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docs/docs/configuration/birdseye.md
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# Birdseye
Birdseye allows a heads-up view of your cameras to see what is going on around your property / space without having to watch all cameras that may have nothing happening. Birdseye allows specific modes that intelligently show and disappear based on what you care about.
Birdseye allows a heads-up view of your cameras to see what is going on around your property / space without having to watch all cameras that may have nothing happening. Birdseye allows specific modes that intelligently show and disappear based on what you care about.
## Birdseye Behavior
### Birdseye Modes
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enabled: False
```
### Birdseye Inactivity
By default birdseye shows all cameras that have had the configured activity in the last 30 seconds, this can be configured:
```yaml
birdseye:
enabled: True
inactivity_threshold: 15
```
## Birdseye Layout
### Birdseye Dimensions
The resolution and aspect ratio of birdseye can be configured. Resolution will increase the quality but does not affect the layout. Changing the aspect ratio of birdseye does affect how cameras are laid out.
```yaml
birdseye:
enabled: True
width: 1280
height: 720
```
### Sorting cameras in the Birdseye view
It is possible to override the order of cameras that are being shown in the Birdseye view.
@@ -55,3 +80,27 @@ cameras:
```
*Note*: Cameras are sorted by default using their name to ensure a constant view inside Birdseye.
### Birdseye Cameras
It is possible to limit the number of cameras shown on birdseye at one time. When this is enabled, birdseye will show the cameras with most recent activity. There is a cooldown to ensure that cameras do not switch too frequently.
For example, this can be configured to only show the most recently active camera.
```yaml
birdseye:
enabled: True
layout:
max_cameras: 1
```
### Birdseye Scaling
By default birdseye tries to fit 2 cameras in each row and then double in size until a suitable layout is found. The scaling can be configured with a value between 1.0 and 5.0 depending on use case.
```yaml
birdseye:
enabled: True
layout:
scaling_factor: 3.0
```
docs/docs/configuration/hardware_acceleration.md
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## Raspberry Pi 3/4
Ensure you increase the allocated RAM for your GPU to at least 128 (raspi-config > Performance Options > GPU Memory).
**NOTICE**: If you are using the addon, you may need to turn off `Protection mode` for hardware acceleration.
Ensure you increase the allocated RAM for your GPU to at least 128 (`raspi-config` > Performance Options > GPU Memory).
If you are using the HA addon, you may need to use the full access variant and turn off `Protection mode` for hardware acceleration.
```yaml
# if you want to decode a h264 stream
@@ -28,16 +28,39 @@ ffmpeg:
:::note
If running Frigate in docker, you either need to run in priviliged mode or be sure to map the /dev/video1x devices to Frigate
If running Frigate in Docker, you either need to run in privileged mode or
map the `/dev/video*` devices to Frigate. With Docker compose add:
```yaml
docker run -d \
--name frigate \
...
--device /dev/video10 \
ghcr.io/blakeblackshear/frigate:stable
services:
frigate:
...
devices:
- /dev/video11:/dev/video11
```
Or with `docker run`:
```bash
docker run -d \
--name frigate \
...
--device /dev/video11 \
ghcr.io/blakeblackshear/frigate:stable
```
`/dev/video11` is the correct device (on Raspberry Pi 4B). You can check
by running the following and looking for `H264`:
```bash
for d in /dev/video*; do
echo -e "---\n$d"
v4l2-ctl --list-formats-ext -d $d
done
```
Or map in all the `/dev/video*` devices.
:::
## Intel-based CPUs
docs/docs/configuration/index.md
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## VSCode Configuration Schema
VSCode (and VSCode addon) supports the JSON schemas which will automatically validate the config. This can be added by adding `# yaml-language-server: $schema=http://frigate_host:5000/api/config/schema.json` to the top of the config file. `frigate_host` being the IP address of Frigate or `ccab4aaf-frigate` if running in the addon.
VSCode supports JSON schemas for automatically validating configuration files. You can enable this feature by adding `# yaml-language-server: $schema=http://frigate_host:5000/api/config/schema.json` to the beginning of the configuration file. Replace `frigate_host` with the IP address or hostname of your Frigate server. If you're using both VSCode and Frigate as an add-on, you should use `ccab4aaf-frigate` instead. Make sure to expose port `5000` for the Web Interface when accessing the config from VSCode on another machine.
## Environment Variable Substitution
docs/docs/configuration/object_detectors.md
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The CPU detector type runs a TensorFlow Lite model utilizing the CPU without hardware acceleration. It is recommended to use a hardware accelerated detector type instead for better performance. To configure a CPU based detector, set the `"type"` attribute to `"cpu"`.
:::tip
If you do not have GPU or Edge TPU hardware, using the [OpenVINO Detector](#openvino-detector) is often more efficient than using the CPU detector.
:::
The number of threads used by the interpreter can be specified using the `"num_threads"` attribute, and defaults to `3.`
A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
@@ -29,17 +35,17 @@ detectors:
When using CPU detectors, you can add one CPU detector per camera. Adding more detectors than the number of cameras should not improve performance.
## Edge-TPU Detector
## Edge TPU Detector
The EdgeTPU detector type runs a TensorFlow Lite model utilizing the Google Coral delegate for hardware acceleration. To configure an EdgeTPU detector, set the `"type"` attribute to `"edgetpu"`.
The Edge TPU detector type runs a TensorFlow Lite model utilizing the Google Coral delegate for hardware acceleration. To configure an Edge TPU detector, set the `"type"` attribute to `"edgetpu"`.
The EdgeTPU device can be specified using the `"device"` attribute according to the [Documentation for the TensorFlow Lite Python API](https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api). If not set, the delegate will use the first device it finds.
The Edge TPU device can be specified using the `"device"` attribute according to the [Documentation for the TensorFlow Lite Python API](https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api). If not set, the delegate will use the first device it finds.
A TensorFlow Lite model is provided in the container at `/edgetpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
:::tip
See [common Edge-TPU troubleshooting steps](/troubleshooting/edgetpu) if the EdgeTPu is not detected.
See [common Edge TPU troubleshooting steps](/troubleshooting/edgetpu) if the Edge TPU is not detected.
:::
@@ -153,11 +159,11 @@ detectors:
## OpenVINO Detector
The OpenVINO detector type runs an OpenVINO IR model on Intel CPU, GPU and VPU hardware. To configure an OpenVINO detector, set the `"type"` attribute to `"openvino"`.
The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel VPU hardware. To configure an OpenVINO detector, set the `"type"` attribute to `"openvino"`.
The OpenVINO device to be used is specified using the `"device"` attribute according to the naming conventions in the [Device Documentation](https://docs.openvino.ai/latest/openvino_docs_OV_UG_Working_with_devices.html). Other supported devices could be `AUTO`, `CPU`, `GPU`, `MYRIAD`, etc. If not specified, the default OpenVINO device will be selected by the `AUTO` plugin.
OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. A supported Intel platform is required to use the `GPU` device with OpenVINO. The `MYRIAD` device may be run on any platform, including Arm devices. For detailed system requirements, see [OpenVINO System Requirements](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/system-requirements.html)
OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will also run on AMD CPUs despite having no official support for it. A supported Intel platform is required to use the `GPU` device with OpenVINO. The `MYRIAD` device may be run on any platform, including Arm devices. For detailed system requirements, see [OpenVINO System Requirements](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/system-requirements.html)
An OpenVINO model is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an FP16 precision IR model. Use the model configuration shown below when using the OpenVINO detector with the default model.
@@ -177,7 +183,7 @@ model:
labelmap_path: /openvino-model/coco_91cl_bkgr.txt
```
This detector also supports some YOLO variants: YOLOX, YOLOv5, and YOLOv8 specifically. Other YOLO variants are not officially supported/tested. Frigate does not come with any yolo models preloaded, so you will need to supply your own models. This detector has been verified to work with the [yolox_tiny](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny) model from Intel's Open Model Zoo. You can follow [these instructions](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny#download-a-model-and-convert-it-into-openvino-ir-format) to retrieve the OpenVINO-compatible `yolox_tiny` model. Make sure that the model input dimensions match the `width` and `height` parameters, and `model_type` is set accordingly. See [Full Configuration Reference](/configuration/index.md#full-configuration-reference) for a list of possible `model_type` options. Below is an example of how `yolox_tiny` can be used in Frigate:
This detector also supports some YOLO variants: YOLOX, YOLOv5, and YOLOv8 specifically. Other YOLO variants are not officially supported/tested. Frigate does not come with any yolo models preloaded, so you will need to supply your own models. This detector has been verified to work with the [yolox_tiny](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny) model from Intel's Open Model Zoo. You can follow [these instructions](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolox-tiny#download-a-model-and-convert-it-into-openvino-ir-format) to retrieve the OpenVINO-compatible `yolox_tiny` model. Make sure that the model input dimensions match the `width` and `height` parameters, and `model_type` is set accordingly. See [Full Configuration Reference](/configuration/reference.md) for a list of possible `model_type` options. Below is an example of how `yolox_tiny` can be used in Frigate:
```yaml
detectors:
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## NVidia TensorRT Detector
NVidia GPUs may be used for object detection using the TensorRT libraries. Due to the size of the additional libraries, this detector is only provided in images with the `-tensorrt` tag suffix, e.g. `ghcr.io/blakeblackshear/frigate:stable-tensorrt`. This detector is designed to work with Yolo models for object detection.
Nvidia GPUs may be used for object detection using the TensorRT libraries. Due to the size of the additional libraries, this detector is only provided in images with the `-tensorrt` tag suffix, e.g. `ghcr.io/blakeblackshear/frigate:stable-tensorrt`. This detector is designed to work with Yolo models for object detection.
### Minimum Hardware Support
docs/docs/configuration/objects.mdx
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Please note:
- `car` is listed twice because `truck` has been renamed to `car` by default. These object types are frequently confused.
- `person` is the only tracked object by default. See the [full configuration reference](index.md#full-configuration-reference) for an example of expanding the list of tracked objects.
- `person` is the only tracked object by default. See the [full configuration reference](reference.md) for an example of expanding the list of tracked objects.
<ul>
{labels.split("\n").map((label) => (
docs/docs/configuration/record.md
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enabled: True
retain:
days: 3
mode: all
mode: motion
events:
retain:
default: 30
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The export page in the Frigate WebUI allows for exporting real time clips with a designated start and stop time as well as exporting a time-lapse for a designated start and stop time. These exports can take a while so it is important to leave the file until it is no longer in progress.
### Time-lapse export
When exporting a time-lapse the default speed-up is 25x with 30 FPS. This means that every 25 seconds of (real-time) recording is condensed into 1 second of time-lapse video (always without audio) with a smoothness of 30 FPS.
To configure the speed-up factor, the frame rate and further custom settings, the configuration parameter `timelapse_args` can be used. The below configuration example would change the time-lapse speed to 60x (for fitting 1 hour of recording into 1 minute of time-lapse) with 25 FPS:
```yaml
record:
enabled: True
export:
timelapse_args: "-vf setpts=PTS/60 -r 25"
```
:::tip
When using `hwaccel_args` globally hardware encoding is used for time lapse generation. The encoder determines its own behavior so the resulting file size may be undesirably large.
To reduce the output file size the ffmpeg parameter `-qp n` can be utilized (where `n` stands for the value of the quantisation parameter). The value can be adjusted to get an acceptable tradeoff between quality and file size for the given scenario.
:::
## Syncing Recordings With Disk
In some cases the recordings files may be deleted but Frigate will not know this has happened. Recordings sync can be enabled which will tell Frigate to check the file system and delete any db entries for files which don't exist.
docs/docs/configuration/reference.md
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# motion - cameras are included if motion was detected in the last 30 seconds
# continuous - all cameras are included always
mode: objects
# Optional: Threshold for camera activity to stop showing camera (default: shown below)
inactivity_threshold: 30
# Optional: Configure the birdseye layout
layout:
# Optional: Scaling factor for the layout calculator (default: shown below)
scaling_factor: 2.0
# Optional: Maximum number of cameras to show at one time, showing the most recent (default: show all cameras)
max_cameras: 1
# Optional: ffmpeg configuration
# More information about presets at https://docs.frigate.video/configuration/ffmpeg_presets