce`, `sheep`, `snake`, `helicopter`, `lizard`, `duck`, `hermes`, `cargus`, `fan_courier`, `sameday`
+- **Vehicles**: `tractor`, `golf_cart`, `bus`, `airplane`, `helicopter`, `rickshaw`, `scooter`
+- **Delivery Logos**: `bpost`, `auspost`, `aramex`, `transoflex`, `parcelforce`, `hermes`, `cargus`, `fan_courier`, `sameday`, `la_poste`
+- **Animals**: `badger`, `chicken`, `duck`, `turkey`, `groundhog`, `boar`, `hedgehog`, `wombat`, `bobcat`, `mustelid`, `mountain_lion`, `crocodile`, `monkey`, `coyote`, `porcupine`, `sheep`, `snake`, `lizard`, `heron`, `elk`, `moose`, `pig`, `donkey`, `civet`
+- **Other**: `sports_ball`, `drone`, `lawnmower`
 
 Candidate labels are not available for automatic suggestions.
 

docs/docs/troubleshooting/faqs.md

@@ -184,6 +184,6 @@ Filters and masks only hide the incorrect result - they don't teach Frigate what
 
 ### Where do I see problems Frigate has detected?
 
-Open System > Health. The Notices list keeps a record of problems Frigate has found, and you can dismiss any entry to acknowledge it. Ongoing conditions, such as an offline camera or recordings deleted before their retention period, appear in the status bar for admins until they clear, and the status bar links to the Notices list while it has undismissed entries. On mobile, tap the warning icon in the bottom navigation bar to see them.
+Open System > Health. The Notices list keeps a record of problems Frigate has found. Acknowledge an entry to hide it until the problem happens again, or mute it to hide it for good. Hidden entries stay listed under Show hidden in the filter. Ongoing conditions, such as an offline camera or recordings deleted before their retention period, appear in the status bar for admins until they clear, and the status bar links to the Notices list while it has entries showing. On mobile, tap the warning icon in the bottom navigation bar to see them.
 
 The Hardware section below the notices shows whether the detection hardware, hardware acceleration, and enrichment devices in your config were found and are being used, so a GPU that silently fell back to the CPU shows up as a warning. Run stream checks to probe every camera's streams for the same problems the camera wizard reports.

docs/docs/troubleshooting/recordings.md

@@ -397,19 +397,11 @@ dmesg | grep -i -E "gpu|drm|reset|hang"
 
 Messages like `trying reset from guc_exec_queue_timedout_job` or similar GPU reset/hang messages indicate a driver or hardware issue. Ensure your kernel and GPU drivers (especially Intel) are up to date.
 
-#### Step 6: Verify hardware acceleration configuration
-
-An incorrect `hwaccel_args` preset can cause ffmpeg to fail silently or consume excessive CPU, starving the detector of resources.
-
-- After upgrading Frigate, verify your preset matches your hardware (e.g., `preset-intel-qsv-h264` instead of the deprecated `preset-vaapi`).
-- For h265 cameras, use the corresponding h265 preset (e.g., `preset-intel-qsv-h265`).
-- Note that `hwaccel_args` are only relevant for the detect stream. Frigate does not decode the record stream.
-
-#### Step 7: Verify go2rtc stream configuration
+#### Step 6: Verify go2rtc stream configuration
 
 Ensure that the ffmpeg source names in your go2rtc configuration match the correct camera stream. A misconfigured stream name (e.g., copying a config from one camera to another without updating the stream reference) will cause the wrong stream to be used or the stream to fail entirely.
 
-#### Step 8: Check system resources
+#### Step 7: Check system resources
 
 If none of the above apply, the issue may be a general resource constraint. Monitor the following on your host:
 

docs/docusaurus.config.ts

@@ -3,6 +3,9 @@ import * as path from "node:path";
 import type { Config, PluginConfig } from "@docusaurus/types";
 import type * as OpenApiPlugin from "docusaurus-plugin-openapi-docs";
 
+// Bump when a new stable release ships
+const STABLE_VERSION = "0.18";
+
 const config: Config = {
   title: "Frigate",
   tagline: "NVR With Realtime Object Detection for IP Cameras",
@@ -23,17 +26,17 @@ const config: Config = {
     mermaid: true,
   },
   i18n: {
-    defaultLocale: 'en',
-    locales: ['en'],
+    defaultLocale: "en",
+    locales: ["en"],
     localeConfigs: {
       en: {
-        label: 'English',
-      }
+        label: "English",
+      },
     },
   },
   themeConfig: {
     announcementBar: {
-      id: 'frigate_plus',
+      id: "frigate_plus",
       content: `
         <span style="margin-right: 8px; display: inline-block; animation: pulse 2s infinite;">🚀</span>
         Get more relevant and accurate detections with Frigate+ models.
@@ -45,8 +48,8 @@ const config: Config = {
             50%       { transform: scale(1.1); }
           }
         </style>`,
-      backgroundColor: '#005f73',
-      textColor: '#e0fbfc',
+      backgroundColor: "#005f73",
+      textColor: "#e0fbfc",
       isCloseable: false,
     },
     docs: {
@@ -85,13 +88,13 @@ const config: Config = {
     prism: {
       magicComments: [
         {
-          className: 'theme-code-block-highlighted-line',
-          line: 'highlight-next-line',
-          block: {start: 'highlight-start', end: 'highlight-end'},
+          className: "theme-code-block-highlighted-line",
+          line: "highlight-next-line",
+          block: { start: "highlight-start", end: "highlight-end" },
         },
         {
-          className: 'code-block-error-line',
-          line: 'highlight-error-line',
+          className: "code-block-error-line",
+          line: "highlight-error-line",
         },
       ],
       additionalLanguages: ["bash", "json"],
@@ -131,6 +134,11 @@ const config: Config = {
         srcDark: "img/branding/logo-dark.svg",
       },
       items: [
+        {
+          href: "https://github.com/blakeblackshear/frigate/releases",
+          label: `${STABLE_VERSION}`,
+          position: "left",
+        },
         {
           to: "/",
           activeBasePath: "docs",
@@ -148,19 +156,19 @@ const config: Config = {
           position: "right",
         },
         {
-          type: 'localeDropdown',
-          position: 'right',
+          type: "localeDropdown",
+          position: "right",
           dropdownItemsAfter: [
             {
-              label: '简体中文（社区翻译）',
-              href: 'https://docs.frigate-cn.video',
-            }
-          ]
+              label: "简体中文（社区翻译）",
+              href: "https://docs.frigate-cn.video",
+            },
+          ],
         },
         {
-          href: 'https://github.com/blakeblackshear/frigate',
-          label: 'GitHub',
-          position: 'right',
+          href: "https://github.com/blakeblackshear/frigate",
+          label: "GitHub",
+          position: "right",
         },
       ],
     },

docs/package-lock.json

@@ -9460,9 +9460,9 @@
       }
     },
     "node_modules/dompurify": {
-      "version": "3.4.13",
-      "resolved": "https://registry.npmjs.org/dompurify/-/dompurify-3.4.13.tgz",
-      "integrity": "sha512-2vmYIoqjze2d+kakP8S/nS5shfsl587kzwEjcGlTdiksUVgFHnFCsLYDVj/JNqJVOQZGSYBTmuycv0PodwmnMQ==",
+      "version": "3.4.16",
+      "resolved": "https://registry.npmjs.org/dompurify/-/dompurify-3.4.16.tgz",
+      "integrity": "sha512-sqo+pNp3qRhCIpbgRi1y8Tgk27Bo2Ry7w0dC1NBeNTdZChWjz9Xb/KOoZbRP/R6pQZ80Qw8YhXw13hWWBbMRnQ==",
       "license": "(MPL-2.0 OR Apache-2.0)",
       "optionalDependencies": {
         "@types/trusted-types": "^2.0.7"
@@ -10125,9 +10125,9 @@
       "license": "MIT"
     },
     "node_modules/fast-uri": {
-      "version": "3.1.7",
-      "resolved": "https://registry.npmjs.org/fast-uri/-/fast-uri-3.1.7.tgz",
-      "integrity": "sha512-dOvZVzjdZdz7phd9v6jCbwxrBW3fK6n8Rc0CtdmM4bumzMnxywBYhuph6J819RRw/ku+rLbelwfMunktuzVVHg==",
+      "version": "3.1.8",
+      "resolved": "https://registry.npmjs.org/fast-uri/-/fast-uri-3.1.8.tgz",
+      "integrity": "sha512-GZMtZUTNRpOVIECoXwLNZS5xUGE+mVNbTB8h/7Rwh2TFWcBQiPzTgyZi05BF9UMZKkLJv8XBRJTlU7zg8+ZfMg==",
       "funding": [
         {
           "type": "github",
@@ -11375,15 +11375,15 @@
       }
     },
     "node_modules/image-size": {
-      "version": "2.0.2",
-      "resolved": "https://registry.npmjs.org/image-size/-/image-size-2.0.2.tgz",
-      "integrity": "sha512-IRqXKlaXwgSMAMtpNzZa1ZAe8m+Sa1770Dhk8VkSsP9LS+iHD62Zd8FQKs8fbPiagBE7BzoFX23cxFnwshpV6w==",
+      "version": "2.0.4",
+      "resolved": "https://registry.npmjs.org/image-size/-/image-size-2.0.4.tgz",
+      "integrity": "sha512-QRUkFFsRV/6fuESxb9Vkq+a0LkSrgKXuc2NEqfikiXxxN/G3tjWt5EVUlMaImRBZRZK/jRBEbYvpPYZL8t08Zw==",
       "license": "MIT",
       "bin": {
         "image-size": "bin/image-size.js"
       },
       "engines": {
-        "node": ">=16.x"
+        "node": ">=18"
       }
     },
     "node_modules/immer": {

docs/sidebars.ts

@@ -130,7 +130,6 @@ const sidebars: SidebarsConfig = {
         label: "Advanced Configuration",
         items: [
           "configuration/advanced/system",
-          "configuration/advanced/analytics",
           "configuration/advanced/reference",
           {
             type: "link",

docs/src/components/AnalyticsFields/index.jsx

@@ -1,60 +0,0 @@
-import React from "react";
-import schema from "@site/static/frigate-analytics-schema.json";
-
-function resolve(node) {
-  if (!node) return node;
-  if (node.$ref) return schema.$defs[node.$ref.split("/").pop()];
-  if (node.anyOf) {
-    const inner = node.anyOf.find((option) => option.type !== "null");
-    return inner ? resolve(inner) : node;
-  }
-  return node;
-}
-
-function rows(properties, prefix = "") {
-  return Object.entries(properties).flatMap(([name, field]) => {
-    const path = prefix ? `${prefix}.${name}` : name;
-    const row = {
-      path,
-      description: field.description,
-      isPublic: field["x-public"],
-    };
-    const target = resolve(field);
-
-    if (target?.properties) return [row, ...rows(target.properties, path)];
-
-    const values = resolve(target?.additionalProperties);
-    if (values?.properties)
-      return [row, ...rows(values.properties, `${path}.<key>`)];
-
-    const items = resolve(target?.items);
-    if (items?.properties) return [row, ...rows(items.properties, `${path}[]`)];
-
-    return [row];
-  });
-}
-
-export default function AnalyticsFields() {
-  return (
-    <table>
-      <thead>
-        <tr>
-          <th>Field</th>
-          <th>Description</th>
-          <th>Public</th>
-        </tr>
-      </thead>
-      <tbody>
-        {rows(schema.properties).map((row) => (
-          <tr key={row.path}>
-            <td>
-              <code>{row.path}</code>
-            </td>
-            <td>{row.description}</td>
-            <td>{row.isPublic ? "Yes" : "No"}</td>
-          </tr>
-        ))}
-      </tbody>
-    </table>
-  );
-}

docs/static/frigate-api.yaml

@@ -10,25 +10,6 @@ servers:
   - url: https://demo.frigate.video/api
   - url: http://localhost:5001/api
 paths:
-  /analytics/preview:
-    get:
-      tags:
-        - Analytics
-      summary: Get Analytics Preview
-      description: |-
-        **Access:** Admin role required.
-
-        Get the analytics report Frigate would send next, without sending it.
-      operationId: get_analytics_preview_analytics_preview_get
-      responses:
-        '200':
-          description: Successful Response
-          content:
-            application/json:
-              schema: {}
-      security:
-        - frigateAdminAuth: []
-      x-required-role: admin
   /auth/first_time_login:
     get:
       tags:
@@ -431,6 +412,39 @@ paths:
       security:
         - frigateAdminAuth: []
       x-required-role: admin
+  /go2rtc/streams/{stream_name}/bitrate:
+    get:
+      tags:
+        - Camera
+      summary: Go2Rtc Stream Bitrate
+      description: |-
+        **Access:** Admin role required.
+
+        Measure a go2rtc stream's bitrate over a few seconds.
+      operationId: 
+        go2rtc_stream_bitrate_go2rtc_streams__stream_name__bitrate_get
+      parameters:
+        - name: stream_name
+          in: path
+          required: true
+          schema:
+            type: string
+            title: Stream Name
+      responses:
+        '200':
+          description: Successful Response
+          content:
+            application/json:
+              schema: {}
+        '422':
+          description: Validation Error
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/HTTPValidationError'
+      security:
+        - frigateAdminAuth: []
+      x-required-role: admin
   /ffprobe:
     get:
       tags:
@@ -4193,19 +4207,20 @@ paths:
         Get notices, most severe first.
 
         Args:
-            include_dismissed: Also return dismissed notices, for the history view
+            include_hidden: Also return acknowledged and muted notices, for the
+                hidden list
 
         Returns:
             The notices
       operationId: get_notices_notices_get
       parameters:
-        - name: include_dismissed
+        - name: include_hidden
           in: query
           required: false
           schema:
             type: boolean
             default: false
-            title: Include Dismissed
+            title: Include Hidden
       responses:
         '200':
           description: Successful Response
@@ -4240,16 +4255,16 @@ paths:
       security:
         - frigateAdminAuth: []
       x-required-role: admin
-  /notices/dismissed_checks:
+  /notices/muted_checks:
     get:
       tags:
         - Notices
-      summary: Get Dismissed Checks
+      summary: Get Muted Checks
       description: |-
         **Access:** Admin role required.
 
-        Get the dismissed config and stream check rows, newest first.
-      operationId: get_dismissed_checks_notices_dismissed_checks_get
+        Get the muted config and stream check rows, newest first.
+      operationId: get_muted_checks_notices_muted_checks_get
       responses:
         '200':
           description: Successful Response
@@ -4259,16 +4274,16 @@ paths:
       security:
         - frigateAdminAuth: []
       x-required-role: admin
-  /notices/dismissed:
+  /notices/hidden:
     delete:
       tags:
         - Notices
-      summary: Purge Dismissed
+      summary: Unhide All Notices
       description: |-
         **Access:** Admin role required.
 
-        Delete every dismissed notice and check row so each can show again.
-      operationId: purge_dismissed_notices_dismissed_delete
+        Show every acknowledged and muted notice and check row again.
+      operationId: unhide_all_notices_notices_hidden_delete
       responses:
         '200':
           description: Successful Response
@@ -4278,18 +4293,83 @@ paths:
       security:
         - frigateAdminAuth: []
       x-required-role: admin
-  /notices/{notice_id}/dismiss:
+  /notices/{notice_id}/acknowledge:
     post:
       tags:
         - Notices
-      summary: Dismiss Notice
+      summary: Acknowledge Notice
       description: |-
         **Access:** Admin role required.
 
-        Hide a notice or a config or stream check row.
+        Hide a notice until it happens again.
 
-        It stays hidden if the same problem happens again.
-      operationId: dismiss_notice_notices__notice_id__dismiss_post
+        Config and stream check rows and the update notice never repeat, so they
+        can only be muted.
+      operationId: acknowledge_notice_notices__notice_id__acknowledge_post
+      parameters:
+        - name: notice_id
+          in: path
+          required: true
+          schema:
+            type: string
+            title: Notice Id
+      responses:
+        '200':
+          description: Successful Response
+          content:
+            application/json:
+              schema: {}
+        '422':
+          description: Validation Error
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/HTTPValidationError'
+      security:
+        - frigateAdminAuth: []
+      x-required-role: admin
+  /notices/{notice_id}/mute:
+    post:
+      tags:
+        - Notices
+      summary: Mute Notice
+      description: |-
+        **Access:** Admin role required.
+
+        Hide a notice or a config or stream check row for good.
+      operationId: mute_notice_notices__notice_id__mute_post
+      parameters:
+        - name: notice_id
+          in: path
+          required: true
+          schema:
+            type: string
+            title: Notice Id
+      responses:
+        '200':
+          description: Successful Response
+          content:
+            application/json:
+              schema: {}
+        '422':
+          description: Validation Error
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/HTTPValidationError'
+      security:
+        - frigateAdminAuth: []
+      x-required-role: admin
+  /notices/{notice_id}/hidden:
+    delete:
+      tags:
+        - Notices
+      summary: Unhide Notice
+      description: |-
+        **Access:** Admin role required.
+
+        Show an acknowledged or muted notice or check row again.
+      operationId: unhide_notice_notices__notice_id__hidden_delete
       parameters:
         - name: notice_id
           in: path
@@ -4413,6 +4493,16 @@ paths:
               - type: 'null'
             default: 100
             title: Limit
+        - name: offset
+          in: query
+          required: false
+          schema:
+            anyOf:
+              - type: integer
+                minimum: 0
+              - type: 'null'
+            default: 0
+            title: Offset
         - name: after
           in: query
           required: false
@@ -4718,6 +4808,16 @@ paths:
               - type: 'null'
             default: 50
             title: Limit
+        - name: offset
+          in: query
+          required: false
+          schema:
+            anyOf:
+              - type: integer
+                minimum: 0
+              - type: 'null'
+            default: 0
+            title: Offset
         - name: cameras
           in: query
           required: false
@@ -7684,6 +7784,11 @@ components:
           type: boolean
           title: Skip Save
           default: false
+        replace_paths:
+          items:
+            type: string
+          type: array
+          title: Replace Paths
       type: object
       title: AppConfigSetBody
     AppPostLoginBody:

frigate/analytics/__init__.py

@@ -1 +0,0 @@
-"""Opt-in anonymous analytics reports."""

frigate/analytics/collectors/__init__.py

@@ -1 +0,0 @@
-"""One collector per report section."""

frigate/analytics/collectors/cameras.py

@@ -1,223 +0,0 @@
-"""Cameras section: counts and histograms across cameras, never per camera."""
-
-from collections import Counter
-from typing import Any
-from urllib.parse import urlsplit
-
-from frigate.analytics.collectors.common import closed, histogram
-from frigate.analytics.context import ReportContext
-from frigate.analytics.schema import (
-    CamerasSection,
-    ConnectionQuality,
-    FpsBucket,
-    HeightBucket,
-    HwaccelKey,
-    InputPresetKey,
-    RetainBucket,
-    RetainDays,
-)
-from frigate.config import CameraConfig
-from frigate.config.camera.camera import CameraTypeEnum
-from frigate.config.camera.ffmpeg import CameraInput, CameraRoleEnum
-from frigate.const import REPLAY_CAMERA_PREFIX
-
-# the upper edge of every height bucket but the last
-HEIGHT_BUCKETS = (
-    (360, HeightBucket.le_360),
-    (540, HeightBucket.h480),
-    (900, HeightBucket.h720),
-    (1260, HeightBucket.h1080),
-    (1800, HeightBucket.h1440),
-)
-RESTREAM_HOSTS = frozenset({"127.0.0.1", "localhost"})
-RESTREAM_PORT = 8554
-QUALITIES = frozenset(ConnectionQuality)
-
-
-def height_bucket(height: int) -> HeightBucket:
-    for limit, bucket in HEIGHT_BUCKETS:
-        if height <= limit:
-            return bucket
-
-    return HeightBucket.ge_2160
-
-
-def fps_bucket(fps: int) -> FpsBucket:
-    if fps <= 5:
-        return FpsBucket.le_5
-
-    if fps <= 10:
-        return FpsBucket.f6_10
-
-    return FpsBucket.gt_10
-
-
-def retain_bucket(days: float) -> RetainBucket:
-    if days <= 0:
-        return RetainBucket.zero
-
-    if days <= 7:
-        return RetainBucket.d1_7
-
-    if days <= 30:
-        return RetainBucket.d8_30
-
-    return RetainBucket.gt_30
-
-
-def preset_key(args: str | list[str], enum: Any) -> Any:
-    """The preset's name, custom for hand written args, or none."""
-    if not args:
-        return enum("none")
-
-    if isinstance(args, str) and args.startswith("preset-"):
-        return closed(enum, args.removeprefix("preset-"), enum("custom"))
-
-    return enum("custom")
-
-
-def is_restream(path: str) -> bool:
-    try:
-        url = urlsplit(path)
-        return url.hostname in RESTREAM_HOSTS and url.port == RESTREAM_PORT
-    except ValueError:
-        return False
-
-
-def role_input(camera: CameraConfig, role: CameraRoleEnum) -> CameraInput | None:
-    return next((i for i in camera.ffmpeg.inputs if role in i.roles), None)
-
-
-def object_masks(camera: CameraConfig) -> int:
-    # parsing copies camera-wide masks into every filter as global_<id>
-    own = sum(1 for mask in camera.objects.mask.values() if mask is not None)
-    per_label = sum(
-        1
-        for label_filter in camera.objects.filters.values()
-        for mask_id, mask in label_filter.mask.items()
-        if mask is not None and not mask_id.startswith("global_")
-    )
-    return own + per_label
-
-
-def collect(ctx: ReportContext) -> CamerasSection:
-    cameras = {
-        name: camera
-        for name, camera in ctx.config.cameras.items()
-        if not name.startswith(REPLAY_CAMERA_PREFIX)
-    }
-    camera_stats = ctx.stats.get("cameras", {})
-
-    flags: Counter[str] = Counter()
-    types: Counter[CameraTypeEnum] = Counter()
-    heights: Counter[HeightBucket] = Counter()
-    fps: Counter[FpsBucket] = Counter()
-    hwaccel: Counter[Any] = Counter()
-    input_presets: Counter[Any] = Counter()
-    quality: Counter[ConnectionQuality] = Counter()
-    retain: dict[str, Counter[RetainBucket]] = {
-        period: Counter() for period in ("continuous", "motion", "alerts", "detections")
-    }
-
-    for name, camera in cameras.items():
-        detect_input = role_input(camera, CameraRoleEnum.detect)
-        record_input = role_input(camera, CameraRoleEnum.record)
-
-        # config validation requires a detect input, so this never skips
-        if detect_input is None:
-            continue
-
-        types[camera.type] += 1
-        fps[fps_bucket(camera.detect.fps)] += 1
-        hwaccel[
-            preset_key(
-                detect_input.hwaccel_args or camera.ffmpeg.hwaccel_args, HwaccelKey
-            )
-        ] += 1
-        input_presets[
-            preset_key(
-                detect_input.input_args or camera.ffmpeg.input_args, InputPresetKey
-            )
-        ] += 1
-
-        if camera.detect.height:
-            heights[height_bucket(camera.detect.height)] += 1
-
-        state = camera_stats.get(name, {}).get("connection_quality")
-
-        if state in QUALITIES:
-            quality[ConnectionQuality(state)] += 1
-
-        if camera.record.enabled:
-            retain["continuous"][retain_bucket(camera.record.continuous.days)] += 1
-            retain["motion"][retain_bucket(camera.record.motion.days)] += 1
-            retain["alerts"][retain_bucket(camera.record.alerts.retain.days)] += 1
-            retain["detections"][
-                retain_bucket(camera.record.detections.retain.days)
-            ] += 1
-
-        zones = len(camera.zones)
-        flags["enabled"] += camera.enabled
-        flags["go2rtc_restream"] += any(
-            is_restream(i.path) for i in camera.ffmpeg.inputs
-        )
-        flags["separate_detect_stream"] += (
-            record_input is not None and record_input.path != detect_input.path
-        )
-        flags["detect"] += camera.detect.enabled
-        flags["record"] += camera.record.enabled
-        flags["sub_stream_record"] += camera.record.sub.enabled
-        flags["snapshots"] += camera.snapshots.enabled
-        flags["audio"] += camera.audio.enabled
-        flags["audio_transcription"] += camera.audio_transcription.enabled
-        flags["birdseye"] += camera.birdseye.enabled
-        flags["onvif"] += bool(camera.onvif.host)
-        flags["autotracking"] += camera.onvif.autotracking.enabled
-        flags["face_recognition"] += camera.face_recognition.enabled
-        flags["lpr"] += camera.lpr.enabled
-        flags["review_genai"] += camera.review.genai.enabled
-        flags["object_genai"] += camera.objects.genai.enabled
-        flags["notifications"] += camera.notifications.enabled
-        flags["zones"] += zones
-        flags["cameras_with_zones"] += zones > 0
-        flags["motion_masks"] += sum(
-            1 for mask in camera.motion.mask.values() if mask is not None
-        )
-        flags["object_masks"] += object_masks(camera)
-
-    return CamerasSection(
-        total=len(cameras),
-        enabled=flags["enabled"],
-        types=histogram(types),
-        detect_height=histogram(heights),
-        detect_fps=histogram(fps),
-        hwaccel=histogram(hwaccel),
-        input_preset=histogram(input_presets),
-        go2rtc_restream=flags["go2rtc_restream"],
-        separate_detect_stream=flags["separate_detect_stream"],
-        detect=flags["detect"],
-        record=flags["record"],
-        sub_stream_record=flags["sub_stream_record"],
-        snapshots=flags["snapshots"],
-        audio=flags["audio"],
-        audio_transcription=flags["audio_transcription"],
-        birdseye=flags["birdseye"],
-        onvif=flags["onvif"],
-        autotracking=flags["autotracking"],
-        face_recognition=flags["face_recognition"],
-        lpr=flags["lpr"],
-        review_genai=flags["review_genai"],
-        object_genai=flags["object_genai"],
-        notifications=flags["notifications"],
-        zones=flags["zones"],
-        cameras_with_zones=flags["cameras_with_zones"],
-        motion_masks=flags["motion_masks"],
-        object_masks=flags["object_masks"],
-        connection_quality=histogram(quality),
-        retain_days=RetainDays(
-            continuous=histogram(retain["continuous"]),
-            motion=histogram(retain["motion"]),
-            alerts=histogram(retain["alerts"]),
-            detections=histogram(retain["detections"]),
-        ),
-    )

frigate/analytics/collectors/common.py

@@ -1,37 +0,0 @@
-"""Helpers the section collectors share."""
-
-import math
-from collections import Counter
-from enum import Enum
-from typing import Any, TypeVar
-
-E = TypeVar("E", bound=Enum)
-
-
-def closed(enum: type[E], value: Any, fallback: E) -> E:
-    """The member for a value, or the fallback for one the enum doesn't know."""
-    try:
-        return enum(str(value))
-    except ValueError:
-        return fallback
-
-
-def rate(value: Any) -> float:
-    """A finite, non-negative number rounded to 2 decimals, else 0.
-
-    A NaN would serialize as null and fail the schema's number type.
-    """
-    try:
-        number = float(value)
-    except (TypeError, ValueError):
-        return 0.0
-
-    if not math.isfinite(number):
-        return 0.0
-
-    return round(max(number, 0.0), 2)
-
-
-def histogram(counter: "Counter[E]") -> dict[E, int]:
-    """Drop the empty buckets, since an absent key means zero."""
-    return {key: total for key, total in counter.items() if total > 0}

frigate/analytics/collectors/detection.py

@@ -1,65 +0,0 @@
-"""Detection section: models, the detectors they run on, and inference speed."""
-
-import os
-
-from frigate.analytics.collectors.common import rate
-from frigate.analytics.context import ReportContext
-from frigate.analytics.schema import DetectionModel, DetectionSection, ModelSource
-from frigate.config.config import DEFAULT_MODEL
-from frigate.const import MODEL_CACHE_DIR
-from frigate.detectors.detector_config import SceneEnum
-from frigate.detectors.detector_types import DetectorTypeEnum
-from frigate.detectors.device import runner_names
-
-# the paths FrigateConfig fills in for a model that sets none
-BUNDLED_MODEL_PATHS = frozenset(
-    {"/cpu_model.tflite", "/edgetpu_model.tflite", str(DEFAULT_MODEL["path"])}
-)
-
-
-def model_source(path: str | None) -> ModelSource:
-    """Default, Frigate+ (a cached model next to its info file), or custom.
-
-    Parsing rewrites plus://<id> to the model cache, so the prefix is gone by now.
-    """
-    if path is None or path in BUNDLED_MODEL_PATHS:
-        return ModelSource.default
-
-    if path.startswith(f"{MODEL_CACHE_DIR}/") and os.path.isfile(f"{path}.json"):
-        return ModelSource.plus
-
-    return ModelSource.custom
-
-
-def collect(ctx: ReportContext) -> DetectionSection:
-    config = ctx.config
-    detectors = ctx.stats.get("detectors", {})
-    model_specs = [(model, config.devices_for_model(model)) for model in config.models]
-
-    # FrigateApp.start_detectors names the processes in this same order
-    names = iter(runner_names([spec for _, specs in model_specs for spec in specs]))
-    models: dict[SceneEnum, DetectionModel] = {}
-
-    for model, specs in model_specs:
-        speeds: list[float] = []
-
-        for _ in specs:
-            speed = detectors.get(next(names), {}).get("inference_speed")
-
-            if isinstance(speed, int | float) and speed > 0:
-                speeds.append(float(speed))
-
-        models[model.scene] = DetectionModel(
-            detector=DetectorTypeEnum(specs[0].detector),
-            devices=len(specs),
-            model_type=model.model_type,
-            input=f"{model.width}x{model.height}",
-            source=model_source(model.path),
-            inference_ms=rate(sum(speeds) / len(speeds)) if speeds else None,
-        )
-
-    return DetectionSection(
-        models=models,
-        detection_fps=rate(ctx.stats.get("detection_fps")),
-        skipped_fps=rate(ctx.stats.get("skipped_fps")),
-    )

frigate/analytics/collectors/features.py

@@ -1,145 +0,0 @@
-"""Features section: enrichments, GenAI, integrations, and users."""
-
-from collections import Counter
-
-from frigate.analytics.collectors.common import closed, histogram
-from frigate.analytics.context import ReportContext
-from frigate.analytics.schema import (
-    BirdseyeUsage,
-    ClassificationUsage,
-    EnrichmentDevice,
-    EnrichmentUsage,
-    FeaturesSection,
-    GenAIUsage,
-    SemanticSearchModel,
-    SemanticSearchUsage,
-    TranscriptionModel,
-    TranscriptionUsage,
-    UserRole,
-)
-from frigate.config.camera.genai import GenAIProviderEnum, GenAIRoleEnum
-from frigate.const import REPLAY_CAMERA_PREFIX
-from frigate.models import User
-
-RUNTIME_DEVICES = {
-    "cpu": EnrichmentDevice.cpu,
-    "cuda": EnrichmentDevice.cuda,
-    "tensorrt": EnrichmentDevice.tensorrt,
-    "migraphx": EnrichmentDevice.migraphx,
-}
-OPENVINO_DEVICES = {
-    "cpu": EnrichmentDevice.openvino_cpu,
-    "gpu": EnrichmentDevice.openvino_gpu,
-    "npu": EnrichmentDevice.openvino_npu,
-}
-
-
-def enrichment_device(label: object) -> EnrichmentDevice | None:
-    """Map a runner's device label, like "CUDA" or "OpenVINO GPU.0,CPU"."""
-    if not isinstance(label, str) or not label:
-        return None
-
-    runtime, _, target = label.partition(" ")
-
-    if runtime == "OpenVINO":
-        first = target.split(",")[0].split(".")[0].strip().lower()
-        return OPENVINO_DEVICES.get(first, EnrichmentDevice.other)
-
-    return RUNTIME_DEVICES.get(label.lower(), EnrichmentDevice.other)
-
-
-def model_name(model: object) -> str | None:
-    if model is None:
-        return None
-
-    return str(getattr(model, "value", model))
-
-
-def semantic_model(model: object) -> SemanticSearchModel | None:
-    # any string that isn't a built-in model names a GenAI provider
-    name = model_name(model)
-
-    if name is None:
-        return None
-
-    if name in ("jinav1", "jinav2"):
-        return SemanticSearchModel(name)
-
-    return SemanticSearchModel.genai
-
-
-def transcription_model(model: object) -> TranscriptionModel | None:
-    name = model_name(model)
-
-    if name is None:
-        return None
-
-    return TranscriptionModel.whisper if name == "whisper" else TranscriptionModel.genai
-
-
-def users() -> dict[UserRole, int]:
-    roles: Counter[UserRole] = Counter(
-        closed(UserRole, user.role, UserRole.custom) for user in User.select(User.role)
-    )
-    return histogram(roles)
-
-
-def collect(ctx: ReportContext) -> FeaturesSection:
-    config = ctx.config
-    devices = ctx.stats.get("embeddings", {}).get("devices", {})
-    cameras = [
-        camera
-        for name, camera in config.cameras.items()
-        if not name.startswith(REPLAY_CAMERA_PREFIX)
-    ]
-    providers: Counter[GenAIProviderEnum] = Counter(
-        genai.provider for genai in config.genai.values()
-    )
-    roles: Counter[GenAIRoleEnum] = Counter(
-        role for genai in config.genai.values() for role in genai.roles
-    )
-    custom = list(config.classification.custom.values())
-
-    return FeaturesSection(
-        face_recognition=EnrichmentUsage(
-            enabled=config.face_recognition.enabled,
-            model_size=config.face_recognition.model_size,
-            device=enrichment_device(devices.get("face_recognition")),
-        ),
-        lpr=EnrichmentUsage(
-            enabled=config.lpr.enabled,
-            model_size=config.lpr.model_size,
-            device=enrichment_device(devices.get("lpr")),
-        ),
-        semantic_search=SemanticSearchUsage(
-            enabled=config.semantic_search.enabled,
-            model=semantic_model(config.semantic_search.model),
-            model_size=config.semantic_search.model_size,
-            device=enrichment_device(devices.get("semantic_search")),
-            triggers=sum(len(camera.semantic_search.triggers) for camera in cameras),
-        ),
-        audio_transcription=TranscriptionUsage(
-            enabled=config.audio_transcription.enabled,
-            model=transcription_model(config.audio_transcription.model),
-            model_size=config.audio_transcription.model_size,
-        ),
-        genai=GenAIUsage(providers=histogram(providers), roles=histogram(roles)),
-        classification_models=ClassificationUsage(
-            state=sum(1 for model in custom if model.state_config is not None),
-            object=sum(1 for model in custom if model.object_config is not None),
-        ),
-        birdseye=BirdseyeUsage(
-            enabled=config.birdseye.enabled,
-            modes=list(config.birdseye.modes),
-            restream=config.birdseye.restream,
-        ),
-        mqtt=config.mqtt.enabled,
-        notifications=config.notifications.enabled,
-        auth=config.auth.enabled,
-        proxy_auth=config.proxy.header_map.user is not None,
-        tls=config.tls.enabled,
-        users=users(),
-        camera_groups=len(config.camera_groups),
-        profiles=len(config.profiles),
-        plus_api_key=config.plus_api.is_active(),
-    )

frigate/analytics/collectors/hardware.py

@@ -1,107 +0,0 @@
-"""Hardware section: CPU, memory, GPUs, decode and detection hardware, storage."""
-
-import os
-from collections import Counter
-from typing import Any
-
-import psutil
-
-from frigate.analytics.collectors.common import closed, histogram
-from frigate.analytics.context import ReportContext
-from frigate.analytics.schema import (
-    DecodeFamily,
-    GpuInfo,
-    GpuVendor,
-    HardwareKey,
-    HardwareSection,
-    StorageInfo,
-)
-from frigate.const import RECORD_DIR
-from frigate.detectors.hardware import hardware_prober
-from frigate.util.hwaccel import hwaccel_options
-
-DEVICE_TREE_MODEL = "/proc/device-tree/model"
-CPUINFO = "/proc/cpuinfo"
-
-
-def cpu_model() -> str:
-    """The board model on ARM boards, else the CPU's model name."""
-    try:
-        with open(DEVICE_TREE_MODEL) as f:
-            board = f.read().strip("\x00\n ")
-
-        if board:
-            return board[:64]
-    except OSError:
-        pass
-
-    try:
-        with open(CPUINFO) as f:
-            for line in f:
-                key, _, value = line.partition(":")
-
-                if key.strip() == "model name" and value.strip():
-                    return value.strip()[:64]
-    except OSError:
-        pass
-
-    return "unknown"
-
-
-def gpus(stats: dict[str, Any]) -> list[GpuInfo]:
-    found: list[GpuInfo] = []
-
-    for name, entry in stats.get("gpu_usages", {}).items():
-        vendor = entry.get("vendor") if isinstance(entry, dict) else None
-        found.append(
-            GpuInfo(
-                vendor=closed(GpuVendor, vendor, GpuVendor.other),
-                name=str(name)[:64],
-            )
-        )
-
-    return found
-
-
-def decode_families() -> list[DecodeFamily]:
-    _, available = hwaccel_options()
-    families = [
-        closed(DecodeFamily, family.key, DecodeFamily.other) for family in available
-    ]
-    return list(dict.fromkeys(families))
-
-
-def detection_hardware() -> dict[HardwareKey, int]:
-    units: Counter[HardwareKey] = Counter()
-
-    for found in hardware_prober.probe():
-        units[closed(HardwareKey, found.key, HardwareKey.other)] += found.count
-
-    return histogram(units)
-
-
-def storage(stats: dict[str, Any]) -> StorageInfo:
-    # stats report sizes in MB
-    entry = stats.get("service", {}).get("storage", {}).get(RECORD_DIR) or {}
-    total_mb = float(entry.get("total") or 0)
-    used_mb = float(entry.get("used") or 0)
-
-    return StorageInfo(
-        record_fs=str(entry.get("mount_type") or "unknown")[:16],
-        record_total_gb=round(total_mb / 1024),
-        record_used_pct=min(round(used_mb / total_mb * 100), 100)
-        if total_mb > 0
-        else 0,
-    )
-
-
-def collect(ctx: ReportContext) -> HardwareSection:
-    return HardwareSection(
-        cpu_model=cpu_model(),
-        cpu_cores=os.cpu_count() or 0,
-        memory_gb=round(psutil.virtual_memory().total / 2**30),
-        gpus=gpus(ctx.stats),
-        decode_families=decode_families(),
-        detection_hardware=detection_hardware(),
-        storage=storage(ctx.stats),
-    )

frigate/analytics/collectors/health.py

@@ -1,59 +0,0 @@
-"""Health section: uptime, CPU, enrichment speed, and notice counts."""
-
-from typing import Any
-
-from frigate.analytics.collectors.common import rate
-from frigate.analytics.context import ReportContext
-from frigate.analytics.schema import (
-    EnrichmentTiming,
-    HealthSection,
-    NoticeCounts,
-    NoticeKindKey,
-)
-
-TIMING_STATS = {
-    EnrichmentTiming.face: "face_recognition_speed",
-    EnrichmentTiming.lpr: "plate_recognition_speed",
-    EnrichmentTiming.plate_detection: "yolov9_plate_detection_speed",
-    EnrichmentTiming.image_embedding: "image_embedding_speed",
-    EnrichmentTiming.text_embedding: "text_embedding_speed",
-    EnrichmentTiming.review_description: "review_description_speed",
-    EnrichmentTiming.object_description: "object_description_speed",
-}
-REPORTABLE_KINDS = frozenset(key.value for key in NoticeKindKey)
-
-
-def notice_deltas(notice_stats: list[dict[str, Any]]) -> dict[Any, NoticeCounts]:
-    """What changed since the last accepted report, per reportable kind."""
-    deltas: dict[Any, NoticeCounts] = {}
-
-    for row in notice_stats:
-        if row["kind"] not in REPORTABLE_KINDS:
-            continue
-
-        occurrences = max(row["occurrences"] - row["reported_occurrences"], 0)
-        dismissals = max(row["dismissals"] - row["reported_dismissals"], 0)
-
-        if occurrences or dismissals:
-            deltas[NoticeKindKey(row["kind"])] = NoticeCounts(
-                occurrences=occurrences, dismissals=dismissals
-            )
-
-    return deltas
-
-
-def collect(ctx: ReportContext) -> HealthSection:
-    service = ctx.stats.get("service", {})
-    embeddings = ctx.stats.get("embeddings", {})
-    cpu = ctx.stats.get("cpu_usages", {}).get("frigate.full_system", {}).get("cpu")
-    timings = {
-        timing: rate(embeddings.get(key)) for timing, key in TIMING_STATS.items()
-    }
-
-    return HealthSection(
-        uptime_hours=int(rate(service.get("uptime")) // 3600),
-        cpu_percent=min(round(rate(cpu)), 100),
-        enrichment_ms={timing: value for timing, value in timings.items() if value > 0},
-        retention_unmet=bool(service.get("retention_unmet", False)),
-        notices=notice_deltas(ctx.notice_stats),
-    )

frigate/analytics/collectors/install.py

@@ -1,63 +0,0 @@
-"""Install section: version, image variant, install type, platform."""
-
-import os
-import platform
-import re
-
-from frigate.analytics.collectors.common import closed
-from frigate.analytics.context import ReportContext
-from frigate.analytics.schema import Arch, ImageVariant, InstallSection, InstallType
-from frigate.version import VERSION
-
-KERNEL_PATTERN = re.compile(r"^(\d{1,3})\.(\d{1,3})")
-
-
-def image_variant(value: str | None) -> ImageVariant:
-    """The published image from the build-time FRIGATE_IMAGE_VARIANT, dev when unset."""
-    if not value:
-        return ImageVariant.dev
-
-    return closed(ImageVariant, value, ImageVariant.other)
-
-
-def install_type() -> InstallType:
-    # the add-on is a container too, so it has to be checked first
-    if os.path.isfile("/data/options.json"):
-        return InstallType.ha_addon
-
-    if os.environ.get("KUBERNETES_SERVICE_HOST"):
-        return InstallType.kubernetes
-
-    if os.path.exists("/run/.containerenv"):
-        return InstallType.podman
-
-    if os.path.exists("/.dockerenv"):
-        return InstallType.docker
-
-    return InstallType.unknown
-
-
-def arch(machine: str) -> Arch:
-    match machine.lower():
-        case "x86_64" | "amd64":
-            return Arch.x86_64
-        case "aarch64" | "arm64":
-            return Arch.aarch64
-        case _:
-            return Arch.other
-
-
-def kernel(release: str) -> str:
-    match = KERNEL_PATTERN.match(release)
-    return f"{match.group(1)}.{match.group(2)}" if match else "unknown"
-
-
-def collect(ctx: ReportContext) -> InstallSection:
-    return InstallSection(
-        version=VERSION[:32],
-        image_variant=image_variant(os.environ.get("FRIGATE_IMAGE_VARIANT")),
-        install_type=install_type(),
-        arch=arch(platform.machine()),
-        kernel=kernel(platform.release()),
-        run_as_root=os.geteuid() == 0,
-    )

frigate/analytics/context.py

@@ -1,13 +0,0 @@
-"""What the collectors read, gathered once per report."""
-
-from dataclasses import dataclass, field
-from typing import Any
-
-from frigate.config import FrigateConfig
-
-
-@dataclass(frozen=True)
-class ReportContext:
-    config: FrigateConfig
-    stats: dict[str, Any]
-    notice_stats: list[dict[str, Any]] = field(default_factory=list)

frigate/analytics/report.py

@@ -1,88 +0,0 @@
-"""Build a report from the section collectors."""
-
-import logging
-import time
-from collections.abc import Callable
-from typing import TYPE_CHECKING, Any
-from uuid import uuid4
-
-from pydantic import BaseModel
-
-from frigate.analytics.collectors import (
-    cameras,
-    detection,
-    features,
-    hardware,
-    health,
-    install,
-)
-from frigate.analytics.context import ReportContext
-from frigate.analytics.schema import SCHEMA_VERSION, AnalyticsReport
-from frigate.analytics.state import load_state
-from frigate.config import FrigateConfig
-
-if TYPE_CHECKING:
-    from frigate.notices.registry import NoticeRegistry
-    from frigate.stats.emitter import StatsEmitter
-
-logger = logging.getLogger(__name__)
-
-COLLECTORS: dict[str, Callable[[ReportContext], BaseModel | None]] = {
-    "install": install.collect,
-    "hardware": hardware.collect,
-    "detection": detection.collect,
-    "cameras": cameras.collect,
-    "features": features.collect,
-    "health": health.collect,
-}
-
-# shown in the preview until the first report creates a real ID
-PREVIEW_INSTALL_ID = "0" * 32
-
-
-def build_report(
-    ctx: ReportContext, install_id: str, sent_at: int | None = None
-) -> AnalyticsReport:
-    """Run every collector; one that fails sends its section as null."""
-    sections: dict[str, Any] = {}
-
-    for name, collect in COLLECTORS.items():
-        try:
-            sections[name] = collect(ctx)
-        except Exception:
-            # a collector bug must cost one section, never the whole report
-            logger.warning("Analytics %s section failed", name, exc_info=True)
-            sections[name] = None
-
-    return AnalyticsReport.model_validate(
-        {
-            "schema_version": SCHEMA_VERSION,
-            "install_id": install_id,
-            "report_id": str(uuid4()),
-            "sent_at": int(time.time()) if sent_at is None else sent_at,
-            **sections,
-        }
-    )
-
-
-def gather_context(
-    config: FrigateConfig,
-    stats_emitter: "StatsEmitter | None",
-    notice_registry: "NoticeRegistry | None",
-) -> ReportContext:
-    return ReportContext(
-        config=config,
-        stats=stats_emitter.get_latest_stats() if stats_emitter is not None else {},
-        notice_stats=notice_registry.stats() if notice_registry is not None else [],
-    )
-
-
-def preview_report(
-    config: FrigateConfig,
-    stats_emitter: "StatsEmitter | None",
-    notice_registry: "NoticeRegistry | None",
-) -> AnalyticsReport:
-    """The report the next send would carry, without sending it."""
-    state = load_state()
-    ctx = gather_context(config, stats_emitter, notice_registry)
-    return build_report(ctx, state.install_id if state else PREVIEW_INSTALL_ID)

frigate/analytics/reporter.py

@@ -1,177 +0,0 @@
-"""Send one analytics report a day while the admin has opted in."""
-
-import logging
-import random
-import threading
-import time
-from collections.abc import Callable
-from multiprocessing.synchronize import Event as MpEvent
-from typing import TYPE_CHECKING
-
-from frigate.analytics.context import ReportContext
-from frigate.analytics.report import build_report
-from frigate.analytics.state import (
-    STATE_PATH,
-    AnalyticsState,
-    delete_state,
-    load_state,
-    new_state,
-    save_state,
-)
-from frigate.analytics.transport import SendOutcome, send_report
-from frigate.config import FrigateConfig
-from frigate.config.holder import ConfigHolder
-from frigate.const import ANALYTICS_URL
-from frigate.notices import raise_notice, resolve_notice
-
-if TYPE_CHECKING:
-    from frigate.notices.registry import NoticeRegistry
-    from frigate.stats.emitter import StatsEmitter
-
-logger = logging.getLogger(__name__)
-
-WAKE_S = 10 * 60
-INTERVAL_S = 24 * 60 * 60
-JITTER_S = 60 * 60
-FIRST_DELAY_S = (15 * 60, 45 * 60)
-PROMPT_KIND = "analytics_prompt"
-
-
-class AnalyticsReporter(threading.Thread):
-    """Follows the live config, so a settings save needs no restart."""
-
-    def __init__(
-        self,
-        config_holder: ConfigHolder,
-        stats_emitter: "StatsEmitter",
-        notice_registry: "NoticeRegistry",
-        stop_event: MpEvent | threading.Event,
-        *,
-        state_path: str = STATE_PATH,
-        url: str = ANALYTICS_URL,
-        send: Callable[[str, str], SendOutcome] = send_report,
-        clock: Callable[[], float] = time.time,
-        rng: random.Random | None = None,
-    ) -> None:
-        super().__init__(name="analytics_reporter", daemon=True)
-        self.config_holder = config_holder
-        self.stats_emitter = stats_emitter
-        self.notice_registry = notice_registry
-        self.stop_event = stop_event
-        self.state_path = state_path
-        self.url = url
-        self.send = send
-        self.clock = clock
-        self.rng = rng or random.Random()
-        self.first_due = clock() + self.rng.uniform(*FIRST_DELAY_S)
-        self.interval = self._next_interval()
-        self.opted_in: bool | None = None
-        self.warned_unwritable = False
-        # a settings save runs on an API thread while a wake may be mid-attempt
-        self._lock = threading.Lock()
-        config_holder.subscribe(self._on_config)
-
-    def _next_interval(self) -> float:
-        return INTERVAL_S + self.rng.uniform(-JITTER_S, JITTER_S)
-
-    def run(self) -> None:
-        while True:
-            try:
-                self.tick()
-            except Exception:
-                logger.exception("Analytics reporter failed")
-
-            if self.stop_event.wait(WAKE_S):
-                break
-
-    def _on_config(self, config: FrigateConfig) -> None:
-        # a save can turn sharing off and back on between two wakes, so an
-        # opt-out is handled when it's saved rather than at the next wake
-        with self._lock:
-            if not config.safe_mode:
-                self._apply_consent(config.telemetry.analytics)
-
-    def _apply_consent(self, opted_in: bool) -> None:
-        # called with the lock held
-        if opted_in == self.opted_in:
-            return
-
-        self.opted_in = opted_in
-
-        if opted_in:
-            resolve_notice(PROMPT_KIND)
-        else:
-            raise_notice(PROMPT_KIND)
-            delete_state(self.state_path)
-
-    def tick(self) -> None:
-        with self._lock:
-            config = self.config_holder.config
-
-            # safe mode parses a default config where analytics reads as off,
-            # and handling that as an opt-out would delete the install ID
-            if config.safe_mode:
-                return
-
-            self._apply_consent(config.telemetry.analytics)
-
-            if not config.telemetry.analytics:
-                return
-
-            now = self.clock()
-
-            if now < self.first_due:
-                return
-
-            state = load_state(self.state_path) or new_state()
-
-            if not self._due(state.last_attempt_at, now):
-                return
-
-            # saved before sending, so a failing endpoint or a crash mid-send
-            # still waits a full interval; without saved state every boot would
-            # send under a new install ID
-            if not save_state(AnalyticsState(state.install_id, now), self.state_path):
-                if not self.warned_unwritable:
-                    logger.warning(
-                        "Analytics is on, but %s isn't writable, so no report is sent",
-                        self.state_path,
-                    )
-                    self.warned_unwritable = True
-
-                return
-
-            self.interval = self._next_interval()
-
-        # the lock stays free during the request, so a save never waits on it
-        self._send(state.install_id, now)
-
-    def _due(self, last_attempt_at: float, now: float) -> bool:
-        # a last attempt stamped in the future came from a wrong clock
-        return (
-            now >= last_attempt_at + self.interval or last_attempt_at > now + INTERVAL_S
-        )
-
-    def _send(self, install_id: str, now: float) -> None:
-        notice_stats = self.notice_registry.stats()
-        ctx = ReportContext(
-            config=self.config_holder.config,
-            stats=self.stats_emitter.get_latest_stats(),
-            notice_stats=notice_stats,
-        )
-        report = build_report(ctx, install_id, sent_at=int(now))
-        body = report.model_dump_json()
-
-        # consent can be withdrawn while the report builds
-        if not self.config_holder.config.telemetry.analytics:
-            return
-
-        if self.send(self.url, body) is not SendOutcome.accepted:
-            return
-
-        # a failed health section sent no notice counts, so they stay pending
-        if report.health is not None:
-            self.notice_registry.mark_reported(notice_stats)
-
-        logger.info("Sent the daily analytics report")
-        logger.debug("Analytics report: %s", body)

frigate/analytics/schema.py

@@ -1,407 +0,0 @@
-"""Models for the analytics report, the contract with the ingest endpoint.
-
-Every field carries a description and an x-public flag, and no field accepts
-user-entered text, so a report can't carry camera names or other free text.
-"""
-
-from enum import StrEnum
-from typing import Any
-
-from pydantic import BaseModel, ConfigDict, Field
-
-from frigate.config.camera.birdseye import BirdseyeModeEnum
-from frigate.config.camera.camera import CameraTypeEnum
-from frigate.config.camera.genai import GenAIProviderEnum, GenAIRoleEnum
-from frigate.config.classification import ModelSizeEnum
-from frigate.detectors.detector_config import ModelTypeEnum, SceneEnum
-from frigate.detectors.detector_types import DetectorTypeEnum
-from frigate.ffmpeg_presets import PRESETS_HW_ACCEL_DECODE, PRESETS_INPUT
-from frigate.notices.types import NOTICE_KINDS
-
-SCHEMA_VERSION = 1
-
-
-class ImageVariant(StrEnum):
-    standard = "standard"
-    rpi = "rpi"
-    tensorrt = "tensorrt"
-    tensorrt_jp6 = "tensorrt-jp6"
-    rocm = "rocm"
-    rk = "rk"
-    synaptics = "synaptics"
-    dev = "dev"
-    other = "other"
-
-
-class InstallType(StrEnum):
-    ha_addon = "ha_addon"
-    docker = "docker"
-    podman = "podman"
-    kubernetes = "kubernetes"
-    unknown = "unknown"
-
-
-class Arch(StrEnum):
-    x86_64 = "x86_64"
-    aarch64 = "aarch64"
-    other = "other"
-
-
-class GpuVendor(StrEnum):
-    intel = "intel"
-    amd = "amd"
-    nvidia = "nvidia"
-    rockchip = "rockchip"
-    rpi = "rpi"
-    other = "other"
-
-
-class DecodeFamily(StrEnum):
-    nvidia = "nvidia"
-    vaapi = "vaapi"
-    rkmpp = "rkmpp"
-    intel_qsv = "intel-qsv"
-    jetson = "jetson"
-    rpi = "rpi"
-    other = "other"
-
-
-class HardwareKey(StrEnum):
-    edgetpu_pci = "edgetpu:pci"
-    edgetpu_usb = "edgetpu:usb"
-    openvino_gpu = "openvino:GPU"
-    openvino_npu = "openvino:NPU"
-    onnx_amd = "onnx:amd"
-    onnx_nvidia = "onnx:nvidia"
-    tensorrt = "tensorrt"
-    hailo = "hailo"
-    memryx = "memryx"
-    deepx = "deepx"
-    rknn = "rknn"
-    axengine = "axengine"
-    synaptics = "synaptics"
-    cpu = "cpu"
-    other = "other"
-
-
-class ModelSource(StrEnum):
-    default = "default"
-    plus = "plus"
-    custom = "custom"
-
-
-class HeightBucket(StrEnum):
-    le_360 = "le_360"
-    h480 = "480"
-    h720 = "720"
-    h1080 = "1080"
-    h1440 = "1440"
-    ge_2160 = "ge_2160"
-
-
-class FpsBucket(StrEnum):
-    le_5 = "le_5"
-    f6_10 = "6_10"
-    gt_10 = "gt_10"
-
-
-class RetainBucket(StrEnum):
-    zero = "0"
-    d1_7 = "1_7"
-    d8_30 = "8_30"
-    gt_30 = "gt_30"
-
-
-class ConnectionQuality(StrEnum):
-    excellent = "excellent"
-    fair = "fair"
-    poor = "poor"
-    unusable = "unusable"
-
-
-class EnrichmentDevice(StrEnum):
-    cpu = "cpu"
-    cuda = "cuda"
-    tensorrt = "tensorrt"
-    migraphx = "migraphx"
-    openvino_cpu = "openvino_cpu"
-    openvino_gpu = "openvino_gpu"
-    openvino_npu = "openvino_npu"
-    other = "other"
-
-
-class SemanticSearchModel(StrEnum):
-    jinav1 = "jinav1"
-    jinav2 = "jinav2"
-    genai = "genai"
-
-
-class TranscriptionModel(StrEnum):
-    whisper = "whisper"
-    genai = "genai"
-
-
-class UserRole(StrEnum):
-    admin = "admin"
-    viewer = "viewer"
-    custom = "custom"
-
-
-class EnrichmentTiming(StrEnum):
-    face = "face"
-    lpr = "lpr"
-    plate_detection = "plate_detection"
-    image_embedding = "image_embedding"
-    text_embedding = "text_embedding"
-    review_description = "review_description"
-    object_description = "object_description"
-
-
-def _preset_keys(presets: dict[str, Any]) -> dict[str, str]:
-    keys = [name.removeprefix("preset-") for name in presets]
-    return {key: key for key in [*keys, "custom", "none"]}
-
-
-# built from the registries they mirror, so a new preset or notice kind reaches
-# the schema through generate_analytics_schema.py instead of a hand edit
-HwaccelKey = StrEnum("HwaccelKey", _preset_keys(PRESETS_HW_ACCEL_DECODE))  # type: ignore[misc]
-InputPresetKey = StrEnum("InputPresetKey", _preset_keys(PRESETS_INPUT))  # type: ignore[misc]
-NoticeKindKey = StrEnum(  # type: ignore[misc]
-    "NoticeKindKey",
-    {key: key for key, kind in NOTICE_KINDS.items() if kind.reportable},
-)
-
-
-class AnalyticsModel(BaseModel):
-    model_config = ConfigDict(extra="forbid")
-
-
-def metric(description: str, *, public: bool = True, **kwargs: Any) -> Any:
-    """Declare a report field; the description and flag land in the schema."""
-    return Field(
-        description=description, json_schema_extra={"x-public": public}, **kwargs
-    )
-
-
-def count(description: str) -> Any:
-    return metric(description, ge=0)
-
-
-class InstallSection(AnalyticsModel):
-    version: str = metric("Frigate version string", max_length=32)
-    image_variant: ImageVariant = metric("Published image the install runs")
-    install_type: InstallType = metric("How Frigate is installed")
-    arch: Arch = metric("CPU architecture")
-    kernel: str = metric(
-        "Host kernel as major.minor", pattern=r"^(\d{1,3}\.\d{1,3}|unknown)$"
-    )
-    run_as_root: bool = metric("Whether the main process runs as root")
-
-
-class GpuInfo(AnalyticsModel):
-    vendor: GpuVendor = metric("GPU vendor")
-    name: str = metric("GPU name as the hardware reports it", max_length=64)
-
-
-class StorageInfo(AnalyticsModel):
-    record_fs: str = metric("Filesystem of the recordings volume", max_length=16)
-    record_total_gb: int = count("Size of the recordings volume in GB")
-    record_used_pct: int = metric(
-        "Percent of the recordings volume in use", ge=0, le=100
-    )
-
-
-class HardwareSection(AnalyticsModel):
-    cpu_model: str = metric(
-        "CPU or board model as the hardware reports it", max_length=64
-    )
-    cpu_cores: int = count("Logical CPU count")
-    memory_gb: int = count("Total memory in GB")
-    gpus: list[GpuInfo] = metric("GPUs the stats collector found")
-    decode_families: list[DecodeFamily] = metric(
-        "Hardware decode families this system can use"
-    )
-    detection_hardware: dict[HardwareKey, int] = metric(
-        "Detection hardware found, as unit counts by kind"
-    )
-    storage: StorageInfo = metric("Recordings storage")
-
-
-class DetectionModel(AnalyticsModel):
-    detector: DetectorTypeEnum = metric("Detector type the model runs on")
-    devices: int = count("Devices the model runs on")
-    model_type: ModelTypeEnum = metric("Model architecture")
-    input: str = metric("Model input size as WxH", pattern=r"^\d{1,5}x\d{1,5}$")
-    source: ModelSource = metric("Where the model came from", public=False)
-    inference_ms: float | None = metric(
-        "Mean inference time across the model's detector processes, null before the first stats",
-        ge=0,
-    )
-
-
-class DetectionSection(AnalyticsModel):
-    models: dict[SceneEnum, DetectionModel] = metric("Detection models keyed by scene")
-    detection_fps: float = metric("Detections per second across cameras", ge=0)
-    skipped_fps: float = metric("Frames per second skipped across cameras", ge=0)
-
-
-class RetainDays(AnalyticsModel):
-    continuous: dict[RetainBucket, int] = metric(
-        "Recording cameras by continuous retention in days"
-    )
-    motion: dict[RetainBucket, int] = metric(
-        "Recording cameras by motion retention in days"
-    )
-    alerts: dict[RetainBucket, int] = metric(
-        "Recording cameras by alert retention in days"
-    )
-    detections: dict[RetainBucket, int] = metric(
-        "Recording cameras by detection retention in days"
-    )
-
-
-class CamerasSection(AnalyticsModel):
-    total: int = count("Configured cameras")
-    enabled: int = count("Enabled cameras")
-    types: dict[CameraTypeEnum, int] = metric("Cameras by type")
-    detect_height: dict[HeightBucket, int] = metric(
-        "Cameras by detect resolution height"
-    )
-    detect_fps: dict[FpsBucket, int] = metric("Cameras by detect fps")
-    hwaccel: dict[HwaccelKey, int] = metric(
-        "Cameras by the resolved hwaccel preset of the detect input"
-    )
-    input_preset: dict[InputPresetKey, int] = metric(
-        "Cameras by the input preset of the detect input"
-    )
-    go2rtc_restream: int = count("Cameras with an input from the go2rtc restream")
-    separate_detect_stream: int = count(
-        "Cameras whose detect input differs from their record input"
-    )
-    detect: int = count("Cameras with detection on")
-    record: int = count("Cameras with recording on")
-    sub_stream_record: int = count("Cameras with sub stream recording on")
-    snapshots: int = count("Cameras with snapshots on")
-    audio: int = count("Cameras with audio detection on")
-    audio_transcription: int = count("Cameras with audio transcription on")
-    birdseye: int = count("Cameras in birdseye")
-    onvif: int = count("Cameras with an ONVIF host")
-    autotracking: int = count("Cameras with PTZ autotracking on")
-    face_recognition: int = count("Cameras with face recognition on")
-    lpr: int = count("Cameras with license plate recognition on")
-    review_genai: int = count("Cameras with GenAI review summaries on")
-    object_genai: int = count("Cameras with GenAI object descriptions on")
-    notifications: int = count("Cameras with notifications on")
-    zones: int = count("Zones across cameras")
-    cameras_with_zones: int = count("Cameras with at least one zone")
-    motion_masks: int = count("Motion masks across cameras")
-    object_masks: int = count("Object masks across cameras")
-    connection_quality: dict[ConnectionQuality, int] = metric(
-        "Cameras by connection quality at send time"
-    )
-    retain_days: RetainDays = metric("Recording retention")
-
-
-class EnrichmentUsage(AnalyticsModel):
-    enabled: bool = metric("Whether the enrichment is on")
-    model_size: ModelSizeEnum = metric("Configured model size")
-    device: EnrichmentDevice | None = metric(
-        "Device the model loaded on, null when it isn't loaded"
-    )
-
-
-class SemanticSearchUsage(AnalyticsModel):
-    enabled: bool = metric("Whether semantic search is on")
-    model: SemanticSearchModel | None = metric(
-        "Embedding model, genai for a GenAI provider"
-    )
-    model_size: ModelSizeEnum = metric("Configured model size")
-    device: EnrichmentDevice | None = metric(
-        "Device the model loaded on, null when it isn't loaded"
-    )
-    triggers: int = count("Semantic search triggers across cameras")
-
-
-class TranscriptionUsage(AnalyticsModel):
-    enabled: bool = metric("Whether audio transcription is on")
-    model: TranscriptionModel | None = metric(
-        "Transcription model, genai for a GenAI provider"
-    )
-    model_size: ModelSizeEnum = metric("Configured model size")
-
-
-class GenAIUsage(AnalyticsModel):
-    providers: dict[GenAIProviderEnum, int] = metric(
-        "Configured GenAI providers by type"
-    )
-    roles: dict[GenAIRoleEnum, int] = metric("Configured GenAI providers by role")
-
-
-class ClassificationUsage(AnalyticsModel):
-    state: int = count("Custom state classification models")
-    object: int = count("Custom object classification models")
-
-
-class BirdseyeUsage(AnalyticsModel):
-    enabled: bool = metric("Whether birdseye is on")
-    modes: list[BirdseyeModeEnum] = metric("Birdseye modes")
-    restream: bool = metric("Whether birdseye is restreamed")
-
-
-class FeaturesSection(AnalyticsModel):
-    face_recognition: EnrichmentUsage = metric("Face recognition")
-    lpr: EnrichmentUsage = metric("License plate recognition")
-    semantic_search: SemanticSearchUsage = metric("Semantic search")
-    audio_transcription: TranscriptionUsage = metric("Audio transcription")
-    genai: GenAIUsage = metric("Generative AI providers")
-    classification_models: ClassificationUsage = metric("Custom classification models")
-    birdseye: BirdseyeUsage = metric("Birdseye")
-    mqtt: bool = metric("Whether MQTT is on")
-    notifications: bool = metric("Whether web push notifications are on")
-    auth: bool = metric("Whether authentication is on")
-    proxy_auth: bool = metric("Whether a proxy supplies the user header")
-    tls: bool = metric("Whether TLS is on")
-    users: dict[UserRole, int] = metric("Users by role")
-    camera_groups: int = count("Camera groups")
-    profiles: int = count("Profiles")
-    plus_api_key: bool = metric("Whether a Frigate+ API key is set", public=False)
-
-
-class NoticeCounts(AnalyticsModel):
-    occurrences: int = count("Occurrences since the last accepted report")
-    dismissals: int = count("Dismissals since the last accepted report")
-
-
-class HealthSection(AnalyticsModel):
-    uptime_hours: int = count("Hours since Frigate started")
-    cpu_percent: int = metric("System CPU use at send time", ge=0, le=100)
-    enrichment_ms: dict[EnrichmentTiming, float] = metric(
-        "Mean enrichment inference times in milliseconds"
-    )
-    retention_unmet: bool = metric(
-        "Whether storage can't keep the configured retention"
-    )
-    notices: dict[NoticeKindKey, NoticeCounts] = metric(
-        "Notice counts by kind since the last accepted report", public=False
-    )
-
-
-class AnalyticsReport(AnalyticsModel):
-    schema_version: int = metric("Report format version", ge=1)
-    install_id: str = metric(
-        "Random install identifier", public=False, pattern=r"^[0-9a-f]{32}$"
-    )
-    report_id: str = metric(
-        "Random identifier of this report",
-        public=False,
-        pattern=r"^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$",
-    )
-    sent_at: int = count("Unix time the report was built")
-    install: InstallSection | None = metric("Install, null if its collector failed")
-    hardware: HardwareSection | None = metric("Hardware, null if its collector failed")
-    detection: DetectionSection | None = metric(
-        "Object detection, null if its collector failed"
-    )
-    cameras: CamerasSection | None = metric("Cameras, null if its collector failed")
-    features: FeaturesSection | None = metric("Features, null if its collector failed")
-    health: HealthSection | None = metric("Health, null if its collector failed")

frigate/analytics/state.py

@@ -1,85 +0,0 @@
-"""The install's analytics identity and last attempt time, kept under /config."""
-
-import json
-import logging
-import os
-import re
-from dataclasses import dataclass
-from uuid import uuid4
-
-from frigate.const import CONFIG_DIR
-
-logger = logging.getLogger(__name__)
-
-STATE_PATH = os.path.join(CONFIG_DIR, ".analytics.json")
-INSTALL_ID_PATTERN = re.compile(r"[0-9a-f]{32}")
-
-
-@dataclass(frozen=True)
-class AnalyticsState:
-    install_id: str
-    last_attempt_at: float
-
-
-def new_state() -> AnalyticsState:
-    return AnalyticsState(install_id=uuid4().hex, last_attempt_at=0.0)
-
-
-def load_state(path: str = STATE_PATH) -> AnalyticsState | None:
-    """The saved state, or None when the file is missing or unusable."""
-    try:
-        with open(path) as f:
-            data = json.load(f)
-    except FileNotFoundError:
-        return None
-    except (OSError, ValueError):
-        logger.warning("Ignoring unreadable analytics state at %s", path)
-        return None
-
-    if not isinstance(data, dict):
-        return None
-
-    install_id = data.get("install_id")
-    last_attempt_at = data.get("last_attempt_at")
-
-    if (
-        not isinstance(install_id, str)
-        or not INSTALL_ID_PATTERN.fullmatch(install_id)
-        or isinstance(last_attempt_at, bool)
-        or not isinstance(last_attempt_at, int | float)
-    ):
-        logger.warning("Ignoring invalid analytics state at %s", path)
-        return None
-
-    return AnalyticsState(install_id=install_id, last_attempt_at=float(last_attempt_at))
-
-
-def save_state(state: AnalyticsState, path: str = STATE_PATH) -> bool:
-    """Write the state atomically, returning False when it can't be written."""
-    temp_path = f"{path}.tmp"
-
-    try:
-        with open(temp_path, "w") as f:
-            json.dump(
-                {
-                    "install_id": state.install_id,
-                    "last_attempt_at": state.last_attempt_at,
-                },
-                f,
-            )
-
-        os.replace(temp_path, path)
-    except OSError:
-        return False
-
-    return True
-
-
-def delete_state(path: str = STATE_PATH) -> None:
-    """Forget the install ID, so a later opt-in starts a fresh identity."""
-    try:
-        os.remove(path)
-    except FileNotFoundError:
-        pass
-    except OSError:
-        logger.warning("Unable to delete analytics state at %s", path)

frigate/analytics/transport.py

@@ -1,54 +0,0 @@
-"""POST a report to the ingest endpoint."""
-
-import logging
-from enum import Enum
-
-import requests
-
-from frigate.version import VERSION
-
-logger = logging.getLogger(__name__)
-
-TIMEOUT_S = 30
-
-
-class SendOutcome(Enum):
-    accepted = "accepted"
-    rejected = "rejected"
-    rate_limited = "rate_limited"
-    failed = "failed"
-
-
-def send_report(url: str, body: str) -> SendOutcome:
-    """Send one report. Never raises; the outcome says what happened."""
-    try:
-        # a redirect would turn the POST into a GET, so it counts as a failure
-        response = requests.post(
-            url,
-            data=body.encode(),
-            headers={
-                "Content-Type": "application/json",
-                "User-Agent": f"Frigate/{VERSION}",
-            },
-            timeout=TIMEOUT_S,
-            allow_redirects=False,
-        )
-    except requests.RequestException as err:
-        logger.warning("Unable to send the analytics report: %s", err)
-        return SendOutcome.failed
-
-    status = response.status_code
-
-    if 200 <= status < 300:
-        return SendOutcome.accepted
-
-    if status == 400:
-        logger.warning("The analytics report was rejected: %s", response.text[:200])
-        return SendOutcome.rejected
-
-    if status == 429:
-        logger.debug("The analytics endpoint is rate limiting this install")
-        return SendOutcome.rate_limited
-
-    logger.warning("The analytics endpoint returned %s", status)
-    return SendOutcome.failed

frigate/api/analytics.py

@@ -1,25 +0,0 @@
-"""Analytics APIs."""
-
-import logging
-
-from fastapi import APIRouter, Depends, Request
-from fastapi.responses import JSONResponse
-
-from frigate.analytics.report import preview_report
-from frigate.api.auth import require_role
-from frigate.api.defs.tags import Tags
-
-logger = logging.getLogger(__name__)
-
-router = APIRouter(tags=[Tags.analytics])
-
-
-@router.get("/analytics/preview", dependencies=[Depends(require_role(["admin"]))])
-def get_analytics_preview(request: Request) -> JSONResponse:
-    """Get the analytics report Frigate would send next, without sending it."""
-    report = preview_report(
-        request.app.frigate_config,
-        request.app.stats_emitter,
-        request.app.notice_registry,
-    )
-    return JSONResponse(content=report.model_dump(mode="json"))

frigate/api/app.py

@@ -63,6 +63,7 @@ from frigate.util.builtin import (
     flatten_config_data,
     load_labels,
     process_config_query_string,
+    split_config_key_path,
     update_yaml_file_bulk,
 )
 from frigate.util.config import (
@@ -70,6 +71,10 @@ from frigate.util.config import (
     find_config_file,
     redact_credential,
 )
+from frigate.util.live_streams import (
+    generated_transcode_streams,
+    sync_transcode_streams,
+)
 from frigate.util.object_names import get_categorized_object_names
 from frigate.util.schema import get_config_schema
 from frigate.util.services import (
@@ -393,6 +398,11 @@ def config(request: Request):
         model_dict["non_logo_attributes"] = model.non_logo_attributes
         model_dict["labelmap"] = model.merged_labelmap
 
+        # report the configured reference rather than the resolved cache path,
+        # so saving the config back doesn't lose the Frigate+ model
+        if model.plus_id:
+            model_dict["path"] = f"plus://{model.plus_id}"
+
         if not config["plus"]["enabled"]:
             continue
 
@@ -429,6 +439,8 @@ def ffmpeg_presets():
         hwaccel_presets = [
             "preset-rpi-64-h264",
             "preset-rpi-64-h265",
+            "preset-apple-silicon-h264",
+            "preset-apple-silicon-h265",
             "preset-jetson-h264",
             "preset-jetson-h265",
             "preset-rkmpp",
@@ -808,6 +820,17 @@ def _config_set_in_memory(request: Request, body: AppConfigSetBody) -> JSONRespo
         )
 
 
+def _config_path_exists(data: Any, key_path: str) -> bool:
+    """Return whether a dotted config path is present in parsed yaml."""
+    for key in split_config_key_path(key_path):
+        if not isinstance(data, dict) or key not in data:
+            return False
+
+        data = data[key]
+
+    return True
+
+
 @router.put("/config/set", dependencies=[Depends(require_role(["admin"]))])
 def config_set(request: Request, body: AppConfigSetBody):
     config_file = find_config_file()
@@ -862,6 +885,19 @@ def config_set(request: Request, body: AppConfigSetBody):
                         status_code=400,
                     )
 
+                # delete replaced paths first so their maps are rewritten in
+                # the order sent; update_yaml would otherwise keep old order
+                if body.replace_paths:
+                    old_yaml = ruamel.yaml.YAML(typ="safe").load(old_raw_config) or {}
+                    updates = {
+                        **{
+                            path: ""
+                            for path in body.replace_paths
+                            if _config_path_exists(old_yaml, path)
+                        },
+                        **updates,
+                    }
+
                 # apply all updates in a single operation
                 update_yaml_file_bulk(config_file, updates)
 
@@ -925,9 +961,15 @@ def config_set(request: Request, body: AppConfigSetBody):
             if request.app.dispatcher is not None:
                 request.app.dispatcher.clear_runtime_state_for_yaml_keys(updates.keys())
 
+            go2rtc_synced = True
+
             if body.requires_restart == 0 or body.update_topic:
                 old_config: FrigateConfig = request.app.frigate_config
                 swap_runtime_config(request.app, config)
+                go2rtc_synced = sync_transcode_streams(
+                    generated_transcode_streams(old_config),
+                    generated_transcode_streams(config),
+                )
 
                 if body.update_topic:
                     if body.update_topic.startswith("config/cameras/"):
@@ -987,6 +1029,7 @@ def config_set(request: Request, body: AppConfigSetBody):
                             if body.requires_restart == 0
                             else "Config successfully updated, restart to apply"
                         ),
+                        "go2rtc_synced": go2rtc_synced,
                     }
                 ),
                 status_code=200,

frigate/api/auth.py

@@ -130,23 +130,18 @@ def require_admin_by_default():
         if path.startswith(EXEMPT_PREFIXES):
             return
 
-        # Dynamic camera path exemption:
-        # Any path whose first segment matches a configured camera name should
-        # bypass the global admin requirement. These endpoints enforce access
-        # via route-level dependencies (e.g. require_camera_access) to ensure
-        # per-camera authorization. This allows non-admin authenticated users
-        # (e.g. viewer role) to access camera-specific resources without
-        # needing admin privileges.
-        try:
-            if path.startswith("/"):
-                first_segment = path.split("/", 2)[1]
+        # Camera routes enforce per-camera access via route-level dependencies
+        # (e.g. require_camera_access). Match on the route template, not the raw
+        # path, so a camera named like another namespace (e.g. "faces") can't
+        # waive the admin check for that namespace's routes.
+        route = request.scope.get("route")
         if (
-                    first_segment
-                    and first_segment in request.app.frigate_config.cameras
+            route is not None
+            and route.path.startswith("/{camera_name}")
+            and request.path_params.get("camera_name")
+            in request.app.frigate_config.cameras
         ):
             return
-        except Exception:
-            pass
 
         # For all other paths, require admin role
         # Internal port requests have admin role set automatically
@@ -324,11 +319,17 @@ def get_remote_addr(request: Request):
             network = ipaddress.ip_network(proxy)
         except ValueError:
             logger.warning(f"Unable to parse trusted network: {proxy}")
+            continue
         trusted_proxies.append(network)
 
     # return the first remote address that is not trusted
     for addr in route:
+        try:
             ip = ipaddress.ip_address(addr.strip())
+        except ValueError:
+            logger.debug("Invalid address in X-Forwarded-For header")
+            return direct_addr or "127.0.0.1"
+
         logger.debug(f"Checking {ip} (v{ip.version})")
         trusted = False
         for trusted_proxy in trusted_proxies:
@@ -473,12 +474,11 @@ def create_encoded_jwt(user, role, expiration, secret):
 
 def set_jwt_cookie(response: Response, cookie_name, encoded_jwt, max_age, secure):
     # TODO: ideally this would set secure as well, but that requires TLS
-    # SameSite is intentionally left unset (browsers default to Lax). Setting
-    # SameSite=Lax/Strict would stop the cookie from being sent in cross-origin
-    # iframes, breaking embedded views such as the Home Assistant Frigate card.
-    # CSRF is instead mitigated by requiring a custom X-CSRF-TOKEN header, which
-    # cross-origin pages cannot set without a CORS preflight that Frigate never
-    # grants (see check_csrf in api/fastapi_app.py).
+    # Starlette sets SameSite=Lax by default. The cookie is still sent to
+    # same-site iframes (e.g. Home Assistant on the same host or domain), but
+    # not to cross-site ones. CSRF is also mitigated by requiring a custom
+    # X-CSRF-TOKEN header, which cross-origin pages cannot set without a CORS
+    # preflight that Frigate never grants (see check_csrf in api/fastapi_app.py).
     response.set_cookie(
         key=cookie_name,
         value=encoded_jwt,

frigate/api/camera.py

@@ -39,6 +39,11 @@ from frigate.util.builtin import clean_camera_user_pass, get_record_segment_time
 from frigate.util.camera_cleanup import cleanup_camera_db, cleanup_camera_files
 from frigate.util.config import find_config_file
 from frigate.util.image import run_ffmpeg_snapshot
+from frigate.util.live_streams import (
+    generated_transcode_streams,
+    measure_stream_bitrate,
+    sync_transcode_streams,
+)
 from frigate.util.services import (
     analyze_record_keyframes,
     ffprobe_stream,
@@ -248,6 +253,34 @@ def go2rtc_delete_stream(stream_name: str):
         )
 
 
+@router.get(
+    "/go2rtc/streams/{stream_name}/bitrate",
+    dependencies=[Depends(require_role(["admin"]))],
+)
+async def go2rtc_stream_bitrate(request: Request, stream_name: str):
+    """Measure a go2rtc stream's bitrate over a few seconds."""
+    config: FrigateConfig = request.app.frigate_config
+    known = set(config.go2rtc.model_dump().get("streams") or {}) | set(
+        generated_transcode_streams(config)
+    )
+
+    if stream_name not in known:
+        return JSONResponse(
+            content={"success": False, "message": "Unknown stream"},
+            status_code=404,
+        )
+
+    kbps = await asyncio.to_thread(measure_stream_bitrate, stream_name)
+
+    if kbps is None:
+        return JSONResponse(
+            content={"success": False, "message": "Stream sent no data"},
+            status_code=502,
+        )
+
+    return JSONResponse(content={"success": True, "kbps": round(kbps)})
+
+
 @router.get("/ffprobe", dependencies=[Depends(require_role(["admin"]))])
 def ffprobe(request: Request, paths: str = "", detailed: bool = False):
     path_param = paths
@@ -1342,6 +1375,12 @@ async def delete_camera(
     except Exception:
         logger.debug("Failed to remove go2rtc stream for %s", camera_name)
 
+    await asyncio.to_thread(
+        sync_transcode_streams,
+        generated_transcode_streams(frigate_config),
+        generated_transcode_streams(request.app.frigate_config),
+    )
+
     return JSONResponse(
         content={
             "success": True,

frigate/api/config_util.py

@@ -51,6 +51,7 @@ def swap_runtime_config(app: FastAPI, config: FrigateConfig) -> None:
 
     if app.stats_emitter is not None:
         app.stats_emitter.config = config
+        app.stats_emitter.hardware_stats.set_config(config)
 
     if app.dispatcher is not None:
         app.dispatcher.config = config

frigate/api/defs/query/events_query_parameters.py

@@ -14,6 +14,7 @@ class EventsQueryParams(BaseModel):
     zone: str | None = "all"
     zones: str | None = "all"
     limit: int | None = 100
+    offset: int | None = Field(0, ge=0)
     after: float | None = None
     before: float | None = None
     time_range: str | None = DEFAULT_TIME_RANGE
@@ -55,6 +56,7 @@ class EventsSearchQueryParams(BaseModel):
         deprecated=True,
     )
     limit: int | None = 50
+    offset: int | None = Field(0, ge=0)
     cameras: str | None = "all"
     labels: str | None = "all"
     sub_labels: str | None = "all"

frigate/api/defs/request/app_body.py

@@ -10,6 +10,8 @@ class AppConfigSetBody(BaseModel):
     update_topic: str | None = None
     config_data: dict[str, Any] | None = None
     skip_save: bool = False
+    # paths rewritten whole, so a map saves in the order sent
+    replace_paths: list[str] = Field(default_factory=list)
 
 
 class GenAIProbeBody(BaseModel):

frigate/api/defs/tags.py

@@ -2,7 +2,6 @@ from enum import Enum
 
 
 class Tags(Enum):
-    analytics = "Analytics"
     app = "App"
     auth = "Auth"
     camera = "Camera"

frigate/api/event.py

@@ -129,6 +129,7 @@ def events(
         zones = zone
 
     limit = params.limit
+    offset = params.offset
     after = params.after
     before = params.before
     time_range = params.time_range
@@ -361,11 +362,15 @@ def events(
     else:
         order_by = Event.start_time.desc()
 
+    # offset paging needs a stable order when scores or speeds tie
+    tiebreaker = [Event.id] if sort and sort.startswith(("score", "speed")) else []
+
     events = (
         Event.select(*selected_columns)
         .where(reduce(operator.and_, clauses))
-        .order_by(order_by)
+        .order_by(order_by, *tiebreaker)
         .limit(limit)
+        .offset(offset)
         .dicts()
         .iterator()
     )
@@ -534,6 +539,7 @@ def events_search(
     search_type = params.search_type
     include_thumbnails = params.include_thumbnails
     limit = params.limit
+    offset = params.offset
     sort = params.sort
 
     # Filters
@@ -840,6 +846,9 @@ def events_search(
     if search_results:
         events_query = events_query.where(Event.id << list(search_results.keys()))
 
+    # sorts below are stable, so this orders ties for offset paging
+    events_query = events_query.order_by(Event.id)
+
     # Fetch events and process them in a single pass
     processed_events = []
     for event in events_query.dicts():
@@ -897,7 +906,7 @@ def events_search(
         processed_events.sort(key=lambda x: x["start_time"], reverse=True)
 
     # Limit the number of events returned
-    processed_events = processed_events[:limit]
+    processed_events = processed_events[offset:][:limit]
 
     return JSONResponse(content=processed_events)
 

frigate/api/fastapi_app.py

@@ -12,8 +12,8 @@ from slowapi.middleware import SlowAPIMiddleware
 from starlette_context import middleware, plugins
 from starlette_context.plugins import Plugin
 
+from frigate.api import app as main_app
 from frigate.api import (
-    analytics,
     auth,
     camera,
     chat,
@@ -30,7 +30,6 @@ from frigate.api import (
     record,
     review,
 )
-from frigate.api import app as main_app
 from frigate.api.auth import get_jwt_secret, limiter, require_admin_by_default
 from frigate.comms.dispatcher import Dispatcher
 from frigate.comms.event_metadata_updater import (
@@ -141,7 +140,6 @@ def create_fastapi_app(
 
     # Routes
     # Order of include_router matters: https://fastapi.tiangolo.com/tutorial/path-params/#order-matters
-    app.include_router(analytics.router)
     app.include_router(auth.router)
     app.include_router(camera.router)
     app.include_router(chat.router)

frigate/api/media.py

@@ -63,7 +63,6 @@ from frigate.util.recording_coverage import (
     null_audio_glitches,
     plan_clip,
     resolve_coverage,
-    stream_has_audio,
 )
 
 logger = logging.getLogger(__name__)
@@ -681,15 +680,10 @@ async def _vod_response(
         end_ts,
         force_discontinuity,
     )
-    intervals = resolve_coverage(camera_name, start_ts, end_ts)
-
     # rows contradicting their stream's audio composition are
     # truncated-shutdown glitches
-    main_audio = stream_has_audio(intervals, main=True)
-    sub_audio = stream_has_audio(intervals, main=False)
-
     spans = build_spans(
-        null_audio_glitches(intervals, main_audio, sub_audio),
+        null_audio_glitches(resolve_coverage(camera_name, start_ts, end_ts)),
         stream_preference,
     )
 

frigate/api/notices.py

@@ -14,17 +14,18 @@ router = APIRouter(tags=[Tags.notices])
 
 
 @router.get("/notices", dependencies=[Depends(require_role(["admin"]))])
-def get_notices(request: Request, include_dismissed: bool = False) -> JSONResponse:
+def get_notices(request: Request, include_hidden: bool = False) -> JSONResponse:
     """Get notices, most severe first.
 
     Args:
-        include_dismissed: Also return dismissed notices, for the history view
+        include_hidden: Also return acknowledged and muted notices, for the
+            hidden list
 
     Returns:
         The notices
     """
     return JSONResponse(
-        content=request.app.notice_registry.active(include_dismissed=include_dismissed)
+        content=request.app.notice_registry.active(include_hidden=include_hidden)
     )
 
 
@@ -34,37 +35,64 @@ def get_notice_stats(request: Request) -> JSONResponse:
     return JSONResponse(content=request.app.notice_registry.stats())
 
 
-@router.get(
-    "/notices/dismissed_checks", dependencies=[Depends(require_role(["admin"]))]
-)
-def get_dismissed_checks(request: Request) -> JSONResponse:
-    """Get the dismissed config and stream check rows, newest first."""
-    return JSONResponse(content=request.app.notice_registry.dismissed_checks())
+@router.get("/notices/muted_checks", dependencies=[Depends(require_role(["admin"]))])
+def get_muted_checks(request: Request) -> JSONResponse:
+    """Get the muted config and stream check rows, newest first."""
+    return JSONResponse(content=request.app.notice_registry.muted_checks())
 
 
-@router.delete("/notices/dismissed", dependencies=[Depends(require_role(["admin"]))])
-def purge_dismissed(request: Request) -> JSONResponse:
-    """Delete every dismissed notice and check row so each can show again."""
-    request.app.notice_registry.purge_dismissed()
-    return JSONResponse(
-        content={"success": True, "message": "Dismissed notices cleared"}
-    )
+@router.delete("/notices/hidden", dependencies=[Depends(require_role(["admin"]))])
+def unhide_all_notices(request: Request) -> JSONResponse:
+    """Show every acknowledged and muted notice and check row again."""
+    request.app.notice_registry.unhide_all()
+    return JSONResponse(content={"success": True, "message": "Notices shown again"})
 
 
 # model notice ids contain a slash, so the id is a path parameter
 @router.post(
-    "/notices/{notice_id:path}/dismiss",
+    "/notices/{notice_id:path}/acknowledge",
     dependencies=[Depends(require_role(["admin"]))],
 )
-def dismiss_notice(request: Request, notice_id: str) -> JSONResponse:
-    """Hide a notice or a config or stream check row.
+def acknowledge_notice(request: Request, notice_id: str) -> JSONResponse:
+    """Hide a notice until it happens again.
 
-    It stays hidden if the same problem happens again.
+    Config and stream check rows and the update notice never repeat, so they
+    can only be muted.
     """
-    if not request.app.notice_registry.dismiss(notice_id):
+    if not request.app.notice_registry.acknowledge(notice_id):
         return JSONResponse(
             content={"success": False, "message": "Notice not found"},
             status_code=404,
         )
 
-    return JSONResponse(content={"success": True, "message": "Notice dismissed"})
+    return JSONResponse(content={"success": True, "message": "Notice acknowledged"})
+
+
+@router.post(
+    "/notices/{notice_id:path}/mute",
+    dependencies=[Depends(require_role(["admin"]))],
+)
+def mute_notice(request: Request, notice_id: str) -> JSONResponse:
+    """Hide a notice or a config or stream check row for good."""
+    if not request.app.notice_registry.mute(notice_id):
+        return JSONResponse(
+            content={"success": False, "message": "Notice not found"},
+            status_code=404,
+        )
+
+    return JSONResponse(content={"success": True, "message": "Notice muted"})
+
+
+@router.delete(
+    "/notices/{notice_id:path}/hidden",
+    dependencies=[Depends(require_role(["admin"]))],
+)
+def unhide_notice(request: Request, notice_id: str) -> JSONResponse:
+    """Show an acknowledged or muted notice or check row again."""
+    if not request.app.notice_registry.unhide(notice_id):
+        return JSONResponse(
+            content={"success": False, "message": "Notice not found"},
+            status_code=404,
+        )
+
+    return JSONResponse(content={"success": True, "message": "Notice shown again"})

frigate/api/review.py

@@ -44,6 +44,22 @@ logger = logging.getLogger(__name__)
 router = APIRouter(tags=[Tags.review])
 
 
+def get_label_clause(label: str, include_audio: bool = True):
+    """Build a clause matching a label within a review segment's data.
+
+    Verified objects are stored with a `-verified` suffix (eg. `person-verified`)
+    so that variant is matched as well.
+    """
+    clause = (ReviewSegment.data["objects"].cast("text") % f'*"{label}"*') | (
+        ReviewSegment.data["objects"].cast("text") % f'*"{label}-verified"*'
+    )
+
+    if include_audio:
+        clause |= ReviewSegment.data["audio"].cast("text") % f'*"{label}"*'
+
+    return clause
+
+
 @router.get(
     "/review",
     response_model=list[ReviewSegmentResponse],
@@ -93,10 +109,7 @@ async def review(
         filtered_labels = labels.split(",")
 
         for label in filtered_labels:
-            label_clauses.append(
-                (ReviewSegment.data["objects"].cast("text") % f'*"{label}"*')
-                | (ReviewSegment.data["audio"].cast("text") % f'*"{label}"*')
-            )
+            label_clauses.append(get_label_clause(label))
         clauses.append(reduce(operator.or_, label_clauses))
 
     if zones != "all":
@@ -239,10 +252,7 @@ async def review_summary(
         filtered_labels = labels.split(",")
 
         for label in filtered_labels:
-            label_clauses.append(
-                (ReviewSegment.data["objects"].cast("text") % f'*"{label}"*')
-                | (ReviewSegment.data["audio"].cast("text") % f'*"{label}"*')
-            )
+            label_clauses.append(get_label_clause(label))
         clauses.append(reduce(operator.or_, label_clauses))
     if zones != "all":
         # use matching so segments with multiple zones
@@ -340,9 +350,8 @@ async def review_summary(
         filtered_labels = labels.split(",")
 
         for label in filtered_labels:
-            label_clauses.append(
-                ReviewSegment.data["objects"].cast("text") % f'*"{label}"*'
-            )
+            label_clauses.append(get_label_clause(label, include_audio=False))
+
         clauses.append(reduce(operator.or_, label_clauses))
 
     # Find the time range of available data

frigate/app.py

@@ -15,7 +15,6 @@ import uvicorn
 from peewee_migrate import Router
 from playhouse.sqlite_ext import SqliteExtDatabase
 
-from frigate.analytics.reporter import AnalyticsReporter
 from frigate.api.auth import hash_password
 from frigate.api.fastapi_app import create_fastapi_app
 from frigate.camera import CameraMetrics, PTZMetrics
@@ -50,7 +49,6 @@ from frigate.debug_replay import (
     DebugReplayManager,
     cleanup_replay_cameras,
 )
-from frigate.detectors.detector_config import SceneEnum
 from frigate.detectors.detector_types import api_types
 from frigate.detectors.device import build_detector_config, runner_names
 from frigate.embeddings import EmbeddingProcess, EmbeddingsContext
@@ -109,7 +107,7 @@ class FrigateApp:
         self.metrics_manager = manager
         self.audio_process: mp.Process | None = None
         self.stop_event = stop_event
-        self.detection_queues: dict[SceneEnum, Queue] = {
+        self.detection_queues: dict[str, Queue] = {
             model.scene: mp.Queue() for model in config.models
         }
         self.detectors: dict[str, ObjectDetectProcess] = {}
@@ -396,7 +394,7 @@ class FrigateApp:
                 logger.error("Unable to prepare the %s runtime: %s", detector_type, err)
 
     def start_detectors(self) -> None:
-        model_cameras: dict[SceneEnum, list[str]] = {
+        model_cameras: dict[str, list[str]] = {
             model.scene: [] for model in self.config.models
         }
 
@@ -530,15 +528,6 @@ class FrigateApp:
         )
         self.stats_emitter.start()
 
-    def start_analytics_reporter(self) -> None:
-        self.analytics_reporter = AnalyticsReporter(
-            self.config_holder,
-            self.stats_emitter,
-            self.notice_registry,
-            self.stop_event,
-        )
-        self.analytics_reporter.start()
-
     def start_watchdog(self) -> None:
         self.frigate_watchdog = FrigateWatchdog(self.detectors, self.stop_event)
 
@@ -690,7 +679,6 @@ class FrigateApp:
         self.start_audio_processor()
         self.start_storage_maintainer()
         self.start_stats_emitter()
-        self.start_analytics_reporter()
         self.start_timeline_processor()
         self.start_event_processor()
         self.start_event_cleanup()
@@ -790,8 +778,6 @@ class FrigateApp:
         self.event_cleanup.join()
         self.record_cleanup.join()
         self.stats_emitter.join()
-        # a send in flight can hold the thread for the whole request timeout
-        self.analytics_reporter.join(timeout=5)
         self.frigate_watchdog.join()
         self.camera_maintainer.join()
         self.db.stop()

frigate/camera/activity_manager.py

@@ -104,12 +104,13 @@ class CameraActivityManager:
         all_objects: list[dict[str, Any]] = []
 
         for camera in new_activity.keys():
-            if camera not in self.config.cameras:
+            camera_config = self.config.cameras.get(camera)
+            if camera_config is None:
                 continue
 
             # handle cameras that were added dynamically
             if camera not in self.camera_all_object_counts:
-                self.__init_camera(self.config.cameras[camera])
+                self.__init_camera(camera_config)
 
             new_objects = new_activity[camera].get("objects", [])
             all_objects.extend(new_objects)
@@ -234,12 +235,13 @@ class AudioActivityManager:
         now = datetime.datetime.now().timestamp()
 
         for camera in new_activity.keys():
-            if camera not in self.config.cameras:
+            camera_config = self.config.cameras.get(camera)
+            if camera_config is None:
                 continue
 
             # handle cameras that were added dynamically
             if camera not in self.current_audio_detections:
-                self.__init_camera(self.config.cameras[camera])
+                self.__init_camera(camera_config)
 
             new_detections = new_activity[camera].get("detections", [])
             if self.compare_audio_activity(camera, new_detections, now):

frigate/camera/maintainer.py

@@ -15,7 +15,6 @@ from frigate.config.camera.updater import (
     CameraConfigUpdateSubscriber,
 )
 from frigate.const import REPLAY_CAMERA_PREFIX
-from frigate.detectors.detector_config import SceneEnum
 from frigate.models import Regions
 from frigate.object_detection.util import detection_frame_size
 from frigate.util.builtin import empty_and_close_queue
@@ -31,7 +30,7 @@ class CameraMaintainer(threading.Thread):
     def __init__(
         self,
         config: FrigateConfig,
-        detection_queues: dict[SceneEnum, Queue],
+        detection_queues: dict[str, Queue],
         detected_frames_queue: Queue,
         camera_metrics: DictProxy,
         ptz_metrics: dict[str, PTZMetrics],

frigate/comms/detections_updater.py

@@ -12,6 +12,7 @@ class DetectionTypeEnum(str, Enum):
     video = "video"
     audio = "audio"
     lpr = "lpr"
+    classification_state = "classification_state"
 
 
 class DetectionPublisher(Publisher):

frigate/comms/dispatcher.py

@@ -782,7 +782,9 @@ class Dispatcher:
         try:
             payload = int(payload)
         except ValueError:
+            logger.warning(
                 f"Received unsupported value for motion contour area: {payload}"
+            )
             return
 
         motion_settings = self.config.cameras[camera_name].motion
@@ -799,7 +801,9 @@ class Dispatcher:
         try:
             payload = int(payload)
         except ValueError:
+            logger.warning(
                 f"Received unsupported value for motion threshold: {payload}"
+            )
             return
 
         motion_settings = self.config.cameras[camera_name].motion
@@ -814,7 +818,9 @@ class Dispatcher:
     def _on_global_notification_command(self, payload: str) -> None:
         """Callback for global notification topic."""
         if payload != "ON" and payload != "OFF":
+            logger.warning(
                 f"Received unsupported value for all notification: {payload}"
+            )
             return
 
         notification_settings = self.config.notifications

frigate/config/camera/detect.py

@@ -1,6 +1,6 @@
 from pydantic import Field, model_validator
 
-from frigate.detectors.detector_config import SceneEnum
+from frigate.detectors.detector_config import DEFAULT_SCENE, SCENE_PATTERN
 
 from ..base import FrigateBaseModel
 
@@ -62,10 +62,11 @@ class DetectConfig(FrigateBaseModel):
         title="Detect width",
         description="Width (pixels) of frames used for the detect stream; leave empty to use the native stream resolution.",
     )
-    scene: SceneEnum = Field(
-        default=SceneEnum.all,
+    scene: str = Field(
+        default=DEFAULT_SCENE,
+        pattern=SCENE_PATTERN,
         title="Detect scene",
-        description="The environment this camera looks at, used to pick which of the configured models runs on it. Cameras left on 'all' run the model configured with a scene of 'all'.",
+        description="The environment this camera looks at, used to pick which of the configured models runs on it. Cameras left on 'default' run the model configured with a scene of 'default'.",
     )
     fps: int = Field(
         default=5,

frigate/config/camera/live.py

@@ -1,8 +1,57 @@
-from pydantic import Field
+from pydantic import Field, field_validator
+
+from frigate.util.live_streams import DEFAULT_TRANSCODE_QUALITIES
 
 from ..base import FrigateBaseModel
 
-__all__ = ["CameraLiveConfig"]
+__all__ = ["CameraLiveConfig", "LiveTranscodeConfig", "LiveTranscodeQualityConfig"]
+
+
+class LiveTranscodeQualityConfig(FrigateBaseModel):
+    height: int = Field(
+        ge=144,
+        le=2160,
+        title="Height",
+        description="Output height in pixels; width follows the source aspect ratio.",
+    )
+    bitrate: int = Field(
+        ge=64,
+        title="Bitrate",
+        description="Target and maximum video bitrate in kbps.",
+    )
+
+
+class LiveTranscodeConfig(FrigateBaseModel):
+    enabled: bool = Field(
+        default=False,
+        title="Enable transcoded streams",
+        description="Add lower-quality live streams that go2rtc transcodes in real time while someone is watching.",
+    )
+    source: str | None = Field(
+        default=None,
+        title="Source stream",
+        description="go2rtc stream to transcode. Defaults to the first live stream.",
+    )
+    qualities: list[LiveTranscodeQualityConfig] = Field(
+        default_factory=lambda: [
+            LiveTranscodeQualityConfig(**quality)
+            for quality in DEFAULT_TRANSCODE_QUALITIES
+        ],
+        title="Qualities",
+        description="One transcoded stream is added per quality.",
+    )
+
+    @field_validator("qualities")
+    @classmethod
+    def validate_unique_heights(
+        cls, qualities: list[LiveTranscodeQualityConfig]
+    ) -> list[LiveTranscodeQualityConfig]:
+        heights = [quality.height for quality in qualities]
+
+        if len(heights) != len(set(heights)):
+            raise ValueError("Transcoded stream heights must be unique.")
+
+        return qualities
 
 
 class CameraLiveConfig(FrigateBaseModel):
@@ -11,6 +60,11 @@ class CameraLiveConfig(FrigateBaseModel):
         title="Live stream names",
         description="Mapping of configured stream names to restream/go2rtc names used for live playback.",
     )
+    transcode: LiveTranscodeConfig = Field(
+        default_factory=LiveTranscodeConfig,
+        title="Transcoded streams",
+        description="Lower-quality live streams transcoded on demand by go2rtc.",
+    )
     height: int = Field(
         default=720,
         title="Live height",

frigate/config/config.py

@@ -19,7 +19,7 @@ from ruamel.yaml import YAML
 
 from frigate.const import REGEX_JSON
 from frigate.detectors import ModelConfig
-from frigate.detectors.detector_config import SceneEnum
+from frigate.detectors.detector_config import DEFAULT_SCENE
 from frigate.detectors.device import DeviceParseError, DeviceSpec, parse_device
 from frigate.plus import PlusApi
 from frigate.util.builtin import (
@@ -35,6 +35,13 @@ from frigate.util.config import (
     migrate_frigate_config,
 )
 from frigate.util.image import create_mask
+from frigate.util.live_streams import (
+    default_transcode_source,
+    is_transcode_stream_name,
+    transcode_stream_name,
+    transcode_streams,
+)
+from frigate.util.runtime_deps import sha256_of
 from frigate.util.services import auto_detect_hwaccel
 
 from .auth import AuthConfig
@@ -282,11 +289,78 @@ def verify_config_roles(camera_config: CameraConfig) -> None:
         )
 
 
+def apply_live_transcode_streams(
+    frigate_config: FrigateConfig, camera_config: CameraConfig
+) -> None:
+    """Fold a camera's transcoded streams into its live stream list.
+
+    Enabled qualities missing from live.streams are appended, and entries the
+    user placed keep their position. Transcoded names that are no longer
+    generated are dropped unless they name a real go2rtc stream.
+    """
+    live = camera_config.live
+    transcode = live.transcode
+    go2rtc_streams = frigate_config.go2rtc.model_dump().get("streams") or {}
+    generated: dict[str, str] = {}
+
+    if transcode.enabled:
+        if transcode.source is None:
+            transcode.source = default_transcode_source(
+                camera_config.name, live.streams
+            )
+
+        if transcode.source not in go2rtc_streams:
+            raise ValueError(
+                f"Camera {camera_config.name} has transcoded streams enabled, but its source {transcode.source} is not a go2rtc stream."
+            )
+
+        generated = transcode_streams(
+            camera_config.name,
+            transcode.source,
+            [quality.model_dump() for quality in transcode.qualities],
+        )
+
+        for name in generated:
+            if name in go2rtc_streams:
+                raise ValueError(
+                    f"Camera {camera_config.name} generates transcoded stream {name}, which collides with a go2rtc stream of the same name."
+                )
+
+    streams = {
+        label: name
+        for label, name in live.streams.items()
+        if name in generated
+        or name in go2rtc_streams
+        or not is_transcode_stream_name(camera_config.name, name)
+    }
+    placed = set(streams.values())
+
+    for quality in transcode.qualities if transcode.enabled else []:
+        name = transcode_stream_name(camera_config.name, quality.height)
+
+        if name in placed:
+            continue
+
+        label = f"{quality.height}p"
+
+        if label in streams:
+            raise ValueError(
+                f"Camera {camera_config.name} already has a live stream named {label}; rename it or place the transcoded stream under another name."
+            )
+
+        streams[label] = name
+
+    live.streams = streams
+
+
 def verify_valid_live_stream_names(
     frigate_config: FrigateConfig, camera_config: CameraConfig
 ) -> ValueError | None:
     """Verify that a restream exists to use for live view."""
     for _, stream_name in camera_config.live.streams.items():
+        if is_transcode_stream_name(camera_config.name, stream_name):
+            continue
+
         if (
             stream_name
             not in frigate_config.go2rtc.model_dump().get("streams", {}).keys()
@@ -536,7 +610,7 @@ class FrigateConfig(FrigateBaseModel):
     models: list[ModelConfig] = Field(
         default_factory=_default_models,
         title="Detection models",
-        description="Object detection models and the hardware each one runs on. Cameras pick a model by matching their detect.scene against a model's scene.",
+        description="Object detection models and the hardware each one runs on. Cameras pick a model by matching their detect.scene against a model's scene, falling back to the 'default' model.",
     )
 
     # GenAI config (named provider configs: name -> GenAIConfig)
@@ -651,7 +725,9 @@ class FrigateConfig(FrigateBaseModel):
     )
 
     _plus_api: PlusApi
-    _model_devices: dict[SceneEnum, list[DeviceSpec]]
+    _model_devices: dict[str, list[DeviceSpec]]
+    # scene -> model, including the scenes of duplicate models folded into another
+    _scene_models: dict[str, ModelConfig]
     _camera_models: dict[str, ModelConfig]
     _all_attributes: list[str]
     _all_attribute_logos: list[str]
@@ -686,7 +762,7 @@ class FrigateConfig(FrigateBaseModel):
     def primary_model(self) -> ModelConfig:
         """The model used when no specific camera is in play."""
         for model in self.models:
-            if model.scene == SceneEnum.all:
+            if model.scene == DEFAULT_SCENE:
                 return model
 
         return self.models[0]
@@ -708,7 +784,7 @@ class FrigateConfig(FrigateBaseModel):
 
         if model is None:
             camera = self.cameras.get(camera_name)
-            scene = camera.detect.scene if camera is not None else SceneEnum.all
+            scene = camera.detect.scene if camera is not None else DEFAULT_SCENE
             model = self._resolve_camera_model(camera_name, scene)
             self._camera_models[camera_name] = model
 
@@ -768,14 +844,14 @@ class FrigateConfig(FrigateBaseModel):
         if not self.models:
             raise ValueError("At least one model must be configured under models")
 
-        model_devices: dict[SceneEnum, list[DeviceSpec]] = {}
+        model_devices: dict[str, list[DeviceSpec]] = {}
         # device string -> the scene of the model that already claimed it
-        claimed_devices: dict[str, SceneEnum] = {}
+        claimed_devices: dict[str, str] = {}
 
         for index, model in enumerate(self.models):
-            scene = model.scene.value
+            scene = model.scene
 
-            if model.scene in model_devices:
+            if scene in model_devices:
                 raise ValueError(
                     f"Multiple models are configured with a scene of '{scene}'. Each model must use a different scene."
                 )
@@ -804,17 +880,20 @@ class FrigateConfig(FrigateBaseModel):
                     other = claimed_devices[device.raw]
                     where = (
                         f"twice by model '{scene}'"
-                        if other == model.scene
-                        else f"by both the '{other.value}' and '{scene}' models"
+                        if other == scene
+                        else f"by both the '{other}' and '{scene}' models"
                     )
                     raise ValueError(
                         f"Device '{device.raw}' is used {where}, but it can only run one detection process."
                     )
 
-                claimed_devices[device.raw] = model.scene
+                claimed_devices[device.raw] = scene
 
             self.models[index] = self._load_model(model, devices[0].detector)
-            model_devices[model.scene] = devices
+            model_devices[scene] = devices
+
+        self._scene_models = {model.scene: model for model in self.models}
+        self._consolidate_duplicate_models(model_devices)
 
         attributes: set[str] = set()
         attribute_logos: set[str] = set()
@@ -838,36 +917,107 @@ class FrigateConfig(FrigateBaseModel):
         }
         self._all_labels = labels
 
-    def _resolve_camera_model(self, name: str, scene: SceneEnum) -> ModelConfig:
+    def _consolidate_duplicate_models(
+        self, model_devices: dict[str, list[DeviceSpec]]
+    ) -> None:
+        """Fold models that load the same model file into a single model.
+
+        Separate scenes for one model only split the same work across separate
+        detection queues, so each device serves fewer cameras and is slower
+        overall than one shared model. The duplicate's devices are moved to the
+        model it duplicates and its scene resolves to that model.
+
+        Args:
+            model_devices: Scene to parsed devices, updated in place
+        """
+        kept: list[ModelConfig] = []
+        hashes: dict[str, str | None] = {}
+
+        def file_hash(path: str) -> str | None:
+            if path not in hashes:
+                hashes[path] = sha256_of(path) if os.path.isfile(path) else None
+
+            return hashes[path]
+
+        def same_model(a: ModelConfig, b: ModelConfig) -> bool:
+            # a model's devices all share a detector, so folding across
+            # detectors would produce an invalid model
+            if model_devices[a.scene][0].detector != model_devices[b.scene][0].detector:
+                return False
+
+            if not a.path or not b.path:
+                return False
+
+            if os.path.realpath(a.path) == os.path.realpath(b.path):
+                return True
+
+            a_hash = file_hash(a.path)
+            return a_hash is not None and a_hash == file_hash(b.path)
+
+        for model in self.models:
+            original = next((other for other in kept if same_model(other, model)), None)
+
+            if original is None:
+                kept.append(model)
+                continue
+
+            # keep the default model so cameras without a scene still find it
+            if model.scene == DEFAULT_SCENE:
+                kept[kept.index(original)] = model
+                original, model = model, original
+
+            logger.warning(
+                "Models '%s' and '%s' use the same model file, so they have been combined into the '%s' model. Defining one model under several scenes to assign detectors to specific cameras is slower and less efficient than letting every detector serve every camera. Remove the '%s' model and list its devices under the '%s' model instead",
+                original.scene,
+                model.scene,
+                original.scene,
+                model.scene,
+                original.scene,
+            )
+            original.devices = [*original.devices, *model.devices]
+            model_devices[original.scene] = [
+                *model_devices[original.scene],
+                *model_devices.pop(model.scene),
+            ]
+            self._scene_models[model.scene] = original
+
+            # anything already folded into the duplicate follows it
+            for scene, target in self._scene_models.items():
+                if target is model:
+                    self._scene_models[scene] = original
+
+        self.models = kept
+
+    def _resolve_camera_model(self, name: str, scene: str) -> ModelConfig:
         """Resolve which model a camera runs on.
 
         A camera may name a scene no model is configured for, which is valid as
-        long as an 'all' model is there to fall back to.
+        long as a 'default' model is there to fall back to.
 
         Args:
             name: Name of the camera
-            scene: The camera's detect scene, which defaults to 'all'
+            scene: The camera's detect scene, which defaults to 'default'
 
         Returns:
             The model the camera runs on
         """
-        by_scene = {model.scene: model for model in self.models}
-        model = by_scene.get(scene)
+        model = self._scene_models.get(scene)
 
         if model is not None:
             return model
 
-        default = by_scene.get(SceneEnum.all)
+        default = self._scene_models.get(DEFAULT_SCENE)
 
         if default is None:
             raise ValueError(
-                f"Camera '{name}' has a detect scene of '{scene.value}', but no model is configured for that scene or for 'all'."
+                f"Camera '{name}' has a detect scene of '{scene}', but no model is configured for that scene or for '{DEFAULT_SCENE}'."
             )
 
         logger.warning(
-            "Camera '%s' has a detect scene of '%s', but no model is configured for that scene, so the 'all' model is used",
+            "Camera '%s' has a detect scene of '%s', but no model is configured for that scene, so the '%s' model is used",
             name,
-            scene.value,
+            scene,
+            DEFAULT_SCENE,
         )
         return default
 
@@ -978,6 +1128,8 @@ class FrigateConfig(FrigateBaseModel):
                 "face_recognition": ["enabled", "min_area"],
                 "lpr": ["enabled", "expire_time", "min_area", "enhancement"],
                 "audio_transcription": ["enabled", "live_enabled"],
+                # transcode is camera-level only
+                "live": ["streams", "height", "quality"],
             }
 
             for section in allowed_fields_map:
@@ -998,6 +1150,10 @@ class FrigateConfig(FrigateBaseModel):
             camera_model = self._resolve_camera_model(name, camera_config.detect.scene)
             self._camera_models[name] = camera_model
 
+            # point cameras at the model their duplicate scene was folded into
+            if camera_config.detect.scene in self._scene_models:
+                camera_config.detect.scene = camera_model.scene
+
             if camera_config.ffmpeg.hwaccel_args == "auto":
                 camera_config.ffmpeg.hwaccel_args = self.ffmpeg.hwaccel_args
 
@@ -1206,6 +1362,8 @@ class FrigateConfig(FrigateBaseModel):
             if not camera_config.live.streams:
                 camera_config.live.streams = {name: name}
 
+            apply_live_transcode_streams(self, camera_config)
+
             # generate the ffmpeg commands
             camera_config.create_ffmpeg_cmds()
             self.cameras[name] = camera_config

frigate/config/holder.py

@@ -1,14 +1,9 @@
 """Shared handle on the config object that is current for this instance."""
 
-import logging
-from collections.abc import Callable
-
 from .config import FrigateConfig
 
 __all__ = ["ConfigHolder"]
 
-logger = logging.getLogger(__name__)
-
 
 class ConfigHolder:
     """Indirection for the most recently parsed config.
@@ -28,24 +23,12 @@ class ConfigHolder:
 
     def __init__(self, config: FrigateConfig) -> None:
         self._config = config
-        self._listeners: list[Callable[[FrigateConfig], None]] = []
 
     @property
     def config(self) -> FrigateConfig:
         """The config as of the most recent successful save."""
         return self._config
 
-    def subscribe(self, listener: Callable[[FrigateConfig], None]) -> None:
-        """Call listener on the saving thread with each config installed later."""
-        self._listeners.append(listener)
-
     def set(self, config: FrigateConfig) -> None:
         """Install a freshly parsed config as the current one."""
         self._config = config
-
-        for listener in self._listeners:
-            try:
-                listener(config)
-            except Exception:
-                # a listener bug must not fail the save that has already applied
-                logger.exception("Config listener failed")

frigate/config/telemetry.py

@@ -29,11 +29,6 @@ class StatsConfig(FrigateBaseModel):
 
 
 class TelemetryConfig(FrigateBaseModel):
-    analytics: bool = Field(
-        default=False,
-        title="Share anonymous analytics",
-        description="Send one anonymous usage report a day to help the Frigate maintainers decide what to support. Nothing is sent until this is on.",
-    )
     network_interfaces: list[str] = Field(
         default=[],
         title="Network interfaces",

frigate/const.py

@@ -101,9 +101,6 @@ MAX_WAL_SIZE = 10  # MB
 
 DEFAULT_FFMPEG_VERSION = os.environ.get("DEFAULT_FFMPEG_VERSION", "")
 INCLUDED_FFMPEG_VERSIONS = os.environ.get("INCLUDED_FFMPEG_VERSIONS", "").split(":")
-ANALYTICS_URL = os.environ.get(
-    "FRIGATE_ANALYTICS_URL", "https://analytics.frigate.video/report"
-)
 LIBAVFORMAT_VERSION_MAJOR = int(os.environ.get("LIBAVFORMAT_VERSION_MAJOR", "59"))
 FFMPEG_HWACCEL_NVIDIA = "preset-nvidia"
 FFMPEG_HWACCEL_VAAPI = "preset-vaapi"

frigate/data_processing/common/license_plate/mixin.py

@@ -176,6 +176,7 @@ class LicensePlateProcessingMixin:
         """
         input_shape = [3, 48, 320]
         num_images = len(images)
+        outputs: list[np.ndarray] = []
 
         for index in range(0, num_images, self.batch_size):
             input_h, input_w = input_shape[1], input_shape[2]
@@ -196,7 +197,7 @@ class LicensePlateProcessingMixin:
                 norm_images.append(norm_image)
 
             try:
-            outputs = self.model_runner.recognition_model(norm_images)  # type: ignore[arg-type]
+                outputs.extend(self.model_runner.recognition_model(norm_images))  # type: ignore[arg-type]
             except Exception as e:
                 logger.warning(f"Error running LPR recognition model: {e}")
                 return [], []

frigate/data_processing/post/review_annotations.py

@@ -1,10 +1,10 @@
 """Frame annotations derived from object tracking data.
 
 Builds short notes describing what changed during a review item, keyed to the
-frames sampled from it. Everything here comes from tracked object data already
-in the database (each event's `path_data` trajectory and the timeline's
-stationary/active changes), so the notes can be stated to the model as fact
-rather than as something it must perceive.
+frames sampled from it. Everything here comes from data already recorded
+(each event's `path_data` trajectory, the timeline's stationary/active
+changes, and the review item's state classification changes), so the notes
+can be stated to the model as fact rather than as something it must perceive.
 """
 
 import logging
@@ -95,6 +95,15 @@ def event_name(event: dict[str, Any]) -> str:
     return f"{article} {label}"
 
 
+def describe_classification_change(change: dict[str, Any]) -> str:
+    """Phrase a state classification change, e.g. 'front gate changed from
+    closed to open'."""
+    model = str(change["model"]).replace("_", " ")
+    before = str(change["from"]).replace("_", " ")
+    after = str(change["to"]).replace("_", " ")
+    return f"{model} changed from {before} to {after}"
+
+
 def path_legs(points: list[Point]) -> list[Leg]:
     """Split a trajectory into runs of travel in a consistent direction.
 
@@ -359,25 +368,38 @@ def get_state_changes(detection_ids: list[str]) -> list[dict[str, Any]]:
 def build_frame_captions(
     detection_ids: list[str],
     frame_times: list[float],
+    classification_changes: Sequence[dict[str, Any]] = (),
 ) -> list[str]:
     """A caption for each sampled frame, in frame order.
 
     Every frame gets its index and elapsed time so the model can tell them
-    apart; frames where something changed also carry the tracker notes for
-    that moment. Returns an empty list when there is nothing to say, which
-    callers treat as a reason to fall back to sending plain frames.
+    apart; frames where something changed also carry the tracker and state
+    classification notes for that moment. Returns an empty list when there is
+    nothing to say, which callers treat as a reason to fall back to sending
+    plain frames.
     """
     if not frame_times:
         return []
 
+    span_end = frame_times[-1]
+    timeline: list[tuple[float, str]] = []
     events = get_tracked_events(detection_ids)
 
-    if not events:
-        logger.debug("No tracked events found for review item, skipping annotations")
-        return []
+    # audio and manual review items can have state changes but no tracked objects
+    if events:
+        timeline.extend(
+            (timestamp, f"[tracker] {note}")
+            for timestamp, note in build_timeline(
+                events, span_end, get_state_changes(detection_ids)
+            )
+        )
 
-    timeline = build_timeline(events, frame_times[-1], get_state_changes(detection_ids))
-    buckets = annotations_by_frame(timeline, frame_times)
+    timeline.extend(
+        (change["timestamp"], f"[state] {describe_classification_change(change)}")
+        for change in classification_changes
+        if change["timestamp"] <= span_end
+    )
+    buckets = annotations_by_frame(sorted(timeline, key=lambda m: m[0]), frame_times)
 
     if not buckets:
         return []
@@ -388,7 +410,7 @@ def build_frame_captions(
 
     for index, timestamp in enumerate(frame_times):
         lines = [f"Frame {index + 1} of {total} (+{timestamp - origin:.1f}s):"]
-        lines.extend(f"[tracker] {note}" for note in buckets.get(index, []))
+        lines.extend(buckets.get(index, []))
         captions.append("\n".join(lines))
 
     return captions

frigate/data_processing/post/review_descriptions.py

@@ -40,7 +40,7 @@ from frigate.util.image import get_image_from_recording
 
 from ..post.api import PostProcessorApi
 from ..types import DataProcessorMetrics
-from .review_annotations import build_frame_captions
+from .review_annotations import build_frame_captions, describe_classification_change
 
 logger = logging.getLogger(__name__)
 
@@ -254,6 +254,10 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                     "start_time": r["start_time"],
                     "end_time": r["end_time"],
                     "metadata": r["data"]["metadata"],
+                    "state_changes": [
+                        describe_classification_change(change)
+                        for change in sorted_classification_state_changes(r["data"])
+                    ],
                 }
                 for r in (
                     ReviewSegment.select(
@@ -298,6 +302,9 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                 primary_item["start_time"] = primary_seg["start_time"]
                 primary_item["end_time"] = primary_seg["end_time"]
 
+                if primary_seg["state_changes"]:
+                    primary_item["state_changes"] = primary_seg["state_changes"]
+
                 # Find overlapping contextual items from other cameras
                 primary_start = primary_seg["start_time"]
                 primary_end = primary_seg["end_time"]
@@ -318,12 +325,23 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                     seg_end = seg["end_time"]
 
                     if seg_start < primary_end and primary_start < seg_end:
-                        # Avoid duplicates if same camera has multiple overlapping segments
-                        if seg_camera not in seen_contextual_cameras:
+                        # Avoid duplicates if same camera has multiple overlapping
+                        # segments. One with state changes is kept as its own item
+                        # so each change stays within its item's time range.
+                        if (
+                            seg_camera in seen_contextual_cameras
+                            and not seg["state_changes"]
+                        ):
+                            continue
+
                         contextual_item = copy.deepcopy(seg["metadata"])
                         contextual_item["camera"] = seg_camera
                         contextual_item["start_time"] = seg_start
                         contextual_item["end_time"] = seg_end
+
+                        if seg["state_changes"]:
+                            contextual_item["state_changes"] = seg["state_changes"]
+
                         contextual_items.append(contextual_item)
                         seen_contextual_cameras.add(seg_camera)
 
@@ -439,6 +457,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
             captions = build_frame_captions(
                 final_data["data"].get("detections") or [],
                 [timestamp for _, timestamp in frames],
+                sorted_classification_state_changes(final_data["data"]),
             )
 
             if not captions:
@@ -688,6 +707,39 @@ def get_recording_buffer_extension(duration: float) -> float:
     return buffer_extension
 
 
+def sorted_classification_state_changes(
+    review_data: dict[str, Any],
+) -> list[dict[str, Any]]:
+    """A review item's state classification changes in time order."""
+    return sorted(
+        review_data.get("classification_state_changes") or [],
+        key=lambda change: change["timestamp"],
+    )
+
+
+def format_classification_state_changes(
+    changes: list[dict[str, Any]], start_time: float, end_time: float
+) -> list[str]:
+    """Phrase state classification changes with their timing in the activity.
+
+    Changes are attached while the review item is active, which runs past its
+    end_time by the review cutoff, and a few seconds before its start.
+    """
+    lines = []
+
+    for change in changes:
+        if change["timestamp"] < start_time:
+            when = "just before the activity started"
+        elif change["timestamp"] > end_time:
+            when = "after the activity ended"
+        else:
+            when = f"{round(change['timestamp'] - start_time)}s into the activity"
+
+        lines.append(f"{describe_classification_change(change)}, {when}")
+
+    return lines
+
+
 def run_analysis(
     requestor: InterProcessRequestor,
     genai_client: GenAIClient,
@@ -743,6 +795,13 @@ def run_analysis(
                 unified_objects.append(object_type)
 
     analytics_data["unified_objects"] = unified_objects
+    analytics_data["classification_state_changes"] = (
+        format_classification_state_changes(
+            sorted_classification_state_changes(final_data["data"]),
+            final_data["start_time"],
+            final_data["end_time"],
+        )
+    )
 
     metadata = genai_client.generate_review_description(
         analytics_data,

frigate/data_processing/real_time/custom_classification.py

@@ -91,8 +91,23 @@ class CustomStateClassificationProcessor(DeferredRealtimeProcessorApi):
         self.tensor_input_details = self.interpreter.get_input_details()
         self.tensor_output_details = self.interpreter.get_output_details()
         self.labelmap = load_labels(labelmap_path, prefill=0, indexed=False)
+        self._forget_unknown_states()
         self.classifications_per_second.start()
 
+    def _forget_unknown_states(self) -> None:
+        """Drop verified states that are not labels of the loaded model.
+
+        A retrained model can rename or remove labels. Keeping a state it can
+        no longer produce would report its first verified state as a change
+        from that obsolete label.
+        """
+        labels = set(self.labelmap.values())
+        self.state_history = {
+            camera: history
+            for camera, history in self.state_history.items()
+            if history["current_state"] in labels
+        }
+
     def __update_metrics(self, duration: float) -> None:
         self.classifications_per_second.update()
         if self.inference_speed:
@@ -134,15 +149,20 @@ class CustomStateClassificationProcessor(DeferredRealtimeProcessorApi):
         # Don't save if state is stable (detected_state == current_state) AND score is 100%
         return False
 
-    def verify_state_change(self, camera: str, detected_state: str) -> str | None:
+    def verify_state_change(
+        self, camera: str, detected_state: str, timestamp: float
+    ) -> tuple[str | None, float] | None:
         """
         Verify state change requires 3 consecutive identical states before publishing.
-        Returns state to publish or None if verification not complete.
+        Returns (previous state, time the new state was first seen) once verified,
+        or None if verification not complete. The previous state is None for the
+        first state verified on a camera.
         """
         if camera not in self.state_history:
             self.state_history[camera] = {
                 "current_state": None,
                 "pending_state": None,
+                "pending_since": 0.0,
                 "consecutive_count": 0,
             }
 
@@ -157,12 +177,14 @@ class CustomStateClassificationProcessor(DeferredRealtimeProcessorApi):
             verification["consecutive_count"] += 1
 
             if verification["consecutive_count"] >= 3:
+                previous_state = verification["current_state"]
                 verification["current_state"] = detected_state
                 verification["pending_state"] = None
                 verification["consecutive_count"] = 0
-                return detected_state
+                return previous_state, verification["pending_since"]
         else:
             verification["pending_state"] = detected_state
+            verification["pending_since"] = timestamp
             verification["consecutive_count"] = 1
             logger.debug(
                 f"New state '{detected_state}' detected for {camera}, need {3 - verification['consecutive_count']} more consecutive detections"
@@ -340,16 +362,19 @@ class CustomStateClassificationProcessor(DeferredRealtimeProcessorApi):
             )
             return
 
-        verified_state = self.verify_state_change(camera, detected_state)
+        verified = self.verify_state_change(camera, detected_state, timestamp)
 
-        if verified_state is not None:
+        if verified is not None:
+            previous_state, changed_at = verified
             self._emit_result(
                 {
                     "type": "classification",
                     "processor": "state",
                     "model_name": self.model_config.name,
                     "camera": camera,
-                    "state": verified_state,
+                    "state": detected_state,
+                    "previous_state": previous_state,
+                    "timestamp": changed_at,
                 }
             )
 

frigate/db/sqlitevecq.py

@@ -1,8 +1,10 @@
 import logging
 import sqlite3
+import threading
 from typing import Any
 
 import regex
+from peewee import DatabaseError
 from playhouse.sqliteq import SqliteQueueDatabase
 
 logger = logging.getLogger(__name__)
@@ -17,6 +19,7 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
         self.load_vec_extension: bool = load_vec_extension
         # no extension necessary, sqlite will load correctly for each platform
         self.sqlite_vec_path = "/usr/local/lib/vec0"
+        self.upsert_lock = threading.Lock()
         super().__init__(*args, **kwargs)
 
     def _connect(self, *args: Any, **kwargs: Any) -> sqlite3.Connection:
@@ -53,6 +56,22 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
 
         conn.create_function("REGEXP", 2, regexp)
 
+    def execute_write(self, sql: str, params: Any = None) -> None:
+        """Run a write and wait for it, so that failures are raised here.
+
+        SqliteQueueDatabase hands non-SELECT statements to a writer thread and
+        stores any exception on the cursor it returns, so callers that ignore
+        that cursor never learn the write failed.
+        """
+        self.execute_sql(sql, params).fetchall()
+
+    def _table_exists(self, table: str) -> bool:
+        cursor = self.execute_sql(
+            "SELECT name FROM sqlite_master WHERE type = 'table' AND name = ?",
+            (table,),
+        )
+        return cursor.fetchone() is not None
+
     def _delete_embeddings(self, table: str, event_ids: list[str]) -> None:
         """Delete embeddings for the given events, if the table exists.
 
@@ -63,17 +82,17 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
             return
 
         # the embeddings tables are only created once semantic search has run
-        cursor = self.execute_sql(
-            "SELECT name FROM sqlite_master WHERE type = 'table' AND name = ?",
-            (table,),
-        )
-
-        if cursor.fetchone() is None:
+        if not self._table_exists(table):
             logger.debug("Skipping %s cleanup, table does not exist", table)
             return
 
         ids = ",".join(["?" for _ in event_ids])
-        self.execute_sql(f"DELETE FROM {table} WHERE id IN ({ids})", event_ids)
+
+        # callers treat cleanup as best effort, so log rather than propagate
+        try:
+            self.execute_write(f"DELETE FROM {table} WHERE id IN ({ids})", event_ids)
+        except DatabaseError:
+            logger.exception("Failed to delete embeddings from %s", table)
 
     def delete_embeddings_thumbnail(self, event_ids: list[str]) -> None:
         self._delete_embeddings("vec_thumbnails", event_ids)
@@ -81,25 +100,67 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
     def delete_embeddings_description(self, event_ids: list[str]) -> None:
         self._delete_embeddings("vec_descriptions", event_ids)
 
+    def _restore_vec_info_table(self, table: str) -> None:
+        """Recreate the _info shadow table a legacy vec0 table is missing.
+
+        sqlite-vec added _info in 0.1.6 and drops it unconditionally when a
+        table is destroyed, so tables written by Frigate 0.17 and earlier fail
+        to drop. An empty stub is enough, and leaving it unseeded keeps the
+        table reading as pre-0.1.10 if the drop does not follow.
+        """
+        if not self._table_exists(table) or self._table_exists(f"{table}_info"):
+            return
+
+        logger.debug("Restoring the %s_info shadow table before dropping", table)
+        self.execute_write(
+            f'CREATE TABLE "{table}_info" (key TEXT PRIMARY KEY, value ANY)'
+        )
+
     def drop_embeddings_tables(self) -> None:
-        self.execute_sql("""
-            DROP TABLE vec_descriptions;
-        """)
-        self.execute_sql("""
-            DROP TABLE vec_thumbnails;
-        """)
+        for table in ("vec_descriptions", "vec_thumbnails"):
+            self._restore_vec_info_table(table)
+            self.execute_write(f"DROP TABLE IF EXISTS {table}")
 
     def create_embeddings_tables(self) -> None:
         """Create vec0 virtual table for embeddings"""
-        self.execute_sql("""
+        self.execute_write("""
             CREATE VIRTUAL TABLE IF NOT EXISTS vec_thumbnails USING vec0(
                 id TEXT PRIMARY KEY,
                 thumbnail_embedding FLOAT[768] distance_metric=cosine
             );
         """)
-        self.execute_sql("""
+        self.execute_write("""
             CREATE VIRTUAL TABLE IF NOT EXISTS vec_descriptions USING vec0(
                 id TEXT PRIMARY KEY,
                 description_embedding FLOAT[768] distance_metric=cosine
             );
         """)
+
+    def upsert_embeddings(
+        self, table: str, column: str, embeddings: dict[str, bytes]
+    ) -> None:
+        """Write embeddings for the given event ids, replacing any that exist.
+
+        vec0 implements neither REPLACE nor UPSERT, so rows that are already
+        there have to be deleted first.
+        """
+        if not embeddings:
+            return
+
+        event_ids = list(embeddings.keys())
+        ids = ",".join(["?" for _ in event_ids])
+        params: list[Any] = []
+
+        for event_id in event_ids:
+            params.extend((event_id, embeddings[event_id]))
+
+        values = ", ".join(["(?, ?)"] * len(event_ids))
+
+        # reindexing and live embedding run on separate threads, and each write
+        # is queued separately, so the delete and the insert have to be held
+        # together or an interleaved pair fails on the vec0 primary key
+        with self.upsert_lock:
+            self.execute_write(f"DELETE FROM {table} WHERE id IN ({ids})", event_ids)
+            self.execute_write(
+                f"INSERT INTO {table}(id, {column}) VALUES {values}", params
+            )

frigate/detectors/detection_runners.py

@@ -46,8 +46,42 @@ _PROVIDER_LABELS = {
     "MIGraphXExecutionProvider": "MIGraphX",
     "OpenVINOExecutionProvider": "OpenVINO",
     "CPUExecutionProvider": "CPU",
+    "LighterANE": "Neural Engine",
 }
 
+# lighter (https://github.com/fieldwork-ai/lighter) places an ONNX Runtime plugin
+# execution provider in a container started with --device lighter.sh/ane=all,
+# which runs models on a Mac's Neural Engine; LIGHTER_ANE_EP names where it is
+LIGHTER_ANE_EP_NAME = "LighterANE"
+LIGHTER_ANE_LIBRARY = "/usr/lib/lighter/liblighter_ane_ep.so"
+
+
+def get_lighter_ane_devices() -> list[Any]:
+    """Get the Neural Engine devices lighter's provider offers, registering it once.
+
+    Returns:
+        The provider's ONNX Runtime devices, or an empty list without lighter's device
+    """
+    library = os.environ.get("LIGHTER_ANE_EP", LIGHTER_ANE_LIBRARY)
+
+    if not os.path.exists(library):
+        return []
+
+    devices = [d for d in ort.get_ep_devices() if d.ep_name == LIGHTER_ANE_EP_NAME]
+
+    if not devices:
+        try:
+            ort.register_execution_provider_library(LIGHTER_ANE_EP_NAME, library)
+        except Exception as e:
+            logger.warning(
+                f"Failed to load the Neural Engine provider from {library}: {e}"
+            )
+            return []
+
+        devices = [d for d in ort.get_ep_devices() if d.ep_name == LIGHTER_ANE_EP_NAME]
+
+    return devices
+
 
 def is_arm64_platform() -> bool:
     """Check if we're running on an ARM platform."""
@@ -689,6 +723,23 @@ def get_optimized_runner(
         if rknn_path:
             return _record_runner(model_path, model_type, RKNNModelRunner(rknn_path))
 
+    if device != "CPU" and (ane_devices := get_lighter_ane_devices()):
+        sess_options = get_ort_session_options(model_type) or ort.SessionOptions()
+        sess_options.add_provider_for_devices(ane_devices, {})
+
+        try:
+            session = ort.InferenceSession(model_path, sess_options=sess_options)
+        except Exception as e:
+            logger.warning(
+                f"Failed to load {model_path} on the Neural Engine, using the default providers: {e}"
+            )
+        else:
+            return _record_runner(
+                model_path,
+                model_type,
+                ONNXModelRunner(session, model_type=model_type),
+            )
+
     providers, options = get_ort_providers(device == "CPU", device, **kwargs)
 
     if providers[0] == "CPUExecutionProvider":

frigate/detectors/detector_config.py

@@ -47,21 +47,17 @@ class ModelTypeEnum(str, Enum):
     yologeneric = "yolo-generic"
 
 
-class SceneEnum(str, Enum):
-    """The camera environment a detection model is intended for."""
-
-    all = "all"
-    indoor = "indoor"
-    outdoor = "outdoor"
-    indoor_thermal = "indoor_thermal"
-    outdoor_thermal = "outdoor_thermal"
+# the scene of the model used by cameras that don't name one
+DEFAULT_SCENE = "default"
+SCENE_PATTERN = r"^[A-Za-z0-9_-]+$"
 
 
 class ModelConfig(BaseModel):
-    scene: SceneEnum = Field(
-        default=SceneEnum.all,
+    scene: str = Field(
+        default=DEFAULT_SCENE,
+        pattern=SCENE_PATTERN,
         title="Model scene",
-        description="The camera environment this model is used for. Cameras select a model by setting detect.scene to a matching value, and 'all' is used by any camera that does not set one.",
+        description="A name for the camera environment this model is used for, such as 'thermal'. Cameras select a model by setting detect.scene to a matching value, and the 'default' model is used by any camera that does not set one.",
     )
     devices: list[str] = Field(
         default_factory=list,
@@ -123,6 +119,7 @@ class ModelConfig(BaseModel):
     _all_attributes: list[str] = PrivateAttr()
     _all_attribute_logos: list[str] = PrivateAttr()
     _model_hash: str = PrivateAttr()
+    _plus_id: str | None = PrivateAttr(default=None)
 
     @property
     def merged_labelmap(self) -> dict[int, str]:
@@ -148,6 +145,11 @@ class ModelConfig(BaseModel):
     def model_hash(self) -> str:
         return self._model_hash
 
+    @property
+    def plus_id(self) -> str | None:
+        """The Frigate+ model id, once a plus:// path has been resolved."""
+        return self._plus_id
+
     def __init__(self, **config):
         super().__init__(**config)
 
@@ -178,6 +180,7 @@ class ModelConfig(BaseModel):
         os.makedirs(MODEL_CACHE_DIR, exist_ok=True)
 
         model_id = self.path[7:]
+        self._plus_id = model_id
         self.path = os.path.join(MODEL_CACHE_DIR, model_id)
         model_info_path = f"{self.path}.json"
 

frigate/detectors/hardware.py

@@ -25,6 +25,7 @@ SYS_ROOT = "/sys"
 DEV_ROOT = "/dev"
 PROC_ROOT = "/proc"
 ETC_ROOT = "/etc"
+LIB_ROOT = "/usr/lib"
 
 # a Coral reports as Global Unichip until its firmware is loaded, then as Google
 CORAL_USB_IDS = {("1a6e", "089a"), ("18d1", "9302")}
@@ -317,6 +318,19 @@ def detect_synaptics() -> DetectionHardware | None:
     return _hardware("synaptics", "synaptics", "Synaptics NPU", units)
 
 
+def detect_lighter_ane() -> DetectionHardware | None:
+    """Find a Mac's Neural Engine by the provider library lighter's device places."""
+    library = os.environ.get(
+        "LIGHTER_ANE_EP", f"{LIB_ROOT}/lighter/liblighter_ane_ep.so"
+    )
+    if not os.path.exists(library):
+        return None
+
+    # runs through onnx, whose session picks lighter's provider when it is present
+    units = [HardwareUnit(device="onnx", label="Neural Engine")]
+    return _hardware("onnx:lighter", "onnx", "Apple Neural Engine", units)
+
+
 def detect_cpu() -> DetectionHardware:
     """The CPU, which is always available."""
     units = [HardwareUnit(device="cpu", label="CPU")]
@@ -338,6 +352,7 @@ PROBES = (
     detect_rockchip,
     detect_axengine,
     detect_synaptics,
+    detect_lighter_ane,
     detect_cpu,
 )
 

frigate/detectors/plugins/zmq_ipc.py

@@ -1,7 +1,7 @@
 import json
 import logging
 import os
-from typing import Any, Literal
+from typing import Any, ClassVar, Literal
 
 import numpy as np
 import zmq
@@ -21,6 +21,7 @@ class ZmqDetectorConfig(BaseDetectorConfig):
     model_config = ConfigDict(
         title="ZMQ IPC",
     )
+    device_spec_field: ClassVar[str] = "endpoint"
 
     type: Literal[DETECTOR_KEY]
     endpoint: str = Field(

frigate/embeddings/embeddings.py

@@ -6,9 +6,10 @@ import logging
 import os
 import threading
 import time
+from typing import Any
 
 import numpy as np
-from peewee import DoesNotExist, IntegrityError
+from peewee import DatabaseError, DoesNotExist, IntegrityError
 from PIL import Image
 from playhouse.shortcuts import model_to_dict
 
@@ -207,12 +208,10 @@ class Embeddings:
         embedding = self.vision_embedding([thumbnail])[0]
 
         if upsert:
-            self.db.execute_sql(
-                """
-                INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
-                VALUES(?, ?)
-                """,
-                (event_id, serialize(embedding)),
+            self.db.upsert_embeddings(
+                "vec_thumbnails",
+                "thumbnail_embedding",
+                {event_id: serialize(embedding)},
             )
 
         self.image_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -251,19 +250,12 @@ class Embeddings:
         embeddings = self.vision_embedding(valid_thumbs)
 
         if upsert:
-            items = []
+            items = {}
             for i in range(len(valid_ids)):
-                items.append(valid_ids[i])
-                items.append(serialize(embeddings[i]))
+                items[valid_ids[i]] = serialize(embeddings[i])
                 self.image_eps.update()
 
-            self.db.execute_sql(
-                """
-                INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
-                VALUES {}
-                """.format(", ".join(["(?, ?)"] * len(valid_ids))),
-                items,
-            )
+            self.db.upsert_embeddings("vec_thumbnails", "thumbnail_embedding", items)
 
         duration = datetime.datetime.now().timestamp() - start
         self.image_inference_speed.update(duration / len(valid_ids))
@@ -277,12 +269,10 @@ class Embeddings:
         embedding = self.text_embedding([description])[0]
 
         if upsert:
-            self.db.execute_sql(
-                """
-                INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
-                VALUES(?, ?)
-                """,
-                (event_id, serialize(embedding)),
+            self.db.upsert_embeddings(
+                "vec_descriptions",
+                "description_embedding",
+                {event_id: serialize(embedding)},
             )
 
         self.text_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -302,19 +292,14 @@ class Embeddings:
 
         if upsert:
             ids = list(event_descriptions.keys())
-            items = []
+            items = {}
 
             for i in range(len(ids)):
-                items.append(ids[i])
-                items.append(serialize(embeddings[i]))
+                items[ids[i]] = serialize(embeddings[i])
                 self.text_eps.update()
 
-            self.db.execute_sql(
-                """
-                INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
-                VALUES {}
-                """.format(", ".join(["(?, ?)"] * len(ids))),
-                items,
+            self.db.upsert_embeddings(
+                "vec_descriptions", "description_embedding", items
             )
 
         self.text_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -322,6 +307,17 @@ class Embeddings:
         return embeddings
 
     def reindex(self) -> None:
+        """Rebuild every tracked object embedding from scratch."""
+        totals: dict[str, Any] = {"status": "indexing"}
+
+        try:
+            self._reindex(totals)
+        except DatabaseError:
+            logger.exception("Unable to reindex tracked object embeddings")
+            totals["status"] = "failed"
+            self.requestor.send_data(UPDATE_EMBEDDINGS_REINDEX_PROGRESS, totals)
+
+    def _reindex(self, totals: dict[str, Any]) -> None:
         logger.info("Indexing tracked object embeddings...")
 
         self.db.drop_embeddings_tables()
@@ -346,17 +342,24 @@ class Embeddings:
             batch_size = 32
         current_page = 1
 
-        totals = {
+        totals.update(
+            {
                 "thumbnails": 0,
                 "descriptions": 0,
-            "processed_objects": total_events - 1 if total_events < batch_size else 0,
+                "processed_objects": total_events - 1
+                if total_events < batch_size
+                else 0,
                 "total_objects": total_events,
                 "time_remaining": 0 if total_events < batch_size else -1,
                 "status": "indexing",
             }
+        )
 
         self.requestor.send_data(UPDATE_EMBEDDINGS_REINDEX_PROGRESS, totals)
 
+        # a single batch sends no progress, so the first message shows it nearly done
+        totals["processed_objects"] = 0
+
         events = (
             Event.select()
             .order_by(Event.start_time.desc())

frigate/embeddings/maintainer.py

@@ -11,7 +11,11 @@ from typing import Any
 from peewee import DoesNotExist
 
 from frigate.comms.config_updater import ConfigSubscriber
-from frigate.comms.detections_updater import DetectionSubscriber, DetectionTypeEnum
+from frigate.comms.detections_updater import (
+    DetectionPublisher,
+    DetectionSubscriber,
+    DetectionTypeEnum,
+)
 from frigate.comms.embeddings_updater import (
     EmbeddingsRequestEnum,
     EmbeddingsResponder,
@@ -168,6 +172,7 @@ class EmbeddingMaintainer(threading.Thread):
         )
         self.review_subscriber = ReviewDataSubscriber("")
         self.detection_subscriber = DetectionSubscriber(DetectionTypeEnum.video.value)
+        self.detection_publisher = DetectionPublisher(DetectionTypeEnum.all.value)
         self.embeddings_responder = EmbeddingsResponder()
         self.frame_manager = SharedMemoryFrameManager()
 
@@ -356,6 +361,7 @@ class EmbeddingMaintainer(threading.Thread):
         self.event_end_subscriber.stop()
         self.recordings_subscriber.stop()
         self.detection_subscriber.stop()
+        self.detection_publisher.stop()
         self.event_metadata_publisher.stop()
         self.event_metadata_subscriber.stop()
         self.embeddings_responder.stop()
@@ -851,6 +857,21 @@ class EmbeddingMaintainer(threading.Thread):
                         f"{result['camera']}/classification/{result['model_name']}",
                         result["state"],
                     )
+
+                    # the first state verified after startup is not a change
+                    if result["previous_state"] is not None:
+                        self.detection_publisher.publish(
+                            (
+                                result["camera"],
+                                {
+                                    "model": result["model_name"],
+                                    "from": result["previous_state"],
+                                    "to": result["state"],
+                                    "timestamp": result["timestamp"],
+                                },
+                            ),
+                            DetectionTypeEnum.classification_state.value,
+                        )
                 elif result["processor"] == "object":
                     object_id = result["object_id"]
                     camera = result["camera"]

frigate/events/cleanup.py

@@ -365,6 +365,7 @@ class EventCleanup(threading.Thread):
                     chunk = ids_to_delete[i : i + CHUNK_SIZE]
                     logger.debug(f"Deleting {len(chunk)} events from the database")
                     Event.delete().where(Event.id << chunk).execute()
+                    Timeline.delete().where(Timeline.source_id << chunk).execute()
 
                     # embeddings are always cleaned up, even when semantic search
                     # is disabled, so that they don't outlive their events

frigate/ffmpeg_presets.py

@@ -84,6 +84,8 @@ _user_agent_args = [
 PRESETS_HW_ACCEL_DECODE = {
     "preset-rpi-64-h264": "-c:v:1 h264_v4l2m2m",
     "preset-rpi-64-h265": "-c:v:1 hevc_v4l2m2m",
+    "preset-apple-silicon-h264": "-c:v h264_v4l2m2m",
+    "preset-apple-silicon-h265": "-c:v hevc_v4l2m2m",
     FFMPEG_HWACCEL_VAAPI: "-hwaccel_flags allow_profile_mismatch -hwaccel vaapi -hwaccel_device {3} -hwaccel_output_format vaapi",
     "preset-intel-qsv-h264": f"-hwaccel qsv -qsv_device {{3}} -hwaccel_output_format qsv -c:v h264_qsv{' -bsf:v dump_extra' if LIBAVFORMAT_VERSION_MAJOR >= 61 else ''}",  # https://trac.ffmpeg.org/ticket/9766#comment:17
     "preset-intel-qsv-h265": f"-load_plugin hevc_hw -hwaccel qsv -qsv_device {{3}} -hwaccel_output_format qsv{' -bsf:v dump_extra' if LIBAVFORMAT_VERSION_MAJOR >= 61 else ''}",  # https://trac.ffmpeg.org/ticket/9766#comment:17
@@ -120,9 +122,12 @@ PRESETS_HW_ACCEL_DECODE["preset-rk-h265"] = PRESETS_HW_ACCEL_DECODE[
 PRESETS_HW_ACCEL_SCALE = {
     "preset-rpi-64-h264": "-r {0} -vf fps={0},scale={1}:{2}",
     "preset-rpi-64-h265": "-r {0} -vf fps={0},scale={1}:{2}",
+    # ffmpeg's v4l2m2m decoders cannot scale, so frames are scaled on the CPU
+    "preset-apple-silicon-h264": "-r {0} -vf fps={0},scale={1}:{2}",
+    "preset-apple-silicon-h265": "-r {0} -vf fps={0},scale={1}:{2}",
     FFMPEG_HWACCEL_VAAPI: "-r {0} -vf fps={0},scale_vaapi=w={1}:h={2},hwdownload,format=nv12",
-    "preset-intel-qsv-h264": "-r {0} -vf vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,fps={0},format=yuv420p",
-    "preset-intel-qsv-h265": "-r {0} -vf vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,fps={0},format=yuv420p",
+    "preset-intel-qsv-h264": "-r {0} -vf fps={0},vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,format=yuv420p",
+    "preset-intel-qsv-h265": "-r {0} -vf fps={0},vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,format=yuv420p",
     FFMPEG_HWACCEL_NVIDIA: "-r {0} -vf fps={0},scale_cuda=w={1}:h={2},hwdownload,format=nv12",
     "preset-jetson-h264": "-r {0}",  # scaled in decoder
     "preset-jetson-h265": "-r {0}",  # scaled in decoder
@@ -150,6 +155,8 @@ PRESETS_HW_ACCEL_SCALE["preset-rk-h265"] = PRESETS_HW_ACCEL_SCALE[FFMPEG_HWACCEL
 PRESETS_HW_ACCEL_ENCODE_BIRDSEYE = {
     "preset-rpi-64-h264": "{0} -hide_banner {1} -c:v h264_v4l2m2m {2}",
     "preset-rpi-64-h265": "{0} -hide_banner {1} -c:v hevc_v4l2m2m {2}",
+    "preset-apple-silicon-h264": "{0} -hide_banner {1} -c:v h264_v4l2m2m {2}",
+    "preset-apple-silicon-h265": "{0} -hide_banner {1} -c:v h264_v4l2m2m {2}",
     # -vaapi_device is required in addition to -hwaccel_device: this is the only
     # birdseye preset that uses hwupload, and ffmpeg 8 initializes filters before
     # the decoder creates a device, so hwupload cannot see an -hwaccel_device one.
@@ -184,6 +191,8 @@ PRESETS_HW_ACCEL_ENCODE_BIRDSEYE["preset-rk-h264"] = PRESETS_HW_ACCEL_ENCODE_BIR
 PRESETS_HW_ACCEL_ENCODE_TIMELAPSE = {
     "preset-rpi-64-h264": "{0} -hide_banner {1} -c:v h264_v4l2m2m -pix_fmt yuv420p {2}",
     "preset-rpi-64-h265": "{0} -hide_banner {1} -c:v hevc_v4l2m2m -pix_fmt yuv420p {2}",
+    "preset-apple-silicon-h264": "{0} -hide_banner {1} -c:v h264_v4l2m2m -pix_fmt yuv420p {2}",
+    "preset-apple-silicon-h265": "{0} -hide_banner {1} -c:v hevc_v4l2m2m -pix_fmt yuv420p {2}",
     FFMPEG_HWACCEL_VAAPI: "{0} -hide_banner -hwaccel vaapi -hwaccel_output_format vaapi -hwaccel_device {3} {1} -c:v h264_vaapi {2}",
     "preset-intel-qsv-h264": "{0} -hide_banner {1} -c:v h264_qsv -profile:v high -level:v 4.1 -async_depth:v 1 {2}",
     "preset-intel-qsv-h265": "{0} -hide_banner {1} -c:v hevc_qsv -profile:v main -level:v 4.1 -async_depth:v 1 {2}",

frigate/genai/plugins/llama_cpp.py

@@ -103,11 +103,10 @@ class LlamaCppClient(GenAIClient):
     _supports_reasoning: bool
     _image_token_cache: dict[tuple[int, int], int]
     _text_baseline_tokens: int | None
-    _media_marker: str
 
     @property
     def supports_embeddings(self) -> bool:
-        """llama.cpp exposes an /embeddings endpoint for any loaded model."""
+        """llama.cpp exposes a /v1/embeddings endpoint for any loaded model."""
         return True
 
     def _auth_headers(self) -> dict | None:
@@ -159,7 +158,6 @@ class LlamaCppClient(GenAIClient):
         self._supports_reasoning = False
         self._image_token_cache = {}
         self._text_baseline_tokens = None
-        self._media_marker = "<__media__>"
 
         base_url = (
             self.genai_config.base_url.rstrip("/")
@@ -187,7 +185,6 @@ class LlamaCppClient(GenAIClient):
         self._supports_audio = info["supports_audio"]
         self._supports_tools = info["supports_tools"]
         self._supports_reasoning = info["supports_reasoning"]
-        self._media_marker = info["media_marker"]
 
         logger.info(
             "llama.cpp model '%s' initialized — context: %s, vision: %s, audio: %s, tools: %s, reasoning: %s",
@@ -215,9 +212,7 @@ class LlamaCppClient(GenAIClient):
         `architecture.input_modalities` (text/image/audio) — the primary
         source. When proxied through llama-swap, the same entry carries
         `status.args` (server launch argv) and, for the loaded model,
-        `meta.n_ctx`. /props remains the only source for `media_marker`,
-        which the server randomizes per startup unless LLAMA_MEDIA_MARKER
-        is set.
+        `meta.n_ctx`.
         """
         info: dict[str, Any] = {
             "context_size": None,
@@ -225,7 +220,6 @@ class LlamaCppClient(GenAIClient):
             "supports_audio": False,
             "supports_tools": False,
             "supports_reasoning": False,
-            "media_marker": "<__media__>",
         }
 
         model_entry: dict[str, Any] | None = None
@@ -314,16 +308,8 @@ class LlamaCppClient(GenAIClient):
             # in the Jinja chat template itself.
             chat_template = props.get("chat_template") or ""
             info["supports_reasoning"] = "enable_thinking" in chat_template
-
-            media_marker = props.get("media_marker")
-            if isinstance(media_marker, str) and media_marker:
-                info["media_marker"] = media_marker
         except Exception as e:
-            logger.warning(
-                "Failed to query llama.cpp /props endpoint: %s. "
-                "Image embeddings may fail if the server randomized its media marker.",
-                e,
-            )
+            logger.warning("Failed to query llama.cpp /props endpoint: %s", e)
 
         return info
 
@@ -474,9 +460,6 @@ class LlamaCppClient(GenAIClient):
     def _transcribe_via_chat(self, audio: bytes, language: str | None) -> str | None:
         """Transcribe through /v1/chat/completions, for servers without the
         transcriptions route.
-
-        The _media_marker / multimodal_data convention is an /embeddings-only
-        protocol, so no marker-refresh retry is needed here.
         """
         prompt = "Transcribe the speech in this audio verbatim. Respond with the transcript only, and with nothing at all if there is no speech."
 
@@ -794,41 +777,16 @@ class LlamaCppClient(GenAIClient):
             )
         return result if result else None
 
-    def _refresh_media_marker(self) -> bool:
-        """Re-fetch /props and update the cached media marker if it changed.
-
-        The server randomizes the marker per startup (unless LLAMA_MEDIA_MARKER
-        is set), so a stale marker indicates a restart. Returns True iff the
-        marker was updated to a new value — used to gate a one-shot retry of
-        a failed embeddings request.
-        """
-        if self.provider is None:
-            return False
-        try:
-            props = self._fetch_llama_props(self.provider, self.genai_config.model)
-        except Exception as e:
-            logger.warning("Failed to refresh llama.cpp media marker: %s", e)
-            return False
-
-        marker = props.get("media_marker")
-
-        if not isinstance(marker, str) or not marker or marker == self._media_marker:
-            return False
-
-        logger.info("llama.cpp media marker changed (server restart); refreshed")
-        self._media_marker = marker
-        return True
-
     def embed(
         self,
         texts: list[str] | None = None,
         images: list[bytes] | None = None,
     ) -> list[np.ndarray]:
-        """Generate embeddings via llama.cpp /embeddings endpoint.
+        """Generate embeddings via llama.cpp /v1/embeddings endpoint.
 
-        Supports batch requests. Uses content format with prompt_string and
-        multimodal_data for images (PR #15108). Server must be started with
-        --embeddings and --mmproj for multimodal support.
+        Each text or image is one entry in `input`, using the chat-style
+        content array from ggml-org/llama.cpp#29556. Server must be started
+        with --embeddings, and --mmproj for image support.
         """
         if self.provider is None:
             logger.warning(
@@ -843,49 +801,42 @@ class LlamaCppClient(GenAIClient):
 
         EMBEDDING_DIM = 768
 
-        encoded_images: list[str] = []
+        inputs: list[dict[str, Any]] = [
+            {"content": [{"type": "text", "text": text}]} for text in texts
+        ]
+
         for img in images:
             # llama.cpp uses STB which does not support WebP; convert to JPEG
             jpeg_bytes = _to_jpeg(img)
             to_encode = jpeg_bytes if jpeg_bytes is not None else img
-            encoded_images.append(base64.b64encode(to_encode).decode("utf-8"))
-
-        def build_content() -> list[dict[str, Any]]:
-            # prompt_string must contain the server's media marker placeholder
-            # for each image. The marker is randomized per server startup.
-            content: list[dict[str, Any]] = []
-            for text in texts:
-                content.append({"prompt_string": text})
-            for encoded in encoded_images:
-                content.append(
+            encoded = base64.b64encode(to_encode).decode("utf-8")
+            # The trailing newline keeps tokenization identical to the older
+            # "<__media__>\n" prompt_string format, so indexed vectors stay valid
+            inputs.append(
                 {
-                        "prompt_string": f"{self._media_marker}\n",
-                        "multimodal_data": [encoded],
+                    "content": [
+                        {
+                            "type": "image_url",
+                            "image_url": {"url": f"data:image/jpeg;base64,{encoded}"},
+                        },
+                        {"type": "text", "text": "\n"},
+                    ]
                 }
             )
-            return content
 
-        def post_embeddings() -> requests.Response:
-            return self._post(
-                f"{self.provider}/embeddings",
-                json={"model": self.genai_config.model, "content": build_content()},
+        try:
+            response = self._post(
+                f"{self.provider}/v1/embeddings",
+                json={
+                    "model": self.genai_config.model,
+                    "input": inputs,
+                    "encoding_format": "float",
+                },
                 timeout=self.timeout,
             )
-
-        try:
-            try:
-                response = post_embeddings()
             response.raise_for_status()
-            except requests.exceptions.RequestException:
-                # The server may have restarted with a new media marker.
-                # Refresh from /props; only retry if the marker actually changed.
-                if not encoded_images or not self._refresh_media_marker():
-                    raise
-                response = post_embeddings()
-                response.raise_for_status()
-            result = response.json()
+            items = response.json().get("data")
 
-            items = result.get("data", result) if isinstance(result, dict) else result
             if not isinstance(items, list):
                 logger.warning("llama.cpp embeddings returned unexpected format")
                 return []
@@ -896,11 +847,7 @@ class LlamaCppClient(GenAIClient):
                 if emb is None:
                     logger.warning("llama.cpp embeddings item missing embedding field")
                     continue
-                arr = np.array(emb, dtype=np.float32)
-                if arr.ndim > 1:
-                    # llama.cpp can return token-level embeddings; pool per item
-                    arr = arr.mean(axis=0)
-                arr = arr.flatten()
+                arr = np.array(emb, dtype=np.float32).flatten()
                 orig_dim = arr.size
                 if orig_dim != EMBEDDING_DIM:
                     if orig_dim > EMBEDDING_DIM:

frigate/genai/prompts.py

@@ -104,6 +104,21 @@ def build_review_description_prompt(
         else:
             return "\n- (No objects detected)"
 
+    def get_state_changes_section() -> str:
+        # empty when nothing changed so the prompt is otherwise unaffected
+        changes = review_data.get("classification_state_changes")
+
+        if not changes:
+            return ""
+
+        return (
+            "\n\n## State Changes\n\n"
+            "The camera's state classifiers watch fixed areas of the scene and "
+            "reported these changes. They come from the classifiers rather than "
+            "from the images, and they are reliable. Describe each one where it "
+            "fits in the sequence of events.\n- " + "\n- ".join(changes)
+        )
+
     fields = get_review_field_guidelines(response_style)
     frame_guidance = f"\n{FRAME_ANNOTATION_GUIDANCE}" if frame_captions else ""
 
@@ -145,7 +160,7 @@ Respond with a JSON object matching the provided schema. Field-specific guidance
 - Camera: {review_data["camera"]}
 - Total frames: {len(thumbnails)} (Frame 1 = earliest, Frame {len(thumbnails)} = latest){frame_guidance}
 - Activity started at {review_data["start"]} and lasted {review_data["duration"]} seconds
-- Zones involved: {", ".join(review_data["zones"]) if review_data["zones"] else "None"}
+- Zones involved: {", ".join(review_data["zones"]) if review_data["zones"] else "None"}{get_state_changes_section()}
 
 ## Objects in Scene
 
@@ -196,6 +211,17 @@ def build_review_summary_prompt(
         f" to "
         f"{datetime.datetime.fromtimestamp(end_ts).strftime('%B %d, %Y at %I:%M %p')}"
     )
+    has_state_changes = any(
+        "state_changes" in item
+        for event in events
+        for item in [event, *event.get("context", [])]
+    )
+    state_changes_format = (
+        '\n- "state_changes" (only on some events): changes to monitored areas '
+        "reported by the camera's state classifiers, which are reliable"
+        if has_state_changes
+        else ""
+    )
     prompt = f"""
 You are a security officer writing a concise security report.
 
@@ -203,7 +229,7 @@ Time range: {time_range}
 
 Input format: Each event is a JSON object with:
 - "title", "scene", "confidence", "potential_threat_level" (0-2), "other_concerns", "camera", "time", "start_time", "end_time"
-- "context": array of related events from other cameras that occurred during overlapping time periods
+- "context": array of related events from other cameras that occurred during overlapping time periods{state_changes_format}
 
 **Note: Use the "scene" field for event descriptions in the report. Ignore any "shortSummary" field if present.**
 

frigate/models.py

@@ -195,15 +195,20 @@ class Notice(Model):
     first_seen = DateTimeField()
     last_seen = DateTimeField()
     count = IntegerField(default=1)
-    dismissed_at = DateTimeField(null=True)
+    # hidden until the next occurrence
+    acknowledged_at = DateTimeField(null=True)
+    # hidden for good
+    muted_at = DateTimeField(null=True)
 
 
 class NoticeStats(Model):
     kind = CharField(null=False, primary_key=True, max_length=50)
     occurrences = IntegerField(default=0)
-    dismissals = IntegerField(default=0)
+    acknowledgements = IntegerField(default=0)
+    mutes = IntegerField(default=0)
     first_seen = DateTimeField()
     last_seen = DateTimeField()
     # watermarks for a future analytics reporter; unused until then
     reported_occurrences = IntegerField(default=0)
-    reported_dismissals = IntegerField(default=0)
+    reported_acknowledgements = IntegerField(default=0)
+    reported_mutes = IntegerField(default=0)

frigate/notices/registry.py

@@ -5,7 +5,7 @@ import threading
 from collections.abc import Callable
 from dataclasses import dataclass
 from datetime import datetime
-from typing import Any
+from typing import Any, cast
 
 from frigate.const import REPLAY_CAMERA_PREFIX
 from frigate.models import Notice, NoticeStats
@@ -77,9 +77,8 @@ class NoticeRegistry:
     ) -> None:
         """Insert a notice or count another occurrence of it.
 
-        A dismissed notice stays dismissed when it is raised again, unless its
-        kind sets reopen_at_count. A kind that should come back after a
-        dismissal gives each episode its own scope.
+        Another occurrence shows an acknowledged notice again. A muted notice
+        stays hidden.
         """
         definition = NOTICE_KINDS.get(kind)
 
@@ -112,7 +111,6 @@ class NoticeRegistry:
                     first_seen=now,
                     last_seen=now,
                     count=1,
-                    dismissed_at=None,
                 )
                 self._bump_occurrences(kind, 1, now)
 
@@ -175,7 +173,7 @@ class NoticeRegistry:
             self._notify()
 
     def resolve_camera(self, camera: str) -> None:
-        """Drop the notices and check dismissals of a camera being deleted."""
+        """Drop the notices and check mutes of a camera being deleted."""
         camera_kinds = [
             key
             for key, definition in NOTICE_KINDS.items()
@@ -193,7 +191,7 @@ class NoticeRegistry:
             )
 
             # a stream id names its camera first; a config id ends with camera.<name>
-            for check in self.dismissed_checks():
+            for check in self.muted_checks():
                 check_id = check["id"]
 
                 if check_id.startswith(f"stream:{camera}:") or (
@@ -205,26 +203,50 @@ class NoticeRegistry:
         if deleted:
             self._notify()
 
-    def purge_dismissed(self) -> int:
-        """Delete every dismissed row so each can show again. Returns how many."""
-        with self._lock:
-            return int(
-                Notice.delete().where(Notice.dismissed_at.is_null(False)).execute()
-            )
+    def acknowledge(self, row_id: str) -> bool:
+        """Hide a notice until it happens again.
 
-    def dismiss(self, row_id: str) -> bool:
-        """Hide a notice or check row for good. Returns False for an unknown id."""
+        Returns False for an unknown id or a kind that never repeats, such as
+        a check row or the update notice.
+        """
         with self._lock:
             existing = Notice.get_or_none(Notice.id == row_id)
 
             if existing is None:
-                # a check row gets a notice row only once it is dismissed
+                return False
+
+            definition = NOTICE_KINDS.get(existing.kind)
+
+            if definition is None or not definition.counts_repeats:
+                return False
+
+            if existing.acknowledged_at is not None or existing.muted_at is not None:
+                return True
+
+            Notice.update(acknowledged_at=datetime.now().timestamp()).where(
+                Notice.id == row_id
+            ).execute()
+            NoticeStats.update(acknowledgements=NoticeStats.acknowledgements + 1).where(
+                NoticeStats.kind == existing.kind
+            ).execute()
+
+        self._notify()
+        return True
+
+    def mute(self, row_id: str) -> bool:
+        """Hide a notice or check row for good. Returns False for an unknown id."""
+        now = datetime.now().timestamp()
+
+        with self._lock:
+            existing = Notice.get_or_none(Notice.id == row_id)
+
+            if existing is None:
+                # a check row gets a notice row only once it is muted
                 kind, _, scope = row_id.partition(":")
 
                 if kind not in CHECK_KINDS or not scope:
                     return False
 
-                now = datetime.now().timestamp()
                 Notice.create(
                     id=row_id,
                     kind=kind,
@@ -233,40 +255,78 @@ class NoticeRegistry:
                     first_seen=now,
                     last_seen=now,
                     count=1,
-                    dismissed_at=now,
+                    muted_at=now,
                 )
                 return True
 
-            if existing.dismissed_at is not None:
+            if existing.muted_at is not None:
                 return True
 
             if existing.kind not in NOTICE_KINDS:
                 return False
 
-            Notice.update(dismissed_at=datetime.now().timestamp()).where(
+            Notice.update(acknowledged_at=None, muted_at=now).where(
                 Notice.id == row_id
             ).execute()
-            NoticeStats.update(dismissals=NoticeStats.dismissals + 1).where(
+            NoticeStats.update(mutes=NoticeStats.mutes + 1).where(
                 NoticeStats.kind == existing.kind
             ).execute()
 
         self._notify()
         return True
 
-    def dismissed_checks(self) -> list[dict[str, Any]]:
-        """Dismissed config and stream check rows, newest first."""
+    def unhide(self, row_id: str) -> bool:
+        """Show an acknowledged or muted row again. Returns False for an unknown id."""
+        with self._lock:
+            existing = Notice.get_or_none(Notice.id == row_id)
+
+            if existing is None:
+                return False
+
+            if existing.kind in CHECK_KINDS:
+                Notice.delete_by_id(row_id)
+                return True
+
+            Notice.update(acknowledged_at=None, muted_at=None).where(
+                Notice.id == row_id
+            ).execute()
+
+        self._notify()
+        return True
+
+    def unhide_all(self) -> None:
+        """Show every acknowledged and muted row again."""
+        with self._lock:
+            for check in self.muted_checks():
+                Notice.delete_by_id(check["id"])
+
+            shown = (
+                Notice.update(acknowledged_at=None, muted_at=None)
+                .where(
+                    Notice.acknowledged_at.is_null(False)
+                    | Notice.muted_at.is_null(False)
+                )
+                .execute()
+            )
+
+        if shown:
+            self._notify()
+
+    def muted_checks(self) -> list[dict[str, Any]]:
+        """Muted config and stream check rows, newest first."""
         rows = (
             Notice.select()
             .where(Notice.kind.in_(list(CHECK_KINDS)))
-            .order_by(Notice.dismissed_at.desc())
+            .order_by(Notice.muted_at.desc())
         )
-        return [{"id": row.id, "dismissed_at": row.dismissed_at} for row in rows]
+        return [{"id": row.id, "muted_at": row.muted_at} for row in rows]
 
-    def active(self, include_dismissed: bool = False) -> list[dict[str, Any]]:
+    def active(self, include_hidden: bool = False) -> list[dict[str, Any]]:
         """Notices most severe first, then most recent first.
 
         Args:
-            include_dismissed: Also return dismissed notices, for the history view
+            include_hidden: Also return acknowledged and muted notices, for the
+                hidden list
         """
         rows = []
 
@@ -276,7 +336,9 @@ class NoticeRegistry:
             if definition is None:
                 continue
 
-            if row.dismissed_at is not None and not include_dismissed:
+            hidden = row.acknowledged_at is not None or row.muted_at is not None
+
+            if hidden and not include_hidden:
                 continue
 
             rows.append(
@@ -291,7 +353,9 @@ class NoticeRegistry:
                     "first_seen": row.first_seen,
                     "last_seen": row.last_seen,
                     "count": row.count,
-                    "dismissed_at": row.dismissed_at,
+                    "acknowledgeable": definition.counts_repeats,
+                    "acknowledged_at": row.acknowledged_at,
+                    "muted_at": row.muted_at,
                 }
             )
 
@@ -309,29 +373,18 @@ class NoticeRegistry:
             {
                 "kind": row.kind,
                 "occurrences": row.occurrences,
-                "dismissals": row.dismissals,
+                "acknowledgements": row.acknowledgements,
+                "mutes": row.mutes,
                 "first_seen": row.first_seen,
                 "last_seen": row.last_seen,
                 "reported_occurrences": row.reported_occurrences,
-                "reported_dismissals": row.reported_dismissals,
+                "reported_acknowledgements": row.reported_acknowledgements,
+                "reported_mutes": row.reported_mutes,
             }
             for row in NoticeStats.select()
             if row.kind in NOTICE_KINDS
         ]
 
-    def mark_reported(self, snapshot: list[dict[str, Any]]) -> None:
-        """Move the analytics watermarks to the counts a sent report was built from.
-
-        Using the snapshot rather than the current counts sends anything raised
-        while the report was in flight with the next one.
-        """
-        with self._lock:
-            for row in snapshot:
-                NoticeStats.update(
-                    reported_occurrences=row["occurrences"],
-                    reported_dismissals=row["dismissals"],
-                ).where(NoticeStats.kind == row["kind"]).execute()
-
     def _write_repeats(
         self,
         row: Notice,
@@ -340,18 +393,18 @@ class NoticeRegistry:
         last_seen: float,
         params: dict[str, Any],
     ) -> None:
-        # called with the lock held
-        fields: dict[str, Any] = {
-            "count": row.count + count,
-            "last_seen": last_seen,
-            "params": params,
-        }
-        reopen_at = NOTICE_KINDS[kind].reopen_at_count
+        # called with the lock held; held repeats from before an acknowledgement
+        # still count but leave the notice hidden
+        still_acknowledged = row.acknowledged_at is not None and (
+            last_seen <= cast(float, row.acknowledged_at)
+        )
 
-        if reopen_at is not None and row.count < reopen_at <= row.count + count:
-            fields["dismissed_at"] = None
-
-        Notice.update(**fields).where(Notice.id == row.id).execute()
+        Notice.update(
+            count=row.count + count,
+            last_seen=last_seen,
+            params=params,
+            acknowledged_at=row.acknowledged_at if still_acknowledged else None,
+        ).where(Notice.id == row.id).execute()
         self._bump_occurrences(kind, count, last_seen)
 
     def _prune(self, kind: str, keep: int) -> None:
@@ -375,7 +428,8 @@ class NoticeRegistry:
             NoticeStats.create(
                 kind=kind,
                 occurrences=count,
-                dismissals=0,
+                acknowledgements=0,
+                mutes=0,
                 first_seen=now,
                 last_seen=now,
             )

frigate/notices/types.py

@@ -28,9 +28,9 @@ class NoticeKind:
         category: camera, detector, model, or system; a camera scope is a
             camera name, and the UI shows it
         link: app route or absolute URL for the row, filled in from params
-        counts_repeats: whether raising an existing notice counts another occurrence
+        counts_repeats: whether raising an existing notice counts another
+            occurrence, which also shows an acknowledged notice again
         batch_repeats: whether repeats wait in memory for the next flush
-        reopen_at_count: count at which a dismissed notice shows again
         keep_latest: rows of this kind to keep; a new row drops the oldest
         reportable: whether a future analytics reporter may send this kind's counts
     """
@@ -41,7 +41,6 @@ class NoticeKind:
     link: str | None = None
     counts_repeats: bool = True
     batch_repeats: bool = False
-    reopen_at_count: int | None = None
     keep_latest: int | None = None
     reportable: bool = True
 
@@ -67,6 +66,12 @@ _KINDS = (
         "camera",
         link="/system#cameras",
     ),
+    NoticeKind(
+        "ffmpeg_high_cpu", NoticeSeverity.warning, "camera", link="/system#cameras"
+    ),
+    NoticeKind(
+        "detect_high_cpu", NoticeSeverity.warning, "camera", link="/system#cameras"
+    ),
     NoticeKind("shm_too_low", NoticeSeverity.warning, "system", link="/system#storage"),
     # one row per user per burst; the login log lines carry the address
     NoticeKind(
@@ -75,10 +80,9 @@ _KINDS = (
         "system",
         link="/logs",
         batch_repeats=True,
-        reopen_at_count=5,
         keep_latest=100,
     ),
-    # one row per release, so a dismissal lasts until the next release
+    # one row per release, so muting it lasts until the next release
     NoticeKind(
         "update_available",
         NoticeSeverity.info,
@@ -86,23 +90,13 @@ _KINDS = (
         link="https://github.com/blakeblackshear/frigate/releases/tag/v{version}",
         counts_repeats=False,
         keep_latest=1,
-        reportable=False,
-    ),
-    # raised while analytics is off; the row keeps a dismissal across restarts
-    NoticeKind(
-        "analytics_prompt",
-        NoticeSeverity.info,
-        "system",
-        link="/settings?page=systemTelemetry",
-        counts_repeats=False,
-        reportable=False,
     ),
 )
 
 NOTICE_KINDS: dict[str, NoticeKind] = {kind.key: kind for kind in _KINDS}
 
 # the Health tab builds config and stream check rows in the browser, so a notice
-# row of these kinds only records a dismissal; its other fields are placeholders
+# row of these kinds only records a mute; its other fields are placeholders
 CHECK_KINDS = frozenset({"config", "stream"})
 
 

frigate/ptz/autotrack.py

@@ -1397,9 +1397,6 @@ class PtzAutoTracker:
     def is_autotracking(self, camera: str):
         return self.tracked_object[camera] is not None
 
-    def autotracked_object_region(self, camera: str):
-        return self.tracked_object[camera]["region"]
-
     def autotrack_object(self, camera: str, obj: TrackedObject):
         if camera not in self.config.cameras:
             return
@@ -1538,8 +1535,6 @@ class PtzAutoTracker:
         # returns camera to preset after timeout when tracking is over
         autotracker_config = self.config.cameras[camera].onvif.autotracking
 
-        if not self.autotracker_init[camera]:
-            self._autotracker_setup(self.config.cameras[camera], camera)
         # regularly update camera status
         if not self.ptz_metrics[camera].motor_stopped.is_set():
             await self.onvif.get_camera_status(camera)