mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-07-26 13:47:03 +02:00
Merge remote-tracking branch 'origin/master' into dev
This commit is contained in:
Blake Blackshear
commit
7238b3ea22
@ -243,3 +243,38 @@ ffmpeg:
|
||||
### TP-Link VIGI Cameras
|
||||
|
||||
TP-Link VIGI cameras need some adjustments to the main stream settings on the camera itself to avoid issues. The stream needs to be configured as `H264` with `Smart Coding` set to `off`. Without these settings you may have problems when trying to watch recorded footage. For example Firefox will stop playback after a few seconds and show the following error message: `The media playback was aborted due to a corruption problem or because the media used features your browser did not support.`.
|
||||
|
||||
## USB Cameras (aka Webcams)
|
||||
|
||||
To use a USB camera (webcam) with Frigate, the recommendation is to use go2rtc's [FFmpeg Device](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg-device) support:
|
||||
|
||||
- Preparation outside of Frigate:
|
||||
- Get USB camera path. Run `v4l2-ctl --list-devices` to get a listing of locally-connected cameras available. (You may need to install `v4l-utils` in a way appropriate for your Linux distribution). In the sample configuration below, we use `video=0` to correlate with a detected device path of `/dev/video0`
|
||||
- Get USB camera formats & resolutions. Run `ffmpeg -f v4l2 -list_formats all -i /dev/video0` to get an idea of what formats and resolutions the USB Camera supports. In the sample configuration below, we use a width of 1024 and height of 576 in the stream and detection settings based on what was reported back.
|
||||
- If using Frigate in a container (e.g. Docker on TrueNAS), ensure you have USB Passthrough support enabled, along with a specific Host Device (`/dev/video0`) + Container Device (`/dev/video0`) listed.
|
||||
|
||||
- In your Frigate Configuration File, add the go2rtc stream and roles as appropriate:
|
||||
|
||||
```
|
||||
go2rtc:
|
||||
streams:
|
||||
usb_camera:
|
||||
- "ffmpeg:device?video=0&video_size=1024x576#video=h264"
|
||||
|
||||
cameras:
|
||||
usb_camera:
|
||||
enabled: true
|
||||
ffmpeg:
|
||||
inputs:
|
||||
- path: rtsp://127.0.0.1:8554/usb_camera
|
||||
input_args: preset-rtsp-restream
|
||||
roles:
|
||||
- detect
|
||||
- record
|
||||
detect:
|
||||
enabled: false # <---- disable detection until you have a working camera feed
|
||||
width: 1024
|
||||
height: 576
|
||||
```
|
||||
|
||||
|
||||
|
||||
@ -101,6 +101,8 @@ This list of working and non-working PTZ cameras is based on user feedback.
|
||||
| Ctronics PTZ | ✅ | ❌ | |
|
||||
| Dahua | ✅ | ✅ | Some low-end Dahuas (lite series, among others) have been reported to not support autotracking |
|
||||
| Dahua DH-SD2A500HB | ✅ | ❌ | |
|
||||
| Dahua DH-SD49825GB-HNR | ✅ | ✅ | |
|
||||
| Dahua DH-P5AE-PV | ❌ | ❌ | |
|
||||
| Foscam R5 | ✅ | ❌ | |
|
||||
| Hanwha XNP-6550RH | ✅ | ❌ | |
|
||||
| Hikvision | ✅ | ❌ | Incomplete ONVIF support (MoveStatus won't update even on latest firmware) - reported with HWP-N4215IH-DE and DS-2DE3304W-DE, but likely others |
|
||||
|
||||
@ -21,12 +21,23 @@ genai:
|
||||
model: gemini-1.5-flash
|
||||
|
||||
cameras:
|
||||
front_camera: ...
|
||||
front_camera:
|
||||
genai:
|
||||
enabled: True # <- enable GenAI for your front camera
|
||||
use_snapshot: True
|
||||
objects:
|
||||
- person
|
||||
required_zones:
|
||||
- steps
|
||||
indoor_camera:
|
||||
genai: # <- disable GenAI for your indoor camera
|
||||
enabled: False
|
||||
genai:
|
||||
enabled: False # <- disable GenAI for your indoor camera
|
||||
```
|
||||
|
||||
By default, descriptions will be generated for all tracked objects and all zones. But you can also optionally specify `objects` and `required_zones` to only generate descriptions for certain tracked objects or zones.
|
||||
|
||||
Optionally, you can generate the description using a snapshot (if enabled) by setting `use_snapshot` to `True`. By default, this is set to `False`, which sends the uncompressed images from the `detect` stream collected over the object's lifetime to the model. Once the object lifecycle ends, only a single compressed and cropped thumbnail is saved with the tracked object. Using a snapshot might be useful when you want to _regenerate_ a tracked object's description as it will provide the AI with a higher-quality image (typically downscaled by the AI itself) than the cropped/compressed thumbnail. Using a snapshot otherwise has a trade-off in that only a single image is sent to your provider, which will limit the model's ability to determine object movement or direction.
|
||||
|
||||
## Ollama
|
||||
|
||||
:::warning
|
||||
@ -185,9 +196,7 @@ genai:
|
||||
car: "Observe the primary vehicle in these images. Focus on its movement, direction, or purpose (e.g., parking, approaching, circling). If it's a delivery vehicle, mention the company."
|
||||
```
|
||||
|
||||
Prompts can also be overriden at the camera level to provide a more detailed prompt to the model about your specific camera, if you desire. By default, descriptions will be generated for all tracked objects and all zones. But you can also optionally specify `objects` and `required_zones` to only generate descriptions for certain tracked objects or zones.
|
||||
|
||||
Optionally, you can generate the description using a snapshot (if enabled) by setting `use_snapshot` to `True`. By default, this is set to `False`, which sends the uncompressed images from the `detect` stream collected over the object's lifetime to the model. Once the object lifecycle ends, only a single compressed and cropped thumbnail is saved with the tracked object. Using a snapshot might be useful when you want to _regenerate_ a tracked object's description as it will provide the AI with a higher-quality image (typically downscaled by the AI itself) than the cropped/compressed thumbnail. Using a snapshot otherwise has a trade-off in that only a single image is sent to your provider, which will limit the model's ability to determine object movement or direction.
|
||||
Prompts can also be overriden at the camera level to provide a more detailed prompt to the model about your specific camera, if you desire.
|
||||
|
||||
```yaml
|
||||
cameras:
|
||||
|
||||
@ -172,7 +172,7 @@ For devices that support two way talk, Frigate can be configured to use the feat
|
||||
|
||||
- Set up go2rtc with [WebRTC](#webrtc-extra-configuration).
|
||||
- Ensure you access Frigate via https (may require [opening port 8971](/frigate/installation/#ports)).
|
||||
- For the Home Assistant Frigate card, [follow the docs](https://github.com/dermotduffy/frigate-hass-card?tab=readme-ov-file#using-2-way-audio) for the correct source.
|
||||
- For the Home Assistant Frigate card, [follow the docs](http://card.camera/#/usage/2-way-audio) for the correct source.
|
||||
|
||||
To use the Reolink Doorbell with two way talk, you should use the [recommended Reolink configuration](/configuration/camera_specific#reolink-doorbell)
|
||||
|
||||
|
||||
@ -498,6 +498,7 @@ detectors:
|
||||
|
||||
model:
|
||||
path: /config/model_cache/tensorrt/yolov7-320.trt
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
input_tensor: nchw
|
||||
input_pixel_format: rgb
|
||||
width: 320
|
||||
|
||||
@ -20,5 +20,5 @@ In order to install Frigate as a PWA, the following requirements must be met:
|
||||
Installation varies slightly based on the device that is being used:
|
||||
|
||||
- Desktop: Use the install button typically found in right edge of the address bar
|
||||
- Android: Use the `Install as App` button in the more options menu
|
||||
- iOS: Use the `Add to Homescreen` button in the share menu
|
||||
- Android: Use the `Install as App` button in the more options menu for Chrome, and the `Add app to Home screen` button for Firefox
|
||||
- iOS: Use the `Add to Homescreen` button in the share menu
|
||||
|
||||
@ -21,6 +21,21 @@ In 0.14 and later, all of that is bundled into a single review item which starts
|
||||
|
||||
Not every segment of video captured by Frigate may be of the same level of interest to you. Video of people who enter your property may be a different priority than those walking by on the sidewalk. For this reason, Frigate 0.14 categorizes review items as _alerts_ and _detections_. By default, all person and car objects are considered alerts. You can refine categorization of your review items by configuring required zones for them.
|
||||
|
||||
:::note
|
||||
|
||||
Alerts and detections categorize the tracked objects in review items, but Frigate must first detect those objects with your configured object detector (Coral, OpenVINO, etc). By default, the object tracker only detects `person`. Setting `labels` for `alerts` and `detections` does not automatically enable detection of new objects. To detect more than `person`, you should add the following to your config:
|
||||
|
||||
```yaml
|
||||
objects:
|
||||
track:
|
||||
- person
|
||||
- car
|
||||
- ...
|
||||
```
|
||||
|
||||
See the [objects documentation](objects.md) for the list of objects that Frigate's default model tracks.
|
||||
:::
|
||||
|
||||
## Restricting alerts to specific labels
|
||||
|
||||
By default a review item will only be marked as an alert if a person or car is detected. This can be configured to include any object or audio label using the following config:
|
||||
|
||||
@ -36,8 +36,8 @@ Note that certbot uses symlinks, and those can't be followed by the container un
|
||||
frigate:
|
||||
...
|
||||
volumes:
|
||||
- /etc/letsencrypt/live/frigate:/etc/letsencrypt/live/frigate:ro
|
||||
- /etc/letsencrypt/archive/frigate:/etc/letsencrypt/archive/frigate:ro
|
||||
- /etc/letsencrypt/live/your.fqdn.net:/etc/letsencrypt/live/frigate:ro
|
||||
- /etc/letsencrypt/archive/your.fqdn.net:/etc/letsencrypt/archive/your.fqdn.net:ro
|
||||
...
|
||||
|
||||
```
|
||||
|
||||
@ -3,7 +3,7 @@ id: camera_setup
|
||||
title: Camera setup
|
||||
---
|
||||
|
||||
Cameras configured to output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. H.265 has better compression, but less compatibility. Chrome 108+, Safari and Edge are the only browsers able to play H.265 and only support a limited number of H.265 profiles. Ideally, cameras should be configured directly for the desired resolutions and frame rates you want to use in Frigate. Reducing frame rates within Frigate will waste CPU resources decoding extra frames that are discarded. There are three different goals that you want to tune your stream configurations around.
|
||||
Cameras configured to output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. H.265 has better compression, but less compatibility. Firefox 134+/136+/137+ (Windows/Mac/Linux & Android), Chrome 108+, Safari and Edge are the only browsers able to play H.265 and only support a limited number of H.265 profiles. Ideally, cameras should be configured directly for the desired resolutions and frame rates you want to use in Frigate. Reducing frame rates within Frigate will waste CPU resources decoding extra frames that are discarded. There are three different goals that you want to tune your stream configurations around.
|
||||
|
||||
- **Detection**: This is the only stream that Frigate will decode for processing. Also, this is the stream where snapshots will be generated from. The resolution for detection should be tuned for the size of the objects you want to detect. See [Choosing a detect resolution](#choosing-a-detect-resolution) for more details. The recommended frame rate is 5fps, but may need to be higher (10fps is the recommended maximum for most users) for very fast moving objects. Higher resolutions and frame rates will drive higher CPU usage on your server.
|
||||
|
||||
|
||||
@ -66,4 +66,4 @@ The time period starting when a tracked object entered the frame and ending when
|
||||
|
||||
## Zone
|
||||
|
||||
Zones are areas of interest, zones can be used for notifications and for limiting the areas where Frigate will create an [event](#event). [See the zone docs for more info](/configuration/zones)
|
||||
Zones are areas of interest, zones can be used for notifications and for limiting the areas where Frigate will create a [review item](#review-item). [See the zone docs for more info](/configuration/zones)
|
||||
|
||||
@ -9,23 +9,36 @@ Cameras that output H.264 video and AAC audio will offer the most compatibility
|
||||
|
||||
I recommend Dahua, Hikvision, and Amcrest in that order. Dahua edges out Hikvision because they are easier to find and order, not because they are better cameras. I personally use Dahua cameras because they are easier to purchase directly. In my experience Dahua and Hikvision both have multiple streams with configurable resolutions and frame rates and rock solid streams. They also both have models with large sensors well known for excellent image quality at night. Not all the models are equal. Larger sensors are better than higher resolutions; especially at night. Amcrest is the fallback recommendation because they are rebranded Dahuas. They are rebranding the lower end models with smaller sensors or less configuration options.
|
||||
|
||||
Many users have reported various issues with Reolink cameras, so I do not recommend them. If you are using Reolink, I suggest the [Reolink specific configuration](../configuration/camera_specific.md#reolink-cameras). Wifi cameras are also not recommended. Their streams are less reliable and cause connection loss and/or lost video data.
|
||||
WiFi cameras are not recommended as [their streams are less reliable and cause connection loss and/or lost video data](https://ipcamtalk.com/threads/camera-conflicts.68142/#post-738821), especially when more than a few WiFi cameras will be used at the same time.
|
||||
|
||||
Here are some of the camera's I recommend:
|
||||
Many users have reported various issues with 4K-plus Reolink cameras, it is best to stick with 5MP and lower for Reolink cameras. If you are using Reolink, I suggest the [Reolink specific configuration](../configuration/camera_specific.md#reolink-cameras).
|
||||
|
||||
Here are some of the cameras I recommend:
|
||||
|
||||
- <a href="https://amzn.to/4fwoNWA" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T549M-ALED-S3</a> (affiliate link)
|
||||
- <a href="https://amzn.to/3YXpcMw" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T54IR-AS</a> (affiliate link)
|
||||
- <a href="https://amzn.to/3AvBHoY" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-AI-V3</a> (affiliate link)
|
||||
- <a href="https://amzn.to/4ltOpaC" target="_blank" rel="nofollow noopener sponsored">HIKVISION DS-2CD2387G2P-LSU/SL ColorVu 8MP Panoramic Turret IP Camera</a> (affiliate link)
|
||||
|
||||
I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
|
||||
|
||||
## Server
|
||||
|
||||
My current favorite is the Beelink EQ13 because of the efficient N100 CPU and dual NICs that allow you to setup a dedicated private network for your cameras where they can be blocked from accessing the internet. There are many used workstation options on eBay that work very well. Anything with an Intel CPU and capable of running Debian should work fine. As a bonus, you may want to look for devices with a M.2 or PCIe express slot that is compatible with the Hailo8 or Google Coral. I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
|
||||
My current favorite is the Beelink EQ13 because of the efficient N100 CPU and dual NICs that allow you to setup a dedicated private network for your cameras where they can be blocked from accessing the internet. There are many used workstation options on eBay that work very well. Anything with an Intel CPU and capable of running Debian should work fine. As a bonus, you may want to look for devices with a M.2 or PCIe express slot that is compatible with the Google Coral, Hailo, or other AI accelerators.
|
||||
|
||||
| Name | Notes |
|
||||
| ------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- |
|
||||
| Beelink EQ13 (<a href="https://amzn.to/4iQaBKu" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras. |
|
||||
Note that many of these mini PCs come with Windows pre-installed, and you will need to install Linux according to the [getting started guide](../guides/getting_started.md).
|
||||
|
||||
I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
|
||||
|
||||
:::warning
|
||||
|
||||
If the EQ13 is out of stock, the link below may take you to a suggested alternative on Amazon. The Beelink EQ14 has some known compatibility issues, so you should avoid that model for now.
|
||||
|
||||
:::
|
||||
|
||||
| Name | Coral Inference Speed | Coral Compatibility | Notes |
|
||||
| ------------------------------------------------------------------------------------------------------------- | --------------------- | ------------------- | ----------------------------------------------------------------------------------------- |
|
||||
| Beelink EQ13 (<a href="https://amzn.to/4jn2qVr" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms | USB | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras. |
|
||||
|
||||
## Detectors
|
||||
|
||||
@ -128,16 +141,29 @@ The TensortRT detector is able to run on x86 hosts that have an Nvidia GPU which
|
||||
Inference speeds will vary greatly depending on the GPU and the model used.
|
||||
`tiny` variants are faster than the equivalent non-tiny model, some known examples are below:
|
||||
|
||||
| Name | YOLOv7 Inference Time | YOLO-NAS Inference Time | RF-DETR Inference Time |
|
||||
<<<<<<< HEAD
|
||||
| Name | YOLOv7 Inference Time | YOLO-NAS Inference Time | RF-DETR Inference Time |
|
||||
| --------------- | --------------------- | ------------------------- | ------------------------- |
|
||||
| GTX 1060 6GB | ~ 7 ms | | |
|
||||
| GTX 1070 | ~ 6 ms | | |
|
||||
| GTX 1660 SUPER | ~ 4 ms | | |
|
||||
| RTX 3050 | 5 - 7 ms | 320: ~ 10 ms 640: ~ 16 ms | 336: ~ 16 ms 560: ~ 40 ms |
|
||||
| RTX 3070 Mobile | ~ 5 ms | | |
|
||||
| RTX 3070 | 4 - 6 ms | 320: ~ 6 ms 640: ~ 12 ms | 336: ~ 14 ms 560: ~ 36 ms |
|
||||
| Quadro P400 2GB | 20 - 25 ms | | |
|
||||
| Quadro P2000 | ~ 12 ms | | |
|
||||
| GTX 1060 6GB | ~ 7 ms | | |
|
||||
| GTX 1070 | ~ 6 ms | | |
|
||||
| GTX 1660 SUPER | ~ 4 ms | | |
|
||||
| RTX 3050 | 5 - 7 ms | 320: ~ 10 ms 640: ~ 16 ms | 336: ~ 16 ms 560: ~ 40 ms |
|
||||
| RTX 3070 Mobile | ~ 5 ms | | |
|
||||
| RTX 3070 | 4 - 6 ms | 320: ~ 6 ms 640: ~ 12 ms | 336: ~ 14 ms 560: ~ 36 ms |
|
||||
| Quadro P400 2GB | 20 - 25 ms | | |
|
||||
| Quadro P2000 | ~ 12 ms | | |
|
||||
=======
|
||||
| Name | YoloV7 Inference Time | YOLO-NAS Inference Time |
|
||||
| --------------- | ---------------------- | --------------------------- |
|
||||
| Quadro P2000 | ~ 12 ms | |
|
||||
| Quadro P400 2GB | 20 - 25 ms | |
|
||||
| RTX 3070 Mobile | ~ 5 ms | |
|
||||
| RTX 3050 | 5 - 7 ms | 320: ~ 10 ms 640: ~ 16 ms |
|
||||
| GTX 1660 SUPER | ~ 4 ms | |
|
||||
| GTX 1070 | ~ 6 ms | |
|
||||
| GTX 1060 6GB | ~ 7 ms | |
|
||||
|
||||
> > > > > > > origin/master
|
||||
|
||||
### AMD GPUs
|
||||
|
||||
@ -193,4 +219,4 @@ Basically - When you increase the resolution and/or the frame rate of the stream
|
||||
|
||||
YES! The Coral does not help with decoding video streams.
|
||||
|
||||
Decompressing video streams takes a significant amount of CPU power. Video compression uses key frames (also known as I-frames) to send a full frame in the video stream. The following frames only include the difference from the key frame, and the CPU has to compile each frame by merging the differences with the key frame. [More detailed explanation](https://blog.video.ibm.com/streaming-video-tips/keyframes-interframe-video-compression/). Higher resolutions and frame rates mean more processing power is needed to decode the video stream, so try and set them on the camera to avoid unnecessary decoding work.
|
||||
Decompressing video streams takes a significant amount of CPU power. Video compression uses key frames (also known as I-frames) to send a full frame in the video stream. The following frames only include the difference from the key frame, and the CPU has to compile each frame by merging the differences with the key frame. [More detailed explanation](https://support.video.ibm.com/hc/en-us/articles/18106203580316-Keyframes-InterFrame-Video-Compression). Higher resolutions and frame rates mean more processing power is needed to decode the video stream, so try and set them on the camera to avoid unnecessary decoding work.
|
||||
|
||||
119
docs/docs/frigate/updating.md
Normal file
119
docs/docs/frigate/updating.md
Normal file
@ -0,0 +1,119 @@
|
||||
---
|
||||
id: updating
|
||||
title: Updating
|
||||
---
|
||||
|
||||
# Updating Frigate
|
||||
|
||||
The current stable version of Frigate is **0.15.0**. The release notes and any breaking changes for this version can be found on the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases/tag/v0.15.0).
|
||||
|
||||
Keeping Frigate up to date ensures you benefit from the latest features, performance improvements, and bug fixes. The update process varies slightly depending on your installation method (Docker, Home Assistant Addon, etc.). Below are instructions for the most common setups.
|
||||
|
||||
## Before You Begin
|
||||
|
||||
- **Stop Frigate**: For most methods, you’ll need to stop the running Frigate instance before backing up and updating.
|
||||
- **Backup Your Configuration**: Always back up your `/config` directory (e.g., `config.yml` and `frigate.db`, the SQLite database) before updating. This ensures you can roll back if something goes wrong.
|
||||
- **Check Release Notes**: Carefully review the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases) for breaking changes or configuration updates that might affect your setup.
|
||||
|
||||
## Updating with Docker
|
||||
|
||||
If you’re running Frigate via Docker (recommended method), follow these steps:
|
||||
|
||||
1. **Stop the Container**:
|
||||
|
||||
- If using Docker Compose:
|
||||
```bash
|
||||
docker compose down frigate
|
||||
```
|
||||
- If using `docker run`:
|
||||
```bash
|
||||
docker stop frigate
|
||||
```
|
||||
|
||||
2. **Update and Pull the Latest Image**:
|
||||
|
||||
- If using Docker Compose:
|
||||
- Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.15.0` instead of `0.14.1`). For example:
|
||||
```yaml
|
||||
services:
|
||||
frigate:
|
||||
image: ghcr.io/blakeblackshear/frigate:0.15.0
|
||||
```
|
||||
- Then pull the image:
|
||||
```bash
|
||||
docker pull ghcr.io/blakeblackshear/frigate:0.15.0
|
||||
```
|
||||
- **Note for `stable` Tag Users**: If your `docker-compose.yml` uses the `stable` tag (e.g., `ghcr.io/blakeblackshear/frigate:stable`), you don’t need to update the tag manually. The `stable` tag always points to the latest stable release after pulling.
|
||||
- If using `docker run`:
|
||||
- Pull the image with the appropriate tag (e.g., `0.15.0`, `0.15.0-tensorrt`, or `stable`):
|
||||
```bash
|
||||
docker pull ghcr.io/blakeblackshear/frigate:0.15.0
|
||||
```
|
||||
|
||||
3. **Start the Container**:
|
||||
|
||||
- If using Docker Compose:
|
||||
```bash
|
||||
docker compose up -d
|
||||
```
|
||||
- If using `docker run`, re-run your original command (e.g., from the [Installation](./installation.md#docker) section) with the updated image tag.
|
||||
|
||||
4. **Verify the Update**:
|
||||
- Check the container logs to ensure Frigate starts successfully:
|
||||
```bash
|
||||
docker logs frigate
|
||||
```
|
||||
- Visit the Frigate Web UI (default: `http://<your-ip>:5000`) to confirm the new version is running. The version number is displayed at the top of the System Metrics page.
|
||||
|
||||
### Notes
|
||||
|
||||
- If you’ve customized other settings (e.g., `shm-size`), ensure they’re still appropriate after the update.
|
||||
- Docker will automatically use the updated image when you restart the container, as long as you pulled the correct version.
|
||||
|
||||
## Updating the Home Assistant Addon
|
||||
|
||||
For users running Frigate as a Home Assistant Addon:
|
||||
|
||||
1. **Check for Updates**:
|
||||
|
||||
- Navigate to **Settings > Add-ons** in Home Assistant.
|
||||
- Find your installed Frigate addon (e.g., "Frigate NVR" or "Frigate NVR (Full Access)").
|
||||
- If an update is available, you’ll see an "Update" button.
|
||||
|
||||
2. **Update the Addon**:
|
||||
|
||||
- Click the "Update" button next to the Frigate addon.
|
||||
- Wait for the process to complete. Home Assistant will handle downloading and installing the new version.
|
||||
|
||||
3. **Restart the Addon**:
|
||||
|
||||
- After updating, go to the addon’s page and click "Restart" to apply the changes.
|
||||
|
||||
4. **Verify the Update**:
|
||||
- Check the addon logs (under the "Log" tab) to ensure Frigate starts without errors.
|
||||
- Access the Frigate Web UI to confirm the new version is running.
|
||||
|
||||
### Notes
|
||||
|
||||
- Ensure your `/config/frigate.yml` is compatible with the new version by reviewing the [Release notes](https://github.com/blakeblackshear/frigate/releases).
|
||||
- If using custom hardware (e.g., Coral or GPU), verify that configurations still work, as addon updates don’t modify your hardware settings.
|
||||
|
||||
## Rolling Back
|
||||
|
||||
If an update causes issues:
|
||||
|
||||
1. Stop Frigate.
|
||||
2. Restore your backed-up config file and database.
|
||||
3. Revert to the previous image version:
|
||||
- For Docker: Specify an older tag (e.g., `ghcr.io/blakeblackshear/frigate:0.14.1`) in your `docker run` command.
|
||||
- For Docker Compose: Edit your `docker-compose.yml`, specify the older version tag (e.g., `ghcr.io/blakeblackshear/frigate:0.14.1`), and re-run `docker compose up -d`.
|
||||
- For Home Assistant: Reinstall the previous addon version manually via the repository if needed and restart the addon.
|
||||
4. Verify the old version is running again.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
- **Container Fails to Start**: Check logs (`docker logs frigate`) for errors.
|
||||
- **UI Not Loading**: Ensure ports (e.g., 5000, 8971) are still mapped correctly and the service is running.
|
||||
- **Hardware Issues**: Revisit hardware-specific setup (e.g., Coral, GPU) if detection or decoding fails post-update.
|
||||
|
||||
Common questions are often answered in the [FAQ](https://github.com/blakeblackshear/frigate/discussions), pinned at the top of the support discussions.
|
||||
@ -35,6 +35,7 @@ There are many solutions available to implement reverse proxies and the communit
|
||||
* [Apache2](#apache2-reverse-proxy)
|
||||
* [Nginx](#nginx-reverse-proxy)
|
||||
* [Traefik](#traefik-reverse-proxy)
|
||||
* [Caddy](#caddy-reverse-proxy)
|
||||
|
||||
## Apache2 Reverse Proxy
|
||||
|
||||
@ -117,7 +118,8 @@ server {
|
||||
set $port 8971;
|
||||
|
||||
listen 80;
|
||||
listen 443 ssl http2;
|
||||
listen 443 ssl;
|
||||
http2 on;
|
||||
|
||||
server_name frigate.domain.com;
|
||||
}
|
||||
@ -177,3 +179,33 @@ The above configuration will create a "service" in Traefik, automatically adding
|
||||
It will also add a router, routing requests to "traefik.example.com" to your local container.
|
||||
|
||||
Note that with this approach, you don't need to expose any ports for the Frigate instance since all traffic will be routed over the internal Docker network.
|
||||
|
||||
## Caddy Reverse Proxy
|
||||
|
||||
This example shows Frigate running under a subdomain with logging and a tls cert (in this case a wildcard domain cert obtained independently of caddy) handled via imports
|
||||
|
||||
```caddy
|
||||
(logging) {
|
||||
log {
|
||||
output file /var/log/caddy/{args[0]}.log {
|
||||
roll_size 10MiB
|
||||
roll_keep 5
|
||||
roll_keep_for 10d
|
||||
}
|
||||
format json
|
||||
level INFO
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
(tls) {
|
||||
tls /var/lib/caddy/wildcard.YOUR_DOMAIN.TLD.fullchain.pem /var/lib/caddy/wildcard.YOUR_DOMAIN.TLD.privkey.pem
|
||||
}
|
||||
|
||||
frigate.YOUR_DOMAIN.TLD {
|
||||
reverse_proxy http://localhost:8971
|
||||
import tls
|
||||
import logging frigate.YOUR_DOMAIN.TLD
|
||||
}
|
||||
|
||||
```
|
||||
|
||||
@ -110,6 +110,14 @@ If you run Frigate on a separate device within your local network, Home Assistan
|
||||
|
||||
Use `http://<frigate_device_ip>:8971` as the URL for the integration so that authentication is required.
|
||||
|
||||
:::tip
|
||||
|
||||
The above URL assumes you have [disabled TLS](../configuration/tls).
|
||||
By default, TLS is enabled and Frigate will be using a self-signed certificate. HomeAssistant will fail to connect HTTPS to port 8971 since it fails to verify the self-signed certificate.
|
||||
Either disable TLS and use HTTP from HomeAssistant, or configure Frigate to be acessible with a valid certificate.
|
||||
|
||||
:::
|
||||
|
||||
```yaml
|
||||
services:
|
||||
frigate:
|
||||
|
||||
@ -28,7 +28,14 @@ Message published for each changed tracked object. The first message is publishe
|
||||
"id": "1607123955.475377-mxklsc",
|
||||
"camera": "front_door",
|
||||
"frame_time": 1607123961.837752,
|
||||
"snapshot_time": 1607123961.837752,
|
||||
"snapshot": {
|
||||
"frame_time": 1607123965.975463,
|
||||
"box": [415, 489, 528, 700],
|
||||
"area": 12728,
|
||||
"region": [260, 446, 660, 846],
|
||||
"score": 0.77546,
|
||||
"attributes": [],
|
||||
},
|
||||
"label": "person",
|
||||
"sub_label": null,
|
||||
"top_score": 0.958984375,
|
||||
@ -62,7 +69,14 @@ Message published for each changed tracked object. The first message is publishe
|
||||
"id": "1607123955.475377-mxklsc",
|
||||
"camera": "front_door",
|
||||
"frame_time": 1607123962.082975,
|
||||
"snapshot_time": 1607123961.837752,
|
||||
"snapshot": {
|
||||
"frame_time": 1607123965.975463,
|
||||
"box": [415, 489, 528, 700],
|
||||
"area": 12728,
|
||||
"region": [260, 446, 660, 846],
|
||||
"score": 0.77546,
|
||||
"attributes": [],
|
||||
},
|
||||
"label": "person",
|
||||
"sub_label": ["John Smith", 0.79],
|
||||
"top_score": 0.958984375,
|
||||
|
||||
@ -43,7 +43,7 @@ Snapshots must be enabled to be able to submit examples to Frigate+
|
||||
|
||||
### Annotate and verify
|
||||
|
||||
You can view all of your submitted images at [https://plus.frigate.video](https://plus.frigate.video). Annotations can be added by clicking an image. For more detailed information about labeling, see the documentation on [improving your model](../plus/improving_model.md).
|
||||
You can view all of your submitted images at [https://plus.frigate.video](https://plus.frigate.video). Annotations can be added by clicking an image. For more detailed information about labeling, see the documentation on [annotating](../plus/annotating.md).
|
||||
|
||||
|
||||
|
||||
|
||||
@ -13,6 +13,10 @@ Please use your own knowledge to assess and vet them before you install anything
|
||||
|
||||
:::
|
||||
|
||||
## [Advanced Camera Card (formerly known as Frigate Card](https://card.camera/#/README)
|
||||
|
||||
The [Advanced Camera Card](https://card.camera/#/README) is a Home Assistant dashboard card with deep Frigate integration.
|
||||
|
||||
## [Double Take](https://github.com/skrashevich/double-take)
|
||||
|
||||
[Double Take](https://github.com/skrashevich/double-take) provides an unified UI and API for processing and training images for facial recognition.
|
||||
|
||||
@ -1,17 +1,9 @@
|
||||
---
|
||||
id: improving_model
|
||||
title: Improving your model
|
||||
id: annotating
|
||||
title: Annotating your images
|
||||
---
|
||||
|
||||
You may find that Frigate+ models result in more false positives initially, but by submitting true and false positives, the model will improve. With all the new images now being submitted by subscribers, future base models will improve as more and more examples are incorporated. Note that only images with at least one verified label will be used when training your model. Submitting an image from Frigate as a true or false positive will not verify the image. You still must verify the image in Frigate+ in order for it to be used in training.
|
||||
|
||||
- **Submit both true positives and false positives**. This will help the model differentiate between what is and isn't correct. You should aim for a target of 80% true positive submissions and 20% false positives across all of your images. If you are experiencing false positives in a specific area, submitting true positives for any object type near that area in similar lighting conditions will help teach the model what that area looks like when no objects are present.
|
||||
- **Lower your thresholds a little in order to generate more false/true positives near the threshold value**. For example, if you have some false positives that are scoring at 68% and some true positives scoring at 72%, you can try lowering your threshold to 65% and submitting both true and false positives within that range. This will help the model learn and widen the gap between true and false positive scores.
|
||||
- **Submit diverse images**. For the best results, you should provide at least 100 verified images per camera. Keep in mind that varying conditions should be included. You will want images from cloudy days, sunny days, dawn, dusk, and night. As circumstances change, you may need to submit new examples to address new types of false positives. For example, the change from summer days to snowy winter days or other changes such as a new grill or patio furniture may require additional examples and training.
|
||||
|
||||
## Properly labeling images
|
||||
|
||||
For the best results, follow the following guidelines.
|
||||
For the best results, follow these guidelines. You may also want to review the documentation on [improving your model](./index.md#improving-your-model).
|
||||
|
||||
**Label every object in the image**: It is important that you label all objects in each image before verifying. If you don't label a car for example, the model will be taught that part of the image is _not_ a car and it will start to get confused. You can exclude labels that you don't want detected on any of your cameras.
|
||||
|
||||
@ -25,9 +17,17 @@ For the best results, follow the following guidelines.
|
||||
|
||||
|
||||
|
||||
## AI suggested labels
|
||||
|
||||
If you have an active Frigate+ subscription, new uploads will be scanned for the objects configured for you camera and you will see suggested labels as light blue boxes when annotating in Frigate+. These suggestions are processed via a queue and typically complete within a minute after uploading, but processing times can be longer.
|
||||
|
||||
|
||||
|
||||
Suggestions are converted to labels when saving, so you should remove any errant suggestions. There is already some logic designed to avoid duplicate labels, but you may still occasionally see some duplicate suggestions. You should keep the most accurate bounding box and delete any duplicates so that you have just one label per object remaining.
|
||||
|
||||
## False positive labels
|
||||
|
||||
False positives will be shown with a read box and the label will have a strike through.
|
||||
False positives will be shown with a read box and the label will have a strike through. These can't be adjusted, but they can be deleted if you accidentally submit a true positive as a false positive from Frigate.
|
||||
|
||||
|
||||
Misidentified objects should have a correct label added. For example, if a person was mistakenly detected as a cat, you should submit it as a false positive in Frigate and add a label for the person. The boxes will overlap.
|
||||
@ -9,11 +9,11 @@ Before requesting your first model, you will need to upload and verify at least
|
||||
|
||||
It is recommended to submit **both** true positives and false positives. This will help the model differentiate between what is and isn't correct. You should aim for a target of 80% true positive submissions and 20% false positives across all of your images. If you are experiencing false positives in a specific area, submitting true positives for any object type near that area in similar lighting conditions will help teach the model what that area looks like when no objects are present.
|
||||
|
||||
For more detailed recommendations, you can refer to the docs on [improving your model](./improving_model.md).
|
||||
For more detailed recommendations, you can refer to the docs on [annotating](./annotating.md).
|
||||
|
||||
## Step 2: Submit a model request
|
||||
|
||||
Once you have an initial set of verified images, you can request a model on the Models page. For guidance on choosing a model type, refer to [this part of the documentation](./index.md#available-model-types). Each model request requires 1 of the 12 trainings that you receive with your annual subscription. This model will support all [label types available](./index.md#available-label-types) even if you do not submit any examples for those labels. Model creation can take up to 36 hours.
|
||||
Once you have an initial set of verified images, you can request a model on the Models page. For guidance on choosing a model type, refer to [this part of the documentation](./index.md#available-model-types). If you are unsure which type to request, you can test the base model for each version from the "Base Models" tab. Each model request requires 1 of the 12 trainings that you receive with your annual subscription. This model will support all [label types available](./index.md#available-label-types) even if you do not submit any examples for those labels. Model creation can take up to 36 hours.
|
||||
|
||||
|
||||
## Step 3: Set your model id in the config
|
||||
|
||||
@ -3,15 +3,9 @@ id: index
|
||||
title: Models
|
||||
---
|
||||
|
||||
<a href="https://frigate.video/plus" target="_blank" rel="nofollow">Frigate+</a> offers models trained on images submitted by Frigate+ users from their security cameras and is specifically designed for the way Frigate analyzes video footage. These models offer higher accuracy with less resources. The images you upload are used to fine tune a baseline model trained from images uploaded by all Frigate+ users. This fine tuning process results in a model that is optimized for accuracy in your specific conditions.
|
||||
<a href="https://frigate.video/plus" target="_blank" rel="nofollow">Frigate+</a> offers models trained on images submitted by Frigate+ users from their security cameras and is specifically designed for the way Frigate NVR analyzes video footage. These models offer higher accuracy with less resources. The images you upload are used to fine tune a base model trained from images uploaded by all Frigate+ users. This fine tuning process results in a model that is optimized for accuracy in your specific conditions.
|
||||
|
||||
:::info
|
||||
|
||||
The baseline model isn't directly available after subscribing. This may change in the future, but for now you will need to submit a model request with the minimum number of images.
|
||||
|
||||
:::
|
||||
|
||||
With a subscription, 12 model trainings per year are included. If you cancel your subscription, you will retain access to any trained models. An active subscription is required to submit model requests or purchase additional trainings.
|
||||
With a subscription, 12 model trainings to fine tune your model per year are included. In addition, you will have access to any base models published while your subscription is active. If you cancel your subscription, you will retain access to any trained and base models in your account. An active subscription is required to submit model requests or purchase additional trainings. New base models are published quarterly with target dates of January 15th, April 15th, July 15th, and October 15th.
|
||||
|
||||
Information on how to integrate Frigate+ with Frigate can be found in the [integration docs](../integrations/plus.md).
|
||||
|
||||
@ -19,7 +13,7 @@ Information on how to integrate Frigate+ with Frigate can be found in the [integ
|
||||
|
||||
There are two model types offered in Frigate+, `mobiledet` and `yolonas`. Both of these models are object detection models and are trained to detect the same set of labels [listed below](#available-label-types).
|
||||
|
||||
Not all model types are supported by all detectors, so it's important to choose a model type to match your detector as shown in the table under [supported detector types](#supported-detector-types).
|
||||
Not all model types are supported by all detectors, so it's important to choose a model type to match your detector as shown in the table under [supported detector types](#supported-detector-types). You can test model types for compatibility and speed on your hardware by using the base models.
|
||||
|
||||
| Model Type | Description |
|
||||
| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
@ -36,19 +30,27 @@ Using Frigate+ models with `onnx` is only available with Frigate 0.15 and later.
|
||||
|
||||
:::
|
||||
|
||||
| Hardware | Recommended Detector Type | Recommended Model Type |
|
||||
| ---------------------------------------------------------------------------------------------------------------------------- | ------------------------- | ---------------------- |
|
||||
| [CPU](/configuration/object_detectors.md#cpu-detector-not-recommended) | `cpu` | `mobiledet` |
|
||||
| [Coral (all form factors)](/configuration/object_detectors.md#edge-tpu-detector) | `edgetpu` | `mobiledet` |
|
||||
| [Intel](/configuration/object_detectors.md#openvino-detector) | `openvino` | `yolonas` |
|
||||
| [NVidia GPU](https://deploy-preview-13787--frigate-docs.netlify.app/configuration/object_detectors#onnx)\* | `onnx` | `yolonas` |
|
||||
| [AMD ROCm GPU](https://deploy-preview-13787--frigate-docs.netlify.app/configuration/object_detectors#amdrocm-gpu-detector)\* | `onnx` | `yolonas` |
|
||||
| Hardware | Recommended Detector Type | Recommended Model Type |
|
||||
| -------------------------------------------------------------------------------- | ------------------------- | ---------------------- |
|
||||
| [CPU](/configuration/object_detectors.md#cpu-detector-not-recommended) | `cpu` | `mobiledet` |
|
||||
| [Coral (all form factors)](/configuration/object_detectors.md#edge-tpu-detector) | `edgetpu` | `mobiledet` |
|
||||
| [Intel](/configuration/object_detectors.md#openvino-detector) | `openvino` | `yolonas` |
|
||||
| [NVidia GPU](/configuration/object_detectors#onnx)\* | `onnx` | `yolonas` |
|
||||
| [AMD ROCm GPU](/configuration/object_detectors#amdrocm-gpu-detector)\* | `rocm` | `yolonas` |
|
||||
|
||||
_\* Requires Frigate 0.15_
|
||||
|
||||
## Improving your model
|
||||
|
||||
Some users may find that Frigate+ models result in more false positives initially, but by submitting true and false positives, the model will improve. With all the new images now being submitted by subscribers, future base models will improve as more and more examples are incorporated. Note that only images with at least one verified label will be used when training your model. Submitting an image from Frigate as a true or false positive will not verify the image. You still must verify the image in Frigate+ in order for it to be used in training.
|
||||
|
||||
- **Submit both true positives and false positives**. This will help the model differentiate between what is and isn't correct. You should aim for a target of 80% true positive submissions and 20% false positives across all of your images. If you are experiencing false positives in a specific area, submitting true positives for any object type near that area in similar lighting conditions will help teach the model what that area looks like when no objects are present.
|
||||
- **Lower your thresholds a little in order to generate more false/true positives near the threshold value**. For example, if you have some false positives that are scoring at 68% and some true positives scoring at 72%, you can try lowering your threshold to 65% and submitting both true and false positives within that range. This will help the model learn and widen the gap between true and false positive scores.
|
||||
- **Submit diverse images**. For the best results, you should provide at least 100 verified images per camera. Keep in mind that varying conditions should be included. You will want images from cloudy days, sunny days, dawn, dusk, and night. As circumstances change, you may need to submit new examples to address new types of false positives. For example, the change from summer days to snowy winter days or other changes such as a new grill or patio furniture may require additional examples and training.
|
||||
|
||||
## Available label types
|
||||
|
||||
Frigate+ models support a more relevant set of objects for security cameras. Currently, the following objects are supported:
|
||||
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`, `license_plate`
|
||||
@ -58,6 +60,16 @@ Frigate+ models support a more relevant set of objects for security cameras. Cur
|
||||
|
||||
Other object types available in the default Frigate model are not available. Additional object types will be added in future releases.
|
||||
|
||||
### Candidate labels
|
||||
|
||||
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.
|
||||
|
||||
The candidate labels are: `baby`, `royal mail`, `canada post`, `bpost`, `skunk`, `badger`, `possum`, `rodent`, `kangaroo`, `chicken`, `groundhog`, `boar`, `hedgehog`, `school bus`, `tractor`, `golf cart`, `garbage truck`, `bus`, `sports ball`
|
||||
|
||||
Candidate labels are not available for automatic suggestions.
|
||||
|
||||
### Label attributes
|
||||
|
||||
Frigate has special handling for some labels when using Frigate+ models. `face`, `license_plate`, and delivery logos such as `amazon`, `ups`, and `fedex` are considered attribute labels which are not tracked like regular objects and do not generate review items directly. In addition, the `threshold` filter will have no effect on these labels. You should adjust the `min_score` and other filter values as needed.
|
||||
|
||||
@ -1,19 +1,24 @@
|
||||
import type * as Preset from '@docusaurus/preset-classic';
|
||||
import * as path from 'node:path';
|
||||
import type { Config, PluginConfig } from '@docusaurus/types';
|
||||
import type * as OpenApiPlugin from 'docusaurus-plugin-openapi-docs';
|
||||
import type * as Preset from "@docusaurus/preset-classic";
|
||||
import * as path from "node:path";
|
||||
import type { Config, PluginConfig } from "@docusaurus/types";
|
||||
import type * as OpenApiPlugin from "docusaurus-plugin-openapi-docs";
|
||||
|
||||
const config: Config = {
|
||||
title: 'Frigate',
|
||||
tagline: 'NVR With Realtime Object Detection for IP Cameras',
|
||||
url: 'https://docs.frigate.video',
|
||||
baseUrl: '/',
|
||||
onBrokenLinks: 'throw',
|
||||
onBrokenMarkdownLinks: 'warn',
|
||||
favicon: 'img/favicon.ico',
|
||||
organizationName: 'blakeblackshear',
|
||||
projectName: 'frigate',
|
||||
themes: ['@docusaurus/theme-mermaid', 'docusaurus-theme-openapi-docs'],
|
||||
title: "Frigate",
|
||||
tagline: "NVR With Realtime Object Detection for IP Cameras",
|
||||
url: "https://docs.frigate.video",
|
||||
baseUrl: "/",
|
||||
onBrokenLinks: "throw",
|
||||
onBrokenMarkdownLinks: "warn",
|
||||
favicon: "img/favicon.ico",
|
||||
organizationName: "blakeblackshear",
|
||||
projectName: "frigate",
|
||||
themes: [
|
||||
"@docusaurus/theme-mermaid",
|
||||
"docusaurus-theme-openapi-docs",
|
||||
"@inkeep/docusaurus/chatButton",
|
||||
"@inkeep/docusaurus/searchBar",
|
||||
],
|
||||
markdown: {
|
||||
mermaid: true,
|
||||
},
|
||||
@ -27,39 +32,79 @@ const config: Config = {
|
||||
},
|
||||
},
|
||||
themeConfig: {
|
||||
algolia: {
|
||||
appId: 'WIURGBNBPY',
|
||||
apiKey: 'd02cc0a6a61178b25da550212925226b',
|
||||
indexName: 'frigate',
|
||||
announcementBar: {
|
||||
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.
|
||||
<a style="margin-left: 12px; padding: 3px 10px; background: #94d2bd; color: #001219; text-decoration: none; border-radius: 4px; font-weight: 500; " target="_blank" rel="noopener noreferrer" href="https://frigate.video/plus/">Learn more</a>
|
||||
<span style="margin-left: 8px; display: inline-block; animation: pulse 2s infinite;">✨</span>
|
||||
<style>
|
||||
@keyframes pulse {
|
||||
0%, 100% { transform: scale(1); }
|
||||
50% { transform: scale(1.1); }
|
||||
}
|
||||
</style>`,
|
||||
backgroundColor: '#005f73',
|
||||
textColor: '#e0fbfc',
|
||||
isCloseable: false,
|
||||
},
|
||||
docs: {
|
||||
sidebar: {
|
||||
hideable: true,
|
||||
},
|
||||
},
|
||||
inkeepConfig: {
|
||||
baseSettings: {
|
||||
apiKey: "b1a4c4d73c9b48aa5b3cdae6e4c81f0bb3d1134eeb5a7100",
|
||||
integrationId: "cm6xmhn9h000gs601495fkkdx",
|
||||
organizationId: "org_map2JQEOco8U1ZYY",
|
||||
primaryBrandColor: "#010101",
|
||||
},
|
||||
aiChatSettings: {
|
||||
chatSubjectName: "Frigate",
|
||||
botAvatarSrcUrl: "https://frigate.video/images/favicon.png",
|
||||
getHelpCallToActions: [
|
||||
{
|
||||
name: "GitHub",
|
||||
url: "https://github.com/blakeblackshear/frigate",
|
||||
icon: {
|
||||
builtIn: "FaGithub",
|
||||
},
|
||||
},
|
||||
],
|
||||
quickQuestions: [
|
||||
"How to configure and setup camera settings?",
|
||||
"How to setup notifications?",
|
||||
"Supported builtin detectors?",
|
||||
"How to restream video feed?",
|
||||
"How can I get sound or audio in my recordings?",
|
||||
],
|
||||
},
|
||||
},
|
||||
prism: {
|
||||
additionalLanguages: ['bash', 'json'],
|
||||
additionalLanguages: ["bash", "json"],
|
||||
},
|
||||
languageTabs: [
|
||||
{
|
||||
highlight: 'python',
|
||||
language: 'python',
|
||||
logoClass: 'python',
|
||||
highlight: "python",
|
||||
language: "python",
|
||||
logoClass: "python",
|
||||
},
|
||||
{
|
||||
highlight: 'javascript',
|
||||
language: 'nodejs',
|
||||
logoClass: 'nodejs',
|
||||
highlight: "javascript",
|
||||
language: "nodejs",
|
||||
logoClass: "nodejs",
|
||||
},
|
||||
{
|
||||
highlight: 'javascript',
|
||||
language: 'javascript',
|
||||
logoClass: 'javascript',
|
||||
highlight: "javascript",
|
||||
language: "javascript",
|
||||
logoClass: "javascript",
|
||||
},
|
||||
{
|
||||
highlight: 'bash',
|
||||
language: 'curl',
|
||||
logoClass: 'curl',
|
||||
highlight: "bash",
|
||||
language: "curl",
|
||||
logoClass: "curl",
|
||||
},
|
||||
{
|
||||
highlight: "rust",
|
||||
@ -68,28 +113,28 @@ const config: Config = {
|
||||
},
|
||||
],
|
||||
navbar: {
|
||||
title: 'Frigate',
|
||||
title: "Frigate",
|
||||
logo: {
|
||||
alt: 'Frigate',
|
||||
src: 'img/logo.svg',
|
||||
srcDark: 'img/logo-dark.svg',
|
||||
alt: "Frigate",
|
||||
src: "img/logo.svg",
|
||||
srcDark: "img/logo-dark.svg",
|
||||
},
|
||||
items: [
|
||||
{
|
||||
to: '/',
|
||||
activeBasePath: 'docs',
|
||||
label: 'Docs',
|
||||
position: 'left',
|
||||
to: "/",
|
||||
activeBasePath: "docs",
|
||||
label: "Docs",
|
||||
position: "left",
|
||||
},
|
||||
{
|
||||
href: 'https://frigate.video',
|
||||
label: 'Website',
|
||||
position: 'right',
|
||||
href: "https://frigate.video",
|
||||
label: "Website",
|
||||
position: "right",
|
||||
},
|
||||
{
|
||||
href: 'http://demo.frigate.video',
|
||||
label: 'Demo',
|
||||
position: 'right',
|
||||
href: "http://demo.frigate.video",
|
||||
label: "Demo",
|
||||
position: "right",
|
||||
},
|
||||
{
|
||||
type: 'localeDropdown',
|
||||
@ -109,18 +154,18 @@ const config: Config = {
|
||||
],
|
||||
},
|
||||
footer: {
|
||||
style: 'dark',
|
||||
style: "dark",
|
||||
links: [
|
||||
{
|
||||
title: 'Community',
|
||||
title: "Community",
|
||||
items: [
|
||||
{
|
||||
label: 'GitHub',
|
||||
href: 'https://github.com/blakeblackshear/frigate',
|
||||
label: "GitHub",
|
||||
href: "https://github.com/blakeblackshear/frigate",
|
||||
},
|
||||
{
|
||||
label: 'Discussions',
|
||||
href: 'https://github.com/blakeblackshear/frigate/discussions',
|
||||
label: "Discussions",
|
||||
href: "https://github.com/blakeblackshear/frigate/discussions",
|
||||
},
|
||||
],
|
||||
},
|
||||
@ -129,19 +174,19 @@ const config: Config = {
|
||||
},
|
||||
},
|
||||
plugins: [
|
||||
path.resolve(__dirname, 'plugins', 'raw-loader'),
|
||||
path.resolve(__dirname, "plugins", "raw-loader"),
|
||||
[
|
||||
'docusaurus-plugin-openapi-docs',
|
||||
"docusaurus-plugin-openapi-docs",
|
||||
{
|
||||
id: 'openapi',
|
||||
docsPluginId: 'classic', // configured for preset-classic
|
||||
id: "openapi",
|
||||
docsPluginId: "classic", // configured for preset-classic
|
||||
config: {
|
||||
frigateApi: {
|
||||
specPath: 'static/frigate-api.yaml',
|
||||
outputDir: 'docs/integrations/api',
|
||||
specPath: "static/frigate-api.yaml",
|
||||
outputDir: "docs/integrations/api",
|
||||
sidebarOptions: {
|
||||
groupPathsBy: 'tag',
|
||||
categoryLinkSource: 'tag',
|
||||
groupPathsBy: "tag",
|
||||
categoryLinkSource: "tag",
|
||||
sidebarCollapsible: true,
|
||||
sidebarCollapsed: true,
|
||||
},
|
||||
@ -149,23 +194,24 @@ const config: Config = {
|
||||
} satisfies OpenApiPlugin.Options,
|
||||
},
|
||||
},
|
||||
]
|
||||
],
|
||||
] as PluginConfig[],
|
||||
presets: [
|
||||
[
|
||||
'classic',
|
||||
"classic",
|
||||
{
|
||||
docs: {
|
||||
routeBasePath: '/',
|
||||
sidebarPath: './sidebars.ts',
|
||||
routeBasePath: "/",
|
||||
sidebarPath: "./sidebars.ts",
|
||||
// Please change this to your repo.
|
||||
editUrl: 'https://github.com/blakeblackshear/frigate/edit/master/docs/',
|
||||
editUrl:
|
||||
"https://github.com/blakeblackshear/frigate/edit/master/docs/",
|
||||
sidebarCollapsible: false,
|
||||
docItemComponent: '@theme/ApiItem', // Derived from docusaurus-theme-openapi
|
||||
docItemComponent: "@theme/ApiItem", // Derived from docusaurus-theme-openapi
|
||||
},
|
||||
|
||||
theme: {
|
||||
customCss: './src/css/custom.css',
|
||||
customCss: "./src/css/custom.css",
|
||||
},
|
||||
} satisfies Preset.Options,
|
||||
],
|
||||
|
||||
7
docs/package-lock.json
generated
7
docs/package-lock.json
generated
@ -12,6 +12,7 @@
|
||||
"@docusaurus/plugin-content-docs": "^3.6.3",
|
||||
"@docusaurus/preset-classic": "^3.7.0",
|
||||
"@docusaurus/theme-mermaid": "^3.6.3",
|
||||
"@inkeep/docusaurus": "^2.0.16",
|
||||
"@mdx-js/react": "^3.1.0",
|
||||
"clsx": "^2.1.1",
|
||||
"docusaurus-plugin-openapi-docs": "^4.3.1",
|
||||
@ -3954,6 +3955,12 @@
|
||||
"url": "https://github.com/sponsors/sindresorhus"
|
||||
}
|
||||
},
|
||||
"node_modules/@inkeep/docusaurus": {
|
||||
"version": "2.0.16",
|
||||
"resolved": "https://registry.npmjs.org/@inkeep/docusaurus/-/docusaurus-2.0.16.tgz",
|
||||
"integrity": "sha512-dQhjlvFnl3CVr0gWeJ/V/qLnDy1XYrCfkdVSa2D3gJTxI9/vOf9639Y1aPxTxO88DiXuW9CertLrZLB6SoJ2yg==",
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/@isaacs/cliui": {
|
||||
"version": "8.0.2",
|
||||
"resolved": "https://registry.npmjs.org/@isaacs/cliui/-/cliui-8.0.2.tgz",
|
||||
|
||||
@ -21,6 +21,7 @@
|
||||
"@docusaurus/plugin-content-docs": "^3.6.3",
|
||||
"@docusaurus/preset-classic": "^3.7.0",
|
||||
"@docusaurus/theme-mermaid": "^3.6.3",
|
||||
"@inkeep/docusaurus": "^2.0.16",
|
||||
"@mdx-js/react": "^3.1.0",
|
||||
"clsx": "^2.1.1",
|
||||
"docusaurus-plugin-openapi-docs": "^4.3.1",
|
||||
|
||||
@ -5,12 +5,13 @@ import frigateHttpApiSidebar from "./docs/integrations/api/sidebar";
|
||||
const sidebars: SidebarsConfig = {
|
||||
docs: {
|
||||
Frigate: [
|
||||
"frigate/index",
|
||||
"frigate/hardware",
|
||||
"frigate/installation",
|
||||
"frigate/camera_setup",
|
||||
"frigate/video_pipeline",
|
||||
"frigate/glossary",
|
||||
'frigate/index',
|
||||
'frigate/hardware',
|
||||
'frigate/installation',
|
||||
'frigate/updating',
|
||||
'frigate/camera_setup',
|
||||
'frigate/video_pipeline',
|
||||
'frigate/glossary',
|
||||
],
|
||||
Guides: [
|
||||
"guides/getting_started",
|
||||
@ -91,11 +92,11 @@ const sidebars: SidebarsConfig = {
|
||||
"configuration/metrics",
|
||||
"integrations/third_party_extensions",
|
||||
],
|
||||
"Frigate+": [
|
||||
"plus/index",
|
||||
"plus/first_model",
|
||||
"plus/improving_model",
|
||||
"plus/faq",
|
||||
'Frigate+': [
|
||||
'plus/index',
|
||||
'plus/annotating',
|
||||
'plus/first_model',
|
||||
'plus/faq',
|
||||
],
|
||||
Troubleshooting: [
|
||||
"troubleshooting/faqs",
|
||||
|
||||
BIN
docs/static/img/plus/suggestions.webp
vendored
Normal file
BIN
docs/static/img/plus/suggestions.webp
vendored
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 71 KiB |
Loading…
Reference in New Issue
Block a user