administrator/frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2026-10-06 23:02:49 +03:00
Code Issues Actions 3 Packages Projects Releases Wiki Activity

Merge remote-tracking branch 'origin/master' into dev

Browse Source
This commit is contained in:
Blake Blackshear
2026-10-02 06:39:23 -05:00
parent d1cb8395ea 321e78e65b
commit 9b02a077e9
57 changed files with 1242 additions and 333 deletions
Download Patch File Download Diff File Expand all files Collapse all files
docs/data/object_detectors_models.yaml
+32
View File
@@ -824,6 +824,38 @@ cpu:
models:
- devices:
- cpu:3
xdna2:
title: AMD XDNA2
models:
- key: yolov9
label: YOLOv9
recommended: true
download: |-
Prepare the model using the frigate-xdna setup instructions linked above. For local YOLO models, Frigate must have access to the same ONNX file bytes as the sidecar. The example below uses YOLOv9-C at 320x320. Frigate+ models may instead use the same `plus://MODEL_ID` in Frigate and the sidecar.
ui: |-
Navigate to **Settings > System > Detection models** and add a model. The ZMQ endpoint is not reported by the hardware probe, so set `devices` to `zmq:tcp://xdna:5555` in YAML. Then, on the same model, open the **Custom Model** tab and configure:
| Field | Value |
| ---------------------------------------- | ------------------------------------------ |
| **Custom object detector model path** | `/config/models/yolov9-c-320.onnx` |
| **Label map for custom object detector** | `/labelmap/coco-80.txt` |
| **Object detection model input width** | `320` |
| **Object detection model input height** | `320` |
| **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
| **Model Input Tensor Shape** | `nchw` |
| **Model Input D Type** | `float` |
| **Object Detection Model Type** | `yolo-generic` |
yaml: |-
models:
- devices:
- zmq:tcp://xdna:5555
model_type: yolo-generic
width: 320
height: 320
input_tensor: nchw
input_dtype: float
path: /config/models/yolov9-c-320.onnx
labelmap_path: /labelmap/coco-80.txt
memryx:
title: MemryX
models:
docs/docs/configuration/advanced/reference.md
+2 -2
View File
@@ -294,9 +294,9 @@ ffmpeg:
# Optional: output args for detect streams (default: shown below)
detect: -threads 2 -f rawvideo -pix_fmt yuv420p
# Optional: output args for record streams (default: shown below)
record: preset-record-generic
record: preset-record-generic-audio-aac
# Optional: output args for sub stream record streams (default: the record output args above)
# record_sub: preset-record-generic
# record_sub: preset-record-generic-audio-aac
# Optional: Time in seconds to wait before ffmpeg retries connecting to the camera. (default: shown below)
# If set too low, frigate will retry a connection to the camera's stream too frequently, using up the limited streams some cameras can allow at once
# If set too high, then if a ffmpeg crash or camera stream timeout occurs, you could potentially lose up to a maximum of retry_interval second(s) of footage
docs/docs/configuration/license_plate_recognition.md
+4 -4
View File
@@ -378,10 +378,10 @@ Navigate to <NavPath path="Settings > Camera configuration > Object detection" /
Navigate to <NavPath path="Settings > Camera configuration > Objects" />.
| Field | Description |
| ---------------------------------------------- | ------------------- |
| **Objects to track** | Add `license_plate` |
| **Object filters > License Plate > Threshold** | Set to `0.7` |
| Field | Description |
| --------------------------------------------------------- | ------------------- |
| **Objects to track** | Add `license_plate` |
| **Object filters > License Plate > Confidence threshold** | Set to `0.7` |
Navigate to <NavPath path="Settings > Camera configuration > Motion detection" />.
docs/docs/configuration/object_detectors.md
+23
View File
@@ -30,6 +30,7 @@ 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**
@@ -567,6 +568,28 @@ When using CPU detectors, you can add one CPU detector per camera. Adding more d
# Community Supported Detectors
## AMD XDNA2
AMD Ryzen AI / XDNA2 NPUs can be used through the community-maintained
[frigate-xdna](https://github.com/mitchins/frigate-xdna) detector sidecar.
The sidecar runs separately from Frigate and connects using Frigate's ZMQ
detector interface.
Currently qualified on **Ryzen AI Max 300 / Strix Halo**. Other XDNA2 devices
are not yet qualified; XDNA1 is unsupported.
Follow the frigate-xdna setup instructions to prepare and start the sidecar
before starting Frigate.
### Configuration {#configuration-xdna2}
Using the detector config below will connect Frigate to the sidecar:
<ModelConfigDropdown detectorTitle="AMD XDNA2" models={objectDetectorsModels.xdna2.models} />
The example assumes Frigate and the sidecar share a Docker network where the
sidecar is named `xdna`.
## MemryX MX3
This detector is available for use with the MemryX MX3 accelerator M.2 module. Frigate supports the MX3 on compatible hardware platforms, providing efficient and high-performance object detection.
docs/docs/configuration/object_filters.md
+10 -10
View File
@@ -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 |
| --------------------------------------- | ---------------------------------------------------------------- |
| **Object filters > Person > Min Score** | Minimum score for a single detection to initiate tracking |
| **Object filters > Person > Threshold** | Minimum computed (median) score to be considered a true positive |
| Field | Description |
| -------------------------------------------------- | ---------------------------------------------------------------- |
| **Object filters > Person > Minimum confidence** | Minimum score for a single detection to initiate tracking |
| **Object filters > Person > Confidence threshold** | Minimum computed (median) score to be considered a true positive |
To override score filters for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Objects" /> and select the camera.
@@ -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 |
| --------------------------------------- | ------------------------------------------------------------------------ |
| **Object filters > Person > Min Area** | Minimum bounding box area in pixels (or decimal for percentage of frame) |
| **Object filters > Person > Max Area** | Maximum bounding box area in pixels (or decimal for percentage of frame) |
| **Object filters > Person > Min Ratio** | Minimum width/height ratio of the bounding box |
| **Object filters > Person > Max Ratio** | Maximum width/height ratio of the bounding box |
| Field | Description |
| -------------------------------------------------- | ------------------------------------------------------------------------ |
| **Object filters > Person > Minimum object area** | Minimum bounding box area in pixels (or decimal for percentage of frame) |
| **Object filters > Person > Maximum object area** | Maximum bounding box area in pixels (or decimal for percentage of frame) |
| **Object filters > Person > Minimum aspect ratio** | Minimum width/height ratio of the bounding box |
| **Object filters > Person > Maximum aspect ratio** | Maximum width/height ratio of the bounding box |
To override shape filters for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Objects" /> and select the camera.
docs/docs/configuration/objects.md
+8 -8
View File
@@ -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 |
| --------------------------------------- | ------------------------------------------------------------------------ |
| **Object filters > Person > Min Area** | Minimum bounding box area in pixels (or decimal for percentage of frame) |
| **Object filters > Person > Max Area** | Maximum bounding box area in pixels (or decimal for percentage of frame) |
| **Object filters > Person > Min Ratio** | Minimum width/height ratio of the bounding box |
| **Object filters > Person > Max Ratio** | Maximum width/height ratio of the bounding box |
| **Object filters > Person > Min Score** | Minimum score for the object to initiate tracking |
| **Object filters > Person > Threshold** | Minimum computed score to be considered a true positive |
| Field | Description |
| -------------------------------------------------- | ------------------------------------------------------------------------ |
| **Object filters > Person > Minimum object area** | Minimum bounding box area in pixels (or decimal for percentage of frame) |
| **Object filters > Person > Maximum object area** | Maximum bounding box area in pixels (or decimal for percentage of frame) |
| **Object filters > Person > Minimum aspect ratio** | Minimum width/height ratio of the bounding box |
| **Object filters > Person > Maximum aspect ratio** | Maximum width/height ratio of the bounding box |
| **Object filters > Person > Minimum confidence** | Minimum score for the object to initiate tracking |
| **Object filters > Person > Confidence threshold** | Minimum computed score to be considered a true positive |
To override filters for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Objects" />.
docs/docs/configuration/profiles.md
+7 -3
View File
@@ -191,14 +191,12 @@ cameras:
detect:
enabled: false
record:
enabled: false
enabled: true
profiles:
away:
enabled: true
detect:
enabled: true
record:
enabled: true
home:
enabled: false
```
@@ -251,6 +249,12 @@ Leaving the `objects` section empty (or omitting `track`) does not clear the lis
Fields that require a Frigate restart to take effect cannot be overridden by profiles, since profiles are applied at runtime without restarting. Those fields are hidden when editing a profile override and can only be changed on the base configuration.
### Why can't a profile enable recording when it's disabled in the base config?
Frigate only sets up a camera's recording stream at startup when recording is enabled in the base config, so enabling it later from a profile has no effect. The same applies to turning recording on from the UI or MQTT.
To keep recording off by default, leave `record.enabled: true` in the base config and create a profile that sets `record.enabled: false`. Activate that profile and it will be restored automatically when Frigate starts.
### Can I schedule profiles to be enabled or disabled at certain times?
Not within Frigate itself. Frigate is an NVR, not an automation platform, so it intentionally does not include a scheduler for activating profiles. Instead, activate profiles from an automation platform that already handles time- and event-based triggers well, such as [Home Assistant](https://www.home-assistant.io/) or [Node-RED](https://nodered.org/). These integrate with Frigate and give you far more robust and flexible scheduling than a built-in scheduler could.
docs/docs/configuration/semantic_search.md
+2 -2
View File
@@ -245,8 +245,8 @@ Triggers are best configured through the Frigate UI.
1. Navigate to <NavPath path="Settings > Enrichments > Triggers" /> and select a camera from the dropdown menu.
2. Click **Add Trigger** to create a new trigger or use the pencil icon to edit an existing one.
3. In the **Create Trigger** wizard:
- Enter a **Name** for the trigger (e.g., "Red Car Alert").
- Enter a descriptive **Friendly Name** for the trigger (e.g., "Red car on the driveway camera").
- Enter a **Name** for the trigger (e.g., "Red Car Alert"). Frigate derives the trigger's
internal **ID** from this name, which can be revealed and edited with the show/hide toggle.
- Select the **Type** (`Thumbnail` or `Description`).
- For `Thumbnail`, select an image to trigger this action when a similar thumbnail image is detected, based on the threshold.
- For `Description`, enter text to trigger this action when a similar tracked object description is detected.
docs/docs/configuration/zones.md
+4 -4
View File
@@ -28,7 +28,7 @@ During testing, enable the Zones option for the [Debug view](/usage/live#the-sin
1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
2. Under the **Zones** section, click the plus icon to add a new zone.
3. Click on the camera's latest image to create the points for the zone boundary. Click the first point again to close the polygon.
4. Configure zone options such as **Friendly name**, **Objects**, **Loitering time**, and **Inertia** in the zone editor.
4. Configure zone options such as **Name**, **Objects**, **Loitering Time**, and **Inertia** in the zone editor.
5. Press **Save** when finished.
</TabItem>
@@ -200,7 +200,7 @@ When using loitering zones, a review item will behave in the following way:
1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
2. Edit or create the zone (e.g., `sidewalk`).
- Set **Loitering time** to the desired number of seconds (e.g., `4`)
- Set **Loitering Time** to the desired number of seconds (e.g., `4`)
- Under **Objects**, add the relevant object types (e.g., `person`)
</TabItem>
@@ -291,7 +291,7 @@ Accurate real-world distance measurements are required to estimate speeds. These
1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
2. Create or edit a zone with exactly 4 points aligned to the ground plane.
3. In the zone editor, enter the real-world **Distances** between each pair of consecutive points.
3. In the zone editor, enable **Speed Estimation** and enter the real-world **Line A distance**, **Line B distance**, **Line C distance**, and **Line D distance** between each pair of consecutive points.
- For example, if the distance between the first and second points is 10 meters, between the second and third is 12 meters, etc.
4. Distances are measured in meters (metric) or feet (imperial), depending on the **Unit system** setting.
@@ -358,7 +358,7 @@ Zones can be configured with a minimum speed requirement, meaning an object must
1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
2. Edit or create the zone with distances configured.
- Set **Speed threshold** to the desired minimum speed (e.g., `20`)
- Set **Speed Threshold** to the desired minimum speed (e.g., `20`)
- The unit is kph or mph, depending on the **Unit system** setting
</TabItem>
docs/docs/frigate/glossary.md
+2 -2
View File
@@ -54,7 +54,7 @@ An object filter mask drops any [bounding box](#bounding-box) whose bottom cente
## Min Score
The lowest score a detected object can have to be kept during tracking. Anything scoring below the minimum is assumed to be a [false positive](#false-positive) and discarded.
The lowest score a detected object can have to be kept during tracking. Anything scoring below the minimum is assumed to be a [false positive](#false-positive) and discarded. Set with `min_score` in the config, shown as **Minimum confidence** in the settings UI.
## Model
@@ -86,7 +86,7 @@ A more specific identity assigned to a [tracked object](#tracked-object-event-in
## Threshold
The median score an object must reach to be considered a true positive.
The median score an object must reach to be considered a true positive. Set with `threshold` in the config, shown as **Confidence threshold** in the settings UI.
## Top Score
docs/docs/frigate/hardware.md
+29
View File
@@ -75,6 +75,9 @@ 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**
@@ -338,6 +341,32 @@ The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms fo
| ---------------- | ----------------------------------- |
| yolov9-tiny | ~ 4 ms |
### AMD Ryzen AI / XDNA2
Frigate supports AMD XDNA2 NPUs through the community-maintained
frigate-xdna ZMQ sidecar. It works with stock Frigate and supports
Frigate+ models or compatible local YOLO ONNX models. Models are compiled
once on the target system and cached for subsequent use.
Currently qualified on **Ryzen AI Max 300 / Strix Halo**. Other XDNA2
devices are not yet qualified; XDNA1 is unsupported.
Measured YOLOv9 detector latency on Strix Halo:
| Model | 320 | 640 |
| ----- | ---: | ---: |
| YOLOv9-T | ~7.4 ms | unsupported |
| YOLOv9-S | ~9.0 ms | ~20.0 ms |
| YOLOv9-M | ~13.1 ms | ~34.4 ms |
| YOLOv9-C | ~14.1 ms | ~35.2 ms |
| YOLOv9-E | ~69.4 ms | ~224.8 ms |
**YOLOv9-C at 320 is the recommended quality/performance balance.**
C at 640 is also usable where the lower throughput is acceptable.
Setup, model preparation, and compatibility details are available
[in the frigate-xdna documentation](https://github.com/mitchins/frigate-xdna).
## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.
docs/docs/integrations/mqtt.md
+6
View File
@@ -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`
docs/docs/integrations/third_party_extensions.md
+4
View File
@@ -27,6 +27,10 @@ The [Advanced Camera Card](https://card.camera/#/README) is a Home Assistant das
It supports automatically setting the sub labels in Frigate for person objects that are detected and recognized.
This is a fork (with fixed errors and new features) of [original Double Take](https://github.com/jakowenko/double-take) project which, unfortunately, isn't being maintained by author.
## [frigate-abr](https://github.com/007hacky007/frigate-abr)
[frigate-abr](https://github.com/007hacky007/frigate-abr) is a drop-in Docker image of Frigate that adds adaptive bitrate (ABR) playback for recordings: a sidecar transcodes footage to lower quality tiers on demand, for reviewing over slow remote connections. Segments are transcoded when played and cached, so no additional stream is recorded. Frigate itself is not modified.
## [Frigate Notify](https://github.com/0x2142/frigate-notify)
[Frigate Notify](https://github.com/0x2142/frigate-notify) is a simple app designed to send notifications from Frigate to your favorite platforms. Intended to be used with standalone Frigate installations - Home Assistant not required, MQTT is optional but recommended.
docs/docs/plus/faq.md
+7 -1
View File
@@ -21,7 +21,13 @@ Yes. Models and metadata are stored in the `model_cache` directory within the co
### Can I keep using my Frigate+ models even if I do not renew my subscription?
Yes. Subscriptions to Frigate+ provide access to the infrastructure used to train the models. Models trained with your subscription are yours to keep and use forever. However, do note that the terms and conditions prohibit you from sharing, reselling, or creating derivative products from the models.
Yes. Subscriptions to Frigate+ provide access to the infrastructure used to train the models. Models you train during an active subscription remain licensed for your continued use even after your subscription ends — models already in your model cache will keep working indefinitely. An active subscription is required to train new models and download new versions.
### Can I use Frigate+ models commercially?
A standard subscription covers use on camera systems you own or operate, including for your business. A shop, restaurant, warehouse, or office running Frigate+ at its own locations (including multiple locations) is exactly the kind of use the subscription is for.
What the standard subscription does not cover is using Frigate+ models to provide a product or service to others. If you're deploying models at your customers' sites, bundling them with hardware you sell, or running them as part of a hosted or managed service, even if your customers never receive the model files themselves, you'll need a commercial license.
Note that professional installers are fine under standard subscriptions when each customer holds their own Frigate+ subscription. The commercial license is for cases where your license powers your customers' sites.
### Why can't I submit images to Frigate+?
docs/docs/plus/first_model.md
+13 -13
View File
@@ -63,20 +63,20 @@ 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 |
| ----------------- | --------- | --------- |
| **dog** | .7 | .9 |
| **cat** | .65 | .8 |
| **face** | .7 | |
| **package** | .65 | .9 |
| **license_plate** | .6 | |
| **amazon** | .75 | |
| **ups** | .75 | |
| **fedex** | .75 | |
| **person** | .65 | .85 |
| **car** | .65 | .85 |
| Object | Minimum confidence | Confidence threshold |
| ----------------- | ------------------ | -------------------- |
| **dog** | .7 | .9 |
| **cat** | .65 | .8 |
| **face** | .7 | |
| **package** | .65 | .9 |
| **license_plate** | .6 | |
| **amazon** | .75 | |
| **ups** | .75 | |
| **fedex** | .75 | |
| **person** | .65 | .85 |
| **car** | .65 | .85 |
</TabItem>
<TabItem value="yaml">
docs/docs/plus/index.md
+9 -6
View File
@@ -65,11 +65,11 @@ Some users may find that Frigate+ models result in more false positives initiall
Frigate+ models support a more relevant set of objects for security cameras. The labels for annotation in Frigate+ are configurable by editing the camera in the Cameras section of Frigate+. Currently, the following objects are supported:
- **People**: `person`, `face`
- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `school_bus`, `license_plate`
- **People**: `person`, `face`, `baby`
- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `school_bus`, `garbage truck`, `license_plate`
- **Delivery Logos**: `amazon`, `usps`, `ups`, `fedex`, `dhl`, `an_post`, `purolator`, `postnl`, `nzpost`, `postnord`, `gls`, `dpd`, `canada_post`, `royal_mail`
- **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`, `skunk`, `kangaroo`
- **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`
- **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`, `skunk`, `kangaroo`, `possum`, `rodent`
- **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`, `baby_stroller`
Other object types available in the default Frigate model are not available. Additional object types will be added in future releases.
@@ -77,9 +77,12 @@ Other object types available in the default Frigate model are not available. Add
Candidate labels are also available for annotation. These labels don't have enough data to be included in the model yet, but using them will help add support sooner. You can enable these labels by editing the camera settings.
Where possible, these labels are mapped to existing labels during training. For example, any `baby` labels are mapped to `person` until support for new labels is added.
Where possible, these labels are mapped to existing labels during training. For example, any `duck` labels are mapped to `bird` until support for new labels is added.
The candidate labels are: `baby`, `bpost`, `badger`, `possum`, `rodent`, `chicken`, `groundhog`, `boar`, `hedgehog`, `tractor`, `golf cart`, `garbage truck`, `bus`, `sports ball`, `la_poste`, `lawnmower`, `heron`, `rickshaw`, `wombat`, `auspost`, `aramex`, `bobcat`, `mustelid`, `transoflex`, `airplane`, `drone`, `mountain_lion`, `crocodile`, `turkey`, `baby_stroller`, `monkey`, `coyote`, `porcupine`, `parcelforce`, `sheep`, `snake`, `helicopter`, `lizard`, `duck`, `hermes`, `cargus`, `fan_courier`, `sameday`
- **Vehicles**: `tractor`, `golf_cart`, `bus`, `airplane`, `helicopter`, `rickshaw`, `scooter`
- **Delivery Logos**: `bpost`, `auspost`, `aramex`, `transoflex`, `parcelforce`, `hermes`, `cargus`, `fan_courier`, `sameday`, `la_poste`
- **Animals**: `badger`, `chicken`, `duck`, `turkey`, `groundhog`, `boar`, `hedgehog`, `wombat`, `bobcat`, `mustelid`, `mountain_lion`, `crocodile`, `monkey`, `coyote`, `porcupine`, `sheep`, `snake`, `lizard`, `heron`, `elk`, `moose`, `pig`, `donkey`, `civet`
- **Other**: `sports_ball`, `drone`, `lawnmower`
Candidate labels are not available for automatic suggestions.
docs/docs/troubleshooting/recordings.md
+2 -10
View File
@@ -397,19 +397,11 @@ dmesg | grep -i -E "gpu|drm|reset|hang"
Messages like `trying reset from guc_exec_queue_timedout_job` or similar GPU reset/hang messages indicate a driver or hardware issue. Ensure your kernel and GPU drivers (especially Intel) are up to date.
#### Step 6: Verify hardware acceleration configuration
An incorrect `hwaccel_args` preset can cause ffmpeg to fail silently or consume excessive CPU, starving the detector of resources.
- After upgrading Frigate, verify your preset matches your hardware (e.g., `preset-intel-qsv-h264` instead of the deprecated `preset-vaapi`).
- For h265 cameras, use the corresponding h265 preset (e.g., `preset-intel-qsv-h265`).
- Note that `hwaccel_args` are only relevant for the detect stream. Frigate does not decode the record stream.
#### Step 7: Verify go2rtc stream configuration
#### Step 6: Verify go2rtc stream configuration
Ensure that the ffmpeg source names in your go2rtc configuration match the correct camera stream. A misconfigured stream name (e.g., copying a config from one camera to another without updating the stream reference) will cause the wrong stream to be used or the stream to fail entirely.
#### Step 8: Check system resources
#### Step 7: Check system resources
If none of the above apply, the issue may be a general resource constraint. Monitor the following on your host:
docs/docusaurus.config.ts
+30 -22
View File
@@ -3,6 +3,9 @@ import * as path from "node:path";
import type { Config, PluginConfig } from "@docusaurus/types";
import type * as OpenApiPlugin from "docusaurus-plugin-openapi-docs";
// Bump when a new stable release ships
const STABLE_VERSION = "0.18";
const config: Config = {
title: "Frigate",
tagline: "NVR With Realtime Object Detection for IP Cameras",
@@ -23,17 +26,17 @@ const config: Config = {
mermaid: true,
},
i18n: {
defaultLocale: 'en',
locales: ['en'],
defaultLocale: "en",
locales: ["en"],
localeConfigs: {
en: {
label: 'English',
}
label: "English",
},
},
},
themeConfig: {
announcementBar: {
id: 'frigate_plus',
id: "frigate_plus",
content: `
<span style="margin-right: 8px; display: inline-block; animation: pulse 2s infinite;">🚀</span>
Get more relevant and accurate detections with Frigate+ models.
@@ -45,8 +48,8 @@ const config: Config = {
50% { transform: scale(1.1); }
}
</style>`,
backgroundColor: '#005f73',
textColor: '#e0fbfc',
backgroundColor: "#005f73",
textColor: "#e0fbfc",
isCloseable: false,
},
docs: {
@@ -83,15 +86,15 @@ const config: Config = {
},
},
prism: {
magicComments:[
magicComments: [
{
className: 'theme-code-block-highlighted-line',
line: 'highlight-next-line',
block: {start: 'highlight-start', end: 'highlight-end'},
className: "theme-code-block-highlighted-line",
line: "highlight-next-line",
block: { start: "highlight-start", end: "highlight-end" },
},
{
className: 'code-block-error-line',
line: 'highlight-error-line',
className: "code-block-error-line",
line: "highlight-error-line",
},
],
additionalLanguages: ["bash", "json"],
@@ -131,6 +134,11 @@ const config: Config = {
srcDark: "img/branding/logo-dark.svg",
},
items: [
{
href: "https://github.com/blakeblackshear/frigate/releases",
label: `${STABLE_VERSION}`,
position: "left",
},
{
to: "/",
activeBasePath: "docs",
@@ -148,19 +156,19 @@ const config: Config = {
position: "right",
},
{
type: 'localeDropdown',
position: 'right',
type: "localeDropdown",
position: "right",
dropdownItemsAfter: [
{
label: '简体中文（社区翻译）',
href: 'https://docs.frigate-cn.video',
}
]
label: "简体中文（社区翻译）",
href: "https://docs.frigate-cn.video",
},
],
},
{
href: 'https://github.com/blakeblackshear/frigate',
label: 'GitHub',
position: 'right',
href: "https://github.com/blakeblackshear/frigate",
label: "GitHub",
position: "right",
},
],
},
docs/static/frigate-api.yaml
Vendored
+20
View File
@@ -4493,6 +4493,16 @@ paths:
- type: 'null'
default: 100
title: Limit
- name: offset
in: query
required: false
schema:
anyOf:
- type: integer
minimum: 0
- type: 'null'
default: 0
title: Offset
- name: after
in: query
required: false
@@ -4798,6 +4808,16 @@ paths:
- type: 'null'
default: 50
title: Limit
- name: offset
in: query
required: false
schema:
anyOf:
- type: integer
minimum: 0
- type: 'null'
default: 0
title: Offset
- name: cameras
in: query
required: false