Mirrors/blakeblackshear.frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2025-03-22 00:17:13 +01:00
Code Issues Projects Releases Wiki Activity

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

Browse Source
This commit is contained in:
Blake Blackshear 2025-02-08 10:42:48 -06:00
commit cc955b1e66
10 changed files with 109 additions and 21 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

22
docs/docs/configuration/advanced.md
View File

@ -4,7 +4,9 @@ title: Advanced Options
sidebar_label: Advanced Options
---
### `logger`
### Logging
#### Frigate `logger`
Change the default log level for troubleshooting purposes.
@ -28,6 +30,18 @@ Examples of available modules are:
- `watchdog.<camera_name>`
- `ffmpeg.<camera_name>.<sorted_roles>` NOTE: All FFmpeg logs are sent as `error` level.
#### Go2RTC Logging
See [the go2rtc docs](for logging configuration)
```yaml
go2rtc:
streams:
...
log:
exec: trace
```
### `environment_vars`
This section can be used to set environment variables for those unable to modify the environment of the container (ie. within HassOS)
@ -189,16 +203,16 @@ When frigate starts up, it checks whether your config file is valid, and if it i
### Via API
Frigate can accept a new configuration file as JSON at the `/config/save` endpoint. When updating the config this way, Frigate will validate the config before saving it, and return a `400` if the config is not valid.
Frigate can accept a new configuration file as JSON at the `/api/config/save` endpoint. When updating the config this way, Frigate will validate the config before saving it, and return a `400` if the config is not valid.
```bash
curl -X POST http://frigate_host:5000/config/save -d @config.json
curl -X POST http://frigate_host:5000/api/config/save -d @config.json
```
if you'd like you can use your yaml config directly by using [`yq`](https://github.com/mikefarah/yq) to convert it to json:
```bash
yq r -j config.yml | curl -X POST http://frigate_host:5000/config/save -d @-
yq r -j config.yml | curl -X POST http://frigate_host:5000/api/config/save -d @-
```
### Via Command Line

5
docs/docs/configuration/authentication.md
View File

@ -24,6 +24,11 @@ On startup, an admin user and password are generated and printed in the logs. It
In the event that you are locked out of your instance, you can tell Frigate to reset the admin password and print it in the logs on next startup using the `reset_admin_password` setting in your config file.
```yaml
auth:
reset_admin_password: true
```
## Login failure rate limiting
In order to limit the risk of brute force attacks, rate limiting is available for login failures. This is implemented with SlowApi, and the string notation for valid values is available in [the documentation](https://limits.readthedocs.io/en/stable/quickstart.html#examples).

37
docs/docs/configuration/camera_specific.md
View File

@ -65,6 +65,18 @@ ffmpeg:
## Model/vendor specific setup
### Amcrest & Dahua
Amcrest & Dahua cameras should be connected to via RTSP using the following format:
```
rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=0 # this is the main stream
rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=1 # this is the sub stream, typically supporting low resolutions only
rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=2 # higher end cameras support a third stream with a mid resolution (1280x720, 1920x1080)
rtsp://USERNAME:PASSWORD@CAMERA-IP/cam/realmonitor?channel=1&subtype=3 # new higher end cameras support a fourth stream with another mid resolution (1280x720, 1920x1080)
```
### Annke C800
This camera is H.265 only. To be able to play clips on some devices (like MacOs or iPhone) the H.265 stream has to be repackaged and the audio stream has to be converted to aac. Unfortunately direct playback of in the browser is not working (yet), but the downloaded clip can be played locally.
@ -77,7 +89,7 @@ cameras:
record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v copy -tag:v hvc1 -bsf:v hevc_mp4toannexb -c:a aac
inputs:
- path: rtsp://user:password@camera-ip:554/H264/ch1/main/av_stream # <----- Update for your camera
- path: rtsp://USERNAME:PASSWORD@CAMERA-IP/H264/ch1/main/av_stream # <----- Update for your camera
roles:
- detect
- record
@ -95,6 +107,29 @@ ffmpeg:
input_args: preset-rtsp-blue-iris
```
### Hikvision Cameras
Hikvision cameras should be connected to via RTSP using the following format:
```
rtsp://USERNAME:PASSWORD@CAMERA-IP/streaming/channels/101 # this is the main stream
rtsp://USERNAME:PASSWORD@CAMERA-IP/streaming/channels/102 # this is the sub stream, typically supporting low resolutions only
rtsp://USERNAME:PASSWORD@CAMERA-IP/streaming/channels/103 # higher end cameras support a third stream with a mid resolution (1280x720, 1920x1080)
```
:::note
[Some users have reported](https://www.reddit.com/r/frigate_nvr/comments/1hg4ze7/hikvision_security_settings) that newer Hikvision cameras require adjustments to the security settings:
```
RTSP Authentication - digest/basic
RTSP Digest Algorithm - MD5
WEB Authentication - digest/basic
WEB Digest Algorithm - MD5
```
:::
### Reolink Cameras
Reolink has older cameras (ex: 410 & 520) as well as newer camera (ex: 520a & 511wa) which support different subsets of options. In both cases using the http stream is recommended.

15
docs/docs/frigate/hardware.md
View File

@ -13,20 +13,19 @@ Many users have reported various issues with Reolink cameras, so I do not recomm
Here are some of the camera's I recommend:
- <a href="https://amzn.to/3uFLtxB" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) T5442TM-AS-LED</a> (affiliate link)
- <a href="https://amzn.to/3isJ3gU" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T5442TM-AS</a> (affiliate link)
- <a href="https://amzn.to/2ZWNWIA" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-28MM</a> (affiliate link)
- <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)
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 EQ12 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. 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. I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
| Name | Coral Inference Speed | Coral Compatibility | Notes |
| ------------------------------------------------------------------------------------------------------------- | --------------------- | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
| Beelink EQ12 (<a href="https://amzn.to/3OlTMJY" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms | USB | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras. |
| Intel NUC (<a href="https://amzn.to/3psFlHi" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms | USB | Overkill for most, but great performance. Can handle many cameras at 5fps depending on typical amounts of motion. Requires extra parts. |
| Name | Coral Inference Speed | Coral Compatibility | Notes |
| ------------------------------------------------------------------------------------------------------------- | --------------------- | ------------------- | ----------------------------------------------------------------------------------------- |
| Beelink EQ13 (<a href="https://amzn.to/4iQaBKu" 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

4
docs/docs/integrations/third_party_extensions.md
View File

@ -19,6 +19,10 @@ Please use your own knowledge to assess and vet them before you install anything
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 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 NVR to your favorite platforms. Intended to be used with standalone Frigate installations - Home Assistant not required, MQTT is optional but recommended.
## [Frigate telegram](https://github.com/OldTyT/frigate-telegram)
[Frigate telegram](https://github.com/OldTyT/frigate-telegram) makes it possible to send events from Frigate to Telegram. Events are sent as a message with a text description, video, and thumbnail.

2
docs/docs/plus/first_model.md
View File

@ -5,7 +5,7 @@ title: Requesting your first model
## Step 1: Upload and annotate your images
Before requesting your first model, you will need to upload and verify at least 1 image to Frigate+. The more images you upload, annotate, and verify the better your results will be. Most users start to see very good results once they have 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. Refer to the [integration docs](../integrations/plus.md#generate-an-api-key) for instructions on how to easily submit images to Frigate+ directly from Frigate.
Before requesting your first model, you will need to upload and verify at least 10 images to Frigate+. The more images you upload, annotate, and verify the better your results will be. Most users start to see very good results once they have 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. Refer to the [integration docs](../integrations/plus.md#generate-an-api-key) for instructions on how to easily submit images to Frigate+ directly from Frigate.
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.

4
docs/docs/plus/improving_model.md
View File

@ -13,7 +13,7 @@ You may find that Frigate+ models result in more false positives initially, but
For the best results, follow the following guidelines.
**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.
**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.
**Make tight bounding boxes**: Tighter bounding boxes improve the recognition and ensure that accurate bounding boxes are predicted at runtime.
@ -21,7 +21,7 @@ For the best results, follow the following guidelines.
**Label objects hard to identify as difficult**: When objects are truly difficult to make out, such as a car barely visible through a bush, or a dog that is hard to distinguish from the background at night, flag it as 'difficult'. This is not used in the model training as of now, but will in the future.
**`amazon`, `ups`, and `fedex` should label the logo**: For a Fedex truck, label the truck as a `car` and make a different bounding box just for the Fedex logo. If there are multiple logos, label each of them.
**Delivery logos such as `amazon`, `ups`, and `fedex` should label the logo**: For a Fedex truck, label the truck as a `car` and make a different bounding box just for the Fedex logo. If there are multiple logos, label each of them.
![Fedex Logo](/img/plus/fedex-logo.jpg)

16
docs/docs/plus/index.md
View File

@ -17,7 +17,7 @@ Information on how to integrate Frigate+ with Frigate can be found in the [integ
## Available model types
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).
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).
@ -48,11 +48,19 @@ _\* Requires Frigate 0.15_
## Available label types
Frigate+ models support a more relevant set of objects for security cameras. Currently, only the following objects are supported: `person`, `face`, `car`, `license_plate`, `amazon`, `ups`, `fedex`, `package`, `dog`, `cat`, `deer`. Other object types available in the default Frigate model are not available. Additional object types will be added in future releases.
Frigate+ models support a more relevant set of objects for security cameras. Currently, the following objects are supported:
- **People**: `person`, `face`
- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `license_plate`
- **Delivery Logos**: `amazon`, `usps`, `ups`, `fedex`, `dhl`, `an_post`, `purolator`, `postnl`, `nzpost`, `postnord`, `gls`, `dpd`
- **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`
- **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`
Other object types available in the default Frigate model are not available. Additional object types will be added in future releases.
### Label attributes
Frigate has special handling for some labels when using Frigate+ models. `face`, `license_plate`, `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.
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.
In order to have Frigate start using these attribute labels, you will need to add them to the list of objects to track:
@ -75,6 +83,6 @@ When using Frigate+ models, Frigate will choose the snapshot of a person object
![Face Attribute](/img/plus/attribute-example-face.jpg)
`amazon`, `ups`, and `fedex` labels are used to automatically assign a sub label to car objects.
Delivery logos such as `amazon`, `ups`, and `fedex` labels are used to automatically assign a sub label to car objects.
![Fedex Attribute](/img/plus/attribute-example-fedex.jpg)

11
docs/docs/troubleshooting/edgetpu.md
View File

@ -54,6 +54,17 @@ The most common reason for the PCIe Coral not being detected is that the driver
- In most cases [the Coral docs](https://coral.ai/docs/m2/get-started/#2-install-the-pcie-driver-and-edge-tpu-runtime) show how to install the driver for the PCIe based Coral.
- For Ubuntu 22.04+ https://github.com/jnicolson/gasket-builder can be used to build and install the latest version of the driver.
### Not detected on Raspberry Pi5
A kernel update to the RPi5 means an upate to config.txt is required, see [the raspberry pi forum for more info](https://forums.raspberrypi.com/viewtopic.php?t=363682&sid=cb59b026a412f0dc041595951273a9ca&start=25)
Specifically, add the following to config.txt
```
dtoverlay=pciex1-compat-pi5,no-mip
dtoverlay=pcie-32bit-dma-pi5
```
## Only One PCIe Coral Is Detected With Coral Dual EdgeTPU
Coral Dual EdgeTPU is one card with two identical TPU cores. Each core has it's own PCIe interface and motherboard needs to have two PCIe busses on the m.2 slot to make them both work.

14
notebooks/YOLO_NAS_Pretrained_Export.ipynb
View File

@ -11,6 +11,18 @@
"! pip install -q super_gradients==3.7.1"
]
},
{
"cell_type": "code",
"source": [
"! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.10/dist-packages/super_gradients/training/pretrained_models.py\n",
"! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.10/dist-packages/super_gradients/training/utils/checkpoint_utils.py"
],
"metadata": {
"id": "NiRCt917KKcL"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
@ -72,4 +84,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
}
}