Fix record status stuck offline after ffmpeg restart (#24522 )

* 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
Fix camera name collision bypassing admin check (#24516 )
2026-10-01 20:36:51 +03:00 · 2026-10-01 08:56:27 -06:00 · 2026-10-01 08:56:04 -06:00 · 2026-10-01 07:36:39 -06:00 · 2026-09-30 08:29:45 -05:00 · 2026-09-30 06:02:45 -06:00
547 changed files with 32589 additions and 11883 deletions
@@ -1,7 +1,7 @@
 default_target: local
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.19.0
+VERSION = 0.18.1
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
 BOARDS= #Initialized empty
@@ -150,7 +150,9 @@ http {
            include auth_request.conf;
            types {
                video/mp4 mp4;
-                image/jpeg jpg;
+                image/jpeg jpg jpeg;
                image/png png;
                image/webp webp;
            }
            expires 7d;
@@ -894,6 +894,41 @@ deepstack:
            api_url: http://<your_codeproject_ai_server_ip>:<port>/v1/vision/detection
            type: deepstack
            api_timeout: 0.1 # seconds
 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 > Detectors and model** and select **ZMQ IPC** from the detector type dropdown and click **Add**, then set the endpoint to `tcp://xdna:5555`. Then on the same page, in the **Custom Model** tab, 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: |-
        detectors:
          xdna:
            type: zmq
            endpoint: tcp://xdna:5555
        model:
          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:
@@ -1100,7 +1135,7 @@ synaptics:
    - key: ssd
      label: SSD MobileNet
      recommended: true
-      download: A synap model is provided in the container at `/mobilenet.synap` and is used by this detector type by default. The model comes from the [Synap-release Github](https://github.com/synaptics-astra/synap-release/tree/v1.5.0/models/dolphin/object_detection/coco/model/mobilenet224_full80).
+      download: A synap model is provided in the container at `/synaptics/mobilenet.synap` and is used by this detector type by default. The model comes from the [Synap-release Github](https://github.com/synaptics-astra/synap-release/tree/v1.5.0/models/dolphin/object_detection/coco/model/mobilenet224_full80).
      ui: |-
        Navigate to **Settings > System > Detectors and model** and select **Synaptics** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
@@ -251,17 +251,11 @@ birdseye:
  # Optional: Encoding quality of the mpeg1 feed (default: shown below)
  # 1 is the highest quality, and 31 is the lowest. Lower quality feeds utilize less CPU resources.
  quality: 8
-  # Optional: Activity types that include cameras in Birdseye (default: shown below)
+  # Optional: Mode of the view. Available options are: objects, motion, and continuous
-  # Multiple activity types can be enabled at the same time.
+  #   objects - cameras are included if they have had a tracked object within the last 30 seconds
-  mode:
+  #   motion - cameras are included if motion was detected in the last 30 seconds
-    # Optional: All cameras are included always (default: shown below)
+  #   continuous - all cameras are included always
-    continuous: False
+  mode: objects
    # Optional: Cameras are included if motion was detected in the last 30 seconds (default: shown below)
    motion: False
    # Optional: Cameras are included if they have had an active tracked object within the last 30 seconds (default: shown below)
    objects: True
    # Optional: Cameras are included while they have a stationary tracked object (default: shown below)
    stationary_objects: False
  # Optional: Threshold for camera activity to stop showing camera (default: shown below)
  inactivity_threshold: 30
  # Optional: Configure the birdseye layout
@@ -292,9 +286,7 @@ 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
  # 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
@@ -645,42 +637,6 @@ record:
      #       For example, if the camera retain mode is "motion", the segments without motion are
      #       never stored, so setting the mode to "all" here won't bring them back.
      mode: motion
  # Optional: Sub stream recording settings
  # Records a second, lower quality stream for quality selection during playback
  # and extended low quality retention. Requires the record_sub role to be assigned
  # to one of the camera's inputs.
  sub:
    # Optional: Enable sub stream recording (default: shown below)
    # NOTE: Recording must also be enabled for sub stream recording to run.
    enabled: False
    # Optional: Continuous retention settings for sub stream recordings
    continuous:
      # Optional: Number of days to retain sub stream recordings regardless of tracked objects or motion (default: shown below)
      days: 0
    # Optional: Motion retention settings for sub stream recordings
    motion:
      # Optional: Number of days to retain sub stream recordings triggered by motion (default: shown below)
      days: 0
    # Optional: Retention settings for sub stream recordings of alerts
    # NOTE: Pre and post capture windows are taken from the main alerts config above.
    alerts:
      # Required: Retention days (default: shown below)
      days: 10
      # Optional: Mode for retention. (default: shown below)
      #   all - save all sub stream recording segments for alerts regardless of activity
      #   motion - save all sub stream recording segments for alerts with any detected motion
      #   active_objects - save all sub stream recording segments for alerts with active/moving objects
      mode: motion
    # Optional: Retention settings for sub stream recordings of detections
    # NOTE: Pre and post capture windows are taken from the main detections config above.
    detections:
      # Required: Retention days (default: shown below)
      days: 10
      # Optional: Mode for retention. (default: shown below)
      #   all - save all sub stream recording segments for detections regardless of activity
      #   motion - save all sub stream recording segments for detections with any detected motion
      #   active_objects - save all sub stream recording segments for detections with active/moving objects
      mode: motion
 # Optional: Configuration for the snapshots written to the clips directory for each tracked object
 # Timestamp, bounding_box, crop and height settings are applied by default to API requests for snapshots.
@@ -932,7 +888,7 @@ cameras:
        # Required: the path to the stream
        # NOTE: path may include environment variables or docker secrets, which must begin with 'FRIGATE_' and be referenced in {}
        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-          # Required: list of roles for this stream. valid values are: audio,detect,record,record_sub
+          # Required: list of roles for this stream. valid values are: audio,detect,record
          # NOTICE: In addition to assigning the audio, detect, and record roles
          # they must also be enabled in the camera config.
          roles:
@@ -18,14 +18,13 @@ Each camera tile in Birdseye is composed from the frames of the stream assigned
 ## Birdseye Behavior
-### Birdseye Activity Types
+### Birdseye Modes
-Birdseye offers independent activity types that control when cameras are shown. Multiple activity types can be enabled together.
+Birdseye offers different modes to customize which cameras show under which circumstances.
- **continuous:** The camera is always included
+- **continuous:** All cameras are always included
- **motion:** The camera is included when motion was detected within the last 30 seconds
+- **motion:** Cameras that have detected motion within the last 30 seconds are included
- **objects:** The camera is included when an active object was tracked within the last 30 seconds
+- **objects:** Cameras that have tracked an active object within the last 30 seconds are included
 - **stationary_objects:** The camera is included while a stationary object is tracked
 ### Custom Birdseye Icon
@@ -40,30 +39,27 @@ To include a camera in Birdseye view only for specific circumstances, or exclude
 **Global settings:** Navigate to <NavPath path="Settings > System > Birdseye" /> to configure the default Birdseye behavior for all cameras.
-**Per-camera overrides:** Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> to override the activity types or disable Birdseye for a specific camera.
+**Per-camera overrides:** Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> to override the mode or disable Birdseye for a specific camera.
-| Field                  | Description                                                |
+| Field               | Description                                                   |
-| ---------------------- | ---------------------------------------------------------- |
+| ------------------- | ------------------------------------------------------------- |
-| **Enable Birdseye**    | Whether this camera appears in Birdseye view               |
+| **Enable Birdseye** | Whether this camera appears in Birdseye view                  |
-| **Activity types**     | Conditions that determine when to show the camera          |
+| **Tracking mode**   | When to show the camera: `continuous`, `motion`, or `objects` |
 </TabItem>
 <TabItem value="yaml">
-```yaml {8-11,13-15}
+```yaml {8-10,12-14}
 # Include all cameras by default in Birdseye view
 birdseye:
  enabled: True
-  mode:
+  mode: continuous
    continuous: True
 cameras:
  front:
    # Only include the "front" camera in Birdseye view when objects are detected
    birdseye:
-      mode:
+      mode: objects
        continuous: False
        objects: True
  back:
    # Exclude the "back" camera from Birdseye view
    birdseye:
@@ -106,5 +106,3 @@ Output arguments are passed to FFmpeg after your camera source and control how r
 | preset-record-mjpeg              | Record - MJPEG Cameras          | Record an MJPEG stream              | Restreaming the MJPEG stream is recommended instead                                                                                                               |
 | preset-record-jpeg               | Record - JPEG Cameras           | Record a live JPEG                  | Restreaming the live JPEG is recommended instead                                                                                                                  |
 | preset-record-ubiquiti           | Record - Ubiquiti Cameras       | Record a Ubiquiti stream with audio | Handles Ubiquiti's non-standard audio format                                                                                                                      |
 These presets apply to the `record` output args. If [sub stream recording](/configuration/record#sub-stream-recording) is enabled, the same args are used for the `record_sub` role unless `output_args.record_sub` is set, which accepts the same presets and manual args.
@@ -59,13 +59,17 @@ Running Generative AI models on CPU is not recommended, as high inference times
 ### Recommended Local Models
 #### Vision models
 You must use a vision-capable model with Frigate. The following models are recommended for local deployment of the `descriptions` and `chat` roles:
-| Model      | Notes                                                                                                                                                                |
+| Model               | Notes                                                                                                                                                                |
-| ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `qwen3-vl` | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object.                                              |
+| `qwen3-vl`          | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object.                                              |
-| `qwen3.6`  | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
+| `qwen3.6`/`qwen3.8` | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
-| `gemma4`   | Strong situational understanding, sometimes resorts to more vague terms like 'interacts' instead of assigning a specific action.                                     |
+| `gemma4`            | Strong situational understanding, sometimes resorts to more vague terms like 'interacts' instead of assigning a specific action.                                     |
 #### Embedding models
 The `embeddings` role needs a different kind of model. Text queries are matched against the stored image embeddings, so the model must be trained to place images and text into the same vector space. A chat or description model will still return vectors when asked, but those vectors are not trained for retrieval and text searches will return poor matches with no error to indicate why.
@@ -498,7 +498,7 @@ cameras:
 ## Synaptics
-Hardware accelerated video de-/encoding is supported on Synpatics SL-series SoC.
+Hardware accelerated video de-/encoding is supported on Synaptics SL-series SoC.
 ### Prerequisites
@@ -8,7 +8,7 @@ import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";
 import FaqItem from "@site/src/components/FaqItem";
-Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car` or `motorcycle`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
+Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car`, `motorcycle`, `bus`, `truck`, `school_bus`, or `garbage_truck`, depending on which of those labels your model detects. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
 LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. When a vehicle becomes stationary, LPR continues to run for a short time after to attempt recognition.
@@ -24,7 +24,7 @@ When a plate is recognized, the details are:
 - Viewable in the Details pane in Review/History.
 - Viewable in the Tracked Object Details pane in Explore (sub labels and recognized license plates).
 - Filterable through the More Filters menu in Explore.
- Published via the `frigate/events` MQTT topic as a `sub_label` ([known](#matching)) or `recognized_license_plate` (unknown) for the `car` or `motorcycle` tracked object.
+- Published via the `frigate/events` MQTT topic as a `sub_label` ([known](#matching)) or `recognized_license_plate` (unknown) for the vehicle tracked object.
 - Published via the `frigate/tracked_object_update` MQTT topic with `name` (if [known](#matching)) and `plate`.
 ## Model Requirements
@@ -35,7 +35,7 @@ Users without a model that detects license plates can still run LPR. Frigate use
 :::note
-In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle` before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a `car` or `motorcycle` will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
+In the default mode, Frigate's LPR needs to first detect a vehicle before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a vehicle will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
 :::
@@ -86,7 +86,7 @@ cameras:
 </TabItem>
 </ConfigTabs>
-For non-dedicated LPR cameras, ensure that your camera is configured to detect objects of type `car` or `motorcycle`, and that a car or motorcycle is actually being detected by Frigate. Otherwise, LPR will not run.
+For non-dedicated LPR cameras, ensure that your camera is configured to detect vehicle objects, and that a vehicle is actually being detected by Frigate. Otherwise, LPR will not run. The object types that can carry a plate are defined by your model's `attributes_map`, so if your model detects other vehicle labels, you can add them there.
 Like the other real-time processors in Frigate, license plate recognition runs on the camera stream defined by the `detect` role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements.
@@ -158,7 +158,7 @@ lpr:
 Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
- **Known plates**: Assign custom `sub_label` values to `car` and `motorcycle` objects when a recognized plate matches a known value. These labels appear in the UI, filters, and notifications. Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
+- **Known plates**: Assign custom `sub_label` values to vehicle objects when a recognized plate matches a known value. These labels appear in the UI, filters, and notifications. Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
 - **Match distance**: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate. For example, setting to `1` allows a plate `ABCDE` to match `ABCBE` or `ABCD`. This parameter will _not_ operate on known plates that are defined as regular expressions.
 </TabItem>
@@ -316,7 +316,7 @@ lpr:
 :::note
-If a camera is configured to detect `car` or `motorcycle` but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
+If a camera is configured to detect vehicles but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
 <ConfigTabs>
 <TabItem value="ui">
@@ -378,10 +378,10 @@ Navigate to <NavPath path="Settings > Camera configuration > Object detection" /
 Navigate to <NavPath path="Settings > Camera configuration > Objects" />.
-| Field                                          | Description         |
+| Field                                                     | Description         |
-| ---------------------------------------------- | ------------------- |
+| --------------------------------------------------------- | ------------------- |
-| **Objects to track**                           | Add `license_plate` |
+| **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" />.
@@ -456,7 +456,7 @@ With this setup:
 - Snapshots will have license plate bounding boxes on them.
 - The `frigate/events` MQTT topic will publish tracked object updates.
 - Debug view will display `license_plate` bounding boxes.
- If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the `car` / `motorcycle` and the `license_plate` in the snapshots on the Frigate+ website, even if the car is barely visible.
+- If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the vehicle and the `license_plate` in the snapshots on the Frigate+ website, even if the vehicle is barely visible.
 ### Using the Secondary LPR Pipeline (Without Frigate+)
@@ -611,9 +611,9 @@ If you are still having issues detecting plates, start with a basic configuratio
 </FaqItem>
-<FaqItem id="can-i-run-lpr-without-detecting-car-or-motorcycle-objects" question={<>Can I run LPR without detecting <code>car</code> or <code>motorcycle</code> objects?</>}>
+<FaqItem id="can-i-run-lpr-without-detecting-car-or-motorcycle-objects" question={<>Can I run LPR without detecting vehicle objects?</>}>
-In normal LPR mode, Frigate requires a `car` or `motorcycle` to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
+In normal LPR mode, Frigate requires a vehicle to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
 </FaqItem>
@@ -699,7 +699,7 @@ lpr:
 4. Ensure the characters on detected plates are being _recognized_.
   - Check the **Plate recognition** inference time in Enrichment metrics (<NavPath path="System metrics > Enrichments" />). High inference times (> 100ms) could lead to poor recognition results, especially for dedicated LPR cameras where the plate crosses the frame quickly.
   - Enable `debug_save_plates` to save images of detected text on plates to the clips directory (`/media/frigate/clips/lpr`). Ensure these images are readable and the text is clear.
-   - Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the `car` or `motorcycle` label will change to the recognized plate when LPR is enabled and working.
+   - Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the vehicle's label will change to the recognized plate when LPR is enabled and working.
   - Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
 </FaqItem>
@@ -714,13 +714,13 @@ LPR's performance impact depends on your hardware. Ensure you have at least 4GB
 The YOLOv9 license plate detector model will run (and the metric will appear) if you've enabled LPR but haven't defined `license_plate` as an object to track, either at the global or camera level.
-If you are detecting `car` or `motorcycle` on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
+If you are detecting vehicles on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
 </FaqItem>
 <FaqItem id="it-looks-like-frigate-picked-up-my-cameras-timestamp-or-overlay-text-as-the-license-plate-how-can-i-prevent-this" question="It looks like Frigate picked up my camera's timestamp or overlay text as the license plate. How can I prevent this?">
-This could happen if cars or motorcycles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.
+This could happen if vehicles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.
 If you are using a model that natively detects `license_plate`, add an _object mask_ of type `license_plate` and a _motion mask_ over your text.
@@ -29,6 +29,7 @@ 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**
@@ -508,6 +509,28 @@ To verify that the integration is working correctly, start Frigate and observe t
 # 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.
@@ -45,10 +45,10 @@ 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                                                      |
+| Field                                              | Description                                                      |
-| --------------------------------------- | ---------------------------------------------------------------- |
+| -------------------------------------------------- | ---------------------------------------------------------------- |
-| **Object filters > Person > Min Score** | Minimum score for a single detection to initiate tracking        |
+| **Object filters > Person > Minimum confidence**   | 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 > 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.
@@ -103,12 +103,12 @@ 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                                                              |
+| Field                                              | Description                                                              |
-| --------------------------------------- | ------------------------------------------------------------------------ |
+| -------------------------------------------------- | ------------------------------------------------------------------------ |
-| **Object filters > Person > Min Area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Minimum object 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 > Maximum object 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 > Minimum aspect 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 > 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.
@@ -70,14 +70,14 @@ Object filters help reduce false positives by constraining the size, shape, and
 Navigate to <NavPath path="Settings > Global configuration > Objects" />.
-| Field                                   | Description                                                              |
+| Field                                              | Description                                                              |
-| --------------------------------------- | ------------------------------------------------------------------------ |
+| -------------------------------------------------- | ------------------------------------------------------------------------ |
-| **Object filters > Person > Min Area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Minimum object 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 > Maximum object 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 > Minimum aspect 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 > Maximum aspect 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 > Minimum confidence**   | Minimum score for the object to initiate tracking                        |
-| **Object filters > Person > Threshold** | Minimum computed score to be considered a true positive                  |
+| **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" />.
@@ -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.
@@ -9,7 +9,7 @@ import NavPath from "@site/src/components/NavPath";
 Recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM-DD/HH/<camera_name>/MM.SS.mp4` in **UTC time**. These recordings are written directly from your camera stream without re-encoding. Each camera supports a configurable retention policy. Frigate chooses the largest matching retention value between the recording retention and the tracked object retention when determining if a recording should be removed.
-New recording segments are written from the camera stream to cache, they are only moved to disk if they match the setup recording retention policy.
+New recording segments are written from the camera stream to cache, they are only moved to disk if they pass a validation check and match the setup recording retention policy.
 :::tip
@@ -275,163 +275,6 @@ record:
 This configuration will retain recording segments that overlap with alerts and detections for 10 days. Because multiple tracked objects can reference the same recording segments, this avoids storing duplicate footage for overlapping tracked objects and reduces overall storage needs.
 ## Sub Stream Recording
 In addition to the main recording stream, Frigate can record a second, lower quality stream for each camera. This serves two purposes:
 - **Quality selection during playback**: A quality selector (`Auto`, `Original`, or `Low`) appears in History view for cameras with sub stream recording enabled. `Original` and `Low` play only that stream's recordings. Time ranges where the selected stream has no footage are skipped during playback, and the selector notes when the selected stream has no recordings at all in the viewed time range. With `Auto` (the default), playback prefers the original quality and automatically falls back to the low quality stream when the connection cannot keep up, or for time ranges where the original recordings have expired. The selector shows each stream's video codec and audio details beneath the options; footage recorded by older Frigate versions shows no details.
 - **Extended retention**: Sub stream recordings have their own retention settings, fully independent of the main recordings. By giving the low quality recordings a longer retention period, you can keep weeks or months of low quality history using a fraction of the storage, and that history remains playable after the main recordings expire. Playback falls back to the low quality recordings automatically, and the timeline shows a muted treatment for time ranges where only low quality footage remains.
 ### Configuring sub stream recording
 Sub stream recording uses the `record_sub` input role. This role can be assigned to the same input as `detect`, so in the common case where detect already uses the camera's sub stream, no additional camera connection is needed. Like the main recording stream, sub stream segments are copied directly from the camera stream without re-encoding, so the recording quality is determined by the source stream.
 The following examples keep 7 days of full quality continuous recordings and 60 days of low quality continuous recordings:
 <ConfigTabs>
 <TabItem value="ui">
 Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and select the camera.
 - In **Camera inputs**, enable the **Record (Sub Stream)** role on the stream you want to record at low quality, commonly the same stream that has the **Detect** role. Only one stream may have this role, and it cannot be assigned to the same stream as the **Record** role.
 Navigate to <NavPath path="Settings > Camera configuration > Recording" /> and select the camera.
 - Set **Enable recording** to on
 - Set **Continuous retention > Retention days** to `7`
 - Set **Sub stream recording > Enable sub stream recording** to on
 - Set **Sub stream recording > Sub stream continuous retention > Retention days** to `60`
 The camera setup wizard also offers the **Record (Sub Stream)** role when assigning stream roles for a newly added camera.
 </TabItem>
 <TabItem value="yaml">
 ```yaml
 cameras:
  front_door:
    ffmpeg:
      inputs:
        - path: rtsp://camera/main
          roles:
            - record
        - path: rtsp://camera/sub
          roles:
            - detect
            - record_sub
    record:
      enabled: true
      continuous:
        days: 7
      sub:
        enabled: true
        continuous:
          days: 60
 ```
 If your camera does not provide a suitable sub stream (or the sub stream is already used at a resolution you don't want to record), you can use a go2rtc transcode as the source for `record_sub` instead:
 ```yaml
 go2rtc:
  streams:
    front_door: rtsp://camera/main
    front_door_lq: ffmpeg:front_door#video=h264#width=854#hardware
 cameras:
  front_door:
    ffmpeg:
      inputs:
        - path: rtsp://127.0.0.1:8554/front_door
          input_args: preset-rtsp-restream
          roles:
            - detect
            - record
        - path: rtsp://127.0.0.1:8554/front_door_lq
          input_args: preset-rtsp-restream
          roles:
            - record_sub
    record:
      enabled: true
      continuous:
        days: 7
      sub:
        enabled: true
        continuous:
          days: 60
 ```
 </TabItem>
 </ConfigTabs>
 The `record.sub` config supports the same retention structure as the main recording config: `continuous`, `motion`, `alerts`, and `detections` each with their own `days` (and `mode` for alerts and detections). The pre-capture and post-capture windows for alerts and detections are taken from the main `record.alerts` and `record.detections` config. Extending `sub.alerts.days` or `sub.detections.days` beyond the main values also keeps those review items visible in the review timeline for the longer window, with playback falling back to the low quality stream once the main recordings expire.
 :::note
 Recording must be enabled (`record.enabled`) for sub stream recording to run, and Frigate will fail to start if `record.sub.enabled` is set without a `record_sub` role assigned to one of the camera's inputs.
 :::
 ### How Auto picks a quality
 `Auto` measures throughput on every segment download and compares it against the original stream's bitrate (computed from the recorded footage itself). Playback drops to the low quality stream when any of these happen:
 - A freeze lasts 4 seconds (10 seconds when it starts within 2 seconds of a seek, since the seek target is rarely buffered), or freezes total 7 seconds within the last minute.
 - 3 downloads in a row measure below the original bitrate plus 10%, dropping quality before a stall ever becomes visible.
 - No first frame appears within 10 seconds, or loading fails outright.
 Playback returns to full quality only when measured throughput exceeds the original bitrate by 50%, checked continuously while playing the low quality stream and again at each new hour. The asymmetric thresholds (1.1x to drop, 1.5x to return) keep a borderline connection from switching back and forth.
 The most recent measurement is remembered on the device: a connection last measured below the original bitrate (or below 3 Mbps when the bitrate is not yet known) starts playback on the low quality stream so a first frame appears immediately, then upgrades within a few segments if the speed allows.
 The quality selector shows which stream Auto is currently playing and why. A browser with Data Saver enabled stays on the low quality stream, a browser that cannot decode the original stream's codec (for example H.265 without HEVC support) plays the low quality stream for that camera, and pinning `Original` or `Low` bypasses Auto entirely.
 ### Sub stream output args
 By default the sub stream is recorded with the same [output args](/configuration/ffmpeg_presets#output-args-presets) as the main recording stream, so it inherits any customization made to `ffmpeg.output_args.record`. Setting `ffmpeg.output_args.record_sub` gives the sub stream its own args instead. Like all `ffmpeg` config, this can be set globally or per camera.
 The most common reason to set this is a pair of streams whose audio differs. Many cameras send AAC on the main stream but PCM on the sub stream, and PCM cannot be copied into an mp4 recording. Copying the main stream's audio avoids re-encoding audio that is already AAC, while the sub stream still needs to be transcoded:
 ```yaml
 ffmpeg:
  output_args:
    # main stream audio is already AAC, so copy it
    record: preset-record-generic-audio-copy
    # sub stream audio is PCM, so transcode it to AAC
    record_sub: preset-record-generic-audio-aac
 ```
 Other reasons to set this are recording a sub stream whose codec needs a different preset than the main stream, such as `preset-record-mjpeg`, or forcing a matching audio sample rate across the two streams with manual args ending in `-c:a aac -ar 16000`.
 :::warning
 Avoid removing audio from only one of the two streams (for example with `-an`). When one stream has audio and the other does not, playback of time ranges that combine both qualities is silent, so stripping audio from the sub stream also silences the merged timeline.
 :::
 ### Which stream do features use?
 As a general rule, features that read recordings prefer the main stream and fall back to the sub stream for time ranges where the main recordings have expired. Analytics features use only the main stream.
 | Feature                                                                                  | Stream used                                                                                                   |
 | ---------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
 | Recording playback (History and Review)                                                  | Both (main preferred with sub fallback by default), or exactly one stream when a quality is selected manually |
 | Tracking details and Explore clip playback                                               | Main, falling back to sub where the main recordings have expired                                              |
 | Exports and clip downloads                                                               | Main; sub is used when no main recordings remain in the range (streams are never mixed in one file)           |
 | Frames grabbed from a recording in History (download snapshot, submit frame to Frigate+) | Main preferred, sub fallback                                                                                  |
 | Audio extraction (e.g., transcription)                                                   | Main preferred, sub fallback                                                                                  |
 | Motion search                                                                            | Main only                                                                                                     |
 | Review timeline motion data                                                              | Main only                                                                                                     |
 | Storage usage statistics                                                                 | Both streams counted, and listed separately per camera                                                        |
 This table covers only features that read recordings from disk. Tracked object snapshots and thumbnails (the images shown in Explore and sent with notifications, and the images submitted to Frigate+ from a tracked object) are captured live from the `detect` stream as the object is tracked, never from recordings, so sub stream recording does not affect them.
 ### Trade-offs
 - Recording a second stream increases overall storage use. The increase is typically small relative to the main recordings, since the low quality stream is much smaller.
 - The go2rtc transcode approach continuously encodes the low quality stream, which uses CPU or GPU resources. This cost only applies to the transcode path; recording the camera's native sub stream does not re-encode. See the [go2rtc hardware acceleration documentation](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg) for accelerating the transcode.
 - Many camera sub streams do not include audio. If the source stream has no audio, the low quality recordings will not have audio.
 - **Matching video codecs and audio settings between the two streams gives the smoothest playback.** When playback combines both qualities on one timeline (the default `Auto` behavior: for example original quality during events with low quality in between, or low quality history after the original recordings expire) and the streams use different video codecs or audio settings, for example H.265 on the main stream and H.264 on the sub stream, or 16 kHz audio on one and 8 kHz on the other, playback still works: Frigate inserts a decoder reset at each quality transition, which can cause a barely-perceptible pause there. Configuring both streams in the camera's firmware to use the same video codec, audio codec, and sample rate makes transitions fully seamless, and a mismatched audio sample rate can also be corrected with [sub stream output args](#sub-stream-output-args). If one stream has audio and the other does not, combined time ranges play **without audio**; selecting a single quality with the playback selector always keeps that stream's audio.
 ## Can I have "continuous" recordings, but only at certain times?
 Using Frigate UI, Home Assistant, or MQTT, cameras can be automated to only record in certain situations or at certain times.
@@ -448,7 +291,7 @@ For advanced use cases, the [custom export HTTP API](../integrations/api/export-
 POST /export/custom/{camera_name}/start/{start_time}/end/{end_time}
 ```
-The request body accepts `ffmpeg_input_args` and `ffmpeg_output_args` to control encoding, frame rate, filters, and other FFmpeg options. If neither is provided, Frigate defaults to time-lapse output settings (25x speed, 30 FPS).
+The request body accepts `ffmpeg_input_args` and `ffmpeg_output_args` to control encoding, frame rate, filters, and other FFmpeg options. If neither is provided, Frigate defaults to time-lapse output settings (25x speed, 30 FPS) with audio removed (`-an`). When providing your own `ffmpeg_input_args`, include `-an` if you want audio stripped from the export.
 The following example exports a time-lapse at 60x speed with 25 FPS:
@@ -197,7 +197,7 @@ For cameras that support two-way talk, go2rtc will automatically establish an au
 To prevent this, you must configure two separate stream instances:
 1. One stream instance with `#backchannel=0` for Frigate's viewing, recording, and detection (prevents go2rtc from establishing the blocking backchannel)
-2. A second stream instance without `#backchannel=0` for two-way talk functionality (can be used by Frigate's WebRTC viewer or other applications)
+2. A second stream instance with no `#` parameters at all for two-way talk functionality (can be used by Frigate's WebRTC viewer or other applications)
 Configuration example:
@@ -215,6 +215,8 @@ In this configuration:
 - `front_door` stream is used by Frigate for viewing, recording, and detection. The `#backchannel=0` parameter prevents go2rtc from establishing the audio output backchannel, so it won't block two-way talk access.
 - `front_door_twoway` stream is used for two-way talk functionality. This stream can be used by Frigate's WebRTC viewer when two-way talk is enabled, or by other applications (like Home Assistant Advanced Camera Card) that need access to the camera's audio output channel.
 Any `#` parameter on a bare `rtsp://` source disables the backchannel unless the URL explicitly contains `#backchannel=1`. A two-way talk stream with something like `#video=h264` on it silently loses two-way audio, and Frigate will report that two-way talk is unavailable for that stream.
 ## Security: Restricted Stream Sources
 For security reasons, the `echo:`, `expr:`, and `exec:` stream sources are disabled by default in go2rtc. These sources allow arbitrary command execution and can pose security risks if misconfigured.
@@ -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 **Name** for the trigger (e.g., "Red Car Alert"). Frigate derives the trigger's
-   - Enter a descriptive **Friendly Name** for the trigger (e.g., "Red car on the driveway camera").
+     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.
@@ -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>
@@ -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
@@ -70,6 +70,9 @@ 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**
@@ -296,6 +299,32 @@ 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.
@@ -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`
@@ -555,21 +561,20 @@ Topic with current state of Birdseye for a camera. Published values are `ON` and
 ### `frigate/<camera_name>/birdseye_mode/set`
-Topic to set the Birdseye activity types for a camera. Send one uppercase activity type or combine multiple types with commas, for example `MOTION,OBJECTS,STATIONARY_OBJECTS`.
+Topic to set Birdseye mode for a camera. Birdseye offers different modes to customize under which circumstances the camera is shown.
-_Note: Changing the value from `CONTINUOUS` to non-continuous activity types will take up to 30 seconds for
+_Note: Changing the value from `CONTINUOUS` -> `MOTION | OBJECTS` will take up to 30 seconds for
 the camera to be removed from the view._
-| Command              | Description                                                      |
+| Command      | Description                                                       |
-| -------------------- | ---------------------------------------------------------------- |
+| ------------ | ----------------------------------------------------------------- |
-| `CONTINUOUS`         | Always included                                                  |
+| `CONTINUOUS` | Always included                                                   |
-| `MOTION`             | Shown if motion was detected within the last 30 seconds          |
+| `MOTION`     | Show when detected motion within the last 30 seconds are included |
-| `OBJECTS`            | Shown if an active object was tracked within the last 30 seconds |
+| `OBJECTS`    | Shown if an active object tracked within the last 30 seconds      |
 | `STATIONARY_OBJECTS` | Shown while a stationary object is tracked                       |
 ### `frigate/<camera_name>/birdseye_mode/state`
-Topic with the current Birdseye activity types for a camera. Multiple enabled types are published as a comma-separated value in the order `OBJECTS`, `MOTION`, `STATIONARY_OBJECTS`, `CONTINUOUS`.
+Topic with current state of the Birdseye mode for a camera. Published values are `CONTINUOUS`, `MOTION`, `OBJECTS`.
 ### `frigate/<camera_name>/notifications/set`
@@ -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.
@@ -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+?
@@ -64,20 +64,20 @@ 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        |
+| **dog**           | .7                 | .9                   |
-| **cat**           | .65       | .8        |
+| **cat**           | .65                | .8                   |
-| **face**          | .7        |           |
+| **face**          | .7                 |                      |
-| **package**       | .65       | .9        |
+| **package**       | .65                | .9                   |
-| **license_plate** | .6        |           |
+| **license_plate** | .6                 |                      |
-| **amazon**        | .75       |           |
+| **amazon**        | .75                |                      |
-| **ups**           | .75       |           |
+| **ups**           | .75                |                      |
-| **fedex**         | .75       |           |
+| **fedex**         | .75                |                      |
-| **person**        | .65       | .85       |
+| **person**        | .65                | .85                  |
-| **car**           | .65       | .85       |
+| **car**           | .65                | .85                  |
 </TabItem>
 <TabItem value="yaml">
@@ -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`
+- **People**: `person`, `face`, `baby`
- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `school_bus`, `license_plate`
+- **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`
+- **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`
+- **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.
@@ -66,17 +66,19 @@ An FFmpeg message meaning it probed the stream but never saw enough decodable vi
 ## Recording
-<FaqItem id="no-new-recording-segments" question="No new recording segments were created for <camera> in the last 120s">
+<FaqItem id="no-new-recording-segments" question="No new recording segments were created (or: No new valid recording segments were created / No valid segments created since last invalid segment) for <camera> in the last 120s">
-Frigate's record watchdog is restarting the record FFmpeg process because no valid segment has reached the cache. This means the record stream is not connecting or the segments are being rejected (see the audio-codec entry below).
+Frigate's record watchdog is restarting the record FFmpeg process because the camera stopped producing usable recordings. The wording distinguishes the cases: `No new recording segments` means no new segment file reached the cache, so ffmpeg isn't getting video out of the record stream; the two `valid` variants mean recordings are arriving but keep failing validation. Either way the fault is on the camera or network side, and the restart is Frigate trying to recover.
-See [Recordings: the record stream isn't connecting](/troubleshooting/recordings#the-record-stream-isnt-connecting).
+See [Recordings: no new recording segments were created](/troubleshooting/recordings#no-new-recording-segments-were-created).
 </FaqItem>
-<FaqItem id="invalid-or-missing-video-stream-in-segment" question="Invalid or missing video stream in segment. Discarding.">
+<FaqItem id="invalid-or-missing-video-stream-in-segment" question="Invalid or missing video stream in segment. Discarding. / Discarding a corrupt recording segment / Failed to probe corrupt segment / Invalid recording segment detected">
-A cached recording segment failed validation (no readable video stream) and was deleted. The most common cause is a segment that was truncated because the record FFmpeg process was killed mid-write, so this often appears alongside, and as a consequence of, the record-stream restarts above. A segment containing only audio triggers it too.
+A cached recording segment failed validation and was deleted, either because it had no readable video stream or because its length was impossible. This nearly always means the camera stopped sending usable video partway through the segment: a camera that rebooted, dropped the connection, or ran out of simultaneous connections, or an unreliable link such as WiFi or a failing switch port. Broken camera timestamps (a "Smart Codec" / H.264+ mode) cause the corrupt-segment variants. The same stream failure trips the record watchdog, so the restarts above usually appear alongside these messages.
 See [Recordings: invalid or missing video stream in segment](/troubleshooting/recordings#invalid-or-missing-video-stream-in-segment).
 </FaqItem>
@@ -3,7 +3,31 @@ id: cpu
 title: High CPU Usage
 ---
-High CPU usage can impact Frigate's performance and responsiveness. This guide outlines the most effective configuration changes to help reduce CPU consumption and optimize resource usage.
+High CPU usage can impact Frigate's performance and responsiveness. This guide explains how to interpret the CPU values Frigate reports and outlines the most effective configuration changes to help reduce CPU consumption and optimize resource usage.
 ## Understanding Frigate's Reported CPU Usage
 Frigate's CPU percentages often look much higher than what the host reports. Usually both numbers are correct and are simply measured against different denominators, so confirm you actually have a problem before tuning anything.
 ### Per-process values are relative to a single core
 The values Frigate reports for FFmpeg, capture, detect, detector, and other processes follow the same convention as `top`: 100% means one CPU core is fully saturated, not that the whole system is saturated. A multithreaded process such as FFmpeg can legitimately report well over 100%.
 Host and hypervisor tools instead report a percentage of the machine's total capacity across all cores. This includes `docker stats`, the `htop` summary, the Proxmox summary graph, the Unraid dashboard, Synology Resource Monitor, and Home Assistant's system monitor sensors. To reconcile the two:
 ```
 host percentage ≈ (sum of Frigate's process percentages) / (number of cores)
 ```
 On a 4 core system, an FFmpeg process reporting 100% is consuming one quarter of the machine, so the host will show roughly 25 to 30% once the remaining Frigate processes are included. That same 100% on a 16 core system is about 6%. Frigate's own warning thresholds use the per-core convention as well, so an FFmpeg process is flagged at 20% of a single core, not 20% of the system.
 ### Instantaneous samples and averages measure different things
 Frigate collects stats every 15 seconds, and the `cpu` value covers only the interval since the previous collection. The `cpu_average` value in the stats API and MQTT payload is the average across the entire life of the process, and it is what the high CPU usage warnings are based on. Host dashboards generally plot data averaged over a longer window, so a single Frigate sample can show a peak that a host graph never displays. A process that has just started, such as FFmpeg after a camera reconnect, reports 0 until it has been sampled twice.
 ### The system-wide value depends on what the container can see
 The system CPU value is read from `/proc/stat`. Under Docker that file belongs to the host, so the value covers the entire machine including workloads unrelated to Frigate, and it will not match `docker stats` for the Frigate container. Under an LXC container, lxcfs virtualizes `/proc/stat` and the value reflects only the cores assigned to the container. In a virtual machine, the guest sees only its assigned vCPUs while the hypervisor divides by every physical thread on the node, so guest and host percentages will not agree even when both are accurate.
 ## 1. Hardware Acceleration for Video Decoding
@@ -72,3 +96,19 @@ The model you use significantly impacts detector performance. Frigate provides d
 - Larger models (640x640): Slower inference, can sometimes have higher accuracy on very large objects that take up a majority of the frame.
 For more detail on picking the right size, see [Choosing a model size](../configuration/object_detectors.md#choosing-a-model-size).
 ## 3. Reducing Detector CPU Usage
 **Priority: High**
 The **Detector CPU Usage** metric measures the CPU spent converting frames into the tensor format the model expects and post-processing the model's output. It does not include inference, so this value can be high even when you've configured a GPU, NPU, or Coral for object detection.
 This metric scales with how many detections per second Frigate runs and how expensive each one is to prepare. Tuning [motion detection](../configuration/motion_detection) is usually the first recommendation to reduce the number of detections. Additionally, you can:
 - **Lower `detect -> fps`.** 5 is the recommended value for nearly all cameras. Running at 10 doubles the frames eligible for detection and is one of the largest contributors to this metric.
 - **Use a 320x320 model.** A 640x640 model has 4 times as many pixels to transpose, convert, and copy on every inference.
 - **Prefer a model that takes integer input.** Models configured with `input_dtype: float` require each frame to be converted to float32 and normalized on the CPU first. Models taking `int` input, such as the tflite models used by the Edge TPU, skip that step.
 - **Do not match the detect resolution to the model resolution.** The detect stream should match your camera's aspect ratio, for example `1280x720`, not the model's input size. Frigate crops and scales regions of motion itself, so an oversized detect stream only adds work.
 - **Tune stationary object behavior.** Objects that never settle into a stationary state are re-detected continuously. Raising `detect -> stationary -> interval` reduces how often detection runs on objects that are already parked. See [stationary objects](../configuration/stationary_objects).
 Adding [more detector instances](#multiple-detector-instances) spreads this work across more CPU cores, but does not reduce the total CPU used.
@@ -39,6 +39,20 @@ To do this efficiently the following setup is required:
 When this is done correctly, the GPU will do the decoding and scaling which will result in a small increase in CPU usage but with better results.
 ### How can I rotate my camera's video feed?
 Rotation is best done in the camera's firmware settings (usually called rotate, flip, or corridor mode) so the video arrives already rotated and no extra processing is needed. Check there first.
 If your camera does not support rotation, go2rtc's ffmpeg module can rotate the stream with the `#rotate` parameter (`90`, `180`, `270`, or `-90`), but this is not recommended: rotation requires transcoding (re-encoding) the video, which significantly increases CPU usage, especially for high resolution streams.
 ```yaml
 go2rtc:
  streams:
    my_camera: "ffmpeg:rtsp://user:password@192.168.1.10:554/stream#video=h264#hardware#rotate=90"
 ```
 Point the camera's inputs at the restream as described in the [restream docs](/configuration/restream.md), and swap `detect -> width` and `detect -> height` to match the rotated resolution.
 ### My mjpeg stream or snapshots look green and crazy
 This almost always means that the width/height defined for your camera are not correct. Double check the resolution with VLC or another player. Also make sure you don't have the width and height values backwards.
@@ -133,6 +147,12 @@ cameras:
      height: 720
 ```
 ### What is the `version` key in my config file?
 `version` records the config format that your config was last migrated to. On startup Frigate compares it against the format the running version expects, and if it is older it copies your config to `/config/backup_config.yaml`, rewrites it to the new format, and updates `version` as the final step. A config with no `version` key is assumed to predate 0.14 and is migrated from there.
 Frigate manages this key for you, so do not set or edit it. Raising it makes Frigate skip migrations your config still needs, and lowering it re-runs migrations against config that has already been converted. Either can leave you with a config that no longer validates.
 ### Why does Frigate keep creating new tracked objects for my parked car?
 Stationary tracking is designed to _prevent_ this: a parked car should remain a single tracked object rather than generating new ones. If you're repeatedly getting new tracked objects for the same car, it's likely that Frigate is losing the object and re-detecting it as a new one.
@@ -78,7 +78,9 @@ go2rtc:
 :::warning
-The `#`-modifiers (`#video=`, `#audio=`, `#hardware`, `#backchannel=0`, …) **only take effect on a source that is prefixed with `ffmpeg:`**. Adding them to a bare `rtsp://…#audio=opus` source does nothing: go2rtc ignores them. Likewise, when a source references another stream by name (e.g. `ffmpeg:back#audio=aac`), the name must match the stream key **exactly** (it is case sensitive), or the transcode is silently never produced. This is the single most common configuration mistake. In the Frigate UI, the **Use compatibility mode (ffmpeg)** toggle adds the `ffmpeg:` prefix for you.
+The transcoding modifiers (`#video=`, `#audio=`, `#hardware`, …) **only take effect on a source that is prefixed with `ffmpeg:`**. Adding them to a bare `rtsp://…#audio=opus` source does nothing: go2rtc ignores them. Likewise, when a source references another stream by name (e.g. `ffmpeg:back#audio=aac`), the name must match the stream key **exactly** (it is case sensitive), or the transcode is silently never produced. This is the single most common configuration mistake. In the Frigate UI, the **Use compatibility mode (ffmpeg)** toggle adds the `ffmpeg:` prefix for you.
 A bare `rtsp://` source reads a different set of modifiers: `#backchannel=`, `#media=`, `#timeout=`, and `#transport=`. These do nothing on an `ffmpeg:` source. Adding **any** modifier to a bare `rtsp://` source also disables the camera's backchannel unless the URL explicitly contains `#backchannel=1`, so a stream dedicated to two-way talk should carry no modifiers at all.
 :::
@@ -153,7 +155,7 @@ WebRTC is only attempted when MSE fails or when using a camera's two-way talk fe
 - **Codec mismatch**: WebRTC cannot carry H.265 or AAC. The stream backing the WebRTC view must provide Opus (or PCMA/PCMU) audio and H.264 video. Add an `ffmpeg:back#audio=opus` source as shown above.
 - **Port `8555` not reachable, or no candidates set**: WebRTC needs port `8555` (both TCP and UDP) open and a reachable candidate advertised. On Docker installs running on a custom/overlay network, go2rtc may advertise unreachable container IPs as ICE candidates; setting `webrtc.filters.candidates: []` and supplying only your host's LAN IP resolves this. See [WebRTC extra configuration](/configuration/live#webrtc-extra-configuration).
- **Two-way talk** additionally requires a secure context (HTTPS or the authenticated port `8971`, because browsers block microphone access on plain HTTP). The camera's RTSP backchannel must also be handled correctly: go2rtc seizes the backchannel by default, which blocks two-way audio for other consumers and can inject static. Disable it on the primary stream with `#backchannel=0` and use a separate dedicated stream for talk, as documented in [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream).
+- **Two-way talk** additionally requires a secure context (HTTPS or the authenticated port `8971`, because browsers block microphone access on plain HTTP). The camera's RTSP backchannel must also be handled correctly: go2rtc seizes the backchannel by default, which blocks two-way audio for other consumers and can inject static. Disable it on the primary stream with `#backchannel=0` and use a separate dedicated stream for talk, carrying no `#` modifiers of any kind, as documented in [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream).
 ## High CPU usage
@@ -209,6 +209,50 @@ If the record stream uses a "Smart Codec"/H.264+ mode or changes encoding parame
 </FaqItem>
 <FaqItem id="invalid-or-missing-video-stream-in-segment" question="I see the message: WARNING : Invalid or missing video stream in segment ... Discarding.">
 Every recording segment is validated before it leaves the cache. Frigate probes each finished `.mp4` in `/tmp/cache` and requires a readable video stream and a valid duration before moving to storage. A segment that fails is deleted, so those ~10 seconds of footage are lost. Three messages come from this check:
 - `Invalid or missing video stream in segment <path>. Discarding.` The segment holds no video, or could not be read at all.
 - `Failed to probe corrupt segment <path>` followed by `Discarding a corrupt recording segment: <path>`. The segment was read, but its length could not be determined.
 - `Discarding a corrupt recording segment: <path>` on its own. The segment's length is impossible (empty, or longer than ten minutes), which points at broken timestamps coming from the camera.
 For each one, the camera watchdog also logs `Invalid recording segment detected for <camera> at <timestamp>`.
 :::warning
 This is almost always a **camera or network problem**, not a Frigate one. A segment is only complete once ffmpeg has finished writing it, so anything that interrupts the stream partway through leaves behind a file that cannot be saved. Frigate is reporting the interruption, not causing it.
 :::
 #### Start with the camera and the network
 - **The camera dropped the connection.** Cameras reboot, reinitialize their stream when switching to night mode, and cut clients off when they are overloaded or out of simultaneous connections. Count everything pulling from the camera at once: Frigate's detect and record streams, go2rtc, a phone app, and any other NVR each use one. Routing all roles through a single [RTSP restream](/configuration/restream#reduce-connections-to-camera) so the camera only ever sees one connection often resolves this by itself.
 - **The link to the camera is unreliable.** WiFi cameras, powerline adapters, a saturated uplink, a failing switch port, or a marginal cable all produce this pattern, and usually only on one camera at a time. WiFi cameras are [not recommended](https://ipcamtalk.com/threads/multiple-cameras-high-bandwidth.77100/#post-861110).
 - **The camera cannot reliably send what it is being asked for.** A high bitrate 4K stream can be more than the camera's own hardware can encode and push out under load. Lower the bitrate, or record a lower-resolution profile.
 - **The camera is using a "Smart Codec", H.264+, or H.265+ mode.** These change encoding parameters mid-stream and produce the broken timestamps behind the corrupt-segment variant. Turn the mode off and set the camera's keyframe interval equal to its frame rate. See [Segments are only ~1 second long](#segments-are-only-1-second-long).
 Read the rest of the Frigate and/or go2rtc log around the **first** occurrence. When the camera or the network is at fault, other messages show up with it, such as `No frames received from <camera> in 20 seconds`, `Non-monotonic DTS`, `RTP: PT=xx: bad cseq`, `error while decoding MB`, or a connection timeout. Each of those is explained in [Common error messages](/troubleshooting/common_errors). To confirm the camera is the source, open its stream in the [go2rtc web interface](/troubleshooting/go2rtc) on port `1984` or play the same URL in VLC, and leave it running long enough for the failures to happen again.
 #### If the camera and network check out
 - **Audio the recording cannot store.** Some cameras send G.711 audio, which cannot be saved in an MP4 and stops segments from finalizing. See [Incompatible audio codec](#incompatible-audio-codec-recordings-silently-fail-to-save).
 - **Frigate itself was stopped or restarted.** A single warning per camera around a restart is expected and needs no action.
 - **The system ran out of room or memory.** A full `/tmp/cache`, or the host killing Frigate for using too much memory, cuts off the segment being written. Both leave other errors in the log alongside this one. See [No space left on device](#errno-28-no-space-left-on-device).
 </FaqItem>
 <FaqItem id="no-new-recording-segments-were-created" question="I see the message: ERROR : No new recording segments were created for <camera> in the last 120s. Restarting the ffmpeg record process...">
 When a camera stops producing usable recordings for two minutes, Frigate restarts that camera's record process to try to recover. The wording tells you how far the recordings got:
 - **`No new recording segments were created`**: no new segment file showed up in the cache at all, so ffmpeg isn't getting video out of the record stream. The camera is unreachable or refusing the connection, the stream URL, path, or credentials are wrong, or the camera accepted the connection and then sent nothing. See [The record stream isn't connecting](#the-record-stream-isnt-connecting).
 - **`No new valid recording segments were created`** and **`No valid segments created since last invalid segment`**: recordings are arriving, but they keep failing validation, so the camera is sending video that cannot be saved. See [Invalid or missing video stream in segment](#invalid-or-missing-video-stream-in-segment) above.
 The restart is Frigate recovering from a problem, not causing one. One of these after a camera reboot or a brief network drop is normal. Seeing them repeat every couple of minutes means the camera or the network is still failing, and the restarts can extend the damage, because each one cuts off the segment that was being written. Work from the earliest failure in that camera's log rather than from the restarts.
 </FaqItem>
 <FaqItem id="i-see-the-message-warning--unable-to-keep-up-with-recording-segments-in-cache-for-camera-keeping-the-5-most-recent-segments-out-of-6-and-discarding-the-rest" question="I see the message: WARNING : Unable to keep up with recording segments in cache for camera. Keeping the 5 most recent segments out of 6 and discarding the rest...">
 This warning means the recording maintainer cannot move recording segments from the RAM cache to disk fast enough. When the cache fills up, Frigate discards the oldest segments to avoid running out of memory and crashing, so you lose recorded footage. This is almost always a storage throughput or system resource problem. Work through the steps below to identify which.
@@ -353,19 +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
+#### Step 6: Verify go2rtc stream 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
 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:
@@ -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',
+    defaultLocale: "en",
-    locales: ['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',
+      backgroundColor: "#005f73",
-      textColor: '#e0fbfc',
+      textColor: "#e0fbfc",
      isCloseable: false,
    },
    docs: {
@@ -83,15 +86,15 @@ const config: Config = {
      },
    },
    prism: {
-      magicComments:[
+      magicComments: [
        {
-          className: 'theme-code-block-highlighted-line',
+          className: "theme-code-block-highlighted-line",
-          line: 'highlight-next-line',
+          line: "highlight-next-line",
-          block: {start: 'highlight-start', end: 'highlight-end'},
+          block: { start: "highlight-start", end: "highlight-end" },
        },
        {
-          className: 'code-block-error-line',
+          className: "code-block-error-line",
-          line: 'highlight-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',
+          type: "localeDropdown",
-          position: 'right',
+          position: "right",
          dropdownItemsAfter: [
            {
-              label: '简体中文（社区翻译）',
+              label: "简体中文（社区翻译）",
-              href: 'https://docs.frigate-cn.video',
+              href: "https://docs.frigate-cn.video",
-            }
+            },
-          ]
+          ],
        },
        {
-          href: 'https://github.com/blakeblackshear/frigate',
+          href: "https://github.com/blakeblackshear/frigate",
-          label: 'GitHub',
+          label: "GitHub",
-          position: 'right',
+          position: "right",
        },
      ],
    },
@@ -1476,7 +1476,7 @@ paths:
        - Classification
      summary: Get custom classification attributes
      description: |-
-        **Access:** Admin role required.
+        **Access:** Authenticated user with access to all cameras.
        Returns custom classification attributes for a given object type.
            Only includes models with classification_type set to 'attribute'.
@@ -1510,8 +1510,8 @@ paths:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
      security:
-        - frigateAdminAuth: []
+        - frigateUserAuth: []
-      x-required-role: admin
+      x-required-role: all_cameras
  /classification/{name}/train:
    get:
      tags:
@@ -2308,15 +2308,15 @@ paths:
                $ref: '#/components/schemas/HTTPValidationError'
      security:
        - frigateUserAuth: []
-      x-required-role: any
+      x-required-role: camera
-      description: '**Access:** Any authenticated user.'
+      description: '**Access:** Authenticated user with access to the referenced camera.'
  /review/summarize/start/{start_ts}/end/{end_ts}:
    post:
      tags:
        - Review
      summary: Generate Review Summary
      description: |-
-        **Access:** Admin role required.
+        **Access:** Authenticated user with access to all cameras.
        Use GenAI to summarize review items over a period of time.
      operationId: 
@@ -2347,8 +2347,8 @@ paths:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
      security:
-        - frigateAdminAuth: []
+        - frigateUserAuth: []
-      x-required-role: admin
+      x-required-role: all_cameras
  /:
    get:
      tags:
@@ -2946,44 +2946,6 @@ paths:
        - frigateUserAuth: []
      x-required-role: any
      description: '**Access:** Any authenticated user.'
  /categorized_object_names:
    get:
      tags:
        - App
      summary: Get known object names by object type
      description: |-
        **Access:** Any authenticated user.
        Returns the sub labels and attributes this install can attach,
            grouped by object type. Unlike /sub_labels, which reflects what has already been
            detected, this reads the config and model files, so it covers recognized face
            names, named license plates, custom object classification categories, and the
            detector attributes of tracked objects.
      operationId: categorized_object_names_categorized_object_names_get
      parameters:
        - name: object_type
          in: query
          required: false
          schema:
            anyOf:
              - type: string
              - type: 'null'
            title: Object Type
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema: {}
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
      security:
        - frigateUserAuth: []
      x-required-role: any
  /audio_labels:
    get:
      tags:
@@ -4111,6 +4073,16 @@ 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
@@ -4416,6 +4388,16 @@ 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
@@ -6022,65 +6004,6 @@ paths:
      security:
        - frigateUserAuth: []
      x-required-role: camera
  /vod/{camera_name}/{stream}/start/{start_ts}/end/{end_ts}:
    get:
      tags:
        - Media
      summary: Vod Ts Stream
      description: |-
        **Access:** Authenticated user with access to the referenced camera.
        Returns an HLS playlist pinned to one stream type (main or sub) for the specified timestamp-range on the specified camera. Append /master.m3u8 or /index.m3u8 for HLS playback.
      operationId: 
        vod_ts_stream_vod__camera_name___stream__start__start_ts__end__end_ts__get
      parameters:
        - name: camera_name
          in: path
          required: true
          schema:
            anyOf:
              - type: string
              - type: 'null'
            title: Camera Name
        - name: stream
          in: path
          required: true
          schema:
            $ref: '#/components/schemas/VodStreamPreference'
        - name: start_ts
          in: path
          required: true
          schema:
            type: number
            title: Start Ts
        - name: end_ts
          in: path
          required: true
          schema:
            type: number
            title: End Ts
        - name: force_discontinuity
          in: query
          required: false
          schema:
            type: boolean
            default: false
            title: Force Discontinuity
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema: {}
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
      security:
        - frigateUserAuth: []
      x-required-role: camera
  /events/{event_id}/snapshot.jpg:
    get:
      tags:
@@ -7019,63 +6942,6 @@ paths:
      security:
        - frigateUserAuth: []
      x-required-role: camera
  /{camera_name}/recordings/coverage:
    get:
      tags:
        - Recordings
      summary: Recordings Coverage
      description: |-
        **Access:** Authenticated user with access to the referenced camera.
        Returns merged recording coverage spans plus codec compatibility.
        codecs_compatible is false only when more than one known video codec
        appears across the range's rows, the case where the merged vod route
        degrades to a single-stream manifest.
      operationId: recordings_coverage__camera_name__recordings_coverage_get
      parameters:
        - name: camera_name
          in: path
          required: true
          schema:
            anyOf:
              - type: string
              - type: 'null'
            title: Camera Name
        - name: after
          in: query
          required: true
          schema:
            type: number
            title: After
        - name: before
          in: query
          required: true
          schema:
            type: number
            title: Before
        - name: timelines
          in: query
          required: false
          schema:
            type: boolean
            default: false
            title: Timelines
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema: {}
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
      security:
        - frigateUserAuth: []
      x-required-role: camera
  /{camera_name}/recordings:
    get:
      tags:
@@ -7263,7 +7129,9 @@ paths:
              schema:
                $ref: '#/components/schemas/DebugReplayStartResponse'
        '400':
-          description: Invalid camera, time range, or no recordings
+          description: Invalid camera or time range
        '404':
          description: No recordings in the requested time range
        '409':
          description: A replay session is already active
        '422':
@@ -9057,17 +8925,6 @@ components:
        - msg
        - type
      title: ValidationError
    VodStreamPreference:
      type: string
      enum:
        - main
        - sub
      title: VodStreamPreference
      description: |-
        Stream pin for the path-segment VOD route.
        nginx-vod derives its mapping fetch URI from the playlist URL path
        (query params are dropped), so the preference must be a path segment.
  securitySchemes:
    frigateAdminAuth:
      type: apiKey
@@ -71,7 +71,6 @@ from frigate.util.config import (
    find_config_file,
    redact_credential,
 )
 from frigate.util.object_names import get_categorized_object_names
 from frigate.util.schema import get_config_schema
 from frigate.util.services import (
    get_nvidia_driver_info,
@@ -1314,28 +1313,6 @@ def get_sub_labels(
    return JSONResponse(content=sub_labels)
@router.get(
    "/categorized_object_names",
    dependencies=[Depends(allow_any_authenticated())],
    summary="Get known object names by object type",
    description="""Returns the sub labels and attributes this install can attach,
    grouped by object type. Unlike /sub_labels, which reflects what has already been
    detected, this reads the config and model files, so it covers recognized face
    names, named license plates, custom object classification categories, and the
    detector attributes of tracked objects.""",
 )
 def categorized_object_names(
    request: Request,
    object_type: str | None = None,
    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
 ):
    return JSONResponse(
        content=get_categorized_object_names(
            request.app.frigate_config, allowed_cameras, object_type
        )
    )
@router.get("/audio_labels", dependencies=[Depends(allow_any_authenticated())])
 def get_audio_labels():
    labels = load_labels("/audio-labelmap.txt", prefill=521)
@@ -83,9 +83,9 @@ def require_admin_by_default():
        "/nvinfo",
        "/labels",
        "/sub_labels",
        "/categorized_object_names",
        "/plus/models",
        "/recognized_license_plates",
        "/classification/attributes",
        "/timeline",
        "/timeline/hourly",
        "/recordings/storage",
@@ -127,23 +127,18 @@ def require_admin_by_default():
        if path.startswith(EXEMPT_PREFIXES):
            return
-        # Dynamic camera path exemption:
+        # Camera routes enforce per-camera access via route-level dependencies
-        # Any path whose first segment matches a configured camera name should
+        # (e.g. require_camera_access). Match on the route template, not the raw
-        # bypass the global admin requirement. These endpoints enforce access
+        # path, so a camera named like another namespace (e.g. "faces") can't
-        # via route-level dependencies (e.g. require_camera_access) to ensure
+        # waive the admin check for that namespace's routes.
-        # per-camera authorization. This allows non-admin authenticated users
+        route = request.scope.get("route")
-        # (e.g. viewer role) to access camera-specific resources without
+        if (
-        # needing admin privileges.
+            route is not None
-        try:
+            and route.path.startswith("/{camera_name}")
-            if path.startswith("/"):
+            and request.path_params.get("camera_name")
-                first_segment = path.split("/", 2)[1]
+            in request.app.frigate_config.cameras
-                if (
+        ):
-                    first_segment
+            return
                    and first_segment 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
@@ -972,6 +967,7 @@ def delete_user(request: Request, username: str):
    summary="Update user password",
    description="Updates a user's password. Users can only change their own password unless they have admin role. Requires the current password to verify identity for non-admin users. Password must be at least 12 characters long. If user changes their own password, a new JWT cookie is automatically issued.",
 )
@limiter.limit(limit_value=rateLimiter.get_limit)
 async def update_password(
    request: Request,
    username: str,
@@ -985,10 +981,11 @@ async def update_password(
    current_username = current_user.get("username")
    current_role = current_user.get("role")
-    # viewers can only change their own password
+    # Only admins may target another account. This has to cover every non-admin
-    if current_role == "viewer" and current_username != username:
+    # role rather than just viewer, since custom roles are arbitrary names
    if current_role != "admin" and current_username != username:
        raise HTTPException(
-            status_code=403, detail="Viewers can only update their own password"
+            status_code=403, detail="Users can only update their own password"
        )
    HASH_ITERATIONS = request.app.frigate_config.auth.hash_iterations
@@ -1252,3 +1249,23 @@ async def get_allowed_cameras_for_filter(request: Request):
    all_camera_names = set(request.app.frigate_config.cameras.keys())
    roles_dict = request.app.frigate_config.auth.roles
    return User.get_allowed_cameras(role, roles_dict, all_camera_names)
 async def require_full_camera_access(
    request: Request,
    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
 ):
    """Dependency for endpoints returning data that spans every camera.
    Some responses cannot be meaningfully scoped to a subset of cameras, so
    rather than filter them the endpoint is limited to callers who can already
    see every camera. Admin and viewer always qualify; a custom role qualifies
    only when its camera list covers all configured cameras.
    """
    all_camera_names = set(request.app.frigate_config.cameras.keys())
    if not all_camera_names.issubset(allowed_cameras):
        raise HTTPException(
            status_code=403,
            detail="Access to all cameras is required for this endpoint",
        )
@@ -651,32 +651,6 @@ async def _connect_onvif_camera(
    raise first_error
 def _supports_continuous_pan_tilt(nodes) -> bool:
    """Whether any PTZ node advertises continuous pan/tilt velocity.
    The web UI's directional controls issue ContinuousMove with a PanTilt
    velocity, so continuous pan/tilt is what makes those controls usable. This
    is intentionally narrower than ptz_supported, which is true for any device
    exposing the ONVIF PTZ service - including zoom/focus-only varifocal lenses.
    """
    for node in nodes or []:
        spaces = getattr(node, "SupportedPTZSpaces", None) or (
            node.get("SupportedPTZSpaces") if isinstance(node, dict) else None
        )
        if spaces is None:
            continue
        continuous = getattr(spaces, "ContinuousPanTiltVelocitySpace", None) or (
            spaces.get("ContinuousPanTiltVelocitySpace")
            if isinstance(spaces, dict)
            else None
        )
        if continuous:
            return True
    return False
@router.get(
    "/onvif/probe",
    dependencies=[Depends(require_role(["admin"]))],
@@ -834,7 +808,6 @@ async def onvif_probe(
        # Check PTZ support and capabilities
        ptz_supported = False
        pan_tilt_supported = False
        presets_count = 0
        autotrack_supported = False
@@ -868,15 +841,6 @@ async def onvif_probe(
                    logger.debug(f"Failed to get presets: {e}")
                    presets_count = 0
            # Check for real (continuous) pan/tilt, which the UI controls need
            if ptz_supported:
                try:
                    nodes = await ptz_service.GetNodes()
                    pan_tilt_supported = _supports_continuous_pan_tilt(nodes)
                    logger.debug(f"Continuous pan/tilt supported: {pan_tilt_supported}")
                except Exception as e:
                    logger.debug(f"Failed to read PTZ nodes for pan/tilt support: {e}")
            # Check for autotracking support - requires both FOV relative movement and MoveStatus
            if ptz_supported and first_profile_token and ptz_config_token:
                # First check for FOV relative movement support
@@ -996,7 +960,6 @@ async def onvif_probe(
            "firmware_version": device_info["firmware_version"],
            "profiles_count": profiles_count,
            "ptz_supported": ptz_supported,
            "pan_tilt_supported": pan_tilt_supported,
            "presets_count": presets_count,
            "autotrack_supported": autotrack_supported,
        }
@@ -50,7 +50,6 @@ from frigate.jobs.vlm_watch import (
    stop_vlm_watch_job,
 )
 from frigate.models import Event
 from frigate.util.object_names import get_categorized_object_names
 logger = logging.getLogger(__name__)
@@ -540,11 +539,6 @@ async def execute_tool(
    if tool_name == "search_objects":
        return await _execute_search_objects(request, arguments, allowed_cameras)
    if tool_name == "get_categorized_object_names":
        return JSONResponse(
            content=_execute_get_categorized_object_names(request, allowed_cameras)
        )
    if tool_name == "find_similar_objects":
        result = await _execute_find_similar_objects(
            request, arguments, allowed_cameras
@@ -597,7 +591,7 @@ async def _execute_get_live_context(
    try:
        frame_processor = request.app.detected_frames_processor
-        camera_state = frame_processor.get_camera_state(camera)
+        camera_state = frame_processor.camera_states.get(camera)
        if camera_state is None:
            return {
@@ -661,7 +655,7 @@ async def _get_live_frame_image_url(
        return None
    try:
        frame_processor = request.app.detected_frames_processor
-        if frame_processor.get_camera_state(camera) is None:
+        if camera not in frame_processor.camera_states:
            return None
        frame = frame_processor.get_current_frame(camera, {})
        if frame is None:
@@ -723,21 +717,6 @@ async def _execute_set_camera_state(
    return {"success": True, "camera": camera, "feature": feature, "value": value}
 def _execute_get_categorized_object_names(
    request: Request,
    allowed_cameras: list[str],
 ) -> dict[str, Any]:
    names = get_categorized_object_names(request.app.frigate_config, allowed_cameras)
    if not names:
        return {
            "names": {},
            "message": "No names configured; search by label or semantic_query.",
        }
    return {"names": names}
 async def _execute_tool_internal(
    tool_name: str,
    arguments: dict[str, Any],
@@ -762,8 +741,6 @@ async def _execute_tool_internal(
        except (json.JSONDecodeError, AttributeError) as e:
            logger.warning(f"Failed to extract tool result: {e}")
            return {"error": "Failed to parse tool result"}
    elif tool_name == "get_categorized_object_names":
        return _execute_get_categorized_object_names(request, allowed_cameras)
    elif tool_name == "find_similar_objects":
        return await _execute_find_similar_objects(request, arguments, allowed_cameras)
    elif tool_name == "set_camera_state":
@@ -796,8 +773,8 @@ async def _execute_tool_internal(
    else:
        logger.error(
            "Tool call failed: unknown tool %r. Expected one of: search_objects, find_similar_objects, "
-            "get_categorized_object_names, get_live_context, start_camera_watch, stop_camera_watch, "
+            "get_live_context, start_camera_watch, stop_camera_watch, get_profile_status, get_recap. "
-            "get_profile_status, get_recap. Arguments received: %s",
+            "Arguments received: %s",
            tool_name,
            json.dumps(arguments),
        )
@@ -14,7 +14,7 @@ from fastapi.responses import JSONResponse
 from peewee import DoesNotExist
 from playhouse.shortcuts import model_to_dict
-from frigate.api.auth import require_role
+from frigate.api.auth import require_full_camera_access, require_role
 from frigate.api.defs.request.classification_body import (
    AudioTranscriptionBody,
    DeleteFaceImagesBody,
@@ -741,6 +741,7 @@ def get_classification_dataset(name: str):
@router.get(
    "/classification/attributes",
    dependencies=[Depends(require_full_camera_access)],
    summary="Get custom classification attributes",
    description="""Returns custom classification attributes for a given object type.
    Only includes models with classification_type set to 'attribute'.
@@ -13,6 +13,7 @@ from frigate.api.auth import require_role
 from frigate.api.defs.tags import Tags
 from frigate.jobs.debug_replay import (
    ExportDebugReplaySource,
    NoRecordingsError,
    RecordingDebugReplaySource,
    start_debug_replay_job,
 )
@@ -74,7 +75,8 @@ class DebugReplayStopResponse(BaseModel):
    response_model=DebugReplayStartResponse,
    status_code=202,
    responses={
-        400: {"description": "Invalid camera, time range, or no recordings"},
+        400: {"description": "Invalid camera or time range"},
        404: {"description": "No recordings in the requested time range"},
        409: {"description": "A replay session is already active"},
    },
    dependencies=[Depends(require_role(["admin"]))],
@@ -113,6 +115,14 @@ async def start_debug_replay(request: Request, body: DebugReplayStartBody):
            },
            status_code=409,
        )
    except NoRecordingsError:
        return JSONResponse(
            content={
                "success": False,
                "message": "No recordings found in the selected time range",
            },
            status_code=404,
        )
    except ValueError:
        logger.exception("Rejected debug replay start request")
        return JSONResponse(
@@ -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"
@@ -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()
    )
@@ -518,6 +523,7 @@ def events_search(
    search_type = params.search_type
    include_thumbnails = params.include_thumbnails
    limit = params.limit
    offset = params.offset
    sort = params.sort
    # Filters
@@ -824,6 +830,9 @@ 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():
@@ -881,7 +890,7 @@ 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)
@@ -1313,7 +1322,7 @@ async def set_sub_label(
    if request.app.detected_frames_processor:
        tracked_obj: TrackedObject = None
-        for state in request.app.detected_frames_processor.get_camera_states():
+        for state in request.app.detected_frames_processor.camera_states.values():
            tracked_obj = state.tracked_objects.get(event_id)
            if tracked_obj is not None:
@@ -1372,7 +1381,7 @@ async def set_plate(
    if request.app.detected_frames_processor:
        tracked_obj: TrackedObject = None
-        for state in request.app.detected_frames_processor.get_camera_states():
+        for state in request.app.detected_frames_processor.camera_states.values():
            tracked_obj = state.tracked_objects.get(event_id)
            if tracked_obj is not None:
@@ -9,6 +9,7 @@ import zipfile
 from collections import deque
 from collections.abc import Iterator
 from pathlib import Path
 from urllib.parse import quote
 import psutil
 from fastapi import APIRouter, Depends, Query, Request
@@ -68,6 +69,7 @@ from frigate.jobs.export import (
 from frigate.models import Export, ExportCase, Previews, Recordings
 from frigate.record.export import (
    DEFAULT_TIME_LAPSE_FFMPEG_ARGS,
    DEFAULT_TIME_LAPSE_FFMPEG_INPUT_ARGS,
    ChaptersEnum,
    PlaybackSourceEnum,
    validate_ffmpeg_args,
@@ -453,6 +455,22 @@ def _stream_case_archive(exports: list[Export]) -> Iterator[bytes]:
    yield from buffer.drain()
 def _content_disposition(filename: str, ascii_fallback: str) -> str:
    """Build an attachment Content-Disposition that survives non-ASCII names.
    Header values are encoded as latin-1, so a name outside that range cannot
    go in filename at all. RFC 6266 handles this with a pair: a plain ASCII
    filename for old clients, plus a percent-encoded UTF-8 filename* that
    every current browser prefers.
    """
    ascii_name = filename if filename.isascii() else ascii_fallback
    return (
        f'attachment; filename="{ascii_name}"; '
        f"filename*=UTF-8''{quote(filename, safe='')}"
    )
@router.get(
    "/cases/{case_id}/download",
    dependencies=[Depends(allow_any_authenticated())],
@@ -495,7 +513,9 @@ def download_export_case(
        _stream_case_archive(exports),
        media_type="application/zip",
        headers={
-            "Content-Disposition": f'attachment; filename="{archive_base}.zip"',
+            "Content-Disposition": _content_disposition(
                f"{archive_base}.zip", f"{case_id}.zip"
            ),
        },
    )
@@ -993,7 +1013,7 @@ def export_recording_custom(
    # Set default values if not provided (timelapse defaults)
    if ffmpeg_input_args is None:
-        ffmpeg_input_args = ""
+        ffmpeg_input_args = DEFAULT_TIME_LAPSE_FFMPEG_INPUT_ARGS
    if ffmpeg_output_args is None:
        ffmpeg_output_args = DEFAULT_TIME_LAPSE_FFMPEG_ARGS
@@ -8,7 +8,6 @@ import os
 import subprocess as sp
 import time
 from datetime import UTC, datetime, timedelta
 from enum import Enum
 from pathlib import Path as FilePath
 from typing import Any
 from urllib.parse import unquote
@@ -40,9 +39,8 @@ from frigate.config.camera.snapshots import SnapshotsConfig
 from frigate.const import (
    CACHE_DIR,
    INSTALL_DIR,
    MAX_SEGMENT_DURATION,
    PREVIEW_FRAME_TYPE,
    STREAM_TYPE_MAIN,
    STREAM_TYPE_SUB,
 )
 from frigate.models import Event, Previews, Recordings, Regions, ReviewSegment
 from frigate.output.preview import get_most_recent_preview_frame
@@ -54,34 +52,12 @@ from frigate.util.file import (
    load_event_snapshot_image,
 )
 from frigate.util.image import get_image_from_recording, get_image_quality_params
 from frigate.util.media import get_keyframe_before
 from frigate.util.object import create_empty_regions_grid
 from frigate.util.recording_coverage import (
    build_spans,
    null_audio_glitches,
    plan_clip,
    resolve_coverage,
    stream_has_audio,
 )
 logger = logging.getLogger(__name__)
 # must match the patched MAX_CLIPS in docker/main/build_nginx.sh; a
 # normal hour needs ~360, one clip per recording file
 NGINX_VOD_MAX_CLIPS = 1080
 class VodStreamPreference(str, Enum):
    """Stream pin for the path-segment VOD route.
    nginx-vod derives its mapping fetch URI from the playlist URL path
    (query params are dropped), so the preference must be a path segment.
    """
    main = STREAM_TYPE_MAIN
    sub = STREAM_TYPE_SUB
 router = APIRouter(tags=[Tags.media])
@@ -343,7 +319,7 @@ async def get_snapshot_from_recording(
                & (frame_time <= Recordings.end_time)
            )
            .where(Recordings.camera == camera_name)
-            .order_by(Recordings.stream_type.asc(), Recordings.start_time.desc())
+            .order_by(Recordings.start_time.desc())
            .limit(1)
            .get()
        )
@@ -362,7 +338,7 @@ async def get_snapshot_from_recording(
                    & (frame_time <= Recordings.end_time)
                )
                .where(Recordings.camera == camera_name)
-                .order_by(Recordings.stream_type.asc(), Recordings.start_time.desc())
+                .order_by(Recordings.start_time.desc())
                .limit(1)
                .get()
            )
@@ -422,7 +398,7 @@ async def submit_recording_snapshot_to_plus(
            (frame_time >= Recordings.start_time) & (frame_time <= Recordings.end_time)
        )
        .where(Recordings.camera == camera_name)
-        .order_by(Recordings.stream_type.asc(), Recordings.start_time.desc())
+        .order_by(Recordings.start_time.desc())
        .limit(1)
    )
@@ -496,29 +472,20 @@ async def recording_clip(
                        FilePath(file_path).unlink(missing_ok=True)
                    break
-    def get_clip_query(stream_type: str):
+    recordings = (
-        return (
+        Recordings.select(
-            Recordings.select(
+            Recordings.path,
-                Recordings.path,
+            Recordings.start_time,
-                Recordings.start_time,
+            Recordings.end_time,
                Recordings.end_time,
            )
            .where(
                (Recordings.start_time.between(start_ts, end_ts))
                | (Recordings.end_time.between(start_ts, end_ts))
                | ((start_ts > Recordings.start_time) & (end_ts < Recordings.end_time))
            )
            .where(Recordings.camera == camera_name)
            .where(Recordings.stream_type == stream_type)
            .order_by(Recordings.start_time.asc())
        )
-
+        .where(
-    # never mix streams in one concat; use main when available and
+            (Recordings.start_time.between(start_ts, end_ts))
-    # fall back to sub for expired-main history
+            | (Recordings.end_time.between(start_ts, end_ts))
-    recordings = get_clip_query(STREAM_TYPE_MAIN)
+            | ((start_ts > Recordings.start_time) & (end_ts < Recordings.end_time))
-
+        )
-    if recordings.count() == 0:
+        .where(Recordings.camera == camera_name)
-        recordings = get_clip_query(STREAM_TYPE_SUB)
+        .order_by(Recordings.start_time.asc())
    )
    if recordings.count() == 0:
        return JSONResponse(
@@ -582,52 +549,17 @@ async def recording_clip(
    )
-def _build_vod_clip(
+@router.get(
-    row: Any, start: float, end: float
+    "/vod/{camera_name}/start/{start_ts}/end/{end_ts}",
-) -> tuple[dict[str, Any], int] | None:
+    dependencies=[Depends(require_camera_access)],
-    """Build one nginx-vod clip dict + duration (ms) for a recording row trimmed to [start, end).
+    description="Returns an HLS playlist for the specified timestamp-range on the specified camera. Append /master.m3u8 or /index.m3u8 for HLS playback.",
-
+)
-    Realization comes entirely from the shared plan_clip, so the coverage
+async def vod_ts(
    endpoint's realized timelines match this manifest by construction.
    """
    plan = plan_clip(row, start, end)
    if plan.skipped:
        return None
    clip: dict[str, Any] = {"type": "source", "path": row.path}
    if plan.clip_from_ms is not None:
        clip["clipFrom"] = plan.clip_from_ms
    clip["keyFrameDurations"] = [plan.duration_ms]
    logger.debug(
        "VOD: added clip %s duration_ms=%s clipFrom=%s",
        row.path,
        plan.duration_ms,
        clip.get("clipFrom"),
    )
    return clip, plan.duration_ms
 async def _vod_response(
    camera_name: str,
    start_ts: float,
    end_ts: float,
    force_discontinuity: bool = False,
-    stream_preference: str | None = None,
+):
 ) -> JSONResponse:
    """Build an nginx-vod mapping JSON for a camera over a timestamp range.
    Always a single-sequence mapping; quality selection happens in the
    frontend by choosing between this route and the stream-pinned routes.
    Args:
        camera_name: The camera to build the mapping for
        start_ts: Range start as a unix timestamp
        end_ts: Range end as a unix timestamp
        force_discontinuity: Emit HLS discontinuity markers between clips
        stream_preference: Pin the manifest to one stream type ("main" or
            "sub"), serving only that stream's recordings
    """
    logger.debug(
        "VOD: Generating VOD for %s from %s to %s with force_discontinuity=%s",
        camera_name,
@@ -635,85 +567,104 @@ async def _vod_response(
        end_ts,
        force_discontinuity,
    )
-    intervals = resolve_coverage(camera_name, start_ts, end_ts)
+    recordings = (
-
+        Recordings.select(
-    # rows contradicting their stream's audio composition are
+            Recordings.path,
-    # truncated-shutdown glitches
+            Recordings.duration,
-    main_audio = stream_has_audio(intervals, main=True)
+            Recordings.end_time,
-    sub_audio = stream_has_audio(intervals, main=False)
+            Recordings.start_time,
-
+        )
-    spans = build_spans(
+        .where(
-        null_audio_glitches(intervals, main_audio, sub_audio),
+            Recordings.start_time.between(start_ts, end_ts)
-        stream_preference,
+            | Recordings.end_time.between(start_ts, end_ts)
            | ((start_ts > Recordings.start_time) & (end_ts < Recordings.end_time))
        )
        .where(Recordings.camera == camera_name)
        .order_by(Recordings.start_time.asc())
        .iterator()
    )
-    durations: list[int] = []
+    clips = []
-    clips: list[dict[str, Any]] = []
+    durations = []
-    # gathered after glitch-nulling and span building, so the policy
+    min_duration_ms = 100  # Minimum 100ms to ensure at least one video frame
-    # decisions below reflect the manifest's real contents
+    max_duration_ms = MAX_SEGMENT_DURATION * 1000
-    video_codecs: set[str] = set()
+
-    audio_presence: set[bool] = set()
+    recording: Recordings
-    audio_params: set[tuple[str | None, int | None]] = set()
+    for recording in recordings:
    span_streams: set[bool] = set()
    for row, span_start, span_end, span_is_main in spans:
        logger.debug(
            "VOD: processing recording: %s start=%s end=%s duration=%s",
-            row.path,
+            recording.path,
-            row.start_time,
+            recording.start_time,
-            row.end_time,
+            recording.end_time,
-            row.duration,
+            recording.duration,
        )
        built = _build_vod_clip(row, span_start, span_end)
-        if built is None:
+        clip = {"type": "source", "path": recording.path}
        duration = int(recording.duration * 1000)
        # adjust start offset if start_ts is after recording.start_time
        if start_ts > recording.start_time:
            inpoint = int((start_ts - recording.start_time) * 1000)
            clip["clipFrom"] = inpoint
            duration -= inpoint
            logger.debug(
                "VOD: applied clipFrom %sms to %s",
                inpoint,
                recording.path,
            )
        # adjust end if recording.end_time is after end_ts
        if recording.end_time > end_ts:
            duration -= int((recording.end_time - end_ts) * 1000)
        # nginx-vod-module pushes clipFrom forward to the next keyframe,
        # which can leave too few frames and produce an empty/unplayable
        # segment. Snap clipFrom back to the preceding keyframe so the
        # segment always starts with a decodable frame.
        if "clipFrom" in clip:
            keyframe_ms = get_keyframe_before(recording.path, clip["clipFrom"])
            if keyframe_ms is not None:
                gained = clip["clipFrom"] - keyframe_ms
                clip["clipFrom"] = keyframe_ms
                duration += gained
                logger.debug(
                    "VOD: snapped clipFrom to keyframe at %sms for %s, duration now %sms",
                    keyframe_ms,
                    recording.path,
                    duration,
                )
            else:
                # could not read keyframes, remove clipFrom to use full recording
                logger.debug(
                    "VOD: no keyframe info for %s, removing clipFrom to use full recording",
                    recording.path,
                )
                del clip["clipFrom"]
                duration = int(recording.duration * 1000)
                if recording.end_time > end_ts:
                    duration -= int((recording.end_time - end_ts) * 1000)
        if duration < min_duration_ms:
            # skip if the clip has no valid duration (too short to contain frames)
            logger.debug(
                "VOD: skipping recording %s - resulting duration %sms too short",
                recording.path,
                duration,
            )
            continue
-        clips.append(built[0])
+        if min_duration_ms <= duration < max_duration_ms:
-        durations.append(built[1])
+            clip["keyFrameDurations"] = [duration]
-        span_streams.add(span_is_main)
+            clips.append(clip)
-        if row.video_codec is not None:
+            durations.append(duration)
-            video_codecs.add(row.video_codec)
+            logger.debug(
-        audio_presence.add(row.has_audio is not False)
+                "VOD: added clip %s duration_ms=%s clipFrom=%s",
-        # legacy rows contribute no signature, so uniformly-unknown
+                recording.path,
-        # history keeps the legacy shape
+                duration,
-        if row.has_audio is not False and (
+                clip.get("clipFrom"),
-            row.audio_codec is not None or row.audio_rate is not None
+            )
-        ):
+        else:
-            audio_params.add((row.audio_codec, row.audio_rate))
+            logger.warning(f"Recording clip is missing or empty: {recording.path}")
    # nginx-vod requires a uniform track count per sequence, and adding or
    # removing an audio track across an MSE discontinuity is unproven
    if len(audio_presence) > 1:
        logger.debug(
            "VOD: %s mixes audio-bearing and audio-less recordings between "
            "%s and %s; serving the range without audio",
            camera_name,
            start_ts,
            end_ts,
        )
        for clip in clips:
            clip["tracks"] = "v"
    # discontinuity mode emits per-clip init segments, letting the decoder
    # reconfigure at each boundary. Stream type counts as a signature of
    # its own: the two encoders differ in SPS/PPS even when codec name and
    # audio params match, and a single-init manifest then decode-fails on
    # players that only configure from the init segment (iOS)
    use_discontinuity = (
        len(video_codecs) > 1 or len(audio_params) > 1 or len(span_streams) > 1
    )
    if use_discontinuity:
        logger.debug(
            "VOD: %s mixes media signatures between %s and %s (video codecs "
            "%s, audio params %s, streams %s); serving a discontinuity "
            "manifest with per-clip init segments",
            camera_name,
            start_ts,
            end_ts,
            sorted(video_codecs),
            sorted(audio_params, key=str),
            sorted(span_streams),
        )
    if not clips:
        logger.error(
@@ -727,49 +678,16 @@ async def _vod_response(
            status_code=404,
        )
    if len(clips) > NGINX_VOD_MAX_CLIPS:
        logger.warning(
            "VOD: %s needs %d clips between %s and %s, exceeding nginx's "
            "limit of %d; playback of this range will fail. This usually "
            "means the camera produced abnormally short recording segments "
            "(check the stream's timestamps)",
            camera_name,
            len(clips),
            start_ts,
            end_ts,
            NGINX_VOD_MAX_CLIPS,
        )
    hour_ago = datetime.now() - timedelta(hours=1)
-    content = {
+    return JSONResponse(
-        "cache": hour_ago.timestamp() > start_ts,
+        content={
-        "discontinuity": force_discontinuity or use_discontinuity,
+            "cache": hour_ago.timestamp() > start_ts,
-        "consistentSequenceMediaInfo": True,
+            "discontinuity": force_discontinuity,
-        "durations": durations,
+            "consistentSequenceMediaInfo": True,
-        # aligns segments to recording file boundaries
+            "durations": durations,
-        "segment_duration": max(durations),
+            "segment_duration": max(durations),
-        "sequences": [{"clips": clips}],
+            "sequences": [{"clips": clips}],
-    }
+        }
    if use_discontinuity:
        # clip-indexed naming is what makes nginx-vod emit per-clip
        # EXT-X-MAP outside of its live mode
        content["initialClipIndex"] = 1
    return JSONResponse(content=content)
@router.get(
    "/vod/{camera_name}/start/{start_ts}/end/{end_ts}",
    dependencies=[Depends(require_camera_access)],
    description="Returns an HLS playlist for the specified timestamp-range on the specified camera. Append /master.m3u8 or /index.m3u8 for HLS playback.",
 )
 async def vod_ts(
    camera_name: str,
    start_ts: float,
    end_ts: float,
    force_discontinuity: bool = False,
 ):
    return await _vod_response(
        camera_name, start_ts, end_ts, force_discontinuity=force_discontinuity
    )
@@ -858,43 +776,7 @@ async def vod_clip(
    start_ts: float,
    end_ts: float,
 ):
-    # the tracking-details player corrects its timeline from
+    return await vod_ts(camera_name, start_ts, end_ts, force_discontinuity=True)
    # sequences[0].clips[0].clipFrom
    return await _vod_response(
        camera_name,
        start_ts,
        end_ts,
        force_discontinuity=True,
    )
 # registered after /vod/clip/... on purpose: both routes are six path
 # segments, Starlette matches structurally in registration order, and the
 # enum validation on {stream} would otherwise 422 every /vod/clip request
@router.get(
    "/vod/{camera_name}/{stream}/start/{start_ts}/end/{end_ts}",
    dependencies=[Depends(require_camera_access)],
    description="Returns an HLS playlist pinned to one stream type (main or sub) for the specified timestamp-range on the specified camera. Append /master.m3u8 or /index.m3u8 for HLS playback.",
 )
 async def vod_ts_stream(
    camera_name: str,
    stream: VodStreamPreference,
    start_ts: float,
    end_ts: float,
    force_discontinuity: bool = False,
 ):
    """VOD for a timestamp range pinned to one stream type.
    How the frontend selects quality, now that mappings are always
    single-sequence.
    """
    return await _vod_response(
        camera_name,
        start_ts,
        end_ts,
        force_discontinuity=force_discontinuity,
        stream_preference=stream.value,
    )
@router.get(
@@ -938,7 +820,7 @@ async def event_snapshot(
        # see if the object is currently being tracked
        try:
            camera_states: list[CameraState] = (
-                request.app.detected_frames_processor.get_camera_states()
+                request.app.detected_frames_processor.camera_states.values()
            )
            for camera_state in camera_states:
                if event_id in camera_state.tracked_objects:
@@ -1016,7 +898,7 @@ async def event_thumbnail(
    if thumbnail_bytes is None:
        # see if the object is currently being tracked
        try:
-            camera_states = request.app.detected_frames_processor.get_camera_states()
+            camera_states = request.app.detected_frames_processor.camera_states.values()
            for camera_state in camera_states:
                if event_id in camera_state.tracked_objects:
                    tracked_obj = camera_state.tracked_objects.get(event_id)
@@ -1245,7 +1127,7 @@ async def event_snapshot_clean(request: Request, event_id: str, download: bool =
            # see if the object is currently being tracked
            try:
                camera_states = (
-                    request.app.detected_frames_processor.get_camera_states()
+                    request.app.detected_frames_processor.camera_states.values()
                )
                for camera_state in camera_states:
                    if event_id in camera_state.tracked_objects:
@@ -1,8 +1,10 @@
 """Notification apis."""
 import ipaddress
 import logging
 import os
 from typing import Any
 from urllib.parse import urlparse
 from cryptography.hazmat.primitives import serialization
 from fastapi import APIRouter, Depends, Request
@@ -19,6 +21,95 @@ logger = logging.getLogger(__name__)
 router = APIRouter(tags=[Tags.notifications])
 # Push endpoints are opaque URLs but stay well under this in practice
 MAX_ENDPOINT_LENGTH = 2048
 # Suffixes that only ever resolve on the local network
 INTERNAL_HOST_SUFFIXES = (".local", ".localdomain", ".internal", ".home.arpa")
 def _validate_push_endpoint(endpoint: Any) -> str | None:
    """Return a reason the endpoint is unusable, or None when it is valid.
    Subscriptions are issued by the browser vendor's push service, so a valid
    endpoint is always a public https URL. Anything else is either a broken
    registration or an attempt to aim the notification sender somewhere it
    should not reach.
    """
    if not isinstance(endpoint, str) or not endpoint:
        return "endpoint must be a url"
    if len(endpoint) > MAX_ENDPOINT_LENGTH:
        return "endpoint is too long"
    try:
        parsed = urlparse(endpoint)
        port = parsed.port
    except ValueError:
        return "endpoint is not a valid url"
    if parsed.scheme != "https":
        return "endpoint must use https"
    if parsed.username or parsed.password:
        return "endpoint must not include credentials"
    if port is not None and port != 443:
        return "endpoint must use the default https port"
    hostname = parsed.hostname
    if not hostname:
        return "endpoint must include a hostname"
    try:
        address = ipaddress.ip_address(hostname)
    except ValueError:
        address = None
    if address is not None:
        # A push service is never reachable at an address only this network can
        # route, so anything non-global is a misconfiguration at best
        if not address.is_global:
            return "endpoint must not use a private address"
    elif hostname == "localhost" or "." not in hostname:
        return "endpoint must use a fully qualified hostname"
    elif hostname.endswith(INTERNAL_HOST_SUFFIXES):
        return "endpoint must not use an internal hostname"
    # The subscription token lives in the path, and webpush.py assumes there is
    # a separator after the host when it builds the VAPID audience
    if len(parsed.path) <= 1:
        return "endpoint must include a subscription path"
    return None
 def _validate_subscription(sub: Any) -> str | None:
    """Return a reason the subscription is unusable, or None when it is valid."""
    if not isinstance(sub, dict):
        return "subscription must be an object"
    reason = _validate_push_endpoint(sub.get("endpoint"))
    if reason:
        return reason
    keys = sub.get("keys")
    if not isinstance(keys, dict):
        return "subscription must include keys"
    # WebPusher raises on a missing key, which would break every send for the
    # user rather than just this registration
    for name in ("p256dh", "auth"):
        value = keys.get(name)
        if not isinstance(value, str) or not value:
            return f"subscription keys must include {name}"
    return None
@router.get(
    "/notifications/pubkey",
@@ -71,6 +162,17 @@ def register_notifications(request: Request, body: dict = None):
            status_code=400,
        )
    reason = _validate_subscription(sub)
    if reason:
        logger.warning(
            "Rejected notification registration for %s: %s", username, reason
        )
        return JSONResponse(
            content={"success": False, "message": f"Invalid subscription: {reason}"},
            status_code=400,
        )
    try:
        User.update(notification_tokens=User.notification_tokens.append(sub)).where(
            User.username == username
@@ -25,20 +25,8 @@ from frigate.api.defs.query.recordings_query_parameters import (
 )
 from frigate.api.defs.response.generic_response import GenericResponse
 from frigate.api.defs.tags import Tags
-from frigate.const import (
+from frigate.const import RECORD_DIR
    MAX_SEGMENT_DURATION,
    RECORD_DIR,
    STREAM_TYPE_MAIN,
    STREAM_TYPE_SUB,
 )
 from frigate.models import Event, Recordings
 from frigate.util.recording_coverage import (
    coverage_spans,
    known_video_codecs,
    realized_timelines,
    resolve_coverage,
    stream_media_summary,
 )
 from frigate.util.time import get_dst_transitions
 logger = logging.getLogger(__name__)
@@ -161,28 +149,23 @@ async def recordings_summary(camera_name: str, timezone: str = "utc"):
        period_hour_modifier = f"{hours_offset} hour"
        period_minute_modifier = f"{minutes_offset} minute"
        hour_expression = fn.strftime(
            "%Y-%m-%d %H",
            fn.datetime(
                Recordings.start_time,
                "unixepoch",
                period_hour_modifier,
                period_minute_modifier,
            ),
        )
        # sub rows duplicate the camera's motion/object stats, so
        # aggregating them too would double-count
        recording_groups = (
            Recordings.select(
-                hour_expression.alias("hour"),
+                fn.strftime(
                    "%Y-%m-%d %H",
                    fn.datetime(
                        Recordings.start_time,
                        "unixepoch",
                        period_hour_modifier,
                        period_minute_modifier,
                    ),
                ).alias("hour"),
                fn.SUM(Recordings.duration).alias("duration"),
                fn.SUM(Recordings.motion).alias("motion"),
                fn.SUM(Recordings.objects).alias("objects"),
            )
            .where(
                (Recordings.camera == camera_name)
                & (Recordings.stream_type == STREAM_TYPE_MAIN)
                & (Recordings.end_time >= period_start)
                & (Recordings.start_time <= period_end)
            )
@@ -191,23 +174,6 @@ async def recordings_summary(camera_name: str, timezone: str = "utc"):
            .namedtuples()
        )
        # sub recordings can outlive main, so hours covered only by sub
        # rows are reported too, flagged as sub_only
        sub_groups = (
            Recordings.select(
                hour_expression.alias("hour"),
                fn.SUM(Recordings.duration).alias("duration"),
            )
            .where(
                (Recordings.camera == camera_name)
                & (Recordings.stream_type == STREAM_TYPE_SUB)
                & (Recordings.end_time >= period_start)
                & (Recordings.start_time <= period_end)
            )
            .group_by((Recordings.start_time + period_offset).cast("int") / 3600)
            .namedtuples()
        )
        event_groups = (
            Event.select(
                fn.strftime(
@@ -231,43 +197,17 @@ async def recordings_summary(camera_name: str, timezone: str = "utc"):
        event_map = {g.hour: g.count for g in event_groups}
-        hour_stats = [
+        for recording_group in recording_groups:
-            (
+            parts = recording_group.hour.split()
                g.hour,
                {
                    "motion": g.motion,
                    "objects": g.objects,
                    "duration": round(g.duration),
                },
            )
            for g in recording_groups
        ]
        main_hours = {group_hour for group_hour, _ in hour_stats}
        hour_stats.extend(
            (
                g.hour,
                {
                    "motion": 0,
                    "objects": 0,
                    "duration": round(g.duration),
                    "sub_only": True,
                },
            )
            for g in sub_groups
            if g.hour not in main_hours
        )
        # restore the most-recent-first ordering after merging in sub hours
        hour_stats.sort(key=lambda entry: entry[0], reverse=True)
        for group_hour, stats in hour_stats:
            parts = group_hour.split()
            hour = parts[1]
            day = parts[0]
-            events_count = event_map.get(group_hour, 0)
+            events_count = event_map.get(recording_group.hour, 0)
            hour_data = {
                "hour": hour,
                "events": events_count,
-                **stats,
+                "motion": recording_group.motion,
                "objects": recording_group.objects,
                "duration": round(recording_group.duration),
            }
            if day in days:
                # merge counts if already present (edge-case at DST boundary)
@@ -283,35 +223,6 @@ async def recordings_summary(camera_name: str, timezone: str = "utc"):
    return JSONResponse(content=list(days.values()))
@router.get(
    "/{camera_name}/recordings/coverage",
    dependencies=[Depends(require_camera_access)],
 )
 async def recordings_coverage(
    camera_name: str, after: float, before: float, timelines: bool = False
 ):
    """Returns merged recording coverage spans plus codec compatibility.
    codecs_compatible is false only when more than one known video codec
    appears across the range's rows, the case where the merged vod route
    degrades to a single-stream manifest.
    """
    intervals = resolve_coverage(camera_name, after, before)
    content = {
        "spans": coverage_spans(intervals),
        "codecs_compatible": len(known_video_codecs(intervals)) <= 1,
        "streams": stream_media_summary(intervals),
    }
    # pure computation (shared plan_clip, record-time keyframe index), but
    # opt-in for payload hygiene: day-level requests need only the spans
    if timelines:
        content["timelines"] = realized_timelines(intervals)
    return JSONResponse(content=content)
@router.get("/{camera_name}/recordings", dependencies=[Depends(require_camera_access)])
 async def recordings(
    camera_name: str,
@@ -332,8 +243,6 @@ async def recordings(
        )
        .where(
            Recordings.camera == camera_name,
            Recordings.stream_type == STREAM_TYPE_MAIN,
            Recordings.start_time >= after - MAX_SEGMENT_DURATION,
            Recordings.end_time >= after,
            Recordings.start_time <= before,
        )
@@ -17,6 +17,7 @@ from frigate.api.auth import (
    get_allowed_cameras_for_filter,
    get_current_user,
    require_camera_access,
    require_full_camera_access,
    require_role,
 )
 from frigate.api.defs.query.review_query_parameters import (
@@ -32,7 +33,6 @@ from frigate.api.defs.response.review_response import (
    ReviewSummaryResponse,
 )
 from frigate.api.defs.tags import Tags
 from frigate.const import STREAM_TYPE_MAIN
 from frigate.embeddings import EmbeddingsContext
 from frigate.models import Recordings, ReviewSegment, UserReviewStatus
 from frigate.review.types import SeverityEnum
@@ -43,6 +43,22 @@ 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],
@@ -92,10 +108,7 @@ async def review(
        filtered_labels = labels.split(",")
        for label in filtered_labels:
-            label_clauses.append(
+            label_clauses.append(get_label_clause(label))
                (ReviewSegment.data["objects"].cast("text") % f'*"{label}"*')
                | (ReviewSegment.data["audio"].cast("text") % f'*"{label}"*')
            )
        clauses.append(reduce(operator.or_, label_clauses))
    if zones != "all":
@@ -236,10 +249,7 @@ async def review_summary(
        filtered_labels = labels.split(",")
        for label in filtered_labels:
-            label_clauses.append(
+            label_clauses.append(get_label_clause(label))
                (ReviewSegment.data["objects"].cast("text") % f'*"{label}"*')
                | (ReviewSegment.data["audio"].cast("text") % f'*"{label}"*')
            )
        clauses.append(reduce(operator.or_, label_clauses))
    if zones != "all":
        # use matching so segments with multiple zones
@@ -337,9 +347,8 @@ async def review_summary(
        filtered_labels = labels.split(",")
        for label in filtered_labels:
-            label_clauses.append(
+            label_clauses.append(get_label_clause(label, include_audio=False))
-                ReviewSegment.data["objects"].cast("text") % f'*"{label}"*'
+
            )
        clauses.append(reduce(operator.or_, label_clauses))
    # Find the time range of available data
@@ -598,8 +607,6 @@ def motion_activity(
    clauses = [(Recordings.start_time > after) & (Recordings.end_time < before)]
    clauses.append(Recordings.motion > 0)
    # sub rows duplicate the camera's motion stats, so only count main rows
    clauses.append(Recordings.stream_type == STREAM_TYPE_MAIN)
    if cameras != "all":
        requested = set(cameras.split(","))
@@ -712,6 +719,7 @@ async def get_review(request: Request, review_id: str):
    dependencies=[Depends(allow_any_authenticated())],
 )
 async def set_not_reviewed(
    request: Request,
    review_id: str,
    current_user: dict = Depends(get_current_user),
 ):
@@ -730,6 +738,8 @@ async def set_not_reviewed(
            status_code=404,
        )
    await require_camera_access(review.camera, request=request)
    try:
        user_review = UserReviewStatus.get(
            UserReviewStatus.user_id == user_id,
@@ -746,9 +756,12 @@ async def set_not_reviewed(
    )
 # Intentionally not camera scoped, as the summary correlates each flagged event
 # with overlapping activity on other cameras. Restricted to callers who can
 # already see every camera, so the unscoped query discloses nothing.
@router.post(
    "/review/summarize/start/{start_ts}/end/{end_ts}",
-    dependencies=[Depends(require_role(["admin"]))],
+    dependencies=[Depends(require_full_camera_access)],
    description="Use GenAI to summarize review items over a period of time.",
 )
 def generate_review_summary(request: Request, start_ts: float, end_ts: float):
@@ -103,12 +103,13 @@ 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)
@@ -233,12 +234,13 @@ 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):
@@ -60,6 +60,11 @@ class CameraState:
        # face/LPR pipelines when using a model without built-in detection.
        self.face_recognition_min_obj_area: int = 0
        self.lpr_min_obj_area: int = 0
        self.lp_objects = {
            label
            for label, attributes in config.model.attributes_map.items()
            if "license_plate" in attributes
        }
        if (
            self.camera_config.face_recognition.enabled
@@ -452,7 +457,7 @@ class CameraState:
                and obj_area >= self.face_recognition_min_obj_area
                and updated_obj.obj_data.get("sub_label") is None
            ) or (
-                obj_label in ("car", "motorcycle")
+                obj_label in self.lp_objects
                and self.lpr_min_obj_area > 0
                and obj_area >= self.lpr_min_obj_area
                and updated_obj.obj_data.get("sub_label") is None
@@ -548,7 +553,7 @@ class CameraState:
                    current_best.thumbnail_data is not None
                    and obj.thumbnail_data is not None
                    and is_better_thumbnail(
-                        object_type,
+                        obj.thumbnail_attributes,
                        current_best.thumbnail_data,
                        obj.thumbnail_data,
                        self.camera_config.frame_shape,
@@ -11,7 +11,7 @@ from frigate.camera.activity_manager import AudioActivityManager, CameraActivity
 from frigate.comms.base_communicator import Communicator
 from frigate.comms.runtime_state import RuntimeStatePersistence
 from frigate.comms.webpush import WebPushClient
-from frigate.config import BirdseyeModeConfig, FrigateConfig
+from frigate.config import BirdseyeModeEnum, FrigateConfig
 from frigate.config.camera.updater import (
    CameraConfigUpdateEnum,
    CameraConfigUpdatePublisher,
@@ -882,9 +882,8 @@ class Dispatcher:
    def _on_birdseye_mode_command(self, camera_name: str, payload: str) -> None:
        """Callback for birdseye mode topic."""
-        mode = BirdseyeModeConfig.from_mqtt_payload(payload)
+        if payload not in ["CONTINUOUS", "MOTION", "OBJECTS"]:
-        if mode is None:
+            logger.info(f"Invalid birdseye_mode command: {payload}")
            logger.info("Invalid birdseye_mode command: %s", payload)
            return
        birdseye_settings = self.config.cameras[camera_name].birdseye
@@ -893,7 +892,7 @@ class Dispatcher:
            logger.info(f"Birdseye mode not enabled for {camera_name}")
            return
-        birdseye_settings.mode = mode
+        birdseye_settings.mode = BirdseyeModeEnum(payload.lower())
        logger.info(
            f"Setting birdseye mode for {camera_name} to {birdseye_settings.mode}"
        )
@@ -902,9 +901,7 @@ class Dispatcher:
            CameraConfigUpdateTopic(CameraConfigUpdateEnum.birdseye, camera_name),
            birdseye_settings,
        )
-        self.publish(
+        self.publish(f"{camera_name}/birdseye_mode/state", payload, retain=True)
            f"{camera_name}/birdseye_mode/state", mode.to_mqtt_payload(), retain=True
        )
    def _on_camera_notification_command(self, camera_name: str, payload: str) -> None:
        """Callback for camera level notifications topic."""
@@ -125,7 +125,7 @@ class MqttClient(Communicator):
            self.publish(
                f"{camera_name}/birdseye_mode/state",
                (
-                    camera.birdseye.mode.to_mqtt_payload()
+                    camera.birdseye.mode.value.upper()
                    if camera.birdseye.enabled
                    else "OFF"
                ),
@@ -89,7 +89,9 @@ class WebPushClient(Communicator):
        # notification and auth config updater
        self.global_config_subscriber = ConfigSubscriber("config/")
        self.config_subscriber = CameraConfigUpdateSubscriber(
-            self.config, self.config.cameras, [CameraConfigUpdateEnum.notifications]
+            self.config,
            self.config.cameras,
            [CameraConfigUpdateEnum.add, CameraConfigUpdateEnum.notifications],
        )
        self._refresh_user_cameras()
@@ -213,12 +215,12 @@ class WebPushClient(Communicator):
                self.suspended_cameras[camera] = 0
                self.last_camera_notification_time[camera] = 0
            self._refresh_user_cameras()
        if topic == "reviews":
            decoded = json.loads(payload)
            camera = decoded["before"]["camera"]
-            camera_config = self.config.cameras.get(camera)
+            if not self.config.cameras[camera].notifications.enabled:
            if camera_config is None or not camera_config.notifications.enabled:
                return
            if self.is_camera_suspended(camera):
                logger.debug(f"Notifications for {camera} are currently suspended.")
@@ -232,14 +234,13 @@ class WebPushClient(Communicator):
            # ensure notifications are enabled and the specific trigger has
            # notification action enabled
            camera_config = self.config.cameras.get(camera)
            if (
-                camera_config is None
+                not self.config.cameras[camera].notifications.enabled
-                or not camera_config.notifications.enabled
+                or name not in self.config.cameras[camera].semantic_search.triggers
                or name not in camera_config.semantic_search.triggers
                or "notification"
-                not in camera_config.semantic_search.triggers[name].actions
+                not in self.config.cameras[camera]
                .semantic_search.triggers[name]
                .actions
            ):
                return
@@ -250,9 +251,7 @@ class WebPushClient(Communicator):
        elif topic == "camera_monitoring":
            decoded = json.loads(payload)
            camera = decoded["camera"]
-            camera_config = self.config.cameras.get(camera)
+            if not self.config.cameras[camera].notifications.enabled:
            if camera_config is None or not camera_config.notifications.enabled:
                return
            if self.is_camera_suspended(camera):
                logger.debug(f"Notifications for {camera} are currently suspended.")
@@ -422,6 +421,7 @@ class WebPushClient(Communicator):
        # Don't notify if message is an update and important fields don't have an update
        if (
            state == "update"
            and payload["before"]["severity"] == payload["after"]["severity"]
            and len(payload["before"]["data"]["objects"])
            == len(payload["after"]["data"]["objects"])
            and len(payload["before"]["data"]["zones"])
@@ -1,3 +1,5 @@
 from enum import Enum
 from pydantic import BaseModel, Field
 from ..base import FrigateBaseModel
@@ -6,78 +8,22 @@ __all__ = [
    "BirdseyeCameraConfig",
    "BirdseyeConfig",
    "BirdseyeLayoutConfig",
-    "BirdseyeModeConfig",
+    "BirdseyeModeEnum",
 ]
 BIRDSEYE_ACTIVITY_TYPES = (
    "objects",
    "motion",
    "stationary_objects",
    "continuous",
 )
-
+class BirdseyeModeEnum(str, Enum):
-class BirdseyeModeConfig(FrigateBaseModel):
+    objects = "objects"
-    continuous: bool = Field(
+    motion = "motion"
-        default=False,
+    continuous = "continuous"
        title="Continuous",
        description="Always include the camera in Birdseye.",
    )
    motion: bool = Field(
        default=False,
        title="Motion",
        description="Include the camera in Birdseye when motion is detected.",
    )
    objects: bool = Field(
        default=False,
        title="Active objects",
        description="Include the camera in Birdseye while an active object is tracked.",
    )
    stationary_objects: bool = Field(
        default=False,
        title="Stationary objects",
        description="Include the camera in Birdseye while a stationary object is tracked.",
    )
    @classmethod
-    def from_mqtt_payload(cls, payload: str) -> "BirdseyeModeConfig | None":
+    def get_index(cls, type):
-        """Create mode options from an uppercase MQTT payload."""
+        return list(cls).index(type)
        raw_modes = payload.split(",")
        if not raw_modes or any(not mode for mode in raw_modes):
            return None
-        modes = [mode.lower() for mode in raw_modes]
+    @classmethod
-        if any(
+    def get(cls, index):
-            raw_mode != mode.upper() or mode not in BIRDSEYE_ACTIVITY_TYPES
+        return list(cls)[index]
            for raw_mode, mode in zip(raw_modes, modes)
        ):
            return None
        if len(modes) != len(set(modes)):
            return None
        return cls(**{mode: True for mode in modes})
    def has_enabled_activity(self) -> bool:
        """Return whether at least one activity type is enabled."""
        return any(getattr(self, activity) for activity in BIRDSEYE_ACTIVITY_TYPES)
    def to_mqtt_payload(self) -> str:
        """Serialize enabled mode options for MQTT state topics."""
        payload = ",".join(
            activity.upper()
            for activity in BIRDSEYE_ACTIVITY_TYPES
            if getattr(self, activity)
        )
        if not payload:
            raise ValueError("At least one Birdseye activity type must be enabled")
        return payload
 def default_birdseye_mode() -> BirdseyeModeConfig:
    """Return the default Birdseye mode configuration."""
    return BirdseyeModeConfig(objects=True)
 class BirdseyeLayoutConfig(FrigateBaseModel):
@@ -101,10 +47,10 @@ class BirdseyeConfig(FrigateBaseModel):
        title="Enable Birdseye",
        description="Enable or disable the Birdseye view feature.",
    )
-    mode: BirdseyeModeConfig = Field(
+    mode: BirdseyeModeEnum = Field(
-        default_factory=default_birdseye_mode,
+        default=BirdseyeModeEnum.objects,
-        title="Activity types",
+        title="Tracking mode",
-        description="Activity types that include cameras in Birdseye.",
+        description="Mode for including cameras in Birdseye: 'objects', 'motion', or 'continuous'.",
    )
    restream: bool = Field(
@@ -156,10 +102,10 @@ class BirdseyeCameraConfig(BaseModel):
        title="Enable Birdseye",
        description="Enable or disable the Birdseye view feature.",
    )
-    mode: BirdseyeModeConfig = Field(
+    mode: BirdseyeModeEnum = Field(
-        default_factory=default_birdseye_mode,
+        default=BirdseyeModeEnum.objects,
-        title="Activity types",
+        title="Tracking mode",
-        description="Activity types that include cameras in Birdseye.",
+        description="Mode for including cameras in Birdseye: 'objects', 'motion', or 'continuous'.",
    )
    order: int = Field(
@@ -3,12 +3,7 @@ from enum import Enum
 from pydantic import Field, PrivateAttr, model_validator
-from frigate.const import (
+from frigate.const import CACHE_DIR, CACHE_SEGMENT_FORMAT, REGEX_CAMERA_NAME
    CACHE_DIR,
    CACHE_SEGMENT_FORMAT,
    REGEX_CAMERA_NAME,
    SUB_CACHE_TAG,
 )
 from frigate.ffmpeg_presets import (
    parse_preset_hardware_acceleration_decode,
    parse_preset_hardware_acceleration_scale,
@@ -299,28 +294,6 @@ class CameraConfig(FrigateBaseModel):
                + ffmpeg_output_args
            )
        if (
            "record_sub" in ffmpeg_input.roles
            and self.record.enabled
            and self.record.sub.enabled
        ):
            sub_output_args = self.ffmpeg.output_args.effective_record_sub
            record_args = get_ffmpeg_arg_list(
                parse_preset_output_record(
                    sub_output_args,
                    self.ffmpeg.apple_compatibility,
                )
                or sub_output_args
            )
            ffmpeg_output_args = (
                record_args
                + [
                    f"{os.path.join(CACHE_DIR, self.name)}{SUB_CACHE_TAG}@{CACHE_SEGMENT_FORMAT}.mp4"
                ]
                + ffmpeg_output_args
            )
        # if there aren't any outputs enabled for this input
        if len(ffmpeg_output_args) == 0:
            return None
@@ -42,20 +42,6 @@ class FfmpegOutputArgsConfig(FrigateBaseModel):
        title="Record output arguments",
        description="Default output arguments for record role streams.",
    )
    record_sub: str | list[str] = Field(
        default_factory=list,
        title="Sub stream record output arguments",
        description="Output arguments for record_sub role streams. The record output arguments are used when this is not set.",
    )
    @property
    def effective_record_sub(self) -> str | list[str]:
        """Output arguments used for the record_sub role.
        Falls back to the record arguments rather than to the stock preset so
        that a customized record value keeps applying to both recorded streams.
        """
        return self.record_sub or self.record
 class FfmpegConfig(FrigateBaseModel):
@@ -113,7 +99,6 @@ class FfmpegConfig(FrigateBaseModel):
 class CameraRoleEnum(str, Enum):
    audio = "audio"
    record = "record"
    record_sub = "record_sub"
    detect = "detect"
@@ -13,7 +13,6 @@ __all__ = [
    "RecordExportConfig",
    "RecordPreviewConfig",
    "RecordQualityEnum",
    "RecordSubConfig",
    "EventsConfig",
    "ReviewRetainConfig",
    "RecordRetainConfig",
@@ -111,34 +110,6 @@ class RecordExportConfig(FrigateBaseModel):
    )
 class RecordSubConfig(FrigateBaseModel):
    enabled: bool = Field(
        default=False,
        title="Enable sub stream recording",
        description="Enable recording of a second, lower quality stream for adaptive quality playback and extended retention.",
    )
    continuous: RecordRetainConfig = Field(
        default_factory=RecordRetainConfig,
        title="Sub stream continuous retention",
        description="Number of days to retain sub stream recordings regardless of tracked objects or motion.",
    )
    motion: RecordRetainConfig = Field(
        default_factory=RecordRetainConfig,
        title="Sub stream motion retention",
        description="Number of days to retain sub stream recordings triggered by motion.",
    )
    alerts: ReviewRetainConfig = Field(
        default_factory=ReviewRetainConfig,
        title="Sub stream alert retention",
        description="Retention settings for sub stream recordings of alerts.",
    )
    detections: ReviewRetainConfig = Field(
        default_factory=ReviewRetainConfig,
        title="Sub stream detection retention",
        description="Retention settings for sub stream recordings of detections.",
    )
 class RecordConfig(FrigateBaseModel):
    enabled: bool = Field(
        default=False,
@@ -180,35 +151,12 @@ class RecordConfig(FrigateBaseModel):
        title="Preview config",
        description="Settings controlling the quality of recording previews shown in the UI.",
    )
    sub: RecordSubConfig = Field(
        default_factory=RecordSubConfig,
        title="Sub stream recording",
        description="Settings for recording a second, lower quality stream.",
    )
    enabled_in_config: bool | None = Field(
        default=None,
        title="Original recording state",
        description="Indicates whether recording was enabled in the original static configuration.",
    )
    @property
    def effective_alert_days(self) -> float:
        """Alert retention extended to the sub stream window when sub is enabled.
        Review items and tracked objects must stay visible for as long as
        either stream still has recordings.
        """
        if self.sub.enabled:
            return max(self.alerts.retain.days, self.sub.alerts.days)
        return self.alerts.retain.days
    @property
    def effective_detection_days(self) -> float:
        """Detection retention extended to the sub window when sub is enabled."""
        if self.sub.enabled:
            return max(self.detections.retain.days, self.sub.detections.days)
        return self.detections.retain.days
    @property
    def event_pre_capture(self) -> int:
        return max(
@@ -129,13 +129,8 @@ class CameraConfigUpdateSubscriber:
            config.objects = updated_config
        elif update_type == CameraConfigUpdateEnum.record:
            old_enabled_in_config = config.record.enabled_in_config
            old_sub_enabled = config.record.sub.enabled
            config.record = updated_config
-            # the record and record_sub ffmpeg outputs are gated on these
+            if old_enabled_in_config != updated_config.enabled_in_config:
            if (
                old_enabled_in_config != updated_config.enabled_in_config
                or old_sub_enabled != updated_config.sub.enabled
            ):
                config.recreate_ffmpeg_cmds()
        elif update_type == CameraConfigUpdateEnum.review:
            config.review = updated_config
@@ -41,7 +41,7 @@ from .auth import AuthConfig
 from .base import FrigateBaseModel
 from .camera import CameraConfig, CameraLiveConfig
 from .camera.audio import AudioConfig, AudioFilterConfig
-from .camera.birdseye import BirdseyeConfig, BirdseyeModeConfig
+from .camera.birdseye import BirdseyeConfig
 from .camera.detect import DetectConfig
 from .camera.ffmpeg import FfmpegConfig
 from .camera.genai import GenAIConfig, GenAIRoleEnum
@@ -255,15 +255,6 @@ def verify_config_roles(camera_config: CameraConfig) -> None:
            f"Camera {camera_config.name} has record enabled, but record is not assigned to an input."
        )
    if (
        camera_config.record.enabled
        and camera_config.record.sub.enabled
        and "record_sub" not in assigned_roles
    ):
        raise ValueError(
            f"Camera {camera_config.name} has sub stream recording enabled, but record_sub is not assigned to an input."
        )
    if camera_config.audio.enabled and "audio" not in assigned_roles:
        raise ValueError(
            f"Camera {camera_config.name} has audio events enabled, but audio is not assigned to an input."
@@ -284,11 +275,13 @@ def verify_valid_live_stream_names(
            )
-def verify_record_output_args_segment_time(
+def verify_recording_segments_setup_with_reasonable_time(
-    camera_config: CameraConfig, output_args: str | list[str], role: str
+    camera_config: CameraConfig,
 ) -> None:
-    """Verify that a recording role's output args segment at a reasonable time."""
+    """Verify that recording segments are setup and segment time is not greater than 60."""
-    record_args: list[str] = get_ffmpeg_arg_list(output_args)
+    record_args: list[str] = get_ffmpeg_arg_list(
        camera_config.ffmpeg.output_args.record
    )
    if record_args[0].startswith("preset"):
        return
@@ -298,32 +291,16 @@ def verify_record_output_args_segment_time(
    except ValueError:
        raise ValueError(
            f"Camera {camera_config.name} has no segment_time in \
-                         {role} output args, segment args are required for record."
+                         recording output args, segment args are required for record."
        ) from None
    if int(record_args[seg_arg_index + 1]) > 60:
        raise ValueError(
-            f"Camera {camera_config.name} has invalid segment_time in {role} output args, \
+            f"Camera {camera_config.name} has invalid segment_time output arg, \
                         segment_time must be 60 or less."
        )
 def verify_recording_segments_setup_with_reasonable_time(
    camera_config: CameraConfig,
 ) -> None:
    """Verify that recording segments are setup and segment time is not greater than 60."""
    verify_record_output_args_segment_time(
        camera_config, camera_config.ffmpeg.output_args.record, "recording"
    )
    if camera_config.record.sub.enabled:
        verify_record_output_args_segment_time(
            camera_config,
            camera_config.ffmpeg.output_args.effective_record_sub,
            "sub stream recording",
        )
 def verify_zone_objects_are_tracked(camera_config: CameraConfig) -> None:
    """Verify that user has not entered zone objects that are not in the tracking config."""
    for zone_name, zone in camera_config.zones.items():
@@ -349,20 +326,8 @@ def verify_required_zones_exist(camera_config: CameraConfig) -> None:
 def verify_profile_overrides_match_base(camera_config: CameraConfig) -> None:
-    """Verify profile overrides against the resolved base camera configuration."""
+    """Verify that profile zone and mask IDs reference entries defined on the base camera."""
    for profile_name, profile in camera_config.profiles.items():
        if profile.birdseye is not None:
            overrides = profile.birdseye.mode.model_dump(exclude_unset=True)
            base_mode = camera_config.birdseye.mode.model_dump()
            resolved_mode = BirdseyeModeConfig.model_validate(
                deep_merge(overrides, base_mode)
            )
            if not resolved_mode.has_enabled_activity():
                raise ValueError(
                    f"Camera '{camera_config.name}' profile '{profile_name}' must "
                    "enable at least one Birdseye activity type"
                )
        if profile.zones:
            for zone_name in profile.zones:
                if zone_name not in camera_config.zones:
@@ -1033,10 +998,6 @@ class FrigateConfig(FrigateBaseModel):
            self.cameras[name] = camera_config
            verify_config_roles(camera_config)
            if not camera_config.birdseye.mode.has_enabled_activity():
                raise ValueError(
                    f"Camera '{name}' must enable at least one Birdseye activity type"
                )
            verify_valid_live_stream_names(self, camera_config)
            verify_recording_segments_setup_with_reasonable_time(camera_config)
            verify_zone_objects_are_tracked(camera_config)
@@ -43,9 +43,7 @@ SECTION_STATE_TOPICS: dict[str, list[tuple[str, Callable[[Any], Any]]]] = {
        ("birdseye", lambda c: "ON" if c.birdseye.enabled else "OFF"),
        (
            "birdseye_mode",
-            lambda c: (
+            lambda c: c.birdseye.mode.value.upper() if c.birdseye.enabled else "OFF",
                c.birdseye.mode.to_mqtt_payload() if c.birdseye.enabled else "OFF"
            ),
        ),
    ],
    "detect": [("detect", lambda c: "ON" if c.detect.enabled else "OFF")],
@@ -23,12 +23,6 @@ SHM_FRAMES_VAR = "SHM_MAX_FRAMES"
 REDACTED_CREDENTIAL_SENTINEL = "__FRIGATE_SAVED_CREDENTIAL__"
 # Stream type constants
 STREAM_TYPE_MAIN = "main"
 STREAM_TYPE_SUB = "sub"
 SUB_CACHE_TAG = "@sub"
 # Attribute & Object constants
 DEFAULT_ATTRIBUTE_LABEL_MAP = {
@@ -50,7 +44,11 @@ DEFAULT_ATTRIBUTE_LABEL_MAP = {
        "ups",
        "usps",
    ],
    "truck": ["license_plate"],
    "garbage_truck": ["license_plate"],
    "motorcycle": ["license_plate"],
    "bus": ["license_plate"],
    "school_bus": ["license_plate"],
 }
 ATTRIBUTE_LABEL_DISPLAY_MAP = {
    "amazon": "Amazon",
@@ -1290,7 +1290,7 @@ class LicensePlateProcessingMixin:
                and obj_data.get("label") != "license_plate"
            ):
                logger.debug(
-                    f"{camera}: Not a processing license plate for non car/motorcycle object."
+                    f"{camera}: Not a processing license plate for {obj_data.get('label', 'unknown')}."
                )
                return
@@ -1367,7 +1367,7 @@ class LicensePlateProcessingMixin:
                if not license_plate:
                    logger.debug(
-                        f"{camera}: Detected no license plates for car/motorcycle object."
+                        f"{camera}: Detected no license plates for {obj_data.get('label', 'unknown')} object."
                    )
                    return
@@ -83,10 +83,6 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
        """
        event_id = data["event_id"]
        camera_name = data["camera"]
        camera_config = self.config.cameras.get(camera_name)
        if camera_config is None:
            return
        if data_type == PostProcessDataEnum.recording:
            start_ts = data["frame_time"]
@@ -108,7 +104,7 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
        try:
            audio_data = get_audio_from_recording(
-                camera_config.ffmpeg,
+                self.config.cameras[camera_name].ffmpeg,
                camera_name,
                start_ts,
                end_ts,
@@ -151,12 +151,7 @@ class ObjectDescriptionProcessor(PostProcessorApi):
            logger.error(f"Event {event_id} not found for description regeneration")
            return
-        camera_config = self.config.cameras.get(str(event.camera))
+        camera_config = self.config.cameras[str(event.camera)]
        if camera_config is None:
            logger.error("Camera %s no longer exists", event.camera)
            return
        if not camera_config.objects.genai.enabled and not force:
            logger.error(f"GenAI not enabled for camera {event.camera}")
            return
@@ -137,10 +137,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
            return
        camera = data["after"]["camera"]
-        camera_config = self.config.cameras.get(camera)
+        camera_config = self.config.cameras[camera]
        if camera_config is None:
            return
        if not camera_config.review.genai.enabled:
            return
@@ -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(
+        if not self._table_exists(table):
            "SELECT name FROM sqlite_master WHERE type = 'table' AND name = ?",
            (table,),
        )
        if cursor.fetchone() is None:
            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("""
+        for table in ("vec_descriptions", "vec_thumbnails"):
-            DROP TABLE vec_descriptions;
+            self._restore_vec_info_table(table)
-        """)
+            self.execute_write(f"DROP TABLE IF EXISTS {table}")
        self.execute_sql("""
            DROP TABLE vec_thumbnails;
        """)
    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
            )
@@ -25,25 +25,31 @@ def is_arm64_platform() -> bool:
    return machine in ("aarch64", "arm64", "armv8", "armv7l")
-def get_ort_session_options(
+def get_ort_session_options(model_type: str | None = None) -> ort.SessionOptions | None:
    is_complex_model: bool = False,
 ) -> ort.SessionOptions | None:
    """Get ONNX Runtime session options with appropriate settings.
    Args:
-        is_complex_model: Whether the model needs basic optimization to avoid graph fusion issues.
+        model_type: Model being loaded, used to pin its graph optimization level.
    Returns:
-        SessionOptions with appropriate optimization level, or None for default settings.
+        SessionOptions with a pinned optimization level, or None for default settings.
    """
-    if is_complex_model:
+    # Import here to avoid circular imports
-        sess_options = ort.SessionOptions()
+    from frigate.embeddings.types import EnrichmentModelTypeEnum
        sess_options.graph_optimization_level = (
            ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
        )
        return sess_options
-    return None
+    if model_type == EnrichmentModelTypeEnum.jina_v2.value:
        # below EXTENDED the CUDA EP returns an identical vector for every image,
        # and ORT_ENABLE_ALL fails to build on CPU with a SimplifiedLayerNormFusion error
        level = ort.GraphOptimizationLevel.ORT_ENABLE_EXTENDED
    elif model_type == EnrichmentModelTypeEnum.jina_v1.value:
        # aggressive optimizations create or expect nodes that don't exist
        level = ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
    else:
        return None
    sess_options = ort.SessionOptions()
    sess_options.graph_optimization_level = level
    return sess_options
 # Import OpenVINO only when needed to avoid circular dependencies
@@ -115,21 +121,6 @@ class BaseModelRunner(ABC):
 class ONNXModelRunner(BaseModelRunner):
    """Run ONNX models using ONNX Runtime."""
    @staticmethod
    def is_cpu_complex_model(model_type: str) -> bool:
        """Check if model needs basic optimization level to avoid graph fusion issues.
        Some models (like Jina-CLIP) have issues with aggressive optimizations like
        SimplifiedLayerNormFusion that create or expect nodes that don't exist.
        """
        # Import here to avoid circular imports
        from frigate.embeddings.types import EnrichmentModelTypeEnum
        return model_type in [
            EnrichmentModelTypeEnum.jina_v1.value,
            EnrichmentModelTypeEnum.jina_v2.value,
        ]
    @staticmethod
    def is_migraphx_complex_model(model_type: str) -> bool:
        # Import here to avoid circular imports
@@ -208,15 +199,20 @@ class CudaGraphRunner(BaseModelRunner):
            EnrichmentModelTypeEnum.yolov9_license_plate.value,
        ]
    # ORT performs two regular runs before it starts capturing, but on some
    # driver / cuDNN combinations the arena still has to extend on the run that
    # captures, and cudaMalloc is not allowed during capture. Running with
    # capture disabled first keeps those allocations outside of the capture.
    GRAPH_FREE_WARMUP_RUNS = 2
    def __init__(self, session: ort.InferenceSession, cuda_device_id: int):
        self._session = session
        self._cuda_device_id = cuda_device_id
-        self._captured = False
+        self._prepared = False
        self._io_binding: ort.IOBinding | None = None
        self._input_name: str | None = None
        self._output_names: list[str] | None = None
        self._input_ortvalue: ort.OrtValue | None = None
        self._output_ortvalues: ort.OrtValue | None = None
    def get_input_names(self) -> list[str]:
        """Get input names for the model."""
@@ -226,35 +222,41 @@ class CudaGraphRunner(BaseModelRunner):
        """Get the input width of the model."""
        return self._session.get_inputs()[0].shape[3]
    def _prepare(self, input_name: str, tensor_input: np.ndarray) -> None:
        """Bind CUDA buffers and warm the session up with capture disabled."""
        self._io_binding = self._session.io_binding()
        self._input_name = input_name
        self._output_names = [o.name for o in self._session.get_outputs()]
        self._input_ortvalue = ort.OrtValue.ortvalue_from_numpy(
            tensor_input, "cuda", self._cuda_device_id
        )
        self._io_binding.bind_ortvalue_input(self._input_name, self._input_ortvalue)
        for name in self._output_names:
            # Bind outputs to CUDA and allow ORT to allocate appropriately
            self._io_binding.bind_output(name, "cuda", self._cuda_device_id)
        # gpu_graph_id -1 disables capture and replay for the run
        warmup_options = ort.RunOptions()
        warmup_options.add_run_config_entry("gpu_graph_id", "-1")
        for _ in range(self.GRAPH_FREE_WARMUP_RUNS):
            self._session.run_with_iobinding(self._io_binding, warmup_options)
        self._prepared = True
    def run(self, input: dict[str, Any]):
        # Extract the single tensor input (assuming one input)
        input_name = list(input.keys())[0]
-        tensor_input = input[input_name]
+        tensor_input = np.ascontiguousarray(input[input_name])
        tensor_input = np.ascontiguousarray(tensor_input)
-        if not self._captured:
+        if not self._prepared:
-            # Prepare IOBinding with CUDA buffers and let ORT allocate outputs on device
+            self._prepare(input_name, tensor_input)
-            self._io_binding = self._session.io_binding()
+        else:
-            self._input_name = input_name
+            # Replay using updated input
-            self._output_names = [o.name for o in self._session.get_outputs()]
+            self._input_ortvalue.update_inplace(tensor_input)
            self._input_ortvalue = ort.OrtValue.ortvalue_from_numpy(
                tensor_input, "cuda", self._cuda_device_id
            )
            self._io_binding.bind_ortvalue_input(self._input_name, self._input_ortvalue)
            for name in self._output_names:
                # Bind outputs to CUDA and allow ORT to allocate appropriately
                self._io_binding.bind_output(name, "cuda", self._cuda_device_id)
            # First IOBinding run to allocate, execute, and capture CUDA Graph
            ro = ort.RunOptions()
            self._session.run_with_iobinding(self._io_binding, ro)
            self._captured = True
            return self._io_binding.copy_outputs_to_cpu()
        # Replay using updated input, copy results to CPU
        self._input_ortvalue.update_inplace(tensor_input)
        ro = ort.RunOptions()
        self._session.run_with_iobinding(self._io_binding, ro)
        return self._io_binding.copy_outputs_to_cpu()
@@ -323,6 +325,12 @@ class OpenVINOModelRunner(BaseModelRunner):
        if device in ["GPU", "AUTO", "NPU"]:
            self.ov_core.set_property(device, {"PERFORMANCE_HINT": "LATENCY"})
        if device in ["GPU", "AUTO"]:
            try:
                self.ov_core.set_property("GPU", {"GPU_QUEUE_THROTTLE": "LOW"})
            except Exception as e:
                logger.debug(f"GPU_QUEUE_THROTTLE not supported: {e}")
        if device == "NPU" and OpenVINOModelRunner.is_detection_model(model_type):
            try:
                self.ov_core.set_property(device, {"NPU_TURBO": "YES"})
@@ -626,9 +634,7 @@ def get_optimized_runner(
    return ONNXModelRunner(
        ort.InferenceSession(
            model_path,
-            sess_options=get_ort_session_options(
+            sess_options=get_ort_session_options(model_type),
                ONNXModelRunner.is_cpu_complex_model(model_type)
            ),
            providers=providers,
            provider_options=options,
        ),
@@ -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(
+            self.db.upsert_embeddings(
-                """
+                "vec_thumbnails",
-                INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
+                "thumbnail_embedding",
-                VALUES(?, ?)
+                {event_id: serialize(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[valid_ids[i]] = serialize(embeddings[i])
                items.append(serialize(embeddings[i]))
                self.image_eps.update()
-            self.db.execute_sql(
+            self.db.upsert_embeddings("vec_thumbnails", "thumbnail_embedding", items)
                """
                INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
                VALUES {}
                """.format(", ".join(["(?, ?)"] * len(valid_ids))),
                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(
+            self.db.upsert_embeddings(
-                """
+                "vec_descriptions",
-                INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
+                "description_embedding",
-                VALUES(?, ?)
+                {event_id: serialize(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[ids[i]] = serialize(embeddings[i])
                items.append(serialize(embeddings[i]))
                self.text_eps.update()
-            self.db.execute_sql(
+            self.db.upsert_embeddings(
-                """
+                "vec_descriptions", "description_embedding", items
                INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
                VALUES {}
                """.format(", ".join(["(?, ?)"] * len(ids))),
                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()
@@ -346,14 +342,18 @@ class Embeddings:
            batch_size = 32
        current_page = 1
-        totals = {
+        totals.update(
-            "thumbnails": 0,
+            {
-            "descriptions": 0,
+                "thumbnails": 0,
-            "processed_objects": total_events - 1 if total_events < batch_size else 0,
+                "descriptions": 0,
-            "total_objects": total_events,
+                "processed_objects": total_events - 1
-            "time_remaining": 0 if total_events < batch_size else -1,
+                if total_events < batch_size
-            "status": "indexing",
+                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)
@@ -609,18 +609,6 @@ class EmbeddingMaintainer(threading.Thread):
                # Embed the thumbnail
                self._embed_thumbnail(event_id, thumbnail)
            # every post processor below reads config.cameras[camera], but
            # tracked_events still has to be released or the thumbnails held
            # for this event leak, same as the two exits above
            if camera not in self.config.cameras:
                logger.debug("Skipping post processing for removed camera %s", camera)
                for processor in self.post_processors:
                    if isinstance(processor, ObjectDescriptionProcessor):
                        processor.cleanup_event(event_id)
                continue
            # call any defined post processors
            for processor in self.post_processors:
                if isinstance(processor, LicensePlatePostProcessor):
@@ -678,18 +666,11 @@ class EmbeddingMaintainer(threading.Thread):
        to_remove = []
        for id, data in self.detected_license_plates.items():
            camera_config = self.config.cameras.get(data["camera"])
            if camera_config is None:
                # camera was removed, drop the entry rather than expiring it
                to_remove.append(id)
                continue
            last_seen = data.get("last_seen", 0)
            if not last_seen:
                continue
-            if now - last_seen > camera_config.lpr.expire_time:
+            if now - last_seen > self.config.cameras[data["camera"]].lpr.expire_time:
                to_remove.append(id)
        for id in to_remove:
            self.event_metadata_publisher.publish(
@@ -197,11 +197,9 @@ class EventCleanup(threading.Thread):
    def expire_clips(self) -> list[str]:
        ## Expire events from unlisted cameras based on the global config
        # effective days cover the sub window, keeping tracked objects in
        # Explore while sub recordings and review items still exist
        expire_days = max(
-            self.config.record.effective_alert_days,
+            self.config.record.alerts.retain.days,
-            self.config.record.effective_detection_days,
+            self.config.record.detections.retain.days,
        )
        file_extension = None  # mp4 clips are no longer stored in /clips
        update_params = {"has_clip": False}
@@ -280,13 +278,15 @@ class EventCleanup(threading.Thread):
        ## Expire events from cameras based on the camera config
        for name, camera in self.config.cameras.items():
-            # effective days cover the sub window, keeping tracked objects
+            expire_days = max(
-            # in Explore while sub recordings and review items still exist
+                camera.record.alerts.retain.days,
                camera.record.detections.retain.days,
            )
            alert_expire_date = (
-                now - datetime.timedelta(days=camera.record.effective_alert_days)
+                now - datetime.timedelta(days=camera.record.alerts.retain.days)
            ).timestamp()
            detection_expire_date = (
-                now - datetime.timedelta(days=camera.record.effective_detection_days)
+                now - datetime.timedelta(days=camera.record.detections.retain.days)
            ).timestamp()
            # grab all events after specific time
            expired_events = (
@@ -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
@@ -121,8 +121,8 @@ 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_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-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 vpp_qsv=w={1}:h={2}:format=nv12,hwdownload,format=nv12,fps={0},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
@@ -245,7 +245,7 @@ class GeminiClient(GenAIClient):
                    )
                    gemini_messages.append(
                        types.Content(
-                            role="function",
+                            role="user",
                            parts=[
                                types.Part.from_function_response(
                                    name=msg.get("name")
@@ -501,7 +501,7 @@ class GeminiClient(GenAIClient):
                    )
                    gemini_messages.append(
                        types.Content(
-                            role="function",
+                            role="user",
                            parts=[
                                types.Part.from_function_response(
                                    name=msg.get("name")
@@ -262,10 +262,6 @@ def get_tool_definitions(
    `attribute` parameter is exposed for filtering by their labels. When the
    embeddings model only understands English (JinaV1), the `semantic_query`
    description instructs the model to write the query in English.
    Descriptions here stay mechanical: which tool to reach for, and how the
    filters relate to each other, is stated once in the system prompt so the
    guidance is not paid for twice on every request.
    """
    search_objects_properties: dict[str, Any] = {
        "camera": {
@@ -274,13 +270,26 @@ def get_tool_definitions(
        },
        "label": {
            "type": "string",
-            "description": "Tracked object class to filter by.",
+            "description": (
                "Generic object class to filter by — one of the tracked detector "
                "labels such as 'person', 'package', 'car', 'dog', 'bird'. Use "
                "this for broad queries like 'show me all cars today'. Combine "
                "with semantic_query when the user also describes appearance or "
                "behavior (e.g. label='person', semantic_query='riding a lawn "
                "mower')."
            ),
        },
        "sub_label": {
            "type": "string",
            "description": (
-                "Name recognized in the detection: a person, delivery company, "
+                "Filter by a DISCRETE NAMED entity recognized in the detection. "
-                "animal species or breed, or license plate."
+                "Use this for: a known person's name ('John'), a delivery "
                "company ('Amazon', 'UPS'), a recognized animal species or "
                "breed ('blue jay', 'cardinal', 'golden retriever'), or a "
                "license plate string. When filtering by a specific name, set "
                "only sub_label and leave label unset. Do NOT use sub_label "
                "for descriptions of appearance, clothing, or actions — those "
                "belong in semantic_query."
            ),
        },
        "after": {
@@ -304,11 +313,20 @@ def get_tool_definitions(
    }
    if attribute_classifications:
        model_outline = "; ".join(
            f"{m['name']} (applies to {', '.join(m['objects']) or 'any object'})"
            for m in attribute_classifications
        )
        search_objects_properties["attribute"] = {
            "type": "string",
            "description": (
-                "Attribute label produced by a configured classification model "
+                "Filter by a classification attribute label produced by a "
-                "(case-sensitive)."
+                "configured attribute classification model. Use this INSTEAD "
                "of semantic_query when the user's request matches one of "
                "these classifications. Configured models: "
                f"{model_outline}. "
                "Set the value to the attribute label that matches the user's "
                "phrasing (case-sensitive)."
            ),
        }
@@ -316,12 +334,29 @@ def get_tool_definitions(
        search_objects_properties["semantic_query"] = {
            "type": "string",
            "description": (
-                "Description of an appearance or activity, used to semantically "
+                "Optional natural-language description of a PHYSICAL "
-                "narrow results."
+                "CHARACTERISTIC, APPEARANCE, or ACTIVITY the user mentioned, "
                "used to semantically narrow results. Only set this when the "
                "user describes something beyond what label and sub_label can "
                "express on their own.\n"
                "USE for descriptive phrases like: 'riding a lawn mower', "
                "'wearing a red jacket', 'carrying a package', 'walking a "
                "dog', 'on a bicycle', 'holding an umbrella'.\n"
                "DO NOT USE for:\n"
                "- specific named people, pets, or delivery companies → use sub_label\n"
                "- animal species or breed names like 'blue jay', 'cardinal', "
                "'golden retriever' → use sub_label\n"
                "- license plate strings → use sub_label\n"
                "- generic object queries like 'all cars today' or 'every "
                "person' → use label alone with no semantic_query\n"
                "When set, combine with label/time/camera/zone filters as "
                "usual (e.g. label='person', semantic_query='riding a lawn "
                "mower', after='2024-05-01T00:00:00Z')."
                + (
-                    " The configured embeddings model only understands English, so "
+                    " The configured embeddings model only understands "
-                    "always write this in English, translating the user's "
+                    "English, so always write semantic_query in English, "
-                    "description if they phrased it in another language."
+                    "translating the user's description if they phrased it "
                    "in another language."
                    if embeddings_language == "english"
                    else ""
                )
@@ -329,10 +364,26 @@ def get_tool_definitions(
        }
    search_objects_description = (
-        "Search the historical record of tracked detections. Use this ONLY for "
+        "Search the historical record of detected objects in Frigate. "
-        "questions about the PAST, e.g. 'did anyone come by today?', 'when was the "
+        "Use this ONLY for questions about the PAST — e.g. 'did anyone come by today?', "
-        "last car?'. For alerting on future events use start_camera_watch instead."
+        "'when was the last car?', 'show me detections from yesterday'. "
        "Do NOT use this for monitoring or alerting requests about future events — "
        "use start_camera_watch instead for those. "
        "An 'object' in Frigate represents a tracked detection (e.g., a person, package, car).\n\n"
        "Choose filters based on what the user is asking for:\n"
        "- Generic class query ('show me all cars today'): set `label` only.\n"
        "- Specific NAMED entity (known person, delivery company, animal "
        "species/breed like 'blue jay' or 'golden retriever', license "
        "plate): set `sub_label` only and leave `label` unset.\n"
    )
    if semantic_search_enabled:
        search_objects_description += (
            "- Physical CHARACTERISTIC, APPEARANCE, or ACTIVITY that is not a "
            "discrete name ('person riding a lawn mower', 'someone in a red "
            "jacket', 'person carrying a package'): set `semantic_query` with "
            "the descriptive phrase, optionally alongside `label` for the "
            "object class. Do NOT put descriptive phrases in sub_label."
        )
    return [
        {
@@ -347,30 +398,20 @@ def get_tool_definitions(
                "required": [],
            },
        },
        {
            "type": "function",
            "function": {
                "name": "get_categorized_object_names",
                "description": (
                    "Every name that can be attached as a sub_label, grouped by object "
                    "type: recognized faces, named license plates, classification "
                    "categories, and delivery logos. Takes no arguments and always "
                    "returns the complete map."
                ),
                "parameters": {
                    "type": "object",
                    "properties": {},
                    "required": [],
                },
            },
        },
        {
            "type": "function",
            "function": {
                "name": "find_similar_objects",
                "description": (
-                    "Find tracked objects visually and semantically similar to a "
+                    "Find tracked objects that are visually and semantically similar "
-                    "specific past event. Requires semantic search to be enabled."
+                    "to a specific past event. Use this when the user references a "
                    "particular object they have seen and wants to find other "
                    "sightings of the same or similar one ('that green car', 'the "
                    "person in the red jacket', 'the package that was delivered'). "
                    "Prefer this over search_objects whenever the user's intent is "
                    "'find more like this specific one.' Use search_objects first "
                    "only if you need to locate the anchor event. Requires semantic "
                    "search to be enabled."
                ),
                "parameters": {
                    "type": "object",
@@ -432,8 +473,9 @@ def get_tool_definitions(
            "function": {
                "name": "set_camera_state",
                "description": (
-                    "Change a camera's feature state, e.g. turn detection on or off. "
+                    "Change a camera's feature state (e.g., turn detection on/off, enable/disable recordings). "
-                    "Only call this when the user explicitly asks to change a setting. "
+                    "Use camera='*' to apply to all cameras at once. "
                    "Only call this tool when the user explicitly asks to change a camera setting. "
                    "Requires admin privileges."
                ),
                "parameters": {
@@ -468,14 +510,14 @@ def get_tool_definitions(
                            ],
                            "description": (
                                "The feature to change. Most features accept ON or OFF. "
-                                "birdseye_mode accepts CONTINUOUS, MOTION, OBJECTS, STATIONARY_OBJECTS, or a comma-separated combination. "
+                                "birdseye_mode accepts CONTINUOUS, MOTION, or OBJECTS. "
                                "motion_contour_area and motion_threshold accept a number. "
                                "profile accepts a profile name or 'none' to deactivate (requires camera='*')."
                            ),
                        },
                        "value": {
                            "type": "string",
-                            "description": "The value to set, as accepted by the chosen feature.",
+                            "description": "The value to set. ON or OFF for toggles, a number for thresholds, a profile name or 'none' for profile.",
                        },
                    },
                    "required": ["camera", "feature", "value"],
@@ -487,9 +529,11 @@ def get_tool_definitions(
            "function": {
                "name": "get_live_context",
                "description": (
-                    "Current live image and detections (tracked objects, zones, "
+                    "Get the current live image and detection information for a single camera: objects being tracked, "
-                    "timestamps) for one camera. Use this for questions about what is "
+                    "zones, timestamps. Use this to understand what is visible in the live view. "
-                    "happening right now. Call it again for each additional camera."
+                    "Call this when answering questions about what is happening right now on a specific camera. "
                    "Operates on one camera at a time; call the tool again for each additional camera. "
                    "Wildcards and empty values are not accepted."
                ),
                "parameters": {
                    "type": "object",
@@ -497,8 +541,8 @@ def get_tool_definitions(
                        "camera": {
                            "type": "string",
                            "description": (
-                                "Exact name of a single camera. Wildcards (e.g. '*', "
+                                "Exact name of a single camera to get live context for. "
-                                "'all') and empty strings are not accepted."
+                                "Wildcards (e.g. '*', 'all') and empty strings are not accepted."
                            ),
                        },
                    },
@@ -511,9 +555,10 @@ def get_tool_definitions(
            "function": {
                "name": "start_camera_watch",
                "description": (
-                    "Start a continuous watch job that monitors a camera and notifies "
+                    "Start a continuous VLM watch job that monitors a camera and sends a notification "
-                    "the user when a condition is met, e.g. 'tell me when guests "
+                    "when a specified condition is met. Use this when the user wants to be alerted about "
-                    "arrive'. Only one watch job can run at a time. Returns a job ID."
+                    "a future event, e.g. 'tell me when guests arrive' or 'notify me when the package is picked up'. "
                    "Only one watch job can run at a time. Returns a job ID."
                ),
                "parameters": {
                    "type": "object",
@@ -553,7 +598,10 @@ def get_tool_definitions(
            "type": "function",
            "function": {
                "name": "stop_camera_watch",
-                "description": "Cancel the currently running watch job.",
+                "description": (
                    "Cancel the currently running VLM watch job. Use this when the user wants to "
                    "stop a previously started watch, e.g. 'stop watching the front door'."
                ),
                "parameters": {
                    "type": "object",
                    "properties": {},
@@ -566,9 +614,11 @@ def get_tool_definitions(
            "function": {
                "name": "get_profile_status",
                "description": (
-                    "Get the active profile and when each profile was last activated. "
+                    "Get the current profile status including the active profile and "
-                    "Call this before get_recap to derive the time window for requests "
+                    "timestamps of when each profile was last activated. Use this to "
-                    "like 'what happened while I was away?'."
+                    "determine time periods for recap requests — e.g. when the user asks "
                    "'what happened while I was away?', call this first to find the relevant "
                    "time window based on profile activation history."
                ),
                "parameters": {
                    "type": "object",
@@ -582,9 +632,11 @@ def get_tool_definitions(
            "function": {
                "name": "get_recap",
                "description": (
-                    "Get all activity (alerts and detections) for a time period, as a "
+                    "Get a recap of all activity (alerts and detections) for a given time period. "
-                    "chronological list with camera, objects, zones, and descriptions "
+                    "Use this after calling get_profile_status to retrieve what happened during "
-                    "when available. Summarize the results for the user."
+                    "a specific window — e.g. 'what happened while I was away?'. Returns a "
                    "chronological list of activity with camera, objects, zones, and GenAI-generated "
                    "descriptions when available. Summarize the results for the user."
                ),
                "parameters": {
                    "type": "object",
@@ -671,13 +723,14 @@ def build_chat_system_prompt(
        )
        speed_units_section = f"\n\nReport object speeds to the user in {speed_unit}."
-    filter_routing_section = (
+    semantic_search_section = ""
        "\n\nWhen routing a search_objects call, pick filters by the shape of the user's request:\n"
        "- Generic class ('show me all cars today'): set `label` only.\n"
        "- Specific named entity — a known person ('John'), delivery company ('Amazon'), animal species/breed ('blue jay', 'golden retriever'), or license plate: set `sub_label` only and leave `label` unset. Call get_categorized_object_names first and use the exact spelling it returns; a guessed spelling matches nothing. If the name is absent, say it is not configured rather than searching for it."
    )
    if semantic_search_enabled:
-        filter_routing_section += "\n- Physical characteristic, appearance, or activity that is NOT a discrete name ('riding a lawn mower', 'someone in a red jacket'): set `semantic_query` with the descriptive phrase, optionally combined with `label`. Never put descriptive phrases in `sub_label`."
+        semantic_search_section = (
            "\n\nWhen routing a search_objects call, pick filters by the shape of the user's request:\n"
            "- Generic class ('show me all cars today'): set `label` only.\n"
            "- Specific named entity — a known person ('John'), delivery company ('Amazon'), animal species/breed ('blue jay', 'cardinal', 'golden retriever'), or license plate: set `sub_label` only and leave `label` unset.\n"
            "- Physical characteristic, appearance, or activity that is NOT a discrete name ('find me people riding a lawn mower', 'someone in a red jacket', 'a person carrying a package'): set `semantic_query` with the descriptive phrase, optionally combined with `label` for the object class. Never put descriptive phrases in `sub_label`."
        )
    attribute_classification_section = ""
    if attribute_classifications:
@@ -686,9 +739,9 @@ def build_chat_system_prompt(
            for m in attribute_classifications
        )
        attribute_classification_section = (
-            "\n\nConfigured attribute classification models:\n"
+            "\n\nAttribute classification models are configured for the following object types:\n"
            f"{model_lines}\n"
-            "When the user's request matches one of these classifications, set the search_objects `attribute` field to the matching label (case-sensitive) rather than using `semantic_query`. Reserve `semantic_query` for descriptive phrases outside the configured attribute labels."
+            "When the user's request matches one of these classifications, set the search_objects `attribute` field to the matching label rather than using `semantic_query`. Reserve `semantic_query` for descriptive phrases that fall outside the configured attribute labels."
        )
    return f"""You are a helpful assistant for Frigate, a security camera NVR system. You help users answer questions about their cameras, detected objects, and events.
@@ -697,6 +750,9 @@ Current server local date and time: {current_date_str} at {current_time_str}
 Do not start your response with phrases like "I will check...", "Let me see...", or "Let me look...". Answer directly.
-Always present times in the server's local timezone. When tool results include start_time_local and end_time_local, quote those strings exactly; never convert or invent timestamps, and fall back to UTC or ISO format only when a result has no local time fields. Resolve relative dates like "today" or "this week" against the current date above, and pass dates to tools in ISO 8601 (e.g. {current_date_str}T00:00:00Z for the start of today).
+Always present times to the user in the server's local timezone. When tool results include start_time_local and end_time_local, use those exact strings when listing or describing detection times—do not convert or invent timestamps. Do not use UTC or ISO format with Z for the user-facing answer unless the tool result only provides Unix timestamps without local time fields.
 When users ask about "today", "yesterday", "this week", etc., use the current date above as reference.
 When searching for objects or events, use ISO 8601 format for dates (e.g., {current_date_str}T00:00:00Z for the start of today).
 Always be accurate with time calculations based on the current date provided.
-When the user refers to a specific object they have seen ("that green car", "the person in the red jacket", "a package left today"), prefer find_similar_objects over search_objects, using search_objects only to locate the anchor event and passing its id along. Keep search_objects for generic queries like "show me all cars today". If a user message begins with [attached_event:<id>], treat that id as the anchor for any similarity or "tell me more" request in the same message.{filter_routing_section}{attribute_classification_section}{cameras_section}{speed_units_section}"""
+When a user refers to a specific object they have seen or describe with identifying details ("that green car", "the person in the red jacket", "a package left today"), prefer the find_similar_objects tool over search_objects. Use search_objects first only to locate the anchor event, then pass its id to find_similar_objects. For generic queries like "show me all cars today", keep using search_objects. If a user message begins with [attached_event:<id>], treat that event id as the anchor for any similarity or "tell me more" request in the same message and call find_similar_objects with that id.{semantic_search_section}{attribute_classification_section}{cameras_section}{speed_units_section}"""
@@ -115,6 +115,10 @@ def query_recordings(source_camera: str, start_ts: float, end_ts: float) -> Mode
    return cast(ModelSelect, query)
 class NoRecordingsError(ValueError):
    """Raised when no recordings exist in the requested time range."""
 class DebugReplaySource(ABC):
    """Abstract source for a debug replay session.
@@ -187,7 +191,7 @@ class RecordingDebugReplaySource(DebugReplaySource):
            raise ValueError("End time must be after start time")
        if not query_recordings(self._camera, self._start_ts, self._end_ts).count():
-            raise ValueError(
+            raise NoRecordingsError(
                f"No recordings found for camera '{self._camera}' in the specified time range"
            )
@@ -15,7 +15,7 @@ import numpy as np
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.config import FrigateConfig
-from frigate.const import STREAM_TYPE_MAIN, UPDATE_JOB_STATE
+from frigate.const import UPDATE_JOB_STATE
 from frigate.jobs.job import Job
 from frigate.jobs.manager import (
    get_job_by_id,
@@ -485,7 +485,6 @@ class MotionSearchRunner(threading.Thread):
                )
            )
            .where(Recordings.camera == camera_name)
            .where(Recordings.stream_type == STREAM_TYPE_MAIN)
            .order_by(Recordings.start_time.asc())
        )
@@ -79,12 +79,6 @@ class Recordings(Model):
    segment_size = FloatField(default=0)  # this should be stored as MB
    regions = IntegerField(null=True)
    motion_heatmap = JSONField(null=True)  # 16x16 grid, 256 values (0-255)
    keyframes = JSONField(null=True)  # ms offsets; NULL = unprobed (legacy rows)
    stream_type = CharField(default="main", max_length=8)
    has_audio = BooleanField(null=True)  # NULL = unknown (legacy rows)
    audio_rate = IntegerField(null=True)  # Hz; NULL = unknown (legacy rows)
    audio_codec = CharField(null=True, max_length=20)  # NULL = unknown (legacy rows)
    video_codec = CharField(null=True, max_length=20)  # NULL = unknown (legacy rows)
 class ExportCase(Model):
@@ -9,7 +9,6 @@ import queue
 import subprocess as sp
 import threading
 import traceback
 from dataclasses import dataclass
 from multiprocessing.synchronize import Event as MpEvent
 from typing import Any
@@ -17,7 +16,7 @@ import cv2
 import numpy as np
 from frigate.comms.inter_process import InterProcessRequestor
-from frigate.config import BirdseyeModeConfig, FfmpegConfig, FrigateConfig
+from frigate.config import BirdseyeModeEnum, FfmpegConfig, FrigateConfig
 from frigate.const import BASE_DIR, BIRDSEYE_PIPE, INSTALL_DIR, UPDATE_BIRDSEYE_LAYOUT
 from frigate.output.ws_auth import ws_has_camera_access
 from frigate.util.image import (
@@ -29,15 +28,6 @@ from frigate.util.image import (
 logger = logging.getLogger(__name__)
@dataclass(frozen=True, slots=True)
 class BirdseyeActivity:
    """Activity signals used to decide whether a camera is shown in Birdseye."""
    has_active_object: bool
    has_stationary_object: bool
    has_motion: bool
 def get_standard_aspect_ratio(width: int, height: int) -> tuple[int, int]:
    """Ensure that only standard aspect ratios are used."""
    # it is important that all ratios have the same scale
@@ -419,16 +409,18 @@ class BirdsEyeFrameManager:
        )
    def camera_active(
-        self,
+        self, mode: Any, object_box_count: int, motion_box_count: int
        mode: BirdseyeModeConfig,
        activity: BirdseyeActivity,
    ) -> bool:
-        return (
+        if mode == BirdseyeModeEnum.continuous:
-            mode.continuous
+            return True
-            or (mode.motion and activity.has_motion)
+
-            or (mode.objects and activity.has_active_object)
+        if mode == BirdseyeModeEnum.motion and motion_box_count > 0:
-            or (mode.stationary_objects and activity.has_stationary_object)
+            return True
-        )
+
        if mode == BirdseyeModeEnum.objects and object_box_count > 0:
            return True
        return False
    def get_camera_coordinates(self) -> dict[str, dict[str, int]]:
        """Return the coordinates of each camera in the current layout."""
@@ -612,92 +604,112 @@ class BirdsEyeFrameManager:
    ) -> list[list[Any]] | None:
        """Calculate the optimal layout for 2+ cameras."""
-        def find_available_x(
+        def map_layout(
-            current_x: int,
+            camera_layout: list[list[Any]], row_height: int
-            width: int,
+        ) -> tuple[int, int, list[list[Any]] | None]:
-            reserved_ranges: list[tuple[int, int]],
+            """Map the calculated layout."""
-            max_width: int,
+            candidate_layout = []
-        ) -> int | None:
+            starting_x = 0
-            """Find the first horizontal slot that does not collide with reservations."""
+            x = 0
            x = current_x
            for reserved_start, reserved_end in sorted(reserved_ranges):
                if x >= reserved_end:
                    continue
                if x + width <= reserved_start:
                    return x
                x = max(x, reserved_end)
            if x + width <= max_width:
                return x
            return None
        def map_layout(row_height: int) -> tuple[int, int, list[list[Any]] | None]:
            """Lay out cameras row by row while reserving portrait spans for the next row."""
            candidate_layout: list[list[Any]] = []
            reserved_ranges: dict[int, list[tuple[int, int]]] = {}
            current_row: list[Any] = []
            row_index = 0
            row_y = 0
            row_x = 0
            max_width = 0
-            max_height = 0
+            y = 0
-            for camera in cameras_to_add:
+            for row in camera_layout:
-                camera_dims = self.cameras[camera]["dimensions"].copy()
+                final_row = []
-                camera_aspect_x, camera_aspect_y = self.canvas.get_camera_aspect(
+                max_width = max(max_width, x)
-                    camera, camera_dims[0], camera_dims[1]
+                x = starting_x
                for cameras in row:
                    camera_dims = self.cameras[cameras[0]]["dimensions"].copy()
                    camera_aspect = cameras[1]
                    if camera_dims[1] > camera_dims[0]:
                        scaled_height = int(row_height * 2)
                        scaled_width = int(scaled_height * camera_aspect)
                        starting_x = scaled_width
                    else:
                        scaled_height = row_height
                        scaled_width = int(scaled_height * camera_aspect)
                    # layout is too large
                    if (
                        x + scaled_width > self.canvas.width
                        or y + scaled_height > self.canvas.height
                    ):
                        return x + scaled_width, y + scaled_height, None
                    final_row.append((cameras[0], (x, y, scaled_width, scaled_height)))
                    x += scaled_width
                y += row_height
                candidate_layout.append(final_row)
            if max_width == 0:
                max_width = x
            return max_width, y, candidate_layout
        canvas_aspect_x, canvas_aspect_y = self.canvas.get_aspect(coefficient)
        camera_layout: list[list[Any]] = []
        camera_layout.append([])
        starting_x = 0
        x = starting_x
        y = 0
        y_i = 0
        max_y = 0
        for camera in cameras_to_add:
            camera_dims = self.cameras[camera]["dimensions"].copy()
            camera_aspect_x, camera_aspect_y = self.canvas.get_camera_aspect(
                camera, camera_dims[0], camera_dims[1]
            )
            if camera_dims[1] > camera_dims[0]:
                portrait = True
            else:
                portrait = False
            if (x + camera_aspect_x) <= canvas_aspect_x:
                # insert if camera can fit on current row
                camera_layout[y_i].append(
                    (
                        camera,
                        camera_aspect_x / camera_aspect_y,
                    )
                )
                portrait = camera_dims[1] > camera_dims[0]
                scaled_height = row_height * 2 if portrait else row_height
                scaled_width = int(scaled_height * (camera_aspect_x / camera_aspect_y))
-                while True:
+                if portrait:
-                    x = find_available_x(
+                    starting_x = camera_aspect_x
-                        row_x,
+                else:
-                        scaled_width,
+                    max_y = max(
-                        reserved_ranges.get(row_index, []),
+                        max_y,
-                        self.canvas.width,
+                        camera_aspect_y,
                    )
-                    if x is not None and row_y + scaled_height <= self.canvas.height:
+                x += camera_aspect_x
-                        current_row.append(
+            else:
-                            (camera, (x, row_y, scaled_width, scaled_height))
+                # move on to the next row and insert
-                        )
+                y += max_y
-                        row_x = x + scaled_width
+                y_i += 1
-                        max_width = max(max_width, row_x)
+                camera_layout.append([])
-                        max_height = max(max_height, row_y + scaled_height)
+                x = starting_x
-                        if portrait:
+                if x + camera_aspect_x > canvas_aspect_x:
-                            reserved_ranges.setdefault(row_index + 1, []).append(
+                    return None
                                (x, row_x)
                            )
-                        break
+                camera_layout[y_i].append(
                    (
                        camera,
                        camera_aspect_x / camera_aspect_y,
                    )
                )
                x += camera_aspect_x
-                    if current_row:
+        if y + max_y > canvas_aspect_y:
-                        candidate_layout.append(current_row)
+            return None
                        current_row = []
-                    row_index += 1
+        row_height = int(self.canvas.height / coefficient)
-                    row_y = row_index * row_height
+        total_width, total_height, standard_candidate_layout = map_layout(
-                    row_x = 0
+            camera_layout, row_height
-
+        )
                    if row_y + scaled_height > self.canvas.height:
                        overflow_width = max(max_width, scaled_width)
                        overflow_height = row_y + scaled_height
                        return overflow_width, overflow_height, None
            if current_row:
                candidate_layout.append(current_row)
            return max_width, max_height, candidate_layout
        row_height = max(1, int(self.canvas.height / coefficient))
        total_width, total_height, standard_candidate_layout = map_layout(row_height)
        if not standard_candidate_layout:
            # if standard layout didn't work
@@ -706,9 +718,9 @@ class BirdsEyeFrameManager:
                total_width / self.canvas.width,
                total_height / self.canvas.height,
            )
-            row_height = max(1, int(row_height / scale_down_percent))
+            row_height = int(row_height / scale_down_percent)
            total_width, total_height, standard_candidate_layout = map_layout(
-                row_height
+                camera_layout, row_height
            )
            if not standard_candidate_layout:
@@ -722,8 +734,8 @@ class BirdsEyeFrameManager:
            1 / (total_width / self.canvas.width),
            1 / (total_height / self.canvas.height),
        )
-        row_height = max(1, int(row_height * scale_up_percent))
+        row_height = int(row_height * scale_up_percent)
-        _, _, scaled_layout = map_layout(row_height)
+        _, _, scaled_layout = map_layout(camera_layout, row_height)
        if scaled_layout:
            return scaled_layout
@@ -733,7 +745,8 @@ class BirdsEyeFrameManager:
    def update(
        self,
        camera: str,
-        activity: BirdseyeActivity,
+        object_count: int,
        motion_count: int,
        frame_time: float,
        frame: np.ndarray,
    ) -> tuple[bool, bool]:
@@ -747,29 +760,22 @@ class BirdsEyeFrameManager:
            return False, False
        force_update = False
        camera_state = self.cameras.get(camera)
        if camera_state is None:
            return False, False
        # disabling birdseye is a little tricky
        if not camera_config.birdseye.enabled or not camera_config.enabled:
            # if we've rendered a frame (we have a value for last_active_frame)
            # then we need to set it to zero
-            if camera_state["last_active_frame"] > 0:
+            if self.cameras[camera]["last_active_frame"] > 0:
-                camera_state["last_active_frame"] = 0
+                self.cameras[camera]["last_active_frame"] = 0
                force_update = True
            else:
                return False, False
        # update the last active frame for the camera
-        camera_state["current_frame"] = frame.copy()
+        self.cameras[camera]["current_frame"] = frame.copy()
-        camera_state["current_frame_time"] = frame_time
+        self.cameras[camera]["current_frame_time"] = frame_time
-        if self.camera_active(
+        if self.camera_active(camera_config.birdseye.mode, object_count, motion_count):
-            camera_config.birdseye.mode,
+            self.cameras[camera]["last_active_frame"] = frame_time
            activity,
        ):
            camera_state["last_active_frame"] = frame_time
        now = datetime.datetime.now().timestamp()
@@ -876,29 +882,10 @@ class Birdseye:
        frame_time: float,
        frame: np.ndarray,
    ) -> None:
        has_active_object = False
        has_stationary_object = False
        for tracked_object in current_tracked_objects:
            if tracked_object["stationary"]:
                if not tracked_object["false_positive"]:
                    has_stationary_object = True
            else:
                # Preserve the existing objects activity behavior, which includes
                # non-stationary trackers before they are confirmed.
                has_active_object = True
            if has_active_object and has_stationary_object:
                break
        activity = BirdseyeActivity(
            has_active_object=has_active_object,
            has_stationary_object=has_stationary_object,
            has_motion=bool(motion_boxes),
        )
        frame_changed, frame_layout_changed = self.birdseye_manager.update(
            camera,
-            activity,
+            len([o for o in current_tracked_objects if not o["stationary"]]),
            len(motion_boxes),
            frame_time,
            frame,
        )
@@ -51,12 +51,8 @@ def check_disabled_camera_update(
    for camera, last_update in write_times.items():
        offline_time = now - last_update
        camera_config = config.cameras.get(camera)
-        if camera_config is None:
+        if config.cameras[camera].enabled:
            continue
        if camera_config.enabled:
            has_enabled_camera = True
        else:
            # flag camera as offline when it is disabled
@@ -66,8 +62,8 @@ def check_disabled_camera_update(
            # last camera update was more than 1 second ago
            # need to send empty data to birdseye because current
            # frame is now out of date
-            cam_width = camera_config.detect.width
+            cam_width = config.cameras[camera].detect.width
-            cam_height = camera_config.detect.height
+            cam_height = config.cameras[camera].detect.height
            if cam_width is None or cam_height is None:
                raise ValueError(f"Camera {camera} detect dimensions not configured")
@@ -313,11 +309,10 @@ class OutputProcess(FrigateProcess):
                regions,
            ) = data
-            camera_config = self.config.cameras.get(camera)
+            frame = frame_manager.get(
-
+                frame_name, self.config.cameras[camera].frame_shape_yuv
-            if camera_config is not None:
+            )
-                frame_manager.get(frame_name, camera_config.frame_shape_yuv)
+            frame_manager.close(frame_name)
                frame_manager.close(frame_name)
        detection_subscriber.stop()
@@ -799,24 +799,14 @@ class PtzAutoTracker:
            except TimeoutError:
                continue
            # both are popped when the camera is deleted, so resolve them once
            # here and use the locals for the rest of the move; a move already
            # in flight then finishes against valid objects
            metrics = self.ptz_metrics.get(camera)
            camera_config = self.config.cameras.get(camera)
            if metrics is None or camera_config is None:
                logger.debug("%s: Dropping queued move, camera was removed", camera)
                continue
            async with self.move_queue_locks[camera]:
                frame_time, pan, tilt, zoom = move_data
                # if we're receiving move requests during a PTZ move, ignore them
                if ptz_moving_at_frame_time(
                    frame_time,
-                    metrics.start_time.value,
+                    self.ptz_metrics[camera].start_time.value,
-                    metrics.stop_time.value,
+                    self.ptz_metrics[camera].stop_time.value,
                ):
                    logger.debug(
                        f"{camera}: Move queue: PTZ moving, dequeueing move request - frame time: {frame_time}, final pan: {pan}, final tilt: {tilt}, final zoom: {zoom}"
@@ -825,7 +815,7 @@ class PtzAutoTracker:
                else:
                    if (
-                        camera_config.onvif.autotracking.zooming
+                        self.config.cameras[camera].onvif.autotracking.zooming
                        == ZoomingModeEnum.relative
                    ):
                        await self.onvif._move_relative(camera, pan, tilt, zoom, 1)
@@ -834,22 +824,25 @@ class PtzAutoTracker:
                            await self.onvif._move_relative(camera, pan, tilt, 0, 1)
                            # Wait until the camera finishes moving
-                            while not metrics.motor_stopped.is_set():
+                            while not self.ptz_metrics[camera].motor_stopped.is_set():
                                await self.onvif.get_camera_status(camera)
-                        if zoom > 0 and metrics.zoom_level.value != zoom:
+                        if (
                            zoom > 0
                            and self.ptz_metrics[camera].zoom_level.value != zoom
                        ):
                            await self.onvif._zoom_absolute(camera, zoom, 1)
                    # Wait until the camera finishes moving
-                    while not metrics.motor_stopped.is_set():
+                    while not self.ptz_metrics[camera].motor_stopped.is_set():
                        await self.onvif.get_camera_status(camera)
-                    if camera_config.onvif.autotracking.movement_weights:
+                    if self.config.cameras[camera].onvif.autotracking.movement_weights:
                        logger.debug(
                            f"{camera}: Predicted movement time: {self._predict_movement_time(camera, pan, tilt)}"
                        )
                        logger.debug(
-                            f"{camera}: Actual movement time: {metrics.stop_time.value - metrics.start_time.value}"
+                            f"{camera}: Actual movement time: {self.ptz_metrics[camera].stop_time.value - self.ptz_metrics[camera].start_time.value}"
                        )
                    # save metrics for better estimate calculations
@@ -858,15 +851,21 @@ class PtzAutoTracker:
                        and len(self.move_metrics[camera])
                        < AUTOTRACKING_MAX_MOVE_METRICS
                        and (pan != 0 or tilt != 0)
-                        and camera_config.onvif.autotracking.calibrate_on_startup
+                        and self.config.cameras[
                            camera
                        ].onvif.autotracking.calibrate_on_startup
                    ):
                        logger.debug(f"{camera}: Adding new values to move metrics")
                        self.move_metrics[camera].append(
                            {
                                "pan": pan,
                                "tilt": tilt,
-                                "start_timestamp": metrics.start_time.value,
+                                "start_timestamp": self.ptz_metrics[
-                                "end_timestamp": metrics.stop_time.value,
+                                    camera
                                ].start_time.value,
                                "end_timestamp": self.ptz_metrics[
                                    camera
                                ].stop_time.value,
                            }
                        )
@@ -72,11 +72,7 @@ class OnvifController:
        self.config_subscriber = CameraConfigUpdateSubscriber(
            self.config,
            self.config.cameras,
-            [
+            [CameraConfigUpdateEnum.onvif],
                CameraConfigUpdateEnum.onvif,
                CameraConfigUpdateEnum.add,
                CameraConfigUpdateEnum.remove,
            ],
        )
        asyncio.run_coroutine_threadsafe(self._init_cameras(), self.loop)
@@ -105,16 +101,6 @@ class OnvifController:
                    if update_type == CameraConfigUpdateEnum.onvif.name:
                        for cam_name in cameras:
                            await self._reinit_camera(cam_name)
                    elif update_type == CameraConfigUpdateEnum.add.name:
                        # a camera added at runtime only needs ONVIF set up if
                        # it actually has an onvif host configured
                        for cam_name in cameras:
                            cam = self.config.cameras.get(cam_name)
                            if cam and cam.onvif.host:
                                await self._reinit_camera(cam_name)
                    elif update_type == CameraConfigUpdateEnum.remove.name:
                        for cam_name in cameras:
                            await self._remove_camera(cam_name)
            except Exception:
                logger.error("Error checking for ONVIF config updates")
@@ -127,18 +113,6 @@ class OnvifController:
            except Exception:
                logger.debug(f"Error closing ONVIF session for {cam_name}")
    async def _remove_camera(self, cam_name: str) -> None:
        """Tear down the ONVIF session for a camera removed at runtime."""
        if cam_name not in self.cams and cam_name not in self.camera_configs:
            return
        logger.debug(f"Tearing down ONVIF for {cam_name} after camera removal")
        await self._close_camera(cam_name)
        self.cams.pop(cam_name, None)
        self.camera_configs.pop(cam_name, None)
        self.failed_cams.pop(cam_name, None)
        self.status_locks.pop(cam_name, None)
    async def _reinit_camera(self, cam_name: str) -> None:
        """Re-initialize a camera after config change."""
        logger.info(f"Re-initializing ONVIF for {cam_name} due to config change")
@@ -206,11 +180,6 @@ class OnvifController:
            return False
    async def _init_onvif(self, camera_name: str) -> bool:
        camera_config = self.config.cameras.get(camera_name)
        if camera_config is None:
            return False
        onvif: ONVIFCamera = self.cams[camera_name]["onvif"]
        try:
            await onvif.update_xaddrs()
@@ -266,7 +235,7 @@ class OnvifController:
                p.token,
            )
-        configured_profile = camera_config.onvif.profile
+        configured_profile = self.config.cameras[camera_name].onvif.profile
        profile = None
        if configured_profile is not None:
@@ -370,7 +339,7 @@ class OnvifController:
            except (AttributeError, TypeError):
                fov_space_id = None
-        autotracking_config = camera_config.onvif.autotracking
+        autotracking_config = self.config.cameras[camera_name].onvif.autotracking
        autotracking_enabled = (
            autotracking_config.enabled_in_config and autotracking_config.enabled
        )
@@ -645,11 +614,6 @@ class OnvifController:
            logger.error(f"{camera_name} does not support ONVIF RelativeMove (FOV).")
            return
        metrics = self.ptz_metrics.get(camera_name)
        if metrics is None:
            return
        logger.debug(
            f"{camera_name} called RelativeMove: pan: {pan} tilt: {tilt} zoom: {zoom}"
        )
@@ -663,11 +627,15 @@ class OnvifController:
        self.cams[camera_name]["active"] = True
        # only track start_time for autotracking
-        if metrics.autotracker_enabled.value:
+        if self.ptz_metrics[camera_name].autotracker_enabled.value:
-            metrics.motor_stopped.clear()
+            self.ptz_metrics[camera_name].motor_stopped.clear()
-            logger.debug(f"{camera_name}: PTZ start time: {metrics.frame_time.value}")
+            logger.debug(
-            metrics.start_time.value = metrics.frame_time.value
+                f"{camera_name}: PTZ start time: {self.ptz_metrics[camera_name].frame_time.value}"
-            metrics.stop_time.value = 0
+            )
            self.ptz_metrics[camera_name].start_time.value = self.ptz_metrics[
                camera_name
            ].frame_time.value
            self.ptz_metrics[camera_name].stop_time.value = 0
        move_request = self.cams[camera_name]["relative_move_request"]
@@ -729,14 +697,9 @@ class OnvifController:
            logger.error(f"{preset} is not a valid preset for {camera_name}")
            return
        metrics = self.ptz_metrics.get(camera_name)
        if metrics is None:
            return
        self.cams[camera_name]["active"] = True
-        metrics.start_time.value = 0
+        self.ptz_metrics[camera_name].start_time.value = 0
-        metrics.stop_time.value = 0
+        self.ptz_metrics[camera_name].stop_time.value = 0
        move_request = self.cams[camera_name]["move_request"]
        preset_token = self.cams[camera_name]["presets"][preset]
@@ -775,11 +738,6 @@ class OnvifController:
            logger.error(f"{camera_name} does not support ONVIF AbsoluteMove zooming.")
            return
        metrics = self.ptz_metrics.get(camera_name)
        if metrics is None:
            return
        logger.debug(f"{camera_name} called AbsoluteMove: zoom: {zoom}")
        if self.cams[camera_name]["active"]:
@@ -789,10 +747,14 @@ class OnvifController:
            return
        self.cams[camera_name]["active"] = True
-        metrics.motor_stopped.clear()
+        self.ptz_metrics[camera_name].motor_stopped.clear()
-        logger.debug(f"{camera_name}: PTZ start time: {metrics.frame_time.value}")
+        logger.debug(
-        metrics.start_time.value = metrics.frame_time.value
+            f"{camera_name}: PTZ start time: {self.ptz_metrics[camera_name].frame_time.value}"
-        metrics.stop_time.value = 0
+        )
        self.ptz_metrics[camera_name].start_time.value = self.ptz_metrics[
            camera_name
        ].frame_time.value
        self.ptz_metrics[camera_name].stop_time.value = 0
        move_request = self.cams[camera_name]["absolute_move_request"]
        # function takes in 0 to 1 for zoom, interpolate to the values of the camera.
@@ -913,18 +875,16 @@ class OnvifController:
        Returns camera details including features and presets if available.
        """
-        camera_config = self.config.cameras.get(camera_name)
+        if not self.config.cameras[camera_name].enabled:
        if camera_config is None:
            return {}
        if not camera_config.enabled:
            logger.debug(
                f"Camera {camera_name} disabled, won't try to initialize ONVIF"
            )
            return {}
-        if camera_name not in self.cams.keys() and (not camera_config.onvif.host):
+        if camera_name not in self.cams.keys() and (
            camera_name not in self.config.cameras
            or not self.config.cameras[camera_name].onvif.host
        ):
            logger.debug(f"ONVIF is not configured for {camera_name}")
            return {}
@@ -1025,12 +985,6 @@ class OnvifController:
                logger.error(f"ONVIF is not configured for {camera_name}")
                return
            metrics = self.ptz_metrics.get(camera_name)
            camera_config = self.config.cameras.get(camera_name)
            if metrics is None or camera_config is None:
                return
            if not self.cams[camera_name]["init"]:
                if not await self._init_onvif(camera_name):
                    return
@@ -1069,29 +1023,36 @@ class OnvifController:
                zoom_status is None or zoom_status == "IDLE"
            ):
                self.cams[camera_name]["active"] = False
-                if not metrics.motor_stopped.is_set():
+                if not self.ptz_metrics[camera_name].motor_stopped.is_set():
-                    metrics.motor_stopped.set()
+                    self.ptz_metrics[camera_name].motor_stopped.set()
                    logger.debug(
-                        f"{camera_name}: PTZ stop time: {metrics.frame_time.value}"
+                        f"{camera_name}: PTZ stop time: {self.ptz_metrics[camera_name].frame_time.value}"
                    )
-                    metrics.stop_time.value = metrics.frame_time.value
+                    self.ptz_metrics[camera_name].stop_time.value = self.ptz_metrics[
                        camera_name
                    ].frame_time.value
            else:
                self.cams[camera_name]["active"] = True
-                if metrics.motor_stopped.is_set():
+                if self.ptz_metrics[camera_name].motor_stopped.is_set():
-                    metrics.motor_stopped.clear()
+                    self.ptz_metrics[camera_name].motor_stopped.clear()
                    logger.debug(
-                        f"{camera_name}: PTZ start time: {metrics.frame_time.value}"
+                        f"{camera_name}: PTZ start time: {self.ptz_metrics[camera_name].frame_time.value}"
                    )
-                    metrics.start_time.value = metrics.frame_time.value
+                    self.ptz_metrics[camera_name].start_time.value = self.ptz_metrics[
-                    metrics.stop_time.value = 0
+                        camera_name
                    ].frame_time.value
                    self.ptz_metrics[camera_name].stop_time.value = 0
-            if camera_config.onvif.autotracking.zooming != ZoomingModeEnum.disabled:
+            if (
                self.config.cameras[camera_name].onvif.autotracking.zooming
                != ZoomingModeEnum.disabled
            ):
                # store absolute zoom level as 0 to 1 interpolated from the values of the camera
-                metrics.zoom_level.value = numpy.interp(
+                self.ptz_metrics[camera_name].zoom_level.value = numpy.interp(
                    round(status.Position.Zoom.x, 2),
                    [
                        self.cams[camera_name]["absolute_zoom_range"]["XRange"]["Min"],
@@ -1100,22 +1061,25 @@ class OnvifController:
                    [0, 1],
                )
                logger.debug(
-                    f"{camera_name}: Camera zoom level: {metrics.zoom_level.value}"
+                    f"{camera_name}: Camera zoom level: {self.ptz_metrics[camera_name].zoom_level.value}"
                )
            # some hikvision cams won't update MoveStatus, so warn if it hasn't changed
            if (
-                not metrics.motor_stopped.is_set()
+                not self.ptz_metrics[camera_name].motor_stopped.is_set()
-                and not metrics.reset.is_set()
+                and not self.ptz_metrics[camera_name].reset.is_set()
-                and metrics.start_time.value != 0
+                and self.ptz_metrics[camera_name].start_time.value != 0
-                and metrics.frame_time.value > (metrics.start_time.value + 10)
+                and self.ptz_metrics[camera_name].frame_time.value
-                and metrics.stop_time.value == 0
+                > (self.ptz_metrics[camera_name].start_time.value + 10)
                and self.ptz_metrics[camera_name].stop_time.value == 0
            ):
                logger.debug(
-                    f"Start time: {metrics.start_time.value}, Stop time: {metrics.stop_time.value}, Frame time: {metrics.frame_time.value}"
+                    f"Start time: {self.ptz_metrics[camera_name].start_time.value}, Stop time: {self.ptz_metrics[camera_name].stop_time.value}, Frame time: {self.ptz_metrics[camera_name].frame_time.value}"
                )
                # set the stop time so we don't come back into this again and spam the logs
-                metrics.stop_time.value = metrics.frame_time.value
+                self.ptz_metrics[camera_name].stop_time.value = self.ptz_metrics[
                    camera_name
                ].frame_time.value
                logger.warning(
                    f"Camera {camera_name} is still in ONVIF 'MOVING' status."
                )
@@ -12,14 +12,7 @@ from typing import Any
 from playhouse.sqlite_ext import SqliteExtDatabase
 from frigate.config import CameraConfig, FrigateConfig, RetainModeEnum
-from frigate.const import (
+from frigate.const import CACHE_DIR, CLIPS_DIR, MAX_WAL_SIZE, RECORD_DIR
    CACHE_DIR,
    CLIPS_DIR,
    MAX_WAL_SIZE,
    RECORD_DIR,
    STREAM_TYPE_MAIN,
    STREAM_TYPE_SUB,
 )
 from frigate.models import Previews, Recordings, ReviewSegment, UserReviewStatus
 from frigate.util.builtin import clear_and_unlink
 from frigate.util.media import remove_empty_directories
@@ -27,29 +20,6 @@ from frigate.util.media import remove_empty_directories
 logger = logging.getLogger(__name__)
 def _filter_reviews_for_pass(
    reviews: list[Any],
    now: datetime.datetime,
    alerts_days: float,
    detections_days: float,
 ) -> list[Any]:
    """Limit reviews to those still within this pass's per-severity retention window.
    Review rows survive to the longer of the main and sub retention windows,
    so a pass that honored all of them would let extended sub retention keep
    main recordings alive too. Filtering preserves sort order for the overlap
    loop in expire_existing_camera_recordings.
    """
    alert_cutoff = (now - datetime.timedelta(days=alerts_days)).timestamp()
    detection_cutoff = (now - datetime.timedelta(days=detections_days)).timestamp()
    return [
        r
        for r in reviews
        if r.end_time is None
        or (r.end_time >= (alert_cutoff if r.severity == "alert" else detection_cutoff))
    ]
 class RecordingCleanup(threading.Thread):
    """Cleanup existing recordings based on retention config."""
@@ -95,14 +65,11 @@ class RecordingCleanup(threading.Thread):
        self, config: CameraConfig, now: datetime.datetime
    ) -> set[Path]:
        """Delete review segments that are expired"""
-        # review rows survive to the longer of the main and sub windows so
+        alert_expire_date = (
-        # they stay visible while either stream still has recordings
+            now - datetime.timedelta(days=config.record.alerts.retain.days)
-        alert_days = config.record.effective_alert_days
+        ).timestamp()
        detection_days = config.record.effective_detection_days
        alert_expire_date = (now - datetime.timedelta(days=alert_days)).timestamp()
        detection_expire_date = (
-            now - datetime.timedelta(days=detection_days)
+            now - datetime.timedelta(days=config.record.detections.retain.days)
        ).timestamp()
        expired_reviews = (
            ReviewSegment.select(ReviewSegment.id, ReviewSegment.thumb_path)
@@ -142,11 +109,8 @@ class RecordingCleanup(threading.Thread):
    def expire_existing_camera_recordings(
        self,
        stream_type: str,
        continuous_expire_date: float,
        motion_expire_date: float,
        alerts_retain_mode: RetainModeEnum,
        detections_retain_mode: RetainModeEnum,
        config: CameraConfig,
        reviews: list[Any],
    ) -> set[Path]:
@@ -166,7 +130,6 @@ class RecordingCleanup(threading.Thread):
            )
            .where(
                (Recordings.camera == config.name)
                & (Recordings.stream_type == stream_type)
                & (
                    (
                        (Recordings.end_time < continuous_expire_date)
@@ -212,9 +175,9 @@ class RecordingCleanup(threading.Thread):
                ):
                    keep = True
                    mode = (
-                        alerts_retain_mode
+                        config.record.alerts.retain.mode
                        if review.severity == "alert"
-                        else detections_retain_mode
+                        else config.record.detections.retain.mode
                    )
                    break
@@ -253,10 +216,6 @@ class RecordingCleanup(threading.Thread):
                Recordings.id << deleted_recordings_list[i : i + max_deletes]
            ).execute()
        # previews follow main retention, so only the main pass expires them
        if stream_type != STREAM_TYPE_MAIN:
            return maybe_empty_dirs
        previews = (
            Previews.select(
                Previews.id,
@@ -383,20 +342,6 @@ class RecordingCleanup(threading.Thread):
                )
            ).timestamp()
            # computed here so the reviews window below covers both passes
            sub_continuous_expire_date = (
                now - datetime.timedelta(days=config.record.sub.continuous.days)
            ).timestamp()
            sub_motion_expire_date = (
                now
                - datetime.timedelta(
                    days=max(
                        config.record.sub.motion.days,
                        config.record.sub.continuous.days,
                    )  # can't keep motion for less than continuous
                )
            ).timestamp()
            # Get all the reviews to check against
            reviews = (
                ReviewSegment.select(
@@ -406,46 +351,18 @@ class RecordingCleanup(threading.Thread):
                )
                .where(
                    ReviewSegment.camera == camera,
-                    # candidate recordings reach the later of the two passes'
+                    # candidate recordings can extend up to continuous_expire_date
-                    # continuous cutoffs, so reviews must cover that whole
+                    # (the no-motion no-audio branch of the recordings query),
-                    # range or segments overlapping recent alerts get deleted
+                    # so reviews must cover that full range to avoid deleting
-                    ReviewSegment.start_time
+                    # segments that overlap recent alerts/detections.
-                    < max(continuous_expire_date, sub_continuous_expire_date),
+                    ReviewSegment.start_time < continuous_expire_date,
                )
                .order_by(ReviewSegment.start_time)
                .namedtuples()
            )
            maybe_empty_dirs |= self.expire_existing_camera_recordings(
-                STREAM_TYPE_MAIN,
+                continuous_expire_date, motion_expire_date, config, reviews
                continuous_expire_date,
                motion_expire_date,
                config.record.alerts.retain.mode,
                config.record.detections.retain.mode,
                config,
                _filter_reviews_for_pass(
                    reviews,
                    now,
                    config.record.alerts.retain.days,
                    config.record.detections.retain.days,
                ),
            )
            # runs even when sub recording is disabled so old rows still
            # expire
            maybe_empty_dirs |= self.expire_existing_camera_recordings(
                STREAM_TYPE_SUB,
                sub_continuous_expire_date,
                sub_motion_expire_date,
                config.record.sub.alerts.mode,
                config.record.sub.detections.mode,
                config,
                _filter_reviews_for_pass(
                    reviews,
                    now,
                    config.record.sub.alerts.days,
                    config.record.sub.detections.days,
                ),
            )
            logger.debug(f"End camera: {camera}.")
@@ -12,7 +12,6 @@ import threading
 from collections.abc import Callable
 from enum import Enum
 from pathlib import Path
 from typing import Any
 import pytz  # type: ignore[import-untyped]
 from peewee import DoesNotExist
@@ -25,8 +24,6 @@ from frigate.const import (
    EXPORT_DIR,
    MAX_PLAYLIST_SECONDS,
    PREVIEW_FRAME_TYPE,
    STREAM_TYPE_MAIN,
    STREAM_TYPE_SUB,
 )
 from frigate.ffmpeg_presets import (
    EncodeTypeEnum,
@@ -39,8 +36,9 @@ from frigate.util.time import is_current_hour
 logger = logging.getLogger(__name__)
 DEFAULT_TIME_LAPSE_FFMPEG_INPUT_ARGS = "-an"
 DEFAULT_TIME_LAPSE_FFMPEG_ARGS = "-vf setpts=0.04*PTS -r 30"
-TIMELAPSE_DATA_INPUT_ARGS = "-an -skip_frame nokey"
+TIMELAPSE_DATA_INPUT_ARGS = "-skip_frame nokey"
 # Matches the setpts factor used in timelapse exports (e.g. setpts=0.04*PTS).
 # Captures the floating-point factor so we can scale expected duration.
@@ -286,29 +284,6 @@ class RecordingExporter(threading.Thread):
        return input_duration * factor
    def _get_recordings_for_range(self, stream_type: str) -> list[Any]:
        """Fetch one stream type's recording rows overlapping the export range."""
        return list(
            Recordings.select(
                Recordings.start_time,
                Recordings.end_time,
            )
            .where(
                Recordings.start_time.between(self.start_time, self.end_time)
                | Recordings.end_time.between(self.start_time, self.end_time)
                | (
                    (self.start_time > Recordings.start_time)
                    & (self.end_time < Recordings.end_time)
                )
            )
            .where(
                (Recordings.camera == self.camera)
                & (Recordings.stream_type == stream_type)
            )
            .order_by(Recordings.start_time.asc())
            .iterator()
        )
    def _sum_source_duration_seconds(self) -> float | None:
        """Sum saved-video seconds inside [start_time, end_time].
@@ -319,12 +294,19 @@ class RecordingExporter(threading.Thread):
        """
        try:
            if self.playback_source == PlaybackSourceEnum.recordings:
-                # never mix streams in one estimate; use main when available
+                rows = (
-                # and fall back to sub for expired-main history
+                    Recordings.select(Recordings.start_time, Recordings.end_time)
-                rows = self._get_recordings_for_range(STREAM_TYPE_MAIN)
+                    .where(
-
+                        Recordings.start_time.between(self.start_time, self.end_time)
-                if not rows:
+                        | Recordings.end_time.between(self.start_time, self.end_time)
-                    rows = self._get_recordings_for_range(STREAM_TYPE_SUB)
+                        | (
                            (self.start_time > Recordings.start_time)
                            & (self.end_time < Recordings.end_time)
                        )
                    )
                    .where(Recordings.camera == self.camera)
                    .iterator()
                )
            else:
                rows = (
                    Previews.select(Previews.start_time, Previews.end_time)
@@ -710,12 +692,23 @@ class RecordingExporter(threading.Thread):
        if type(internal_port) is str:
            internal_port = int(internal_port.split(":")[-1])
-        # never mix streams in one playlist; use main when available and
+        recordings = list(
-        # fall back to sub for expired-main history
+            Recordings.select(
-        recordings = self._get_recordings_for_range(STREAM_TYPE_MAIN)
+                Recordings.start_time,
-
+                Recordings.end_time,
-        if not recordings:
+            )
-            recordings = self._get_recordings_for_range(STREAM_TYPE_SUB)
+            .where(
                Recordings.start_time.between(self.start_time, self.end_time)
                | Recordings.end_time.between(self.start_time, self.end_time)
                | (
                    (self.start_time > Recordings.start_time)
                    & (self.end_time < Recordings.end_time)
                )
            )
            .where(Recordings.camera == self.camera)
            .order_by(Recordings.start_time.asc())
            .iterator()
        )
        playlist_lines: list[str] = []
        if (self.end_time - self.start_time) <= MAX_PLAYLIST_SECONDS:
@@ -745,7 +738,7 @@ class RecordingExporter(threading.Thread):
                parse_preset_hardware_acceleration_encode(
                    self.config.ffmpeg.ffmpeg_path,
                    hwaccel_args,
-                    f"{self.ffmpeg_input_args} -an {ffmpeg_input}".strip(),
+                    f"{self.ffmpeg_input_args} {ffmpeg_input}".strip(),
                    f"{self.ffmpeg_output_args} -movflags +faststart".strip(),
                    EncodeTypeEnum.timelapse,
                )
@@ -15,7 +15,6 @@ from typing import Any
 import numpy as np
 import psutil
 from peewee import fn
 from frigate.comms.detections_updater import DetectionSubscriber, DetectionTypeEnum
 from frigate.comms.inter_process import InterProcessRequestor
@@ -36,48 +35,15 @@ from frigate.const import (
    MAX_SEGMENT_DURATION,
    MAX_SEGMENTS_IN_CACHE,
    RECORD_DIR,
    STREAM_TYPE_MAIN,
    STREAM_TYPE_SUB,
    SUB_CACHE_TAG,
 )
 from frigate.models import Recordings, ReviewSegment
 from frigate.review.types import SeverityEnum
 from frigate.util.media import get_keyframe_offsets
 from frigate.util.services import get_video_properties
 logger = logging.getLogger(__name__)
 STALE_RECORDINGS_INFO_TTL = MAX_SEGMENTS_IN_CACHE * MAX_SEGMENT_DURATION * 2
 # cache filenames have whole-second resolution, so a contiguous segment's
 # parsed start lands up to 1s before the previous segment's true end
 SEGMENT_CHAIN_TOLERANCE_S = 1.0
 # against an mtime-measured start, disagreement beyond this means
 # accumulated probe-duration error and the chain re-anchors on the mtime
 SEGMENT_CHAIN_DRIFT_LIMIT_S = 0.5
 # probing every cached segment at once starves the camera and detection
 # processes, and the probes then blow their own timeouts together, so
 # segments get discarded as corrupt and the record watchdog restarts ffmpeg
 MAX_CONCURRENT_SEGMENT_PROBES = 4
 def parse_cache_segment_name(basename: str) -> tuple[str, str, str] | None:
    """Parse a cache segment basename into (camera, stream_type, date).
    Main segments are named {camera}@{date}; sub segments {camera}@sub@{date}.
    """
    try:
        prefix, date = basename.rsplit("@", maxsplit=1)
    except ValueError:
        return None
    if prefix.endswith(SUB_CACHE_TAG):
        return (prefix[: -len(SUB_CACHE_TAG)], STREAM_TYPE_SUB, date)
    return (prefix, STREAM_TYPE_MAIN, date)
 class SegmentInfo:
    def __init__(
@@ -117,10 +83,6 @@ class SegmentInfo:
 class RecordingMaintainer(threading.Thread):
    # move_files replaces this per cycle: an asyncio primitive binds to the
    # first event loop that contends it, and every cycle runs in a new loop
    probe_semaphore = asyncio.Semaphore(MAX_CONCURRENT_SEGMENT_PROBES)
    def __init__(self, config: FrigateConfig, stop_event: MpEvent):
        super().__init__(name="recording_maintainer")
        self.config = config
@@ -138,101 +100,10 @@ class RecordingMaintainer(threading.Thread):
        self.stop_event = stop_event
        self.object_recordings_info: dict[str, list] = defaultdict(list)
        self.audio_recordings_info: dict[str, list] = defaultdict(list)
-        # cache_path -> (end_time, duration, has_audio, audio_rate,
+        self.end_time_cache: dict[str, tuple[datetime.datetime, float]] = {}
        # audio_codec, video_codec, keyframes)
        self.end_time_cache: dict[
            str,
            tuple[
                datetime.datetime,
                float,
                bool | None,
                int | None,
                str | None,
                str | None,
                list[int] | None,
            ],
        ] = {}
        # last known capture end per (camera, stream_type); 0.0 marks a key
        # whose DB seed found no rows
        self.last_segment_end: dict[tuple[str, str], float] = {}
        self.unexpected_cache_files_logged: bool = False
    def _get_last_segment_end(self, camera: str, stream_type: str) -> float | None:
        """Return the last known capture end time for a camera stream.
        Lazily seeds from the most recent stored recording so start-time
        chains survive restarts.
        """
        key = (camera, stream_type)
        if key not in self.last_segment_end:
            last_db_end = (
                Recordings.select(fn.MAX(Recordings.end_time))
                .where(
                    Recordings.camera == camera,
                    Recordings.stream_type == stream_type,
                )
                .scalar()
            )
            # the 0.0 sentinel keeps the seed query from repeating
            self.last_segment_end[key] = last_db_end if last_db_end is not None else 0.0
        return self.last_segment_end[key] or None
    def _resolve_segment_start(
        self,
        camera: str,
        stream_type: str,
        filename_start: datetime.datetime,
        duration: float,
        cache_path: str,
    ) -> datetime.datetime:
        """Resolve a segment's true start time from its cache file.
        Cache filenames carry whole-second resolution, so the parsed start
        sits up to 1s early. The cache file's mtime is the wall clock when
        ffmpeg rolled the segment, so mtime minus the probed duration
        restores the fractional start. Contiguous segments still chain to
        the previous segment's end so rows stay exactly adjacent.
        """
        filename_ts = filename_start.timestamp()
        measured: float | None = None
        try:
            mtime = os.path.getmtime(cache_path)
        except OSError:
            mtime = None
        if mtime is not None:
            candidate = mtime - duration
            # media shorter than its wall span (a stalled stream, an early
            # close) derives a start past the truncation window, where the
            # floored filename start is safer
            if 0 <= candidate - filename_ts < SEGMENT_CHAIN_TOLERANCE_S:
                measured = candidate
        last_end = self._get_last_segment_end(camera, stream_type)
        if measured is not None:
            if (
                last_end is not None
                and abs(last_end - measured) < SEGMENT_CHAIN_DRIFT_LIMIT_S
            ):
                return datetime.datetime.fromtimestamp(last_end, tz=datetime.UTC)
            return datetime.datetime.fromtimestamp(measured, tz=datetime.UTC)
        # no usable mtime: capture is continuous within a run, so a
        # filename start just before the previous end chains to that end
        if (
            last_end is not None
            and 0 <= last_end - filename_ts < SEGMENT_CHAIN_TOLERANCE_S
        ):
            return datetime.datetime.fromtimestamp(last_end, tz=datetime.UTC)
        return filename_start
    async def move_files(self) -> None:
        self.probe_semaphore = asyncio.Semaphore(MAX_CONCURRENT_SEGMENT_PROBES)
        cache_files = [
            d
            for d in os.listdir(CACHE_DIR)
@@ -246,17 +117,13 @@ class RecordingMaintainer(threading.Thread):
        for cache in cache_files:
            cache_path = os.path.join(CACHE_DIR, cache)
            basename = os.path.splitext(cache)[0]
-            parsed = parse_cache_segment_name(basename)
+            try:
-            if parsed is None:
+                camera, date = basename.rsplit("@", maxsplit=1)
            except ValueError:
                if not self.unexpected_cache_files_logged:
                    logger.warning("Skipping unexpected files in cache")
                    self.unexpected_cache_files_logged = True
                continue
            camera, stream_type, date = parsed
            # this topic feeds main-stream health/sync consumers only
            if stream_type == STREAM_TYPE_SUB:
                continue
            start_time = datetime.datetime.strptime(
                date, CACHE_SEGMENT_FORMAT
@@ -300,10 +167,8 @@ class RecordingMaintainer(threading.Thread):
            except psutil.Error:
                continue
-        # group recordings by camera and stream type (skip in-use for validation/moving)
+        # group recordings by camera (skip in-use for validation/moving)
-        grouped_recordings: defaultdict[tuple[str, str], list[dict[str, Any]]] = (
+        grouped_recordings: defaultdict[str, list[dict[str, Any]]] = defaultdict(list)
            defaultdict(list)
        )
        for cache in cache_files:
            # Skip files currently in use
            if cache in files_in_use:
@@ -311,35 +176,32 @@ class RecordingMaintainer(threading.Thread):
            cache_path = os.path.join(CACHE_DIR, cache)
            basename = os.path.splitext(cache)[0]
-            parsed = parse_cache_segment_name(basename)
+            try:
-            if parsed is None:
+                camera, date = basename.rsplit("@", maxsplit=1)
            except ValueError:
                if not self.unexpected_cache_files_logged:
                    logger.warning("Skipping unexpected files in cache")
                    self.unexpected_cache_files_logged = True
                continue
            camera, stream_type, date = parsed
            # important that start_time is utc because recordings are stored and compared in utc
            start_time = datetime.datetime.strptime(
                date, CACHE_SEGMENT_FORMAT
            ).astimezone(datetime.UTC)
-            grouped_recordings[(camera, stream_type)].append(
+            grouped_recordings[camera].append(
                {
                    "cache_path": cache_path,
                    "start_time": start_time,
                    "stream_type": stream_type,
                }
            )
        # delete all cached files past the most recent MAX_SEGMENTS_IN_CACHE
        keep_count = MAX_SEGMENTS_IN_CACHE
-        for key in grouped_recordings.keys():
+        for camera in grouped_recordings.keys():
            camera, stream_type = key
            # sort based on start time
-            grouped_recordings[key] = sorted(
+            grouped_recordings[camera] = sorted(
-                grouped_recordings[key], key=lambda s: s["start_time"]
+                grouped_recordings[camera], key=lambda s: s["start_time"]
            )
            camera_info = self.object_recordings_info[camera]
@@ -354,7 +216,7 @@ class RecordingMaintainer(threading.Thread):
                            r["start_time"].timestamp()
                            < most_recently_processed_frame_time
                        ),
-                        grouped_recordings[key],
+                        grouped_recordings[camera],
                    )
                )
            )
@@ -364,133 +226,103 @@ class RecordingMaintainer(threading.Thread):
                logger.warning(
                    f"Unable to keep up with recording segments in cache for {camera}. Keeping the {keep_count} most recent segments out of {processed_segment_count} and discarding the rest..."
                )
-                to_remove = grouped_recordings[key][:-keep_count]
+                to_remove = grouped_recordings[camera][:-keep_count]
                for rec in to_remove:
                    cache_path = rec["cache_path"]
                    Path(cache_path).unlink(missing_ok=True)
                    self.end_time_cache.pop(cache_path, None)
-                grouped_recordings[key] = grouped_recordings[key][-keep_count:]
+                grouped_recordings[camera] = grouped_recordings[camera][-keep_count:]
            # see if detection has failed and unprocessed segments need to be deleted
            unprocessed_segment_count = (
-                len(grouped_recordings[key]) - processed_segment_count
+                len(grouped_recordings[camera]) - processed_segment_count
            )
            if unprocessed_segment_count > keep_count:
                logger.warning(
                    f"Too many unprocessed recording segments in cache for {camera}. This likely indicates an issue with the detect stream, keeping the {keep_count} most recent segments out of {unprocessed_segment_count} and discarding the rest..."
                )
-                to_remove = grouped_recordings[key][:-keep_count]
+                to_remove = grouped_recordings[camera][:-keep_count]
                for rec in to_remove:
                    cache_path = rec["cache_path"]
                    Path(cache_path).unlink(missing_ok=True)
                    self.end_time_cache.pop(cache_path, None)
-                grouped_recordings[key] = grouped_recordings[key][-keep_count:]
+                grouped_recordings[camera] = grouped_recordings[camera][-keep_count:]
-        # frame stats are shared per camera across stream types, so trimming
+        tasks = []
-        # to one stream's oldest cache would pop frames the other still needs
+        for camera, recordings in grouped_recordings.items():
        min_start_per_camera: dict[str, float] = {}
        for key, recordings in grouped_recordings.items():
            camera, _ = key
            oldest_start = recordings[0]["start_time"].timestamp()
            if (
                camera not in min_start_per_camera
                or oldest_start < min_start_per_camera[camera]
            ):
                min_start_per_camera[camera] = oldest_start
        for camera, min_start in min_start_per_camera.items():
            # clear out all the object recording info for old frames
            while (
                len(self.object_recordings_info[camera]) > 0
-                and self.object_recordings_info[camera][0][0] < min_start
+                and self.object_recordings_info[camera][0][0]
                < recordings[0]["start_time"].timestamp()
            ):
                self.object_recordings_info[camera].pop(0)
            # clear out all the audio recording info for old frames
            while (
                len(self.audio_recordings_info[camera]) > 0
-                and self.audio_recordings_info[camera][0][0] < min_start
+                and self.audio_recordings_info[camera][0][0]
                < recordings[0]["start_time"].timestamp()
            ):
                self.audio_recordings_info[camera].pop(0)
-        tasks = []
+            # get all reviews with the end time after the start of the oldest cache file
-        reviews_by_camera: dict[str, Any] = {}
+            # or with end_time None
-        for key, recordings in grouped_recordings.items():
+            reviews = (
-            camera, stream_type = key
+                ReviewSegment.select(
-
+                    ReviewSegment.start_time,
-            # get all reviews with the end time after the start of the oldest
+                    ReviewSegment.end_time,
-            # cache file or with end_time None; shared across stream types
+                    ReviewSegment.severity,
-            if camera not in reviews_by_camera:
+                    ReviewSegment.data,
                reviews_by_camera[camera] = (
                    ReviewSegment.select(
                        ReviewSegment.start_time,
                        ReviewSegment.end_time,
                        ReviewSegment.severity,
                        ReviewSegment.data,
                    )
                    .where(
                        ReviewSegment.camera == camera,
                        (ReviewSegment.end_time == None)
                        | (ReviewSegment.end_time >= min_start_per_camera[camera]),
                    )
                    .order_by(ReviewSegment.start_time)
                )
-            reviews = reviews_by_camera[camera]
+                .where(
                    ReviewSegment.camera == camera,
                    (ReviewSegment.end_time == None)
                    | (
                        ReviewSegment.end_time
                        >= recordings[0]["start_time"].timestamp()
                    ),
                )
                .order_by(ReviewSegment.start_time)
            )
            tasks.extend(
                [self.validate_and_move_segment(camera, reviews, r) for r in recordings]
            )
            # publish most recently available recording time and None if disabled
-            if stream_type == STREAM_TYPE_MAIN:
+            camera_cfg = self.config.cameras.get(camera)
-                camera_cfg = self.config.cameras.get(camera)
+            self.recordings_publisher.publish(
-                self.recordings_publisher.publish(
+                (
-                    (
+                    camera,
-                        camera,
+                    recordings[0]["start_time"].timestamp()
-                        recordings[0]["start_time"].timestamp()
+                    if camera_cfg and camera_cfg.record.enabled
-                        if camera_cfg and camera_cfg.record.enabled
+                    else None,
-                        else None,
+                    None,
-                        None,
+                ),
-                    ),
+                RecordingsDataTypeEnum.saved.value,
-                    RecordingsDataTypeEnum.saved.value,
+            )
                )
        self._expire_stale_recordings_info(grouped_recordings)
-        # one segment must not abort the cycle: an exception propagating out
+        recordings_to_insert: list[dict[str, Any] | None] = await asyncio.gather(*tasks)
        # of gather would abandon the other segments' in-flight probes
        results: list[dict[str, Any] | None | BaseException] = await asyncio.gather(
            *tasks, return_exceptions=True
        )
        recordings_to_insert: list[dict[str, Any]] = []
        for result in results:
            if isinstance(result, BaseException):
                logger.error(
                    "Failed to validate and move a recording segment", exc_info=result
                )
                continue
            if result is not None:
                recordings_to_insert.append(result)
        # fire and forget recordings entries
-        self.requestor.send_data(INSERT_MANY_RECORDINGS, recordings_to_insert)
+        self.requestor.send_data(
            INSERT_MANY_RECORDINGS,
            [r for r in recordings_to_insert if r is not None],
        )
    def _expire_stale_recordings_info(
-        self, grouped_recordings: defaultdict[tuple[str, str], list[dict[str, Any]]]
+        self, grouped_recordings: defaultdict[str, list[dict[str, Any]]]
    ) -> None:
        expire_before = datetime.datetime.now().timestamp() - STALE_RECORDINGS_INFO_TTL
        # a camera is still active when any of its streams cached segments
        cameras_with_cache = {camera for camera, _ in grouped_recordings}
        for recordings_info in (
            self.object_recordings_info,
            self.audio_recordings_info,
        ):
            for camera in list(recordings_info.keys()):
-                if camera in cameras_with_cache:
+                if camera in grouped_recordings:
                    continue
                info = recordings_info[camera]
                while info and info[0][0] < expire_before:
@@ -505,164 +337,102 @@ class RecordingMaintainer(threading.Thread):
    ) -> dict[str, Any] | None:
        cache_path: str = recording["cache_path"]
        start_time: datetime.datetime = recording["start_time"]
        stream_type: str = recording["stream_type"]
        # Just delete files if camera removed or recordings are turned off
        if (
            camera not in self.config.cameras
            or not self.config.cameras[camera].record.enabled
            or (
                stream_type == STREAM_TYPE_SUB
                and not self.config.cameras[camera].record.sub.enabled
            )
        ):
            self.drop_segment(cache_path)
            return None
        if cache_path in self.end_time_cache:
-            (
+            end_time, duration = self.end_time_cache[cache_path]
                end_time,
                duration,
                has_audio,
                audio_rate,
                audio_codec,
                video_codec,
                keyframes,
            ) = self.end_time_cache[cache_path]
            # recover the resolved start rather than reusing the truncated
            # filename timestamp
            start_time = end_time - datetime.timedelta(seconds=duration)
        else:
-            async with self.probe_semaphore:
+            segment_info = await get_video_properties(
-                segment_info = await get_video_properties(
+                self.config.ffmpeg, cache_path, get_duration=True
-                    self.config.ffmpeg, cache_path, get_duration=True
+            )
                )
            if not segment_info.get("has_valid_video", False):
                logger.warning(
                    f"Invalid or missing video stream in segment {cache_path}. Discarding."
                )
-                if stream_type == STREAM_TYPE_MAIN:
+                self.recordings_publisher.publish(
-                    self.recordings_publisher.publish(
+                    (camera, start_time.timestamp(), cache_path),
-                        (camera, start_time.timestamp(), cache_path),
+                    RecordingsDataTypeEnum.invalid.value,
-                        RecordingsDataTypeEnum.invalid.value,
+                )
                    )
                self.drop_segment(cache_path)
                return None
            duration = float(segment_info.get("duration", -1))
            has_audio = segment_info.get("has_audio")
            audio_rate = segment_info.get("audio_rate")
            audio_codec = segment_info.get("audio_codec")
            video_codec = segment_info.get("video_codec")
            # ensure duration is within expected length
            if 0 < duration < MAX_SEGMENT_DURATION:
                # playback snaps mid-file entry points against these offsets
                # instead of probing files on demand
                async with self.probe_semaphore:
                    keyframes = await get_keyframe_offsets(cache_path)
                start_time = self._resolve_segment_start(
                    camera, stream_type, start_time, duration, cache_path
                )
                end_time = start_time + datetime.timedelta(seconds=duration)
-                self.end_time_cache[cache_path] = (
+                self.end_time_cache[cache_path] = (end_time, duration)
                    end_time,
                    duration,
                    has_audio,
                    audio_rate,
                    audio_codec,
                    video_codec,
                    keyframes,
                )
                # segments later discarded by retention still advance the
                # chain for the next kept segment
                self.last_segment_end[(camera, stream_type)] = end_time.timestamp()
            else:
                if duration == -1:
                    logger.warning(f"Failed to probe corrupt segment {cache_path}")
                logger.warning(f"Discarding a corrupt recording segment: {cache_path}")
-                if stream_type == STREAM_TYPE_MAIN:
+                self.recordings_publisher.publish(
-                    self.recordings_publisher.publish(
+                    (camera, start_time.timestamp(), cache_path),
-                        (camera, start_time.timestamp(), cache_path),
+                    RecordingsDataTypeEnum.invalid.value,
-                        RecordingsDataTypeEnum.invalid.value,
+                )
                    )
                self.drop_segment(cache_path)
                return None
            # this segment has a valid duration and has video data, so publish an update
-            if stream_type == STREAM_TYPE_MAIN:
+            self.recordings_publisher.publish(
-                self.recordings_publisher.publish(
+                (camera, start_time.timestamp(), cache_path),
-                    (camera, start_time.timestamp(), cache_path),
+                RecordingsDataTypeEnum.valid.value,
-                    RecordingsDataTypeEnum.valid.value,
+            )
-                )
+
        # assume that empty means the relevant recording info has not been received yet
        camera_info = self.object_recordings_info[camera]
        most_recently_processed_frame_time = (
            camera_info[-1][0] if len(camera_info) > 0 else 0
        )
        # ensure delayed segment info does not lead to lost segments, every
        # retention decision below depends on complete stats for the segment
        if (
            datetime.datetime.fromtimestamp(
                most_recently_processed_frame_time
            ).astimezone(datetime.UTC)
            < end_time
        ):
            return None
        record_config = self.config.cameras[camera].record
        # sub's alerts/detections carry the retain mode directly, unlike
        # main's nested retain config
        if stream_type == STREAM_TYPE_SUB:
            continuous_days = record_config.sub.continuous.days
            motion_days = record_config.sub.motion.days
            alerts_retain_mode = record_config.sub.alerts.mode
            detections_retain_mode = record_config.sub.detections.mode
        else:
            continuous_days = record_config.continuous.days
            motion_days = record_config.motion.days
            alerts_retain_mode = record_config.alerts.retain.mode
            detections_retain_mode = record_config.detections.retain.mode
        segment_stats: SegmentInfo | None = None
        highest = None
-        if continuous_days > 0:
+        if record_config.continuous.days > 0:
            highest = "continuous"
-        elif motion_days > 0:
+        elif record_config.motion.days > 0:
            highest = "motion"
        # if we have continuous or motion recording enabled
        # we should first just check if this segment matches that
        # and avoid any DB calls
        if highest is not None:
-            # assume that empty means the relevant recording info has not been received yet
+            record_mode = (
-            camera_info = self.object_recordings_info[camera]
+                RetainModeEnum.all if highest == "continuous" else RetainModeEnum.motion
            most_recently_processed_frame_time = (
                camera_info[-1][0] if len(camera_info) > 0 else 0
            )
            segment_stats = self.segment_stats(camera, start_time, end_time)
-            # ensure delayed segment info does not lead to lost segments
+            # Here we only check if we should move the segment based on non-object recording retention
-            if (
+            # we will always want to check for overlapping review items below before dropping the segment
-                datetime.datetime.fromtimestamp(
+            if not segment_stats.should_discard_segment(record_mode):
-                    most_recently_processed_frame_time
+                return await self.move_segment(
-                ).astimezone(datetime.UTC)
+                    camera,
-                >= end_time
+                    start_time,
-            ):
+                    end_time,
-                record_mode = (
+                    duration,
-                    RetainModeEnum.all
+                    cache_path,
-                    if highest == "continuous"
+                    segment_stats,
                    else RetainModeEnum.motion
                )
                segment_stats = self.segment_stats(camera, start_time, end_time)
                # Here we only check if we should move the segment based on non-object recording retention
                # we will always want to check for overlapping review items below before dropping the segment
                if not segment_stats.should_discard_segment(record_mode):
                    return await self.move_segment(
                        camera,
                        stream_type,
                        start_time,
                        end_time,
                        duration,
                        cache_path,
                        segment_stats,
                        has_audio,
                        audio_rate,
                        audio_codec,
                        video_codec,
                        keyframes,
                    )
        # we fell through the continuous / motion check, so we need to check the review items
        # if the cached segment overlaps with the review items:
@@ -690,9 +460,9 @@ class RecordingMaintainer(threading.Thread):
        if overlaps:
            record_mode = (
-                alerts_retain_mode
+                record_config.alerts.retain.mode
                if review.severity == "alert"
-                else detections_retain_mode
+                else record_config.detections.retain.mode
            )
            if segment_stats is None:
@@ -702,17 +472,11 @@ class RecordingMaintainer(threading.Thread):
                # move from cache to recordings immediately
                return await self.move_segment(
                    camera,
                    stream_type,
                    start_time,
                    end_time,
                    duration,
                    cache_path,
                    segment_stats,
                    has_audio,
                    audio_rate,
                    audio_codec,
                    video_codec,
                    keyframes,
                )
            else:
                self.drop_segment(cache_path)
@@ -724,10 +488,6 @@ class RecordingMaintainer(threading.Thread):
        # continuous/motion retention (either disabled or segment_stats said
        # discard), so waiting longer just fills the cache.
        else:
            camera_info = self.object_recordings_info[camera]
            most_recently_processed_frame_time = (
                camera_info[-1][0] if len(camera_info) > 0 else 0
            )
            retain_cutoff = datetime.datetime.fromtimestamp(
                most_recently_processed_frame_time - record_config.event_pre_capture
            ).astimezone(datetime.UTC)
@@ -851,24 +611,17 @@ class RecordingMaintainer(threading.Thread):
    async def move_segment(
        self,
        camera: str,
        stream_type: str,
        start_time: datetime.datetime,
        end_time: datetime.datetime,
        duration: float,
        cache_path: str,
        segment_info: SegmentInfo,
        has_audio: bool | None = None,
        audio_rate: int | None = None,
        audio_codec: str | None = None,
        video_codec: str | None = None,
        keyframes: list[int] | None = None,
    ) -> dict[str, Any] | None:
        # directory will be in utc due to start_time being in utc
        # sub segments get a tagged directory to avoid filename collisions
        directory = os.path.join(
            RECORD_DIR,
            start_time.strftime("%Y-%m-%d/%H"),
-            camera if stream_type == STREAM_TYPE_MAIN else f"{camera}{SUB_CACHE_TAG}",
+            camera,
        )
        os.makedirs(directory, exist_ok=True)
@@ -928,7 +681,6 @@ class RecordingMaintainer(threading.Thread):
                return {
                    Recordings.id.name: f"{start_time.timestamp()}-{rand_id}",
                    Recordings.camera.name: camera,
                    Recordings.stream_type.name: stream_type,
                    Recordings.path.name: file_path,
                    Recordings.start_time.name: start_time.timestamp(),
                    Recordings.end_time.name: end_time.timestamp(),
@@ -940,11 +692,6 @@ class RecordingMaintainer(threading.Thread):
                    Recordings.dBFS.name: segment_info.average_dBFS,
                    Recordings.segment_size.name: segment_size,
                    Recordings.motion_heatmap.name: segment_info.motion_heatmap,
                    Recordings.has_audio.name: has_audio,
                    Recordings.audio_rate.name: audio_rate,
                    Recordings.audio_codec.name: audio_codec,
                    Recordings.video_codec.name: video_codec,
                    Recordings.keyframes.name: keyframes,
                }
        except Exception as e:
            logger.error(f"Unable to store recording segment {cache_path}")
@@ -995,9 +742,7 @@ class RecordingMaintainer(threading.Thread):
                        regions,
                    ) = data
-                    camera_config = self.config.cameras.get(camera)
+                    if self.config.cameras[camera].record.enabled:
                    if camera_config is not None and camera_config.record.enabled:
                        self.object_recordings_info[camera].append(
                            (
                                frame_time,
@@ -1014,9 +759,7 @@ class RecordingMaintainer(threading.Thread):
                        audio_detections,
                    ) = data
-                    camera_config = self.config.cameras.get(camera)
+                    if self.config.cameras[camera].record.enabled:
                    if camera_config is not None and camera_config.record.enabled:
                        self.audio_recordings_info[camera].append(
                            (
                                frame_time,
@@ -1038,10 +781,11 @@ class RecordingMaintainer(threading.Thread):
            try:
                asyncio.run(self.move_files())
-            except Exception:
+            except Exception as e:
-                logger.exception(
+                logger.error(
                    "Error occurred when attempting to maintain recording cache"
                )
                logger.error(e)
            duration = datetime.datetime.now().timestamp() - run_start
            wait_time = max(0, 5 - duration)
@@ -418,11 +418,6 @@ class ReviewSegmentMaintainer(threading.Thread):
        return None
    def _handle_camera_removed(self, camera: str) -> None:
        """Close out a deleted camera's segment so a reused name cannot inherit it."""
        self.forcibly_end_segment(camera)
        self.indefinite_events.pop(camera, None)
    def update_existing_segment(
        self,
        segment: PendingReviewSegment,
@@ -671,10 +666,6 @@ class ReviewSegmentMaintainer(threading.Thread):
                for camera in updated_topics["enabled"]:
                    self.forcibly_end_segment(camera)
            if "remove" in updated_topics:
                for camera in updated_topics["remove"]:
                    self._handle_camera_removed(camera)
            result = self.detection_subscriber.check_for_update(timeout=1)
            if not result:
@@ -352,8 +352,9 @@ def stats_snapshot(
    total_camera_fps = total_process_fps = total_skipped_fps = total_detection_fps = 0
    stats["cameras"] = {}
-    for name, camera_stats in camera_metrics.items():
+    for name, camera_stats in list(camera_metrics.items()):
-        if name not in config.cameras:
+        camera_config = config.cameras.get(name)
        if camera_config is None:
            continue
        total_camera_fps += camera_stats.camera_fps.value
@@ -370,7 +371,7 @@ def stats_snapshot(
        # Calculate connection quality based on current state
        # This is computed at stats-collection time so offline cameras
        # correctly show as unusable rather than excellent
-        expected_fps = config.cameras[name].detect.fps
+        expected_fps = camera_config.detect.fps
        current_fps = camera_stats.camera_fps.value
        reconnects = camera_stats.reconnects_last_hour.value
        stalls = camera_stats.stalls_last_hour.value
@@ -398,7 +399,7 @@ def stats_snapshot(
            "process_fps": round(camera_stats.process_fps.value, 2),
            "skipped_fps": round(camera_stats.skipped_fps.value, 2),
            "detection_fps": round(camera_stats.detection_fps.value, 2),
-            "detection_enabled": config.cameras[name].detect.enabled,
+            "detection_enabled": camera_config.detect.enabled,
            "pid": pid,
            "capture_pid": capture_pid,
            "ffmpeg_pid": ffmpeg_pid,
@@ -9,12 +9,7 @@ from pathlib import Path
 from peewee import SQL, fn
 from frigate.config import FrigateConfig
-from frigate.const import (
+from frigate.const import RECORD_DIR, REPLAY_CAMERA_PREFIX
    RECORD_DIR,
    REPLAY_CAMERA_PREFIX,
    STREAM_TYPE_MAIN,
    STREAM_TYPE_SUB,
 )
 from frigate.models import Event, Recordings
 from frigate.util.builtin import clear_and_unlink
@@ -54,55 +49,32 @@ class StorageMaintainer(threading.Thread):
                    )
                }
-                # calculate MB/hr from the last 100 segments of each stream
+                # calculate MB/hr from last 100 segments
-                # type and sum the rates; mixing streams would average small
+                try:
-                # sub segments against large main segments and underestimate
+                    # Subquery to get last 100 segments, then average their bandwidth
-                # the true write rate
+                    last_100 = (
-                bandwidth_by_stream: dict[str, float] = {}
+                        Recordings.select(bandwidth_equation.alias("bw"))
-                for stream_type in (STREAM_TYPE_MAIN, STREAM_TYPE_SUB):
+                        .where(Recordings.camera == camera, Recordings.segment_size > 0)
-                    try:
+                        .order_by(Recordings.start_time.desc())
-                        # Subquery to get last 100 segments, then average their bandwidth
+                        .limit(100)
-                        last_100 = (
+                        .alias("recent")
                            Recordings.select(bandwidth_equation.alias("bw"))
                            .where(
                                Recordings.camera == camera,
                                Recordings.segment_size > 0,
                                Recordings.stream_type == stream_type,
                            )
                            .order_by(Recordings.start_time.desc())
                            .limit(100)
                            .alias("recent")
                        )
                        bandwidth_by_stream[stream_type] = round(
                            Recordings.select(fn.AVG(SQL("bw")))
                            .from_(last_100)
                            .scalar()
                            * 3600,
                            2,
                        )
                    except TypeError:
                        pass
                bandwidth = round(sum(bandwidth_by_stream.values()), 2)
                if bandwidth > MAX_CALCULATED_BANDWIDTH:
                    logger.warning(
                        f"{camera} has a bandwidth of {bandwidth} MB/hr which exceeds the expected maximum. This typically indicates an issue with the cameras recordings."
                    )
-                    # scale each stream so the per stream values still sum to
+
-                    # the clamped total the UI displays alongside them
+                    bandwidth = round(
-                    scale = MAX_CALCULATED_BANDWIDTH / bandwidth
+                        Recordings.select(fn.AVG(SQL("bw"))).from_(last_100).scalar()
-                    bandwidth_by_stream = {
+                        * 3600,
-                        stream_type: round(value * scale, 2)
+                        2,
-                        for stream_type, value in bandwidth_by_stream.items()
+                    )
-                    }
+
-                    bandwidth = MAX_CALCULATED_BANDWIDTH
+                    if bandwidth > MAX_CALCULATED_BANDWIDTH:
                        logger.warning(
                            f"{camera} has a bandwidth of {bandwidth} MB/hr which exceeds the expected maximum. This typically indicates an issue with the cameras recordings."
                        )
                        bandwidth = MAX_CALCULATED_BANDWIDTH
                except TypeError:
                    bandwidth = 0
                self.camera_storage_stats[camera]["bandwidth"] = bandwidth
                self.camera_storage_stats[camera]["bandwidth_by_stream"] = (
                    bandwidth_by_stream
                )
                logger.debug(f"{camera} has a bandwidth of {bandwidth} MiB/hr.")
    def calculate_camera_usages(self) -> dict[str, dict]:
@@ -114,40 +86,20 @@ class StorageMaintainer(threading.Thread):
            if camera.startswith(REPLAY_CAMERA_PREFIX):
                continue
-            stream_usages = {
+            camera_storage = (
-                row["stream_type"]: row["usage"] or 0
+                Recordings.select(fn.SUM(Recordings.segment_size))
-                for row in (
+                .where(Recordings.camera == camera, Recordings.segment_size != 0)
-                    Recordings.select(
+                .scalar()
                        Recordings.stream_type,
                        fn.SUM(Recordings.segment_size).alias("usage"),
                    )
                    .where(Recordings.camera == camera, Recordings.segment_size != 0)
                    .group_by(Recordings.stream_type)
                    .dicts()
                )
            }
            stream_bandwidths = self.camera_storage_stats.get(camera, {}).get(
                "bandwidth_by_stream", {}
            )
            camera_key = (
                getattr(self.config.cameras[camera], "friendly_name", None) or camera
            )
            usages[camera_key] = {
-                "usage": sum(stream_usages.values()),
+                "usage": camera_storage,
                "bandwidth": self.camera_storage_stats.get(camera, {}).get(
                    "bandwidth", 0
                ),
                # only streams with segments on disk are reported, so a camera
                # keeps its sub entry until sub retention expires those segments
                "streams": {
                    stream_type: {
                        "usage": stream_usages[stream_type],
                        "bandwidth": stream_bandwidths.get(stream_type, 0),
                    }
                    for stream_type in (STREAM_TYPE_MAIN, STREAM_TYPE_SUB)
                    if stream_usages.get(stream_type)
                },
            }
        return usages
@@ -144,7 +144,9 @@ class BaseTestHttp(unittest.TestCase):
        except OSError:
            pass
-    def create_app(self, stats=None, event_metadata_publisher=None):
+    def create_app(
        self, stats=None, event_metadata_publisher=None, enforce_default_admin=False
    ):
        from frigate.api.auth import get_allowed_cameras_for_filter, get_current_user
        app = create_fastapi_app(
@@ -158,7 +160,7 @@ class BaseTestHttp(unittest.TestCase):
            event_metadata_publisher,
            None,
            DebugReplayManager(),
-            enforce_default_admin=False,
+            enforce_default_admin=enforce_default_admin,
        )
        # Default test mocks for authentication
@@ -2,6 +2,7 @@
 from unittest.mock import patch
 from frigate.jobs.debug_replay import NoRecordingsError
 from frigate.models import Event, Recordings, ReviewSegment
 from frigate.test.http_api.base_http_test import AuthTestClient, BaseTestHttp
@@ -66,6 +67,32 @@ class TestDebugReplayAPI(BaseTestHttp):
        # (CodeQL: information exposure through an exception).
        self.assertEqual(body["message"], "Invalid debug replay parameters")
    def test_start_returns_404_when_no_recordings(self):
        with patch(
            "frigate.api.debug_replay.start_debug_replay_job",
            side_effect=NoRecordingsError(
                "No recordings found for camera 'front' in the specified time range"
            ),
        ):
            with AuthTestClient(self.app) as client:
                resp = client.post(
                    "/debug_replay/start",
                    json={
                        "camera": "front",
                        "start_time": 100,
                        "end_time": 200,
                    },
                )
        self.assertEqual(resp.status_code, 404)
        body = resp.json()
        self.assertFalse(body["success"])
        # Message is hard-coded so we don't echo exception text back to clients
        # (CodeQL: information exposure through an exception).
        self.assertEqual(
            body["message"], "No recordings found in the selected time range"
        )
    def test_start_returns_409_when_session_already_active(self):
        with patch(
            "frigate.api.debug_replay.start_debug_replay_job",
@@ -47,6 +47,25 @@ class TestHttpApp(BaseTestHttp):
            assert response.status_code == 200
            assert response.json()["front_door"]["usage_percent"] == 25.0
    def test_camera_name_collision_keeps_admin_default(self):
        self.minimal_config["cameras"]["faces"] = self.minimal_config["cameras"].pop(
            "front_door"
        )
        app = super().create_app(enforce_default_admin=True)
        viewer = {"remote-user": "viewer", "remote-role": "viewer"}
        with AuthTestClient(app) as client:
            assert client.get("/faces", headers=viewer).status_code == 403
            assert client.get("/faces").status_code == 200
            assert (
                client.post("/faces/train/person/classify", headers=viewer).status_code
                == 403
            )
            # Camera routes for the same name stay reachable by viewers
            response = client.get("/faces/recordings/summary", headers=viewer)
            assert response.status_code == 200
    def test_config_set_in_memory_replaces_objects_track_list(self):
        self.minimal_config["cameras"]["front_door"]["objects"] = {
            "track": ["person", "car"],
@@ -440,3 +440,68 @@ class TestGo2rtcStreamAccess(BaseTestHttp):
            f"limited_user should be denied on alias back_door_main; "
            f"got {resp.status_code}"
        )
 class TestReviewSummaryAccess(BaseTestHttp):
    """Tests for POST /review/summarize/start/{start_ts}/end/{end_ts}.
    The summary correlates each flagged event with overlapping activity on
    other cameras, so it is gated on full camera access rather than scoped to
    the caller's cameras. These tests pin that decision so the dependency is
    not loosened without first scoping the query.
    GenAI is not configured in unit tests, so an authorized request returns 400
    while an unauthorized one is rejected with 403 before the handler runs.
    """
    def setUp(self):
        super().setUp([Event, ReviewSegment, Recordings])
        self.minimal_config = _MULTI_CAMERA_CONFIG
        self.app = super().create_app()
    def tearDown(self):
        self.app.dependency_overrides.clear()
        super().tearDown()
    def _summarize(self, allowed_cameras: list[str]):
        async def mock_cameras(request: Request):
            return allowed_cameras
        self.app.dependency_overrides[get_allowed_cameras_for_filter] = mock_cameras
        with AuthTestClient(self.app) as client:
            return client.post("/review/summarize/start/0/end/9999999999")
    def _assert_allowed(self, resp):
        assert resp.status_code not in (401, 403), (
            f"Caller should not be blocked; got {resp.status_code}"
        )
    def test_partial_camera_access_blocked(self):
        assert self._summarize(["front_door"]).status_code == 403
    def test_no_camera_access_blocked(self):
        assert self._summarize([]).status_code == 403
    def test_full_camera_access_allowed(self):
        # Covers admin and viewer, which always resolve to every camera, and a
        # custom role whose list happens to name them all.
        self._assert_allowed(self._summarize(["front_door", "back_door"]))
    def _summarize_as_role(self, role: str):
        """Summarize using the real role to allowed-cameras resolution."""
        self.app.dependency_overrides.pop(get_allowed_cameras_for_filter, None)
        with AuthTestClient(self.app) as client:
            return client.post(
                "/review/summarize/start/0/end/9999999999",
                headers={"remote-user": "test", "remote-role": role},
            )
    def test_viewer_role_allowed(self):
        # viewer is never camera restricted, so it resolves to every camera.
        self._assert_allowed(self._summarize_as_role("viewer"))
    def test_admin_role_allowed(self):
        self._assert_allowed(self._summarize_as_role("admin"))
    def test_restricted_role_blocked(self):
        assert self._summarize_as_role("limited_user").status_code == 403
@@ -386,19 +386,9 @@ class TestConfigSetWildcardPropagation(BaseTestHttp):
        guess (mode still equals the previous global) wrongly claims a camera
        whose explicit yaml mode happens to match.
        """
-        self.minimal_config["birdseye"] = {
+        self.minimal_config["birdseye"] = {"enabled": True, "mode": "motion"}
            "enabled": True,
            "mode": {"motion": True},
        }
        # explicit override that matches the global value being replaced
-        self.minimal_config["cameras"]["front_door"]["birdseye"] = {
+        self.minimal_config["cameras"]["front_door"]["birdseye"] = {"mode": "motion"}
            "mode": {
                "continuous": False,
                "motion": True,
                "objects": False,
                "stationary_objects": False,
            }
        }
        config_path = self._write_config_file()
        mock_find_config.return_value = config_path
@@ -409,16 +399,7 @@ class TestConfigSetWildcardPropagation(BaseTestHttp):
                resp = client.put(
                    "/config/set",
                    json={
-                        "config_data": {
+                        "config_data": {"birdseye": {"mode": "continuous"}},
                            "birdseye": {
                                "mode": {
                                    "continuous": True,
                                    "motion": False,
                                    "objects": False,
                                    "stationary_objects": False,
                                }
                            }
                        },
                        "update_topic": "config/birdseye",
                        "requires_restart": 0,
                    },
@@ -430,7 +411,7 @@ class TestConfigSetWildcardPropagation(BaseTestHttp):
                mock_publisher.publisher.publish.assert_called_once()
                topic, settings = mock_publisher.publisher.publish.call_args[0]
                self.assertEqual(topic, "config/birdseye")
-                self.assertEqual(settings.mode.to_mqtt_payload(), "CONTINUOUS")
+                self.assertEqual(settings.mode.value, "continuous")
                published = {
                    call[0][0].camera: call[0][1]
@@ -444,12 +425,8 @@ class TestConfigSetWildcardPropagation(BaseTestHttp):
                    )
                # the override survives, the inheriting camera follows global
-                self.assertEqual(
+                self.assertEqual(published["front_door"].mode.value, "motion")
-                    published["front_door"].mode.to_mqtt_payload(), "MOTION"
+                self.assertEqual(published["back_yard"].mode.value, "continuous")
                )
                self.assertEqual(
                    published["back_yard"].mode.to_mqtt_payload(), "CONTINUOUS"
                )
        finally:
            os.unlink(config_path)
@@ -168,6 +168,29 @@ class TestHttpApp(BaseTestHttp):
            assert events[0]["id"] == id
            assert events[1]["id"] == id2
    def test_get_event_list_offset_pages_score_sort(self):
        now = datetime.now().timestamp()
        scores = [0.6, 0.9, 0.7, 0.95, 0.8]
        with AuthTestClient(self.app) as client:
            for i, score in enumerate(scores):
                super().insert_mock_event(
                    f"event-{i}", start_time=now + i, data={"score": score}
                )
            params = {"sort": "score_desc"}
            full = [e["id"] for e in client.get("/events", params=params).json()]
            paged = [
                e["id"]
                for offset in (0, 2, 4)
                for e in client.get(
                    "/events", params={**params, "limit": 2, "offset": offset}
                ).json()
            ]
            assert full == ["event-3", "event-1", "event-4", "event-2", "event-0"]
            assert paged == full
    def test_get_event_list_match_multilingual_attribute(self):
        event_id = "123456.zh"
        attribute = "中文标签"
@@ -219,6 +242,85 @@ class TestHttpApp(BaseTestHttp):
            assert len(events) == 1
            assert events[0]["id"] == event_id
    def test_events_search_offset_pages_score_sort(self):
        now = datetime.now().timestamp()
        scores = [0.6, 0.9, 0.7, 0.95, 0.8]
        ids = [f"event-{i}" for i in range(len(scores))]
        mock_embeddings = Mock()
        mock_embeddings.search_thumbnail.return_value = [
            (event_id, 0.1 * i) for i, event_id in enumerate(ids)
        ]
        self.app.frigate_config.semantic_search.enabled = True
        self.app.embeddings = mock_embeddings
        with AuthTestClient(self.app) as client:
            for i, score in enumerate(scores):
                super().insert_mock_event(
                    ids[i], start_time=now + i, data={"score": score}
                )
            params = {
                "search_type": "similarity",
                "event_id": ids[0],
                "sort": "score_desc",
            }
            paged = [
                e["id"]
                for offset in (0, 2, 4)
                for e in client.get(
                    "/events/search",
                    params={**params, "limit": 2, "offset": offset},
                ).json()
            ]
            assert paged == ["event-3", "event-1", "event-4", "event-2", "event-0"]
    def test_events_search_offset_pages_orders_ties_by_id(self):
        now = datetime.now().timestamp()
        ids = ["event-c", "event-a", "event-b"]
        mock_embeddings = Mock()
        mock_embeddings.search_thumbnail.return_value = [
            (event_id, 0.1) for event_id in ids
        ]
        self.app.frigate_config.semantic_search.enabled = True
        self.app.embeddings = mock_embeddings
        with AuthTestClient(self.app) as client:
            for i, event_id in enumerate(ids):
                super().insert_mock_event(
                    event_id, start_time=now + i, data={"score": 0.8}
                )
            for sort in ("score_desc", "relevance"):
                params = {
                    "search_type": "similarity",
                    "event_id": ids[0],
                    "sort": sort,
                }
                paged = [
                    e["id"]
                    for offset in (0, 1, 2)
                    for e in client.get(
                        "/events/search",
                        params={**params, "limit": 1, "offset": offset},
                    ).json()
                ]
                assert paged == ["event-a", "event-b", "event-c"]
    def test_event_list_rejects_negative_offset(self):
        with AuthTestClient(self.app) as client:
            response = client.get("/events", params={"offset": -5})
            assert response.status_code == 422
            response = client.get(
                "/events/search",
                params={"query": "car", "offset": -5},
            )
            assert response.status_code == 422
    def test_similarity_search_hides_unauthorized_anchor_event(self):
        mock_embeddings = Mock()
        self.app.frigate_config.semantic_search.enabled = True
@@ -1,5 +1,7 @@
 import io
 import os
 import tempfile
 import zipfile
 from unittest.mock import patch
 from frigate.jobs.export import (
@@ -1431,3 +1433,79 @@ class TestHttpExport(BaseTestHttp):
            )
        assert response.status_code == 403
    def test_download_export_case_with_multibyte_name(self):
        """A case name outside latin-1 must not break the response headers."""
        case = ExportCase.create(
            id="case_multibyte",
            name="テスト事案",
            description="",
            created_at=10,
            updated_at=10,
        )
        with tempfile.TemporaryDirectory() as tmpdir:
            video_path = os.path.join(tmpdir, "multibyte_export.mp4")
            with open(video_path, "wb") as handle:
                handle.write(b"video")
            Export.create(
                id="export_multibyte",
                camera="front_door",
                name="現場カメラ",
                date=100,
                video_path=video_path,
                thumb_path=os.path.join(tmpdir, "multibyte_export.webp"),
                in_progress=False,
                export_case=case,
            )
            with AuthTestClient(self.app) as client:
                response = client.get(f"/cases/{case.id}/download")
        assert response.status_code == 200
        # RFC 5987/6266: the UTF-8 name rides in filename*, and a latin-1 safe
        # fallback stays in filename for old clients.
        assert response.headers["content-disposition"] == (
            'attachment; filename="case_multibyte.zip"; '
            "filename*=UTF-8''%E3%83%86%E3%82%B9%E3%83%88%E4%BA%8B%E6%A1%88.zip"
        )
        archive = zipfile.ZipFile(io.BytesIO(response.content))
        assert archive.namelist() == ["現場カメラ.mp4"]
    def test_download_export_case_with_ascii_name(self):
        """An ASCII case name still gets a plain, readable filename."""
        case = ExportCase.create(
            id="case_ascii",
            name="Burglary 2026-08",
            description="",
            created_at=10,
            updated_at=10,
        )
        with tempfile.TemporaryDirectory() as tmpdir:
            video_path = os.path.join(tmpdir, "ascii_export.mp4")
            with open(video_path, "wb") as handle:
                handle.write(b"video")
            Export.create(
                id="export_ascii",
                camera="front_door",
                name="Front door",
                date=100,
                video_path=video_path,
                thumb_path=os.path.join(tmpdir, "ascii_export.webp"),
                in_progress=False,
                export_case=case,
            )
            with AuthTestClient(self.app) as client:
                response = client.get(f"/cases/{case.id}/download")
        assert response.status_code == 200
        assert (
            response.headers["content-disposition"]
            == 'attachment; filename="Burglary 2026-08.zip"; '
            "filename*=UTF-8''Burglary%202026-08.zip"
        )
@@ -0,0 +1,113 @@
 """Tests for password change authorization."""
 from fastapi import Request
 from frigate.api.auth import get_current_user, hash_password, verify_password
 from frigate.models import Event, Recordings, ReviewSegment, User
 from frigate.test.http_api.base_http_test import AuthTestClient, BaseTestHttp
 # Config carrying a custom role, which is the class of user the literal
 # "viewer" check used to let through.
 _CUSTOM_ROLE_CONFIG = {
    "mqtt": {"host": "mqtt"},
    "auth": {"roles": {"neighbor": ["front_door"]}, "hash_iterations": 10},
    "cameras": {
        "front_door": {
            "ffmpeg": {
                "inputs": [{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}]
            },
            "detect": {"height": 1080, "width": 1920, "fps": 5},
        },
    },
 }
 ADMIN_PASSWORD = "admin-real-password"
 NEW_PASSWORD = "AttackerChosenPassword123!"
 class TestUpdatePasswordAccess(BaseTestHttp):
    def setUp(self):
        super().setUp([Event, ReviewSegment, Recordings, User])
        self.minimal_config = _CUSTOM_ROLE_CONFIG
        self.app = super().create_app()
        User.insert(
            username="admin",
            password_hash=hash_password(ADMIN_PASSWORD, iterations=10),
            role="admin",
            notification_tokens=[],
        ).execute()
        async def mock_get_current_user(request: Request):
            return {
                "username": request.headers.get("remote-user"),
                "role": request.headers.get("remote-role"),
            }
        self.app.dependency_overrides[get_current_user] = mock_get_current_user
    def tearDown(self):
        self.app.dependency_overrides.clear()
        super().tearDown()
    def _change_password(self, actor: str, role: str, target: str, old_password: str):
        with AuthTestClient(self.app) as client:
            return client.put(
                f"/users/{target}/password",
                json={"password": NEW_PASSWORD, "old_password": old_password},
                headers={"remote-user": actor, "remote-role": role},
            )
    def _admin_password_unchanged(self) -> bool:
        return verify_password(ADMIN_PASSWORD, User.get_by_id("admin").password_hash)
    def test_custom_role_cannot_target_another_account(self):
        resp = self._change_password("neighbor", "neighbor", "admin", "wrong-guess")
        assert resp.status_code == 403
        assert self._admin_password_unchanged()
    def test_custom_role_cannot_target_another_account_with_correct_password(self):
        # The 403 must land before old_password is checked, so knowing the
        # target's password is not a way through
        resp = self._change_password("neighbor", "neighbor", "admin", ADMIN_PASSWORD)
        assert resp.status_code == 403
        assert self._admin_password_unchanged()
    def test_viewer_cannot_target_another_account(self):
        resp = self._change_password("viewer_user", "viewer", "admin", ADMIN_PASSWORD)
        assert resp.status_code == 403
        assert self._admin_password_unchanged()
    def test_admin_can_target_another_account(self):
        User.insert(
            username="neighbor",
            password_hash=hash_password("neighbor-password", iterations=10),
            role="neighbor",
            notification_tokens=[],
        ).execute()
        resp = self._change_password("admin", "admin", "neighbor", "")
        assert resp.status_code == 200
    def test_non_admin_can_change_own_password(self):
        User.insert(
            username="neighbor",
            password_hash=hash_password("neighbor-password", iterations=10),
            role="neighbor",
            notification_tokens=[],
        ).execute()
        resp = self._change_password(
            "neighbor", "neighbor", "neighbor", "neighbor-password"
        )
        assert resp.status_code == 200
    def test_non_admin_own_password_still_requires_old_password(self):
        User.insert(
            username="neighbor",
            password_hash=hash_password("neighbor-password", iterations=10),
            role="neighbor",
            notification_tokens=[],
        ).execute()
        resp = self._change_password("neighbor", "neighbor", "neighbor", "wrong-guess")
        assert resp.status_code == 401
@@ -240,9 +240,101 @@ class TestHttpReview(BaseTestHttp):
            assert len(response_json) == 1
            assert response_json[0]["id"] == id_reviewed
    def test_get_review_with_label_filter_matches_verified(self):
        """Test that a label filter also matches the `-verified` variant."""
        now = datetime.now().timestamp()
        with AuthTestClient(self.app) as client:
            super().insert_mock_review_segment(
                "123456.person", now, now + 2, data={"objects": ["person"]}
            )
            super().insert_mock_review_segment(
                "123456.verified", now, now + 2, data={"objects": ["person-verified"]}
            )
            super().insert_mock_review_segment(
                "123456.car", now, now + 2, data={"objects": ["car"]}
            )
            params = {
                "labels": "person",
                "after": now - 1,
                "before": now + 3,
            }
            response = client.get("/review", params=params)
            assert response.status_code == 200
            response_json = response.json()
            assert {r["id"] for r in response_json} == {
                "123456.person",
                "123456.verified",
            }
    def test_get_review_with_label_filter_does_not_match_prefix(self):
        """Test that a label filter does not match labels that only share a prefix."""
        now = datetime.now().timestamp()
        with AuthTestClient(self.app) as client:
            super().insert_mock_review_segment(
                "123456.carrot", now, now + 2, data={"objects": ["carrot"]}
            )
            params = {
                "labels": "car",
                "after": now - 1,
                "before": now + 3,
            }
            response = client.get("/review", params=params)
            assert response.status_code == 200
            assert len(response.json()) == 0
    def test_get_review_with_audio_label_filter(self):
        """Test that a label filter still matches audio labels."""
        now = datetime.now().timestamp()
        with AuthTestClient(self.app) as client:
            super().insert_mock_review_segment(
                "123456.audio", now, now + 2, data={"audio": ["speech"]}
            )
            params = {
                "labels": "speech",
                "after": now - 1,
                "before": now + 3,
            }
            response = client.get("/review", params=params)
            assert response.status_code == 200
            response_json = response.json()
            assert len(response_json) == 1
            assert response_json[0]["id"] == "123456.audio"
    ####################################################################################################################
    ###################################  GET /review/summary Endpoint   #################################################
    ####################################################################################################################
    def test_get_review_summary_label_filter_matches_verified(self):
        """Test that the summary label filter also matches the `-verified` variant."""
        with AuthTestClient(self.app) as client:
            super().insert_mock_review_segment(
                "123456.verified", data={"objects": ["person-verified"]}
            )
            super().insert_mock_review_segment(
                "123456.car", data={"objects": ["car"]}, severity=SeverityEnum.detection
            )
            params = {
                "cameras": "front_door",
                "labels": "person",
                "zones": "all",
                "timezone": "utc",
            }
            response = client.get("/review/summary", params=params)
            assert response.status_code == 200
            response_json = response.json()
            assert response_json["last24Hours"]["total_alert"] == 1
            assert response_json["last24Hours"]["total_detection"] == 0
            today_formatted = datetime.today().strftime("%Y-%m-%d")
            assert response_json[today_formatted]["total_alert"] == 1
            assert response_json[today_formatted]["total_detection"] == 0
    def test_get_review_summary_all_filters(self):
        with AuthTestClient(self.app) as client:
            super().insert_mock_review_segment("123456.random")
@@ -1,70 +1,13 @@
-"""Tests for Birdseye canvas sizing and layout behavior."""
+"""Test camera user and password cleanup."""
 import multiprocessing as mp
 import unittest
 from unittest.mock import Mock
-from frigate.config import BirdseyeModeConfig, FrigateConfig
+from frigate.config import FrigateConfig
-from frigate.output.birdseye import (
+from frigate.output.birdseye import BirdsEyeFrameManager, get_canvas_shape
    Birdseye,
    BirdseyeActivity,
    BirdsEyeFrameManager,
    get_canvas_shape,
 )
 class TestBirdseye(unittest.TestCase):
    def _build_manager(
        self, camera_dimensions: dict[str, tuple[int, int]]
    ) -> BirdsEyeFrameManager:
        config = {
            "mqtt": {"host": "mqtt"},
            "birdseye": {"width": 1280, "height": 720},
            "cameras": {},
        }
        for order, (camera, dimensions) in enumerate(
            camera_dimensions.items(), start=1
        ):
            config["cameras"][camera] = {
                "ffmpeg": {
                    "inputs": [
                        {
                            "path": f"rtsp://10.0.0.1:554/{camera}",
                            "roles": ["detect"],
                        }
                    ]
                },
                "detect": {
                    "width": dimensions[0],
                    "height": dimensions[1],
                    "fps": 5,
                },
                "birdseye": {"order": order},
            }
        return BirdsEyeFrameManager(FrigateConfig(**config), mp.Event())
    def _assert_no_overlaps(
        self, layout: list[list[tuple[str, tuple[int, int, int, int]]]]
    ):
        rectangles = [position for row in layout for _, position in row]
        for index, rect in enumerate(rectangles):
            x1, y1, width1, height1 = rect
            for other in rectangles[index + 1 :]:
                x2, y2, width2, height2 = other
                overlap = (
                    x1 < x2 + width2
                    and x2 < x1 + width1
                    and y1 < y2 + height2
                    and y2 < y1 + height1
                )
                self.assertFalse(
                    overlap,
                    msg=f"Overlapping rectangles found: {rect} and {other}",
                )
    def test_16x9(self):
        """Test 16x9 aspect ratio works as expected for birdseye."""
        width = 1280
@@ -105,212 +48,6 @@ class TestBirdseye(unittest.TestCase):
        assert canvas_width == width  # width will be the same
        assert canvas_height != height
    def test_portrait_camera_does_not_overlap_next_row(self):
        """Portrait cameras should reserve their real horizontal position on the next row."""
        manager = self._build_manager(
            {
                "cam_a": (1280, 720),
                "cam_p": (360, 640),
                "cam_b": (1280, 720),
                "cam_c": (640, 480),
            }
        )
        layout = manager.calculate_layout(["cam_a", "cam_p", "cam_b", "cam_c"], 3)
        self.assertIsNotNone(layout)
        assert layout is not None
        self._assert_no_overlaps(layout)
        cam_c = [
            position for row in layout for camera, position in row if camera == "cam_c"
        ][0]
        self.assertEqual(cam_c[0], 0)
    def test_portrait_reservation_only_applies_to_next_row(self):
        """Portrait reservations should not push later rows after the span ends."""
        manager = self._build_manager(
            {
                "cam_a": (1280, 720),
                "cam_p": (360, 640),
                "cam_b": (1280, 720),
                "cam_c": (1280, 720),
                "cam_d": (1280, 720),
                "cam_e": (1280, 720),
            }
        )
        layout = manager.calculate_layout(
            ["cam_a", "cam_p", "cam_b", "cam_c", "cam_d", "cam_e"],
            3,
        )
        self.assertIsNotNone(layout)
        assert layout is not None
        self._assert_no_overlaps(layout)
        cam_e = [
            position for row in layout for camera, position in row if camera == "cam_e"
        ][0]
        self.assertEqual(cam_e[0], 0)
    def test_multiple_portraits_reserve_distinct_ranges(self):
        """Multiple portrait cameras in one row should reserve separate spans below them."""
        manager = self._build_manager(
            {
                "cam_a": (640, 480),
                "cam_p1": (360, 640),
                "cam_p2": (360, 640),
                "cam_b": (640, 480),
                "cam_c": (1280, 720),
                "cam_d": (640, 480),
            }
        )
        layout = manager.calculate_layout(
            ["cam_a", "cam_p1", "cam_p2", "cam_b", "cam_c", "cam_d"],
            4,
        )
        self.assertIsNotNone(layout)
        assert layout is not None
        self._assert_no_overlaps(layout)
    def test_two_landscapes_then_portrait_then_two_landscapes(self):
        """A portrait after two landscapes should reserve only its own tail span."""
        manager = self._build_manager(
            {
                "cam_a": (1280, 720),
                "cam_b": (1280, 720),
                "cam_p": (360, 640),
                "cam_c": (1280, 720),
                "cam_d": (1280, 720),
            }
        )
        layout = manager.calculate_layout(
            ["cam_a", "cam_b", "cam_p", "cam_c", "cam_d"],
            3,
        )
        self.assertIsNotNone(layout)
        assert layout is not None
        self._assert_no_overlaps(layout)
        cam_c = [
            position for row in layout for camera, position in row if camera == "cam_c"
        ][0]
        cam_d = [
            position for row in layout for camera, position in row if camera == "cam_d"
        ][0]
        self.assertEqual(cam_c[0], 0)
        self.assertEqual(cam_d[0], cam_c[0] + cam_c[2])
 class TestBirdseyeActivity(unittest.TestCase):
    """Test which camera activity is included in each Birdseye mode."""
    def setUp(self):
        config = {
            "mqtt": {"enabled": False},
            "birdseye": {
                "enabled": True,
                "mode": {
                    "motion": True,
                    "objects": True,
                    "stationary_objects": True,
                },
            },
            "cameras": {
                "front": {
                    "ffmpeg": {
                        "inputs": [
                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
                        ]
                    },
                    "detect": {"height": 1080, "width": 1920, "fps": 5},
                }
            },
        }
        self.manager = BirdsEyeFrameManager(FrigateConfig(**config), mp.Event())
    def test_existing_modes_keep_their_activity_rules(self):
        continuous = BirdseyeModeConfig(continuous=True)
        motion = BirdseyeModeConfig(motion=True)
        objects = BirdseyeModeConfig(objects=True)
        no_activity = BirdseyeActivity(False, False, False)
        motion_activity = BirdseyeActivity(False, False, True)
        stationary_activity = BirdseyeActivity(False, True, False)
        active_object_activity = BirdseyeActivity(True, False, False)
        assert self.manager.camera_active(continuous, no_activity)
        assert self.manager.camera_active(motion, motion_activity)
        assert not self.manager.camera_active(motion, stationary_activity)
        assert self.manager.camera_active(objects, active_object_activity)
        assert not self.manager.camera_active(objects, stationary_activity)
    def test_modes_can_be_combined(self):
        mode = BirdseyeModeConfig(motion=True, stationary_objects=True)
        assert self.manager.camera_active(mode, BirdseyeActivity(False, False, True))
        assert self.manager.camera_active(mode, BirdseyeActivity(False, True, False))
        assert not self.manager.camera_active(
            mode, BirdseyeActivity(False, False, False)
        )
    def test_stationary_objects_are_independent_from_active_objects(self):
        stationary_objects = BirdseyeModeConfig(stationary_objects=True)
        assert self.manager.camera_active(
            stationary_objects, BirdseyeActivity(False, True, False)
        )
        assert not self.manager.camera_active(
            stationary_objects, BirdseyeActivity(True, False, False)
        )
    def test_write_data_preserves_active_and_confirms_stationary_activity(self):
        birdseye = Birdseye.__new__(Birdseye)
        birdseye.birdseye_manager = Mock()
        birdseye.birdseye_manager.update.return_value = (False, False)
        birdseye._idle_interval = None
        frame = Mock()
        birdseye.write_data(
            "front",
            [
                {"stationary": True, "false_positive": True},
                {"stationary": False, "false_positive": True},
                {"stationary": True, "false_positive": False},
            ],
            [[0, 0, 10, 10]],
            1.0,
            frame,
        )
        birdseye.birdseye_manager.update.assert_called_once_with(
            "front", BirdseyeActivity(True, True, True), 1.0, frame
        )
    def test_stationary_false_positive_does_not_activate_birdseye(self):
        birdseye = Birdseye.__new__(Birdseye)
        birdseye.birdseye_manager = Mock()
        birdseye.birdseye_manager.update.return_value = (False, False)
        birdseye._idle_interval = None
        frame = Mock()
        birdseye.write_data(
            "front",
            [{"stationary": True, "false_positive": True}],
            [],
            1.0,
            frame,
        )
        birdseye.birdseye_manager.update.assert_called_once_with(
            "front", BirdseyeActivity(False, False, False), 1.0, frame
        )
 class TestBirdseyeCameraOrder(unittest.TestCase):
    """Test that birdseye reacts to camera order changes without a restart."""
@@ -318,7 +55,7 @@ class TestBirdseyeCameraOrder(unittest.TestCase):
    def setUp(self):
        config = {
            "mqtt": {"enabled": False},
-            "birdseye": {"enabled": True, "mode": {"continuous": True}},
+            "birdseye": {"enabled": True, "mode": "continuous"},
            "cameras": {
                camera: {
                    "ffmpeg": {
@@ -1,102 +0,0 @@
 """Tests for dynamic camera config updates recreating ffmpeg commands."""
 import unittest
 from unittest.mock import patch
 from frigate.config import CameraConfig, FrigateConfig
 from frigate.config.camera.updater import (
    CameraConfigUpdateEnum,
    CameraConfigUpdateSubscriber,
 )
 from frigate.const import SUB_CACHE_TAG
 def _build_camera_config(sub_enabled: bool) -> CameraConfig:
    config = FrigateConfig(
        **{
            "mqtt": {"host": "mqtt"},
            "cameras": {
                "front_door": {
                    "ffmpeg": {
                        "inputs": [
                            {
                                "path": "rtsp://10.0.0.1:554/video",
                                "roles": ["detect", "record"],
                            },
                            {
                                "path": "rtsp://10.0.0.1:554/video2",
                                "roles": ["record_sub"],
                            },
                        ]
                    },
                    "record": {"enabled": True, "sub": {"enabled": sub_enabled}},
                }
            },
        }
    )
    return config.cameras["front_door"]
 def _has_sub_output(camera_config: CameraConfig) -> bool:
    return any(
        SUB_CACHE_TAG in part for c in camera_config.ffmpeg_cmds for part in c["cmd"]
    )
 class TestRecordUpdateRecreatesFfmpegCmds(unittest.TestCase):
    def setUp(self):
        # avoid binding a real ZMQ socket; updates are fed directly through
        # the mocked subscriber below
        patcher = patch("frigate.config.camera.updater.ConfigSubscriber")
        patcher.start()
        self.addCleanup(patcher.stop)
    def _push_record_update(
        self, subscriber: CameraConfigUpdateSubscriber, record_config
    ) -> None:
        subscriber.subscriber.check_for_update.side_effect = [
            ("config/cameras/front_door/record", record_config),
            (None, None),
        ]
        subscriber.check_for_updates()
    def test_enabling_sub_recreates_ffmpeg_cmds(self):
        camera_config = _build_camera_config(sub_enabled=False)
        subscriber = CameraConfigUpdateSubscriber(
            None, {"front_door": camera_config}, [CameraConfigUpdateEnum.record]
        )
        assert not _has_sub_output(camera_config)
        self._push_record_update(
            subscriber, _build_camera_config(sub_enabled=True).record
        )
        assert _has_sub_output(camera_config)
    def test_disabling_sub_recreates_ffmpeg_cmds(self):
        camera_config = _build_camera_config(sub_enabled=True)
        subscriber = CameraConfigUpdateSubscriber(
            None, {"front_door": camera_config}, [CameraConfigUpdateEnum.record]
        )
        assert _has_sub_output(camera_config)
        self._push_record_update(
            subscriber, _build_camera_config(sub_enabled=False).record
        )
        assert not _has_sub_output(camera_config)
    def test_unchanged_record_update_keeps_existing_cmds(self):
        camera_config = _build_camera_config(sub_enabled=False)
        subscriber = CameraConfigUpdateSubscriber(
            None, {"front_door": camera_config}, [CameraConfigUpdateEnum.record]
        )
        cmds_before = camera_config.ffmpeg_cmds
        # neither enabled_in_config nor sub.enabled changed, so the
        # commands should not be rebuilt
        self._push_record_update(
            subscriber, _build_camera_config(sub_enabled=False).record
        )
        assert camera_config.ffmpeg_cmds is cmds_before
@@ -1,227 +0,0 @@
 """Regression tests for runtime camera add and delete handling."""
 import asyncio
 import threading
 import unittest
 from types import SimpleNamespace
 from unittest.mock import MagicMock
 # LicensePlatePostProcessor is imported via the maintainer rather than from
 # data_processing.post.license_plate, which circularly imports back through
 # frigate.embeddings before that package finishes initializing
 from frigate.embeddings.maintainer import (
    EmbeddingMaintainer,
    LicensePlatePostProcessor,
 )
 from frigate.ptz.autotrack import PtzAutoTracker
 from frigate.review.maintainer import ReviewSegmentMaintainer
 from frigate.track.object_processing import TrackedObjectProcessor
 def _make_processor() -> TrackedObjectProcessor:
    """Build a processor with no cameras, bypassing __init__."""
    processor = TrackedObjectProcessor.__new__(TrackedObjectProcessor)
    processor.camera_states = {}
    processor.camera_states_lock = threading.Lock()
    processor.config = SimpleNamespace(cameras={})
    processor.event_sender = MagicMock()
    processor.detection_publisher = MagicMock()
    processor.ongoing_manual_events = {}
    return processor
 class TestObjectProcessorUnknownCamera(unittest.TestCase):
    def test_save_lpr_snapshot_ignores_unknown_camera(self):
        processor = _make_processor()
        # 1x1 png, base64; decoding must not be what fails
        payload = (
            "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8z8BQDwAEhQGAhKmMIQAAAABJRU5ErkJggg==",
            "1234.5-abcdef",
            "deleted_cam",
        )
        processor.save_lpr_snapshot(payload)
        processor.event_sender.publish.assert_not_called()
    def test_create_manual_event_ignores_unknown_camera(self):
        processor = _make_processor()
        payload = (
            1234.5,
            "deleted_cam",
            "person",
            "1234.5-abcdef",
            True,
            0.9,
            None,
            None,
            "api",
            False,
            None,
        )
        processor.create_manual_event(payload)
        processor.event_sender.publish.assert_not_called()
        self.assertEqual(processor.ongoing_manual_events, {})
    def test_create_lpr_event_ignores_unknown_camera(self):
        processor = _make_processor()
        payload = (
            1234.5,
            "deleted_cam",
            "license_plate",
            "1234.5-abcdef",
            True,
            0.9,
            None,
            "ABC123",
        )
        processor.create_lpr_event(payload)
        processor.event_sender.publish.assert_not_called()
        self.assertEqual(processor.ongoing_manual_events, {})
    def test_create_manual_event_ignores_camera_added_but_not_yet_drained(self):
        """The add window: present in config.cameras, absent from camera_states.
        debug_replay writes the camera into the shared config before publishing
        add, so a guard on config.cameras passes here and falls through to
        camera_states. This test fails against such a guard.
        """
        processor = _make_processor()
        processor.config = SimpleNamespace(
            cameras={
                "new_cam": SimpleNamespace(
                    record=SimpleNamespace(event_pre_capture=5, enabled=True)
                )
            }
        )
        payload = (
            1234.5,
            "new_cam",
            "person",
            "1234.5-abcdef",
            True,
            0.9,
            None,
            None,
            "api",
            False,
            None,
        )
        processor.create_manual_event(payload)
        processor.event_sender.publish.assert_not_called()
 class TestEmbeddingsUnknownCamera(unittest.TestCase):
    def _make_maintainer(self) -> EmbeddingMaintainer:
        maintainer = EmbeddingMaintainer.__new__(EmbeddingMaintainer)
        maintainer.config = SimpleNamespace(cameras={})
        maintainer.event_end_subscriber = MagicMock()
        maintainer.realtime_processors = [MagicMock()]
        # spec is required: the dispatch loop is a chain of isinstance checks,
        # and a bare MagicMock matches none of them, so the crashing branch
        # would never run and the test would pass against unfixed code
        maintainer.post_processors = [MagicMock(spec=LicensePlatePostProcessor)]
        maintainer.detected_license_plates = {"1234.5-abcdef": {"obj_data": {}}}
        maintainer.recordings_available_through = {"deleted_cam": 1234.5}
        maintainer.event_metadata_publisher = MagicMock()
        return maintainer
    def test_process_finalized_skips_unknown_camera(self):
        maintainer = self._make_maintainer()
        # updated_db=False bypasses the Event.get branch, which would hit the
        # database and mask the KeyError this test is about
        maintainer.event_end_subscriber.check_for_update.side_effect = [
            ("1234.5-abcdef", "deleted_cam", False),
            None,
        ]
        maintainer._process_finalized()
        maintainer.post_processors[0].process_data.assert_not_called()
    def test_process_finalized_still_expires_realtime_state(self):
        """The guard must not skip per-event cleanup, only post processing."""
        maintainer = self._make_maintainer()
        maintainer.event_end_subscriber.check_for_update.side_effect = [
            ("1234.5-abcdef", "deleted_cam", False),
            None,
        ]
        maintainer._process_finalized()
        maintainer.realtime_processors[0].expire_object.assert_called_once_with(
            "1234.5-abcdef", "deleted_cam"
        )
    def test_expire_dedicated_lpr_drops_entry_for_unknown_camera(self):
        maintainer = self._make_maintainer()
        maintainer.detected_license_plates = {
            "1234.5-abcdef": {"camera": "deleted_cam", "last_seen": 1.0}
        }
        maintainer._expire_dedicated_lpr()
        self.assertEqual(maintainer.detected_license_plates, {})
 class TestReviewMaintainerRemoval(unittest.TestCase):
    def test_camera_removal_ends_segment_and_clears_state(self):
        maintainer = ReviewSegmentMaintainer.__new__(ReviewSegmentMaintainer)
        maintainer.active_review_segments = {"deleted_cam": MagicMock()}
        maintainer.indefinite_events = {"deleted_cam": {"1234.5-abcdef": 1.0}}
        maintainer.forcibly_end_segment = MagicMock()
        maintainer._handle_camera_removed("deleted_cam")
        maintainer.forcibly_end_segment.assert_called_once_with("deleted_cam")
        self.assertNotIn("deleted_cam", maintainer.indefinite_events)
 class TestAutotrackerMoveQueue(unittest.TestCase):
    def test_move_queue_drops_move_for_removed_camera(self):
        tracker = PtzAutoTracker.__new__(PtzAutoTracker)
        tracker.stop_event = MagicMock()
        # one pass through the loop, then stop
        tracker.stop_event.is_set.side_effect = [False, True]
        tracker.ptz_metrics = {}
        tracker.move_queues = {"deleted_cam": asyncio.Queue()}
        tracker.move_queue_locks = {"deleted_cam": asyncio.Lock()}
        tracker.onvif = MagicMock()
        tracker.config = SimpleNamespace(cameras={})
        tracker.move_queues["deleted_cam"].put_nowait((1234.5, 0.1, 0.1, 0.0))
        asyncio.run(tracker._process_move_queue("deleted_cam"))
        tracker.onvif._move_relative.assert_not_called()
 class TestCameraStateAccessors(unittest.TestCase):
    def test_get_camera_state_returns_none_for_unknown_camera(self):
        processor = _make_processor()
        self.assertIsNone(processor.get_camera_state("deleted_cam"))
    def test_get_camera_states_returns_a_snapshot_not_a_view(self):
        """A live values() view raises RuntimeError if the writer pops mid-iteration."""
        processor = _make_processor()
        processor.camera_states = {"one": MagicMock(), "two": MagicMock()}
        states = processor.get_camera_states()
        processor.camera_states.pop("one")
        self.assertEqual(len(states), 2)
    def test_get_current_frame_time_is_zero_for_unknown_camera(self):
        processor = _make_processor()
        self.assertEqual(processor.get_current_frame_time("deleted_cam"), 0.0)
@@ -1,14 +1,13 @@
 import json
 import os
 import unittest
 from copy import deepcopy
 from unittest.mock import patch
 import numpy as np
 from pydantic import ValidationError
 from ruamel.yaml.constructor import DuplicateKeyError
-from frigate.config import FrigateConfig, RetainModeEnum
+from frigate.config import BirdseyeModeEnum, FrigateConfig
 from frigate.const import MODEL_CACHE_DIR
 from frigate.detectors import DetectorTypeEnum
 from frigate.util.builtin import deep_merge
@@ -171,7 +170,7 @@ class TestConfig(unittest.TestCase):
    def test_override_birdseye(self):
        config = {
            "mqtt": {"host": "mqtt"},
-            "birdseye": {"enabled": True, "mode": {"continuous": True}},
+            "birdseye": {"enabled": True, "mode": "continuous"},
            "cameras": {
                "back": {
                    "ffmpeg": {
@@ -184,30 +183,19 @@ class TestConfig(unittest.TestCase):
                        "width": 1920,
                        "fps": 5,
                    },
-                    "birdseye": {
+                    "birdseye": {"enabled": False, "mode": "motion"},
                        "enabled": False,
                        "mode": {"continuous": False, "motion": True},
                    },
                }
            },
        }
        frigate_config = FrigateConfig(**config)
        assert not frigate_config.cameras["back"].birdseye.enabled
-        mode = frigate_config.cameras["back"].birdseye.mode
+        assert frigate_config.cameras["back"].birdseye.mode is BirdseyeModeEnum.motion
        assert mode.motion
        assert not mode.continuous
        assert not mode.objects
        assert not mode.stationary_objects
    def test_override_birdseye_non_inheritable(self):
        config = {
            "mqtt": {"host": "mqtt"},
-            "birdseye": {
+            "birdseye": {"enabled": True, "mode": "continuous", "height": 1920},
                "enabled": True,
                "mode": {"continuous": True},
                "height": 1920,
            },
            "cameras": {
                "back": {
                    "ffmpeg": {
@@ -230,7 +218,7 @@ class TestConfig(unittest.TestCase):
    def test_inherit_birdseye(self):
        config = {
            "mqtt": {"host": "mqtt"},
-            "birdseye": {"enabled": True, "mode": {"continuous": True}},
+            "birdseye": {"enabled": True, "mode": "continuous"},
            "cameras": {
                "back": {
                    "ffmpeg": {
@@ -249,89 +237,9 @@ class TestConfig(unittest.TestCase):
        frigate_config = FrigateConfig(**config)
        assert frigate_config.cameras["back"].birdseye.enabled
-        mode = frigate_config.cameras["back"].birdseye.mode
+        assert (
-        assert mode.continuous
+            frigate_config.cameras["back"].birdseye.mode is BirdseyeModeEnum.continuous
-        assert not mode.motion
+        )
        assert not mode.objects
        assert not mode.stationary_objects
    def test_combine_birdseye_activity_types(self):
        config = {
            **self.minimal,
            "birdseye": {
                "mode": {
                    "motion": True,
                    "stationary_objects": True,
                }
            },
        }
        frigate_config = FrigateConfig(**config)
        mode = frigate_config.cameras["back"].birdseye.mode
        assert mode.motion
        assert mode.stationary_objects
        assert not mode.continuous
        assert not mode.objects
    def test_birdseye_requires_an_activity_type(self):
        config = {
            **self.minimal,
            "birdseye": {
                "mode": {
                    "continuous": False,
                    "motion": False,
                    "objects": False,
                    "stationary_objects": False,
                }
            },
        }
        with self.assertRaisesRegex(
            ValidationError, "must enable at least one Birdseye activity type"
        ):
            FrigateConfig(**config)
    def test_camera_can_disable_an_inherited_activity_type(self):
        config = {
            **self.minimal,
            "birdseye": {"mode": {"motion": True, "objects": True}},
        }
        config["cameras"]["back"]["birdseye"] = {"mode": {"motion": False}}
        frigate_config = FrigateConfig(**config)
        mode = frigate_config.cameras["back"].birdseye.mode
        assert not mode.motion
        assert mode.objects
    def test_profile_must_leave_an_activity_type_enabled(self):
        config = {
            **self.minimal,
            "profiles": {"away": {"friendly_name": "Away"}},
            "birdseye": {"mode": {"objects": True}},
        }
        config["cameras"]["back"]["profiles"] = {
            "away": {"birdseye": {"mode": {"objects": False}}}
        }
        with self.assertRaisesRegex(
            ValidationError, "must enable at least one Birdseye activity type"
        ):
            FrigateConfig(**config)
    def test_camera_birdseye_activity_types_override_global_values(self):
        config = {
            **self.minimal,
            "birdseye": {"mode": {"motion": True, "objects": True}},
        }
        config["cameras"]["back"]["birdseye"] = {
            "mode": {"motion": False, "stationary_objects": True}
        }
        frigate_config = FrigateConfig(**config)
        mode = frigate_config.cameras["back"].birdseye.mode
        assert not mode.motion
        assert mode.objects
        assert mode.stationary_objects
    def test_override_tracked_objects(self):
        config = {
@@ -969,162 +877,6 @@ class TestConfig(unittest.TestCase):
        assert len(ffmpeg_cmds) == 1
        assert "clips" not in ffmpeg_cmds[0]["roles"]
    def test_record_sub_cmd_writes_sub_cache_path(self):
        config = {
            "mqtt": {"host": "mqtt"},
            "cameras": {
                "back": {
                    "ffmpeg": {
                        "inputs": [
                            {
                                "path": "rtsp://10.0.0.1:554/video",
                                "roles": ["detect", "record"],
                            },
                            {
                                "path": "rtsp://10.0.0.1:554/video2",
                                "roles": ["record_sub"],
                            },
                        ]
                    },
                    "record": {"enabled": True, "sub": {"enabled": True}},
                }
            },
        }
        frigate_config = FrigateConfig(**config)
        cmds = frigate_config.cameras["back"].ffmpeg_cmds
        sub_cmds = [c for c in cmds if "record_sub" in c["roles"]]
        assert len(sub_cmds) == 1
        joined = " ".join(sub_cmds[0]["cmd"])
        assert "back@sub@" in joined
    def test_record_sub_disabled_no_sub_cache_path(self):
        config = {
            "mqtt": {"host": "mqtt"},
            "cameras": {
                "back": {
                    "ffmpeg": {
                        "inputs": [
                            {
                                "path": "rtsp://10.0.0.1:554/video",
                                "roles": ["detect", "record"],
                            },
                        ]
                    },
                    "record": {"enabled": True, "sub": {"enabled": False}},
                }
            },
        }
        frigate_config = FrigateConfig(**config)
        cmds = frigate_config.cameras["back"].ffmpeg_cmds
        assert all("@sub@" not in " ".join(c["cmd"]) for c in cmds)
    def _sub_record_config(self, ffmpeg_extra: dict | None = None) -> dict:
        return {
            "mqtt": {"host": "mqtt"},
            "cameras": {
                "back": {
                    "ffmpeg": {
                        "inputs": [
                            {
                                "path": "rtsp://10.0.0.1:554/video",
                                "roles": ["detect", "record"],
                            },
                            {
                                "path": "rtsp://10.0.0.1:554/video2",
                                "roles": ["record_sub"],
                            },
                        ],
                        **(ffmpeg_extra or {}),
                    },
                    "record": {"enabled": True, "sub": {"enabled": True}},
                }
            },
        }
    def _sub_record_cmd(self, config: dict) -> str:
        cmds = FrigateConfig(**config).cameras["back"].ffmpeg_cmds
        sub_cmds = [c for c in cmds if "record_sub" in c["roles"]]
        assert len(sub_cmds) == 1
        return " ".join(sub_cmds[0]["cmd"])
    def test_record_sub_output_args_inherit_record(self):
        config = self._sub_record_config(
            {"output_args": {"record": "preset-record-generic-audio-copy"}}
        )
        cmd = self._sub_record_cmd(config)
        # the customized record args, not the stock aac default
        assert "-c copy" in cmd
        assert "-c:a aac" not in cmd
    def test_record_sub_output_args_override_record(self):
        config = self._sub_record_config(
            {
                "output_args": {
                    "record": "preset-record-generic-audio-aac",
                    "record_sub": "preset-record-generic",
                }
            }
        )
        cmd = self._sub_record_cmd(config)
        assert "-c copy -an" in cmd
        assert "-c:a aac" not in cmd
    def test_record_output_args_unaffected_by_record_sub(self):
        config = self._sub_record_config(
            {
                "output_args": {
                    "record": "preset-record-generic-audio-aac",
                    "record_sub": "preset-record-generic",
                }
            }
        )
        cmds = FrigateConfig(**config).cameras["back"].ffmpeg_cmds
        record_cmd = " ".join(next(c for c in cmds if "record" in c["roles"])["cmd"])
        assert "-c:a aac" in record_cmd
    def test_record_sub_manual_output_args(self):
        config = self._sub_record_config(
            {
                "output_args": {
                    "record_sub": "-f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v copy -c:a aac -ar 16000"
                }
            }
        )
        assert "-ar 16000" in self._sub_record_cmd(config)
    def test_fails_on_bad_record_sub_segment_time(self):
        config = self._sub_record_config(
            {
                "output_args": {
                    "record_sub": "-f segment -segment_time 70 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c copy -an"
                }
            }
        )
        self.assertRaisesRegex(
            ValueError,
            "segment_time",
            lambda: FrigateConfig(**config).cameras,
        )
    def test_record_sub_segment_time_not_checked_when_disabled(self):
        config = self._sub_record_config(
            {
                "output_args": {
                    "record_sub": "-f segment -segment_time 70 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c copy -an"
                }
            }
        )
        config["cameras"]["back"]["record"]["sub"]["enabled"] = False
        FrigateConfig(**config).cameras
    def test_max_disappeared_default(self):
        config = {
            "mqtt": {"host": "mqtt"},
@@ -1367,44 +1119,6 @@ class TestConfig(unittest.TestCase):
        self.assertRaises(ValueError, lambda: FrigateConfig(**config))
    def test_record_sub_config_defaults(self):
        config = FrigateConfig(**self.minimal)
        record = config.cameras["back"].record
        assert record.sub.enabled is False
        assert record.sub.continuous.days == 0
        assert record.sub.alerts.mode == RetainModeEnum.motion
    def test_record_sub_enabled_requires_role(self):
        config = deepcopy(self.minimal)
        config["cameras"]["back"]["ffmpeg"]["inputs"] = [
            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect", "record"]},
        ]
        config["cameras"]["back"]["record"] = {
            "enabled": True,
            "sub": {"enabled": True},
        }
        # no record_sub role assigned -> must raise
        self.assertRaisesRegex(
            ValueError,
            "record_sub is not assigned",
            lambda: FrigateConfig(**config),
        )
    def test_record_sub_role_accepted(self):
        config = deepcopy(self.minimal)
        config["cameras"]["back"]["ffmpeg"]["inputs"] = [
            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect", "record"]},
            {"path": "rtsp://10.0.0.1:554/video2", "roles": ["record_sub"]},
        ]
        config["cameras"]["back"]["record"] = {
            "enabled": True,
            "sub": {"enabled": True, "continuous": {"days": 30}},
        }
        parsed = FrigateConfig(**config)
        assert parsed.cameras["back"].record.sub.continuous.days == 30
    def test_works_on_missing_role_multiple_cams(self):
        config = {
            "mqtt": {"host": "mqtt"},
@@ -9,6 +9,7 @@ from unittest.mock import MagicMock, patch
 from frigate.debug_replay import DebugReplayManager
 from frigate.jobs.debug_replay import (
    DebugReplayJob,
    NoRecordingsError,
    RecordingDebugReplaySource,
    cancel_debug_replay_job,
    get_active_runner,
@@ -129,7 +130,7 @@ class TestStartDebugReplayJob(unittest.TestCase):
        empty_qs = MagicMock()
        empty_qs.count.return_value = 0
        with patch("frigate.jobs.debug_replay.query_recordings", return_value=empty_qs):
-            with self.assertRaises(ValueError):
+            with self.assertRaises(NoRecordingsError):
                start_debug_replay_job(
                    source=RecordingDebugReplaySource(
                        source_camera="front",
@@ -0,0 +1,88 @@
 """Tests for ONNX Runtime session option selection."""
 import unittest
 from unittest.mock import MagicMock, patch
 import numpy as np
 import onnxruntime as ort
 from frigate.detectors.detection_runners import (
    CudaGraphRunner,
    get_ort_session_options,
 )
 from frigate.detectors.detector_config import ModelTypeEnum
 from frigate.embeddings.types import EnrichmentModelTypeEnum
 class TestGetOrtSessionOptions(unittest.TestCase):
    def test_jina_v2_uses_extended(self):
        """jina-clip-v2 returns an identical vector for every image on the CUDA
        execution provider at anything below EXTENDED."""
        options = get_ort_session_options(EnrichmentModelTypeEnum.jina_v2.value)
        self.assertIsNotNone(options)
        self.assertEqual(
            options.graph_optimization_level,
            ort.GraphOptimizationLevel.ORT_ENABLE_EXTENDED,
        )
    def test_jina_v1_uses_basic(self):
        options = get_ort_session_options(EnrichmentModelTypeEnum.jina_v1.value)
        self.assertIsNotNone(options)
        self.assertEqual(
            options.graph_optimization_level,
            ort.GraphOptimizationLevel.ORT_ENABLE_BASIC,
        )
    def test_other_models_use_defaults(self):
        for model_type in [
            None,
            EnrichmentModelTypeEnum.paddleocr.value,
            EnrichmentModelTypeEnum.arcface.value,
            ModelTypeEnum.rfdetr.value,
        ]:
            with self.subTest(model_type=model_type):
                self.assertIsNone(get_ort_session_options(model_type))
 class TestCudaGraphRunner(unittest.TestCase):
    """CUDA graph capture fails if the arena has to allocate during capture, so
    the session is warmed up with capture disabled before the first real run."""
    def setUp(self):
        self.session = MagicMock()
        self.session.get_outputs.return_value = [MagicMock(name="output")]
        self.io_binding = self.session.io_binding.return_value
        self.input = {"images": np.zeros((1, 3, 320, 320), np.float32)}
    def _annotations(self) -> list[str | None]:
        """Graph annotation id passed with each run, None when unset."""
        annotations = []
        for call in self.session.run_with_iobinding.call_args_list:
            try:
                annotations.append(call.args[1].get_run_config_entry("gpu_graph_id"))
            except RuntimeError:
                annotations.append(None)
        return annotations
    def test_first_run_warms_up_with_capture_disabled(self):
        with patch.object(ort.OrtValue, "ortvalue_from_numpy"):
            CudaGraphRunner(self.session, 0).run(self.input)
        self.assertEqual(
            self._annotations(),
            ["-1"] * CudaGraphRunner.GRAPH_FREE_WARMUP_RUNS + [None],
        )
    def test_later_runs_allow_capture(self):
        with patch.object(ort.OrtValue, "ortvalue_from_numpy"):
            runner = CudaGraphRunner(self.session, 0)
            runner.run(self.input)
            self.session.run_with_iobinding.reset_mock()
            runner.run(self.input)
        self.assertEqual(self._annotations(), [None])
        runner._input_ortvalue.update_inplace.assert_called_once()
@@ -8,7 +8,6 @@ from unittest.mock import MagicMock, patch
 from frigate.app import FrigateApp
 from frigate.comms.dispatcher import Dispatcher
 from frigate.comms.runtime_state import RuntimeStatePersistence
 from frigate.config import BirdseyeModeConfig
 def _make_camera_mock(
@@ -52,58 +51,6 @@ def _build_dispatcher(cameras: dict[str, MagicMock]) -> Dispatcher:
        return Dispatcher(config, config_updater, onvif, ptz_metrics, communicators)
 class TestBirdseyeModeCommands(unittest.TestCase):
    """Verify Birdseye mode commands use the boolean mode contract."""
    def setUp(self) -> None:
        self.camera = _make_camera_mock()
        self.camera.birdseye.enabled = True
        self.dispatcher = _build_dispatcher({"front_door": self.camera})
        self.dispatcher.publish = MagicMock()
    def test_combined_modes_are_accepted(self) -> None:
        self.dispatcher._on_birdseye_mode_command(
            "front_door", "STATIONARY_OBJECTS,MOTION"
        )
        self.assertEqual(
            self.camera.birdseye.mode,
            BirdseyeModeConfig(motion=True, stationary_objects=True),
        )
        self.dispatcher.config_updater.publish_update.assert_called_once()
        self.dispatcher.publish.assert_called_once_with(
            "front_door/birdseye_mode/state",
            "MOTION,STATIONARY_OBJECTS",
            retain=True,
        )
    def test_single_activity_type_is_accepted(self) -> None:
        self.dispatcher._on_birdseye_mode_command("front_door", "OBJECTS")
        self.assertEqual(
            self.camera.birdseye.mode,
            BirdseyeModeConfig(objects=True),
        )
        self.dispatcher.publish.assert_called_once_with(
            "front_door/birdseye_mode/state", "OBJECTS", retain=True
        )
    def test_unknown_mode_is_rejected(self) -> None:
        for payload in (
            "UNKNOWN",
            "motion",
            "MOTION_OBJECTS",
            "NONE",
            "NONE,MOTION",
            "MOTION,MOTION",
            "MOTION,",
        ):
            with self.subTest(payload=payload):
                self.dispatcher._on_birdseye_mode_command("front_door", payload)
        self.dispatcher.config_updater.publish_update.assert_not_called()
 class TestRestoreRuntimeState(unittest.TestCase):
    """Verify replay routes through handlers and tolerates missing entries."""
@@ -1,127 +0,0 @@
 """Tests for sub stream retention extending event clip lifetimes."""
 import datetime
 import unittest
 from unittest.mock import MagicMock
 from playhouse.sqlite_ext import SqliteExtDatabase
 from frigate.config import FrigateConfig
 from frigate.events.cleanup import EventCleanup
 from frigate.models import Event, Timeline
 class TestEventCleanupSubRetention(unittest.TestCase):
    def setUp(self):
        # in-memory database keeps these tests isolated from the shared
        # on-disk test.db used by the http api tests
        self.db = SqliteExtDatabase(":memory:")
        models = [Event, Timeline]
        self.db.bind(models)
        self.db.create_tables(models)
    def tearDown(self):
        self.db.close()
    def _build_cleanup(self, record_config: dict) -> EventCleanup:
        config = FrigateConfig(
            **{
                "mqtt": {"host": "mqtt"},
                "cameras": {
                    "front_door": {
                        "ffmpeg": {
                            "inputs": [
                                {
                                    "path": "rtsp://10.0.0.1:554/video",
                                    "roles": ["detect", "record"],
                                },
                                {
                                    "path": "rtsp://10.0.0.1:554/video2",
                                    "roles": ["record_sub"],
                                },
                            ]
                        },
                        "record": record_config,
                    }
                },
            }
        )
        return EventCleanup(config, MagicMock(), MagicMock())
    def _insert_event(self, id: str, age_days: float, severity: str = "alert") -> None:
        end_time = (
            datetime.datetime.now() - datetime.timedelta(days=age_days)
        ).timestamp()
        Event.create(
            id=id,
            label="person",
            camera="front_door",
            start_time=end_time - 10,
            end_time=end_time,
            top_score=0.9,
            score=0.9,
            false_positive=False,
            zones=[],
            thumbnail="",
            has_clip=True,
            has_snapshot=False,
            region=[],
            box=[],
            area=0,
            retain_indefinitely=False,
            plus_id="",
            model_hash="",
            detector_type="cpu",
            model_type="ssd",
            data={"max_severity": severity},
        )
    def test_sub_alerts_days_extends_event_clip_retention(self):
        # a 20-day-old alert event keeps its clip for the 60 day sub window
        # so Explore stays coherent with the surviving sub recordings
        cleanup = self._build_cleanup(
            {
                "enabled": True,
                "alerts": {"retain": {"days": 10}},
                "sub": {"enabled": True, "alerts": {"days": 60}},
            }
        )
        self._insert_event("e1", 20)
        expired = cleanup.expire_clips()
        assert "e1" not in expired
        assert Event.get(Event.id == "e1").has_clip is True
    def test_event_clip_expires_when_sub_disabled(self):
        # with sub recording disabled, the 20-day-old alert event expires
        # under the 10 day main alerts retention exactly as before
        cleanup = self._build_cleanup(
            {
                "enabled": True,
                "alerts": {"retain": {"days": 10}},
                "sub": {"enabled": False, "alerts": {"days": 60}},
            }
        )
        self._insert_event("e1", 20)
        expired = cleanup.expire_clips()
        assert "e1" in expired
        assert Event.get(Event.id == "e1").has_clip is False
    def test_sub_detections_days_extends_event_clip_retention(self):
        # detection severity uses the sub detections window
        cleanup = self._build_cleanup(
            {
                "enabled": True,
                "detections": {"retain": {"days": 10}},
                "sub": {"enabled": True, "detections": {"days": 60}},
            }
        )
        self._insert_event("e1", 20, severity="detection")
        expired = cleanup.expire_clips()
        assert "e1" not in expired
        assert Event.get(Event.id == "e1").has_clip is True
--- a/Show More
+++ b/Show More